langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.

An AI developer that writes LangChain Expression Language (LCEL).

You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.

The "lcel-tutorial" repo is designed for mastering LangChain Expression Language (LCEL), offering exercises to build stateful, multi-actor LLM applications. It's a hands-on guide to leveraging LCEL for complex workflows and agent-like behaviors. Perfect for enthusiasts eager to explore LLM's potential.

Langchain RAG model, with output streaming on Streamlit and using persistent VectorStore in disk

Explore the realms of graph databases with Neo4j, dive into Cypher queries, and integrate LLMs for dynamic data insights with Langchain. A personal journey to master graph data.

This project combines the power of vector databases, large language models, and chat history management to create an interactive PDF chatbot