https://github.com/ravirch/generative-ai-with-langchain-python

This repository is a comprehensive guide and hands-on implementation of Generative AI projects using LangChain with Python.
https://github.com/ravirch/generative-ai-with-langchain-python

generative-ai langchain-python large-language-models

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This repository is a comprehensive guide and hands-on implementation of Generative AI projects using LangChain with Python.

• Host: GitHub
• URL: https://github.com/ravirch/generative-ai-with-langchain-python
• Owner: ravirch
• Created: 2025-01-09T04:05:03.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-02-19T12:35:47.000Z (8 months ago)
• Last Synced: 2025-02-19T13:36:00.729Z (8 months ago)
• Topics: generative-ai, langchain-python, large-language-models
• Language: Jupyter Notebook
• Homepage:
• Size: 1.54 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Generative AI with LangChain Python

This repository is a comprehensive guide and hands-on implementation of Generative AI projects using LangChain with Python. The focus of this project is to explore, implement, and demonstrate various capabilities of the LangChain ecosystem, including data ingestion, transformations, embeddings, vector databases, and practical applications like building chatbots and language models. 

The repository is designed to be a continually growing resource, with new modules and examples added over time to showcase the power and flexibility of LangChain for generative AI tasks.

## Project Index

### 1. [LangChain Fundamentals](https://github.com/ravirch/Generative-AI-with-LangChain-Python/tree/main/LangChain-Fundamentals)

   Learn the LangChain ecosystem with this foundational module. It covers essential topics like:

   - Data ingestion techniques (text and PDF loaders)

   - Data transformation (recursive, character, and text splitting)

   - Embeddings

   - Creating and querying vector databases (FAISS and Chroma)

### 2. [OpenAI-Ollama-RAG Framework](https://github.com/ravirch/Generative-AI-with-LangChain-Python/tree/main/OpenAI-Ollama-RAG-Framework)

   Build an end-to-end Retrieval-Augmented Generation (RAG) framework using OpenAI and Ollama. This module includes:

   - Data ingestion and splitting

   - Storing and managing FAISS vector databases

   - Retrieving and generating efficient responses

### 3. [LangChain Expression Language](https://github.com/ravirch/Generative-AI-with-LangChain-Python/tree/main/LangChain-Expression-Language)

   Explore the LangChain Expression Language (LCEL) and build a simple application to translate text from English to other languages. This module demonstrates:

   - Text transformations

   - Utilizing large language models for translations

### 4. [Building Chatbots with LangChain](https://github.com/ravirch/Generative-AI-with-LangChain-Python/tree/main/Building-Chatbots-with-LangChain)

   Create intelligent chatbots with LangChain, focusing on:

   - Managing conversation history

   - Utilizing prompt templates

   - Integrating vector databases for enhanced responses

### 5. [Chat Interfaces with LangChain](https://github.com/ravirch/Chat-Interfaces-with-LangChain)

   Create interactive chat applications using LangChain, focusing on:  

   - Implementing dynamic chat-based interfaces  

   - Enhancing user interactions with conversational memory  

   - Integrating real-time data sources for intelligent responses

### 6. [Text Summarization with LangChain](https://github.com/ravirch/Generative-AI-with-LangChain-Python/tree/main/Text%20Summarization%20with%20LangChain)

Build intelligent summarization pipelines using LangChain, focusing on:

   - Using "Stuff" method to summarize combined documents in one prompt

   - Applying "Map-Reduce" to generate summaries from document chunks and merge results

   - Implementing "Refine" to iteratively improve summaries using previous outputs

   - Choosing the right strategy based on document size and summarization goals

## Future Enhancements

This repository is a work in progress. Future updates may include:

- Advanced RAG frameworks with hybrid search techniques

- End-to-end AI pipelines for domain-specific tasks

- Integration with other tools like LlamaIndex or Pinecone

- Tutorials for deploying LangChain applications on the cloud

Stay tuned for regular updates!

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Feel free to suggest improvements or contribute by submitting a pull request. Let's build the future of AI together!

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