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DocArray

DocArray 是一个多功能的开源工具，用于管理您的多模态数据。它允许您以任何方式处理您的数据，并提供使用各种文档索引后端存储和搜索它的灵活性。此外，还有更棒的地方 - 您可以利用您的 DocArray 文档索引来创建一个 DocArrayRetriever，并构建出色的 Langchain 应用程序！

这本笔记本分为两个部分。第一个部分提供了所有五种支持的文档索引后端的介绍。它提供了一些建立和索引每个后端的方法，并指导您如何构建一个DocArrayRetriever来查找相关文档。 在第二个部分中，我们将选择其中一个这些后端并通过一个基本示例说明其用法。

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)

在开始构建索引之前，定义文档架构非常重要。这确定了您的文档将包含哪些字段以及每个字段将存储哪种类型的数据。

对于这个演示，我们将创建一个包含 'title'（字符串），'title_embedding'（numpy数组），'year'（整数）和 'color'（字符串）的相对随机的模式

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

InMemoryExactNNIndex​

InMemoryExactNNIndex 将所有文档存储在内存中。对于小型数据集来说，这是一个很好的起点，在这种情况下，你可能不想启动一个数据库服务器。

学习更多：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'})]

HnswDocumentIndex​

HnswDocumentIndex 是一个轻量级的文档索引实现，完全在本地运行，并且最适合用于小型到中型数据集。它将向量存储在 hnswlib 中，在磁盘上保存所有其他数据，并使用 SQLite 数据库进行存储。

了解更多请访问：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'})]

Weaviate文档索引​

WeaviateDocumentIndex 是一个基于 Weaviate 向量数据库 的文档索引。

学习更多内容请访问: 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'})]

ElasticDocIndex​

ElasticDocIndex 是一个基于 ElasticSearch 的文档索引

学习更多 这里

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​

QdrantDocumentIndex 是一个基于 Qdrant 向量数据库的文档索引

学习更多 这里

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'})]

Movie Retrieval using HnswDocumentIndex​

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:OpenAI 嵌入
from docarray.index import HnswDocumentIndex

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

# add data
db.index(docs)

正常检索器​

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)
[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'})]

Retriever 与过滤器​

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)
[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'})]
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)
[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'})]