163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
3.8 KiB
RAG - Zep - conversation
This template demonstrates building a RAG conversation app using Zep.
Included in this template:
- Populating a Zep Document Collection with a set of documents (a Collection is analogous to an index in other Vector Databases).
- Using Zep's integrated embedding functionality to embed the documents as vectors.
- Configuring a LangChain ZepVectorStore Retriever to retrieve documents using Zep's built, hardware accelerated in Maximal Marginal Relevance (MMR) re-ranking.
- Prompts, a simple chat history data structure, and other components required to build a RAG conversation app.
- The RAG conversation chain.
About Zep
Zep - Fast, scalable building blocks for LLM Apps
Zep is an open source platform for productionizing LLM apps. Go from a prototype built in LangChain or LlamaIndex, or a custom app, to production in minutes without rewriting code.
Key Features:
- Fast! Zep’s async extractors operate independently of the chat loop, ensuring a snappy user experience.
- Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.
- Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.
- Hybrid search over memories and metadata, with messages automatically embedded on creation.
- Entity Extractor that automatically extracts named entities from messages and stores them in the message metadata.
- Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.
- Python and JavaScript SDKs.
Zep project: https://github.com/getzep/zep | Docs: https://docs.getzep.com/
Environment Setup
Set up a Zep service by following the Quick Start Guide.
Ingesting Documents into a Zep Collection
Run python ingest.py to ingest the test documents into a Zep Collection. Review the file to modify the Collection name and document source.
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U "langchain-cli[serve]"
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package rag-conversation-zep
If you want to add this to an existing project, you can just run:
langchain app add rag-conversation-zep
And add the following code to your server.py file:
from rag_conversation_zep import chain as rag_conversation_zep_chain
add_routes(app, rag_conversation_zep_chain, path="/rag-conversation-zep")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-conversation-zep/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-conversation-zep")