文档数组

DocArray 是一种多功能的开源工具，用于管理您的多模态数据。它允许您根据需要调整数据，并提供使用各种文档索引后端存储和搜索数据的灵活性。此外，它变得更好 - 您可以利用您的DocArraydocument 索引创建DocArrayRetriever，并构建超棒的 Langchain 应用程序！

此笔记本分为两个部分。第一部分介绍了所有五个受支持的文档索引后端。它提供了有关设置和索引每个后端的指导，并指导您如何构建DocArrayRetriever用于查找相关文件。 在第二部分中，我们将选择其中一个后端，并通过一个基本示例来说明如何使用它。

文档索引后端

import random

from docarray import BaseDoc
from docarray.typing import NdArray
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.retrievers import DocArrayRetriever

embeddings = FakeEmbeddings(size=32)

API 参考：FakeEmbeddings | 文档数组检索器

在开始构建索引之前，定义文档架构非常重要。这决定了您的文档将包含哪些字段以及每个字段将包含的数据类型。

在本演示中，我们将创建一个有点随机的架构，其中包含 'title' （str）、'title_embedding' （numpy array）、'year' （int） 和 'color' （str）

class MyDoc(BaseDoc):
    title: str
    title_embedding: NdArray[32]
    year: int
    color: str

InMemoryExactNNIndex

InMemoryExactNNIndex将所有 Documents 存储在内存中。对于您可能不想启动数据库服务器的小型数据集，这是一个很好的起点。

在此处了解更多信息：https://docs.docarray.org/user_guide/storing/index_in_memory/

from docarray.index import InMemoryExactNNIndex

# initialize the index
db = InMemoryExactNNIndex[MyDoc]()
# index data
db.index(
    [
        MyDoc(
            title=f"My document {i}",
            title_embedding=embeddings.embed_query(f"query {i}"),
            year=i,
            color=random.choice(["red", "green", "blue"]),
        )
        for i in range(100)
    ]
)
# optionally, you can create a filter query
filter_query = {"year": {"$lte": 90}}

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="title_embedding",
    content_field="title",
    filters=filter_query,
)

# find the relevant document
doc = retriever.invoke("some query")
print(doc)

[Document(page_content='My document 56', metadata={'id': '1f33e58b6468ab722f3786b96b20afe6', 'year': 56, 'color': 'red'})]

Hnsw文档索引

HnswDocumentIndex是一种轻量级 Document Index 实现，它完全在本地运行，最适合中小型数据集。它将向量存储在磁盘上的 hnswlib 中，并将所有其他数据存储在 SQLite 中。

在此处了解更多信息：https://docs.docarray.org/user_guide/storing/index_hnswlib/

from docarray.index import HnswDocumentIndex

# initialize the index
db = HnswDocumentIndex[MyDoc](work_dir="hnsw_index")

# index data
db.index(
    [
        MyDoc(
            title=f"My document {i}",
            title_embedding=embeddings.embed_query(f"query {i}"),
            year=i,
            color=random.choice(["red", "green", "blue"]),
        )
        for i in range(100)
    ]
)
# optionally, you can create a filter query
filter_query = {"year": {"$lte": 90}}

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="title_embedding",
    content_field="title",
    filters=filter_query,
)

# find the relevant document
doc = retriever.invoke("some query")
print(doc)

[Document(page_content='My document 28', metadata={'id': 'ca9f3f4268eec7c97a7d6e77f541cb82', 'year': 28, 'color': 'red'})]

WeaviateDocumentIndex（维维亚特文档索引）

WeaviateDocumentIndex是建立在 Weaviate 向量数据库上的文档索引。

在此处了解更多信息：https://docs.docarray.org/user_guide/storing/index_weaviate/

# There's a small difference with the Weaviate backend compared to the others.
# Here, you need to 'mark' the field used for vector search with 'is_embedding=True'.
# So, let's create a new schema for Weaviate that takes care of this requirement.

from pydantic import Field


class WeaviateDoc(BaseDoc):
    title: str
    title_embedding: NdArray[32] = Field(is_embedding=True)
    year: int
    color: str

from docarray.index import WeaviateDocumentIndex

# initialize the index
dbconfig = WeaviateDocumentIndex.DBConfig(host="http://localhost:8080")
db = WeaviateDocumentIndex[WeaviateDoc](db_config=dbconfig)

# index data
db.index(
    [
        MyDoc(
            title=f"My document {i}",
            title_embedding=embeddings.embed_query(f"query {i}"),
            year=i,
            color=random.choice(["red", "green", "blue"]),
        )
        for i in range(100)
    ]
)
# optionally, you can create a filter query
filter_query = {"path": ["year"], "operator": "LessThanEqual", "valueInt": "90"}

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="title_embedding",
    content_field="title",
    filters=filter_query,
)

# find the relevant document
doc = retriever.invoke("some query")
print(doc)

[Document(page_content='My document 17', metadata={'id': '3a5b76e85f0d0a01785dc8f9d965ce40', 'year': 17, 'color': 'red'})]

弹性文档索引

ElasticDocIndex是基于 Elasticsearch 构建的文档索引

在此处了解更多信息

from docarray.index import ElasticDocIndex

# initialize the index
db = ElasticDocIndex[MyDoc](
    hosts="http://localhost:9200", index_name="docarray_retriever"
)

# index data
db.index(
    [
        MyDoc(
            title=f"My document {i}",
            title_embedding=embeddings.embed_query(f"query {i}"),
            year=i,
            color=random.choice(["red", "green", "blue"]),
        )
        for i in range(100)
    ]
)
# optionally, you can create a filter query
filter_query = {"range": {"year": {"lte": 90}}}

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="title_embedding",
    content_field="title",
    filters=filter_query,
)

# find the relevant document
doc = retriever.invoke("some query")
print(doc)

[Document(page_content='My document 46', metadata={'id': 'edbc721bac1c2ad323414ad1301528a4', 'year': 46, 'color': 'green'})]

QdrantDocumentIndex（Qdrant文档索引）

QdrantDocumentIndex是基于 Qdrant 向量数据库构建的文档索引

在此处了解更多信息

from docarray.index import QdrantDocumentIndex
from qdrant_client.http import models as rest

# initialize the index
qdrant_config = QdrantDocumentIndex.DBConfig(path=":memory:")
db = QdrantDocumentIndex[MyDoc](qdrant_config)

# index data
db.index(
    [
        MyDoc(
            title=f"My document {i}",
            title_embedding=embeddings.embed_query(f"query {i}"),
            year=i,
            color=random.choice(["red", "green", "blue"]),
        )
        for i in range(100)
    ]
)
# optionally, you can create a filter query
filter_query = rest.Filter(
    must=[
        rest.FieldCondition(
            key="year",
            range=rest.Range(
                gte=10,
                lt=90,
            ),
        )
    ]
)

WARNING:root:Payload indexes have no effect in the local Qdrant. Please use server Qdrant if you need payload indexes.

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="title_embedding",
    content_field="title",
    filters=filter_query,
)

# find the relevant document
doc = retriever.invoke("some query")
print(doc)

[Document(page_content='My document 80', metadata={'id': '97465f98d0810f1f330e4ecc29b13d20', 'year': 80, 'color': 'blue'})]

使用 HnswDocumentIndex 检索影片

movies = [
    {
        "title": "Inception",
        "description": "A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.",
        "director": "Christopher Nolan",
        "rating": 8.8,
    },
    {
        "title": "The Dark Knight",
        "description": "When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.",
        "director": "Christopher Nolan",
        "rating": 9.0,
    },
    {
        "title": "Interstellar",
        "description": "Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.",
        "director": "Christopher Nolan",
        "rating": 8.6,
    },
    {
        "title": "Pulp Fiction",
        "description": "The lives of two mob hitmen, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption.",
        "director": "Quentin Tarantino",
        "rating": 8.9,
    },
    {
        "title": "Reservoir Dogs",
        "description": "When a simple jewelry heist goes horribly wrong, the surviving criminals begin to suspect that one of them is a police informant.",
        "director": "Quentin Tarantino",
        "rating": 8.3,
    },
    {
        "title": "The Godfather",
        "description": "An aging patriarch of an organized crime dynasty transfers control of his empire to his reluctant son.",
        "director": "Francis Ford Coppola",
        "rating": 9.2,
    },
]

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

OpenAI API Key: ········

from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from langchain_openai import OpenAIEmbeddings


# define schema for your movie documents
class MyDoc(BaseDoc):
    title: str
    description: str
    description_embedding: NdArray[1536]
    rating: float
    director: str


embeddings = OpenAIEmbeddings()


# get "description" embeddings, and create documents
docs = DocList[MyDoc](
    [
        MyDoc(
            description_embedding=embeddings.embed_query(movie["description"]), **movie
        )
        for movie in movies
    ]
)

API 参考：OpenAIEmbeddings

from docarray.index import HnswDocumentIndex

# initialize the index
db = HnswDocumentIndex[MyDoc](work_dir="movie_search")

# add data
db.index(docs)

普通检索器

from langchain_community.retrievers import DocArrayRetriever

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="description_embedding",
    content_field="description",
)

# find the relevant document
doc = retriever.invoke("movie about dreams")
print(doc)

API 参考：DocArrayRetriever

[Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'})]

带过滤器的检索器

from langchain_community.retrievers import DocArrayRetriever

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="description_embedding",
    content_field="description",
    filters={"director": {"$eq": "Christopher Nolan"}},
    top_k=2,
)

# find relevant documents
docs = retriever.invoke("space travel")
print(docs)

API 参考：DocArrayRetriever

[Document(page_content='Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.', metadata={'id': 'ab704cc7ae8573dc617f9a5e25df022a', 'title': 'Interstellar', 'rating': 8.6, 'director': 'Christopher Nolan'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'})]

带有 MMR 搜索的检索器

from langchain_community.retrievers import DocArrayRetriever

# create a retriever
retriever = DocArrayRetriever(
    index=db,
    embeddings=embeddings,
    search_field="description_embedding",
    content_field="description",
    filters={"rating": {"$gte": 8.7}},
    search_type="mmr",
    top_k=3,
)

# find relevant documents
docs = retriever.invoke("action movies")
print(docs)

API 参考：DocArrayRetriever

[Document(page_content="The lives of two mob hitmen, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption.", metadata={'id': 'e6aa313bbde514e23fbc80ab34511afd', 'title': 'Pulp Fiction', 'rating': 8.9, 'director': 'Quentin Tarantino'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'}), Document(page_content='When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.', metadata={'id': '91dec17d4272041b669fd113333a65f7', 'title': 'The Dark Knight', 'rating': 9.0, 'director': 'Christopher Nolan'})]

相关

• Retriever概念指南
• Retriever操作指南