Getting Started

Prerequisites

• Python 3.10 or 3.11
• Poetry (Follow this Poetry installation tutorial to install Poetry on your system)

Installation

1. Clone the repository:

   <!-- TODO: UPDATE TO MY  -->
   git clone https://github.com/add path

2. Install dependencies using Poetry:

   poetry install --no-root

3. Set up your environment variables:

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

   mv .env.example .env

4. Activate the Poetry shell to run the examples:

   poetry shell

5. Run the code examples:

    python 1_chat_models/1_chat_model_basic.py

Repository Structure

Here's a breakdown of the folders and what you'll find in each:

1. Chat Models

• 1_chat_model_basic.py
• 2_chat_model_basic_conversation.py
• 3_chat_model_alternatives.py
• 4_chat_model_conversation_with_user.py
• 5_chat_model_save_message_history_firestore.py

Learn how to interact with models like ChatGPT, Claude, and Gemini.

2. Prompt Templates

• 1_prompt_template_basic.py
• 2_prompt_template_with_chat_model.py

Understand the basics of prompt templates and how to use them effectively.

3. Chains

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

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

4. RAG (Retrieval-Augmented Generation)

• 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

Explore the technologies like documents, embeddings, and vector stores that enable RAG queries.

5. Agents & Tools

• 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

Learn about agents, how they work, and how to build custom tools to enhance their capabilities.

FAQ

Q: What is LangChain?
A: LangChain is a framework designed to simplify the process of building applications that utilize language models.

Q: How do I set up my environment?
A: Follow the instructions in the "Getting Started" section above. Ensure you have Python 3.10 or 3.11 installed, install Poetry, clone the repository, install dependencies, rename the .env.example file to .env, and activate the Poetry shell.

Q: I am getting an error when running the examples. What should I do?
A: Ensure all dependencies are installed correctly and your environment variables are set up properly. If the issue persists, seek help in the Skool community or open an issue on GitHub.

Q: Where can I find more information about LangChain?
A: Check out the official LangChain documentation and join the Skool community for additional resources and support.

Support

If you encounter any issues or have questions, feel free to open an issue on GitHub or ask for help in the Skool community.

License

This project is licensed under the MIT License.