Databricks Vector Search

Databricks Vector Search is a vector database that is built into the Databricks Intelligence Platform and integrated with its governance and productivity tools. Full docs here: https://docs.databricks.com/en/generative-ai/vector-search.html

Install llama-index and databricks-vectorsearch. You must be inside a Databricks runtime to use the Vector Search python client.

%pip install llama-index llama-index-vector-stores-databricks
%pip install databricks-vectorsearch

Import databricks dependencies

from databricks.vector_search.client import (
    VectorSearchIndex,
    VectorSearchClient,
)

Import LlamaIndex dependencies

from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    ServiceContext,
    StorageContext,
)
from llama_index.vector_stores.databricks import DatabricksVectorSearch

Load example data

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

Read the data

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(f"Total documents: {len(documents)}")
print(f"First document, id: {documents[0].doc_id}")
print(f"First document, hash: {documents[0].hash}")
print(
    "First document, text"
    f" ({len(documents[0].text)} characters):\n{'='*20}\n{documents[0].text[:360]} ..."
)

Create a Databricks Vector Search endpoint which will serve the index

# Create a vector search endpoint
client = VectorSearchClient()
client.create_endpoint(
    name="llamaindex_dbx_vector_store_test_endpoint", endpoint_type="STANDARD"
)

Create the Databricks Vector Search index, and build it from the documents

# Create a vector search index
# it must be placed inside a Unity Catalog-enabled schema

# We'll use self-managed embeddings (i.e. managed by LlamaIndex) rather than a Databricks-managed index
databricks_index = client.create_direct_access_index(
    endpoint_name="llamaindex_dbx_vector_store_test_endpoint",
    index_name="my_catalog.my_schema.my_test_table",
    primary_key="my_primary_key_name",
    embedding_dimension=1536,  # match the embeddings model dimension you're going to use
    embedding_vector_column="my_embedding_vector_column_name",  # you name this anything you want - it'll be picked up by the LlamaIndex class
    schema={
        "my_primary_key_name": "string",
        "my_embedding_vector_column_name": "array<double>",
        "text": "string",  # one column must match the text_column in the DatabricksVectorSearch instance created below; this will hold the raw node text,
        "doc_id": "string",  # one column must contain the reference document ID (this will be populated by LlamaIndex automatically)
        # add any other metadata you may have in your nodes (Databricks Vector Search supports metadata filtering)
        # NOTE THAT THESE FIELDS MUST BE ADDED EXPLICITLY TO BE USED FOR METADATA FILTERING
    },
)

databricks_vector_store = DatabricksVectorSearch(
    index=databricks_index,
    text_column="text",
    columns=None,  # YOU MUST ALSO RECORD YOUR METADATA FIELD NAMES HERE
)  # text_column is required for self-managed embeddings
storage_context = StorageContext.from_defaults(
    vector_store=databricks_vector_store
)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

Query the index

query_engine = index.as_query_engine()
response = query_engine.query("Why did the author choose to work on AI?")

print(response.response)