From d833a96a6837574dc8429f425ad336003cac3454 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Tue, 8 Aug 2023 08:21:27 -0700
Subject: [PATCH 01/36] ingest with new docs adding expression language to chat
 attempt at stream, not working correctly right now

---
 ingest.py        | 22 ++++++++++++++++----
 ingest.sh        |  5 +++--
 main.py          | 53 +++++++++++++++++++++++++++++++++++++++---------
 query_data.py    |  2 +-
 requirements.txt |  3 ++-
 5 files changed, 67 insertions(+), 18 deletions(-)

diff --git a/ingest.py b/ingest.py
index 148a8a5f4..9ea050c3d 100644
--- a/ingest.py
+++ b/ingest.py
@@ -5,20 +5,33 @@
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores.faiss import FAISS
-
+from langchain.document_loaders import RecursiveUrlLoader
+import numpy as np
 
 def ingest_docs():
     """Get documents from web pages."""
-    loader = ReadTheDocsLoader("langchain.readthedocs.io/en/latest/")
+    loader = ReadTheDocsLoader("api.python.langchain.com/en/latest", features="lxml")
+    loader2 = ReadTheDocsLoader("python.langchain.com", features="lxml")
+    # loader2 = RecursiveUrlLoader(url="https://python.langchain.com/docs/get_started/introduction", max_depth=2, extractor=lambda x: Soup(x, "lxml").text)
+
     raw_documents = loader.load()
+    print(len(raw_documents))
+    raw_documents += loader2.load()
+    print(len(raw_documents))
     text_splitter = RecursiveCharacterTextSplitter(
         chunk_size=1000,
         chunk_overlap=200,
     )
     documents = text_splitter.split_documents(raw_documents)
-    embeddings = OpenAIEmbeddings()
+    print(len(documents))
+    embeddings = OpenAIEmbeddings(chunk_size=100)
+    print(embeddings)
     vectorstore = FAISS.from_documents(documents, embeddings)
-
+    
+    # Ensure all embeddings have the same length
+    max_length = max(len(e) for e in embeddings)
+    padded_embeddings = np.array([e + tuple([0]*(max_length-len(e))) for e in embeddings])
+    
     # Save vectorstore
     with open("vectorstore.pkl", "wb") as f:
         pickle.dump(vectorstore, f)
@@ -26,3 +39,4 @@ def ingest_docs():
 
 if __name__ == "__main__":
     ingest_docs()
+    
\ No newline at end of file
diff --git a/ingest.sh b/ingest.sh
index 73b75a899..fa19fa4d5 100755
--- a/ingest.sh
+++ b/ingest.sh
@@ -2,5 +2,6 @@
 # This involves scraping the data from the web and then cleaning up and putting in Weaviate.
 # Error if any command fails
 set -e
-wget -r -A.html https://langchain.readthedocs.io/en/latest/
-python3 ingest.py
+wget -r -A.html https://api.python.langchain.com/en/latest
+wget -r -A.html https://python.langchain.com/docs/get_started/introduction
+python3 ingest.py
\ No newline at end of file
diff --git a/main.py b/main.py
index b829756ff..7d355618f 100644
--- a/main.py
+++ b/main.py
@@ -12,6 +12,19 @@
 from query_data import get_chain
 from schemas import ChatResponse
 
+from langchain.chat_models import ChatOpenAI, ChatAnthropic, ChatGooglePalm
+from langchain.llms import Anthropic
+
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.prompts import ChatPromptTemplate
+from langchain.vectorstores import Chroma
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+import langchain
+from typing import Any
+
+# langchain.debug = True
+
 app = FastAPI()
 templates = Jinja2Templates(directory="templates")
 vectorstore: Optional[VectorStore] = None
@@ -31,17 +44,29 @@ async def startup_event():
 async def get(request: Request):
     return templates.TemplateResponse("index.html", {"request": request})
 
+class TokenHandler:
+    def __init__(self, websocket):
+        self.websocket = websocket
+
+    async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        resp = ChatResponse(sender="bot", message=token, type="stream")
+        await self.websocket.send_json(resp.dict())
 
 @app.websocket("/chat")
 async def websocket_endpoint(websocket: WebSocket):
     await websocket.accept()
-    question_handler = QuestionGenCallbackHandler(websocket)
-    stream_handler = StreamingLLMCallbackHandler(websocket)
+    token_handler = TokenHandler(websocket)
+    model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo", streaming=True)
+    # model = ChatAnthropic()
+    # model = ChatGooglePalm()
+    prompt = ChatPromptTemplate.from_template("Using Langchain docs, help me answer a question about {question} with context {context}.")
+    chain = (
+        {"context": vectorstore.as_retriever(), "question": RunnablePassthrough()} 
+        | prompt 
+        | model 
+        | StrOutputParser()
+    )
     chat_history = []
-    qa_chain = get_chain(vectorstore, question_handler, stream_handler)
-    # Use the below line instead of the above line to enable tracing
-    # Ensure `langchain-server` is running
-    # qa_chain = get_chain(vectorstore, question_handler, stream_handler, tracing=True)
 
     while True:
         try:
@@ -49,15 +74,23 @@ async def websocket_endpoint(websocket: WebSocket):
             question = await websocket.receive_text()
             resp = ChatResponse(sender="you", message=question, type="stream")
             await websocket.send_json(resp.dict())
+            
+            print("Recieved question: ", question)
 
             # Construct a response
             start_resp = ChatResponse(sender="bot", message="", type="start")
             await websocket.send_json(start_resp.dict())
 
-            result = await qa_chain.acall(
-                {"question": question, "chat_history": chat_history}
-            )
-            chat_history.append((question, result["answer"]))
+            result = ""
+            num = 0
+            
+            for s in chain.stream(question):
+                print(num)
+                num += 1
+                await token_handler.on_llm_new_token(s)
+                result += s
+                
+            chat_history.append((question, result))
 
             end_resp = ChatResponse(sender="bot", message="", type="end")
             await websocket.send_json(end_resp.dict())
diff --git a/query_data.py b/query_data.py
index c0028317f..167146587 100644
--- a/query_data.py
+++ b/query_data.py
@@ -1,5 +1,5 @@
 """Create a ChatVectorDBChain for question/answering."""
-from langchain.callbacks.base import AsyncCallbackManager
+from langchain.callbacks.manager import AsyncCallbackManager
 from langchain.callbacks.tracers import LangChainTracer
 from langchain.chains import ChatVectorDBChain
 from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
diff --git a/requirements.txt b/requirements.txt
index 1b7831d96..bccd70fa7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,6 +8,7 @@ langchain
 uvicorn
 jinja2
 faiss-cpu
-bs4
+beautifulsoup4
 unstructured
 libmagic
+tiktoken
\ No newline at end of file

From 0d21c56ccf89497499b537dcad51fbfbf2b7bf04 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Wed, 9 Aug 2023 13:54:13 -0700
Subject: [PATCH 02/36] switch to weaviate, recursiveurlloader,
 openaiembeddings

---
 ingest.py | 71 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 41 insertions(+), 30 deletions(-)

diff --git a/ingest.py b/ingest.py
index 9ea050c3d..ecc4a0972 100644
--- a/ingest.py
+++ b/ingest.py
@@ -1,42 +1,53 @@
 """Load html from files, clean up, split, ingest into Weaviate."""
-import pickle
+from bs4 import BeautifulSoup as Soup
+import weaviate
+import os
 
-from langchain.document_loaders import ReadTheDocsLoader
-from langchain.embeddings import OpenAIEmbeddings
+from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
+from langchain.embeddings import OpenAIEmbeddings, CohereEmbeddings
+from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores.faiss import FAISS
-from langchain.document_loaders import RecursiveUrlLoader
-import numpy as np
+from langchain.vectorstores import Weaviate
+
+WEAVIATE_URL=os.environ["WEAVIATE_URL"]
+WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
 
 def ingest_docs():
     """Get documents from web pages."""
-    loader = ReadTheDocsLoader("api.python.langchain.com/en/latest", features="lxml")
-    loader2 = ReadTheDocsLoader("python.langchain.com", features="lxml")
-    # loader2 = RecursiveUrlLoader(url="https://python.langchain.com/docs/get_started/introduction", max_depth=2, extractor=lambda x: Soup(x, "lxml").text)
-
-    raw_documents = loader.load()
-    print(len(raw_documents))
-    raw_documents += loader2.load()
-    print(len(raw_documents))
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1000,
-        chunk_overlap=200,
-    )
-    documents = text_splitter.split_documents(raw_documents)
-    print(len(documents))
-    embeddings = OpenAIEmbeddings(chunk_size=100)
-    print(embeddings)
-    vectorstore = FAISS.from_documents(documents, embeddings)
-    
-    # Ensure all embeddings have the same length
-    max_length = max(len(e) for e in embeddings)
-    padded_embeddings = np.array([e + tuple([0]*(max_length-len(e))) for e in embeddings])
+
+    urls = [
+        "https://api.python.langchain.com/en/latest/api_reference.html#module-langchain",
+        "https://python.langchain.com/docs/get_started", 
+        "https://python.langchain.com/docs/modules", 
+        "https://python.langchain.com/docs/guides",
+        "https://python.langchain.com/docs/ecosystem",
+        "https://python.langchain.com/docs/additional_resources",
+    ]
+
+    documents = []
+    for url in urls:
+        loader = RecursiveUrlLoader(url=url, max_depth=1 if url == urls[0] else 3, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=False)
+        documents += loader.load()
     
-    # Save vectorstore
-    with open("vectorstore.pkl", "wb") as f:
-        pickle.dump(vectorstore, f)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
 
+    print('before splitting', len(documents))
+    docs = text_splitter.split_documents(documents)
+    print('after splitting', len(docs))
 
+    embeddings = OpenAIEmbeddings(chunk_size=200) # rate limit
+
+    client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
+    client.schema.delete_all()
+    print(client.schema.get()) # should be empty
+
+    batch_size = 100 # to handle batch size limit 
+    for i in range(0, len(docs), batch_size):
+        batch = docs[i:i+batch_size]
+        Weaviate.from_documents(batch, embeddings, client=client, by_text=False, index_name="LangChain_idx")
+
+    print("LangChain now has this many vectors", client.query.aggregate("LangChain_idx").with_meta_count().do())
+    
 if __name__ == "__main__":
     ingest_docs()
     
\ No newline at end of file

From 483363260a7b7e8d267d1509180c61135568e15d Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Wed, 9 Aug 2023 14:35:10 -0700
Subject: [PATCH 03/36] streaming working

---
 main.py | 92 ++++++++++++++++++++++++---------------------------------
 1 file changed, 38 insertions(+), 54 deletions(-)

diff --git a/main.py b/main.py
index 7d355618f..8523d9a88 100644
--- a/main.py
+++ b/main.py
@@ -1,27 +1,21 @@
 """Main entrypoint for the app."""
-import logging
-import pickle
-from pathlib import Path
-from typing import Optional
-
 from fastapi import FastAPI, Request, WebSocket, WebSocketDisconnect
 from fastapi.templating import Jinja2Templates
-from langchain.vectorstores import VectorStore
-
-from callback import QuestionGenCallbackHandler, StreamingLLMCallbackHandler
-from query_data import get_chain
-from schemas import ChatResponse
-
-from langchain.chat_models import ChatOpenAI, ChatAnthropic, ChatGooglePalm
-from langchain.llms import Anthropic
+from typing import Any, Optional
+import os
+import logging
 
-from langchain.schema.runnable import RunnablePassthrough
+import langchain
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
-from langchain.vectorstores import Chroma
-from langchain.schema.runnable import RunnablePassthrough
 from langchain.schema.output_parser import StrOutputParser
-import langchain
-from typing import Any
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.vectorstores import VectorStore, Weaviate
+
+import weaviate
+from schemas import ChatResponse
+import asyncio
 
 # langchain.debug = True
 
@@ -29,44 +23,27 @@
 templates = Jinja2Templates(directory="templates")
 vectorstore: Optional[VectorStore] = None
 
-
-@app.on_event("startup")
-async def startup_event():
-    logging.info("loading vectorstore")
-    if not Path("vectorstore.pkl").exists():
-        raise ValueError("vectorstore.pkl does not exist, please run ingest.py first")
-    with open("vectorstore.pkl", "rb") as f:
-        global vectorstore
-        vectorstore = pickle.load(f)
-
-
 @app.get("/")
 async def get(request: Request):
     return templates.TemplateResponse("index.html", {"request": request})
 
-class TokenHandler:
-    def __init__(self, websocket):
-        self.websocket = websocket
-
-    async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        resp = ChatResponse(sender="bot", message=token, type="stream")
-        await self.websocket.send_json(resp.dict())
-
 @app.websocket("/chat")
 async def websocket_endpoint(websocket: WebSocket):
+    WEAVIATE_URL=os.environ["WEAVIATE_URL"]
+    WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
+
     await websocket.accept()
-    token_handler = TokenHandler(websocket)
-    model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo", streaming=True)
-    # model = ChatAnthropic()
-    # model = ChatGooglePalm()
-    prompt = ChatPromptTemplate.from_template("Using Langchain docs, help me answer a question about {question} with context {context}.")
-    chain = (
-        {"context": vectorstore.as_retriever(), "question": RunnablePassthrough()} 
-        | prompt 
-        | model 
-        | StrOutputParser()
-    )
+    
+    # if model_type == "anthropic":
+    #     model = ChatAnthropic()
+    # else:
+    model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")
+
     chat_history = []
+    embeddings = OpenAIEmbeddings()
+    client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
+    print(client.query.aggregate("LangChain_idx").with_meta_count().do())
+    weaviate_client = Weaviate(client=client, index_name="LangChain_idx", text_key="text", embedding=embeddings, by_text=False)
 
     while True:
         try:
@@ -74,21 +51,28 @@ async def websocket_endpoint(websocket: WebSocket):
             question = await websocket.receive_text()
             resp = ChatResponse(sender="you", message=question, type="stream")
             await websocket.send_json(resp.dict())
-            
+            await asyncio.sleep(0)
+
             print("Recieved question: ", question)
 
             # Construct a response
             start_resp = ChatResponse(sender="bot", message="", type="start")
             await websocket.send_json(start_resp.dict())
+            
+            prompt = ChatPromptTemplate.from_template("Using Langchain docs, help me answer a question about {question} with the context provided {context}")
+            chain = (
+                {"context": weaviate_client.as_retriever(), "question": RunnablePassthrough()} 
+                | prompt 
+                | model 
+            )
 
             result = ""
-            num = 0
             
             for s in chain.stream(question):
-                print(num)
-                num += 1
-                await token_handler.on_llm_new_token(s)
-                result += s
+                resp = ChatResponse(sender="bot", message=s.content, type="stream")
+                await websocket.send_json(resp.dict())
+                await asyncio.sleep(0)
+                result += s.content
                 
             chat_history.append((question, result))
 

From 9f7a9ca7aa565cb2eea213c0df55df58df48ac6f Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Thu, 10 Aug 2023 17:22:26 -0700
Subject: [PATCH 04/36] nextjs transition and fly server

---
 .gitignore                             |    1 +
 Procfile                               |    2 +
 chat-langchain/.eslintrc.json          |    3 +
 chat-langchain/.gitignore              |   35 +
 chat-langchain/README.md               |   34 +
 chat-langchain/app/components/chat.tsx |  174 +
 chat-langchain/app/favicon.ico         |  Bin 0 -> 25931 bytes
 chat-langchain/app/globals.css         |   27 +
 chat-langchain/app/layout.tsx          |   22 +
 chat-langchain/app/page.tsx            |   15 +
 chat-langchain/next.config.js          |    4 +
 chat-langchain/package-lock.json       | 6372 ++++++++++++++++++++++++
 chat-langchain/package.json            |   25 +
 chat-langchain/postcss.config.js       |    6 +
 chat-langchain/public/next.svg         |    1 +
 chat-langchain/public/vercel.svg       |    1 +
 chat-langchain/tailwind.config.ts      |   20 +
 chat-langchain/tsconfig.json           |   28 +
 fly.toml                               |   21 +
 main.py                                |   67 +-
 requirements.txt                       |    4 +-
 21 files changed, 6823 insertions(+), 39 deletions(-)
 create mode 100644 Procfile
 create mode 100644 chat-langchain/.eslintrc.json
 create mode 100644 chat-langchain/.gitignore
 create mode 100644 chat-langchain/README.md
 create mode 100644 chat-langchain/app/components/chat.tsx
 create mode 100644 chat-langchain/app/favicon.ico
 create mode 100644 chat-langchain/app/globals.css
 create mode 100644 chat-langchain/app/layout.tsx
 create mode 100644 chat-langchain/app/page.tsx
 create mode 100644 chat-langchain/next.config.js
 create mode 100644 chat-langchain/package-lock.json
 create mode 100644 chat-langchain/package.json
 create mode 100644 chat-langchain/postcss.config.js
 create mode 100644 chat-langchain/public/next.svg
 create mode 100644 chat-langchain/public/vercel.svg
 create mode 100644 chat-langchain/tailwind.config.ts
 create mode 100644 chat-langchain/tsconfig.json
 create mode 100644 fly.toml

diff --git a/.gitignore b/.gitignore
index 74ef9d7a8..603f175ad 100644
--- a/.gitignore
+++ b/.gitignore
@@ -137,3 +137,4 @@ dmypy.json
 
 vectorstore.pkl
 langchain.readthedocs.io/
+.vercel
diff --git a/Procfile b/Procfile
new file mode 100644
index 000000000..17cffd989
--- /dev/null
+++ b/Procfile
@@ -0,0 +1,2 @@
+# Modify this Procfile to fit your needs
+web: uvicorn main:app --host 0.0.0.0 --port 8080
\ No newline at end of file
diff --git a/chat-langchain/.eslintrc.json b/chat-langchain/.eslintrc.json
new file mode 100644
index 000000000..bffb357a7
--- /dev/null
+++ b/chat-langchain/.eslintrc.json
@@ -0,0 +1,3 @@
+{
+  "extends": "next/core-web-vitals"
+}
diff --git a/chat-langchain/.gitignore b/chat-langchain/.gitignore
new file mode 100644
index 000000000..8f322f0d8
--- /dev/null
+++ b/chat-langchain/.gitignore
@@ -0,0 +1,35 @@
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# local env files
+.env*.local
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
diff --git a/chat-langchain/README.md b/chat-langchain/README.md
new file mode 100644
index 000000000..f4da3c4c1
--- /dev/null
+++ b/chat-langchain/README.md
@@ -0,0 +1,34 @@
+This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).
+
+## Getting Started
+
+First, run the development server:
+
+```bash
+npm run dev
+# or
+yarn dev
+# or
+pnpm dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
+
+You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
+
+This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
+
+## Learn More
+
+To learn more about Next.js, take a look at the following resources:
+
+- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
+- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
+
+You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
+
+## Deploy on Vercel
+
+The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
+
+Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
diff --git a/chat-langchain/app/components/chat.tsx b/chat-langchain/app/components/chat.tsx
new file mode 100644
index 000000000..9b7e5e8cd
--- /dev/null
+++ b/chat-langchain/app/components/chat.tsx
@@ -0,0 +1,174 @@
+"use client";
+import React, { useEffect, useRef, useState } from "react";
+
+const Chat = () => {
+  const [messages, setMessages] = useState<
+    Array<{ sender: string; message: string; isCode: boolean }>
+  >([]);
+  const [model, setModel] = useState("openai");
+  const messagesEndRef = useRef<HTMLDivElement>(null);
+  const [message, setMessage] = useState(""); // Add this line
+
+  const sendMessage = (event: { preventDefault: () => void }) => {
+    event.preventDefault();
+    if (message === "") {
+      return;
+    }
+    let isCode = false;
+
+    fetch("http://0.0.0.0:8080/chat", {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        message: message,
+        model: model,
+      }),
+    })
+      .then((response) => {
+        const reader = response.body.getReader();
+        const decoder = new TextDecoder("utf-8");
+
+        let accumulatedMessage = "";
+        let prevIsCode = null;
+
+        reader
+          .read()
+          .then(function processText({
+            done,
+            value,
+          }: {
+            done: any;
+            value: any;
+          }) {
+            if (done) {
+              console.log("Stream complete");
+              return;
+            }
+
+            let result = decoder.decode(value);
+            if (result.includes("``")) {
+              isCode = !isCode;
+            } else {
+              result = result.replace(/`/g, "");
+              accumulatedMessage += result;
+            }
+
+            setMessages((prevMessages) => {
+              let newMessages = [...prevMessages];
+              if (isCode !== prevIsCode) {
+                newMessages.push({
+                  sender: isCode ? "" : "Bot",
+                  message: accumulatedMessage.trim(),
+                  isCode: isCode,
+                });
+                if (newMessages.length > 1) {
+                  accumulatedMessage = "";
+                }
+              } else if (newMessages.length > 0) {
+                newMessages[newMessages.length - 1] = {
+                  sender: isCode ? "" : "Bot",
+                  message: accumulatedMessage.trim(),
+                  isCode: isCode,
+                };
+              }
+              prevIsCode = isCode;
+              return newMessages;
+            });
+            setMessage("");
+            return reader.read().then(processText);
+          });
+      })
+      .catch((error) => {
+        console.error("Error:", error);
+      });
+  };
+
+  useEffect(() => {
+    messagesEndRef.current?.scrollIntoView({ behavior: "smooth" });
+  }, [messagesEndRef]);
+
+  return (
+    <div className="rounded-2xl border-zinc-100  lg:border lg:p-6 bg-slate-200">
+      <div className="w-full mb-5 rounded-lg px-4 py-5 shadow-lg ring-1 ring-zinc-100 sm:px-6 bg-slate-100">
+        <h4 className="text-center text-3xl font-sans font-medium">
+          Chat Langchain
+        </h4>
+        <span className="mx-auto flex font-mono flex-grow text-gray-400 clear-both mb-5 mt-1 text-sm justify-center">
+          A chatbot for everything Langchain.
+        </span>
+        <hr className="border-gray-500 my-5" />
+        <div
+          id="messages"
+          className="overflow-auto m-8"
+          style={{ maxHeight: "500px" }}
+        >
+          {messages.map((msg, index) => (
+            <div
+              key={index}
+              className={`flex ${
+                msg.sender === "You" ? "justify-end" : "justify-start"
+              }`}
+            >
+              <div
+                className={
+                  msg.sender === "You" ? "client-message" : "server-message"
+                }
+              >
+                {msg.sender && <strong>{msg.sender}: </strong>}
+                {msg.isCode ? (
+                  <code
+                    style={{
+                      color: "white",
+                      backgroundColor: "rgba(0, 0, 0, 0.8)",
+                      opacity: "0.9",
+                      padding: "4px",
+                      margin: "4px",
+                      borderRadius: "5px",
+                      overflow: "auto",
+                      boxShadow: "0px 0px 10px rgba(0, 0, 0, 0.5))",
+                      transition: "ease-in-out 0.15s",
+                    }}
+                    dangerouslySetInnerHTML={{ __html: msg.message }}
+                  ></code>
+                ) : (
+                  msg.message
+                )}
+              </div>
+            </div>
+          ))}
+          <div ref={messagesEndRef} />
+        </div>
+        <form className="flex justify-between mt-5" onSubmit={sendMessage}>
+          <input
+            type="text"
+            className="w-4/5 bg-gray-800 text-white rounded p-2 mr-2"
+            placeholder="Write your question"
+            id="messageText"
+            value={message}
+            onChange={(e) => setMessage(e.target.value)}
+          />
+          <select
+            id="modelType"
+            className="w-1/5 bg-gray-800 text-white rounded p-2 mr-2"
+            value={model}
+            onChange={(e) => setModel(e.target.value)}
+          >
+            <option value="openai">OpenAI</option>
+            <option value="anthropic">Anthropic</option>
+          </select>
+          <button
+            id="send"
+            type="submit"
+            className="bg-blue-500 text-white rounded p-2"
+          >
+            Send
+          </button>
+        </form>
+      </div>
+    </div>
+  );
+};
+
+export default Chat;
diff --git a/chat-langchain/app/favicon.ico b/chat-langchain/app/favicon.ico
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diff --git a/chat-langchain/app/globals.css b/chat-langchain/app/globals.css
new file mode 100644
index 000000000..fd81e8858
--- /dev/null
+++ b/chat-langchain/app/globals.css
@@ -0,0 +1,27 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+:root {
+  --foreground-rgb: 0, 0, 0;
+  --background-start-rgb: 214, 219, 220;
+  --background-end-rgb: 255, 255, 255;
+}
+
+@media (prefers-color-scheme: dark) {
+  :root {
+    --foreground-rgb: 255, 255, 255;
+    --background-start-rgb: 0, 0, 0;
+    --background-end-rgb: 0, 0, 0;
+  }
+}
+
+body {
+  color: rgb(var(--foreground-rgb));
+  background: linear-gradient(
+      to bottom,
+      transparent,
+      rgb(var(--background-end-rgb))
+    )
+    rgb(var(--background-start-rgb));
+}
diff --git a/chat-langchain/app/layout.tsx b/chat-langchain/app/layout.tsx
new file mode 100644
index 000000000..ae8456212
--- /dev/null
+++ b/chat-langchain/app/layout.tsx
@@ -0,0 +1,22 @@
+import './globals.css'
+import type { Metadata } from 'next'
+import { Inter } from 'next/font/google'
+
+const inter = Inter({ subsets: ['latin'] })
+
+export const metadata: Metadata = {
+  title: 'Create Next App',
+  description: 'Generated by create next app',
+}
+
+export default function RootLayout({
+  children,
+}: {
+  children: React.ReactNode
+}) {
+  return (
+    <html lang="en">
+      <body className={inter.className}>{children}</body>
+    </html>
+  )
+}
diff --git a/chat-langchain/app/page.tsx b/chat-langchain/app/page.tsx
new file mode 100644
index 000000000..dc95882c2
--- /dev/null
+++ b/chat-langchain/app/page.tsx
@@ -0,0 +1,15 @@
+import Image from 'next/image'
+import axios from 'axios'
+import Chat from "../app/components/chat"
+
+export default function Home() {
+  return (
+    <main className="flex min-h-screen flex-col items-center justify-between p-24 bg-white">
+      <div className="z-10 max-w-5xl w-full items-center justify-between font-light text-sm lg:flex">
+        <Chat/>
+      </div>
+    </main>
+  )
+}
+
+
diff --git a/chat-langchain/next.config.js b/chat-langchain/next.config.js
new file mode 100644
index 000000000..767719fc4
--- /dev/null
+++ b/chat-langchain/next.config.js
@@ -0,0 +1,4 @@
+/** @type {import('next').NextConfig} */
+const nextConfig = {}
+
+module.exports = nextConfig
diff --git a/chat-langchain/package-lock.json b/chat-langchain/package-lock.json
new file mode 100644
index 000000000..e5842bf94
--- /dev/null
+++ b/chat-langchain/package-lock.json
@@ -0,0 +1,6372 @@
+{
+  "name": "chat-langchain",
+  "version": "0.1.0",
+  "lockfileVersion": 2,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "chat-langchain",
+      "version": "0.1.0",
+      "dependencies": {
+        "@types/node": "20.4.9",
+        "@types/react": "18.2.20",
+        "@types/react-dom": "18.2.7",
+        "autoprefixer": "10.4.14",
+        "eslint": "8.46.0",
+        "eslint-config-next": "13.4.13",
+        "next": "13.4.13",
+        "postcss": "8.4.27",
+        "react": "18.2.0",
+        "react-dom": "18.2.0",
+        "tailwindcss": "3.3.3",
+        "typescript": "5.1.6"
+      }
+    },
+    "node_modules/@aashutoshrathi/word-wrap": {
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+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/@alloc/quick-lru": {
+      "version": "5.2.0",
+      "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
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+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
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+    "node_modules/@babel/runtime": {
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+      "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.22.10.tgz",
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+      "dependencies": {
+        "regenerator-runtime": "^0.14.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@eslint-community/eslint-utils": {
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+      },
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+      }
+    },
+    "node_modules/@eslint-community/regexpp": {
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+      "resolved": "https://registry.npmjs.org/@eslint-community/regexpp/-/regexpp-4.6.2.tgz",
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+    "node_modules/@eslint/eslintrc": {
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+        "debug": "^4.3.2",
+        "espree": "^9.6.0",
+        "globals": "^13.19.0",
+        "ignore": "^5.2.0",
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+    "node_modules/@eslint/js": {
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+      "dependencies": {
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+        "debug": "^4.1.1",
+        "minimatch": "^3.0.5"
+      },
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+}
diff --git a/chat-langchain/package.json b/chat-langchain/package.json
new file mode 100644
index 000000000..2e264cf65
--- /dev/null
+++ b/chat-langchain/package.json
@@ -0,0 +1,25 @@
+{
+  "name": "chat-langchain",
+  "version": "0.1.0",
+  "private": true,
+  "scripts": {
+    "dev": "next dev",
+    "build": "next build",
+    "start": "next start",
+    "lint": "next lint"
+  },
+  "dependencies": {
+    "@types/node": "20.4.9",
+    "@types/react": "18.2.20",
+    "@types/react-dom": "18.2.7",
+    "autoprefixer": "10.4.14",
+    "eslint": "8.46.0",
+    "eslint-config-next": "13.4.13",
+    "next": "13.4.13",
+    "postcss": "8.4.27",
+    "react": "18.2.0",
+    "react-dom": "18.2.0",
+    "tailwindcss": "3.3.3",
+    "typescript": "5.1.6"
+  }
+}
diff --git a/chat-langchain/postcss.config.js b/chat-langchain/postcss.config.js
new file mode 100644
index 000000000..33ad091d2
--- /dev/null
+++ b/chat-langchain/postcss.config.js
@@ -0,0 +1,6 @@
+module.exports = {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}
diff --git a/chat-langchain/public/next.svg b/chat-langchain/public/next.svg
new file mode 100644
index 000000000..5174b28c5
--- /dev/null
+++ b/chat-langchain/public/next.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 394 80"><path fill="#000" d="M262 0h68.5v12.7h-27.2v66.6h-13.6V12.7H262V0ZM149 0v12.7H94v20.4h44.3v12.6H94v21h55v12.6H80.5V0h68.7zm34.3 0h-17.8l63.8 79.4h17.9l-32-39.7 32-39.6h-17.9l-23 28.6-23-28.6zm18.3 56.7-9-11-27.1 33.7h17.8l18.3-22.7z"/><path fill="#000" d="M81 79.3 17 0H0v79.3h13.6V17l50.2 62.3H81Zm252.6-.4c-1 0-1.8-.4-2.5-1s-1.1-1.6-1.1-2.6.3-1.8 1-2.5 1.6-1 2.6-1 1.8.3 2.5 1a3.4 3.4 0 0 1 .6 4.3 3.7 3.7 0 0 1-3 1.8zm23.2-33.5h6v23.3c0 2.1-.4 4-1.3 5.5a9.1 9.1 0 0 1-3.8 3.5c-1.6.8-3.5 1.3-5.7 1.3-2 0-3.7-.4-5.3-1s-2.8-1.8-3.7-3.2c-.9-1.3-1.4-3-1.4-5h6c.1.8.3 1.6.7 2.2s1 1.2 1.6 1.5c.7.4 1.5.5 2.4.5 1 0 1.8-.2 2.4-.6a4 4 0 0 0 1.6-1.8c.3-.8.5-1.8.5-3V45.5zm30.9 9.1a4.4 4.4 0 0 0-2-3.3 7.5 7.5 0 0 0-4.3-1.1c-1.3 0-2.4.2-3.3.5-.9.4-1.6 1-2 1.6a3.5 3.5 0 0 0-.3 4c.3.5.7.9 1.3 1.2l1.8 1 2 .5 3.2.8c1.3.3 2.5.7 3.7 1.2a13 13 0 0 1 3.2 1.8 8.1 8.1 0 0 1 3 6.5c0 2-.5 3.7-1.5 5.1a10 10 0 0 1-4.4 3.5c-1.8.8-4.1 1.2-6.8 1.2-2.6 0-4.9-.4-6.8-1.2-2-.8-3.4-2-4.5-3.5a10 10 0 0 1-1.7-5.6h6a5 5 0 0 0 3.5 4.6c1 .4 2.2.6 3.4.6 1.3 0 2.5-.2 3.5-.6 1-.4 1.8-1 2.4-1.7a4 4 0 0 0 .8-2.4c0-.9-.2-1.6-.7-2.2a11 11 0 0 0-2.1-1.4l-3.2-1-3.8-1c-2.8-.7-5-1.7-6.6-3.2a7.2 7.2 0 0 1-2.4-5.7 8 8 0 0 1 1.7-5 10 10 0 0 1 4.3-3.5c2-.8 4-1.2 6.4-1.2 2.3 0 4.4.4 6.2 1.2 1.8.8 3.2 2 4.3 3.4 1 1.4 1.5 3 1.5 5h-5.8z"/></svg>
\ No newline at end of file
diff --git a/chat-langchain/public/vercel.svg b/chat-langchain/public/vercel.svg
new file mode 100644
index 000000000..d2f842227
--- /dev/null
+++ b/chat-langchain/public/vercel.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 283 64"><path fill="black" d="M141 16c-11 0-19 7-19 18s9 18 20 18c7 0 13-3 16-7l-7-5c-2 3-6 4-9 4-5 0-9-3-10-7h28v-3c0-11-8-18-19-18zm-9 15c1-4 4-7 9-7s8 3 9 7h-18zm117-15c-11 0-19 7-19 18s9 18 20 18c6 0 12-3 16-7l-8-5c-2 3-5 4-8 4-5 0-9-3-11-7h28l1-3c0-11-8-18-19-18zm-10 15c2-4 5-7 10-7s8 3 9 7h-19zm-39 3c0 6 4 10 10 10 4 0 7-2 9-5l8 5c-3 5-9 8-17 8-11 0-19-7-19-18s8-18 19-18c8 0 14 3 17 8l-8 5c-2-3-5-5-9-5-6 0-10 4-10 10zm83-29v46h-9V5h9zM37 0l37 64H0L37 0zm92 5-27 48L74 5h10l18 30 17-30h10zm59 12v10l-3-1c-6 0-10 4-10 10v15h-9V17h9v9c0-5 6-9 13-9z"/></svg>
\ No newline at end of file
diff --git a/chat-langchain/tailwind.config.ts b/chat-langchain/tailwind.config.ts
new file mode 100644
index 000000000..c7ead8046
--- /dev/null
+++ b/chat-langchain/tailwind.config.ts
@@ -0,0 +1,20 @@
+import type { Config } from 'tailwindcss'
+
+const config: Config = {
+  content: [
+    './pages/**/*.{js,ts,jsx,tsx,mdx}',
+    './components/**/*.{js,ts,jsx,tsx,mdx}',
+    './app/**/*.{js,ts,jsx,tsx,mdx}',
+  ],
+  theme: {
+    extend: {
+      backgroundImage: {
+        'gradient-radial': 'radial-gradient(var(--tw-gradient-stops))',
+        'gradient-conic':
+          'conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))',
+      },
+    },
+  },
+  plugins: [],
+}
+export default config
diff --git a/chat-langchain/tsconfig.json b/chat-langchain/tsconfig.json
new file mode 100644
index 000000000..23ba4fd54
--- /dev/null
+++ b/chat-langchain/tsconfig.json
@@ -0,0 +1,28 @@
+{
+  "compilerOptions": {
+    "target": "es5",
+    "lib": ["dom", "dom.iterable", "esnext"],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "strict": true,
+    "forceConsistentCasingInFileNames": true,
+    "noEmit": true,
+    "esModuleInterop": true,
+    "module": "esnext",
+    "moduleResolution": "bundler",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "jsx": "preserve",
+    "incremental": true,
+    "plugins": [
+      {
+        "name": "next"
+      }
+    ],
+    "paths": {
+      "@/*": ["./*"]
+    }
+  },
+  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+  "exclude": ["node_modules"]
+}
diff --git a/fly.toml b/fly.toml
new file mode 100644
index 000000000..465ba8980
--- /dev/null
+++ b/fly.toml
@@ -0,0 +1,21 @@
+# fly.toml app configuration file generated for chat-langchain on 2023-08-10T11:47:36-07:00
+#
+# See https://fly.io/docs/reference/configuration/ for information about how to use this file.
+#
+
+app = "chat-langchain"
+primary_region = "lax"
+
+[build]
+  builder = "paketobuildpacks/builder:base"
+
+[env]
+  PORT = "8080"
+
+[http_service]
+  internal_port = 8080
+  force_https = true
+  auto_stop_machines = true
+  auto_start_machines = true
+  min_machines_running = 0
+  processes = ["app"]
diff --git a/main.py b/main.py
index 8523d9a88..5ecf854cb 100644
--- a/main.py
+++ b/main.py
@@ -1,12 +1,15 @@
 """Main entrypoint for the app."""
-from fastapi import FastAPI, Request, WebSocket, WebSocketDisconnect
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.templating import Jinja2Templates
-from typing import Any, Optional
+from typing import Any, Optional, Generator
 import os
 import logging
+import json
 
 import langchain
-from langchain.chat_models import ChatOpenAI
+from langchain.chat_models import ChatOpenAI, ChatAnthropic
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema.output_parser import StrOutputParser
@@ -20,6 +23,14 @@
 # langchain.debug = True
 
 app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 templates = Jinja2Templates(directory="templates")
 vectorstore: Optional[VectorStore] = None
 
@@ -27,39 +38,28 @@
 async def get(request: Request):
     return templates.TemplateResponse("index.html", {"request": request})
 
-@app.websocket("/chat")
-async def websocket_endpoint(websocket: WebSocket):
+@app.post("/chat")
+async def chat_endpoint(request: Request):
+    data = await request.json()
+    question = data.get('message')
+    model_type = data.get('model')
+    
     WEAVIATE_URL=os.environ["WEAVIATE_URL"]
     WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
 
-    await websocket.accept()
-    
-    # if model_type == "anthropic":
-    #     model = ChatAnthropic()
-    # else:
-    model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")
-
     chat_history = []
     embeddings = OpenAIEmbeddings()
     client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
     print(client.query.aggregate("LangChain_idx").with_meta_count().do())
     weaviate_client = Weaviate(client=client, index_name="LangChain_idx", text_key="text", embedding=embeddings, by_text=False)
 
-    while True:
+    async def stream():
         try:
-            # Receive and send back the client message
-            question = await websocket.receive_text()
-            resp = ChatResponse(sender="you", message=question, type="stream")
-            await websocket.send_json(resp.dict())
-            await asyncio.sleep(0)
+            model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") if model_type == "openai" else ChatAnthropic()
 
             print("Recieved question: ", question)
-
-            # Construct a response
-            start_resp = ChatResponse(sender="bot", message="", type="start")
-            await websocket.send_json(start_resp.dict())
             
-            prompt = ChatPromptTemplate.from_template("Using Langchain docs, help me answer a question about {question} with the context provided {context}")
+            prompt = ChatPromptTemplate.from_template("You are an expert programming prodigy creator of Langchain. Answer this question to the best of your ability {question} with the provided context {context}")
             chain = (
                 {"context": weaviate_client.as_retriever(), "question": RunnablePassthrough()} 
                 | prompt 
@@ -69,29 +69,20 @@ async def websocket_endpoint(websocket: WebSocket):
             result = ""
             
             for s in chain.stream(question):
-                resp = ChatResponse(sender="bot", message=s.content, type="stream")
-                await websocket.send_json(resp.dict())
+                yield formatted_content
                 await asyncio.sleep(0)
-                result += s.content
+                print(formatted_content, end="", flush=True)
                 
+            result += formatted_content
             chat_history.append((question, result))
 
-            end_resp = ChatResponse(sender="bot", message="", type="end")
-            await websocket.send_json(end_resp.dict())
-        except WebSocketDisconnect:
-            logging.info("websocket disconnect")
-            break
         except Exception as e:
             logging.error(e)
-            resp = ChatResponse(
-                sender="bot",
-                message="Sorry, something went wrong. Try again.",
-                type="error",
-            )
-            await websocket.send_json(resp.dict())
+            yield  "Sorry, something went wrong. Try again." + "\n"
 
+    return StreamingResponse(stream())
 
 if __name__ == "__main__":
     import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8080)
 
-    uvicorn.run(app, host="0.0.0.0", port=9000)
diff --git a/requirements.txt b/requirements.txt
index bccd70fa7..9f01d3572 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,4 +11,6 @@ faiss-cpu
 beautifulsoup4
 unstructured
 libmagic
-tiktoken
\ No newline at end of file
+tiktoken
+weaviate-client
+anthropic
\ No newline at end of file

From da1af44e31ec4aa7d1a007c7014a51a9f70c5231 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Thu, 10 Aug 2023 17:30:03 -0700
Subject: [PATCH 05/36] repo ingestion

---
 ingest.py | 42 ++++++++++++++++++++++++++++++++++++++----
 1 file changed, 38 insertions(+), 4 deletions(-)

diff --git a/ingest.py b/ingest.py
index ecc4a0972..50b78cb8c 100644
--- a/ingest.py
+++ b/ingest.py
@@ -2,16 +2,44 @@
 from bs4 import BeautifulSoup as Soup
 import weaviate
 import os
+from git import Repo
+import shutil
 
 from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
 from langchain.embeddings import OpenAIEmbeddings, CohereEmbeddings
 from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Weaviate
+from langchain.document_transformers import Html2TextTransformer
+from langchain.text_splitter import Language
+from langchain.document_loaders.generic import GenericLoader
+from langchain.document_loaders.parsers import LanguageParser
 
 WEAVIATE_URL=os.environ["WEAVIATE_URL"]
 WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
 
+def ingest_repo():
+    repo_path = "/Users/mollycantillon/Desktop/test_repo"
+    if os.path.exists(repo_path):
+        shutil.rmtree(repo_path)
+
+    repo = Repo.clone_from("https://github.com/langchain-ai/langchain", to_path=repo_path)
+
+    loader = GenericLoader.from_filesystem(
+        repo_path+"/libs/langchain/langchain",
+        glob="**/*",
+        suffixes=[".py"],
+        parser=LanguageParser(language=Language.PYTHON, parser_threshold=500)
+    )
+    documents_repo = loader.load()
+    len(documents_repo)
+
+    python_splitter = RecursiveCharacterTextSplitter.from_language(language=Language.PYTHON, 
+                                                                chunk_size=1000, 
+                                                                chunk_overlap=200)
+    texts = python_splitter.split_documents(documents_repo)
+    return texts
+
 def ingest_docs():
     """Get documents from web pages."""
 
@@ -26,14 +54,20 @@ def ingest_docs():
 
     documents = []
     for url in urls:
-        loader = RecursiveUrlLoader(url=url, max_depth=1 if url == urls[0] else 3, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=False)
+        loader = RecursiveUrlLoader(url=url, max_depth=1 if url == urls[0] else 3, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=False if url=urls[0] else True)
         documents += loader.load()
     
+    html2text = Html2TextTransformer()
+    docs_transformed = html2text.transform_documents(documents)
+    
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-
-    print('before splitting', len(documents))
-    docs = text_splitter.split_documents(documents)
+    
+    print('before splitting', len(docs_transformed))
+    docs = text_splitter.split_documents(docs_transformed)
     print('after splitting', len(docs))
+    
+    repo_docs = ingest_repo()
+    docs += repo_docs
 
     embeddings = OpenAIEmbeddings(chunk_size=200) # rate limit
 

From 5992bb518f187120406ae6f65d5bc227968ccb67 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 14 Aug 2023 18:21:27 -0500
Subject: [PATCH 06/36] adding chat history, changing frontend

---
 .../app/components/ChatMessageBubble.tsx      |   16 +
 chat-langchain/app/components/ChatWindow.tsx  |  290 ++
 chat-langchain/app/components/chat.tsx        |  345 +-
 chat-langchain/app/globals.css                |   42 +-
 chat-langchain/app/layout.tsx                 |    6 +-
 chat-langchain/app/page.tsx                   |   84 +-
 chat-langchain/package-lock.json              | 2957 ++++++++++++++++-
 chat-langchain/package.json                   |    8 +
 ingest.py                                     |    4 +-
 main.py                                       |   99 +-
 10 files changed, 3647 insertions(+), 204 deletions(-)
 create mode 100644 chat-langchain/app/components/ChatMessageBubble.tsx
 create mode 100644 chat-langchain/app/components/ChatWindow.tsx

diff --git a/chat-langchain/app/components/ChatMessageBubble.tsx b/chat-langchain/app/components/ChatMessageBubble.tsx
new file mode 100644
index 000000000..f8d3c3462
--- /dev/null
+++ b/chat-langchain/app/components/ChatMessageBubble.tsx
@@ -0,0 +1,16 @@
+import type { Message } from "ai/react";
+
+export function ChatMessageBubble(props: { message: Message, aiEmoji?: string }) {
+  const colorClassName =
+    props.message.role === "user" ? "bg-sky-600" : "bg-slate-50 text-black";
+  const alignmentClassName =
+    props.message.role === "user" ? "mr-auto" : "ml-auto";
+  const prefix = props.message.role === "user" ? "🧑" : props.aiEmoji;
+  return (
+    <div
+      className={`${alignmentClassName} ${colorClassName} rounded px-4 py-2 max-w-[80%] mb-8 inline-block whitespace-pre-wrap`}
+    >
+        {prefix} <div dangerouslySetInnerHTML={{ __html: props.message.content }}></div>
+    </div>
+  );
+}
\ No newline at end of file
diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
new file mode 100644
index 000000000..54cba92e2
--- /dev/null
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -0,0 +1,290 @@
+"use client";
+
+import React, { useRef, useState, useEffect } from "react";
+import type { FormEvent } from "react";
+import { ChatMessageBubble } from "../components/ChatMessageBubble";
+import { marked } from "marked";
+import { Renderer } from "marked";
+import hljs from "highlight.js";
+import "highlight.js/styles/gradient-dark.css";
+
+export function ChatWindow(props: {
+  endpoint: string;
+  emptyStateComponent: React.ReactElement;
+  placeholder?: string;
+  titleText?: string;
+}) {
+  const messageContainerRef = useRef<HTMLDivElement | null>(null);
+  const [messages, setMessages] = useState<
+    Array<{
+      id: string;
+      message: string;
+      role: "function" | "user" | "assistant" | "system";
+    }>
+  >([]);
+  const [input, setInput] = useState("");
+  const [model, setModel] = useState("openai");
+  const [isLoading, setIsLoading] = useState(false);
+  const [feedback, setFeedback] = useState<number | null>(null);
+  const [hasInteracted, setHasInteracted] = useState(false);
+  const [chatHistory, setChatHistory] = useState<{question: string, result: string}[]>([]);
+
+  const {
+    endpoint,
+    emptyStateComponent,
+    placeholder,
+    titleText = "An LLM",
+  } = props;
+
+  function sendMessage(e: FormEvent<HTMLFormElement>) {
+    e.preventDefault();
+    if (messageContainerRef.current) {
+      messageContainerRef.current.classList.add("grow");
+    }
+    if (isLoading) {
+      return;
+    }
+    setMessages((prevMessages) => [
+      ...prevMessages,
+      { id: Math.random().toString(), message: input.trim(), role: "user" },
+    ]);
+    setFeedback(null);
+    setIsLoading(true);
+    fetch(endpoint, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        message: input,
+        model: model,
+        history: chatHistory,
+      }),
+    }).then((response) => {
+      if (!response.body) {
+        throw new Error("Response body is null");
+      }
+      const reader = response.body.getReader();
+      const decoder = new TextDecoder("utf-8");
+
+      let accumulatedMessage = "";
+      let messageIndex: number | null = null;
+
+      let renderer = new Renderer();
+      renderer.paragraph = function(text) {
+        return text;
+      };
+      renderer.code = function (code, language) {
+        const validLanguage = hljs.getLanguage(language || "")
+          ? language
+          : "plaintext";
+        const highlightedCode = hljs.highlight(
+          validLanguage || "plaintext",
+          code
+        ).value;
+        return `<pre class="highlight bg-gray-700" style="padding: 5px; border-radius: 5px; overflow: auto; word-wrap: break-word;"><code class="${language}" style="color: #d6e2ef;">${highlightedCode}</code></pre>`;
+      };
+      marked.setOptions({ renderer });
+
+      reader
+      .read()
+      .then(function processText(
+        res: ReadableStreamReadResult<Uint8Array>
+      ): Promise<void> {
+        const { done, value } = res;
+        if (done) {
+          console.log("Stream complete");
+          setChatHistory(prevChatHistory => [...prevChatHistory, {question: input, result: accumulatedMessage}]);
+          return Promise.resolve();
+        }
+    
+        let result = decoder.decode(value);
+        accumulatedMessage += result;
+        let parsedResult = marked.parse(accumulatedMessage);
+    
+        setMessages((prevMessages) => {
+            let newMessages = [...prevMessages];
+            if (messageIndex === null) {
+              messageIndex = newMessages.length;
+              newMessages.push({ id: Math.random().toString(), message: parsedResult.trim(), role: "assistant" });
+            } else {
+              newMessages[messageIndex].message = parsedResult.trim();
+            }
+            return newMessages;
+          });
+
+        setIsLoading(false);
+        return reader.read().then(processText);
+      })
+      .catch((error) => {
+        console.error("Error:", error);
+      });
+    });
+  }
+
+  const sendFeedback = (score: number | null) => {
+    if (feedback !== null) return;
+
+    setFeedback(score);
+    fetch("http://0.0.0.0:8080/feedback", {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        score: score,
+      }),
+    })
+      .then((response) => response.json())
+      .then((data) => {
+        if (data.code === 200) {
+          score == 1 ? animateButton("upButton") : animateButton("downButton");
+        }
+        console.log(data);
+      })
+      .catch((error) => {
+        console.error("Error:", error);
+      });
+  };
+
+  const animateButton = (buttonId: string) => {
+    const button = document.getElementById(buttonId);
+    button!.classList.add("animate-ping");
+    setTimeout(() => {
+      button!.classList.remove("animate-ping");
+    }, 500);
+  };
+
+  const viewTrace = () => {
+    console.log("IN VIEW TRACE")
+    fetch("http://0.0.0.0:8080/get_trace", {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+    })
+      .then((response) => response.text())
+      .then((data) => {
+        console.log(data);
+        const url = data.replace(/['"]+/g, "");
+        window.open(url, "_blank");
+      })
+      .catch((error) => {
+        console.error("Error:", error);
+      });
+  };
+
+  return (
+    <div
+      className={`flex flex-col items-center p-8 rounded grow max-h-[calc(100%-2rem)] ${
+        messages.length > 0 ? "border" : ""
+      }`}
+    >
+      <h2 className={`${messages.length > 0 ? "" : "hidden"} text-2xl mb-1`}>
+        {titleText}
+      </h2>
+      <div
+        className="flex flex-col-reverse w-full mb-2 overflow-auto"
+        ref={messageContainerRef}
+      >
+        {messages.length > 0
+          ? [...messages]
+              .reverse()
+              .map((m) => (
+                <ChatMessageBubble
+                  key={m.id}
+                  message={{ id: m.id, content: m.message, role: m.role }}
+                  aiEmoji="🦜"
+                ></ChatMessageBubble>
+              ))
+          : emptyStateComponent}
+      </div>
+
+      <div className="flex w-full flex-row-reverse mb-2">
+            <button
+              type="button"
+              className="text-xs border rounded p-1 float-right"
+              onClick={() => viewTrace()}
+            >
+              🛠️ view trace
+            </button>
+        </div>
+
+      <form onSubmit={sendMessage} className="flex w-full">
+        <input
+          className="flex-grow mr-2 p-4 rounded"
+          placeholder={placeholder}
+          onChange={(e) => setInput(e.target.value)}
+          style={{minWidth: '50px'}}
+        />
+        <select
+          id="modelType"
+          className="bg-gray-800 text-white rounded p-2 mr-2 w-full sm:w-auto"
+          value={model}
+          onChange={(e) => setModel(e.target.value)}
+        >
+          <option value="openai">OpenAI</option>
+          <option value="anthropic">Anthropic</option>
+        </select>
+        <button
+          type="button"
+          className={`w-16 sm:w-auto text-white border rounded p-2 mr-1 hover:border-green-400 ${
+            feedback === 1 ? "bg-green-100" : ""
+          }`}
+          onClick={() => {
+            if (feedback === null) {
+              sendFeedback(1);
+            }
+          }}
+          disabled={feedback !== null || !hasInteracted}
+          id="upButton"
+        >
+          👍
+        </button>
+        <button
+          type="button"
+          className={`w-16 sm:w-auto text-white border rounded p-2 mr-2 hover:border-red-400 ${
+            feedback === 0 ? "bg-red-100" : ""
+          }`}
+          onClick={() => {
+            if (feedback === null) {
+              sendFeedback(0);
+            }
+          }}
+          disabled={feedback !== null || !hasInteracted}
+          id="downButton"
+        >
+          👎
+        </button>
+        <button
+          type="submit"
+          className="flex-shrink p-2 w-16 sm:w-auto bg-sky-600 rounded"
+        >
+          <div
+            role="status"
+            className={`${isLoading ? "" : "hidden"} flex justify-center`}
+          >
+            <svg
+              aria-hidden="true"
+              className="w-6 h-6 text-white animate-spin dark:text-white fill-sky-800"
+              viewBox="0 0 100 101"
+              fill="none"
+              xmlns="http://www.w3.org/2000/svg"
+            >
+              <path
+                d="M100 50.5908C100 78.2051 77.6142 100.591 50 100.591C22.3858 100.591 0 78.2051 0 50.5908C0 22.9766 22.3858 0.59082 50 0.59082C77.6142 0.59082 100 22.9766 100 50.5908ZM9.08144 50.5908C9.08144 73.1895 27.4013 91.5094 50 91.5094C72.5987 91.5094 90.9186 73.1895 90.9186 50.5908C90.9186 27.9921 72.5987 9.67226 50 9.67226C27.4013 9.67226 9.08144 27.9921 9.08144 50.5908Z"
+                fill="currentColor"
+              />
+              <path
+                d="M93.9676 39.0409C96.393 38.4038 97.8624 35.9116 97.0079 33.5539C95.2932 28.8227 92.871 24.3692 89.8167 20.348C85.8452 15.1192 80.8826 10.7238 75.2124 7.41289C69.5422 4.10194 63.2754 1.94025 56.7698 1.05124C51.7666 0.367541 46.6976 0.446843 41.7345 1.27873C39.2613 1.69328 37.813 4.19778 38.4501 6.62326C39.0873 9.04874 41.5694 10.4717 44.0505 10.1071C47.8511 9.54855 51.7191 9.52689 55.5402 10.0491C60.8642 10.7766 65.9928 12.5457 70.6331 15.2552C75.2735 17.9648 79.3347 21.5619 82.5849 25.841C84.9175 28.9121 86.7997 32.2913 88.1811 35.8758C89.083 38.2158 91.5421 39.6781 93.9676 39.0409Z"
+                fill="currentFill"
+              />
+            </svg>
+            <span className="sr-only">Loading...</span>
+          </div>
+          <span className={isLoading ? "hidden" : ""}>Send</span>
+        </button>
+      </form>
+    </div>
+  );
+}
diff --git a/chat-langchain/app/components/chat.tsx b/chat-langchain/app/components/chat.tsx
index 9b7e5e8cd..c1597be50 100644
--- a/chat-langchain/app/components/chat.tsx
+++ b/chat-langchain/app/components/chat.tsx
@@ -1,22 +1,32 @@
 "use client";
+
 import React, { useEffect, useRef, useState } from "react";
+import { marked } from "marked";
+import { Renderer } from "marked";
+import hljs from "highlight.js";
+import "highlight.js/styles/gradient-dark.css";
 
 const Chat = () => {
   const [messages, setMessages] = useState<
-    Array<{ sender: string; message: string; isCode: boolean }>
+    Array<{ sender: string; message: string }>
   >([]);
   const [model, setModel] = useState("openai");
   const messagesEndRef = useRef<HTMLDivElement>(null);
-  const [message, setMessage] = useState(""); // Add this line
+  const [message, setMessage] = useState("");
+  const [hasInteracted, setHasInteracted] = useState(false);
 
   const sendMessage = (event: { preventDefault: () => void }) => {
     event.preventDefault();
     if (message === "") {
       return;
     }
-    let isCode = false;
+    setFeedback(null);
+    setMessages((prevMessages) => [
+      ...prevMessages,
+      { sender: "You", message },
+    ]);
 
-    fetch("http://0.0.0.0:8080/chat", {
+    fetch("https://chat-langchain.fly.dev/chat", {
       method: "POST",
       headers: {
         "Content-Type": "application/json",
@@ -25,147 +35,234 @@ const Chat = () => {
         message: message,
         model: model,
       }),
-    })
-      .then((response) => {
-        const reader = response.body.getReader();
-        const decoder = new TextDecoder("utf-8");
-
-        let accumulatedMessage = "";
-        let prevIsCode = null;
-
-        reader
-          .read()
-          .then(function processText({
-            done,
-            value,
-          }: {
-            done: any;
-            value: any;
-          }) {
-            if (done) {
-              console.log("Stream complete");
-              return;
-            }
+    }).then((response) => {
+      if (!response.body) {
+        throw new Error("Response body is null");
+      }
+      setMessage("");
+      setHasInteracted(true);
+      const reader = response.body.getReader();
+      const decoder = new TextDecoder("utf-8");
+
+      let accumulatedMessage = "";
+      let messageIndex: number | null = null;
+
+      let renderer = new Renderer();
+      renderer.code = function (code, language) {
+        const validLanguage = hljs.getLanguage(language || "")
+          ? language
+          : "plaintext";
+        const highlightedCode = hljs.highlight(
+          validLanguage || "plaintext",
+          code
+        ).value;
+        return `<pre class="highlight bg-gray-700" style="padding: 10px; margin: 10px; border-radius: 5px; overflow: auto;"><code class="${language}" style="color: #d6e2ef;">${highlightedCode}</code></pre>`;
+      };
+      marked.setOptions({ renderer });
+
+      reader
+        .read()
+        .then(function processText(
+          res: ReadableStreamReadResult<Uint8Array>
+        ): Promise<void> {
+          const { done, value } = res;
+          if (done) {
+            console.log("Stream complete");
+            return Promise.resolve();
+          }
 
-            let result = decoder.decode(value);
-            if (result.includes("``")) {
-              isCode = !isCode;
+          let result = decoder.decode(value);
+          accumulatedMessage += result;
+          let parsedResult = marked.parse(accumulatedMessage);
+
+          setMessages((prevMessages) => {
+            let newMessages = [...prevMessages];
+            if (messageIndex === null) {
+              messageIndex = newMessages.length;
+              newMessages.push({
+                sender: "Bot",
+                message: parsedResult.trim(),
+              });
             } else {
-              result = result.replace(/`/g, "");
-              accumulatedMessage += result;
+              newMessages[messageIndex].message = parsedResult.trim();
             }
-
-            setMessages((prevMessages) => {
-              let newMessages = [...prevMessages];
-              if (isCode !== prevIsCode) {
-                newMessages.push({
-                  sender: isCode ? "" : "Bot",
-                  message: accumulatedMessage.trim(),
-                  isCode: isCode,
-                });
-                if (newMessages.length > 1) {
-                  accumulatedMessage = "";
-                }
-              } else if (newMessages.length > 0) {
-                newMessages[newMessages.length - 1] = {
-                  sender: isCode ? "" : "Bot",
-                  message: accumulatedMessage.trim(),
-                  isCode: isCode,
-                };
-              }
-              prevIsCode = isCode;
-              return newMessages;
-            });
-            setMessage("");
-            return reader.read().then(processText);
+            return newMessages;
           });
+          return reader.read().then(processText);
+        })
+        .catch((error) => {
+          console.error("Error:", error);
+        });
+    });
+  };
+
+  useEffect(() => {
+    if (messagesEndRef.current) {
+      messagesEndRef.current.scrollIntoView({ behavior: "smooth" });
+    }
+  }, [messages]);
+
+  const [feedback, setFeedback] = useState<number | null>(null);
+
+  const sendFeedback = (score: number | null) => {
+    if (feedback !== null) return;
+
+    setFeedback(score);
+    fetch("https://chat-langchain.fly.dev/feedback", {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        score: score,
+      }),
+    })
+      .then((response) => response.json())
+      .then((data) => {
+        if (data.code === 200) {
+          score == 1 ? animateButton("upButton") : animateButton("downButton");
+        }
+        console.log(data);
       })
       .catch((error) => {
         console.error("Error:", error);
       });
   };
 
-  useEffect(() => {
-    messagesEndRef.current?.scrollIntoView({ behavior: "smooth" });
-  }, [messagesEndRef]);
+  const animateButton = (buttonId: string) => {
+    const button = document.getElementById(buttonId);
+    button!.classList.add("animate-ping");
+    setTimeout(() => {
+      button!.classList.remove("animate-ping");
+    }, 500);
+  };
+
+  const viewTrace = () => {
+    fetch("https://chat-langchain.fly.dev/get_trace", {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+    })
+      .then((response) => response.text()) // change this to handle text response
+      .then((data) => {
+        console.log(data);
+        const url = data.replace(/['"]+/g, "");
+        window.open(url, "_blank");
+      })
+      .catch((error) => {
+        console.error("Error:", error);
+      });
+  };
 
   return (
-    <div className="rounded-2xl border-zinc-100  lg:border lg:p-6 bg-slate-200">
+    <div className="rounded-2xl border-zinc-100 lg:border lg:p-6 bg-slate-200 w-full sm:w-[60%] lg:w-[75%]">
       <div className="w-full mb-5 rounded-lg px-4 py-5 shadow-lg ring-1 ring-zinc-100 sm:px-6 bg-slate-100">
-        <h4 className="text-center text-3xl font-sans font-medium">
-          Chat Langchain
+        <h4 className="text-center text-3xl font-sans font-bold pb-2 text-transparent bg-clip-text bg-gradient-to-r from-teal-600 to-teal-800">
+          Chat LangChain
         </h4>
-        <span className="mx-auto flex font-mono flex-grow text-gray-400 clear-both mb-5 mt-1 text-sm justify-center">
-          A chatbot for everything Langchain.
+        <span className="mx-auto flex font-light flex-grow text-gray-400 clear-both text-sm justify-center font-mono">
+          A chatbot for everything LangChain.
         </span>
-        <hr className="border-gray-500 my-5" />
-        <div
-          id="messages"
-          className="overflow-auto m-8"
-          style={{ maxHeight: "500px" }}
-        >
-          {messages.map((msg, index) => (
-            <div
-              key={index}
-              className={`flex ${
-                msg.sender === "You" ? "justify-end" : "justify-start"
-              }`}
+        <hr className="border-gray-500 my-4" />
+        <div className="flex flex-col">
+          <div>
+            <button
+              type="button"
+              className="text-black text-xs border rounded p-1 float-right"
+              onClick={() => viewTrace()}
             >
+              🛠️ view trace
+            </button>
+          </div>
+          <div
+            className="my-2 font-sans font-normal max-h-[calc(100vh-28rem)] overflow-y-scroll"
+          >
+            {messages.map((msg, index) => (
               <div
-                className={
-                  msg.sender === "You" ? "client-message" : "server-message"
-                }
+                key={index}
+                className={`flex ${
+                  msg.sender === "You" ? "justify-end" : "justify-start"
+                }`}
               >
-                {msg.sender && <strong>{msg.sender}: </strong>}
-                {msg.isCode ? (
-                  <code
-                    style={{
-                      color: "white",
-                      backgroundColor: "rgba(0, 0, 0, 0.8)",
-                      opacity: "0.9",
-                      padding: "4px",
-                      margin: "4px",
-                      borderRadius: "5px",
-                      overflow: "auto",
-                      boxShadow: "0px 0px 10px rgba(0, 0, 0, 0.5))",
-                      transition: "ease-in-out 0.15s",
-                    }}
-                    dangerouslySetInnerHTML={{ __html: msg.message }}
-                  ></code>
-                ) : (
-                  msg.message
-                )}
+                <div
+                  className={
+                    msg.sender === "You"
+                      ? "bg-gray-700 border border-gray-600 rounded-md p-2 m-2 text-xs sm:text-base text-white text-right max-w-[80%] inline-block"
+                      : "bg-slate-200 border border-gray-200 rounded-md p-2 m-2 text-xs sm:text-base text-left max-w-[80%] inline-block"
+                  }
+                >
+                  {msg.sender && (
+                    <strong className="font-medium">{msg.sender}: </strong>
+                  )}
+                  <div dangerouslySetInnerHTML={{ __html: msg.message }}></div>
+                </div>
               </div>
+            ))}
+            <div ref={messagesEndRef} />
+          </div>
+          <form
+            className="flex flex-col sm:flex-row justify-between mt-5"
+            onSubmit={sendMessage}
+          >
+            <input
+              type="text"
+              className="w-full sm:w-[70%] text-gray-800 rounded p-2 mr-2 focus:outline-none focus:ring-1 focus:ring-teal-600"
+              placeholder="Write your question"
+              id="messageText"
+              value={message}
+              onChange={(e) => setMessage(e.target.value)}
+            />
+            <div className="flex flex-row mt-2 sm:mt-0">
+              <select
+                id="modelType"
+                className="w-full bg-gray-800 text-white rounded p-2 mr-2"
+                value={model}
+                onChange={(e) => setModel(e.target.value)}
+              >
+                <option value="openai">OpenAI</option>
+                <option value="anthropic">Anthropic</option>
+              </select>
+              <button
+                type="button"
+                className={`w-16 sm:w-auto text-white border rounded p-2 mr-1 hover:border-green-400 ${
+                  feedback === 1 ? "bg-green-100" : ""
+                }`}
+                onClick={() => {
+                  if (feedback === null) {
+                    sendFeedback(1);
+                  }
+                }}
+                disabled={feedback !== null || !hasInteracted}
+                id="upButton"
+              >
+                👍
+              </button>
+              <button
+                type="button"
+                className={`w-16 sm:w-auto text-white border rounded p-2 mr-2 hover:border-red-400 ${
+                  feedback === 0 ? "bg-red-100" : ""
+                }`}
+                onClick={() => {
+                  if (feedback === null) {
+                    sendFeedback(0);
+                  }
+                }}
+                disabled={feedback !== null || !hasInteracted}
+                id="downButton"
+              >
+                👎
+              </button>
+              <button
+                id="send"
+                type="submit"
+                className="w-16 sm:w-auto text-sm bg-gradient-to-r from-teal-600 to-teal-800 text-white rounded p-2"
+              >
+                Send
+              </button>
             </div>
-          ))}
-          <div ref={messagesEndRef} />
+          </form>
         </div>
-        <form className="flex justify-between mt-5" onSubmit={sendMessage}>
-          <input
-            type="text"
-            className="w-4/5 bg-gray-800 text-white rounded p-2 mr-2"
-            placeholder="Write your question"
-            id="messageText"
-            value={message}
-            onChange={(e) => setMessage(e.target.value)}
-          />
-          <select
-            id="modelType"
-            className="w-1/5 bg-gray-800 text-white rounded p-2 mr-2"
-            value={model}
-            onChange={(e) => setModel(e.target.value)}
-          >
-            <option value="openai">OpenAI</option>
-            <option value="anthropic">Anthropic</option>
-          </select>
-          <button
-            id="send"
-            type="submit"
-            className="bg-blue-500 text-white rounded p-2"
-          >
-            Send
-          </button>
-        </form>
       </div>
     </div>
   );
diff --git a/chat-langchain/app/globals.css b/chat-langchain/app/globals.css
index fd81e8858..bf85ac97b 100644
--- a/chat-langchain/app/globals.css
+++ b/chat-langchain/app/globals.css
@@ -2,26 +2,32 @@
 @tailwind components;
 @tailwind utilities;
 
-:root {
-  --foreground-rgb: 0, 0, 0;
-  --background-start-rgb: 214, 219, 220;
-  --background-end-rgb: 255, 255, 255;
+body {
+  color: #f8f8f8;
+  background: #131318;
 }
 
-@media (prefers-color-scheme: dark) {
-  :root {
-    --foreground-rgb: 255, 255, 255;
-    --background-start-rgb: 0, 0, 0;
-    --background-end-rgb: 0, 0, 0;
-  }
+body input,
+body textarea {
+  color: black;
 }
 
-body {
-  color: rgb(var(--foreground-rgb));
-  background: linear-gradient(
-      to bottom,
-      transparent,
-      rgb(var(--background-end-rgb))
-    )
-    rgb(var(--background-start-rgb));
+a {
+  color: #2d7bd4;
 }
+
+a:hover {
+  border-bottom: 1px solid;
+}
+
+p {
+  margin: 8px 0;
+}
+
+code {
+  color: #ffa500;
+}
+
+li {
+  padding: 4px;
+}
\ No newline at end of file
diff --git a/chat-langchain/app/layout.tsx b/chat-langchain/app/layout.tsx
index ae8456212..42f08bbee 100644
--- a/chat-langchain/app/layout.tsx
+++ b/chat-langchain/app/layout.tsx
@@ -16,7 +16,11 @@ export default function RootLayout({
 }) {
   return (
     <html lang="en">
-      <body className={inter.className}>{children}</body>
+      <body className={inter.className}>
+        <div className="flex flex-col m-12 h-[calc(100vh-6rem)]">
+          {children}
+        </div>
+      </body>
     </html>
   )
 }
diff --git a/chat-langchain/app/page.tsx b/chat-langchain/app/page.tsx
index dc95882c2..da50b19fc 100644
--- a/chat-langchain/app/page.tsx
+++ b/chat-langchain/app/page.tsx
@@ -1,15 +1,77 @@
-import Image from 'next/image'
-import axios from 'axios'
-import Chat from "../app/components/chat"
+import Image from "next/image";
+import Chat from "../app/components/chat";
+import { ChatWindow } from "../app/components/ChatWindow";
 
 export default function Home() {
+  const InfoCard = (
+    <div className="p-8 rounded bg-[#25252d]">
+      <h1 className="text-4xl mb-4">Chat LangChain 🦜🔗</h1>
+      <ul>
+        <li className="text-l">
+          🤝
+          <span className="ml-2">
+            This template showcases a simple chatbot using{" "}
+            <a href="https://js.langchain.com/" target="_blank">
+              LangChain.js
+            </a>{" "}
+            in a{" "}
+            <a href="https://nextjs.org/" target="_blank">
+              Next.js
+            </a>{" "}
+            project.
+          </span>
+        </li>
+        <li className="text-l">
+          🔁
+          <span className="ml-2">
+            It also uses the Vercel{" "}
+            <a href="https://sdk.vercel.ai/docs" target="_blank">
+              AI SDK
+            </a>{" "}
+            for streaming HTTP responses and the UI.
+          </span>
+        </li>
+        <li className="text-l">
+          💻
+          <span className="ml-2">
+            You can find the prompt and model logic for this use-case in{" "}
+            <code>app/api/chat/route.ts</code>.
+          </span>
+        </li>
+        <li>
+          🏴‍☠️
+          <span className="ml-2">
+            By default, the bot is pretending to be a pirate, but you can change
+            the prompt to whatever you want!
+          </span>
+        </li>
+        <li className="text-l">
+          🎨
+          <span className="ml-2">
+            The main frontend logic is found in <code>app/page.tsx</code>.
+          </span>
+        </li>
+        <li className="text-l">
+          👇<span className="ml-2">Try asking, "What is it like to be a pirate?" or another question below!</span>
+        </li>
+      </ul>
+    </div>
+  );
   return (
-    <main className="flex min-h-screen flex-col items-center justify-between p-24 bg-white">
-      <div className="z-10 max-w-5xl w-full items-center justify-between font-light text-sm lg:flex">
-        <Chat/>
-      </div>
-    </main>
-  )
+    <>
+      {/* <main className="flex h-[100vh] overflow-hidden flex-col items-center justify-center bg-grey-100">
+        <div className="z-10 w-[100vw] w items-center justify-center font-normal text-sm lg:flex lg:max-h-[40rem] sm:max-h-[20rem] text-center p-4 md:p-0"> */}
+          {/* <Chat/> */}
+          <ChatWindow
+            // endpoint="https://chat-langchain.fly.dev/chat"
+            endpoint="http://0.0.0.0:8080/chat"
+            emoji="🏴‍☠️"
+            titleText="ChatLangChain"
+            placeholder="Write your question"
+            emptyStateComponent={InfoCard}
+          ></ChatWindow>
+        {/* </div>
+      </main> */}
+    </>
+  );
 }
-
-
diff --git a/chat-langchain/package-lock.json b/chat-langchain/package-lock.json
index e5842bf94..3b4db8860 100644
--- a/chat-langchain/package-lock.json
+++ b/chat-langchain/package-lock.json
@@ -8,16 +8,24 @@
       "name": "chat-langchain",
       "version": "0.1.0",
       "dependencies": {
+        "@emotion/react": "^11.11.1",
+        "@nanostores/react": "github:ai/react",
+        "@types/marked": "^5.0.1",
         "@types/node": "20.4.9",
         "@types/react": "18.2.20",
         "@types/react-dom": "18.2.7",
+        "ai": "^2.1.33",
         "autoprefixer": "10.4.14",
         "eslint": "8.46.0",
         "eslint-config-next": "13.4.13",
+        "highlight.js": "^11.8.0",
+        "langchain": "^0.0.127",
+        "marked": "^7.0.2",
         "next": "13.4.13",
         "postcss": "8.4.27",
         "react": "18.2.0",
         "react-dom": "18.2.0",
+        "react-toastify": "^9.1.3",
         "tailwindcss": "3.3.3",
         "typescript": "5.1.6"
       }
@@ -41,6 +49,231 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/@ampproject/remapping": {
+      "version": "2.2.1",
+      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.2.1.tgz",
+      "integrity": "sha512-lFMjJTrFL3j7L9yBxwYfCq2k6qqwHyzuUl/XBnif78PWTJYyL/dfowQHWE3sp6U6ZzqWiiIZnpTMO96zhkjwtg==",
+      "peer": true,
+      "dependencies": {
+        "@jridgewell/gen-mapping": "^0.3.0",
+        "@jridgewell/trace-mapping": "^0.3.9"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@anthropic-ai/sdk": {
+      "version": "0.5.10",
+      "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.5.10.tgz",
+      "integrity": "sha512-P8xrIuTUO/6wDzcjQRUROXp4WSqtngbXaE4GpEu0PhEmnq/1Q8vbF1s0o7W07EV3j8zzRoyJxAKovUJtNXH7ew==",
+      "dependencies": {
+        "@types/node": "^18.11.18",
+        "@types/node-fetch": "^2.6.4",
+        "abort-controller": "^3.0.0",
+        "agentkeepalive": "^4.2.1",
+        "digest-fetch": "^1.3.0",
+        "form-data-encoder": "1.7.2",
+        "formdata-node": "^4.3.2",
+        "node-fetch": "^2.6.7"
+      }
+    },
+    "node_modules/@anthropic-ai/sdk/node_modules/@types/node": {
+      "version": "18.17.5",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.17.5.tgz",
+      "integrity": "sha512-xNbS75FxH6P4UXTPUJp/zNPq6/xsfdJKussCWNOnz4aULWIRwMgP1LgaB5RiBnMX1DPCYenuqGZfnIAx5mbFLA=="
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.22.10",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.10.tgz",
+      "integrity": "sha512-/KKIMG4UEL35WmI9OlvMhurwtytjvXoFcGNrOvyG9zIzA8YmPjVtIZUf7b05+TPO7G7/GEmLHDaoCgACHl9hhA==",
+      "dependencies": {
+        "@babel/highlight": "^7.22.10",
+        "chalk": "^2.4.2"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/ansi-styles": {
+      "version": "3.2.1",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz",
+      "integrity": "sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==",
+      "dependencies": {
+        "color-convert": "^1.9.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/chalk": {
+      "version": "2.4.2",
+      "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz",
+      "integrity": "sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==",
+      "dependencies": {
+        "ansi-styles": "^3.2.1",
+        "escape-string-regexp": "^1.0.5",
+        "supports-color": "^5.3.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/color-convert": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-1.9.3.tgz",
+      "integrity": "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==",
+      "dependencies": {
+        "color-name": "1.1.3"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/color-name": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz",
+      "integrity": "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw=="
+    },
+    "node_modules/@babel/code-frame/node_modules/escape-string-regexp": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz",
+      "integrity": "sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg==",
+      "engines": {
+        "node": ">=0.8.0"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/has-flag": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
+      "integrity": "sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw==",
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/supports-color": {
+      "version": "5.5.0",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
+      "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==",
+      "dependencies": {
+        "has-flag": "^3.0.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/helper-module-imports": {
+      "version": "7.22.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.22.5.tgz",
+      "integrity": "sha512-8Dl6+HD/cKifutF5qGd/8ZJi84QeAKh+CEe1sBzz8UayBBGg1dAIJrdHOcOM5b2MpzWL2yuotJTtGjETq0qjXg==",
+      "dependencies": {
+        "@babel/types": "^7.22.5"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-string-parser": {
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@@ -6173,6 +9019,11 @@
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@@ -6181,6 +9032,11 @@
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@@ -6297,11 +9153,35 @@
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@@ -6311,6 +9191,25 @@
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   }
 }
diff --git a/chat-langchain/package.json b/chat-langchain/package.json
index 2e264cf65..5d1b63404 100644
--- a/chat-langchain/package.json
+++ b/chat-langchain/package.json
@@ -9,16 +9,24 @@
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+    "@emotion/react": "^11.11.1",
+    "@nanostores/react": "github:ai/react",
+    "@types/marked": "^5.0.1",
     "@types/node": "20.4.9",
     "@types/react": "18.2.20",
     "@types/react-dom": "18.2.7",
+    "ai": "^2.1.33",
     "autoprefixer": "10.4.14",
     "eslint": "8.46.0",
     "eslint-config-next": "13.4.13",
+    "highlight.js": "^11.8.0",
+    "langchain": "^0.0.127",
+    "marked": "^7.0.2",
     "next": "13.4.13",
     "postcss": "8.4.27",
     "react": "18.2.0",
     "react-dom": "18.2.0",
+    "react-toastify": "^9.1.3",
     "tailwindcss": "3.3.3",
     "typescript": "5.1.6"
   }
diff --git a/ingest.py b/ingest.py
index 50b78cb8c..a43b9110e 100644
--- a/ingest.py
+++ b/ingest.py
@@ -35,7 +35,7 @@ def ingest_repo():
     len(documents_repo)
 
     python_splitter = RecursiveCharacterTextSplitter.from_language(language=Language.PYTHON, 
-                                                                chunk_size=1000, 
+                                                                chunk_size=2000, 
                                                                 chunk_overlap=200)
     texts = python_splitter.split_documents(documents_repo)
     return texts
@@ -54,7 +54,7 @@ def ingest_docs():
 
     documents = []
     for url in urls:
-        loader = RecursiveUrlLoader(url=url, max_depth=1 if url == urls[0] else 3, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=False if url=urls[0] else True)
+        loader = RecursiveUrlLoader(url=url, max_depth=1 if url == urls[0] else 3, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=False if url == urls[0] else True)        
         documents += loader.load()
     
     html2text = Html2TextTransformer()
diff --git a/main.py b/main.py
index 5ecf854cb..e53da6884 100644
--- a/main.py
+++ b/main.py
@@ -6,22 +6,24 @@
 from typing import Any, Optional, Generator
 import os
 import logging
-import json
 
 import langchain
 from langchain.chat_models import ChatOpenAI, ChatAnthropic
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnablePassthrough
 from langchain.vectorstores import VectorStore, Weaviate
+from langchain.prompts.prompt import PromptTemplate
+from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
+from langchain.schema.runnable import RunnableConfig, RunnableMap, RunnablePassthrough
+from langchain.schema import format_document
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import RetrievalQA
 
+from operator import itemgetter
 import weaviate
-from schemas import ChatResponse
 import asyncio
 
-# langchain.debug = True
-
 app = FastAPI()
 app.add_middleware(
     CORSMiddleware,
@@ -29,10 +31,16 @@
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
+    expose_headers=["*"],
 )
 
 templates = Jinja2Templates(directory="templates")
 vectorstore: Optional[VectorStore] = None
+run_collector = RunCollectorCallbackHandler()
+runnable_config = RunnableConfig(callbacks=[run_collector])
+run_id = None
+from langsmith import Client
+client = Client()
 
 @app.get("/")
 async def get(request: Request):
@@ -40,41 +48,59 @@ async def get(request: Request):
 
 @app.post("/chat")
 async def chat_endpoint(request: Request):
+    global run_id, access_counter
+    run_id = None
+    access_counter = 0
+    run_collector.traced_runs = []
+    
     data = await request.json()
     question = data.get('message')
     model_type = data.get('model')
+    chat_history = data.get('history', [])
     
     WEAVIATE_URL=os.environ["WEAVIATE_URL"]
     WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
 
-    chat_history = []
     embeddings = OpenAIEmbeddings()
     client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
     print(client.query.aggregate("LangChain_idx").with_meta_count().do())
-    weaviate_client = Weaviate(client=client, index_name="LangChain_idx", text_key="text", embedding=embeddings, by_text=False)
-
+    weaviate_client = Weaviate(client=client, index_name="LangChain_idx", text_key="text", embedding=embeddings, by_text=False, attributes=["source"])
+    
     async def stream():
+        result = ''
         try:
             model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") if model_type == "openai" else ChatAnthropic()
 
             print("Recieved question: ", question)
             
-            prompt = ChatPromptTemplate.from_template("You are an expert programming prodigy creator of Langchain. Answer this question to the best of your ability {question} with the provided context {context}")
-            chain = (
-                {"context": weaviate_client.as_retriever(), "question": RunnablePassthrough()} 
-                | prompt 
-                | model 
-            )
+            _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
+            
+            Here you have multiple separate sources about Langchain.{context} 
+                            
+            Conversation History:               
+            {history}
+            
+            Answer the user's question to the best of your ability: {question}
+            Helpful Answer:"""
 
-            result = ""
+            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=_template)
+            memory = ConversationBufferMemory(input_key="question", memory_key="history")
             
-            for s in chain.stream(question):
-                yield formatted_content
-                await asyncio.sleep(0)
-                print(formatted_content, end="", flush=True)
+            for message in chat_history:
+                memory.save_context({'question': message['question']}, {'result': message['result']})
                 
-            result += formatted_content
-            chat_history.append((question, result))
+            qa_chain = RetrievalQA.from_chain_type(
+                model,
+                chain_type="stuff",
+                retriever=weaviate_client.as_retriever(search_kwargs=dict(k=10)),
+                return_source_documents=True,
+                chain_type_kwargs={"prompt": prompt, "memory": memory},
+            )
+            
+            for s in qa_chain.stream(question, config=runnable_config):
+                result += s["result"]
+                yield s["result"]
+                await asyncio.sleep(0)
 
         except Exception as e:
             logging.error(e)
@@ -82,7 +108,36 @@ async def stream():
 
     return StreamingResponse(stream())
 
+access_counter = 0
+
+@app.post("/feedback")
+async def send_feedback(request: Request):
+    global run_id, access_counter
+    data = await request.json()
+    score = data.get('score')
+    if not run_id:  
+        run = run_collector.traced_runs[0]
+        run_id = run.id
+        access_counter += 1
+        if access_counter >= 2:
+            run_collector.traced_runs = []
+            access_counter = 0
+    client.create_feedback(run_id, "user_score", score=score)
+    return {"result": "posted feedback successfully", "code": 200}
+    
+@app.post("/get_trace")
+async def get_trace(request: Request):
+    global run_id, access_counter
+    if run_id == None:
+        run = run_collector.traced_runs[0]
+        run_id = run.id
+        access_counter += 1
+        if access_counter >= 2:
+            run_collector.traced_runs = []
+            access_counter = 0
+    url = client.share_run(run_id)
+    return url
+    
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8080)
-

From 4125c7eee46fa520952efb427d31fa77498c528f Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 14 Aug 2023 18:34:06 -0500
Subject: [PATCH 07/36] a bit of cleanup -> archive_recent

---
 .../archive_recent/callback.py                |  2 +-
 ingest.sh => archive/archive_recent/ingest.sh |  0
 .../archive_recent/query_data.py              |  0
 .../archive_recent/schemas.py                 |  0
 .../archive_recent/templates}/index.html      | 23 +++++++++++++++++--
 main.py                                       |  7 +-----
 6 files changed, 23 insertions(+), 9 deletions(-)
 rename callback.py => archive/archive_recent/callback.py (94%)
 rename ingest.sh => archive/archive_recent/ingest.sh (100%)
 rename query_data.py => archive/archive_recent/query_data.py (100%)
 rename schemas.py => archive/archive_recent/schemas.py (100%)
 rename {templates => archive/archive_recent/templates}/index.html (85%)

diff --git a/callback.py b/archive/archive_recent/callback.py
similarity index 94%
rename from callback.py
rename to archive/archive_recent/callback.py
index d55ad494d..6eb078de7 100644
--- a/callback.py
+++ b/archive/archive_recent/callback.py
@@ -3,7 +3,7 @@
 
 from langchain.callbacks.base import AsyncCallbackHandler
 
-from schemas import ChatResponse
+from archive.archive_recent.schemas import ChatResponse
 
 
 class StreamingLLMCallbackHandler(AsyncCallbackHandler):
diff --git a/ingest.sh b/archive/archive_recent/ingest.sh
similarity index 100%
rename from ingest.sh
rename to archive/archive_recent/ingest.sh
diff --git a/query_data.py b/archive/archive_recent/query_data.py
similarity index 100%
rename from query_data.py
rename to archive/archive_recent/query_data.py
diff --git a/schemas.py b/archive/archive_recent/schemas.py
similarity index 100%
rename from schemas.py
rename to archive/archive_recent/schemas.py
diff --git a/templates/index.html b/archive/archive_recent/templates/index.html
similarity index 85%
rename from templates/index.html
rename to archive/archive_recent/templates/index.html
index c123510e4..2a7b77e29 100644
--- a/templates/index.html
+++ b/archive/archive_recent/templates/index.html
@@ -49,9 +49,20 @@
         .form-message {
           margin-top: 10px;
         }
+        /* New styles for the dropdown */
+        #modelType {
+            width: 20%;
+            background-color: #333;
+            color: #fff;
+            border: none;
+            border-radius: 5px;
+            padding: 5px;
+            margin-right: 10px;
+        }
     </style>
     <script>
-        var endpoint = "ws://localhost:9000/chat";
+        // var endpoint = "ws://localhost:9000/chat";
+        var endpoint = "wss://" + window.location.host + "/chat";
         var ws = new WebSocket(endpoint);
         // Receive message from server word by word. Display the words as they are received.
         ws.onmessage = function (event) {
@@ -110,10 +121,12 @@
         function sendMessage(event) {
             event.preventDefault();
             var message = document.getElementById('messageText').value;
+            var model = document.getElementById('modelType').value;
             if (message === "") {
                 return;
             }
-            ws.send(message);
+            console.log(JSON.stringify({message: message, model: model}));
+            ws.send(JSON.stringify({message: message, model: model}));
             document.getElementById('messageText').value = "";
 
             // Turn the button into a loading button
@@ -125,6 +138,12 @@
 </head>
 <body class="bg-black">
     <div class="chat-body card">
+        <form action="" class="form-inline m-5" id="chat-form" onsubmit="sendMessage(event)">
+            <select id="modelType" class="form-control">
+                <option value="openai" selected>OpenAI</option>
+                <option value="anthropic">Anthropic</option>
+            </select>
+        </form>
         <div class="card-body p-5">
             <h4 class="card-title text-center text-xl font-medium"> Chat Your Data </h4>
             <p class="card-text text-center text-sm" id="header"> Ask a question </p>
diff --git a/main.py b/main.py
index e53da6884..6c067c22f 100644
--- a/main.py
+++ b/main.py
@@ -34,18 +34,13 @@
     expose_headers=["*"],
 )
 
-templates = Jinja2Templates(directory="templates")
-vectorstore: Optional[VectorStore] = None
 run_collector = RunCollectorCallbackHandler()
 runnable_config = RunnableConfig(callbacks=[run_collector])
 run_id = None
+
 from langsmith import Client
 client = Client()
 
-@app.get("/")
-async def get(request: Request):
-    return templates.TemplateResponse("index.html", {"request": request})
-
 @app.post("/chat")
 async def chat_endpoint(request: Request):
     global run_id, access_counter

From 2255281f6af4c20933bbd5c46e4f6a779e46803e Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 14 Aug 2023 18:38:31 -0500
Subject: [PATCH 08/36] prompt upgrades

---
 main.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/main.py b/main.py
index 6c067c22f..a42584497 100644
--- a/main.py
+++ b/main.py
@@ -70,7 +70,10 @@ async def stream():
             
             _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
             
-            Here you have multiple separate sources about Langchain.{context} 
+            Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
+            <context>
+                {context} 
+            <context/>
                             
             Conversation History:               
             {history}

From 7fb9ee4a88412b4b5cde105fccf7131a9aa5f6f9 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 14 Aug 2023 19:36:41 -0500
Subject: [PATCH 09/36] feedback edge case with toast error, claude 100k model

---
 chat-langchain/app/components/ChatWindow.tsx | 14 ++++++++++----
 chat-langchain/app/page.tsx                  |  2 ++
 main.py                                      |  5 +++--
 3 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 54cba92e2..da11db706 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -8,6 +8,9 @@ import { Renderer } from "marked";
 import hljs from "highlight.js";
 import "highlight.js/styles/gradient-dark.css";
 
+import { toast } from 'react-toastify';
+import 'react-toastify/dist/ReactToastify.css';
+
 export function ChatWindow(props: {
   endpoint: string;
   emptyStateComponent: React.ReactElement;
@@ -44,6 +47,7 @@ export function ChatWindow(props: {
     if (isLoading) {
       return;
     }
+    setHasInteracted(true);
     setMessages((prevMessages) => [
       ...prevMessages,
       { id: Math.random().toString(), message: input.trim(), role: "user" },
@@ -232,11 +236,12 @@ export function ChatWindow(props: {
             feedback === 1 ? "bg-green-100" : ""
           }`}
           onClick={() => {
-            if (feedback === null) {
+            if (feedback === null && hasInteracted) {
               sendFeedback(1);
+            } else {
+              toast.error("You have already provided your feedback.");
             }
           }}
-          disabled={feedback !== null || !hasInteracted}
           id="upButton"
         >
           👍
@@ -247,11 +252,12 @@ export function ChatWindow(props: {
             feedback === 0 ? "bg-red-100" : ""
           }`}
           onClick={() => {
-            if (feedback === null) {
+            if (feedback === null && hasInteracted) {
               sendFeedback(0);
+            } else {
+              toast.error("You have already provided your feedback.");
             }
           }}
-          disabled={feedback !== null || !hasInteracted}
           id="downButton"
         >
           👎
diff --git a/chat-langchain/app/page.tsx b/chat-langchain/app/page.tsx
index da50b19fc..e9fac4d39 100644
--- a/chat-langchain/app/page.tsx
+++ b/chat-langchain/app/page.tsx
@@ -1,6 +1,7 @@
 import Image from "next/image";
 import Chat from "../app/components/chat";
 import { ChatWindow } from "../app/components/ChatWindow";
+import { ToastContainer } from 'react-toastify';
 
 export default function Home() {
   const InfoCard = (
@@ -62,6 +63,7 @@ export default function Home() {
       {/* <main className="flex h-[100vh] overflow-hidden flex-col items-center justify-center bg-grey-100">
         <div className="z-10 w-[100vw] w items-center justify-center font-normal text-sm lg:flex lg:max-h-[40rem] sm:max-h-[20rem] text-center p-4 md:p-0"> */}
           {/* <Chat/> */}
+          <ToastContainer />
           <ChatWindow
             // endpoint="https://chat-langchain.fly.dev/chat"
             endpoint="http://0.0.0.0:8080/chat"
diff --git a/main.py b/main.py
index a42584497..b07e6ac81 100644
--- a/main.py
+++ b/main.py
@@ -64,7 +64,7 @@ async def chat_endpoint(request: Request):
     async def stream():
         result = ''
         try:
-            model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") if model_type == "openai" else ChatAnthropic()
+            model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") if model_type == "openai" else ChatAnthropic(model='claude-instant-v1-100k')
 
             print("Recieved question: ", question)
             
@@ -75,8 +75,9 @@ async def stream():
                 {context} 
             <context/>
                             
-            Conversation History:               
+            <conversation_history>               
             {history}
+            <conversation_history/>
             
             Answer the user's question to the best of your ability: {question}
             Helpful Answer:"""

From 7091c5fee8d0cc671a78d21c46703e2c523b1ce3 Mon Sep 17 00:00:00 2001
From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Date: Mon, 14 Aug 2023 17:49:38 -0700
Subject: [PATCH 10/36] Add an eval script

---
 _scripts/evaluate_chains.py | 117 ++++++++++++++++++++++
 main.py                     | 193 +++++++++++++++++++++++-------------
 2 files changed, 241 insertions(+), 69 deletions(-)
 create mode 100644 _scripts/evaluate_chains.py

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
new file mode 100644
index 000000000..3667c9260
--- /dev/null
+++ b/_scripts/evaluate_chains.py
@@ -0,0 +1,117 @@
+import argparse
+import functools
+import os
+from typing import Literal, Optional, Union
+
+import weaviate
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatAnthropic, ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema.retriever import BaseRetriever
+from langchain.schema.runnable import Runnable
+from langchain.smith import RunEvalConfig
+from langchain.vectorstores import Weaviate
+from langsmith import Client
+
+_PROVIDER_MAP = {
+    "openai": ChatOpenAI,
+    "anthropic": ChatAnthropic,
+}
+
+_MODEL_MAP = {
+    "openai": "gpt-3.5-turbo",
+    "anthropic": "claude-2",
+}
+
+
+def create_chain(
+    retriever: BaseRetriever,
+    model_provider: Union[Literal["openai"], Literal["anthropic"]],
+    chat_history: Optional[list] = None,
+    model: Optional[str] = None,
+    temperature: float = 0.0,
+) -> Runnable:
+    model_name = model or _MODEL_MAP[model_provider]
+    model = _PROVIDER_MAP[model_provider](model=model_name, temperature=temperature)
+
+    _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
+    
+    Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
+    <context>
+        {context} 
+    <context/>
+                    
+    Conversation History:               
+    {history}
+    
+    Answer the user's question to the best of your ability: {question}
+    Helpful Answer:"""
+
+    prompt = PromptTemplate(
+        input_variables=["history", "context", "question"], template=_template
+    )
+    memory = ConversationBufferMemory(input_key="question", memory_key="history")
+    chat_history_ = chat_history or []
+    for message in chat_history_:
+        memory.save_context(
+            {"question": message["question"]}, {"result": message["result"]}
+        )
+
+    qa_chain = RetrievalQA.from_chain_type(
+        model,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=True,
+        chain_type_kwargs={"prompt": prompt, "memory": memory},
+    )
+    return qa_chain
+
+
+def _get_retriever():
+    WEAVIATE_URL = os.environ["WEAVIATE_URL"]
+    WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
+
+    embeddings = OpenAIEmbeddings()
+    client = weaviate.Client(
+        url=WEAVIATE_URL,
+        auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
+    )
+    print(client.query.aggregate("LangChain_idx").with_meta_count().do())
+    weaviate_client = Weaviate(
+        client=client,
+        index_name="LangChain_idx",
+        text_key="text",
+        embedding=embeddings,
+        by_text=False,
+        attributes=["source"],
+    )
+    return weaviate_client.as_retriever(search_kwargs=dict(k=10))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset-name", default="Chat LangChain Questions")
+    parser.add_argument("--model-provider", default="openai")
+    args = parser.parse_args()
+    client = Client()
+    # Check dataset exists
+    ds = client.read_dataset(dataset_name=args.dataset_name)
+    retriever = _get_retriever()
+    constructor = functools.partial(
+        create_chain,
+        retriever=retriever,
+        model_provider=args.model_provider,
+    )
+    chain = constructor()
+    print(chain.invoke({"query": "What is LangChain?"}))
+    eval_config = RunEvalConfig(evaluators=["qa"], prediction_key="result")
+    results = client.run_on_dataset(
+        dataset_name=args.dataset_name,
+        llm_or_chain_factory=constructor,
+        evaluation=eval_config,
+        verbose=True,
+    )
+    proj = client.read_project(project_name=results["project_name"])
+    print(proj.feedback_stats)
diff --git a/main.py b/main.py
index a42584497..ba87de2a7 100644
--- a/main.py
+++ b/main.py
@@ -1,28 +1,39 @@
 """Main entrypoint for the app."""
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.responses import StreamingResponse
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.templating import Jinja2Templates
-from typing import Any, Optional, Generator
-import os
+import asyncio
 import logging
+import os
 
-import langchain
-from langchain.chat_models import ChatOpenAI, ChatAnthropic
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-from langchain.vectorstores import VectorStore, Weaviate
-from langchain.prompts.prompt import PromptTemplate
+import weaviate
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
 from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
-from langchain.schema.runnable import RunnableConfig, RunnableMap, RunnablePassthrough
-from langchain.schema import format_document
-from langchain.memory import ConversationBufferMemory
 from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatAnthropic, ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema.retriever import BaseRetriever
+from langchain.schema.runnable import Runnable, RunnableConfig
+from langchain.vectorstores import Weaviate
+from langsmith import Client
+
+from typing import Literal, Optional, Union
 
-from operator import itemgetter
 import weaviate
-import asyncio
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatAnthropic, ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema.retriever import BaseRetriever
+from langchain.schema.runnable import Runnable
+from langchain.smith import RunEvalConfig
+from langchain.vectorstores import Weaviate
+from langsmith import Client
+
+
+client = Client()
 
 app = FastAPI()
 app.add_middleware(
@@ -38,8 +49,80 @@
 runnable_config = RunnableConfig(callbacks=[run_collector])
 run_id = None
 
-from langsmith import Client
-client = Client()
+_PROVIDER_MAP = {
+    "openai": ChatOpenAI,
+    "anthropic": ChatAnthropic,
+}
+
+_MODEL_MAP = {
+    "openai": "gpt-3.5-turbo",
+    "anthropic": "claude-2",
+}
+
+
+def create_chain(
+    retriever: BaseRetriever,
+    model_provider: Union[Literal["openai"], Literal["anthropic"]],
+    chat_history: Optional[list] = None,
+    model: Optional[str] = None,
+    temperature: float = 0.0,
+) -> Runnable:
+    model_name = model or _MODEL_MAP[model_provider]
+    model = _PROVIDER_MAP[model_provider](model=model_name, temperature=temperature)
+
+    _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
+    
+    Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
+    <context>
+        {context} 
+    <context/>
+                    
+    Conversation History:               
+    {history}
+    
+    Answer the user's question to the best of your ability: {question}
+    Helpful Answer:"""
+
+    prompt = PromptTemplate(
+        input_variables=["history", "context", "question"], template=_template
+    )
+    memory = ConversationBufferMemory(input_key="question", memory_key="history")
+    chat_history_ = chat_history or []
+    for message in chat_history_:
+        memory.save_context(
+            {"question": message["question"]}, {"result": message["result"]}
+        )
+
+    qa_chain = RetrievalQA.from_chain_type(
+        model,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=True,
+        chain_type_kwargs={"prompt": prompt, "memory": memory},
+    )
+    return qa_chain
+
+
+def _get_retriever():
+    WEAVIATE_URL = os.environ["WEAVIATE_URL"]
+    WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
+
+    embeddings = OpenAIEmbeddings()
+    client = weaviate.Client(
+        url=WEAVIATE_URL,
+        auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
+    )
+    print(client.query.aggregate("LangChain_idx").with_meta_count().do())
+    weaviate_client = Weaviate(
+        client=client,
+        index_name="LangChain_idx",
+        text_key="text",
+        embedding=embeddings,
+        by_text=False,
+        attributes=["source"],
+    )
+    return weaviate_client.as_retriever(search_kwargs=dict(k=10))
+
 
 @app.post("/chat")
 async def chat_endpoint(request: Request):
@@ -47,54 +130,21 @@ async def chat_endpoint(request: Request):
     run_id = None
     access_counter = 0
     run_collector.traced_runs = []
-    
+
     data = await request.json()
-    question = data.get('message')
-    model_type = data.get('model')
-    chat_history = data.get('history', [])
-    
-    WEAVIATE_URL=os.environ["WEAVIATE_URL"]
-    WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
+    question = data.get("message")
+    model_type = data.get("model")
+    chat_history = data.get("history", [])
+
+    retriever = _get_retriever()
+    qa_chain = create_chain(
+        retriever=retriever, model_provider=model_type, chat_history=chat_history
+    )
+    print("Recieved question: ", question)
 
-    embeddings = OpenAIEmbeddings()
-    client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
-    print(client.query.aggregate("LangChain_idx").with_meta_count().do())
-    weaviate_client = Weaviate(client=client, index_name="LangChain_idx", text_key="text", embedding=embeddings, by_text=False, attributes=["source"])
-    
     async def stream():
-        result = ''
+        result = ""
         try:
-            model = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") if model_type == "openai" else ChatAnthropic()
-
-            print("Recieved question: ", question)
-            
-            _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
-            
-            Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
-            <context>
-                {context} 
-            <context/>
-                            
-            Conversation History:               
-            {history}
-            
-            Answer the user's question to the best of your ability: {question}
-            Helpful Answer:"""
-
-            prompt = PromptTemplate(input_variables=["history", "context", "question"], template=_template)
-            memory = ConversationBufferMemory(input_key="question", memory_key="history")
-            
-            for message in chat_history:
-                memory.save_context({'question': message['question']}, {'result': message['result']})
-                
-            qa_chain = RetrievalQA.from_chain_type(
-                model,
-                chain_type="stuff",
-                retriever=weaviate_client.as_retriever(search_kwargs=dict(k=10)),
-                return_source_documents=True,
-                chain_type_kwargs={"prompt": prompt, "memory": memory},
-            )
-            
             for s in qa_chain.stream(question, config=runnable_config):
                 result += s["result"]
                 yield s["result"]
@@ -102,18 +152,20 @@ async def stream():
 
         except Exception as e:
             logging.error(e)
-            yield  "Sorry, something went wrong. Try again." + "\n"
+            yield "Sorry, something went wrong. Try again." + "\n"
 
     return StreamingResponse(stream())
 
+
 access_counter = 0
 
+
 @app.post("/feedback")
 async def send_feedback(request: Request):
     global run_id, access_counter
     data = await request.json()
-    score = data.get('score')
-    if not run_id:  
+    score = data.get("score")
+    if not run_id:
         run = run_collector.traced_runs[0]
         run_id = run.id
         access_counter += 1
@@ -122,7 +174,8 @@ async def send_feedback(request: Request):
             access_counter = 0
     client.create_feedback(run_id, "user_score", score=score)
     return {"result": "posted feedback successfully", "code": 200}
-    
+
+
 @app.post("/get_trace")
 async def get_trace(request: Request):
     global run_id, access_counter
@@ -135,7 +188,9 @@ async def get_trace(request: Request):
             access_counter = 0
     url = client.share_run(run_id)
     return url
-    
+
+
 if __name__ == "__main__":
     import uvicorn
+
     uvicorn.run(app, host="0.0.0.0", port=8080)

From 92a418c3e3fb6125c510065f691ef7b89c470ad9 Mon Sep 17 00:00:00 2001
From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Date: Mon, 14 Aug 2023 18:12:29 -0700
Subject: [PATCH 11/36] chat format

---
 _scripts/evaluate_chains.py | 64 ++++++++++++++++++++++++++-----------
 1 file changed, 46 insertions(+), 18 deletions(-)

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
index 3667c9260..3065a9dde 100644
--- a/_scripts/evaluate_chains.py
+++ b/_scripts/evaluate_chains.py
@@ -4,11 +4,11 @@
 from typing import Literal, Optional, Union
 
 import weaviate
+from langchain import prompts
 from langchain.chains import RetrievalQA
 from langchain.chat_models import ChatAnthropic, ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.memory import ConversationBufferMemory
-from langchain.prompts.prompt import PromptTemplate
 from langchain.schema.retriever import BaseRetriever
 from langchain.schema.runnable import Runnable
 from langchain.smith import RunEvalConfig
@@ -26,33 +26,60 @@
 }
 
 
+def _get_prompt(prompt_type: str) -> prompts.BasePromptTemplate:
+    if prompt_type == "completion":
+        _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
+        
+Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
+<context>
+    {context} 
+<context/>
+                
+Conversation History:               
+{history}
+
+Answer the user's question to the best of your ability: {question}
+Helpful Answer:"""
+
+        return prompts.PromptTemplate(
+            input_variables=["history", "context", "question"], template=_template
+        )
+    else:
+        return prompts.ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are an expert programmer, tasked with answering any question about Langchain. Be as helpful as possible.",
+                ),
+                prompts.MessagesPlaceholder(variable_name="history"),
+                ("human", "{question}"),
+                (
+                    "system",
+                    "Respond to the user's question as best and truthfully as you are able."
+                    " You can choose to use the following retrieved information if it is relevant.",
+                ),
+                ("system", "<retrieved_context>\n{context}\n</retrieved_context>"),
+            ]
+        )
+
+
 def create_chain(
     retriever: BaseRetriever,
     model_provider: Union[Literal["openai"], Literal["anthropic"]],
     chat_history: Optional[list] = None,
     model: Optional[str] = None,
     temperature: float = 0.0,
+    prompt_type: Union[Literal["chat"], Literal["completion"]] = "chat",
 ) -> Runnable:
     model_name = model or _MODEL_MAP[model_provider]
     model = _PROVIDER_MAP[model_provider](model=model_name, temperature=temperature)
 
-    _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
-    
-    Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
-    <context>
-        {context} 
-    <context/>
-                    
-    Conversation History:               
-    {history}
-    
-    Answer the user's question to the best of your ability: {question}
-    Helpful Answer:"""
-
-    prompt = PromptTemplate(
-        input_variables=["history", "context", "question"], template=_template
+    prompt = _get_prompt(prompt_type)
+    return_messages = True if prompt_type == "chat" else False
+
+    memory = ConversationBufferMemory(
+        input_key="question", memory_key="history", return_messages=return_messages
     )
-    memory = ConversationBufferMemory(input_key="question", memory_key="history")
     chat_history_ = chat_history or []
     for message in chat_history_:
         memory.save_context(
@@ -94,6 +121,7 @@ def _get_retriever():
     parser = argparse.ArgumentParser()
     parser.add_argument("--dataset-name", default="Chat LangChain Questions")
     parser.add_argument("--model-provider", default="openai")
+    parser.add_argument("--prompt-type", default="chat")
     args = parser.parse_args()
     client = Client()
     # Check dataset exists
@@ -103,9 +131,9 @@ def _get_retriever():
         create_chain,
         retriever=retriever,
         model_provider=args.model_provider,
+        prompt_type=args.prompt_type,
     )
     chain = constructor()
-    print(chain.invoke({"query": "What is LangChain?"}))
     eval_config = RunEvalConfig(evaluators=["qa"], prediction_key="result")
     results = client.run_on_dataset(
         dataset_name=args.dataset_name,

From 94b86212a638bcc60536c9899ea3246bb1ae7289 Mon Sep 17 00:00:00 2001
From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Date: Mon, 14 Aug 2023 21:08:04 -0700
Subject: [PATCH 12/36] some testing

---
 _scripts/evaluate_chains.py | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
index 3065a9dde..5d9532259 100644
--- a/_scripts/evaluate_chains.py
+++ b/_scripts/evaluate_chains.py
@@ -3,6 +3,9 @@
 import os
 from typing import Literal, Optional, Union
 
+from langsmith.evaluation.evaluator import EvaluationResult
+from langsmith.schemas import Example, Run
+
 import weaviate
 from langchain import prompts
 from langchain.chains import RetrievalQA
@@ -14,6 +17,9 @@
 from langchain.smith import RunEvalConfig
 from langchain.vectorstores import Weaviate
 from langsmith import Client
+from langsmith import RunEvaluator
+from langchain import load as langchain_load
+import json
 
 _PROVIDER_MAP = {
     "openai": ChatOpenAI,
@@ -63,6 +69,7 @@ def _get_prompt(prompt_type: str) -> prompts.BasePromptTemplate:
         )
 
 
+
 def create_chain(
     retriever: BaseRetriever,
     model_provider: Union[Literal["openai"], Literal["anthropic"]],
@@ -117,6 +124,31 @@ def _get_retriever():
     return weaviate_client.as_retriever(search_kwargs=dict(k=10))
 
 
+class CustomHallucinationEvaluator(RunEvaluator):
+
+    @staticmethod
+    def _get_llm_runs(run: Run) -> Run:
+        runs = []
+        for child in (run.child_runs or []):
+            if run.run_type == "llm":
+                runs.append(child)
+            else:
+                runs.extend(CustomHallucinationEvaluator._get_llm_runs(child))
+
+
+    def evaluate_run(self, run: Run, example: Example | None = None) -> EvaluationResult:
+        llm_runs = self._get_llm_runs(run)
+        if not llm_runs:
+            return EvaluationResult(key="hallucination", comment="No LLM runs found")
+        if len(llm_runs) > 0:
+            return EvaluationResult(key="hallucination", comment="Too many LLM runs found")
+        llm_run = llm_runs[0]
+        messages = llm_run.inputs["messages"]
+        langchain_load(json.dumps(messages))
+
+
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--dataset-name", default="Chat LangChain Questions")

From aff17b5e9125d8a5e97d8608d431a23d2a24076d Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Tue, 15 Aug 2023 15:09:39 -0500
Subject: [PATCH 13/36] fixed streaming

---
 main.py | 47 +++++++++++++++++++++++++----------------------
 1 file changed, 25 insertions(+), 22 deletions(-)

diff --git a/main.py b/main.py
index 5b5058cda..9ab33a0ca 100644
--- a/main.py
+++ b/main.py
@@ -49,7 +49,6 @@
 def create_chain(
     retriever: BaseRetriever,
     model_provider: Union[Literal["openai"], Literal["anthropic"]],
-    chat_history: Optional[list] = None,
     model: Optional[str] = None,
     temperature: float = 0.0,
 ) -> Runnable:
@@ -65,7 +64,7 @@ def create_chain(
                     
     Conversation History:               
     <conversation_history>
-    {history}
+        {history}
     <conversation_history/>
     
     Answer the user's question to the best of your ability: {question}
@@ -74,21 +73,13 @@ def create_chain(
     prompt = PromptTemplate(
         input_variables=["history", "context", "question"], template=_template
     )
-    memory = ConversationBufferMemory(input_key="question", memory_key="history")
-    chat_history_ = chat_history or []
-    for message in chat_history_:
-        memory.save_context(
-            {"question": message["question"]}, {"result": message["result"]}
-        )
-
-    qa_chain = RetrievalQA.from_chain_type(
-        model,
-        chain_type="stuff",
-        retriever=retriever,
-        return_source_documents=True,
-        chain_type_kwargs={"prompt": prompt, "memory": memory},
+    
+    chain = (
+        prompt 
+        | model 
     )
-    return qa_chain
+    
+    return chain
 
 
 def _get_retriever():
@@ -100,7 +91,7 @@ def _get_retriever():
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
     )
-    print(client.query.aggregate("LangChain_idx").with_meta_count().do())
+    # print(client.query.aggregate("LangChain_idx").with_meta_count().do())
     weaviate_client = Weaviate(
         client=client,
         index_name="LangChain_idx",
@@ -111,6 +102,13 @@ def _get_retriever():
     )
     return weaviate_client.as_retriever(search_kwargs=dict(k=10))
 
+def _process_chat_history(chat_history):
+    for chat in chat_history:
+        if 'question' in chat:
+            chat['HumanChatMessage'] = chat.pop('question')
+        if 'result' in chat:
+            chat['AIChatMessage'] = chat.pop('result')
+    return chat_history
 
 @app.post("/chat")
 async def chat_endpoint(request: Request):
@@ -125,18 +123,23 @@ async def chat_endpoint(request: Request):
     chat_history = data.get("history", [])
 
     retriever = _get_retriever()
+    source_docs = retriever.invoke(question) # opportunity to return source documents
+    context = [doc.page_content for doc in source_docs]
+    
+    chat_history = _process_chat_history(chat_history)
+     
     qa_chain = create_chain(
-        retriever=retriever, model_provider=model_type, chat_history=chat_history
+        retriever=retriever, model_provider=model_type
     )
     print("Recieved question: ", question)
 
     async def stream():
         result = ""
         try:
-            for s in qa_chain.stream(question, config=runnable_config):
-                result += s["result"]
-                yield s["result"]
-                await asyncio.sleep(0)
+            async for s in qa_chain.astream({"context": context, "question": question, "history": chat_history}, config=runnable_config):
+                print(s.content, end="", flush=True)
+                result += s.content
+                yield s.content
 
         except Exception as e:
             logging.error(e)

From 826eb589046d08b199fab684adaeff37427807cf Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Tue, 15 Aug 2023 15:19:45 -0500
Subject: [PATCH 14/36] frontend nit

---
 .../app/components/ChatMessageBubble.tsx          | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/chat-langchain/app/components/ChatMessageBubble.tsx b/chat-langchain/app/components/ChatMessageBubble.tsx
index f8d3c3462..a4744151f 100644
--- a/chat-langchain/app/components/ChatMessageBubble.tsx
+++ b/chat-langchain/app/components/ChatMessageBubble.tsx
@@ -1,6 +1,9 @@
 import type { Message } from "ai/react";
 
-export function ChatMessageBubble(props: { message: Message, aiEmoji?: string }) {
+export function ChatMessageBubble(props: {
+  message: Message;
+  aiEmoji?: string;
+}) {
   const colorClassName =
     props.message.role === "user" ? "bg-sky-600" : "bg-slate-50 text-black";
   const alignmentClassName =
@@ -8,9 +11,13 @@ export function ChatMessageBubble(props: { message: Message, aiEmoji?: string })
   const prefix = props.message.role === "user" ? "🧑" : props.aiEmoji;
   return (
     <div
-      className={`${alignmentClassName} ${colorClassName} rounded px-4 py-2 max-w-[80%] mb-8 inline-block whitespace-pre-wrap`}
+      className={`${alignmentClassName} ${colorClassName} rounded px-4 py-2 max-w-[80%] mb-8 flex`}
     >
-        {prefix} <div dangerouslySetInnerHTML={{ __html: props.message.content }}></div>
+      <div className="mr-2">{prefix}</div>
+      <div
+        className="whitespace-pre-wrap"
+        dangerouslySetInnerHTML={{ __html: props.message.content }}
+      ></div>
     </div>
   );
-}
\ No newline at end of file
+}

From 6f2973a4add104fea716ea44ef47c22c49e216e8 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Tue, 15 Aug 2023 15:42:14 -0500
Subject: [PATCH 15/36] info element rewrite

---
 chat-langchain/app/components/ChatWindow.tsx |  4 +-
 chat-langchain/app/page.tsx                  | 78 +++++++++++---------
 2 files changed, 46 insertions(+), 36 deletions(-)

diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index da11db706..2bac4e0e2 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -130,7 +130,7 @@ export function ChatWindow(props: {
     if (feedback !== null) return;
 
     setFeedback(score);
-    fetch("http://0.0.0.0:8080/feedback", {
+    fetch("https://chat-langchain.fly.dev/feedback", {
       method: "POST",
       headers: {
         "Content-Type": "application/json",
@@ -161,7 +161,7 @@ export function ChatWindow(props: {
 
   const viewTrace = () => {
     console.log("IN VIEW TRACE")
-    fetch("http://0.0.0.0:8080/get_trace", {
+    fetch("https://chat-langchain.fly.dev/get_trace", {
       method: "POST",
       headers: {
         "Content-Type": "application/json",
diff --git a/chat-langchain/app/page.tsx b/chat-langchain/app/page.tsx
index e9fac4d39..2202119bf 100644
--- a/chat-langchain/app/page.tsx
+++ b/chat-langchain/app/page.tsx
@@ -1,7 +1,7 @@
 import Image from "next/image";
 import Chat from "../app/components/chat";
 import { ChatWindow } from "../app/components/ChatWindow";
-import { ToastContainer } from 'react-toastify';
+import { ToastContainer } from "react-toastify";
 
 export default function Home() {
   const InfoCard = (
@@ -11,39 +11,51 @@ export default function Home() {
         <li className="text-l">
           🤝
           <span className="ml-2">
-            This template showcases a simple chatbot using{" "}
-            <a href="https://js.langchain.com/" target="_blank">
-              LangChain.js
+            This is a simple chatbot over LangChain's{" "}
+            <a href="https://python.langchain.com/" target="_blank">
+              Python Documentation.
             </a>{" "}
-            in a{" "}
-            <a href="https://nextjs.org/" target="_blank">
-              Next.js
-            </a>{" "}
-            project.
           </span>
         </li>
         <li className="text-l">
           🔁
           <span className="ml-2">
-            It also uses the Vercel{" "}
-            <a href="https://sdk.vercel.ai/docs" target="_blank">
-              AI SDK
+            It uses the{" "}
+            <a href="https://python.langchain.com/docs/integrations/document_loaders/recursive_url_loader">
+              RecursiveURLLoader
+            </a>{" "}
+            in tandem with the{" "}
+            <a href="https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.generic.GenericLoader.html">
+              GenericLoader
             </a>{" "}
-            for streaming HTTP responses and the UI.
+            to ingest all of our API and repository pages.
+          </span>
+        </li>
+        <li className="text-l">
+          ⚡️
+          <span className="ml-2">
+            The documents are chunked and stored in a{" "}
+            <a
+              href="https://python.langchain.com/docs/integrations/vectorstores/weaviate"
+              target="_blank"
+            >
+              Weaviate
+            </a>{" "}
+            instance for lightning fast retrieval.
           </span>
         </li>
         <li className="text-l">
           💻
           <span className="ml-2">
-            You can find the prompt and model logic for this use-case in{" "}
-            <code>app/api/chat/route.ts</code>.
+            You can find the prompt and model logic which uses expression language
+            and chat history in <code> main.py</code>.
           </span>
         </li>
-        <li>
-          🏴‍☠️
+        <li className="text-l">
+          💻
           <span className="ml-2">
-            By default, the bot is pretending to be a pirate, but you can change
-            the prompt to whatever you want!
+            Feedback and Tracing are additional neat features, thanks to {" "}
+            <a href="https://smith.langchain.com" target="_blank">LangSmith</a>.
           </span>
         </li>
         <li className="text-l">
@@ -53,27 +65,25 @@ export default function Home() {
           </span>
         </li>
         <li className="text-l">
-          👇<span className="ml-2">Try asking, "What is it like to be a pirate?" or another question below!</span>
+          👇
+          <span className="ml-2">
+            Begin by asking, "Show me how to use a RecursiveUrlLoader?" or another
+            question below!
+          </span>
         </li>
       </ul>
     </div>
   );
   return (
     <>
-      {/* <main className="flex h-[100vh] overflow-hidden flex-col items-center justify-center bg-grey-100">
-        <div className="z-10 w-[100vw] w items-center justify-center font-normal text-sm lg:flex lg:max-h-[40rem] sm:max-h-[20rem] text-center p-4 md:p-0"> */}
-          {/* <Chat/> */}
-          <ToastContainer />
-          <ChatWindow
-            // endpoint="https://chat-langchain.fly.dev/chat"
-            endpoint="http://0.0.0.0:8080/chat"
-            emoji="🏴‍☠️"
-            titleText="ChatLangChain"
-            placeholder="Write your question"
-            emptyStateComponent={InfoCard}
-          ></ChatWindow>
-        {/* </div>
-      </main> */}
+      <ToastContainer />
+      <ChatWindow
+        endpoint="https://chat-langchain.fly.dev/chat"
+        // emoji="🏴‍☠️"
+        titleText="ChatLangChain"
+        placeholder="Write your question"
+        emptyStateComponent={InfoCard}
+      ></ChatWindow>
     </>
   );
 }

From cd53931d863fb6288ff22ab56d20d0882646d920 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Tue, 15 Aug 2023 15:46:11 -0500
Subject: [PATCH 16/36] nit

---
 chat-langchain/app/page.tsx | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/chat-langchain/app/page.tsx b/chat-langchain/app/page.tsx
index 2202119bf..4c5d48d46 100644
--- a/chat-langchain/app/page.tsx
+++ b/chat-langchain/app/page.tsx
@@ -11,7 +11,7 @@ export default function Home() {
         <li className="text-l">
           🤝
           <span className="ml-2">
-            This is a simple chatbot over LangChain's{" "}
+            This is a simple chatbot over LangChain&apos;s{" "}
             <a href="https://python.langchain.com/" target="_blank">
               Python Documentation.
             </a>{" "}
@@ -67,7 +67,7 @@ export default function Home() {
         <li className="text-l">
           👇
           <span className="ml-2">
-            Begin by asking, "Show me how to use a RecursiveUrlLoader?" or another
+            Begin by asking, &quot;Show me how to use a RecursiveUrlLoader?&quot; or another
             question below!
           </span>
         </li>

From a01f8795a928b21017484db99c9ea1d4cb3a9589 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Tue, 15 Aug 2023 17:45:15 -0500
Subject: [PATCH 17/36] condensing question pre-process step

---
 chat-langchain/app/components/ChatWindow.tsx |  3 +-
 main.py                                      | 87 +++++++++++++-------
 2 files changed, 61 insertions(+), 29 deletions(-)

diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 2bac4e0e2..7e33e8fee 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -47,6 +47,7 @@ export function ChatWindow(props: {
     if (isLoading) {
       return;
     }
+    setInput("");
     setHasInteracted(true);
     setMessages((prevMessages) => [
       ...prevMessages,
@@ -160,7 +161,6 @@ export function ChatWindow(props: {
   };
 
   const viewTrace = () => {
-    console.log("IN VIEW TRACE")
     fetch("https://chat-langchain.fly.dev/get_trace", {
       method: "POST",
       headers: {
@@ -219,6 +219,7 @@ export function ChatWindow(props: {
           className="flex-grow mr-2 p-4 rounded"
           placeholder={placeholder}
           onChange={(e) => setInput(e.target.value)}
+          value={input}
           style={{minWidth: '50px'}}
         />
         <select
diff --git a/main.py b/main.py
index 9ab33a0ca..d98fc2114 100644
--- a/main.py
+++ b/main.py
@@ -14,10 +14,14 @@
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.memory import ConversationBufferMemory
 from langchain.prompts.prompt import PromptTemplate
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.schema.messages import HumanMessage, AIMessage
 from langchain.schema.retriever import BaseRetriever
-from langchain.schema.runnable import Runnable, RunnableConfig
+from langchain.schema.runnable import Runnable, RunnableConfig, RunnableMap
+from langchain.schema.output_parser import StrOutputParser
 from langchain.vectorstores import Weaviate
 from langsmith import Client
+from operator import itemgetter
 
 client = Client()
 
@@ -45,37 +49,74 @@
     "anthropic": "claude-instant-v1-100k",
 }
 
+def _process_chat_history(chat_history):
+    processed_chat_history = []
+    for chat in chat_history:
+        if 'question' in chat:
+            processed_chat_history.append(HumanMessage(content=chat.pop('question')))
+        if 'result' in chat:
+            processed_chat_history.append(AIMessage(content=chat.pop('result')))
+    return processed_chat_history
 
 def create_chain(
     retriever: BaseRetriever,
     model_provider: Union[Literal["openai"], Literal["anthropic"]],
+    chat_history: Optional[list] = None,
     model: Optional[str] = None,
     temperature: float = 0.0,
 ) -> Runnable:
     model_name = model or _MODEL_MAP[model_provider]
     model = _PROVIDER_MAP[model_provider](model=model_name, temperature=temperature)
+        
+    _template = """Given the following chat history and a follow up question, respond with a standalone question with all the necessary context to understand what the user is truly asking.
+
+    Chat History:
+    {chat_history}
+    Follow Up Input: {question}
+    Standalone Question:"""
+    
+    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+    
+    _inputs = RunnableMap(
+        {
+            "standalone_question": {
+                "question": lambda x: x["question"],
+                "chat_history": lambda x: _process_chat_history(x['chat_history'])
+            } | CONDENSE_QUESTION_PROMPT | model | StrOutputParser(),
+            "chat_history": lambda x: _process_chat_history(x['chat_history']),
+        }
+    )
 
     _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
     
     Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
     <context>
         {context} 
-    <context/>
-                    
-    Conversation History:               
-    <conversation_history>
-        {history}
-    <conversation_history/>
+    <context/>"""
     
-    Answer the user's question to the best of your ability: {question}
-    Helpful Answer:"""
+    if len(chat_history) > 1:
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", _template),
+            MessagesPlaceholder(variable_name="chat_history"),
+            ("human", "{question}"),
+        ])
+    else:
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", _template),
+            ("human", "{question}"),
+        ])
 
-    prompt = PromptTemplate(
-        input_variables=["history", "context", "question"], template=_template
-    )
+    
+    _context = {
+        "context": itemgetter("standalone_question") | retriever,
+        "question": lambda x: x["standalone_question"], 
+        "chat_history": lambda x: x["chat_history"]
+    }
     
     chain = (
-        prompt 
+        _inputs
+        | _context
+        | prompt 
         | model 
     )
     
@@ -102,14 +143,6 @@ def _get_retriever():
     )
     return weaviate_client.as_retriever(search_kwargs=dict(k=10))
 
-def _process_chat_history(chat_history):
-    for chat in chat_history:
-        if 'question' in chat:
-            chat['HumanChatMessage'] = chat.pop('question')
-        if 'result' in chat:
-            chat['AIChatMessage'] = chat.pop('result')
-    return chat_history
-
 @app.post("/chat")
 async def chat_endpoint(request: Request):
     global run_id, access_counter
@@ -123,20 +156,18 @@ async def chat_endpoint(request: Request):
     chat_history = data.get("history", [])
 
     retriever = _get_retriever()
-    source_docs = retriever.invoke(question) # opportunity to return source documents
-    context = [doc.page_content for doc in source_docs]
-    
-    chat_history = _process_chat_history(chat_history)
-     
+    # source_docs = retriever.invoke(question) # opportunity to return source documents
+    # context = [doc.page_content for doc in source_docs]
+             
     qa_chain = create_chain(
-        retriever=retriever, model_provider=model_type
+        retriever=retriever, model_provider=model_type, chat_history=chat_history
     )
     print("Recieved question: ", question)
 
     async def stream():
         result = ""
         try:
-            async for s in qa_chain.astream({"context": context, "question": question, "history": chat_history}, config=runnable_config):
+            async for s in qa_chain.astream({"question": question, "chat_history": chat_history}, config=runnable_config):
                 print(s.content, end="", flush=True)
                 result += s.content
                 yield s.content

From 111b9ec82e25e78406e661d7e3c19b8757ead049 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Wed, 16 Aug 2023 00:51:36 -0500
Subject: [PATCH 18/36] fixing title + desc

---
 chat-langchain/app/layout.tsx |  4 +-
 main.py                       | 78 +++++++++++++++++++++--------------
 2 files changed, 48 insertions(+), 34 deletions(-)

diff --git a/chat-langchain/app/layout.tsx b/chat-langchain/app/layout.tsx
index 42f08bbee..53a589e10 100644
--- a/chat-langchain/app/layout.tsx
+++ b/chat-langchain/app/layout.tsx
@@ -5,8 +5,8 @@ import { Inter } from 'next/font/google'
 const inter = Inter({ subsets: ['latin'] })
 
 export const metadata: Metadata = {
-  title: 'Create Next App',
-  description: 'Generated by create next app',
+  title: 'Chat LangChain',
+  description: 'Chatbot for LangChain',
 }
 
 export default function RootLayout({
diff --git a/main.py b/main.py
index d98fc2114..7c84413c9 100644
--- a/main.py
+++ b/main.py
@@ -49,15 +49,6 @@
     "anthropic": "claude-instant-v1-100k",
 }
 
-def _process_chat_history(chat_history):
-    processed_chat_history = []
-    for chat in chat_history:
-        if 'question' in chat:
-            processed_chat_history.append(HumanMessage(content=chat.pop('question')))
-        if 'result' in chat:
-            processed_chat_history.append(AIMessage(content=chat.pop('result')))
-    return processed_chat_history
-
 def create_chain(
     retriever: BaseRetriever,
     model_provider: Union[Literal["openai"], Literal["anthropic"]],
@@ -67,62 +58,75 @@ def create_chain(
 ) -> Runnable:
     model_name = model or _MODEL_MAP[model_provider]
     model = _PROVIDER_MAP[model_provider](model=model_name, temperature=temperature)
-        
-    _template = """Given the following chat history and a follow up question, respond with a standalone question with all the necessary context to understand what the user is truly asking.
+    
+    _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
 
     Chat History:
     {chat_history}
     Follow Up Input: {question}
     Standalone Question:"""
     
+    
     CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
     
-    _inputs = RunnableMap(
-        {
-            "standalone_question": {
-                "question": lambda x: x["question"],
-                "chat_history": lambda x: _process_chat_history(x['chat_history'])
-            } | CONDENSE_QUESTION_PROMPT | model | StrOutputParser(),
-            "chat_history": lambda x: _process_chat_history(x['chat_history']),
-        }
-    )
-
-    _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
+    _template = """You are a helpful AI assistant. Use the following pieces of context to answer the question at the end.
+    If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
+    If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context
     
     Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
     <context>
         {context} 
     <context/>"""
-    
-    if len(chat_history) > 1:
+
+    if chat_history:
+        _inputs = RunnableMap(
+                {
+                    "standalone_question": {
+                        "question": lambda x: x["question"],
+                        "chat_history": lambda x: x["chat_history"],
+                    } | CONDENSE_QUESTION_PROMPT | model | StrOutputParser(),
+                    "question": lambda x: x["question"],
+                    "chat_history": lambda x: x["chat_history"],
+                }
+            )
+        _context = {
+            "context": itemgetter("standalone_question") | retriever,
+            "question": lambda x: x["question"], 
+            "chat_history": lambda x: x["chat_history"],
+        }
         prompt = ChatPromptTemplate.from_messages([
             ("system", _template),
             MessagesPlaceholder(variable_name="chat_history"),
             ("human", "{question}"),
         ])
     else:
+        _inputs = RunnableMap(
+            {
+                "question": lambda x: x["question"],
+                "chat_history": lambda x: x["chat_history"],
+            }
+        )
+        _context = {
+            "context": itemgetter("question") | retriever,
+            "question": lambda x: x["question"], 
+            "chat_history": lambda x: x["chat_history"],
+        }
         prompt = ChatPromptTemplate.from_messages([
             ("system", _template),
             ("human", "{question}"),
         ])
-
     
-    _context = {
-        "context": itemgetter("standalone_question") | retriever,
-        "question": lambda x: x["standalone_question"], 
-        "chat_history": lambda x: x["chat_history"]
-    }
+
     
     chain = (
         _inputs
         | _context
         | prompt 
-        | model 
+        | ChatOpenAI(model="gpt-4", temperature=temperature)
     )
     
     return chain
 
-
 def _get_retriever():
     WEAVIATE_URL = os.environ["WEAVIATE_URL"]
     WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
@@ -143,6 +147,15 @@ def _get_retriever():
     )
     return weaviate_client.as_retriever(search_kwargs=dict(k=10))
 
+def _process_chat_history(chat_history):
+    processed_chat_history = []
+    for chat in chat_history:
+        if 'question' in chat:
+            processed_chat_history.append(HumanMessage(content=chat.pop('question')))
+        if 'result' in chat:
+            processed_chat_history.append(AIMessage(content=chat.pop('result')))
+    return processed_chat_history
+    
 @app.post("/chat")
 async def chat_endpoint(request: Request):
     global run_id, access_counter
@@ -156,6 +169,7 @@ async def chat_endpoint(request: Request):
     chat_history = data.get("history", [])
 
     retriever = _get_retriever()
+    chat_history = _process_chat_history(chat_history)
     # source_docs = retriever.invoke(question) # opportunity to return source documents
     # context = [doc.page_content for doc in source_docs]
              

From ff64c0fa985c4a703e338300c6102aa335a01eab Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Wed, 16 Aug 2023 13:59:05 -0500
Subject: [PATCH 19/36] frontend nit, prompt change

---
 chat-langchain/app/components/ChatMessageBubble.tsx | 2 +-
 chat-langchain/app/components/ChatWindow.tsx        | 2 +-
 main.py                                             | 8 +++-----
 3 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/chat-langchain/app/components/ChatMessageBubble.tsx b/chat-langchain/app/components/ChatMessageBubble.tsx
index a4744151f..66881d843 100644
--- a/chat-langchain/app/components/ChatMessageBubble.tsx
+++ b/chat-langchain/app/components/ChatMessageBubble.tsx
@@ -11,7 +11,7 @@ export function ChatMessageBubble(props: {
   const prefix = props.message.role === "user" ? "🧑" : props.aiEmoji;
   return (
     <div
-      className={`${alignmentClassName} ${colorClassName} rounded px-4 py-2 max-w-[80%] mb-8 flex`}
+      className={`${alignmentClassName} ${colorClassName} rounded px-4 py-2 max-w-[80%] mb-8 flex break-words`}
     >
       <div className="mr-2">{prefix}</div>
       <div
diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 7e33e8fee..5b33a3c01 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -87,7 +87,7 @@ export function ChatWindow(props: {
           validLanguage || "plaintext",
           code
         ).value;
-        return `<pre class="highlight bg-gray-700" style="padding: 5px; border-radius: 5px; overflow: auto; word-wrap: break-word;"><code class="${language}" style="color: #d6e2ef;">${highlightedCode}</code></pre>`;
+        return `<pre class="highlight bg-gray-700" style="padding: 5px; border-radius: 5px; overflow: auto; overflow-wrap: anywhere; white-space: pre-wrap; max-width: 100%; display: block; line-height: 1.2"><code class="${language}" style="color: #d6e2ef; font-size: 12px; ">${highlightedCode}</code></pre>`;
       };
       marked.setOptions({ renderer });
 
diff --git a/main.py b/main.py
index 7c84413c9..856470b26 100644
--- a/main.py
+++ b/main.py
@@ -69,9 +69,8 @@ def create_chain(
     
     CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
     
-    _template = """You are a helpful AI assistant. Use the following pieces of context to answer the question at the end.
-    If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
-    If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context
+    _template = """
+    You are an expert programmer, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability.
     
     Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
     <context>
@@ -116,8 +115,6 @@ def create_chain(
             ("human", "{question}"),
         ])
     
-
-    
     chain = (
         _inputs
         | _context
@@ -170,6 +167,7 @@ async def chat_endpoint(request: Request):
 
     retriever = _get_retriever()
     chat_history = _process_chat_history(chat_history)
+    
     # source_docs = retriever.invoke(question) # opportunity to return source documents
     # context = [doc.page_content for doc in source_docs]
              

From c7e0567ef23d0c4254b0ed4a4a19650220c77b42 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Wed, 16 Aug 2023 13:59:16 -0500
Subject: [PATCH 20/36] moving chat.tsx

---
 chat-langchain/app/components/chat.tsx | 271 -------------------------
 1 file changed, 271 deletions(-)
 delete mode 100644 chat-langchain/app/components/chat.tsx

diff --git a/chat-langchain/app/components/chat.tsx b/chat-langchain/app/components/chat.tsx
deleted file mode 100644
index c1597be50..000000000
--- a/chat-langchain/app/components/chat.tsx
+++ /dev/null
@@ -1,271 +0,0 @@
-"use client";
-
-import React, { useEffect, useRef, useState } from "react";
-import { marked } from "marked";
-import { Renderer } from "marked";
-import hljs from "highlight.js";
-import "highlight.js/styles/gradient-dark.css";
-
-const Chat = () => {
-  const [messages, setMessages] = useState<
-    Array<{ sender: string; message: string }>
-  >([]);
-  const [model, setModel] = useState("openai");
-  const messagesEndRef = useRef<HTMLDivElement>(null);
-  const [message, setMessage] = useState("");
-  const [hasInteracted, setHasInteracted] = useState(false);
-
-  const sendMessage = (event: { preventDefault: () => void }) => {
-    event.preventDefault();
-    if (message === "") {
-      return;
-    }
-    setFeedback(null);
-    setMessages((prevMessages) => [
-      ...prevMessages,
-      { sender: "You", message },
-    ]);
-
-    fetch("https://chat-langchain.fly.dev/chat", {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        message: message,
-        model: model,
-      }),
-    }).then((response) => {
-      if (!response.body) {
-        throw new Error("Response body is null");
-      }
-      setMessage("");
-      setHasInteracted(true);
-      const reader = response.body.getReader();
-      const decoder = new TextDecoder("utf-8");
-
-      let accumulatedMessage = "";
-      let messageIndex: number | null = null;
-
-      let renderer = new Renderer();
-      renderer.code = function (code, language) {
-        const validLanguage = hljs.getLanguage(language || "")
-          ? language
-          : "plaintext";
-        const highlightedCode = hljs.highlight(
-          validLanguage || "plaintext",
-          code
-        ).value;
-        return `<pre class="highlight bg-gray-700" style="padding: 10px; margin: 10px; border-radius: 5px; overflow: auto;"><code class="${language}" style="color: #d6e2ef;">${highlightedCode}</code></pre>`;
-      };
-      marked.setOptions({ renderer });
-
-      reader
-        .read()
-        .then(function processText(
-          res: ReadableStreamReadResult<Uint8Array>
-        ): Promise<void> {
-          const { done, value } = res;
-          if (done) {
-            console.log("Stream complete");
-            return Promise.resolve();
-          }
-
-          let result = decoder.decode(value);
-          accumulatedMessage += result;
-          let parsedResult = marked.parse(accumulatedMessage);
-
-          setMessages((prevMessages) => {
-            let newMessages = [...prevMessages];
-            if (messageIndex === null) {
-              messageIndex = newMessages.length;
-              newMessages.push({
-                sender: "Bot",
-                message: parsedResult.trim(),
-              });
-            } else {
-              newMessages[messageIndex].message = parsedResult.trim();
-            }
-            return newMessages;
-          });
-          return reader.read().then(processText);
-        })
-        .catch((error) => {
-          console.error("Error:", error);
-        });
-    });
-  };
-
-  useEffect(() => {
-    if (messagesEndRef.current) {
-      messagesEndRef.current.scrollIntoView({ behavior: "smooth" });
-    }
-  }, [messages]);
-
-  const [feedback, setFeedback] = useState<number | null>(null);
-
-  const sendFeedback = (score: number | null) => {
-    if (feedback !== null) return;
-
-    setFeedback(score);
-    fetch("https://chat-langchain.fly.dev/feedback", {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        score: score,
-      }),
-    })
-      .then((response) => response.json())
-      .then((data) => {
-        if (data.code === 200) {
-          score == 1 ? animateButton("upButton") : animateButton("downButton");
-        }
-        console.log(data);
-      })
-      .catch((error) => {
-        console.error("Error:", error);
-      });
-  };
-
-  const animateButton = (buttonId: string) => {
-    const button = document.getElementById(buttonId);
-    button!.classList.add("animate-ping");
-    setTimeout(() => {
-      button!.classList.remove("animate-ping");
-    }, 500);
-  };
-
-  const viewTrace = () => {
-    fetch("https://chat-langchain.fly.dev/get_trace", {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-    })
-      .then((response) => response.text()) // change this to handle text response
-      .then((data) => {
-        console.log(data);
-        const url = data.replace(/['"]+/g, "");
-        window.open(url, "_blank");
-      })
-      .catch((error) => {
-        console.error("Error:", error);
-      });
-  };
-
-  return (
-    <div className="rounded-2xl border-zinc-100 lg:border lg:p-6 bg-slate-200 w-full sm:w-[60%] lg:w-[75%]">
-      <div className="w-full mb-5 rounded-lg px-4 py-5 shadow-lg ring-1 ring-zinc-100 sm:px-6 bg-slate-100">
-        <h4 className="text-center text-3xl font-sans font-bold pb-2 text-transparent bg-clip-text bg-gradient-to-r from-teal-600 to-teal-800">
-          Chat LangChain
-        </h4>
-        <span className="mx-auto flex font-light flex-grow text-gray-400 clear-both text-sm justify-center font-mono">
-          A chatbot for everything LangChain.
-        </span>
-        <hr className="border-gray-500 my-4" />
-        <div className="flex flex-col">
-          <div>
-            <button
-              type="button"
-              className="text-black text-xs border rounded p-1 float-right"
-              onClick={() => viewTrace()}
-            >
-              🛠️ view trace
-            </button>
-          </div>
-          <div
-            className="my-2 font-sans font-normal max-h-[calc(100vh-28rem)] overflow-y-scroll"
-          >
-            {messages.map((msg, index) => (
-              <div
-                key={index}
-                className={`flex ${
-                  msg.sender === "You" ? "justify-end" : "justify-start"
-                }`}
-              >
-                <div
-                  className={
-                    msg.sender === "You"
-                      ? "bg-gray-700 border border-gray-600 rounded-md p-2 m-2 text-xs sm:text-base text-white text-right max-w-[80%] inline-block"
-                      : "bg-slate-200 border border-gray-200 rounded-md p-2 m-2 text-xs sm:text-base text-left max-w-[80%] inline-block"
-                  }
-                >
-                  {msg.sender && (
-                    <strong className="font-medium">{msg.sender}: </strong>
-                  )}
-                  <div dangerouslySetInnerHTML={{ __html: msg.message }}></div>
-                </div>
-              </div>
-            ))}
-            <div ref={messagesEndRef} />
-          </div>
-          <form
-            className="flex flex-col sm:flex-row justify-between mt-5"
-            onSubmit={sendMessage}
-          >
-            <input
-              type="text"
-              className="w-full sm:w-[70%] text-gray-800 rounded p-2 mr-2 focus:outline-none focus:ring-1 focus:ring-teal-600"
-              placeholder="Write your question"
-              id="messageText"
-              value={message}
-              onChange={(e) => setMessage(e.target.value)}
-            />
-            <div className="flex flex-row mt-2 sm:mt-0">
-              <select
-                id="modelType"
-                className="w-full bg-gray-800 text-white rounded p-2 mr-2"
-                value={model}
-                onChange={(e) => setModel(e.target.value)}
-              >
-                <option value="openai">OpenAI</option>
-                <option value="anthropic">Anthropic</option>
-              </select>
-              <button
-                type="button"
-                className={`w-16 sm:w-auto text-white border rounded p-2 mr-1 hover:border-green-400 ${
-                  feedback === 1 ? "bg-green-100" : ""
-                }`}
-                onClick={() => {
-                  if (feedback === null) {
-                    sendFeedback(1);
-                  }
-                }}
-                disabled={feedback !== null || !hasInteracted}
-                id="upButton"
-              >
-                👍
-              </button>
-              <button
-                type="button"
-                className={`w-16 sm:w-auto text-white border rounded p-2 mr-2 hover:border-red-400 ${
-                  feedback === 0 ? "bg-red-100" : ""
-                }`}
-                onClick={() => {
-                  if (feedback === null) {
-                    sendFeedback(0);
-                  }
-                }}
-                disabled={feedback !== null || !hasInteracted}
-                id="downButton"
-              >
-                👎
-              </button>
-              <button
-                id="send"
-                type="submit"
-                className="w-16 sm:w-auto text-sm bg-gradient-to-r from-teal-600 to-teal-800 text-white rounded p-2"
-              >
-                Send
-              </button>
-            </div>
-          </form>
-        </div>
-      </div>
-    </div>
-  );
-};
-
-export default Chat;

From 745b719d0ae0c3bcca6cd8adbf7f7b92f93c9fad Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Wed, 16 Aug 2023 19:17:04 -0500
Subject: [PATCH 21/36] StrOutputParser and feedback change

---
 .../app/components/ChatMessageBubble.tsx      | 84 ++++++++++++++++++-
 chat-langchain/app/components/ChatWindow.tsx  | 40 +--------
 chat-langchain/app/page.tsx                   |  1 -
 chat-langchain/package-lock.json              | 11 +++
 chat-langchain/package.json                   |  1 +
 main.py                                       |  8 +-
 6 files changed, 103 insertions(+), 42 deletions(-)

diff --git a/chat-langchain/app/components/ChatMessageBubble.tsx b/chat-langchain/app/components/ChatMessageBubble.tsx
index 66881d843..081f7c8b0 100644
--- a/chat-langchain/app/components/ChatMessageBubble.tsx
+++ b/chat-langchain/app/components/ChatMessageBubble.tsx
@@ -1,23 +1,105 @@
 import type { Message } from "ai/react";
+import { toast } from 'react-toastify';
+import 'react-toastify/dist/ReactToastify.css';
+import { emojisplosion } from "emojisplosion";
+import { useState } from "react";
 
 export function ChatMessageBubble(props: {
   message: Message;
   aiEmoji?: string;
+  feedback: number | null;
+  sendFeedback: (feedback: 0 | 1) => void;
+  isMostRecent: boolean;
 }) {
   const colorClassName =
     props.message.role === "user" ? "bg-sky-600" : "bg-slate-50 text-black";
   const alignmentClassName =
     props.message.role === "user" ? "mr-auto" : "ml-auto";
   const prefix = props.message.role === "user" ? "🧑" : props.aiEmoji;
+
+  const [feedbackColor, setFeedbackColor] = useState("");
+
+  var cumulativeOffset = function(element: HTMLElement | null) {
+      var top = 0, left = 0;
+      do {
+          top += element?.offsetTop  || 0;
+          left += element?.offsetLeft || 0;
+          element = (element?.offsetParent as HTMLElement) || null;
+      } while(element);
+
+      return {
+          top: top,
+          left: left
+      };
+  };
+
+  const animateButton = (buttonId: string) => {
+    const button = document.getElementById(buttonId);
+    button!.classList.add("animate-ping");
+    setTimeout(() => {
+      button!.classList.remove("animate-ping");
+    }, 500);
+
+    emojisplosion({
+      emojiCount: 10,
+      uniqueness: 1,
+      position() {
+        const offset = cumulativeOffset(button);
+
+        return {
+          x: offset.left + button!.clientWidth / 2,
+          y: offset.top + button!.clientHeight / 2,
+        };
+      },
+      emojis: buttonId === "upButton" ? ["👍"] : ["👎"],
+    });
+  };
+
   return (
     <div
-      className={`${alignmentClassName} ${colorClassName} rounded px-4 py-2 max-w-[80%] mb-8 flex break-words`}
+      className={`${alignmentClassName} ${colorClassName} ${feedbackColor} rounded px-4 py-2 max-w-[80%] mb-8 flex break-words`}
     >
       <div className="mr-2">{prefix}</div>
       <div
         className="whitespace-pre-wrap"
         dangerouslySetInnerHTML={{ __html: props.message.content }}
       ></div>
+      {props.message.role !== "user" && props.isMostRecent && (
+        <div className="relative flex space-x-4 items-start translate-y-[-35px]">
+          <button
+            className={`top-0 text-sm rounded ${props.feedback === null ? "hover:bg-green-200" : ""}`}
+            id="upButton"
+            type="button"
+            onClick={() => {
+              if (props.feedback === null) {
+                props.sendFeedback(1);
+                animateButton("upButton")
+                setFeedbackColor("border-4 border-green-300");
+              } else {
+                toast.error("You have already provided your feedback.");
+              }
+            }}
+          >
+            👍
+          </button>
+          <button
+            className={`absolute top-0 ml-6 text-sm rounded ${props.feedback === null ? "hover:bg-red-200" : ""}`}
+            id="downButton"
+            type="button"
+            onClick={() => {
+              if (props.feedback === null) {
+                props.sendFeedback(0);
+                animateButton("downButton")
+                setFeedbackColor("border-4 border-red-300")
+              } else {
+                toast.error("You have already provided your feedback.");
+              }
+            }}
+          >
+            👎
+          </button>
+        </div>
+      )}
     </div>
   );
 }
diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 5b33a3c01..665fc6a30 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -8,9 +8,6 @@ import { Renderer } from "marked";
 import hljs from "highlight.js";
 import "highlight.js/styles/gradient-dark.css";
 
-import { toast } from 'react-toastify';
-import 'react-toastify/dist/ReactToastify.css';
-
 export function ChatWindow(props: {
   endpoint: string;
   emptyStateComponent: React.ReactElement;
@@ -194,11 +191,14 @@ export function ChatWindow(props: {
         {messages.length > 0
           ? [...messages]
               .reverse()
-              .map((m) => (
+              .map((m, index) => (
                 <ChatMessageBubble
                   key={m.id}
                   message={{ id: m.id, content: m.message, role: m.role }}
                   aiEmoji="🦜"
+                  sendFeedback={sendFeedback}
+                  feedback={feedback}
+                  isMostRecent={index === 0}
                 ></ChatMessageBubble>
               ))
           : emptyStateComponent}
@@ -231,38 +231,6 @@ export function ChatWindow(props: {
           <option value="openai">OpenAI</option>
           <option value="anthropic">Anthropic</option>
         </select>
-        <button
-          type="button"
-          className={`w-16 sm:w-auto text-white border rounded p-2 mr-1 hover:border-green-400 ${
-            feedback === 1 ? "bg-green-100" : ""
-          }`}
-          onClick={() => {
-            if (feedback === null && hasInteracted) {
-              sendFeedback(1);
-            } else {
-              toast.error("You have already provided your feedback.");
-            }
-          }}
-          id="upButton"
-        >
-          👍
-        </button>
-        <button
-          type="button"
-          className={`w-16 sm:w-auto text-white border rounded p-2 mr-2 hover:border-red-400 ${
-            feedback === 0 ? "bg-red-100" : ""
-          }`}
-          onClick={() => {
-            if (feedback === null && hasInteracted) {
-              sendFeedback(0);
-            } else {
-              toast.error("You have already provided your feedback.");
-            }
-          }}
-          id="downButton"
-        >
-          👎
-        </button>
         <button
           type="submit"
           className="flex-shrink p-2 w-16 sm:w-auto bg-sky-600 rounded"
diff --git a/chat-langchain/app/page.tsx b/chat-langchain/app/page.tsx
index 4c5d48d46..bfbc879a8 100644
--- a/chat-langchain/app/page.tsx
+++ b/chat-langchain/app/page.tsx
@@ -1,5 +1,4 @@
 import Image from "next/image";
-import Chat from "../app/components/chat";
 import { ChatWindow } from "../app/components/ChatWindow";
 import { ToastContainer } from "react-toastify";
 
diff --git a/chat-langchain/package-lock.json b/chat-langchain/package-lock.json
index 3b4db8860..649ab723b 100644
--- a/chat-langchain/package-lock.json
+++ b/chat-langchain/package-lock.json
@@ -16,6 +16,7 @@
         "@types/react-dom": "18.2.7",
         "ai": "^2.1.33",
         "autoprefixer": "10.4.14",
+        "emojisplosion": "^2.6.1",
         "eslint": "8.46.0",
         "eslint-config-next": "13.4.13",
         "highlight.js": "^11.8.0",
@@ -1881,6 +1882,11 @@
       "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-9.2.2.tgz",
       "integrity": "sha512-L18DaJsXSUk2+42pv8mLs5jJT2hqFkFE4j21wOmgbUqsZ2hL72NsUU785g9RXgo3s0ZNgVl42TiHp3ZtOv/Vyg=="
     },
+    "node_modules/emojisplosion": {
+      "version": "2.6.1",
+      "resolved": "https://registry.npmjs.org/emojisplosion/-/emojisplosion-2.6.1.tgz",
+      "integrity": "sha512-a+yvHzHHP9R8qvt7ZeZS8yReuHjwXvbBbSxJHVMMWs4+sf0eOE/v/+Iz6fQq0CdF8G04jsJMF1MNDlrIBCC1pw=="
+    },
     "node_modules/enhanced-resolve": {
       "version": "5.15.0",
       "resolved": "https://registry.npmjs.org/enhanced-resolve/-/enhanced-resolve-5.15.0.tgz",
@@ -6876,6 +6882,11 @@
       "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-9.2.2.tgz",
       "integrity": "sha512-L18DaJsXSUk2+42pv8mLs5jJT2hqFkFE4j21wOmgbUqsZ2hL72NsUU785g9RXgo3s0ZNgVl42TiHp3ZtOv/Vyg=="
     },
+    "emojisplosion": {
+      "version": "2.6.1",
+      "resolved": "https://registry.npmjs.org/emojisplosion/-/emojisplosion-2.6.1.tgz",
+      "integrity": "sha512-a+yvHzHHP9R8qvt7ZeZS8yReuHjwXvbBbSxJHVMMWs4+sf0eOE/v/+Iz6fQq0CdF8G04jsJMF1MNDlrIBCC1pw=="
+    },
     "enhanced-resolve": {
       "version": "5.15.0",
       "resolved": "https://registry.npmjs.org/enhanced-resolve/-/enhanced-resolve-5.15.0.tgz",
diff --git a/chat-langchain/package.json b/chat-langchain/package.json
index 5d1b63404..a29ab5a65 100644
--- a/chat-langchain/package.json
+++ b/chat-langchain/package.json
@@ -17,6 +17,7 @@
     "@types/react-dom": "18.2.7",
     "ai": "^2.1.33",
     "autoprefixer": "10.4.14",
+    "emojisplosion": "^2.6.1",
     "eslint": "8.46.0",
     "eslint-config-next": "13.4.13",
     "highlight.js": "^11.8.0",
diff --git a/main.py b/main.py
index 856470b26..3262aa92f 100644
--- a/main.py
+++ b/main.py
@@ -120,6 +120,7 @@ def create_chain(
         | _context
         | prompt 
         | ChatOpenAI(model="gpt-4", temperature=temperature)
+        | StrOutputParser()
     )
     
     return chain
@@ -133,7 +134,6 @@ def _get_retriever():
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
     )
-    # print(client.query.aggregate("LangChain_idx").with_meta_count().do())
     weaviate_client = Weaviate(
         client=client,
         index_name="LangChain_idx",
@@ -180,9 +180,9 @@ async def stream():
         result = ""
         try:
             async for s in qa_chain.astream({"question": question, "chat_history": chat_history}, config=runnable_config):
-                print(s.content, end="", flush=True)
-                result += s.content
-                yield s.content
+                print(s, end="", flush=True)
+                result += s
+                yield s
 
         except Exception as e:
             logging.error(e)

From c00984c1727cbaed9d5a6c302ff84906b02a1421 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Fri, 18 Aug 2023 12:40:47 -0700
Subject: [PATCH 22/36] feedback banner

---
 chat-langchain/app/components/ChatMessageBubble.tsx | 13 ++++++++-----
 chat-langchain/app/components/ChatWindow.tsx        | 12 ++++++++----
 main.py                                             |  2 +-
 3 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/chat-langchain/app/components/ChatMessageBubble.tsx b/chat-langchain/app/components/ChatMessageBubble.tsx
index 081f7c8b0..31634eb7b 100644
--- a/chat-langchain/app/components/ChatMessageBubble.tsx
+++ b/chat-langchain/app/components/ChatMessageBubble.tsx
@@ -10,6 +10,7 @@ export function ChatMessageBubble(props: {
   feedback: number | null;
   sendFeedback: (feedback: 0 | 1) => void;
   isMostRecent: boolean;
+  messageCompleted: boolean;
 }) {
   const colorClassName =
     props.message.role === "user" ? "bg-sky-600" : "bg-slate-50 text-black";
@@ -56,18 +57,20 @@ export function ChatMessageBubble(props: {
   };
 
   return (
+    <div className="mb-8">
     <div
-      className={`${alignmentClassName} ${colorClassName} ${feedbackColor} rounded px-4 py-2 max-w-[80%] mb-8 flex break-words`}
+      className={`${alignmentClassName} ${colorClassName} ${feedbackColor} rounded px-4 py-2 max-w-[80%] mb-1 flex break-words`}
     >
       <div className="mr-2">{prefix}</div>
       <div
         className="whitespace-pre-wrap"
         dangerouslySetInnerHTML={{ __html: props.message.content }}
       ></div>
-      {props.message.role !== "user" && props.isMostRecent && (
-        <div className="relative flex space-x-4 items-start translate-y-[-35px]">
+    </div>
+    {props.message.role !== "user" && props.isMostRecent && props.messageCompleted && (
+        <div className="relative flex space-x-1 items-start justify-end">
           <button
-            className={`top-0 text-sm rounded ${props.feedback === null ? "hover:bg-green-200" : ""}`}
+            className={`text-sm rounded ${props.feedback === null ? "hover:bg-green-200" : ""}`}
             id="upButton"
             type="button"
             onClick={() => {
@@ -83,7 +86,7 @@ export function ChatMessageBubble(props: {
             👍
           </button>
           <button
-            className={`absolute top-0 ml-6 text-sm rounded ${props.feedback === null ? "hover:bg-red-200" : ""}`}
+            className={`text-sm rounded ${props.feedback === null ? "hover:bg-red-200" : ""}`}
             id="downButton"
             type="button"
             onClick={() => {
diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 665fc6a30..837d4c849 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -184,6 +184,9 @@ export function ChatWindow(props: {
       <h2 className={`${messages.length > 0 ? "" : "hidden"} text-2xl mb-1`}>
         {titleText}
       </h2>
+      <h4 className={`${messages.length > 0 ? "" : "hidden"} text-sm mb-4`}>
+        We appreciate feedback!
+        </h4>
       <div
         className="flex flex-col-reverse w-full mb-2 overflow-auto"
         ref={messageContainerRef}
@@ -199,6 +202,7 @@ export function ChatWindow(props: {
                   sendFeedback={sendFeedback}
                   feedback={feedback}
                   isMostRecent={index === 0}
+                  messageCompleted={!isLoading}
                 ></ChatMessageBubble>
               ))
           : emptyStateComponent}
@@ -215,7 +219,7 @@ export function ChatWindow(props: {
         </div>
 
       <form onSubmit={sendMessage} className="flex w-full">
-        <input
+        <textarea
           className="flex-grow mr-2 p-4 rounded"
           placeholder={placeholder}
           onChange={(e) => setInput(e.target.value)}
@@ -224,7 +228,7 @@ export function ChatWindow(props: {
         />
         <select
           id="modelType"
-          className="bg-gray-800 text-white rounded p-2 mr-2 w-full sm:w-auto"
+          className="bg-gray-800 text-white rounded p-2 mr-2 w-full sm:w-auto max-h-[40px]"
           value={model}
           onChange={(e) => setModel(e.target.value)}
         >
@@ -233,7 +237,7 @@ export function ChatWindow(props: {
         </select>
         <button
           type="submit"
-          className="flex-shrink p-2 w-16 sm:w-auto bg-sky-600 rounded"
+          className="flex-shrink p-2 w-16 sm:w-auto bg-sky-600 rounded max-h-[40px]"
         >
           <div
             role="status"
@@ -257,7 +261,7 @@ export function ChatWindow(props: {
             </svg>
             <span className="sr-only">Loading...</span>
           </div>
-          <span className={isLoading ? "hidden" : ""}>Send</span>
+          <span className={`${isLoading ? "hidden" : ""}`}>Send</span>
         </button>
       </form>
     </div>
diff --git a/main.py b/main.py
index 3262aa92f..de70a4c73 100644
--- a/main.py
+++ b/main.py
@@ -136,7 +136,7 @@ def _get_retriever():
     )
     weaviate_client = Weaviate(
         client=client,
-        index_name="LangChain_idx",
+        index_name="LangChain_newest_idx",
         text_key="text",
         embedding=embeddings,
         by_text=False,

From 90e3600754a8da122bd16e0bfbe71cbfbb1c9c62 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 21 Aug 2023 11:34:51 -0700
Subject: [PATCH 23/36] ingestion/retriever tweets and evaluation catchup

---
 _scripts/evaluate_chains.py | 142 +++++++++++++++++++-----------------
 main.py                     |   8 +-
 2 files changed, 80 insertions(+), 70 deletions(-)

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
index 5d9532259..970bffb50 100644
--- a/_scripts/evaluate_chains.py
+++ b/_scripts/evaluate_chains.py
@@ -13,12 +13,15 @@
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.memory import ConversationBufferMemory
 from langchain.schema.retriever import BaseRetriever
-from langchain.schema.runnable import Runnable
+from langchain.schema.runnable import Runnable, RunnableMap
+from langchain.schema.output_parser import StrOutputParser
 from langchain.smith import RunEvalConfig
 from langchain.vectorstores import Weaviate
+from langchain.prompts import PromptTemplate, ChatPromptTemplate, MessagesPlaceholder
 from langsmith import Client
 from langsmith import RunEvaluator
 from langchain import load as langchain_load
+from operator import itemgetter
 import json
 
 _PROVIDER_MAP = {
@@ -31,98 +34,104 @@
     "anthropic": "claude-2",
 }
 
-
-def _get_prompt(prompt_type: str) -> prompts.BasePromptTemplate:
-    if prompt_type == "completion":
-        _template = """You are an expert programmer, tasked to answer any question about Langchain. Be as helpful as possible. 
-        
-Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
-<context>
-    {context} 
-<context/>
-                
-Conversation History:               
-{history}
-
-Answer the user's question to the best of your ability: {question}
-Helpful Answer:"""
-
-        return prompts.PromptTemplate(
-            input_variables=["history", "context", "question"], template=_template
-        )
-    else:
-        return prompts.ChatPromptTemplate.from_messages(
-            [
-                (
-                    "system",
-                    "You are an expert programmer, tasked with answering any question about Langchain. Be as helpful as possible.",
-                ),
-                prompts.MessagesPlaceholder(variable_name="history"),
-                ("human", "{question}"),
-                (
-                    "system",
-                    "Respond to the user's question as best and truthfully as you are able."
-                    " You can choose to use the following retrieved information if it is relevant.",
-                ),
-                ("system", "<retrieved_context>\n{context}\n</retrieved_context>"),
-            ]
-        )
-
-
-
 def create_chain(
     retriever: BaseRetriever,
     model_provider: Union[Literal["openai"], Literal["anthropic"]],
     chat_history: Optional[list] = None,
     model: Optional[str] = None,
     temperature: float = 0.0,
-    prompt_type: Union[Literal["chat"], Literal["completion"]] = "chat",
 ) -> Runnable:
     model_name = model or _MODEL_MAP[model_provider]
     model = _PROVIDER_MAP[model_provider](model=model_name, temperature=temperature)
-
-    prompt = _get_prompt(prompt_type)
-    return_messages = True if prompt_type == "chat" else False
-
-    memory = ConversationBufferMemory(
-        input_key="question", memory_key="history", return_messages=return_messages
-    )
-    chat_history_ = chat_history or []
-    for message in chat_history_:
-        memory.save_context(
-            {"question": message["question"]}, {"result": message["result"]}
+    
+    _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
+
+    Chat History:
+    {chat_history}
+    Follow Up Input: {question}
+    Standalone Question:"""
+    
+    
+    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+    
+    _template = """
+    You are an expert programmer, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability.
+    If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
+    Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
+    <context>
+        {context} 
+    <context/>"""
+
+    if chat_history:
+        _inputs = RunnableMap(
+                {
+                    "standalone_question": {
+                        "question": lambda x: x["question"],
+                        "chat_history": lambda x: x["chat_history"],
+                    } | CONDENSE_QUESTION_PROMPT | model | StrOutputParser(),
+                    "question": lambda x: x["question"],
+                    "chat_history": lambda x: x["chat_history"],
+                }
+            )
+        _context = {
+            "context": itemgetter("standalone_question") | retriever,
+            "question": lambda x: x["question"], 
+            "chat_history": lambda x: x["chat_history"],
+        }
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", _template),
+            MessagesPlaceholder(variable_name="chat_history"),
+            ("human", "{question}"),
+        ])
+    else:
+        _inputs = RunnableMap(
+            {
+                "question": lambda x: x["question"],
+                "chat_history": lambda x: [],
+            }
         )
-
-    qa_chain = RetrievalQA.from_chain_type(
-        model,
-        chain_type="stuff",
-        retriever=retriever,
-        return_source_documents=True,
-        chain_type_kwargs={"prompt": prompt, "memory": memory},
+        _context = {
+            "context": itemgetter("question") | retriever,
+            "question": lambda x: x["question"], 
+            "chat_history": lambda x: [],
+        }
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", _template),
+            ("human", "{question}"),
+        ])
+    
+    chain = (
+        _inputs
+        | _context
+        | prompt 
+        | ChatOpenAI(model="gpt-4", temperature=temperature)
+        | StrOutputParser()
     )
-    return qa_chain
+    
+    return chain
 
 
 def _get_retriever():
     WEAVIATE_URL = os.environ["WEAVIATE_URL"]
     WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
 
-    embeddings = OpenAIEmbeddings()
+    embeddings = OpenAIEmbeddings(chunk_size=200)
     client = weaviate.Client(
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
     )
-    print(client.query.aggregate("LangChain_idx").with_meta_count().do())
+    print(client.query.aggregate("LangChain_newest_idx").with_meta_count().do())
     weaviate_client = Weaviate(
         client=client,
-        index_name="LangChain_idx",
+        index_name="LangChain_newest_idx",
         text_key="text",
         embedding=embeddings,
         by_text=False,
         attributes=["source"],
     )
-    return weaviate_client.as_retriever(search_kwargs=dict(k=10))
-
+    return weaviate_client.as_retriever(
+        search_kwargs={'score_threshold': 0.7}
+    )
 
 class CustomHallucinationEvaluator(RunEvaluator):
 
@@ -163,10 +172,9 @@ def evaluate_run(self, run: Run, example: Example | None = None) -> EvaluationRe
         create_chain,
         retriever=retriever,
         model_provider=args.model_provider,
-        prompt_type=args.prompt_type,
     )
     chain = constructor()
-    eval_config = RunEvalConfig(evaluators=["qa"], prediction_key="result")
+    eval_config = RunEvalConfig(evaluators=["qa"], prediction_key="output")
     results = client.run_on_dataset(
         dataset_name=args.dataset_name,
         llm_or_chain_factory=constructor,
diff --git a/main.py b/main.py
index de70a4c73..59d853727 100644
--- a/main.py
+++ b/main.py
@@ -70,8 +70,8 @@ def create_chain(
     CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
     
     _template = """
-    You are an expert programmer, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability.
-    
+    You are an expert programmer, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability. 
+    If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
     Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
     <context>
         {context} 
@@ -142,7 +142,9 @@ def _get_retriever():
         by_text=False,
         attributes=["source"],
     )
-    return weaviate_client.as_retriever(search_kwargs=dict(k=10))
+    return weaviate_client.as_retriever(
+        search_kwargs={'score_threshold': 0.7}
+    )
 
 def _process_chat_history(chat_history):
     processed_chat_history = []

From 0bfe3192eec0d586b32764ef14fc86fe2957d3eb Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 21 Aug 2023 14:13:09 -0700
Subject: [PATCH 24/36] edge case, can't submit if message is empty

---
 chat-langchain/app/components/ChatWindow.tsx | 1 +
 1 file changed, 1 insertion(+)

diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 837d4c849..e83f7385c 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -44,6 +44,7 @@ export function ChatWindow(props: {
     if (isLoading) {
       return;
     }
+    if (input.trim() === "") return;
     setInput("");
     setHasInteracted(true);
     setMessages((prevMessages) => [

From fb7c931329b80a38af4da46f42aa8e7d9cdf6a8a Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Tue, 22 Aug 2023 15:03:26 -0700
Subject: [PATCH 25/36] ingest via parent and redis mem store

---
 _scripts/evaluate_chains.py | 57 ++++++++++++++++++---
 ingest.py                   | 98 ++++++++++++++++++++++++++++++-------
 main.py                     | 64 +++++++++++++++++++-----
 3 files changed, 182 insertions(+), 37 deletions(-)

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
index 970bffb50..0fcb27c42 100644
--- a/_scripts/evaluate_chains.py
+++ b/_scripts/evaluate_chains.py
@@ -23,6 +23,12 @@
 from langchain import load as langchain_load
 from operator import itemgetter
 import json
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.retrievers import ParentDocumentRetriever
+from langchain.storage import InMemoryStore
+from langchain.storage import RedisStore, EncoderBackedStore
+from langchain.utilities.redis import get_client
+from langchain.schema.document import Document
 
 _PROVIDER_MAP = {
     "openai": ChatOpenAI,
@@ -55,8 +61,8 @@ def create_chain(
     CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
     
     _template = """
-    You are an expert programmer, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability.
-    If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
+    You are an expert programmer and problem-solver, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability using the resources provided.
+    If you really don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
     Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
     <context>
         {context} 
@@ -116,22 +122,59 @@ def _get_retriever():
     WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
 
     embeddings = OpenAIEmbeddings(chunk_size=200)
+    child_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
+    parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
+    
     client = weaviate.Client(
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
     )
-    print(client.query.aggregate("LangChain_newest_idx").with_meta_count().do())
+    print("Index has this many vectors", client.query.aggregate("LangChain_parents_idx").with_meta_count().do())
     weaviate_client = Weaviate(
         client=client,
-        index_name="LangChain_newest_idx",
+        index_name="LangChain_parents_idx",
         text_key="text",
         embedding=embeddings,
         by_text=False,
-        attributes=["source"],
+        attributes=["source", "doc_id"],
     )
-    return weaviate_client.as_retriever(
-        search_kwargs={'score_threshold': 0.7}
+
+    def key_encoder(key: int) -> str:
+        return json.dumps(key)
+
+    def value_serializer(value: float) -> str:
+        if isinstance(value, Document):
+            value = {
+                'page_content': value.page_content,
+                'metadata': value.metadata,
+            }
+        return json.dumps(value)
+
+    def value_deserializer(serialized_value: str) -> Document:
+        value = json.loads(serialized_value)
+        if 'page_content' in value and 'metadata' in value:
+            return Document(page_content=value['page_content'], metadata=value['metadata'])
+        else:
+            return value
+
+    client = get_client('redis://default:c16f99d1cc694b7fb9380db03abbe341@fly-chat-langchain.upstash.io')
+    abstract_store = RedisStore(client=client)
+    store = EncoderBackedStore(
+        store=abstract_store,
+        key_encoder=key_encoder,
+        value_serializer=value_serializer,
+        value_deserializer=value_deserializer
     )
+    
+    retriever = ParentDocumentRetriever(
+        vectorstore=weaviate_client, 
+        docstore=store, 
+        child_splitter=child_splitter,
+        parent_splitter=parent_splitter,
+        search_kwargs={'k': 10}
+    )
+    
+    return retriever
 
 class CustomHallucinationEvaluator(RunEvaluator):
 
diff --git a/ingest.py b/ingest.py
index a43b9110e..f461cde36 100644
--- a/ingest.py
+++ b/ingest.py
@@ -14,12 +14,16 @@
 from langchain.text_splitter import Language
 from langchain.document_loaders.generic import GenericLoader
 from langchain.document_loaders.parsers import LanguageParser
+from langchain.storage import RedisStore, EncoderBackedStore
+from langchain.retrievers import ParentDocumentRetriever
+from langchain.utilities.redis import get_client
+import json
 
 WEAVIATE_URL=os.environ["WEAVIATE_URL"]
 WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
 
 def ingest_repo():
-    repo_path = "/Users/mollycantillon/Desktop/test_repo"
+    repo_path = os.path.join(os.getcwd(), "test_repo")
     if os.path.exists(repo_path):
         shutil.rmtree(repo_path)
 
@@ -40,47 +44,105 @@ def ingest_repo():
     texts = python_splitter.split_documents(documents_repo)
     return texts
 
+def key_encoder(key: int) -> str:
+    return json.dumps(key)
+
+def value_serializer(value: float) -> str:
+    if isinstance(value, Document):
+        value = {
+            'page_content': value.page_content,
+            'metadata': value.metadata,
+        }
+    return json.dumps(value)
+
+def value_deserializer(serialized_value: str) -> Document:
+    value = json.loads(serialized_value)
+    if 'page_content' in value and 'metadata' in value:
+        return Document(page_content=value['page_content'], metadata=value['metadata'])
+    else:
+        return value
+    
 def ingest_docs():
     """Get documents from web pages."""
 
     urls = [
         "https://api.python.langchain.com/en/latest/api_reference.html#module-langchain",
         "https://python.langchain.com/docs/get_started", 
+        "https://python.langchain.com/docs/use_cases",
+        "https://python.langchain.com/docs/integrations",
         "https://python.langchain.com/docs/modules", 
         "https://python.langchain.com/docs/guides",
         "https://python.langchain.com/docs/ecosystem",
         "https://python.langchain.com/docs/additional_resources",
+        "https://python.langchain.com/docs/community",
     ]
 
     documents = []
     for url in urls:
-        loader = RecursiveUrlLoader(url=url, max_depth=1 if url == urls[0] else 3, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=False if url == urls[0] else True)        
-        documents += loader.load()
+        loader = RecursiveUrlLoader(url=url, max_depth=2 if url == urls[0] else 10, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=True)
+        temp_docs = loader.load()
+        temp_docs = [doc for i, doc in enumerate(temp_docs) if doc not in temp_docs[:i]]        
+        documents += temp_docs
+        print("Loaded", len(temp_docs), "documents from", url)
+    
+    print("Loaded", len(documents), "documents from all URLs")
     
     html2text = Html2TextTransformer()
     docs_transformed = html2text.transform_documents(documents)
     
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    repo_docs = ingest_repo()
+    docs_transformed += repo_docs
     
-    print('before splitting', len(docs_transformed))
-    docs = text_splitter.split_documents(docs_transformed)
-    print('after splitting', len(docs))
+    print("Transformed", len(docs_transformed), "documents in total")
     
-    repo_docs = ingest_repo()
-    docs += repo_docs
+    # OPTION TO PICKLE
+    # import pickle
+    # with open('docs_transformed.pkl', 'wb') as f:
+    #     pickle.dump(docs_transformed, f)
+    # with open('docs_transformed.pkl', 'rb') as f:
+    #     docs_transformed = pickle.load(f)
+    
+    client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
+    child_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
+    parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
+    client.schema.delete_class("LangChain_parents_idx") # delete the class if it already exists
+    
+    vectorstore = Weaviate(
+        embedding=OpenAIEmbeddings(chunk_size=200),
+        client=client,
+        index_name="LangChain_parents_idx",
+        by_text=False,
+        text_key="text",
+        attributes=["doc_id"],
+    )
 
-    embeddings = OpenAIEmbeddings(chunk_size=200) # rate limit
+    redis_client = get_client('redis://localhost:6379')
+    abstract_store = RedisStore(client=redis_client)
 
-    client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
-    client.schema.delete_all()
-    print(client.schema.get()) # should be empty
+    # Create an instance of the encoder-backed store
+    store = EncoderBackedStore(
+        store=abstract_store,
+        key_encoder=key_encoder,
+        value_serializer=value_serializer,
+        value_deserializer=value_deserializer
+    )
 
-    batch_size = 100 # to handle batch size limit 
-    for i in range(0, len(docs), batch_size):
-        batch = docs[i:i+batch_size]
-        Weaviate.from_documents(batch, embeddings, client=client, by_text=False, index_name="LangChain_idx")
+    retriever = ParentDocumentRetriever(
+        vectorstore=vectorstore, 
+        docstore=store, 
+        child_splitter=child_splitter,
+        parent_splitter=parent_splitter
+    )
+            
+    # client.schema.delete_all()
+    # print(client.schema.get()) # should be empty
+        
+    batch = 100
+    for i in range(0, len(docs_transformed), batch):
+        chunk = docs_transformed[i:i+batch]
+        retriever.add_documents(chunk, ids=None)
 
-    print("LangChain now has this many vectors", client.query.aggregate("LangChain_idx").with_meta_count().do())
+    print("LangChain now has this many vectors", client.query.aggregate("LangChain_parents_idx").with_meta_count().do())
     
 if __name__ == "__main__":
     ingest_docs()
diff --git a/main.py b/main.py
index 59d853727..f98391e8b 100644
--- a/main.py
+++ b/main.py
@@ -1,5 +1,4 @@
 """Main entrypoint for the app."""
-import asyncio
 import logging
 import os
 from typing import Literal, Optional, Union
@@ -9,10 +8,8 @@
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse
 from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
-from langchain.chains import RetrievalQA
 from langchain.chat_models import ChatAnthropic, ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
-from langchain.memory import ConversationBufferMemory
 from langchain.prompts.prompt import PromptTemplate
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.schema.messages import HumanMessage, AIMessage
@@ -22,6 +19,12 @@
 from langchain.vectorstores import Weaviate
 from langsmith import Client
 from operator import itemgetter
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.retrievers import ParentDocumentRetriever
+from langchain.storage import RedisStore, EncoderBackedStore
+from langchain.utilities.redis import get_client
+import json
+from langchain.schema.document import Document
 
 client = Client()
 
@@ -66,12 +69,12 @@ def create_chain(
     Follow Up Input: {question}
     Standalone Question:"""
     
-    
+
     CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
     
     _template = """
-    You are an expert programmer, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability. 
-    If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
+    You are an expert programmer and problem-solver, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability using the resources provided.
+    If you really don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
     Anything between the following markdown blocks is retrieved from a knowledge bank, not part of the conversation with the user. 
     <context>
         {context} 
@@ -129,23 +132,60 @@ def _get_retriever():
     WEAVIATE_URL = os.environ["WEAVIATE_URL"]
     WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
 
-    embeddings = OpenAIEmbeddings()
+    embeddings = OpenAIEmbeddings(chunk_size=200)
+    child_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
+    parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
+    
     client = weaviate.Client(
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
     )
     weaviate_client = Weaviate(
         client=client,
-        index_name="LangChain_newest_idx",
+        index_name="LangChain_parents_idx",
         text_key="text",
         embedding=embeddings,
         by_text=False,
-        attributes=["source"],
-    )
-    return weaviate_client.as_retriever(
-        search_kwargs={'score_threshold': 0.7}
+        attributes=["source", "doc_id"],
     )
 
+    def key_encoder(key: int) -> str:
+        return json.dumps(key)
+
+    def value_serializer(value: float) -> str:
+        if isinstance(value, Document):
+            value = {
+                'page_content': value.page_content,
+                'metadata': value.metadata,
+            }
+        return json.dumps(value)
+
+    def value_deserializer(serialized_value: str) -> Document:
+        value = json.loads(serialized_value)
+        if 'page_content' in value and 'metadata' in value:
+            return Document(page_content=value['page_content'], metadata=value['metadata'])
+        else:
+            return value
+
+    client = get_client('redis://localhost:6379')
+    abstract_store = RedisStore(client=client)
+    store = EncoderBackedStore(
+        store=abstract_store,
+        key_encoder=key_encoder,
+        value_serializer=value_serializer,
+        value_deserializer=value_deserializer
+    )
+    
+    retriever = ParentDocumentRetriever(
+        vectorstore=weaviate_client, 
+        docstore=store, 
+        child_splitter=child_splitter,
+        parent_splitter=parent_splitter,
+        search_kwargs={'k': 10}
+    )
+    
+    return retriever
+    
 def _process_chat_history(chat_history):
     processed_chat_history = []
     for chat in chat_history:

From 009f8e14cf27bdc0c2daafdee175e32cde23f560 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Thu, 24 Aug 2023 10:35:21 -0700
Subject: [PATCH 26/36] layout changes

---
 .../app/components/ChatMessageBubble.tsx      |  4 ++--
 chat-langchain/app/components/ChatWindow.tsx  | 23 +++++++++++--------
 chat-langchain/app/layout.tsx                 |  2 +-
 3 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/chat-langchain/app/components/ChatMessageBubble.tsx b/chat-langchain/app/components/ChatMessageBubble.tsx
index 31634eb7b..fb3ca0351 100644
--- a/chat-langchain/app/components/ChatMessageBubble.tsx
+++ b/chat-langchain/app/components/ChatMessageBubble.tsx
@@ -15,7 +15,7 @@ export function ChatMessageBubble(props: {
   const colorClassName =
     props.message.role === "user" ? "bg-sky-600" : "bg-slate-50 text-black";
   const alignmentClassName =
-    props.message.role === "user" ? "mr-auto" : "ml-auto";
+    props.message.role === "user" ? "ml-auto" : "mr-auto";
   const prefix = props.message.role === "user" ? "🧑" : props.aiEmoji;
 
   const [feedbackColor, setFeedbackColor] = useState("");
@@ -68,7 +68,7 @@ export function ChatMessageBubble(props: {
       ></div>
     </div>
     {props.message.role !== "user" && props.isMostRecent && props.messageCompleted && (
-        <div className="relative flex space-x-1 items-start justify-end">
+        <div className="relative flex space-x-1 items-start justify-start">
           <button
             className={`text-sm rounded ${props.feedback === null ? "hover:bg-green-200" : ""}`}
             id="upButton"
diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index e83f7385c..6b4c94251 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -36,8 +36,7 @@ export function ChatWindow(props: {
     titleText = "An LLM",
   } = props;
 
-  function sendMessage(e: FormEvent<HTMLFormElement>) {
-    e.preventDefault();
+  function sendMessage() {
     if (messageContainerRef.current) {
       messageContainerRef.current.classList.add("grow");
     }
@@ -220,13 +219,19 @@ export function ChatWindow(props: {
         </div>
 
       <form onSubmit={sendMessage} className="flex w-full">
-        <textarea
-          className="flex-grow mr-2 p-4 rounded"
-          placeholder={placeholder}
-          onChange={(e) => setInput(e.target.value)}
-          value={input}
-          style={{minWidth: '50px'}}
-        />
+      <textarea
+        className="flex-grow mr-2 p-2 rounded max-h-[40px]"
+        placeholder={placeholder}
+        onChange={(e) => setInput(e.target.value)}
+        value={input}
+        style={{minWidth: '50px'}}
+        onKeyDown={(e) => {
+          if (e.key === 'Enter' && !e.shiftKey) {
+            e.preventDefault();
+            sendMessage();
+          }
+        }}
+      />
         <select
           id="modelType"
           className="bg-gray-800 text-white rounded p-2 mr-2 w-full sm:w-auto max-h-[40px]"
diff --git a/chat-langchain/app/layout.tsx b/chat-langchain/app/layout.tsx
index 53a589e10..57c105f4a 100644
--- a/chat-langchain/app/layout.tsx
+++ b/chat-langchain/app/layout.tsx
@@ -17,7 +17,7 @@ export default function RootLayout({
   return (
     <html lang="en">
       <body className={inter.className}>
-        <div className="flex flex-col m-12 h-[calc(100vh-6rem)]">
+        <div className="flex flex-col h-[calc(100vh-6rem)] m-[calc(2rem)]">
           {children}
         </div>
       </body>

From 2fdfa3d451941a97c6df337f3abb12f194b824d0 Mon Sep 17 00:00:00 2001
From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Date: Fri, 25 Aug 2023 17:14:24 -0700
Subject: [PATCH 27/36] Add conversation id

---
 chat-langchain/app/components/ChatWindow.tsx | 107 +++++++++++--------
 main.py                                      | 107 +++++++++++--------
 2 files changed, 123 insertions(+), 91 deletions(-)

diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 6b4c94251..75b04574c 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -2,6 +2,7 @@
 
 import React, { useRef, useState, useEffect } from "react";
 import type { FormEvent } from "react";
+import { v4 as uuidv4 } from "uuid";
 import { ChatMessageBubble } from "../components/ChatMessageBubble";
 import { marked } from "marked";
 import { Renderer } from "marked";
@@ -26,8 +27,12 @@ export function ChatWindow(props: {
   const [model, setModel] = useState("openai");
   const [isLoading, setIsLoading] = useState(false);
   const [feedback, setFeedback] = useState<number | null>(null);
+
+  const [conversationId, setConversationId] = useState<string | null>(uuidv4());
   const [hasInteracted, setHasInteracted] = useState(false);
-  const [chatHistory, setChatHistory] = useState<{question: string, result: string}[]>([]);
+  const [chatHistory, setChatHistory] = useState<
+    { question: string; result: string }[]
+  >([]);
 
   const {
     endpoint,
@@ -61,6 +66,7 @@ export function ChatWindow(props: {
         message: input,
         model: model,
         history: chatHistory,
+        conversation_id: conversationId,
       }),
     }).then((response) => {
       if (!response.body) {
@@ -73,7 +79,7 @@ export function ChatWindow(props: {
       let messageIndex: number | null = null;
 
       let renderer = new Renderer();
-      renderer.paragraph = function(text) {
+      renderer.paragraph = function (text) {
         return text;
       };
       renderer.code = function (code, language) {
@@ -89,38 +95,45 @@ export function ChatWindow(props: {
       marked.setOptions({ renderer });
 
       reader
-      .read()
-      .then(function processText(
-        res: ReadableStreamReadResult<Uint8Array>
-      ): Promise<void> {
-        const { done, value } = res;
-        if (done) {
-          console.log("Stream complete");
-          setChatHistory(prevChatHistory => [...prevChatHistory, {question: input, result: accumulatedMessage}]);
-          return Promise.resolve();
-        }
-    
-        let result = decoder.decode(value);
-        accumulatedMessage += result;
-        let parsedResult = marked.parse(accumulatedMessage);
-    
-        setMessages((prevMessages) => {
+        .read()
+        .then(function processText(
+          res: ReadableStreamReadResult<Uint8Array>
+        ): Promise<void> {
+          const { done, value } = res;
+          if (done) {
+            console.log("Stream complete");
+            setChatHistory((prevChatHistory) => [
+              ...prevChatHistory,
+              { question: input, result: accumulatedMessage },
+            ]);
+            return Promise.resolve();
+          }
+
+          let result = decoder.decode(value);
+          accumulatedMessage += result;
+          let parsedResult = marked.parse(accumulatedMessage);
+
+          setMessages((prevMessages) => {
             let newMessages = [...prevMessages];
             if (messageIndex === null) {
               messageIndex = newMessages.length;
-              newMessages.push({ id: Math.random().toString(), message: parsedResult.trim(), role: "assistant" });
+              newMessages.push({
+                id: Math.random().toString(),
+                message: parsedResult.trim(),
+                role: "assistant",
+              });
             } else {
               newMessages[messageIndex].message = parsedResult.trim();
             }
             return newMessages;
           });
 
-        setIsLoading(false);
-        return reader.read().then(processText);
-      })
-      .catch((error) => {
-        console.error("Error:", error);
-      });
+          setIsLoading(false);
+          return reader.read().then(processText);
+        })
+        .catch((error) => {
+          console.error("Error:", error);
+        });
     });
   }
 
@@ -186,7 +199,7 @@ export function ChatWindow(props: {
       </h2>
       <h4 className={`${messages.length > 0 ? "" : "hidden"} text-sm mb-4`}>
         We appreciate feedback!
-        </h4>
+      </h4>
       <div
         className="flex flex-col-reverse w-full mb-2 overflow-auto"
         ref={messageContainerRef}
@@ -209,29 +222,29 @@ export function ChatWindow(props: {
       </div>
 
       <div className="flex w-full flex-row-reverse mb-2">
-            <button
-              type="button"
-              className="text-xs border rounded p-1 float-right"
-              onClick={() => viewTrace()}
-            >
-              🛠️ view trace
-            </button>
-        </div>
+        <button
+          type="button"
+          className="text-xs border rounded p-1 float-right"
+          onClick={() => viewTrace()}
+        >
+          🛠️ view trace
+        </button>
+      </div>
 
       <form onSubmit={sendMessage} className="flex w-full">
-      <textarea
-        className="flex-grow mr-2 p-2 rounded max-h-[40px]"
-        placeholder={placeholder}
-        onChange={(e) => setInput(e.target.value)}
-        value={input}
-        style={{minWidth: '50px'}}
-        onKeyDown={(e) => {
-          if (e.key === 'Enter' && !e.shiftKey) {
-            e.preventDefault();
-            sendMessage();
-          }
-        }}
-      />
+        <textarea
+          className="flex-grow mr-2 p-2 rounded max-h-[40px]"
+          placeholder={placeholder}
+          onChange={(e) => setInput(e.target.value)}
+          value={input}
+          style={{ minWidth: "50px" }}
+          onKeyDown={(e) => {
+            if (e.key === "Enter" && !e.shiftKey) {
+              e.preventDefault();
+              sendMessage();
+            }
+          }}
+        />
         <select
           id="modelType"
           className="bg-gray-800 text-white rounded p-2 mr-2 w-full sm:w-auto max-h-[40px]"
diff --git a/main.py b/main.py
index f98391e8b..1231eb85e 100644
--- a/main.py
+++ b/main.py
@@ -52,6 +52,7 @@
     "anthropic": "claude-instant-v1-100k",
 }
 
+
 def create_chain(
     retriever: BaseRetriever,
     model_provider: Union[Literal["openai"], Literal["anthropic"]],
@@ -61,17 +62,16 @@ def create_chain(
 ) -> Runnable:
     model_name = model or _MODEL_MAP[model_provider]
     model = _PROVIDER_MAP[model_provider](model=model_name, temperature=temperature)
-    
+
     _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
 
     Chat History:
     {chat_history}
     Follow Up Input: {question}
     Standalone Question:"""
-    
 
     CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-    
+
     _template = """
     You are an expert programmer and problem-solver, tasked to answer any question about Langchain. Using the provided context, answer the user's question to the best of your ability using the resources provided.
     If you really don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
@@ -82,25 +82,30 @@ def create_chain(
 
     if chat_history:
         _inputs = RunnableMap(
-                {
-                    "standalone_question": {
-                        "question": lambda x: x["question"],
-                        "chat_history": lambda x: x["chat_history"],
-                    } | CONDENSE_QUESTION_PROMPT | model | StrOutputParser(),
+            {
+                "standalone_question": {
                     "question": lambda x: x["question"],
                     "chat_history": lambda x: x["chat_history"],
                 }
-            )
+                | CONDENSE_QUESTION_PROMPT
+                | model
+                | StrOutputParser(),
+                "question": lambda x: x["question"],
+                "chat_history": lambda x: x["chat_history"],
+            }
+        )
         _context = {
             "context": itemgetter("standalone_question") | retriever,
-            "question": lambda x: x["question"], 
+            "question": lambda x: x["question"],
             "chat_history": lambda x: x["chat_history"],
         }
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", _template),
-            MessagesPlaceholder(variable_name="chat_history"),
-            ("human", "{question}"),
-        ])
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", _template),
+                MessagesPlaceholder(variable_name="chat_history"),
+                ("human", "{question}"),
+            ]
+        )
     else:
         _inputs = RunnableMap(
             {
@@ -110,24 +115,27 @@ def create_chain(
         )
         _context = {
             "context": itemgetter("question") | retriever,
-            "question": lambda x: x["question"], 
+            "question": lambda x: x["question"],
             "chat_history": lambda x: x["chat_history"],
         }
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", _template),
-            ("human", "{question}"),
-        ])
-    
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", _template),
+                ("human", "{question}"),
+            ]
+        )
+
     chain = (
         _inputs
         | _context
-        | prompt 
+        | prompt
         | ChatOpenAI(model="gpt-4", temperature=temperature)
         | StrOutputParser()
     )
-    
+
     return chain
 
+
 def _get_retriever():
     WEAVIATE_URL = os.environ["WEAVIATE_URL"]
     WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
@@ -135,7 +143,7 @@ def _get_retriever():
     embeddings = OpenAIEmbeddings(chunk_size=200)
     child_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
     parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
-    
+
     client = weaviate.Client(
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
@@ -155,46 +163,50 @@ def key_encoder(key: int) -> str:
     def value_serializer(value: float) -> str:
         if isinstance(value, Document):
             value = {
-                'page_content': value.page_content,
-                'metadata': value.metadata,
+                "page_content": value.page_content,
+                "metadata": value.metadata,
             }
         return json.dumps(value)
 
     def value_deserializer(serialized_value: str) -> Document:
         value = json.loads(serialized_value)
-        if 'page_content' in value and 'metadata' in value:
-            return Document(page_content=value['page_content'], metadata=value['metadata'])
+        if "page_content" in value and "metadata" in value:
+            return Document(
+                page_content=value["page_content"], metadata=value["metadata"]
+            )
         else:
             return value
 
-    client = get_client('redis://localhost:6379')
+    client = get_client("redis://localhost:6379")
     abstract_store = RedisStore(client=client)
     store = EncoderBackedStore(
         store=abstract_store,
         key_encoder=key_encoder,
         value_serializer=value_serializer,
-        value_deserializer=value_deserializer
+        value_deserializer=value_deserializer,
     )
-    
+
     retriever = ParentDocumentRetriever(
-        vectorstore=weaviate_client, 
-        docstore=store, 
+        vectorstore=weaviate_client,
+        docstore=store,
         child_splitter=child_splitter,
         parent_splitter=parent_splitter,
-        search_kwargs={'k': 10}
+        search_kwargs={"k": 10},
     )
-    
+
     return retriever
-    
+
+
 def _process_chat_history(chat_history):
     processed_chat_history = []
     for chat in chat_history:
-        if 'question' in chat:
-            processed_chat_history.append(HumanMessage(content=chat.pop('question')))
-        if 'result' in chat:
-            processed_chat_history.append(AIMessage(content=chat.pop('result')))
+        if "question" in chat:
+            processed_chat_history.append(HumanMessage(content=chat.pop("question")))
+        if "result" in chat:
+            processed_chat_history.append(AIMessage(content=chat.pop("result")))
     return processed_chat_history
-    
+
+
 @app.post("/chat")
 async def chat_endpoint(request: Request):
     global run_id, access_counter
@@ -206,13 +218,14 @@ async def chat_endpoint(request: Request):
     question = data.get("message")
     model_type = data.get("model")
     chat_history = data.get("history", [])
+    conversation_id = data.get("conversation_id")
 
     retriever = _get_retriever()
     chat_history = _process_chat_history(chat_history)
-    
+
     # source_docs = retriever.invoke(question) # opportunity to return source documents
     # context = [doc.page_content for doc in source_docs]
-             
+
     qa_chain = create_chain(
         retriever=retriever, model_provider=model_type, chat_history=chat_history
     )
@@ -221,7 +234,13 @@ async def chat_endpoint(request: Request):
     async def stream():
         result = ""
         try:
-            async for s in qa_chain.astream({"question": question, "chat_history": chat_history}, config=runnable_config):
+            async for s in qa_chain.astream(
+                {"question": question, "chat_history": chat_history},
+                config={
+                    **runnable_config,
+                    "metadata": {"conversation_id": conversation_id},
+                },
+            ):
                 print(s, end="", flush=True)
                 result += s
                 yield s

From 54f0f6848c60672751568b57b59c5a4a170d144d Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Fri, 25 Aug 2023 17:28:04 -0700
Subject: [PATCH 28/36] delete archive and parentsplit retriever

---
 _scripts/evaluate_chains.py                 |  46 +---
 archive/app.py                              |  98 ---------
 archive/archive_recent/callback.py          |  33 ---
 archive/archive_recent/ingest.sh            |   7 -
 archive/archive_recent/query_data.py        |  54 -----
 archive/archive_recent/schemas.py           |  22 --
 archive/archive_recent/templates/index.html | 160 --------------
 archive/chain.py                            | 127 ------------
 archive/ingest.py                           |  92 --------
 archive/ingest.sh                           |   6 -
 archive/ingest_examples.py                  | 219 --------------------
 archive/requirements.txt                    |   9 -
 ingest.py                                   |  83 ++------
 main.py                                     |  91 ++------
 14 files changed, 45 insertions(+), 1002 deletions(-)
 delete mode 100644 archive/app.py
 delete mode 100644 archive/archive_recent/callback.py
 delete mode 100755 archive/archive_recent/ingest.sh
 delete mode 100644 archive/archive_recent/query_data.py
 delete mode 100644 archive/archive_recent/schemas.py
 delete mode 100644 archive/archive_recent/templates/index.html
 delete mode 100644 archive/chain.py
 delete mode 100644 archive/ingest.py
 delete mode 100755 archive/ingest.sh
 delete mode 100644 archive/ingest_examples.py
 delete mode 100644 archive/requirements.txt

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
index 0fcb27c42..919e154fd 100644
--- a/_scripts/evaluate_chains.py
+++ b/_scripts/evaluate_chains.py
@@ -122,59 +122,19 @@ def _get_retriever():
     WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
 
     embeddings = OpenAIEmbeddings(chunk_size=200)
-    child_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
-    parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
-    
     client = weaviate.Client(
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
     )
-    print("Index has this many vectors", client.query.aggregate("LangChain_parents_idx").with_meta_count().do())
     weaviate_client = Weaviate(
         client=client,
-        index_name="LangChain_parents_idx",
+        index_name="LangChain_test_idx",
         text_key="text",
         embedding=embeddings,
         by_text=False,
-        attributes=["source", "doc_id"],
-    )
-
-    def key_encoder(key: int) -> str:
-        return json.dumps(key)
-
-    def value_serializer(value: float) -> str:
-        if isinstance(value, Document):
-            value = {
-                'page_content': value.page_content,
-                'metadata': value.metadata,
-            }
-        return json.dumps(value)
-
-    def value_deserializer(serialized_value: str) -> Document:
-        value = json.loads(serialized_value)
-        if 'page_content' in value and 'metadata' in value:
-            return Document(page_content=value['page_content'], metadata=value['metadata'])
-        else:
-            return value
-
-    client = get_client('redis://default:c16f99d1cc694b7fb9380db03abbe341@fly-chat-langchain.upstash.io')
-    abstract_store = RedisStore(client=client)
-    store = EncoderBackedStore(
-        store=abstract_store,
-        key_encoder=key_encoder,
-        value_serializer=value_serializer,
-        value_deserializer=value_deserializer
-    )
-    
-    retriever = ParentDocumentRetriever(
-        vectorstore=weaviate_client, 
-        docstore=store, 
-        child_splitter=child_splitter,
-        parent_splitter=parent_splitter,
-        search_kwargs={'k': 10}
+        attributes=["source"],
     )
-    
-    return retriever
+    return weaviate_client.as_retriever(search_kwargs=dict(k=10))
 
 class CustomHallucinationEvaluator(RunEvaluator):
 
diff --git a/archive/app.py b/archive/app.py
deleted file mode 100644
index d1e3b9c40..000000000
--- a/archive/app.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import datetime
-import os
-
-import gradio as gr
-import langchain
-import weaviate
-from chain import get_new_chain1
-from langchain.vectorstores import Weaviate
-
-WEAVIATE_URL = os.environ["WEAVIATE_URL"]
-
-
-def get_weaviate_store():
-    client = weaviate.Client(
-        url=WEAVIATE_URL,
-        additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
-    )
-    return Weaviate(client, "Paragraph", "content", attributes=["source"])
-
-
-def set_openai_api_key(api_key, agent):
-    if api_key:
-        os.environ["OPENAI_API_KEY"] = api_key
-        vectorstore = get_weaviate_store()
-        qa_chain = get_new_chain1(vectorstore)
-        os.environ["OPENAI_API_KEY"] = ""
-        return qa_chain
-
-
-def chat(inp, history, agent):
-    history = history or []
-    if agent is None:
-        history.append((inp, "Please paste your OpenAI key to use"))
-        return history, history
-    print("\n==== date/time: " + str(datetime.datetime.now()) + " ====")
-    print("inp: " + inp)
-    history = history or []
-    output = agent({"question": inp, "chat_history": history})
-    answer = output["answer"]
-    history.append((inp, answer))
-    print(history)
-    return history, history
-
-
-block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
-
-with block:
-    with gr.Row():
-        gr.Markdown("<h3><center>LangChain AI</center></h3>")
-
-        openai_api_key_textbox = gr.Textbox(
-            placeholder="Paste your OpenAI API key (sk-...)",
-            show_label=False,
-            lines=1,
-            type="password",
-        )
-
-    chatbot = gr.Chatbot()
-
-    with gr.Row():
-        message = gr.Textbox(
-            label="What's your question?",
-            placeholder="What's the answer to life, the universe, and everything?",
-            lines=1,
-        )
-        submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
-
-    gr.Examples(
-        examples=[
-            "What are agents?",
-            "How do I summarize a long document?",
-            "What types of memory exist?",
-        ],
-        inputs=message,
-    )
-
-    gr.HTML(
-        """
-    This simple application is an implementation of ChatGPT but over an external dataset (in this case, the LangChain documentation)."""
-    )
-
-    gr.HTML(
-        "<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
-    )
-
-    state = gr.State()
-    agent_state = gr.State()
-
-    submit.click(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
-    message.submit(chat, inputs=[message, state, agent_state], outputs=[chatbot, state])
-
-    openai_api_key_textbox.change(
-        set_openai_api_key,
-        inputs=[openai_api_key_textbox, agent_state],
-        outputs=[agent_state],
-    )
-
-block.launch(debug=True)
diff --git a/archive/archive_recent/callback.py b/archive/archive_recent/callback.py
deleted file mode 100644
index 6eb078de7..000000000
--- a/archive/archive_recent/callback.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""Callback handlers used in the app."""
-from typing import Any, Dict, List
-
-from langchain.callbacks.base import AsyncCallbackHandler
-
-from archive.archive_recent.schemas import ChatResponse
-
-
-class StreamingLLMCallbackHandler(AsyncCallbackHandler):
-    """Callback handler for streaming LLM responses."""
-
-    def __init__(self, websocket):
-        self.websocket = websocket
-
-    async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        resp = ChatResponse(sender="bot", message=token, type="stream")
-        await self.websocket.send_json(resp.dict())
-
-
-class QuestionGenCallbackHandler(AsyncCallbackHandler):
-    """Callback handler for question generation."""
-
-    def __init__(self, websocket):
-        self.websocket = websocket
-
-    async def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> None:
-        """Run when LLM starts running."""
-        resp = ChatResponse(
-            sender="bot", message="Synthesizing question...", type="info"
-        )
-        await self.websocket.send_json(resp.dict())
diff --git a/archive/archive_recent/ingest.sh b/archive/archive_recent/ingest.sh
deleted file mode 100755
index fa19fa4d5..000000000
--- a/archive/archive_recent/ingest.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-# Bash script to ingest data
-# This involves scraping the data from the web and then cleaning up and putting in Weaviate.
-# Error if any command fails
-set -e
-wget -r -A.html https://api.python.langchain.com/en/latest
-wget -r -A.html https://python.langchain.com/docs/get_started/introduction
-python3 ingest.py
\ No newline at end of file
diff --git a/archive/archive_recent/query_data.py b/archive/archive_recent/query_data.py
deleted file mode 100644
index 167146587..000000000
--- a/archive/archive_recent/query_data.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""Create a ChatVectorDBChain for question/answering."""
-from langchain.callbacks.manager import AsyncCallbackManager
-from langchain.callbacks.tracers import LangChainTracer
-from langchain.chains import ChatVectorDBChain
-from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
-                                                     QA_PROMPT)
-from langchain.chains.llm import LLMChain
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import OpenAI
-from langchain.vectorstores.base import VectorStore
-
-
-def get_chain(
-    vectorstore: VectorStore, question_handler, stream_handler, tracing: bool = False
-) -> ChatVectorDBChain:
-    """Create a ChatVectorDBChain for question/answering."""
-    # Construct a ChatVectorDBChain with a streaming llm for combine docs
-    # and a separate, non-streaming llm for question generation
-    manager = AsyncCallbackManager([])
-    question_manager = AsyncCallbackManager([question_handler])
-    stream_manager = AsyncCallbackManager([stream_handler])
-    if tracing:
-        tracer = LangChainTracer()
-        tracer.load_default_session()
-        manager.add_handler(tracer)
-        question_manager.add_handler(tracer)
-        stream_manager.add_handler(tracer)
-
-    question_gen_llm = OpenAI(
-        temperature=0,
-        verbose=True,
-        callback_manager=question_manager,
-    )
-    streaming_llm = OpenAI(
-        streaming=True,
-        callback_manager=stream_manager,
-        verbose=True,
-        temperature=0,
-    )
-
-    question_generator = LLMChain(
-        llm=question_gen_llm, prompt=CONDENSE_QUESTION_PROMPT, callback_manager=manager
-    )
-    doc_chain = load_qa_chain(
-        streaming_llm, chain_type="stuff", prompt=QA_PROMPT, callback_manager=manager
-    )
-
-    qa = ChatVectorDBChain(
-        vectorstore=vectorstore,
-        combine_docs_chain=doc_chain,
-        question_generator=question_generator,
-        callback_manager=manager,
-    )
-    return qa
diff --git a/archive/archive_recent/schemas.py b/archive/archive_recent/schemas.py
deleted file mode 100644
index f4d83a085..000000000
--- a/archive/archive_recent/schemas.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Schemas for the chat app."""
-from pydantic import BaseModel, validator
-
-
-class ChatResponse(BaseModel):
-    """Chat response schema."""
-
-    sender: str
-    message: str
-    type: str
-
-    @validator("sender")
-    def sender_must_be_bot_or_you(cls, v):
-        if v not in ["bot", "you"]:
-            raise ValueError("sender must be bot or you")
-        return v
-
-    @validator("type")
-    def validate_message_type(cls, v):
-        if v not in ["start", "stream", "end", "error", "info"]:
-            raise ValueError("type must be start, stream or end")
-        return v
diff --git a/archive/archive_recent/templates/index.html b/archive/archive_recent/templates/index.html
deleted file mode 100644
index 2a7b77e29..000000000
--- a/archive/archive_recent/templates/index.html
+++ /dev/null
@@ -1,160 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <title>Chat Your Data</title>
-    <link href="https://unpkg.com/tailwindcss@^2/dist/tailwind.min.css" rel="stylesheet">
-    <style>
-        .chat-body {
-            width: 550px;
-            margin: 50px auto;
-        }
-        .card-body {
-            background-color: #333;
-            color: #fff;
-            border-radius: 10px;
-        }
-        .server-message {
-            background-color: #444;
-            padding: 10px;
-            margin: 10px;
-            border-radius: 10px;
-        }
-        .client-message {
-            background-color: #555;
-            padding: 10px;
-            margin: 10px;
-            border-radius: 10px;
-        }
-        .form-inline {
-            display: flex;
-            justify-content: space-between;
-        }
-        .form-control {
-            width: 80%;
-            background-color: #333;
-            color: #fff;
-            border: none;
-            border-radius: 5px;
-            padding: 10px;
-            margin-right: 10px;
-        }
-        #send {
-            background-color: #4C4CFF;
-            color: #fff;
-            border: none;
-            border-radius: 5px;
-            padding: 10px 20px;
-        }
-        .form-message {
-          margin-top: 10px;
-        }
-        /* New styles for the dropdown */
-        #modelType {
-            width: 20%;
-            background-color: #333;
-            color: #fff;
-            border: none;
-            border-radius: 5px;
-            padding: 5px;
-            margin-right: 10px;
-        }
-    </style>
-    <script>
-        // var endpoint = "ws://localhost:9000/chat";
-        var endpoint = "wss://" + window.location.host + "/chat";
-        var ws = new WebSocket(endpoint);
-        // Receive message from server word by word. Display the words as they are received.
-        ws.onmessage = function (event) {
-            var messages = document.getElementById('messages');
-            var data = JSON.parse(event.data);
-            if (data.sender === "bot") {
-                if (data.type === "start") {
-                    var header = document.getElementById('header');
-                    header.innerHTML = "Computing answer...";
-                    var div = document.createElement('div');
-                    div.className = 'server-message';
-                    var p = document.createElement('p');
-                    p.innerHTML = "<strong>" + "Chatbot: " + "</strong>";
-                    div.appendChild(p);
-                    messages.appendChild(div);
-                } else if (data.type === "stream") {
-                    var header = document.getElementById('header');
-                    header.innerHTML = "Chatbot is typing...";
-                    var p = messages.lastChild.lastChild;
-                    if (data.message === "\n") {
-                        p.innerHTML += "<br>";
-                    } else {
-                        p.innerHTML += data.message;
-                    }
-                } else if (data.type === "info") {
-                    var header = document.getElementById('header');
-                    header.innerHTML = data.message;
-                } else if (data.type === "end") {
-                    var header = document.getElementById('header');
-                    header.innerHTML = "Ask a question";
-                    var button = document.getElementById('send');
-                    button.innerHTML = "Send";
-                    button.disabled = false;
-                } else if (data.type === "error") {
-                    var header = document.getElementById('header');
-                    header.innerHTML = "Ask a question";
-                    var button = document.getElementById('send');
-                    button.innerHTML = "Send";
-                    button.disabled = false;
-                    var p = messages.lastChild.lastChild;
-                    p.innerHTML += data.message;
-                }
-            } else {
-                var div = document.createElement('div');
-                div.className = 'client-message';
-                var p = document.createElement('p');
-                p.innerHTML = "<strong>" + "You: " + "</strong>";
-                p.innerHTML += data.message;
-                div.appendChild(p);
-                messages.appendChild(div);
-            }
-            // Scroll to the bottom of the chat
-            messages.scrollTop = messages.scrollHeight;
-        };
-        // Send message to server
-        function sendMessage(event) {
-            event.preventDefault();
-            var message = document.getElementById('messageText').value;
-            var model = document.getElementById('modelType').value;
-            if (message === "") {
-                return;
-            }
-            console.log(JSON.stringify({message: message, model: model}));
-            ws.send(JSON.stringify({message: message, model: model}));
-            document.getElementById('messageText').value = "";
-
-            // Turn the button into a loading button
-            var button = document.getElementById('send');
-            button.innerHTML = "Loading...";
-            button.disabled = true;
-        }
-    </script>
-</head>
-<body class="bg-black">
-    <div class="chat-body card">
-        <form action="" class="form-inline m-5" id="chat-form" onsubmit="sendMessage(event)">
-            <select id="modelType" class="form-control">
-                <option value="openai" selected>OpenAI</option>
-                <option value="anthropic">Anthropic</option>
-            </select>
-        </form>
-        <div class="card-body p-5">
-            <h4 class="card-title text-center text-xl font-medium"> Chat Your Data </h4>
-            <p class="card-text text-center text-sm" id="header"> Ask a question </p>
-            <hr class="border-gray-500 mb-5" style="margin-top: 20px;">
-            <div id="messages" class="overflow-auto" style="max-height: 500px;">
-            </div>
-            <form action="" class="form-inline mt-5" id="chat-form" onsubmit="sendMessage(event)">
-                <input type="text" class="form-control" placeholder="Write your question" id="messageText">
-                <button id="send" type="submit" class="btn btn-primary">Send</button>
-            </form>
-        </div>
-    </div>
-</body>
-</html>
\ No newline at end of file
diff --git a/archive/chain.py b/archive/chain.py
deleted file mode 100644
index b62406047..000000000
--- a/archive/chain.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import json
-import os
-import pathlib
-from typing import Dict, List, Tuple
-
-import weaviate
-from langchain import OpenAI, PromptTemplate
-from langchain.chains import LLMChain
-from langchain.chains.base import Chain
-from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
-from langchain.chains.conversation.memory import ConversationBufferMemory
-from langchain.chains.question_answering import load_qa_chain
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.prompts import FewShotPromptTemplate, PromptTemplate
-from langchain.prompts.example_selector import \
-    SemanticSimilarityExampleSelector
-from langchain.vectorstores import FAISS, Weaviate
-from pydantic import BaseModel
-
-
-class CustomChain(Chain, BaseModel):
-    vstore: Weaviate
-    chain: BaseCombineDocumentsChain
-    key_word_extractor: Chain
-
-    @property
-    def input_keys(self) -> List[str]:
-        return ["question"]
-
-    @property
-    def output_keys(self) -> List[str]:
-        return ["answer"]
-
-    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
-        question = inputs["question"]
-        chat_history_str = _get_chat_history(inputs["chat_history"])
-        if chat_history_str:
-            new_question = self.key_word_extractor.run(
-                question=question, chat_history=chat_history_str
-            )
-        else:
-            new_question = question
-        print(new_question)
-        docs = self.vstore.similarity_search(new_question, k=4)
-        new_inputs = inputs.copy()
-        new_inputs["question"] = new_question
-        new_inputs["chat_history"] = chat_history_str
-        answer, _ = self.chain.combine_docs(docs, **new_inputs)
-        return {"answer": answer}
-
-
-def get_new_chain1(vectorstore) -> Chain:
-    WEAVIATE_URL = os.environ["WEAVIATE_URL"]
-    client = weaviate.Client(
-        url=WEAVIATE_URL,
-        additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
-    )
-
-    _eg_template = """## Example:
-
-    Chat History:
-    {chat_history}
-    Follow Up Input: {question}
-    Standalone question: {answer}"""
-    _eg_prompt = PromptTemplate(
-        template=_eg_template,
-        input_variables=["chat_history", "question", "answer"],
-    )
-
-    _prefix = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. You should assume that the question is related to LangChain."""
-    _suffix = """## Example:
-
-    Chat History:
-    {chat_history}
-    Follow Up Input: {question}
-    Standalone question:"""
-    eg_store = Weaviate(
-        client,
-        "Rephrase",
-        "content",
-        attributes=["question", "answer", "chat_history"],
-    )
-    example_selector = SemanticSimilarityExampleSelector(vectorstore=eg_store, k=4)
-    prompt = FewShotPromptTemplate(
-        prefix=_prefix,
-        suffix=_suffix,
-        example_selector=example_selector,
-        example_prompt=_eg_prompt,
-        input_variables=["question", "chat_history"],
-    )
-    llm = OpenAI(temperature=0, model_name="text-davinci-003")
-    key_word_extractor = LLMChain(llm=llm, prompt=prompt)
-
-    EXAMPLE_PROMPT = PromptTemplate(
-        template=">Example:\nContent:\n---------\n{page_content}\n----------\nSource: {source}",
-        input_variables=["page_content", "source"],
-    )
-    template = """You are an AI assistant for the open source library LangChain. The documentation is located at https://langchain.readthedocs.io.
-You are given the following extracted parts of a long document and a question. Provide a conversational answer with a hyperlink to the documentation.
-You should only use hyperlinks that are explicitly listed as a source in the context. Do NOT make up a hyperlink that is not listed.
-If the question includes a request for code, provide a code block directly from the documentation.
-If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
-If the question is not about LangChain, politely inform them that you are tuned to only answer questions about LangChain.
-Question: {question}
-=========
-{context}
-=========
-Answer in Markdown:"""
-    PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
-    doc_chain = load_qa_chain(
-        OpenAI(temperature=0, model_name="text-davinci-003", max_tokens=-1),
-        chain_type="stuff",
-        prompt=PROMPT,
-        document_prompt=EXAMPLE_PROMPT,
-    )
-    return CustomChain(
-        chain=doc_chain, vstore=vectorstore, key_word_extractor=key_word_extractor
-    )
-
-
-def _get_chat_history(chat_history: List[Tuple[str, str]]):
-    buffer = ""
-    for human_s, ai_s in chat_history:
-        human = f"Human: " + human_s
-        ai = f"Assistant: " + ai_s
-        buffer += "\n" + "\n".join([human, ai])
-    return buffer
diff --git a/archive/ingest.py b/archive/ingest.py
deleted file mode 100644
index c3e86cb8a..000000000
--- a/archive/ingest.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""Load html from files, clean up, split, ingest into Weaviate."""
-import os
-from pathlib import Path
-
-import weaviate
-from bs4 import BeautifulSoup
-from langchain.text_splitter import CharacterTextSplitter
-
-
-def clean_data(data):
-    soup = BeautifulSoup(data)
-    text = soup.find_all("main", {"id": "main-content"})[0].get_text()
-    return "\n".join([t for t in text.split("\n") if t])
-
-
-docs = []
-metadatas = []
-for p in Path("langchain.readthedocs.io/en/latest/").rglob("*"):
-    if p.is_dir():
-        continue
-    with open(p) as f:
-        docs.append(clean_data(f.read()))
-        metadatas.append({"source": p})
-
-
-text_splitter = CharacterTextSplitter(
-    separator="\n",
-    chunk_size=1000,
-    chunk_overlap=200,
-    length_function=len,
-)
-
-documents = text_splitter.create_documents(docs, metadatas=metadatas)
-
-
-WEAVIATE_URL = os.environ["WEAVIATE_URL"]
-client = weaviate.Client(
-    url=WEAVIATE_URL,
-    additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
-)
-
-client.schema.delete_class("Paragraph")
-client.schema.get()
-schema = {
-    "classes": [
-        {
-            "class": "Paragraph",
-            "description": "A written paragraph",
-            "vectorizer": "text2vec-openai",
-            "moduleConfig": {
-                "text2vec-openai": {
-                    "model": "ada",
-                    "modelVersion": "002",
-                    "type": "text",
-                }
-            },
-            "properties": [
-                {
-                    "dataType": ["text"],
-                    "description": "The content of the paragraph",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": False,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "content",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "source",
-                },
-            ],
-        },
-    ]
-}
-
-client.schema.create(schema)
-
-with client.batch as batch:
-    for text in documents:
-        batch.add_data_object(
-            {"content": text.page_content, "source": str(text.metadata["source"])},
-            "Paragraph",
-        )
diff --git a/archive/ingest.sh b/archive/ingest.sh
deleted file mode 100755
index aa5c68d96..000000000
--- a/archive/ingest.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-# Bash script to ingest data
-# This involves scraping the data from the web and then cleaning up and putting in Weaviate.
-!set -eu
-wget -r -A.html https://langchain.readthedocs.io/en/latest/
-python3 ingest.py
-python3 ingest_examples.py
diff --git a/archive/ingest_examples.py b/archive/ingest_examples.py
deleted file mode 100644
index d2a1e7a7c..000000000
--- a/archive/ingest_examples.py
+++ /dev/null
@@ -1,219 +0,0 @@
-"""Ingest examples into Weaviate."""
-import os
-from pathlib import Path
-
-import weaviate
-
-WEAVIATE_URL = os.environ["WEAVIATE_URL"]
-client = weaviate.Client(
-    url=WEAVIATE_URL,
-    additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
-)
-
-client.schema.delete_class("Rephrase")
-client.schema.delete_class("QA")
-client.schema.get()
-schema = {
-    "classes": [
-        {
-            "class": "Rephrase",
-            "description": "Rephrase Examples",
-            "vectorizer": "text2vec-openai",
-            "moduleConfig": {
-                "text2vec-openai": {
-                    "model": "ada",
-                    "modelVersion": "002",
-                    "type": "text",
-                }
-            },
-            "properties": [
-                {
-                    "dataType": ["text"],
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": False,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "content",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "question",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "answer",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "chat_history",
-                },
-            ],
-        },
-    ]
-}
-
-client.schema.create(schema)
-
-documents = [
-    {
-        "question": "how do i load those?",
-        "chat_history": "Human: What types of memory exist?\nAssistant: \n\nThere are a few different types of memory: Buffer, Summary, and Conversational Memory.",
-        "answer": "How do I load Buffer, Summary, and Conversational Memory",
-    },
-    {
-        "question": "how do i install this package?",
-        "chat_history": "",
-        "answer": "How do I install langchain?",
-    },
-    {
-        "question": "how do I set serpapi_api_key?",
-        "chat_history": "Human: can you write me a code snippet for that?\nAssistant: \n\nYes, you can create an Agent with a custom LLMChain in LangChain. Here is a [link](https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html) to the documentation that provides a code snippet for creating a custom Agent.",
-        "answer": "How do I set the serpapi_api_key?",
-    },
-    {
-        "question": "What are some methods for data augmented generation?",
-        "chat_history": "Human: List all methods of an Agent class please\nAssistant: \n\nTo answer your question, you can find a list of all the methods of the Agent class in the [API reference documentation](https://langchain.readthedocs.io/en/latest/modules/agents/reference.html).",
-        "answer": "What are some methods for data augmented generation?",
-    },
-    {
-        "question": "can you write me a code snippet for that?",
-        "chat_history": "Human: how do I create an agent with custom LLMChain?\nAssistant: \n\nTo create an Agent with a custom LLMChain in LangChain, you can use the [Custom Agent example](https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html). This example shows how to create a custom LLMChain and use an existing Agent class to parse the output. For more information on Agents and Tools, check out the [Key Concepts](https://langchain.readthedocs.io/en/latest/modules/agents/key_concepts.html) documentation.",
-        "answer": "Can you provide a code snippet for creating an Agent with a custom LLMChain?",
-    },
-]
-from langchain.prompts.example_selector.semantic_similarity import \
-    sorted_values
-
-for d in documents:
-    d["content"] = " ".join(sorted_values(d))
-with client.batch as batch:
-    for text in documents:
-        batch.add_data_object(
-            text,
-            "Rephrase",
-        )
-
-client.schema.get()
-schema = {
-    "classes": [
-        {
-            "class": "QA",
-            "description": "Rephrase Examples",
-            "vectorizer": "text2vec-openai",
-            "moduleConfig": {
-                "text2vec-openai": {
-                    "model": "ada",
-                    "modelVersion": "002",
-                    "type": "text",
-                }
-            },
-            "properties": [
-                {
-                    "dataType": ["text"],
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": False,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "content",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "question",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "answer",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "summaries",
-                },
-                {
-                    "dataType": ["text"],
-                    "description": "The link",
-                    "moduleConfig": {
-                        "text2vec-openai": {
-                            "skip": True,
-                            "vectorizePropertyName": False,
-                        }
-                    },
-                    "name": "sources",
-                },
-            ],
-        },
-    ]
-}
-
-client.schema.create(schema)
-
-documents = [
-    {
-        "question": "how do i install langchain?",
-        "answer": "```pip install langchain```",
-        "summaries": ">Example:\nContent:\n---------\nYou can pip install langchain package by running 'pip install langchain'\n----------\nSource: foo.html",
-        "sources": "foo.html",
-    },
-    {
-        "question": "how do i import an openai LLM?",
-        "answer": "```from langchain.llm import OpenAI```",
-        "summaries": ">Example:\nContent:\n---------\nyou can import the open ai wrapper (OpenAI) from the langchain.llm module\n----------\nSource: bar.html",
-        "sources": "bar.html",
-    },
-]
-from langchain.prompts.example_selector.semantic_similarity import \
-    sorted_values
-
-for d in documents:
-    d["content"] = " ".join(sorted_values(d))
-with client.batch as batch:
-    for text in documents:
-        batch.add_data_object(
-            text,
-            "QA",
-        )
diff --git a/archive/requirements.txt b/archive/requirements.txt
deleted file mode 100644
index d0c82e2ea..000000000
--- a/archive/requirements.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-langchain==0.0.64
-beautifulsoup4
-weaviate-client
-openai
-black
-isort
-Flask
-transformers
-gradio
diff --git a/ingest.py b/ingest.py
index f461cde36..e460ca96b 100644
--- a/ingest.py
+++ b/ingest.py
@@ -44,24 +44,6 @@ def ingest_repo():
     texts = python_splitter.split_documents(documents_repo)
     return texts
 
-def key_encoder(key: int) -> str:
-    return json.dumps(key)
-
-def value_serializer(value: float) -> str:
-    if isinstance(value, Document):
-        value = {
-            'page_content': value.page_content,
-            'metadata': value.metadata,
-        }
-    return json.dumps(value)
-
-def value_deserializer(serialized_value: str) -> Document:
-    value = json.loads(serialized_value)
-    if 'page_content' in value and 'metadata' in value:
-        return Document(page_content=value['page_content'], metadata=value['metadata'])
-    else:
-        return value
-    
 def ingest_docs():
     """Get documents from web pages."""
 
@@ -79,7 +61,7 @@ def ingest_docs():
 
     documents = []
     for url in urls:
-        loader = RecursiveUrlLoader(url=url, max_depth=2 if url == urls[0] else 10, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=True)
+        loader = RecursiveUrlLoader(url=url, max_depth=2 if url == urls[0] else 8, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=True)
         temp_docs = loader.load()
         temp_docs = [doc for i, doc in enumerate(temp_docs) if doc not in temp_docs[:i]]        
         documents += temp_docs
@@ -90,60 +72,33 @@ def ingest_docs():
     html2text = Html2TextTransformer()
     docs_transformed = html2text.transform_documents(documents)
     
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
+    print('before splitting', len(docs_transformed))
+    docs_transformed = text_splitter.split_documents(docs_transformed)
+    print('after splitting', len(docs_transformed))
+    
     repo_docs = ingest_repo()
     docs_transformed += repo_docs
-    
-    print("Transformed", len(docs_transformed), "documents in total")
-    
+        
     # OPTION TO PICKLE
-    # import pickle
-    # with open('docs_transformed.pkl', 'wb') as f:
-    #     pickle.dump(docs_transformed, f)
+    import pickle
+    with open('docs_transformed.pkl', 'wb') as f:
+        pickle.dump(docs_transformed, f)
+        
     # with open('docs_transformed.pkl', 'rb') as f:
     #     docs_transformed = pickle.load(f)
     
     client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY))
-    child_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
-    parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
-    client.schema.delete_class("LangChain_parents_idx") # delete the class if it already exists
-    
-    vectorstore = Weaviate(
-        embedding=OpenAIEmbeddings(chunk_size=200),
-        client=client,
-        index_name="LangChain_parents_idx",
-        by_text=False,
-        text_key="text",
-        attributes=["doc_id"],
-    )
+    client.schema.delete_class("LangChain_newest_idx") # delete the class if it already exists
 
-    redis_client = get_client('redis://localhost:6379')
-    abstract_store = RedisStore(client=redis_client)
+    embeddings = OpenAIEmbeddings(chunk_size=200) # rate limit
 
-    # Create an instance of the encoder-backed store
-    store = EncoderBackedStore(
-        store=abstract_store,
-        key_encoder=key_encoder,
-        value_serializer=value_serializer,
-        value_deserializer=value_deserializer
-    )
-
-    retriever = ParentDocumentRetriever(
-        vectorstore=vectorstore, 
-        docstore=store, 
-        child_splitter=child_splitter,
-        parent_splitter=parent_splitter
-    )
-            
-    # client.schema.delete_all()
-    # print(client.schema.get()) # should be empty
-        
-    batch = 100
-    for i in range(0, len(docs_transformed), batch):
-        chunk = docs_transformed[i:i+batch]
-        retriever.add_documents(chunk, ids=None)
+    batch_size = 100 # to handle batch size limit 
+    for i in range(0, len(docs_transformed), batch_size):
+        batch = docs_transformed[i:i+batch_size]
+        Weaviate.from_documents(batch, embeddings, client=client, by_text=False, index_name="LangChain_newest_idx")
 
-    print("LangChain now has this many vectors", client.query.aggregate("LangChain_parents_idx").with_meta_count().do())
+    print("LangChain now has this many vectors", client.query.aggregate("LangChain_newest_idx").with_meta_count().do())
     
 if __name__ == "__main__":
-    ingest_docs()
-    
\ No newline at end of file
+    ingest_docs()
\ No newline at end of file
diff --git a/main.py b/main.py
index f98391e8b..5a37f29a6 100644
--- a/main.py
+++ b/main.py
@@ -41,6 +41,7 @@
 run_collector = RunCollectorCallbackHandler()
 runnable_config = RunnableConfig(callbacks=[run_collector])
 run_id = None
+feedback_recorded = False
 
 _PROVIDER_MAP = {
     "openai": ChatOpenAI,
@@ -132,59 +133,20 @@ def _get_retriever():
     WEAVIATE_URL = os.environ["WEAVIATE_URL"]
     WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
 
-    embeddings = OpenAIEmbeddings(chunk_size=200)
-    child_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
-    parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
-    
+    embeddings = OpenAIEmbeddings()
     client = weaviate.Client(
         url=WEAVIATE_URL,
         auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
     )
     weaviate_client = Weaviate(
         client=client,
-        index_name="LangChain_parents_idx",
+        index_name="LangChain_test_idx",
         text_key="text",
         embedding=embeddings,
         by_text=False,
-        attributes=["source", "doc_id"],
-    )
-
-    def key_encoder(key: int) -> str:
-        return json.dumps(key)
-
-    def value_serializer(value: float) -> str:
-        if isinstance(value, Document):
-            value = {
-                'page_content': value.page_content,
-                'metadata': value.metadata,
-            }
-        return json.dumps(value)
-
-    def value_deserializer(serialized_value: str) -> Document:
-        value = json.loads(serialized_value)
-        if 'page_content' in value and 'metadata' in value:
-            return Document(page_content=value['page_content'], metadata=value['metadata'])
-        else:
-            return value
-
-    client = get_client('redis://localhost:6379')
-    abstract_store = RedisStore(client=client)
-    store = EncoderBackedStore(
-        store=abstract_store,
-        key_encoder=key_encoder,
-        value_serializer=value_serializer,
-        value_deserializer=value_deserializer
-    )
-    
-    retriever = ParentDocumentRetriever(
-        vectorstore=weaviate_client, 
-        docstore=store, 
-        child_splitter=child_splitter,
-        parent_splitter=parent_splitter,
-        search_kwargs={'k': 10}
+        attributes=["source"],
     )
-    
-    return retriever
+    return weaviate_client.as_retriever(search_kwargs=dict(k=10))
     
 def _process_chat_history(chat_history):
     processed_chat_history = []
@@ -197,16 +159,17 @@ def _process_chat_history(chat_history):
     
 @app.post("/chat")
 async def chat_endpoint(request: Request):
-    global run_id, access_counter
+    global run_id, feedback_recorded, trace_url
     run_id = None
-    access_counter = 0
+    trace_url = None
+    feedback_recorded = False
     run_collector.traced_runs = []
 
     data = await request.json()
     question = data.get("message")
     model_type = data.get("model")
     chat_history = data.get("history", [])
-
+    
     retriever = _get_retriever()
     chat_history = _process_chat_history(chat_history)
     
@@ -219,12 +182,16 @@ async def chat_endpoint(request: Request):
     print("Recieved question: ", question)
 
     async def stream():
+        global run_id, trace_url, feedback_recorded
         result = ""
         try:
             async for s in qa_chain.astream({"question": question, "chat_history": chat_history}, config=runnable_config):
                 print(s, end="", flush=True)
                 result += s
                 yield s
+            if not run_id and run_collector.traced_runs:
+                run = run_collector.traced_runs[0]
+                run_id = run.id
 
         except Exception as e:
             logging.error(e)
@@ -233,38 +200,26 @@ async def stream():
     return StreamingResponse(stream())
 
 
-access_counter = 0
-
-
 @app.post("/feedback")
 async def send_feedback(request: Request):
-    global run_id, access_counter
+    global run_id, feedback_recorded
+    if feedback_recorded or run_id is None:
+        return {"result": "Feedback already recorded or no chat session found", "code": 400}
     data = await request.json()
     score = data.get("score")
-    if not run_id:
-        run = run_collector.traced_runs[0]
-        run_id = run.id
-        access_counter += 1
-        if access_counter >= 2:
-            run_collector.traced_runs = []
-            access_counter = 0
     client.create_feedback(run_id, "user_score", score=score)
+    feedback_recorded = True
     return {"result": "posted feedback successfully", "code": 200}
 
-
+trace_url = None
 @app.post("/get_trace")
 async def get_trace(request: Request):
-    global run_id, access_counter
+    global run_id, trace_url
+    if trace_url is None and run_id is not None:
+        trace_url = client.share_run(run_id)
     if run_id is None:
-        run = run_collector.traced_runs[0]
-        run_id = run.id
-        access_counter += 1
-        if access_counter >= 2:
-            run_collector.traced_runs = []
-            access_counter = 0
-    url = client.share_run(run_id)
-    return url
-
+        return {"result": "No chat session found", "code": 400}
+    return trace_url if trace_url else {"result": "Trace URL not found", "code": 400}
 
 if __name__ == "__main__":
     import uvicorn

From 3a5d796e83749f0564fe25b2d07871a75e155179 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Fri, 25 Aug 2023 17:30:50 -0700
Subject: [PATCH 29/36] delete unused imports

---
 _scripts/evaluate_chains.py | 6 ------
 ingest.py                   | 7 +------
 main.py                     | 6 ------
 3 files changed, 1 insertion(+), 18 deletions(-)

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
index 919e154fd..08401b96f 100644
--- a/_scripts/evaluate_chains.py
+++ b/_scripts/evaluate_chains.py
@@ -23,12 +23,6 @@
 from langchain import load as langchain_load
 from operator import itemgetter
 import json
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.retrievers import ParentDocumentRetriever
-from langchain.storage import InMemoryStore
-from langchain.storage import RedisStore, EncoderBackedStore
-from langchain.utilities.redis import get_client
-from langchain.schema.document import Document
 
 _PROVIDER_MAP = {
     "openai": ChatOpenAI,
diff --git a/ingest.py b/ingest.py
index e460ca96b..126e3832d 100644
--- a/ingest.py
+++ b/ingest.py
@@ -6,18 +6,13 @@
 import shutil
 
 from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
-from langchain.embeddings import OpenAIEmbeddings, CohereEmbeddings
-from langchain.schema import Document
+from langchain.embeddings import OpenAIEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Weaviate
 from langchain.document_transformers import Html2TextTransformer
 from langchain.text_splitter import Language
 from langchain.document_loaders.generic import GenericLoader
 from langchain.document_loaders.parsers import LanguageParser
-from langchain.storage import RedisStore, EncoderBackedStore
-from langchain.retrievers import ParentDocumentRetriever
-from langchain.utilities.redis import get_client
-import json
 
 WEAVIATE_URL=os.environ["WEAVIATE_URL"]
 WEAVIATE_API_KEY=os.environ["WEAVIATE_API_KEY"]
diff --git a/main.py b/main.py
index 5a37f29a6..5dbe72122 100644
--- a/main.py
+++ b/main.py
@@ -19,12 +19,6 @@
 from langchain.vectorstores import Weaviate
 from langsmith import Client
 from operator import itemgetter
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.retrievers import ParentDocumentRetriever
-from langchain.storage import RedisStore, EncoderBackedStore
-from langchain.utilities.redis import get_client
-import json
-from langchain.schema.document import Document
 
 client = Client()
 

From 3bf20fa049e77beaa259109454c939a060aaa1b6 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 28 Aug 2023 01:22:18 -0700
Subject: [PATCH 30/36] emoji changes

---
 chat-langchain/app/components/ChatWindow.tsx | 144 ++++++++++---------
 chat-langchain/app/page.tsx                  |   4 +-
 2 files changed, 82 insertions(+), 66 deletions(-)

diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 6b4c94251..4f5cdf149 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -8,6 +8,9 @@ import { Renderer } from "marked";
 import hljs from "highlight.js";
 import "highlight.js/styles/gradient-dark.css";
 
+import { toast } from 'react-toastify';
+import 'react-toastify/dist/ReactToastify.css';
+
 export function ChatWindow(props: {
   endpoint: string;
   emptyStateComponent: React.ReactElement;
@@ -28,6 +31,7 @@ export function ChatWindow(props: {
   const [feedback, setFeedback] = useState<number | null>(null);
   const [hasInteracted, setHasInteracted] = useState(false);
   const [chatHistory, setChatHistory] = useState<{question: string, result: string}[]>([]);
+  const [mode, setMode] = useState("novice");
 
   const {
     endpoint,
@@ -55,74 +59,72 @@ export function ChatWindow(props: {
     fetch(endpoint, {
       method: "POST",
       headers: {
-        "Content-Type": "application/json",
+          "Content-Type": "application/json",
       },
       body: JSON.stringify({
-        message: input,
-        model: model,
-        history: chatHistory,
+          message: input,
+          model: model,
+          history: chatHistory,
+          mode: mode,
       }),
-    }).then((response) => {
-      if (!response.body) {
-        throw new Error("Response body is null");
-      }
-      const reader = response.body.getReader();
-      const decoder = new TextDecoder("utf-8");
+  }).then(response => { 
+    if (!response.body) {
+      throw new Error("Response body is null");
+    }
+    const reader = response.body.getReader();
+    let decoder = new TextDecoder();
 
-      let accumulatedMessage = "";
-      let messageIndex: number | null = null;
+    let accumulatedMessage = "";
+    let messageIndex: number | null = null;
 
-      let renderer = new Renderer();
-      renderer.paragraph = function(text) {
-        return text;
-      };
-      renderer.code = function (code, language) {
-        const validLanguage = hljs.getLanguage(language || "")
-          ? language
-          : "plaintext";
-        const highlightedCode = hljs.highlight(
-          validLanguage || "plaintext",
-          code
-        ).value;
-        return `<pre class="highlight bg-gray-700" style="padding: 5px; border-radius: 5px; overflow: auto; overflow-wrap: anywhere; white-space: pre-wrap; max-width: 100%; display: block; line-height: 1.2"><code class="${language}" style="color: #d6e2ef; font-size: 12px; ">${highlightedCode}</code></pre>`;
-      };
-      marked.setOptions({ renderer });
+    let renderer = new Renderer();
+    renderer.paragraph = function(text) {
+      return text;
+    };
+    renderer.code = function (code, language) {
+      const validLanguage = hljs.getLanguage(language || "")
+        ? language
+        : "plaintext";
+      const highlightedCode = hljs.highlight(
+        validLanguage || "plaintext",
+        code
+      ).value;
+      return `<pre class="highlight bg-gray-700" style="padding: 5px; border-radius: 5px; overflow: auto; overflow-wrap: anywhere; white-space: pre-wrap; max-width: 100%; display: block; line-height: 1.2"><code class="${language}" style="color: #d6e2ef; font-size: 12px; ">${highlightedCode}</code></pre>`;
+    };
+    marked.setOptions({ renderer });
 
-      reader
-      .read()
-      .then(function processText(
-        res: ReadableStreamReadResult<Uint8Array>
-      ): Promise<void> {
-        const { done, value } = res;
-        if (done) {
-          console.log("Stream complete");
-          setChatHistory(prevChatHistory => [...prevChatHistory, {question: input, result: accumulatedMessage}]);
-          return Promise.resolve();
-        }
-    
-        let result = decoder.decode(value);
-        accumulatedMessage += result;
-        let parsedResult = marked.parse(accumulatedMessage);
-    
-        setMessages((prevMessages) => {
-            let newMessages = [...prevMessages];
-            if (messageIndex === null) {
-              messageIndex = newMessages.length;
-              newMessages.push({ id: Math.random().toString(), message: parsedResult.trim(), role: "assistant" });
-            } else {
-              newMessages[messageIndex].message = parsedResult.trim();
-            }
-            return newMessages;
-          });
+    reader
+    .read()
+    .then(function processText(
+      res: ReadableStreamReadResult<Uint8Array>
+    ): Promise<void> {
+      const { done, value } = res;
+      if (done) {
+        console.log("Stream complete");
+        setChatHistory(prevChatHistory => [...prevChatHistory, {question: input, result: accumulatedMessage}]);
+        return Promise.resolve();
+      }
 
-        setIsLoading(false);
-        return reader.read().then(processText);
-      })
-      .catch((error) => {
-        console.error("Error:", error);
-      });
+      let result = decoder.decode(value);
+      accumulatedMessage += result;
+      let parsedResult = marked.parse(accumulatedMessage);
+
+      setMessages((prevMessages) => {
+          let newMessages = [...prevMessages];
+          if (messageIndex === null) {
+            messageIndex = newMessages.length;
+            newMessages.push({ id: Math.random().toString(), message: parsedResult.trim(), role: "assistant" });
+          } else {
+            newMessages[messageIndex].message = parsedResult.trim();
+          }
+          return newMessages;
+        });
+
+      setIsLoading(false);
+      return reader.read().then(processText);
     });
-  }
+})
+}
 
   const sendFeedback = (score: number | null) => {
     if (feedback !== null) return;
@@ -164,11 +166,16 @@ export function ChatWindow(props: {
         "Content-Type": "application/json",
       },
     })
-      .then((response) => response.text())
+      .then((response) => response.json())
       .then((data) => {
-        console.log(data);
-        const url = data.replace(/['"]+/g, "");
-        window.open(url, "_blank");
+        if (data.code === 400 && data.result === "No chat session found") {
+          toast.error("Unable to view trace");
+          throw new Error("Unable to view trace");
+        } else {
+          console.log(data)
+          const url = data.replace(/['"]+/g, "");
+          window.open(url, "_blank");
+        }
       })
       .catch((error) => {
         console.error("Error:", error);
@@ -216,6 +223,15 @@ export function ChatWindow(props: {
             >
               🛠️ view trace
             </button>
+            <div className="flex items-center mr-2">
+              <label htmlFor="modeToggle" className="mr-1 text-xs">Expert Mode</label>
+              <input
+                type="checkbox"
+                id="modeToggle"
+                className="toggle toggle-blue"
+                onChange={() => setMode(mode === "expert" ? "novice" : "expert")}
+              />
+            </div>
         </div>
 
       <form onSubmit={sendMessage} className="flex w-full">
diff --git a/chat-langchain/app/page.tsx b/chat-langchain/app/page.tsx
index bfbc879a8..7e39b2ec2 100644
--- a/chat-langchain/app/page.tsx
+++ b/chat-langchain/app/page.tsx
@@ -44,14 +44,14 @@ export default function Home() {
           </span>
         </li>
         <li className="text-l">
-          💻
+          🦾
           <span className="ml-2">
             You can find the prompt and model logic which uses expression language
             and chat history in <code> main.py</code>.
           </span>
         </li>
         <li className="text-l">
-          💻
+          👣
           <span className="ml-2">
             Feedback and Tracing are additional neat features, thanks to {" "}
             <a href="https://smith.langchain.com" target="_blank">LangSmith</a>.

From 6dfb13709dda880858221a39977926cac36fcc1a Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 28 Aug 2023 11:57:14 -0700
Subject: [PATCH 31/36] README changes and nits

---
 Makefile  |  2 +-
 README.md | 51 +++++++++++++++++++++++++++++++++------------------
 ingest.py | 11 +++++------
 3 files changed, 39 insertions(+), 25 deletions(-)

diff --git a/Makefile b/Makefile
index 62c80d675..e25010441 100644
--- a/Makefile
+++ b/Makefile
@@ -1,6 +1,6 @@
 .PHONY: start
 start:
-	uvicorn main:app --reload --port 9000
+	uvicorn main:app --reload --port 8080
 
 .PHONY: format
 format:
diff --git a/README.md b/README.md
index fd2de7e20..74860af28 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# 🦜️🔗 ChatLangChain
+# 🦜️🔗 Chat LangChain
 
 This repo is an implementation of a locally hosted chatbot specifically focused on question answering over the [LangChain documentation](https://langchain.readthedocs.io/en/latest/).
 Built with [LangChain](https://github.com/hwchase17/langchain/) and [FastAPI](https://fastapi.tiangolo.com/).
@@ -7,21 +7,27 @@ The app leverages LangChain's streaming support and async API to update the page
 
 ## ✅ Running locally
 1. Install dependencies: `pip install -r requirements.txt`
-1. Run `ingest.sh` to ingest LangChain docs data into the vectorstore (only needs to be done once).
+1. Run `python ingest.py` to ingest LangChain docs data into the Weaviate vectorstore (only needs to be done once).
    1. You can use other [Document Loaders](https://langchain.readthedocs.io/en/latest/modules/document_loaders.html) to load your own data into the vectorstore.
-1. Run the app: `make start`
-   1. To enable tracing, make sure `langchain-server` is running locally and pass `tracing=True` to `get_chain` in `main.py`. You can find more documentation [here](https://langchain.readthedocs.io/en/latest/tracing.html).
-1. Open [localhost:9000](http://localhost:9000) in your browser.
+1. Run the app: `make start` for backend and `npm run dev` for frontend (cd into chat-langchain first)
+   1. Make sure to enter your environment variables to configure the application: 
+   ```
+   export OPENAI_API_KEY=
+   export WEAVIATE_URL=
+   export WEAVIATE_API_KEY=
+   
+   # for tracing
+   export LANGCHAIN_TRACING_V2=true
+   export LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
+   export LANGCHAIN_API_KEY=
+   export LANGCHAIN_PROJECT=
+   ```
+1. Open [localhost:3000](http://localhost:3000) in your browser.
 
 ## 🚀 Important Links
 
-Deployed version (to be updated soon): [chat.langchain.dev](https://chat.langchain.dev)
+Deployed version: [chat.langchain.com](https://chat.langchain.com)
 
-Hugging Face Space (to be updated soon): [huggingface.co/spaces/hwchase17/chat-langchain](https://huggingface.co/spaces/hwchase17/chat-langchain)
-
-Blog Posts: 
-* [Initial Launch](https://blog.langchain.dev/langchain-chat/)
-* [Streaming Support](https://blog.langchain.dev/streaming-support-in-langchain/)
 
 ## 📚 Technical description
 
@@ -29,13 +35,22 @@ There are two components: ingestion and question-answering.
 
 Ingestion has the following steps:
 
-1. Pull html from documentation site
-2. Load html with LangChain's [ReadTheDocs Loader](https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/readthedocs_documentation.html)
-3. Split documents with LangChain's [TextSplitter](https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html)
-4. Create a vectorstore of embeddings, using LangChain's [vectorstore wrapper](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html) (with OpenAI's embeddings and FAISS vectorstore).
+1. Pull html from documentation site as well as the Github Codebase
+2. Load html with LangChain's [RecursiveURLLoader Loader](https://python.langchain.com/docs/integrations/document_loaders/recursive_url_loader)
+2. Transform html to text with [Html2TextTransformer](https://python.langchain.com/docs/integrations/document_transformers/html2text)
+3. Split documents with LangChain's [RecursiveCharacterTextSplitter](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.RecursiveCharacterTextSplitter.html)
+4. Create a vectorstore of embeddings, using LangChain's [Weaviate vectorstore wrapper](https://python.langchain.com/docs/integrations/vectorstores/weaviate) (with OpenAI's embeddings).
 
-Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://langchain.readthedocs.io/en/latest/modules/indexes/chain_examples/chat_vector_db.html):
+Question-Answering has the following steps, all handled by [OpenAIFunctionsAgent](https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent):
 
-1. Given the chat history and new user input, determine what a standalone question would be (using GPT-3).
+1. Given the chat history and new user input, determine what a standalone question would be (using GPT-3.5).
 2. Given that standalone question, look up relevant documents from the vectorstore.
-3. Pass the standalone question and relevant documents to GPT-3 to generate a final answer.
+3. Pass the standalone question and relevant documents to GPT-4 to generate and stream the final answer.
+4. Generate a trace URL for the current chat session, as well as the endpoint to collect feedback. 
+
+## Deprecated Links
+Hugging Face Space (to be updated soon): [huggingface.co/spaces/hwchase17/chat-langchain](https://huggingface.co/spaces/hwchase17/chat-langchain)
+
+Blog Posts: 
+* [Initial Launch](https://blog.langchain.dev/langchain-chat/)
+* [Streaming Support](https://blog.langchain.dev/streaming-support-in-langchain/)
\ No newline at end of file
diff --git a/ingest.py b/ingest.py
index 126e3832d..c223eaae8 100644
--- a/ingest.py
+++ b/ingest.py
@@ -31,7 +31,6 @@ def ingest_repo():
         parser=LanguageParser(language=Language.PYTHON, parser_threshold=500)
     )
     documents_repo = loader.load()
-    len(documents_repo)
 
     python_splitter = RecursiveCharacterTextSplitter.from_language(language=Language.PYTHON, 
                                                                 chunk_size=2000, 
@@ -55,10 +54,10 @@ def ingest_docs():
     ]
 
     documents = []
-    for url in urls:
-        loader = RecursiveUrlLoader(url=url, max_depth=2 if url == urls[0] else 8, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=True)
-        temp_docs = loader.load()
-        temp_docs = [doc for i, doc in enumerate(temp_docs) if doc not in temp_docs[:i]]        
+    for j, url in enumerate(urls):
+        max_depth = 2 if j == 0 else 10
+        loader = RecursiveUrlLoader(url=url, max_depth=max_depth, extractor=lambda x: Soup(x, "lxml").text, prevent_outside=True)
+        temp_docs = loader.load()           
         documents += temp_docs
         print("Loaded", len(temp_docs), "documents from", url)
     
@@ -91,7 +90,7 @@ def ingest_docs():
     batch_size = 100 # to handle batch size limit 
     for i in range(0, len(docs_transformed), batch_size):
         batch = docs_transformed[i:i+batch_size]
-        Weaviate.from_documents(batch, embeddings, client=client, by_text=False, index_name="LangChain_newest_idx")
+        Weaviate.add_documents(batch, embeddings, client=client, by_text=False, index_name="LangChain_newest_idx")
 
     print("LangChain now has this many vectors", client.query.aggregate("LangChain_newest_idx").with_meta_count().do())
     

From ddaebae96aea37ded7a54a44dac4faea446cff46 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 28 Aug 2023 12:11:26 -0700
Subject: [PATCH 32/36] delete vercel images add favicon

---
 chat-langchain/app/favicon.ico    | Bin 25931 -> 0 bytes
 chat-langchain/public/favicon.ico | Bin 0 -> 15406 bytes
 chat-langchain/public/next.svg    |   1 -
 chat-langchain/public/vercel.svg  |   1 -
 4 files changed, 2 deletions(-)
 delete mode 100644 chat-langchain/app/favicon.ico
 create mode 100644 chat-langchain/public/favicon.ico
 delete mode 100644 chat-langchain/public/next.svg
 delete mode 100644 chat-langchain/public/vercel.svg

diff --git a/chat-langchain/app/favicon.ico b/chat-langchain/app/favicon.ico
deleted file mode 100644
index 718d6fea4835ec2d246af9800eddb7ffb276240c..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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\ No newline at end of file
diff --git a/chat-langchain/public/vercel.svg b/chat-langchain/public/vercel.svg
deleted file mode 100644
index d2f842227..000000000
--- a/chat-langchain/public/vercel.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 283 64"><path fill="black" d="M141 16c-11 0-19 7-19 18s9 18 20 18c7 0 13-3 16-7l-7-5c-2 3-6 4-9 4-5 0-9-3-10-7h28v-3c0-11-8-18-19-18zm-9 15c1-4 4-7 9-7s8 3 9 7h-18zm117-15c-11 0-19 7-19 18s9 18 20 18c6 0 12-3 16-7l-8-5c-2 3-5 4-8 4-5 0-9-3-11-7h28l1-3c0-11-8-18-19-18zm-10 15c2-4 5-7 10-7s8 3 9 7h-19zm-39 3c0 6 4 10 10 10 4 0 7-2 9-5l8 5c-3 5-9 8-17 8-11 0-19-7-19-18s8-18 19-18c8 0 14 3 17 8l-8 5c-2-3-5-5-9-5-6 0-10 4-10 10zm83-29v46h-9V5h9zM37 0l37 64H0L37 0zm92 5-27 48L74 5h10l18 30 17-30h10zm59 12v10l-3-1c-6 0-10 4-10 10v15h-9V17h9v9c0-5 6-9 13-9z"/></svg>
\ No newline at end of file

From ab7f764d3b6fc05a906242e0ad1d659b37d78dbb Mon Sep 17 00:00:00 2001
From: Molly Cantillon <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 28 Aug 2023 13:45:42 -0700
Subject: [PATCH 33/36] Update main.py

---
 main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main.py b/main.py
index dd5694652..5503c5adf 100644
--- a/main.py
+++ b/main.py
@@ -124,7 +124,7 @@ def create_chain(
         _inputs
         | _context
         | prompt
-        | ChatOpenAI(model="gpt-4", temperature=temperature)
+        | model
         | StrOutputParser()
     )
 

From 7fecfc719121e3ec363b6b4aabe317f9df95d73e Mon Sep 17 00:00:00 2001
From: Molly Cantillon <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 28 Aug 2023 13:46:17 -0700
Subject: [PATCH 34/36] Update evaluate_chains.py

---
 _scripts/evaluate_chains.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py
index 08401b96f..6c1d0f151 100644
--- a/_scripts/evaluate_chains.py
+++ b/_scripts/evaluate_chains.py
@@ -104,7 +104,7 @@ def create_chain(
         _inputs
         | _context
         | prompt 
-        | ChatOpenAI(model="gpt-4", temperature=temperature)
+        | model
         | StrOutputParser()
     )
     

From 6aad426b3cbcb20a61c89cf87b05217f24fa81d3 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 28 Aug 2023 13:46:53 -0700
Subject: [PATCH 35/36] nits

---
 chat-langchain/README.md                      | 34 -------------------
 .../app/components/ChatMessageBubble.tsx      |  2 +-
 main.py                                       |  2 +-
 3 files changed, 2 insertions(+), 36 deletions(-)
 delete mode 100644 chat-langchain/README.md

diff --git a/chat-langchain/README.md b/chat-langchain/README.md
deleted file mode 100644
index f4da3c4c1..000000000
--- a/chat-langchain/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).
-
-## Getting Started
-
-First, run the development server:
-
-```bash
-npm run dev
-# or
-yarn dev
-# or
-pnpm dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
-
-This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
diff --git a/chat-langchain/app/components/ChatMessageBubble.tsx b/chat-langchain/app/components/ChatMessageBubble.tsx
index fb3ca0351..c9e9524b7 100644
--- a/chat-langchain/app/components/ChatMessageBubble.tsx
+++ b/chat-langchain/app/components/ChatMessageBubble.tsx
@@ -20,7 +20,7 @@ export function ChatMessageBubble(props: {
 
   const [feedbackColor, setFeedbackColor] = useState("");
 
-  var cumulativeOffset = function(element: HTMLElement | null) {
+  const cumulativeOffset = function(element: HTMLElement | null) {
       var top = 0, left = 0;
       do {
           top += element?.offsetTop  || 0;
diff --git a/main.py b/main.py
index dd5694652..b05fddf55 100644
--- a/main.py
+++ b/main.py
@@ -183,7 +183,7 @@ async def chat_endpoint(request: Request):
     qa_chain = create_chain(
         retriever=retriever, model_provider=model_type, chat_history=chat_history
     )
-    print("Recieved question: ", question)
+    print("Received question: ", question)
 
     async def stream():
         global run_id, trace_url, feedback_recorded

From 87bace8502b1903b6f4cb96d1fed258dea48ef94 Mon Sep 17 00:00:00 2001
From: mcantillon21 <60069300+mcantillon21@users.noreply.github.com>
Date: Mon, 28 Aug 2023 13:53:34 -0700
Subject: [PATCH 36/36] final model change

---
 chat-langchain/app/components/ChatWindow.tsx | 1 -
 main.py                                      | 4 +++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/chat-langchain/app/components/ChatWindow.tsx b/chat-langchain/app/components/ChatWindow.tsx
index 30cc4ab99..520e7db67 100644
--- a/chat-langchain/app/components/ChatWindow.tsx
+++ b/chat-langchain/app/components/ChatWindow.tsx
@@ -140,7 +140,6 @@ export function ChatWindow(props: {
           console.error("Error:", error);
         });
     });
-})
 }
 
   const sendFeedback = (score: number | null) => {
diff --git a/main.py b/main.py
index d521a2e4e..4f66a7ad3 100644
--- a/main.py
+++ b/main.py
@@ -120,11 +120,13 @@ def create_chain(
             ]
         )
 
+    final_model = "gpt-4" if model_provider == "openai" else "claude-2"
+    
     chain = (
         _inputs
         | _context
         | prompt
-        | model
+        | final_model
         | StrOutputParser()
     )