## 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/)[​](#about-zep---fast-scalable-building-blocks-for-llm-apps "Direct link to about-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 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](https://github.com/getzep/zep) | Docs: [https://docs.getzep.com/](https://docs.getzep.com/)

## Environment Setup[​](#environment-setup "Direct link to 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[​](#ingesting-documents-into-a-zep-collection "Direct link to 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[​](#usage "Direct link to 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_chainadd_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](https://smith.langchain.com/). If you don't have access, you can skip this section

```
export LANGCHAIN_TRACING_V2=trueexport 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:

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:

```
from langserve.client import RemoteRunnablerunnable = RemoteRunnable("http://localhost:8000/rag-conversation-zep")
```