Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/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.
https://github.com/bhavik-jikadara/langchain-course
artificial-intelligence automation chat-model langchain retrieval-augmented-generation vector-database
Last synced: about 1 month ago
JSON representation
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.
- Host: GitHub
- URL: https://github.com/bhavik-jikadara/langchain-course
- Owner: Bhavik-Jikadara
- License: apache-2.0
- Created: 2024-06-26T11:15:21.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-06-26T11:53:20.000Z (6 months ago)
- Last Synced: 2024-06-26T14:14:26.498Z (6 months ago)
- Topics: artificial-intelligence, automation, chat-model, langchain, retrieval-augmented-generation, vector-database
- Language: Python
- Homepage:
- Size: 4.38 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Complete Beginner's Guide to LangChain: Build AI Agents and Chatbots
The [LangChain](https://www.langchain.com/) 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](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](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](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)](4_rag): 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](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:
```bash
cp .env.example .env
```
* Add all api keys to the `.env` file.
```bash
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:
* Github Link:
* Facebook Link:
* Instagram Link:
* twitter Link:
## Subscribe
*
## Donate & Support us
*
## License
The Multiple PDFs QueryBot is released under the [Apache License 2.0](https://github.com/Bhavik-Jikadara/langchain-course/blob/main/LICENSE).