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https://github.com/hwchase17/langchain
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93 lines
3.8 KiB
Markdown
93 lines
3.8 KiB
Markdown
# rag-conversation-zep
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This template demonstrates building a RAG conversation app using Zep.
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Included in this template:
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- 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).
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- Using Zep's [integrated embedding](https://docs.getzep.com/deployment/embeddings/) functionality to embed the documents as vectors.
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- 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.
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- Prompts, a simple chat history data structure, and other components required to build a RAG conversation app.
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- The RAG conversation chain.
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## About [Zep - Fast, scalable building blocks for LLM Apps](https://www.getzep.com/)
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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.
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Key Features:
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- Fast! Zep’s async extractors operate independently of the your chat loop, ensuring a snappy user experience.
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- Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.
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- Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.
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- Hybrid search over memories and metadata, with messages automatically embedded on creation.
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- Entity Extractor that automatically extracts named entities from messages and stores them in the message metadata.
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- Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.
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- Python and JavaScript SDKs.
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Zep project: https://github.com/getzep/zep | Docs: https://docs.getzep.com/
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## Environment Setup
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Set up a Zep service by following the [Quick Start Guide](https://docs.getzep.com/deployment/quickstart/).
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## Ingesting Documents into a Zep Collection
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Run `python ingest.py` to ingest the test documents into a Zep Collection. Review the file to modify the Collection name and document source.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U "langchain-cli[serve]"
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-conversation-zep
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-conversation-zep
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```
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And add the following code to your `server.py` file:
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```python
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from rag_conversation_zep import chain as rag_conversation_zep_chain
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add_routes(app, rag_conversation_zep_chain, path="/rag-conversation-zep")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-conversation-zep")
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``` |