A comprehensive collection of LangChain tutorials, examples, and practical implementations to unlock the full potential of building AI-powered applications.
LangChain Unlocked is your complete guide to mastering LangChain - from basic concepts to advanced implementation patterns. This repository contains hands-on examples, real-world use cases, and best practices for building production-ready AI applications.
- Core LangChain Components: Models, Prompts, Chains, and Agents
- Document Processing: Loading, splitting, and vectorizing documents
- Memory Management: Conversation buffers and retrieval systems
- RAG Implementation: Retrieval-Augmented Generation patterns
- Agent Development: Tool-using AI agents for complex tasks
- Production Deployment: Scaling and monitoring LangChain applications
├── 01_fundamentals/ # Core LangChain concepts
│ ├── models_and_prompts/
│ ├── chains_basics/
│ └── output_parsers/
├── 02_document_processing/ # Document handling and RAG
│ ├── loaders/
│ ├── text_splitters/
│ └── vector_stores/
├── 03_memory_systems/ # Conversation and context management
│ ├── conversation_buffer/
│ ├── summary_memory/
│ └── retrieval_memory/
├── 04_agents_and_tools/ # Autonomous agents
│ ├── basic_agents/
│ ├── custom_tools/
│ └── multi_agent_systems/
├── 05_advanced_patterns/ # Complex implementations
│ ├── rag_systems/
│ ├── guardrails/
│ └── evaluation/
├── 06_production/ # Deployment and monitoring
│ ├── api_deployment/
│ ├── streaming/
│ └── monitoring/
├── projects/ # End-to-end projects
├── notebooks/ # Jupyter notebooks
├── requirements.txt
└── README.md
- Python 3.8 or higher
- OpenAI API key (or other LLM provider)
- Git
-
Clone the repository
git clone https://github.com/Muhammad-Hassan-Farid/Langchain-Unlocked.git cd Langchain-Unlocked -
Create a virtual environment
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
-
Install dependencies
pip install -r requirements.txt
-
Set up environment variables
cp .env.example .env # Edit .env with your API keys
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
# Initialize the model
llm = ChatOpenAI(temperature=0.7)
# Create a simple chat
response = llm.invoke([HumanMessage(content="Hello, LangChain!")])
print(response.content)- Start Here:
01_fundamentals/models_and_prompts/ - Build Chains:
01_fundamentals/chains_basics/ - Handle Documents:
02_document_processing/loaders/
- Add Memory:
03_memory_systems/conversation_buffer/ - Create Agents:
04_agents_and_tools/basic_agents/ - Build RAG Systems:
05_advanced_patterns/rag_systems/
- Custom Tools:
04_agents_and_tools/custom_tools/ - Production Patterns:
06_production/ - Complete Projects:
projects/
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
# Load and process documents
loader = PyPDFLoader("document.pdf")
docs = loader.load_and_split()
# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)
# Build Q&A chain
qa_chain = RetrievalQA.from_chain_type(
llm=ChatOpenAI(),
retriever=vectorstore.as_retriever()
)from langchain.agents import create_openai_functions_agent
from langchain_community.tools import DuckDuckGoSearchRun
# Create tools
search = DuckDuckGoSearchRun()
tools = [search]
# Build agent
agent = create_openai_functions_agent(
llm=ChatOpenAI(),
tools=tools,
prompt=agent_prompt
)- Models: OpenAI, Anthropic, Hugging Face integration
- Prompts: Template management and optimization
- Chains: Sequential and parallel processing
- Memory: Context retention strategies
- Retrieval-Augmented Generation (RAG)
- Agent-based architectures
- Custom tool development
- Streaming responses
- Error handling and retries
- API rate limiting
- Cost optimization
- Performance monitoring
- Security best practices
- Never commit API keys to version control
- Use environment variables for sensitive data
- Implement proper input validation
- Set up usage monitoring and alerts
- Cache frequently used embeddings
- Implement proper retry mechanisms
- Use streaming for long responses
- Monitor token usage and costs
- Follow PEP 8 style guidelines
- Include comprehensive error handling
- Write unit tests for custom components
- Document your code thoroughly
- Upload PDFs, analyze content, ask questions
- Technologies: RAG, FAISS, Streamlit
- Autonomous research with web search capabilities
- Technologies: Agents, Tools, Memory
- Analyze GitHub repositories and provide insights
- Technologies: GitHub API, Code parsing, Summarization
- Context-aware customer service automation
- Technologies: Memory, Classification, Intent detection
langchain>=0.1.0
langchain-openai>=0.1.0
langchain-community>=0.0.20
python-dotenv>=1.0.0
streamlit>=1.28.0
faiss-cpu>=1.7.4
pypdf>=3.17.0
chromadb>=0.4.0Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Follow existing code style and structure
- Add tests for new features
- Update documentation as needed
- Provide clear commit messages
This project is licensed under the MIT License - see the LICENSE file for details.
Muhammad Hassan Farid
- LangChain Team for the amazing framework
- OpenAI for the powerful language models
- The open-source community for continuous inspiration
⭐ Star this repository if you find it helpful!
🐛 Found a bug? Open an issue
💡 Have a suggestion? Start a discussion