Zep 向量存储¶

专为LLM应用设计的长期记忆存储¶

本笔记本演示如何将Zep向量存储与LlamaIndex结合使用。

关于Zep¶

Zep帮助开发者轻松地将相关文档、聊天历史记忆及丰富的用户数据整合到LLM应用的提示中。

注意事项¶

Zep支持自动文档嵌入。LlamaIndex实现的Zep向量存储利用LlamaIndex自带的嵌入器完成此功能。

快速开始¶

快速入门指南: https://docs.getzep.com/deployment/quickstart/ GitHub仓库: https://github.com/getzep/zep

如果您在 Colab 上打开此 Notebook，可能需要安装 LlamaIndex 🦙。

In [ ]:

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

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

In [ ]:

!pip install llama-index

!pip install llama-index

In [ ]:

# !pip install zep-python

# !pip install zep-python

In [ ]:

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"]

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"]

In [ ]:

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

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

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

下载数据

In [ ]:

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

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

In [ ]:

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

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

创建 Zep 向量存储与索引¶

您可以使用现有的 Zep 集合，或新建一个集合。

In [ ]:

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
)

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 )

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"

In [ ]:

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

print(str(response))

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.

使用元数据过滤器进行查询¶

In [ ]:

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",
        },
    ),
]

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", }, ), ]

In [ ]:

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)

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"

In [ ]:

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

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

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters filters = MetadataFilters( filters=[ExactMatchFilter(key="theme", value="Mafia")] )

In [ ]:

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)

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