https://github.com/md-emon-hasan/langchain
Powerful framework for building applications with Large Language Models (LLMs), enabling seamless integration with memory, agents, and external data sources.
https://github.com/md-emon-hasan/langchain
ai-chatbot chromadb custom-llm document-processing faiss fine-tuning-llms generative-ai huggingface knowledge-graph langchain large-language-models llm-agents llm-applications multi-modal-ai prompt-engineering rag-pipeline retrieval-augmented-generation semantic-search text-embedding vector-search
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Powerful framework for building applications with Large Language Models (LLMs), enabling seamless integration with memory, agents, and external data sources.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/langchain
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2025-02-13T16:38:00.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-02-13T16:58:17.000Z (over 1 year ago)
- Last Synced: 2025-03-02T04:38:10.512Z (over 1 year ago)
- Topics: ai-chatbot, chromadb, custom-llm, document-processing, faiss, fine-tuning-llms, generative-ai, huggingface, knowledge-graph, langchain, large-language-models, llm-agents, llm-applications, multi-modal-ai, prompt-engineering, rag-pipeline, retrieval-augmented-generation, semantic-search, text-embedding, vector-search
- Language: Jupyter Notebook
- Homepage:
- Size: 737 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LangChain
## Overview
LangChain is a cutting-edge open-source framework designed to streamline the development of applications leveraging Large Language Models (LLMs). It provides robust tools for seamless integration with external data sources, memory management, and agent-based decision-making, empowering developers to build sophisticated AI-powered applications efficiently.
## Key Features
- **Seamless LLM Integration**: Effortlessly connect with models such as OpenAI, Hugging Face, Cohere, and others.
- **Advanced Prompt Engineering**: Optimize and refine prompts for improved model interactions.
- **Persistent Memory Management**: Store and retrieve conversation history to enhance contextual awareness.
- **Intelligent Agents and Tools**: Automate decision-making workflows with flexible agents.
- **Efficient Vector Databases**: Implement state-of-the-art knowledge indexing and retrieval.
## Installation
To get started with LangChain, install the necessary dependencies:
```bash
pip install langchain
pip install openai # If using OpenAI models
pip install chromadb # For vector storage
pip install faiss-cpu # Alternative vector store
```
## Quick Start Guide
### 1. Basic LLM Usage
```python
from langchain.llms import OpenAI
llm = OpenAI(model_name="text-davinci-003", openai_api_key="your-api-key")
response = llm.predict("What is LangChain?")
print(response)
```
### 2. Creating a Conversational AI with Memory
```python
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
memory = ConversationBufferMemory()
llm = OpenAI(model_name="gpt-3.5-turbo", openai_api_key="your-api-key")
conversation = ConversationChain(llm=llm, memory=memory)
print(conversation.predict(input="Hello!"))
print(conversation.predict(input="How are you today?"))
```
### 3. Utilizing Vector Databases (ChromaDB)
```python
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(openai_api_key="your-api-key")
vectorstore = Chroma(embedding_function=embeddings)
vectorstore.add_texts(["LangChain is a powerful framework for LLM applications."])
query = "What is LangChain?"
results = vectorstore.similarity_search(query)
print(results)
```
### 4. Implementing an AI Agent with Custom Tools
```python
from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools import Tool
def custom_tool(query):
return f"Processed query: {query}"
tools = [Tool(name="CustomTool", func=custom_tool, description="A sample tool")]
llm = OpenAI(model_name="gpt-3.5-turbo", openai_api_key="your-api-key")
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
response = agent.run("Use CustomTool to process 'Hello World'")
print(response)
```
## Advanced Topics
- **Fine-tuning LangChain with Hugging Face Models**
- **Building a Retrieval-Augmented Generation (RAG) Pipeline**
- **Deploying LangChain Applications with Streamlit and FastAPI**
## Best Practices
- Implement caching mechanisms to optimize API costs.
- Use prompt engineering techniques like few-shot learning to improve performance.
- Secure API keys by leveraging environment variables.
## Resources
- [LangChain Official Documentation](https://python.langchain.com/)
- [LangChain GitHub Repository](https://github.com/hwchase17/langchain)
- [Community Support and Discussions](https://discord.com/invite/langchain)
## Contributing
Contributions are highly encouraged! Feel free to submit issues, feature requests, or pull requests to help enhance this repository.
## Contact Information
For any inquiries or collaboration opportunities, feel free to reach out:
- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.