LangChain Models

A comprehensive repository showcasing the integration of various Language Models (LLMs), Chat Models, and Embedding Models using the latest version of LangChain.

🔍 Overview

This project demonstrates how to leverage both open-source and closed-source models from providers such as OpenAI, Anthropic, Google, and Hugging Face, as well as locally hosted models running on your own system. Built using the latest LangChain release, it provides a modular framework for experimenting with and combining different model types.

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🧠 Language Models (LLMs)

⚠️ Traditional LLMs are gradually being replaced in many applications by chat-optimized models, which provide better instruction-following and contextual interactions.

LLMs refer to general-purpose large language models that take a prompt as input and return a text completion. Examples include:

• OpenAI GPT-3 (text-davinci-003)
• Google PaLM API
• Anthropic Claude (non-chat versions)
• Open-source models on Hugging Face (e.g., EleutherAI/gpt-j, bigscience/bloom)

These models can still be useful for straightforward completions and basic NLP tasks, but Chat Models are now the preferred choice for most interactive or task-specific use cases.

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💬 Chat Models

Chat Models are fine-tuned versions of LLMs optimized for dialogue-based interactions. They support multi-turn conversations, tool usage, and function calling in LangChain workflows.

Examples include:

• OpenAI GPT-4 / GPT-3.5 Turbo
• Anthropic Claude 3
• Google Gemini Pro
• Local chat-tuned models like mistralai/Mistral-7B-Instruct, Meta-Llama-3, or Nous Hermes variants

These models are ideal for agents, conversational interfaces, and structured workflows in LangChain.

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📌 Embedding Models

Embedding models convert text into vector representations, enabling powerful capabilities like semantic search, retrieval-augmented generation (RAG), clustering, and more.

Examples used in this repo:

• OpenAI Embeddings (e.g., text-embedding-3-large)
• Local embedding models like sentence-transformers/all-MiniLM-L6-v2

LangChain makes it easy to switch between embedding providers and manage vector stores across different backends.

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⚙️ Supported Model Sources

• ✅ Open Source: Hugging Face models, local GGUF/transformer models
• ✅ Closed Source: OpenAI, Anthropic, Google APIs
• ✅ Local Execution: Models running via llama.cpp, ctransformers, or transformers on your own hardware

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🧱 Built with

• LangChain (latest version)
• Python 3.10+
• Hugging Face Transformers
• OpenAI Python SDK
• Anthropic SDK
• Google Generative AI SDK