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42 lines
2.6 KiB
Markdown
42 lines
2.6 KiB
Markdown
# 🦜️🔗 ChatLangChain
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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/).
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Built with [LangChain](https://github.com/hwchase17/langchain/) and [FastAPI](https://fastapi.tiangolo.com/).
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The app leverages LangChain's streaming support and async API to update the page in real time for multiple users.
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## ✅ Running locally
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1. Install dependencies: `pip install -r requirements.txt`
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1. Run `ingest.sh` to ingest LangChain docs data into the vectorstore (only needs to be done once).
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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.
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1. Run the app: `make start`
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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).
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1. Open [localhost:9000](http://localhost:9000) in your browser.
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## 🚀 Important Links
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Deployed version (to be updated soon): [chat.langchain.dev](https://chat.langchain.dev)
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Hugging Face Space (to be updated soon): [huggingface.co/spaces/hwchase17/chat-langchain](https://huggingface.co/spaces/hwchase17/chat-langchain)
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Blog Posts:
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* [Initial Launch](https://blog.langchain.dev/langchain-chat/)
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* [Streaming Support](https://blog.langchain.dev/streaming-support-in-langchain/)
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## 📚 Technical description
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There are two components: ingestion and question-answering.
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Ingestion has the following steps:
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1. Pull html from documentation site
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2. Load html with LangChain's [ReadTheDocs Loader](https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/readthedocs_documentation.html)
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3. Split documents with LangChain's [TextSplitter](https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html)
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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).
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Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://langchain.readthedocs.io/en/latest/modules/indexes/chain_examples/chat_vector_db.html):
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1. Given the chat history and new user input, determine what a standalone question would be (using GPT-3).
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2. Given that standalone question, look up relevant documents from the vectorstore.
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3. Pass the standalone question and relevant documents to GPT-3 to generate a final answer.
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