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413 | class DatabricksVectorSearch(BasePydanticVectorStore):
"""Vector store for Databricks Vector Search.
在Databricks笔记本中使用以下命令安装``databricks-vectorsearch``包：
%pip install databricks-vectorsearch
dbutils.library.restartPython()"""
stores_text: bool = True
text_column: Optional[str]
columns: Optional[List[str]]
_index: VectorSearchIndex = PrivateAttr()
_primary_key: str = PrivateAttr()
_index_type: str = PrivateAttr()
_delta_sync_index_spec: dict = PrivateAttr()
_direct_access_index_spec: dict = PrivateAttr()
_doc_id_to_pk: dict = PrivateAttr()
def __init__(
self,
index: VectorSearchIndex,
text_column: Optional[str] = None,
columns: Optional[List[str]] = None,
) -> None:
try:
from databricks.vector_search.client import VectorSearchIndex
except ImportError:
raise ImportError(
"`databricks-vectorsearch` package not found: "
"please run `pip install databricks-vectorsearch`"
)
if not isinstance(index, VectorSearchIndex):
raise TypeError(
f"index must be of type `VectorSearchIndex`, not {type(index)}"
)
self._index = index
# unpack the index spec
index_description = _DatabricksIndexDescription.parse_obj(
self._index.describe()
)
self._primary_key = index_description.primary_key
self._index_type = index_description.index_type
self._delta_sync_index_spec = index_description.delta_sync_index_spec
self._direct_access_index_spec = index_description.direct_access_index_spec
self._doc_id_to_pk = {}
if columns is None:
columns = []
if "doc_id" not in columns:
columns = columns[:19] + ["doc_id"]
super().__init__(
text_column=text_column,
columns=columns,
)
# initialize the column name for the text column in the delta table
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:
if text_column is None:
raise ValueError("text_column is required for self-managed embeddings.")
self.text_column = text_column
# Fold primary key and text column into columns if they're not empty.
columns_to_add = set(columns or [])
columns_to_add.add(self._primary_key)
columns_to_add.add(self.text_column)
columns_to_add -= {"", None}
self.columns = list(columns_to_add)
# If the index schema is known, all our columns should be in that index.
# Validate specified columns are in the index
index_schema = self._index_schema()
if self._is_direct_access_index() and index_schema:
missing_columns = columns_to_add - set(index_schema.keys())
if missing_columns:
raise ValueError(
f"columns missing from schema: {', '.join(missing_columns)}"
)
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
"""将节点添加到索引中。
Args:
节点: List[BaseNode]: 带有嵌入的节点列表
"""
if self._is_databricks_managed_embeddings():
raise ValueError(
"Adding nodes is not supported for Databricks-managed embeddings."
)
# construct the entries to upsert
entries = []
ids = []
for node in nodes:
node_id = node.node_id
metadata = node_to_metadata_dict(node, remove_text=True, flat_metadata=True)
metadata_columns = self.columns or []
# explicitly record doc_id as metadata (for delete)
if "doc_id" not in metadata_columns:
metadata_columns.append("doc_id")
entry = {
self._primary_key: node_id,
self.text_column: node.get_content(),
self._embedding_vector_column_name(): node.get_embedding(),
**{
col: metadata.get(col)
for col in filter(
lambda column: column
not in (self._primary_key, self.text_column),
metadata_columns,
)
},
}
doc_id = metadata.get("doc_id")
self._doc_id_to_pk[doc_id] = list(
set(self._doc_id_to_pk.get(doc_id, []) + [node_id]) # noqa: RUF005
) # associate this node_id with this doc_id
entries.append(entry)
ids.append(node_id)
# attempt the upsert
upsert_resp = self._index.upsert(
entries,
)
# return the successful IDs
response_status = upsert_resp.get("status")
failed_ids = (
set(upsert_resp["result"]["failed_primary_keys"] or [])
if "result" in upsert_resp
and "failed_primary_keys" in upsert_resp["result"]
else set()
)
if response_status not in ("PARTIAL_SUCCESS", "FAILURE") or not failed_ids:
return ids
elif response_status == "PARTIAL_SUCCESS":
_logger.warning(
"failed to add %d out of %d texts to the index",
len(failed_ids),
len(ids),
)
elif response_status == "FAILURE":
_logger.error("failed to add all %d texts to the index", len(ids))
return list(filter(lambda id_: id_ not in failed_ids, ids))
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""删除具有ref_doc_id的节点。
Args:
ref_doc_id (str): 要删除的文档的doc_id。
"""
primary_keys = self._doc_id_to_pk.get(
ref_doc_id, None
) # get the node_ids associated with the doc_id
if primary_keys is not None:
self._index.delete(
primary_keys=primary_keys,
)
self._doc_id_to_pk.pop(
ref_doc_id
) # remove this doc_id from the doc_id-to-node_id map
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
"""查询前k个最相似节点的索引。"""
if self._is_databricks_managed_embeddings():
query_text = query.query_str
query_vector = None
else:
query_text = None
query_vector = cast(List[float], query.query_embedding)
if query.mode not in (
VectorStoreQueryMode.DEFAULT,
VectorStoreQueryMode.HYBRID,
):
raise ValueError(
"Only DEFAULT and HYBRID modes are supported for Databricks Vector Search."
)
if query.filters is not None:
filters = _to_databricks_filter(query.filters)
else:
filters = None
search_resp = self._index.similarity_search(
columns=self.columns,
query_text=query_text,
query_vector=query_vector,
filters=filters,
num_results=query.similarity_top_k,
)
columns = [
col["name"] for col in search_resp.get("manifest", {}).get("columns", [])
]
top_k_nodes = []
top_k_ids = []
top_k_scores = []
for result in search_resp.get("result", {}).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]
}
metadata[self._primary_key] = doc_id
score = result[-1]
node = TextNode(
text=text_content, id_=doc_id, metadata=metadata
) # TODO star_char, end_char, relationships? https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py
top_k_ids.append(doc_id)
top_k_nodes.append(node)
top_k_scores.append(score)
return VectorStoreQueryResult(
nodes=top_k_nodes, similarities=top_k_scores, ids=top_k_ids
)
@property
def client(self) -> Any:
"""返回VectorStoreIndex。"""
return self._index
# The remaining utilities (and snippets of the above) are taken from
# https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/vectorstores/databricks_vector_search.py
def _index_schema(self) -> Optional[dict]:
"""返回索引模式作为字典。
如果未找到模式，则返回None。
"""
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]:
"""返回嵌入向量列的名称。
如果索引不是自管理的嵌入索引，则返回None。
"""
return self._embedding_vector_column().get("name")
def _embedding_vector_column(self) -> dict:
"""返回嵌入向量列配置的字典。
如果索引不是自管理的嵌入索引，则为空。
"""
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 []), {})
def _embedding_source_column_name(self) -> Optional[str]:
"""返回嵌入源列的名称。
如果索引不是由Databricks管理的嵌入索引，则返回None。
"""
return self._embedding_source_column().get("name")
def _embedding_source_column(self) -> dict:
"""返回嵌入源列配置的字典。
如果索引不是由Databricks管理的嵌入索引，则为空。
"""
return next(
iter(self._delta_sync_index_spec.get("embedding_source_columns") or []),
{},
)
def _is_delta_sync_index(self) -> bool:
"""如果索引是增量同步索引，则返回True。"""
return self._index_type == _DatabricksIndexType.DELTA_SYNC
def _is_direct_access_index(self) -> bool:
"""如果索引是直接访问索引，则返回True。"""
return self._index_type == _DatabricksIndexType.DIRECT_ACCESS
def _is_databricks_managed_embeddings(self) -> bool:
"""如果嵌入由Databricks Vector Search 管理，则返回True。"""
return (
self._is_delta_sync_index()
and self._embedding_source_column_name() is not None
)
|