Add files via upload

app/builds/main1.py

@@ -0,0 +1,91 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import boto3
+from langchain import FewShotPromptTemplate, LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms.bedrock import Bedrock
+from langchain_community.document_loaders import UnstructuredHTMLLoader
+import uvicorn
+
+app = FastAPI()
+
+class Question(BaseModel):
+    user_question: str
+
+# Define AWS credentials globally
+role_arn = 'arn:aws:iam::992382738258:role/gsa-rag'
+session_name = 'AssumedRoleSession'
+region_name = 'us-east-1'
+sts_client = boto3.client('sts', region_name=region_name)
+
+# Initialize Bedrock client globally
+def initialize_bedrock_client():
+    response = sts_client.assume_role(RoleArn=role_arn, RoleSessionName=session_name)
+    credentials = response['Credentials']
+    return boto3.Session(
+        aws_access_key_id=credentials['AccessKeyId'],
+        aws_secret_access_key=credentials['SecretAccessKey'],
+        aws_session_token=credentials['SessionToken'],
+        region_name=region_name
+    ).client(service_name="bedrock-runtime", region_name="us-east-1")
+
+bedrock_runtime = initialize_bedrock_client()
+
+# List of document paths
+document_paths = [
+    "/home/ubuntu/gsa-rag-backend/docs/0VV35P.3RLG4G_47QTCA20D00B2_INDUCTIVEHEALTH123020.html",
+    "/home/ubuntu/gsa-rag-backend/docs/0WG872.3S6L5T_47QTCA20D006Y_TMETRICSIFSSPRICELISTREV.html",
+    "/home/ubuntu/gsa-rag-backend/docs/0WTKWJ.3SJXV8_47QTCA20D00EY_47QTCA20D00EY.html"
+]
+
+# Load documents and concatenate their contents
+fulltext = ""
+for path in document_paths:
+    loader = UnstructuredHTMLLoader(path)
+    document = loader.load()
+    for page in document:
+        fulltext += page.page_content
+    fulltext += "\n****************End of this Document**************\n"
+    fulltext += "****************Start of next Document**************\n"
+
+# Model ID for Bedrock
+model_id = 'anthropic.claude-v2:1'
+
+# Hello World endpoint
+@app.get("/hello")
+async def read_root():
+    return {"message": "Hello, World!"}
+
+@app.post("/generate_answer/")
+async def generate_answer(question: Question):
+    # Q&A prompt template
+    user_question = question.user_question
+    question_template = """
+    You are a helpful assistant. The {doc_text} contains quotes from 4 companies.
+    Answer the following questions only on provided info.
+    List all company names.
+    """
+
+    question_template = question_template + user_question
+    prompt = PromptTemplate(template=question_template, input_variables=[""])
+
+    # Initialize LLM chain and run
+    bedrock_llm = Bedrock(
+        model_id=model_id,
+        client=bedrock_runtime,
+        model_kwargs={
+            'max_tokens_to_sample': 4096,
+            'temperature': 0.0,
+            'top_k': 250,
+            'top_p': 0.999
+        }
+    )
+
+    llm_chain = LLMChain(prompt=prompt, llm=bedrock_llm)
+    output = llm_chain.run({"doc_text": fulltext})
+    return output
+
+# Run the FastAPI app using uvicorn when the script is executed directly
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
+

app/builds/main2.py

@@ -0,0 +1,101 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import boto3
+from langchain import FewShotPromptTemplate, LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms.bedrock import Bedrock
+from langchain_community.document_loaders import UnstructuredHTMLLoader
+import uvicorn
+
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Set this to the appropriate origins if needed
+    allow_credentials=True,
+    allow_methods=["GET", "POST"],
+    allow_headers=["*"],
+)
+
+class Question(BaseModel):
+    user_question: str
+
+# Define AWS credentials globally
+role_arn = 'arn:aws:iam::992382738258:role/gsa-rag'
+session_name = 'AssumedRoleSession'
+region_name = 'us-east-1'
+sts_client = boto3.client('sts', region_name=region_name)
+
+# Initialize Bedrock client globally
+def initialize_bedrock_client():
+    response = sts_client.assume_role(RoleArn=role_arn, RoleSessionName=session_name)
+    credentials = response['Credentials']
+    return boto3.Session(
+        aws_access_key_id=credentials['AccessKeyId'],
+        aws_secret_access_key=credentials['SecretAccessKey'],
+        aws_session_token=credentials['SessionToken'],
+        region_name=region_name
+    ).client(service_name="bedrock-runtime", region_name="us-east-1")
+
+bedrock_runtime = initialize_bedrock_client()
+
+# List of document paths
+document_paths = [
+    "/home/ubuntu/gsa-rag-backend/docs/0VV35P.3RLG4G_47QTCA20D00B2_INDUCTIVEHEALTH123020.html",
+    "/home/ubuntu/gsa-rag-backend/docs/0WG872.3S6L5T_47QTCA20D006Y_TMETRICSIFSSPRICELISTREV.html",    "/home/ubuntu/gsa-rag-backend/docs/0WTKWJ.3SJXV8_47QTCA20D00EY_47QTCA20D00EY.html"
+]
+
+# Load documents and concatenate their contents
+fulltext = ""
+for path in document_paths:
+    loader = UnstructuredHTMLLoader(path)
+    document = loader.load()
+    for page in document:
+        fulltext += page.page_content
+    fulltext += "\n****************End of this Document**************\n"
+    fulltext += "****************Start of next Document**************\n"
+
+# Model ID for Bedrock
+model_id = 'anthropic.claude-v2:1'
+
+# Hello World endpoint
+@app.get("/hello")
+async def read_root():
+    return {"message": "Hello, World!"}
+
+@app.post("/generate_answer/")
+async def generate_answer(question: Question):
+    # Q&A prompt template
+    user_question = question.user_question
+    question_template = """
+    You are a helpful assistant. The {doc_text} contains quotes from 4 companies.
+    Answer the following questions only on provided info.
+    List all company names.
+    """
+
+    question_template = question_template + user_question
+    prompt = PromptTemplate(template=question_template, input_variables=[""])
+
+    # Initialize LLM chain and run
+    bedrock_llm = Bedrock(
+        model_id=model_id,
+        client=bedrock_runtime,
+        model_kwargs={
+            'max_tokens_to_sample': 4096,
+            'temperature': 0.0,
+            'top_k': 250,
+            'top_p': 0.999
+        }
+    )
+
+    llm_chain = LLMChain(prompt=prompt, llm=bedrock_llm)
+    output = llm_chain.run({"doc_text": fulltext})
+    return output
+
+# Run the FastAPI app using uvicorn when the script is executed directly
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
+

app/builds/main3.py

@@ -0,0 +1,101 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import boto3
+from langchain import FewShotPromptTemplate, LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms.bedrock import Bedrock
+from langchain_community.document_loaders import UnstructuredHTMLLoader
+import uvicorn
+
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Set this to the appropriate origins if needed
+    allow_credentials=True,
+    allow_methods=["GET", "POST"],
+    allow_headers=["*"],
+)
+
+class Question(BaseModel):
+    user_question: str
+
+# Define AWS credentials globally
+role_arn = 'arn:aws:iam::992382738258:role/gsa-rag'
+session_name = 'AssumedRoleSession'
+region_name = 'us-east-1'
+sts_client = boto3.client('sts', region_name=region_name)
+
+# Initialize Bedrock client globally
+def initialize_bedrock_client():
+    response = sts_client.assume_role(RoleArn=role_arn, RoleSessionName=session_name)
+    credentials = response['Credentials']
+    return boto3.Session(
+        aws_access_key_id=credentials['AccessKeyId'],
+        aws_secret_access_key=credentials['SecretAccessKey'],
+        aws_session_token=credentials['SessionToken'],
+        region_name=region_name
+    ).client(service_name="bedrock-runtime", region_name="us-east-1")
+
+bedrock_runtime = initialize_bedrock_client()
+
+# List of document paths
+document_paths = [
+    "/home/ubuntu/gsa-rag-backend/docs/0VV35P.3RLG4G_47QTCA20D00B2_INDUCTIVEHEALTH123020.html",
+    "/home/ubuntu/gsa-rag-backend/docs/0WG872.3S6L5T_47QTCA20D006Y_TMETRICSIFSSPRICELISTREV.html",    "/home/ubuntu/gsa-rag-backend/docs/0WTKWJ.3SJXV8_47QTCA20D00EY_47QTCA20D00EY.html"
+]
+
+# Load documents and concatenate their contents
+fulltext = ""
+for path in document_paths:
+    loader = UnstructuredHTMLLoader(path)
+    document = loader.load()
+    for page in document:
+        fulltext += page.page_content
+    fulltext += "\n****************End of this Document**************\n"
+    fulltext += "****************Start of next Document**************\n"
+
+# Model ID for Bedrock
+model_id = 'anthropic.claude-v2:1'
+
+# Hello World endpoint
+@app.get("/hello")
+async def read_root():
+    return {"message": "Hello, World!"}
+
+@app.post("/generate_answer/")
+async def generate_answer(question: Question):
+    # Q&A prompt template
+    user_question = question.user_question
+    question_template = """
+    You are a helpful assistant. Read the document inside <doc> tags that contains proposals for products and services from 4 vendors.
+    <doc>
+    {doc_text}
+    </doc>
+    """
+
+    question_template = question_template + user_question + "Think step by Step"
+    prompt = PromptTemplate(template=question_template, input_variables=[""])
+
+    # Initialize LLM chain and run
+    bedrock_llm = Bedrock(
+        model_id=model_id,
+        client=bedrock_runtime,
+        model_kwargs={
+            'max_tokens_to_sample': 4096,
+            'temperature': 1.0,
+            'top_k': 250,
+            'top_p': 0.999
+        }
+    )
+
+    llm_chain = LLMChain(prompt=prompt, llm=bedrock_llm)
+    output = llm_chain.run({"doc_text": fulltext})
+    return output
+
+# Run the FastAPI app using uvicorn when the script is executed directly
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=8000)