https://github.com/04bhavyaa/langchain-models
This project explores various LLMs and embedding models using LangChain, integrating OpenAI, Hugging Face, Google Gemini, and Anthropic. It includes chat models, document similarity search, and embeddings with cosine similarity for retrieval. The setup is simple, making it easy to experiment with LLMs and vector search. 🚀 (Big Thankyou to CampusX)
https://github.com/04bhavyaa/langchain-models
anthropic-claude chatmodel embedding-models embeddings google-generative-ai huggingface langchain langchain-models llm-model openai-api vectorization
Last synced: about 2 months ago
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This project explores various LLMs and embedding models using LangChain, integrating OpenAI, Hugging Face, Google Gemini, and Anthropic. It includes chat models, document similarity search, and embeddings with cosine similarity for retrieval. The setup is simple, making it easy to experiment with LLMs and vector search. 🚀 (Big Thankyou to CampusX)
- Host: GitHub
- URL: https://github.com/04bhavyaa/langchain-models
- Owner: 04bhavyaa
- Created: 2025-02-16T16:16:32.000Z (over 1 year ago)
- Default Branch: Main
- Last Pushed: 2025-02-16T16:35:36.000Z (over 1 year ago)
- Last Synced: 2025-05-22T10:56:52.697Z (about 1 year ago)
- Topics: anthropic-claude, chatmodel, embedding-models, embeddings, google-generative-ai, huggingface, langchain, langchain-models, llm-model, openai-api, vectorization
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LangChain Models
This repository contains implementations of various **Language Models (LLMs)**, **Chat Models** and **Embedding Models** using LangChain. It demonstrates different model integrations for LLMs and embeddings, including OpenAI, Hugging Face, Google, Sentence Transformers and Anthropic.
## 📂 Directory Structure
```plaintext
04bhavyaa-langchain-models/
├── README.md # Project documentation
├── requirements.txt # Required dependencies
├── LLMs/
│ └── llm_demo.py # Basic LLM demonstration
├── chat-models/ # Various chat models
│ ├── chatmodel_anthropic.py # Anthropic's Claude integration
│ ├── chatmodel_google.py # Google Gemini (PaLM) integration
│ ├── chatmodel_hf_api.py # Hugging Face API integration
│ ├── chatmodel_hf_local.py # Local Hugging Face LLM integration
│ └── chatmodel_openai.py # OpenAI ChatGPT integration
└── embedding-models/ # Embedding models for similarity search
├── document_similarity.py # Cosine similarity for document search
├── embedding_hf_local.py # Local Hugging Face embeddings
├── embedding_openai_docs.py # OpenAI document embeddings
└── embedding_openai_query.py # OpenAI query embeddings
```
## âš¡ Tech Stack
- **LangChain** (Core framework for chaining LLMs)
- **OpenAI API** (ChatGPT & Embeddings)
- **Anthropic API** (Claude models)
- **Google Generative AI** (Gemini/PaLM models)
- **Hugging Face** (Transformer-based LLMs & embeddings)
- **Scikit-learn** (Cosine similarity for document search)
- **Pandas & NumPy** (Data processing utilities)
## 🧠Theory Behind LangChain Models
- **LLMs:** These are large-scale neural networks trained on vast text corpora. LangChain provides a structured way to interact with them.
- **Chat Models:** Variants of LLMs optimized for dialogue, such as OpenAI's ChatGPT, Claude, and Gemini.
- **Embedding Models:** Convert text into high-dimensional vectors to perform similarity search, useful for document retrieval and semantic search.
## 🔧 Setup Instructions
### 1. Clone the repository:
```
git clone https://github.com/yourusername/04bhavyaa-langchain-models.git
cd 04bhavyaa-langchain-models
```
### 2. Create and activate a virtual environment:
```
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
### 3. Install dependencies:
```
pip install -r requirements.txt
```
### 4. Set Up Environment Variables
Create a `.env` file and add your API keys:
```
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
HUGGINGFACEHUB_API_TOKEN=your_huggingface_api_key
```
### 5. Run the Models
Example usage for document similarity:
```bash
python embedding-models/document_similarity.py
```
Sample Output:
---
💡 **Contributions are welcome!** Feel free to fork, open issues, or submit PRs.