updated readme

README.md

@@ -1,41 +1,11 @@
-# 🦜️🔗 ChatLangChain
+# 🦜️🔗 MockGPT4
 
-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/).
+This repo is an implementation of a locally hosted GPT4 or GPT3.5 chatbot.
 Built with [LangChain](https://github.com/hwchase17/langchain/) and [FastAPI](https://fastapi.tiangolo.com/).
 
 The app leverages LangChain's streaming support and async API to update the page in real time for multiple users.
 
 ## ✅ 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. 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.
+Donwload and run this colab notebook-https://colab.research.google.com/drive/1tFQWh7CUryLX6PthFuZyNIpz3_nALXAm?authuser=1#scrollTo=FmvlU7Af2XhZ
 
-## 🚀 Important Links
 
-Deployed version (to be updated soon): [chat.langchain.dev](https://chat.langchain.dev)
-
-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
-
-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/modules/utils/combine_docs_examples/textsplitter.html)
-4. Create a vectorstore of embeddings, using LangChain's [vectorstore wrapper](https://langchain.readthedocs.io/en/latest/modules/utils/combine_docs_examples/vectorstores.html) (with OpenAI's embeddings and FAISS vectorstore).
-
-Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://langchain.readthedocs.io/en/latest/modules/chains/combine_docs_examples/chat_vector_db.html):
-
-1. Given the chat history and new user input, determine what a standalone question would be (using GPT-3).
-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.