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.
- 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.
To get started with LangChain, install the necessary dependencies:
pip install langchain
pip install openai # If using OpenAI models
pip install chromadb # For vector storage
pip install faiss-cpu # Alternative vector storefrom 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)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?"))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)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)- Fine-tuning LangChain with Hugging Face Models
- Building a Retrieval-Augmented Generation (RAG) Pipeline
- Deploying LangChain Applications with Streamlit and FastAPI
- 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.
Contributions are highly encouraged! Feel free to submit issues, feature requests, or pull requests to help enhance this repository.
For any inquiries or collaboration opportunities, feel free to reach out:
- Email: [email protected]
- WhatsApp: +8801834363533
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Facebook: Md Emon Hasan
This project is licensed under the MIT License - see the LICENSE file for details.