Bhavik-Jikadara / langchain-course

The LangChain Crash Course repository serves as a comprehensive resource for beginners who are ready to learn LangChain, a programming framework designed for creating AI agents, building RAG (Retrieval-Augmented Generation) chatbots, and automating tasks using artificial intelligence.

Complete Beginner's Guide to LangChain: Build AI Agents and Chatbots

The LangChain Crash Course repository serves as a comprehensive resource for beginners who are ready to learn LangChain, a programming framework designed for creating AI agents, building RAG (Retrieval-Augmented Generation) chatbots, and automating tasks using artificial intelligence.

Learning Objectives

• Creating AI Agents: Understand and implement agent-based programming principles using LangChain.
• Building RAG Chatbots: Develop RAG chatbots integrating retrieval-based and generative AI techniques.
• Automating Tasks with AI: Use LangChain for automating repetitive tasks and improving workflow efficiency.

Course Outline

1. Chat Models: Learn how to interact with models like ChatGPT, Claude, and Gemini.

   • 1_chat_model_basic.py
   • 2_chat_model_basic_conversation.py
   • 3_chat_model_conversation_with_user.py

2. Prompt Templates: Understand the basics of prompt templates and how to use them effectively.

   • 1_prompt_template_basic.py
   • 2_prompt_template_with_chat_model.py

3. Chains: Learn how to create chains using Chat Models and Prompts to automate tasks.

   • 1_chains_basics.py
   • 2_chains_under_the_hood.py
   • 3_chains_extended.py
   • 4_chains_parallel.py
   • 5_chains_branching.py

4. RAG (Retrieval-Augmented Generation): Explore the technologies like documents, embeddings, and vector stores that enable RAG queries.

   • 1a_rag_basics.py
   • 1b_rag_basics.py
   • 2a_rag_basics_metadata.py
   • 2b_rag_basics_metadata.py
   • 3_rag_text_splitting_deep_dive.py
   • 4_rag_embedding_deep_dive.py
   • 5_rag_retriever_deep_dive.py
   • 6_rag_one_off_question.py
   • 7_rag_conversational.py
   • 8_rag_web_scrape_firecrawl.py
   • 8_rag_web_scrape.py

5. Agents & Tools: Learn about agents, how they work, and how to build custom tools to enhance their capabilities.

   • 1_agent_and_tools_basics.py
   • agent_deep_dive/
     • 1_agent_react_chat.py
     • 2_react_docstore.py
   • tools_deep_dive/
     • 1_tool_constructor.py
     • 2_tool_decorator.py
     • 3_tool_base_tool.py

Set up your environment variables:

• Rename the .env.example file to .env and update the variables inside with your own values. Example:

  cp .env.example .env

• Add all api keys to the .env file.

   OPENAI_API_KEY="Enter the OPENAI api key"
   GOOGLE_API_KEY="Enter the GOOGLE api key"
   FIRECRAWL_API_KEY="Enter the FIRECRAWL api key"
   TAVILY_API_KEY="Enter the TAVILY api key"
   OPENAI_MODEL_NAME="gpt-3.5-turbo"

Follow

• Linkedin Link: https://www.linkedin.com/in/bhavikjikadara
• Github Link: https://github.com/Bhavik-Jikadara
• Facebook Link: https://www.facebook.com/Bhavikjikadara07
• Instagram Link: https://www.instagram.com/bhavikjikadara/
• twitter Link: https://twitter.com/BhavikJikadara1

Subscribe

• https://www.youtube.com/channel/UC7Bp_sYQmAryrrPqvUp6PwQ

Donate & Support us

• https://www.paypal.com/paypalme/bhavikjikadara

License

The Multiple PDFs QueryBot is released under the Apache License 2.0.