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| 2026-08-06 | 962 |
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# LangChain Crash Course
Welcome to the LangChain Crash Course repository! This repo contains all the code examples you'll need to follow along with the LangChain Master Class for Beginners video. By the end of this course, you'll know how to use LangChain to create your own AI agents, build RAG chatbots, and automate tasks with AI.
## Course Outline
1. **Setup Environment**
2. **Chat Models**
3. **Prompt Templates**
4. **Chains**
5. **RAG (Retrieval-Augmented Generation)**
6. **Agents & Tools**
## Getting Started
### Prerequisites
- Python 3.10 or 3.11
- Poetry (Follow this [Poetry installation tutorial](https://python-poetry.org/docs/#installation) to install Poetry on your system)
### Installation
1. Clone the repository:
```bash
<!-- TODO: UPDATE TO MY -->
git clone https://github.com/bhancockio/langchain-crash-course
cd langchain-crash-course
```
2. Install dependencies using Poetry:
```bash
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:
```bash
mv .env.example .env
```
4. Activate the Poetry shell to run the examples:
```bash
poetry shell
```
5. Run the code examples:
```bash
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.
## How to Use This Repository
1. **Watch the Video:** Start by watching the LangChain Master Class for Beginners video on YouTube at 2X speed for a high-level overview.
2. **Run the Code Examples:** Follow along with the code examples provided in this repository. Each section in the video corresponds to a folder in this repo.
3. **Join the Community:** If you get stuck or want to connect with other AI developers, join the FREE Skool community [here](https://www.skool.com/ai-developer-accelerator/about).
## Comprehensive Documentation
Each script in this repository contains detailed comments explaining the purpose and functionality of the code. This will help you understand the flow and logic behind each example.
## 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 dependenExcerpt of 4,866 characters
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