LangChain Zero-to-Mastery

About

This repository serves as a comprehensive guide to mastering LangChain, a framework designed to integrate Large Language Models (LLMs) into workflows. It covers foundational concepts, advanced techniques, and practical projects to help you build robust LLM-powered applications.

Structure

1. Introduction to LangChain [1-Introduction-to-LangChain.ipynb]

In this part, we explore the basics of LangChain and how to set up your environment to build applications with LLMs. It includes:

1. What is LangChain?
   • Overview of LangChain's capabilities and use cases.
   • Core concepts like Chains, Memory, and Tools.
2. Setting Up LangChain
   • Installation of required libraries.
   • Configuring API keys for OpenAI or other LLMs.
3. Your First Chain
   • Creating a simple prompt-based chain using PromptTemplate and LLMChain.
   • Example: A chatbot that answers questions.

This part provides the foundation for understanding LangChain and its modular design.

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2. Chains and Memories [2-Chains-Memories.ipynb]

This part dives into building sequential chains and adding memory for context retention. Key topics include:

1. Sequential Chains
   • Creating workflows where the output of one chain feeds into another.
   • Example: Generating ideas and expanding them.
2. Memory Integration
   • Adding memory to chains using ConversationBufferMemory and other memory modules.
   • Example: A chatbot that remembers the context of previous conversations.

This part demonstrates how to build context-aware applications with LangChain.

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3. Document Processing with RAG [3-Document-Processing-with-RAG.ipynb]

In this part, we implement a Retrieval-Augmented Generation (RAG) system to process documents and answer user queries. Topics include:

1. Document Splitting
   • Splitting long documents into manageable chunks using CharacterTextSplitter.
   • Example: Breaking PDFs into smaller sections for processing.
2. Vector Store Creation
   • Building a vector store using FAISS and OpenAI embeddings.
   • Saving and reloading the vector store for reuse.
3. Retrieval-Augmented Generation
   • Retrieving relevant document chunks for user queries.
   • Creating a RetrievalQA system using a retriever and a combining chain.
   • Example: Answering questions based on uploaded PDFs.

This part is essential for building document-based question-answering systems.

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4. MORE WILL COME OUT SOON

Stay tuned for future updates covering advanced LangChain topics like:

• Custom Agents and Tool Integration.
• API Automation with LangChain.
• Knowledge Graphs for advanced data representation.
• Multi-Agent Systems for collaborative workflows.

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We hope this series accelerates your journey to mastering LangChain! Contributions are welcome—feel free to open issues or submit pull requests.