MongoDB Atlas

MongoDB Atlas 是一個文件資料庫，可用作向量資料庫。

在逐步解說中，我們將示範搭配 MongoDB Atlas 向量儲存區的 SelfQueryRetriever。

建立 MongoDB Atlas 向量儲存區

首先，我們會想要建立 MongoDB Atlas VectorStore，並使用一些資料來植入它。我們建立了一小組示範文件，其中包含電影摘要。

注意：自我查詢檢索器需要您安裝 lark (pip install lark)。我們也需要 pymongo 套件。

%pip install --upgrade --quiet  lark pymongo

我們要使用 OpenAIEmbeddings，因此我們必須取得 OpenAI API 金鑰。

import os

OPENAI_API_KEY = "Use your OpenAI key"

os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
from pymongo import MongoClient

CONNECTION_STRING = "Use your MongoDB Atlas connection string"
DB_NAME = "Name of your MongoDB Atlas database"
COLLECTION_NAME = "Name of your collection in the database"
INDEX_NAME = "Name of a search index defined on the collection"

MongoClient = MongoClient(CONNECTION_STRING)
collection = MongoClient[DB_NAME][COLLECTION_NAME]

embeddings = OpenAIEmbeddings()

API 參考：MongoDBAtlasVectorSearch | Document | OpenAIEmbeddings

docs = [
    Document(
        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
        metadata={"year": 1993, "rating": 7.7, "genre": "action"},
    ),
    Document(
        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
        metadata={"year": 2010, "genre": "thriller", "rating": 8.2},
    ),
    Document(
        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
        metadata={"year": 2019, "rating": 8.3, "genre": "drama"},
    ),
    Document(
        page_content="Three men walk into the Zone, three men walk out of the Zone",
        metadata={"year": 1979, "rating": 9.9, "genre": "science fiction"},
    ),
    Document(
        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",
        metadata={"year": 2006, "genre": "thriller", "rating": 9.0},
    ),
    Document(
        page_content="Toys come alive and have a blast doing so",
        metadata={"year": 1995, "genre": "animated", "rating": 9.3},
    ),
]

vectorstore = MongoDBAtlasVectorSearch.from_documents(
    docs,
    embeddings,
    collection=collection,
    index_name=INDEX_NAME,
)

現在，讓我們在您的叢集上建立向量搜尋索引。在以下範例中，embedding 是包含嵌入向量的欄位名稱。請參閱文件，以取得關於如何定義 Atlas Vector Search 索引的更多詳細資訊。您可以將索引命名為 {COLLECTION_NAME}，並在命名空間 {DB_NAME}.{COLLECTION_NAME} 上建立索引。最後，在 MongoDB Atlas 上的 JSON 編輯器中寫入以下定義

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "embedding": {
        "dimensions": 1536,
        "similarity": "cosine",
        "type": "knnVector"
      },
      "genre": {
        "type": "token"
      },
      "ratings": {
        "type": "number"
      },
      "year": {
        "type": "number"
      }
    }
  }
}

建立我們的自我查詢檢索器

現在我們可以實例化我們的檢索器。為此，我們需要預先提供一些關於我們的文件支援的中繼資料欄位資訊，以及文件內容的簡短描述。

from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import OpenAI

metadata_field_info = [
    AttributeInfo(
        name="genre",
        description="The genre of the movie",
        type="string",
    ),
    AttributeInfo(
        name="year",
        description="The year the movie was released",
        type="integer",
    ),
    AttributeInfo(
        name="rating", description="A 1-10 rating for the movie", type="float"
    ),
]
document_content_description = "Brief summary of a movie"

API 參考：AttributeInfo | SelfQueryRetriever | OpenAI

llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
    llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)

測試它

現在我們可以嘗試實際使用我們的檢索器！

# This example only specifies a relevant query
retriever.invoke("What are some movies about dinosaurs")

# This example specifies a filter
retriever.invoke("What are some highly rated movies (above 9)?")

# This example only specifies a query and a filter
retriever.invoke("I want to watch a movie about toys rated higher than 9")

# This example specifies a composite filter
retriever.invoke("What's a highly rated (above or equal 9) thriller film?")

# This example specifies a query and composite filter
retriever.invoke(
    "What's a movie after 1990 but before 2005 that's all about dinosaurs, \
    and preferably has a lot of action"
)

篩選 k

我們也可以使用自我查詢檢索器來指定 k：要提取的文件數量。

我們可以透過將 enable_limit=True 傳遞給建構函式來做到這一點。

retriever = SelfQueryRetriever.from_llm(
    llm,
    vectorstore,
    document_content_description,
    metadata_field_info,
    verbose=True,
    enable_limit=True,
)

# This example only specifies a relevant query
retriever.invoke("What are two movies about dinosaurs?")