Welcome to the Langchain Series Project! This project encompasses a wide range of applications and integrations using Langchain, showcasing its versatility and power in various natural language processing tasks. Below you'll find detailed descriptions of each component, the technologies used, and how to get started.
The Langchain Series Project consists of several interconnected modules, each focusing on a different aspect of natural language processing and AI-driven solutions. These modules include:
- ChatBot: An interactive chatbot using advanced language models.
- Chains: Building complex workflows and pipelines with language models.
- Agents: Autonomous agents that perform specific tasks.
- Tools: Various utilities and tools to enhance Langchain applications.
- LangServe: A server framework for deploying Langchain applications.
- LangSmith: Enhancing interpretability and debugging for language models.
- RAG: Retrieval-Augmented Generation for document-based Q&A.
- GROQ: Fast inferencing using cutting-edge hardware acceleration.
- OpenAI gpt-3.5-turbo
- Meta Llama3
- FAISS
- Chromadb
- FastAPI
- Huggingface Hub
An intelligent chatbot built using OpenAI gpt-3.5-turbo and Meta Llama3, providing conversational AI capabilities for various use cases.
Complex workflows and pipelines are created using Langchain, integrating multiple models and data sources for advanced processing.
Autonomous agents perform specific tasks by leveraging language models and other tools, automating workflows and decision-making processes.
A set of utilities and tools designed to enhance the functionality and performance of Langchain applications.
A robust server framework using FastAPI for deploying Langchain-based applications, ensuring scalability and reliability.
Enhances interpretability and debugging of language models, improving development and deployment processes.
Utilizes Retrieval-Augmented Generation to answer questions based on document content, integrating FAISS and Chromadb for efficient retrieval.
Leverages advanced hardware acceleration for fast inferencing, optimizing the performance of language models.
To get started with the Langchain Series Project, follow these steps:
-
Clone the Repository:
git clone https://github.com/yourusername/langchain-series-project cd langchain-series-project -
Set Up Environment Variables: Ensure all necessary environment variables are set up in your development environment.
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Install Dependencies:
pip install -r requirements.txt
-
Run the Application:
uvicorn main:app --reload
Each component of the Langchain Series Project is designed to be modular and can be used independently or as part of a larger system. Detailed documentation for each module can be found in their respective directories.
We welcome contributions to improve this project! Please fork the repository and submit pull requests. Make sure to follow the contribution guidelines.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions or feedback, feel free to reach out!
- WebSite: decodeai.in
- GitHub: manoharpalanisamy
- Email: email@decodeai.in
Happy coding! 💻✨