# rag-conversation-zep This template demonstrates building a RAG conversation app using Zep. Included in this template: - Populating a [Zep Document Collection](https://docs.getzep.com/sdk/documents/) with a set of documents (a Collection is analogous to an index in other Vector Databases). - Using Zep's [integrated embedding](https://docs.getzep.com/deployment/embeddings/) functionality to embed the documents as vectors. - Configuring a LangChain [ZepVectorStore Retriever](https://docs.getzep.com/sdk/documents/) to retrieve documents using Zep's built, hardware accelerated in [Maximal Marginal Relevance](https://docs.getzep.com/sdk/search_query/) (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 - Fast, scalable building blocks for LLM Apps](https://www.getzep.com/) 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 your 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](https://docs.getzep.com/deployment/quickstart/). ## 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: ```shell pip install -U "langchain-cli[serve]" ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package rag-conversation-zep ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-conversation-zep ``` And add the following code to your `server.py` file: ```python 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. LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/rag-conversation-zep/playground](http://127.0.0.1:8000/rag-conversation-zep/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-conversation-zep") ```