This repository provides practical examples and experiments for building applications with the LangChain library, targeting developers and data scientists interested in leveraging Large Language Models (LLMs). It demonstrates creating searchable databases from YouTube transcripts and answering questions using similarity search with FAISS and OpenAI's GPT models.

How It Works

The project utilizes LangChain's modular framework, which connects LLMs with external data sources and enables agentic behavior. It specifically showcases the "Indexes" module for data integration and "Chains" for sequential LLM calls. The approach involves processing YouTube transcripts, embedding them using FAISS for efficient similarity search, and then querying the LLM with context derived from these searches to provide accurate answers.

Quick Start & Requirements

• Installation: Clone the repository, create a Python environment (3.6+), and install dependencies via pip install -r requirements.txt.
• Prerequisites: OpenAI API key, SerpAPI API key.
• Setup: Create a .env file in the root directory with OPENAI_API_KEY="your_api_key_here".
• Resources: Video tutorials are available on YouTube: youtube.com/@daveebbelaar.

Highlighted Details

• Demonstrates creating a searchable database from YouTube video transcripts.
• Implements similarity search using FAISS for efficient data retrieval.
• Showcases answering user questions with context from video transcripts and LLMs.
• Leverages OpenAI's GPT-3.5 Turbo model, with plans for GPT-4 integration.

Maintenance & Community

The repository is associated with Datalumina, a service that helps data professionals build freelance businesses. Further tutorials are available on a dedicated YouTube channel.

Licensing & Compatibility

The repository's license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking would require clarification of the licensing terms.

Limitations & Caveats

The README does not specify the license, which may impact commercial use. It also does not detail specific hardware requirements beyond standard Python execution environments.