from __future__ import annotations
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from langchain_core._api import warn_deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VST, VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from databricks.vector_search.client import VectorSearchIndex
logger = logging.getLogger(__name__)
[docs]class DatabricksVectorSearch(VectorStore):
"""`Databricks Vector Search` vector store.
To use, you should have the ``databricks-vectorsearch`` python package installed.
Example:
.. code-block:: python
from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
endpoint_name="vs_endpoint",
index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(vs_index)
Args:
index: A Databricks Vector Search index object.
embedding: The embedding model.
Required for direct-access index or delta-sync index
with self-managed embeddings.
text_column: The name of the text column to use for the embeddings.
Required for direct-access index or delta-sync index
with self-managed embeddings.
Make sure the text column specified is in the index.
columns: The list of column names to get when doing the search.
Defaults to ``[primary_key, text_column]``.
Delta-sync index with Databricks-managed embeddings manages the ingestion, deletion,
and embedding for you.
Manually ingestion/deletion of the documents/texts is not supported for delta-sync
index.
If you want to use a delta-sync index with self-managed embeddings, you need to
provide the embedding model and text column name to use for the embeddings.
Example:
.. code-block:: python
from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
from langchain_community.embeddings.openai import OpenAIEmbeddings
vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
endpoint_name="vs_endpoint",
index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(
index=vs_index,
embedding=OpenAIEmbeddings(),
text_column="document_content"
)
If you want to manage the documents ingestion/deletion yourself, you can use a
direct-access index.
Example:
.. code-block:: python
from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
from langchain_community.embeddings.openai import OpenAIEmbeddings
vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
endpoint_name="vs_endpoint",
index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(
index=vs_index,
embedding=OpenAIEmbeddings(),
text_column="document_content"
)
vectorstore.add_texts(
texts=["text1", "text2"]
)
For more information on Databricks Vector Search, see `Databricks Vector Search
documentation: https://docs.databricks.com/en/generative-ai/vector-search.html.
"""
[docs] def __init__(
self,
index: VectorSearchIndex,
*,
embedding: Optional[Embeddings] = None,
text_column: Optional[str] = None,
columns: Optional[List[str]] = None,
):
try:
from databricks.vector_search.client import VectorSearchIndex
except ImportError as e:
raise ImportError(
"Could not import databricks-vectorsearch python package. "
"Please install it with `pip install databricks-vectorsearch`."
) from e
# index
self.index = index
if not isinstance(index, VectorSearchIndex):
raise TypeError("index must be of type VectorSearchIndex.")
# index_details
index_details = self.index.describe()
self.primary_key = index_details["primary_key"]
self.index_type = index_details.get("index_type")
self._delta_sync_index_spec = index_details.get("delta_sync_index_spec", dict())
self._direct_access_index_spec = index_details.get(
"direct_access_index_spec", dict()
)
# text_column
if self._is_databricks_managed_embeddings():
index_source_column = self._embedding_source_column_name()
# check if input text column matches the source column of the index
if text_column is not None and text_column != index_source_column:
raise ValueError(
f"text_column '{text_column}' does not match with the "
f"source column of the index: '{index_source_column}'."
)
self.text_column = index_source_column
else:
self._require_arg(text_column, "text_column")
self.text_column = text_column
# columns
self.columns = columns or []
# add primary key column and source column if not in columns
if self.primary_key not in self.columns:
self.columns.append(self.primary_key)
if self.text_column and self.text_column not in self.columns:
self.columns.append(self.text_column)
# Validate specified columns are in the index
if self._is_direct_access_index():
index_schema = self._index_schema()
if index_schema:
for col in self.columns:
if col not in index_schema:
raise ValueError(
f"column '{col}' is not in the index's schema."
)
# embedding model
if not self._is_databricks_managed_embeddings():
# embedding model is required for direct-access index
# or delta-sync index with self-managed embedding
self._require_arg(embedding, "embedding")
self._embedding = embedding
# validate dimension matches
index_embedding_dimension = self._embedding_vector_column_dimension()
if index_embedding_dimension is not None:
inferred_embedding_dimension = self._infer_embedding_dimension()
if inferred_embedding_dimension != index_embedding_dimension:
raise ValueError(
f"embedding model's dimension '{inferred_embedding_dimension}' "
f"does not match with the index's dimension "
f"'{index_embedding_dimension}'."
)
else:
if embedding is not None:
logger.warning(
"embedding model is not used in delta-sync index with "
"Databricks-managed embeddings."
)
self._embedding = None
[docs] @classmethod
def from_texts(
cls: Type[VST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict]] = None,
**kwargs: Any,
) -> VST:
raise NotImplementedError(
"`from_texts` is not supported. "
"Use `add_texts` to add to existing direct-access index."
)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict]] = None,
ids: Optional[List[Any]] = None,
**kwargs: Any,
) -> List[str]:
"""Add texts to the index.
Only support direct-access index.
Args:
texts: List of texts to add.
metadatas: List of metadata for each text. Defaults to None.
ids: List of ids for each text. Defaults to None.
If not provided, a random uuid will be generated for each text.
Returns:
List of ids from adding the texts into the index.
"""
self._op_require_direct_access_index("add_texts")
assert self.embeddings is not None, "embedding model is required."
# Wrap to list if input texts is a single string
if isinstance(texts, str):
texts = [texts]
texts = list(texts)
vectors = self.embeddings.embed_documents(texts)
ids = ids or [str(uuid.uuid4()) for _ in texts]
metadatas = metadatas or [{} for _ in texts]
updates = [
{
self.primary_key: id_,
self.text_column: text,
self._embedding_vector_column_name(): vector,
**metadata,
}
for text, vector, id_, metadata in zip(texts, vectors, ids, metadatas)
]
upsert_resp = self.index.upsert(updates)
if upsert_resp.get("status") in ("PARTIAL_SUCCESS", "FAILURE"):
failed_ids = upsert_resp.get("result", dict()).get(
"failed_primary_keys", []
)
if upsert_resp.get("status") == "FAILURE":
logger.error("Failed to add texts to the index.")
else:
logger.warning("Some texts failed to be added to the index.")
return [id_ for id_ in ids if id_ not in failed_ids]
return ids
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
return self._embedding
[docs] def delete(self, ids: Optional[List[Any]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete documents from the index.
Only support direct-access index.
Args:
ids: List of ids of documents to delete.
Returns:
True if successful.
"""
self._op_require_direct_access_index("delete")
if ids is None:
raise ValueError("ids must be provided.")
self.index.delete(ids)
return True
[docs] def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents most similar to the embedding.
"""
docs_with_score = self.similarity_search_with_score(
query=query,
k=k,
filter=filter,
query_type=query_type,
**kwargs,
)
return [doc for doc, _ in docs_with_score]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents most similar to the embedding and score for each.
"""
if self._is_databricks_managed_embeddings():
query_text = query
query_vector = None
else:
assert self.embeddings is not None, "embedding model is required."
# The value for `query_text` needs to be specified only for hybrid search.
if query_type is not None and query_type.upper() == "HYBRID":
query_text = query
else:
query_text = None
query_vector = self.embeddings.embed_query(query)
search_resp = self.index.similarity_search(
columns=self.columns,
query_text=query_text,
query_vector=query_vector,
filters=filter or _alias_filters(kwargs),
num_results=k,
query_type=query_type,
)
return self._parse_search_response(search_resp)
@staticmethod
def _identity_fn(score: float) -> float:
return score
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
Databricks Vector search uses a normalized score 1/(1+d) where d
is the L2 distance. Hence, we simply return the identity function.
"""
return self._identity_fn
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, Any]] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents selected by maximal marginal relevance.
"""
if not self._is_databricks_managed_embeddings():
assert self.embeddings is not None, "embedding model is required."
query_vector = self.embeddings.embed_query(query)
else:
raise ValueError(
"`max_marginal_relevance_search` is not supported for index with "
"Databricks-managed embeddings."
)
docs = self.max_marginal_relevance_search_by_vector(
query_vector,
k,
fetch_k,
lambda_mult=lambda_mult,
filter=filter or _alias_filters(kwargs),
query_type=query_type,
)
return docs
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Any] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents selected by maximal marginal relevance.
"""
if not self._is_databricks_managed_embeddings():
embedding_column = self._embedding_vector_column_name()
else:
raise ValueError(
"`max_marginal_relevance_search` is not supported for index with "
"Databricks-managed embeddings."
)
search_resp = self.index.similarity_search(
columns=list(set(self.columns + [embedding_column])),
query_text=None,
query_vector=embedding,
filters=filter or _alias_filters(kwargs),
num_results=fetch_k,
query_type=query_type,
)
embeddings_result_index = (
search_resp.get("manifest").get("columns").index({"name": embedding_column})
)
embeddings = [
doc[embeddings_result_index]
for doc in search_resp.get("result").get("data_array")
]
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
ignore_cols: List = (
[embedding_column] if embedding_column not in self.columns else []
)
candidates = self._parse_search_response(search_resp, ignore_cols=ignore_cols)
selected_results = [r[0] for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Any] = None,
*,
query_type: Optional[str] = None,
query: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents most similar to the embedding.
"""
docs_with_score = self.similarity_search_by_vector_with_score(
embedding=embedding,
k=k,
filter=filter,
query_type=query_type,
query=query,
**kwargs,
)
return [doc for doc, _ in docs_with_score]
[docs] def similarity_search_by_vector_with_score(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Any] = None,
*,
query_type: Optional[str] = None,
query: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector, along with scores.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents most similar to the embedding and score for each.
"""
if self._is_databricks_managed_embeddings():
raise ValueError(
"`similarity_search_by_vector` is not supported for index with "
"Databricks-managed embeddings."
)
if query_type is not None and query_type.upper() == "HYBRID":
if query is None:
raise ValueError(
"A value for `query` must be specified for hybrid search."
)
query_text = query
else:
if query is not None:
raise ValueError(
(
"Cannot specify both `embedding` and "
'`query` unless `query_type="HYBRID"'
)
)
query_text = None
search_resp = self.index.similarity_search(
columns=self.columns,
query_vector=embedding,
query_text=query_text,
filters=filter or _alias_filters(kwargs),
num_results=k,
query_type=query_type,
)
return self._parse_search_response(search_resp)
def _parse_search_response(
self, search_resp: Dict, ignore_cols: Optional[List[str]] = None
) -> List[Tuple[Document, float]]:
"""Parse the search response into a list of Documents with score."""
if ignore_cols is None:
ignore_cols = []
columns = [
col["name"]
for col in search_resp.get("manifest", dict()).get("columns", [])
]
docs_with_score = []
for result in search_resp.get("result", dict()).get("data_array", []):
doc_id = result[columns.index(self.primary_key)]
text_content = result[columns.index(self.text_column)]
metadata = {
col: value
for col, value in zip(columns[:-1], result[:-1])
if col not in ([self.primary_key, self.text_column] + ignore_cols)
}
metadata[self.primary_key] = doc_id
score = result[-1]
doc = Document(page_content=text_content, metadata=metadata)
docs_with_score.append((doc, score))
return docs_with_score
def _index_schema(self) -> Optional[Dict]:
"""Return the index schema as a dictionary.
Return None if no schema found.
"""
if self._is_direct_access_index():
schema_json = self._direct_access_index_spec.get("schema_json")
if schema_json is not None:
return json.loads(schema_json)
return None
def _embedding_vector_column_name(self) -> Optional[str]:
"""Return the name of the embedding vector column.
None if the index is not a self-managed embedding index.
"""
return self._embedding_vector_column().get("name")
def _embedding_vector_column_dimension(self) -> Optional[int]:
"""Return the dimension of the embedding vector column.
None if the index is not a self-managed embedding index.
"""
return self._embedding_vector_column().get("embedding_dimension")
def _embedding_vector_column(self) -> Dict:
"""Return the embedding vector column configs as a dictionary.
Empty if the index is not a self-managed embedding index.
"""
index_spec = (
self._delta_sync_index_spec
if self._is_delta_sync_index()
else self._direct_access_index_spec
)
return next(iter(index_spec.get("embedding_vector_columns") or list()), dict())
def _embedding_source_column_name(self) -> Optional[str]:
"""Return the name of the embedding source column.
None if the index is not a Databricks-managed embedding index.
"""
return self._embedding_source_column().get("name")
def _embedding_source_column(self) -> Dict:
"""Return the embedding source column configs as a dictionary.
Empty if the index is not a Databricks-managed embedding index.
"""
index_spec = self._delta_sync_index_spec
return next(iter(index_spec.get("embedding_source_columns") or list()), dict())
def _is_delta_sync_index(self) -> bool:
"""Return True if the index is a delta-sync index."""
return self.index_type == "DELTA_SYNC"
def _is_direct_access_index(self) -> bool:
"""Return True if the index is a direct-access index."""
return self.index_type == "DIRECT_ACCESS"
def _is_databricks_managed_embeddings(self) -> bool:
"""Return True if the embeddings are managed by Databricks Vector Search."""
return (
self._is_delta_sync_index()
and self._embedding_source_column_name() is not None
)
def _infer_embedding_dimension(self) -> int:
"""Infer the embedding dimension from the embedding function."""
assert self.embeddings is not None, "embedding model is required."
return len(self.embeddings.embed_query("test"))
def _op_require_direct_access_index(self, op_name: str) -> None:
"""
Raise ValueError if the operation is not supported for direct-access index."""
if not self._is_direct_access_index():
raise ValueError(f"`{op_name}` is only supported for direct-access index.")
@staticmethod
def _require_arg(arg: Any, arg_name: str) -> None:
"""Raise ValueError if the required arg with name `arg_name` is None."""
if not arg:
raise ValueError(f"`{arg_name}` is required for this index.")
def _alias_filters(kwargs: Dict[str, Any]) -> Optional[Dict[str, Any]]:
"""
The `filters` argument was used in the previous versions. It is now
replaced with `filter` for consistency with other vector stores, but
we still support `filters` for backward compatibility.
"""
if "filters" in kwargs:
warn_deprecated(
since="0.2.11",
removal="1.0",
message="DatabricksVectorSearch received a key `filters` in search_kwargs. "
"`filters` was deprecated since langchain-community 0.2.11 and will "
"be removed in 0.3. Please use `filter` instead.",
)
return kwargs.pop("filters", None)
