Moss LangChain Cookbook

This cookbook demonstrates how to integrate Moss with LangChain.

Overview

Moss is a semantic search platform that allows you to build and query high-performance vector indices without managing infrastructure. This integration provides:

1. MossRetriever: A LangChain-compatible retriever for semantic search.
2. MossSearchTool: A tool for LangChain agents to search your knowledge base.

Installation

Ensure you have the required packages installed:

pip install inferedge-moss langchain langchain-openai python-dotenv

Setup

Create a .env file with your Moss credentials:

MOSS_PROJECT_ID=your_project_id
MOSS_PROJECT_KEY=your_project_key
MOSS_INDEX_NAME=your_index_name
OPENAI_API_KEY=your_openai_api_key

Usage

Using the Retriever

The MossRetriever can be used in any LangChain chain.

from moss_langchain import MossRetriever

retriever = MossRetriever(
    project_id="your_id",
    project_key="your_key",
    index_name="your_index",
    top_k=3,
    alpha=0
)

docs = retriever.invoke("What is the refund policy?")

Using the Agent Tool

You can also use Moss as a tool for an agent.

from moss_langchain import get_moss_tool

tool = get_moss_tool(
    project_id="your_id",
    project_key="your_key",
    index_name="your_index"
)

# Add to agent tools
tools = [tool]

Examples

Check out the moss_langchain.ipynb notebook for complete examples including:

• Direct index querying
• Retrieval-Augmented Generation (RAG)
• ReAct Agent with Moss search