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 🦙.

!pip install llama-index

# !pip install zep-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"]

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

INFO:numexpr.utils:NumExpr defaulting to 8 threads.
NumExpr defaulting to 8 threads.

Download Data

!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'

# 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.

from llama_index.storage.storage_context 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
)

INFO:httpx:HTTP Request: GET http://localhost:8000/healthz "HTTP/1.1 200 OK"
HTTP Request: GET http://localhost:8000/healthz "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df "HTTP/1.1 404 Not Found"
HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df "HTTP/1.1 404 Not Found"
INFO:llama_index.vector_stores.zep:Collection grahamfbf0c456a2ad46c2887a707ccc7bb5df does not exist, will try creating one with dimensions=1536
Collection grahamfbf0c456a2ad46c2887a707ccc7bb5df does not exist, will try creating one with dimensions=1536
INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df "HTTP/1.1 200 OK"
HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/document "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/document "HTTP/1.1 200 OK"

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

print(str(response))

INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/search?limit=2 "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/search?limit=2 "HTTP/1.1 200 OK"
The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer and started programming more extensively, writing simple games, a program to predict rocket heights, and a word processor. They initially planned to study philosophy in college but switched to AI. They also started publishing essays online and realized the potential of the web as a medium for publishing.

Querying with Metadata filters

from llama_index.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",
        },
    ),
]

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)

INFO:httpx:HTTP Request: GET http://localhost:8000/healthz "HTTP/1.1 200 OK"
HTTP Request: GET http://localhost:8000/healthz "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 "HTTP/1.1 404 Not Found"
HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 "HTTP/1.1 404 Not Found"
INFO:llama_index.vector_stores.zep:Collection movies40ffd4f8a68c4822ae1680bb752c07e1 does not exist, will try creating one with dimensions=1536
Collection movies40ffd4f8a68c4822ae1680bb752c07e1 does not exist, will try creating one with dimensions=1536
INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 "HTTP/1.1 200 OK"
HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/document "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/document "HTTP/1.1 200 OK"

from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters

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

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)

INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/search?limit=2 "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/search?limit=2 "HTTP/1.1 200 OK"

 Node ID: 2b5ad50a-8ec0-40fa-b401-6e6b7ac3d304
Text: The Godfather
Score: 0.8841066656525941