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Databricks Vector Search

Databricks Vector Search is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.

In the walkthrough, we'll demo the SelfQueryRetriever with a Databricks Vector Search.

create Databricks vector store index​

First we'll want to create a databricks vector store index 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) along with integration-specific requirements.

%pip install --upgrade --quiet  langchain-core databricks-vectorsearch langchain-openai tiktoken
Note: you may need to restart the kernel to use updated packages.

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

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
databricks_host = getpass.getpass("Databricks host:")
databricks_token = getpass.getpass("Databricks token:")
OpenAI API Key: ········
Databricks host: ········
Databricks token: ········
from databricks.vector_search.client import VectorSearchClient
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
emb_dim = len(embeddings.embed_query("hello"))

vector_search_endpoint_name = "vector_search_demo_endpoint"


vsc = VectorSearchClient(
    workspace_url=databricks_host, personal_access_token=databricks_token
)
vsc.create_endpoint(name=vector_search_endpoint_name, endpoint_type="STANDARD")
API Reference:OpenAIEmbeddings
[NOTICE] Using a Personal Authentication Token (PAT). Recommended for development only. For improved performance, please use Service Principal based authentication. To disable this message, pass disable_notice=True to VectorSearchClient().
index_name = "udhay_demo.10x.demo_index"

index = vsc.create_direct_access_index(
    endpoint_name=vector_search_endpoint_name,
    index_name=index_name,
    primary_key="id",
    embedding_dimension=emb_dim,
    embedding_vector_column="text_vector",
    schema={
        "id": "string",
        "page_content": "string",
        "year": "int",
        "rating": "float",
        "genre": "string",
        "text_vector": "array<float>",
    },
)

index.describe()
index = vsc.get_index(endpoint_name=vector_search_endpoint_name, index_name=index_name)

index.describe()
from langchain_core.documents import Document

docs = [
    Document(
        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
        metadata={"id": 1, "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={"id": 2, "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={"id": 3, "year": 2019, "rating": 8.3, "genre": "drama"},
    ),
    Document(
        page_content="Three men walk into the Zone, three men walk out of the Zone",
        metadata={"id": 4, "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={"id": 5, "year": 2006, "genre": "thriller", "rating": 9.0},
    ),
    Document(
        page_content="Toys come alive and have a blast doing so",
        metadata={"id": 6, "year": 1995, "genre": "animated", "rating": 9.3},
    ),
]
API Reference:Document
from langchain_community.vectorstores import DatabricksVectorSearch

vector_store = DatabricksVectorSearch(
    index,
    text_column="page_content",
    embedding=embeddings,
    columns=["year", "rating", "genre"],
)
vector_store.add_documents(docs)

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

Test it out​

And now we can try actually using our retriever!

# This example only specifies a relevant query
retriever.invoke("What are some movies about dinosaurs")
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993.0, 'rating': 7.7, 'genre': 'action', 'id': 1.0}),
 Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995.0, 'rating': 9.3, 'genre': 'animated', 'id': 6.0}),
 Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979.0, 'rating': 9.9, 'genre': 'science fiction', 'id': 4.0}),
 Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006.0, 'rating': 9.0, 'genre': 'thriller', 'id': 5.0})]
# This example specifies a filter
retriever.invoke("What are some highly rated movies (above 9)?")
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995.0, 'rating': 9.3, 'genre': 'animated', 'id': 6.0}),
 Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979.0, 'rating': 9.9, 'genre': 'science fiction', 'id': 4.0})]
# This example specifies both a relevant query and a filter
retriever.invoke("What are the thriller movies that are highly rated?")
[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006.0, 'rating': 9.0, 'genre': 'thriller', 'id': 5.0}),
 Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010.0, 'rating': 8.2, 'genre': 'thriller', 'id': 2.0})]
# 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"
)
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993.0, 'rating': 7.7, 'genre': 'action', 'id': 1.0})]

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.

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,
    vector_store,
    document_content_description,
    metadata_field_info,
    verbose=True,
    enable_limit=True,
)
retriever.invoke("What are two movies about dinosaurs?")

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