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Astra DB

DataStax Astra DB is a serverless vector-capable database built on 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 we’ll want to create an Astra DB VectorStore and seed it with some data. We’ve created a small demo set of documents that contain summaries of movies.

NOTE: The self-query retriever requires you to have lark installed (pip install lark). We also need the astrapy package.

%pip install --upgrade --quiet lark astrapy langchain-openai

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import os
from getpass import getpass

from langchain_openai.embeddings import OpenAIEmbeddings

os.environ["OPENAI_API_KEY"] = getpass("OpenAI API Key:")

embeddings = OpenAIEmbeddings()

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:6gBhNmsk135....
ASTRA_DB_API_ENDPOINT = input("ASTRA_DB_API_ENDPOINT = ")
ASTRA_DB_APPLICATION_TOKEN = getpass("ASTRA_DB_APPLICATION_TOKEN = ")
from langchain.schema import Document
from langchain.vectorstores import AstraDB

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

Creating our self-querying retriever​

Now we can instantiate our retriever. To do this we’ll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.

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

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​

And now we can try actually using our retriever!

# This example only specifies a relevant query
retriever.get_relevant_documents("What are some movies about dinosaurs?")
# This example specifies a filter
retriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")
# This example only specifies a query and a filter
retriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")
# This example specifies a composite filter
retriever.get_relevant_documents(
"What's a highly rated (above 8.5), science fiction movie ?"
)
# This example specifies a query and composite filter
retriever.get_relevant_documents(
"What's a movie about toys after 1990 but before 2005, and is animated"
)

Filter k​

We can also use the self query retriever to specify k: the number of documents to fetch.

We can do this by passing enable_limit=True to the constructor.

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.get_relevant_documents("What are two movies about dinosaurs?")

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()