DocArray
DocArray 是一个通用的开源工具，用于管理您的多模态数据。它允许您以任何您想要的方式塑造数据，并提供灵活地使用各种文档索引后端存储和搜索数据。此外，它变得更好 - 您可以利用您的
DocArray文档索引来创建一个DocArrayRetriever，并构建很棒的 Langchain 应用程序！
此笔记本分为两个部分。该第一部分介绍所有五个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南，还指导您如何构建一个 DocArrayRetriever 来查找相关文档。在该第二部分，我们将选择其中一个后端并通过一个基本示例说明如何使用它。
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 | DocArrayRetriever
在开始构建索引之前，定义文档模式非常重要。这决定了您的文档将包含哪些字段以及每个字段将保存哪种类型的数据。
为了演示，我们将创建一个包含“标题”（str）、“标题嵌入”（numpy 数组）、“年份”（int）和“颜色”（str）的随机模式。
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，这是一个很好的起点，您可能不想启动数据库服务器。
在此处了解详情：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": {"$lte": 90}}
# create a retriever
retriever = DocArrayRetriever(
index=db,
embeddings=embeddings,
search_field="title_embedding",
content_field="title",
filters=filter_query,
)
# find the relevant document
doc = retriever.invoke("some query")
print(doc)
[Document(page_content='My document 56', metadata={'id': '1f33e58b6468ab722f3786b96b20afe6', 'year': 56, 'color': 'red'})]
HnswDocumentIndex
HnswDocumentIndex 是一种轻量级的文档索引实现，它完全在本地运行，最适合小型到中型数据集。它将向量存储在磁盘上的 hnswlib 中，并将所有其他数据存储在 SQLite 中。
在此处了解详情：https://docs.docarray.org/user_guide/storing/index_hnswlib/
from docarray.index import HnswDocumentIndex
# initialize the index
db = HnswDocumentIndex[MyDoc](work_dir="hnsw_index")
# index data
db.index(
[
MyDoc(
title=f"My document {i}",
title_embedding=embeddings.embed_query(f"query {i}"),
year=i,
color=random.choice(["red", "green", "blue"]),
)
for i in range(100)
]
)
# optionally, you can create a filter query
filter_query = {"year": {"$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
WeaviateDocumentIndex 是一个建立在 Weaviate 向量数据库上的文档索引。
在此处了解详情：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 是一个建立在 ElasticSearch 上的文档索引。
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
QdrantDocumentIndex 是一个建立在 Qdrant 向量数据库上的文档索引。
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 进行电影检索
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": {"$eq": "Christopher Nolan"}},
top_k=2,
)
# find relevant documents
docs = retriever.invoke("space travel")
print(docs)
API 参考：DocArrayRetriever
[Document(page_content='Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.', metadata={'id': 'ab704cc7ae8573dc617f9a5e25df022a', 'title': 'Interstellar', 'rating': 8.6, 'director': 'Christopher Nolan'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'})]
带 MMR 搜索的检索器
from langchain_community.retrievers import DocArrayRetriever
# create a retriever
retriever = DocArrayRetriever(
index=db,
embeddings=embeddings,
search_field="description_embedding",
content_field="description",
filters={"rating": {"$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'})]