Langchain Projects

Visual Overviews

General Ecosystem

This image provides a high-level view of Langchain's ecosystem, showcasing its modular design and the integration of various components such as chains, agents, retrievers, and vector databases.

Architecture Diagram

This diagram highlights key components of Langchain, including:
- Chains & Agents: Modules to manage sequential tasks and decision-making.
- Retrievers & RAG: Systems to fetch and augment data for improved responses.
- Vector Databases: Integration with FAISS, Chroma, and more for efficient data storage and retrieval.
- LLMs & Embeddings: Support for models like OpenAI and Ollama, with embedding techniques from Hugging Face and others.
- Langsmith & Langserve: Tools to monitor, serve, and scale applications using FastAPI.

RAG Pipeline Overview

This image illustrates the Retrieval-Augmented Generation (RAG) pipeline. It outlines how external data is retrieved and combined with language model outputs to improve response quality. The pipeline typically involves:
- Data Retrieval: Searching external databases for relevant context.
- Integration: Merging retrieved data with generated content.
- Generation: Producing enriched outputs that leverage both internal and external information.

Hybrid Search Overview

This image demonstrates the hybrid search mechanism, which combines dense (vector-based) and sparse (keyword-based) retrieval techniques. It employs Reciprocal Rank Fusion (RRF) to integrate results from multiple search methods, assigning higher scores to consistently high-ranking items for improved overall search accuracy.

About This Repository

This repository holds a variety of projects related to Langchain, including:

• Chains, Retrievers, Tools & Agents: Build and integrate dynamic components.
• Retrieval-Augmented Generation (RAG): Enhance outputs using external data sources.
• Vector Databases: Utilize FAISS, Pinecone, Chroma, and similar tools for efficient storage and retrieval.
• Langsmith & Langserve: Develop and serve applications with integrated monitoring and FastAPI support.
• Large Language Models: Experiment with OpenAI, Ollama models, and more.
• Word Embeddings: Implement diverse embedding techniques from Hugging Face, OpenAI, Ollama, etc.