📌 Project Overview
In this project, we:
Experiment with different embedding models via API calls using LangChain.
Create and manage LLM agents for retrieval tasks.
Test vector databases for efficient storage and querying.
Build, refine, and benchmark a custom RAG pipeline.
This repository serves as a playground for testing and evaluating the performance of RAG systems under various conditions and configurations.
🚀 Key Features
Embedding Model Testing: Evaluate performance of different embedding models using APIs (e.g., OpenAI, Hugging Face, Cohere).
Agent Creation: Build custom agents with LangChain for intelligent querying and response generation.
Vector Database Integration: Experiment with ChromaDB, FAISS, Pinecone, and Milvus.
Dynamic RAG Pipelines: Implement RAG pipelines with different configurations to handle unstructured data retrieval and response generation.
Benchmarking and Analysis: Compare accuracy, retrieval speed, and resource usage across various setups.