need langchain project repo where all implementation code present along with notebook , file as per industry , if any one have please share

[imnaveed092]

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Example Code

looking for all demo example projects

Description

please share

System Info

looking for help

[dosubot[bot]]

Hey @imnaveed092, I'm here to help you with any issues or questions you have. Feel free to ask anything!

You can find the LangChain repository, which includes both implementation code and Jupyter notebooks, at the following URL: https://github.com/langchain-ai/langchain ^[1].

The repository has a specific directory structure for .py files and notebooks. The .py files are primarily located within the /libs directory, while notebooks and tutorials are found in the /cookbook directory. Here is an overview of the directory structure:

.
├── cookbook # Tutorials and examples
├── docs # Contains content for the documentation here: https://python.langchain.com/
├── libs
│   ├── langchain
│   │   ├── langchain
│   │   ├── tests/unit_tests # Unit tests (present in each package not shown for brevity)
│   │   ├── tests/integration_tests # Integration tests (present in each package not shown for brevity)
│   ├── community # Third-party integrations
│   │   ├── langchain-community
│   ├── core # Base interfaces for key abstractions
│   │   ├── langchain-core
│   ├── experimental # Experimental components and chains
│   │   ├── langchain-experimental
|   ├── cli # Command line interface
│   │   ├── langchain-cli
│   ├── text-splitters
│   │   ├── langchain-text-splitters
│   ├── standard-tests
│   │   ├── langchain-standard-tests
│   ├── partners
│       ├── langchain-partner-1
│       ├── langchain-partner-2
│       ├── ...
│
├── templates # A collection of easily deployable reference architectures for a wide variety of tasks.

^[2]

For example projects and demo implementations, you can refer to the cookbook/README.md file, which lists numerous example notebooks. Some notable examples include:

• agent_fireworks_ai_langchain_mongodb.ipynb: Build an AI Agent With Memory Using MongoDB, LangChain, and FireWorksAI.
• mongodb-langchain-cache-memory.ipynb: Build a RAG Application with Semantic Cache Using MongoDB and LangChain.
• LLaMA2_sql_chat.ipynb: Build a chat application that interacts with a SQL database using an open-source LLM (LLaMA2), specifically demonstrated on an SQLite database containing rosters.
• Semi_Structured_RAG.ipynb: Perform retrieval-augmented generation (RAG) on documents with semi-structured data, including text and tables.

These examples and many others are provided to help users build and understand applications using LangChain ^[3][4].

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[imnaveed092]

hi want all demo projects of langchain as per industry,