Astra DB

DataStax Astra DB is a serverless AI-ready database built on Apache Cassandra® and made conveniently available through an easy-to-use JSON API.

In the walkthrough, we’ll demo the SelfQueryRetriever with an Astra DB vector store.

Creating an Astra DB vector store

First, create an Astra DB vector store and seed it with some data. We’ve created a small demo set of documents containing movie summaries. NOTE: The self-query retriever requires the lark package installed (pip install lark).

!pip install "langchain-astradb>=0.6,<0.7" \
  "langchain_openai>=0.3,<0.4" \
  "lark>=1.2,<2.0"

In this example, you’ll use the OpenAIEmbeddings. Please enter an OpenAI API Key.

import os
from getpass import getpass

from langchain_openai.embeddings import OpenAIEmbeddings

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

embeddings = OpenAIEmbeddings()

OpenAI API Key: ········

Create the Astra DB VectorStore:

the API Endpoint looks like https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com
the Token looks like AstraCS:aBcD0123...

ASTRA_DB_API_ENDPOINT = input("ASTRA_DB_API_ENDPOINT = ")
ASTRA_DB_APPLICATION_TOKEN = getpass("ASTRA_DB_APPLICATION_TOKEN = ")

ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com
ASTRA_DB_APPLICATION_TOKEN =  ········

from langchain_astradb import AstraDBVectorStore
from langchain_core.documents import Document

docs = [
    Document(
        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
    ),
    Document(
        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
        metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},
    ),
    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, "director": "Satoshi Kon", "rating": 8.6},
    ),
    Document(
        page_content="A bunch of normal-sized women are supremely wholesome and some men "
        "pine after them",
        metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
    ),
    Document(
        page_content="Toys come alive and have a blast doing so",
        metadata={"year": 1995, "genre": "animated"},
    ),
    Document(
        page_content="Three men walk into the Zone, three men walk out of the Zone",
        metadata={
            "year": 1979,
            "director": "Andrei Tarkovsky",
            "genre": "science fiction",
            "rating": 9.9,
        },
    ),
]

vectorstore = AstraDBVectorStore.from_documents(
    docs,
    embeddings,
    collection_name="astra_self_query_demo",
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)

Creating a self-querying retriever

Now you can instantiate the retriever. To do this, you need to provide some information upfront about the metadata fields that the documents support, along with a short description of the documents’ contents.

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 or list[string]",
    ),
    AttributeInfo(
        name="year",
        description="The year the movie was released",
        type="integer",
    ),
    AttributeInfo(
        name="director",
        description="The name of the movie director",
        type="string",
    ),
    AttributeInfo(
        name="rating", description="A 1-10 rating for the movie", type="float"
    ),
]
document_content_description = "Brief summary of a movie"
llm = OpenAI(temperature=0)

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

Testing it out

Now you can try actually using our retriever:

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

[Document(id='d7b9ec1edafa467caab524455e8c1f5d', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),
 Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so'),
 Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone'),
 Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]

# This example specifies a filter
retriever.invoke("I want to watch a movie rated higher than 8.5")

[Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone'),
 Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]

# This example only specifies a query and a filter
retriever.invoke("Has Greta Gerwig directed any movies about women")

[Document(id='0539843fd203484c9be486c2a0e2454c', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3}, page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them')]

# This example specifies a composite filter
retriever.invoke("What's a highly rated (above 8.5), science fiction movie ?")

[Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea'),
 Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone')]

# This example specifies a query and composite filter
retriever.invoke(
    "What's a movie about toys after 1990 but before 2005, and is animated"
)

[Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so')]

Set a limit (‘k’)

you can also use the self-query retriever to specify k, the number of documents to fetch. You achieve this by passing enable_limit=True to the constructor.

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

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

[Document(id='d7b9ec1edafa467caab524455e8c1f5d', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),
 Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so')]

Cleanup

If you want to completely delete the collection from your Astra DB instance, run this. (You will lose the data you stored in it.)

vectorstore.delete_collection()

快速开始

核心组件

进阶使用

在生产环境使用

Creating an Astra DB vector store

Creating a self-querying retriever

Testing it out

Set a limit (‘k’)

Cleanup

​Creating an Astra DB vector store

​Creating a self-querying retriever

​Testing it out

​Set a limit (‘k’)

​Cleanup

Creating an Astra DB vector store

Creating a self-querying retriever

Testing it out

Set a limit (‘k’)

Cleanup