Zep Vector Store

A long-term memory store for LLM applications

This notebook demonstrates how to use the Zep Vector Store with LlamaIndex.

About Zep

Zep makes it easy for developers to add relevant documents, chat history memory & rich user data to their LLM app's prompts.

Note

Zep can automatically embed your documents. The LlamaIndex implementation of the Zep Vector Store utilizes LlamaIndex's embedders to do so.

Getting Started

Quick Start Guide: https://docs.getzep.com/deployment/quickstart/ GitHub: https://github.com/getzep/zep

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

python

%pip install llama-index-vector-stores-zep

python

!pip install llama-index

python

# !pip install zep-python

python

import logging
import sys
from uuid import uuid4

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import os
import openai
from dotenv import load_dotenv

load_dotenv()

# os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]

python

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.zep import ZepVectorStore

Download Data

python

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

python

# load documents
documents = SimpleDirectoryReader("../data/paul_graham/").load_data()

Create a Zep Vector Store and Index

You can use an existing Zep Collection, or create a new one.

python

from llama_index.core import StorageContext

zep_api_url = "http://localhost:8000"
collection_name = f"graham{uuid4().hex}"

vector_store = ZepVectorStore(
    api_url=zep_api_url,
    collection_name=collection_name,
    embedding_dimensions=1536,
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

python

query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

print(str(response))

Querying with Metadata filters

python

from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text="The Shawshank Redemption",
        metadata={
            "author": "Stephen King",
            "theme": "Friendship",
        },
    ),
    TextNode(
        text="The Godfather",
        metadata={
            "director": "Francis Ford Coppola",
            "theme": "Mafia",
        },
    ),
    TextNode(
        text="Inception",
        metadata={
            "director": "Christopher Nolan",
        },
    ),
]

python

collection_name = f"movies{uuid4().hex}"

vector_store = ZepVectorStore(
    api_url=zep_api_url,
    collection_name=collection_name,
    embedding_dimensions=1536,
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)

python

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="theme", value="Mafia")]
)

python

retriever = index.as_retriever(filters=filters)
result = retriever.retrieve("What is inception about?")

for r in result:
    print("\n", r.node)
    print("Score:", r.score)