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1146 | class AzureAISearchVectorStore(BasePydanticVectorStore):
"""
Azure AI Search vector store.
Examples:
`pip install llama-index-vector-stores-azureaisearch`
```python
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore
from llama_index.vector_stores.azureaisearch import IndexManagement, MetadataIndexFieldType
# Azure AI Search setup
search_service_api_key = "YOUR-AZURE-SEARCH-SERVICE-ADMIN-KEY"
search_service_endpoint = "YOUR-AZURE-SEARCH-SERVICE-ENDPOINT"
search_service_api_version = "2024-07-01"
credential = AzureKeyCredential(search_service_api_key)
# Index name to use
index_name = "llamaindex-vector-demo"
# Use index client to demonstrate creating an index
index_client = SearchIndexClient(
endpoint=search_service_endpoint,
credential=credential,
)
metadata_fields = {
"author": "author",
"theme": ("topic", MetadataIndexFieldType.STRING),
"director": "director",
}
# Creating an Azure AI Search Vector Store
vector_store = AzureAISearchVectorStore(
search_or_index_client=index_client,
filterable_metadata_field_keys=metadata_fields,
index_name=index_name,
index_management=IndexManagement.CREATE_IF_NOT_EXISTS,
id_field_key="id",
chunk_field_key="chunk",
embedding_field_key="embedding",
embedding_dimensionality=1536,
metadata_string_field_key="metadata",
doc_id_field_key="doc_id",
language_analyzer="en.lucene",
vector_algorithm_type="exhaustiveKnn",
)
```
"""
stores_text: bool = True
flat_metadata: bool = False
_index_client: SearchIndexClient = PrivateAttr()
_index_name: Optional[str] = PrivateAttr()
_async_index_client: AsyncSearchIndexClient = PrivateAttr()
_search_client: SearchClient = PrivateAttr()
_async_search_client: AsyncSearchClient = PrivateAttr()
_embedding_dimensionality: int = PrivateAttr()
_language_analyzer: str = PrivateAttr()
_field_mapping: Dict[str, str] = PrivateAttr()
_index_management: IndexManagement = PrivateAttr()
_index_mapping: Callable[
[Dict[str, str], Dict[str, Any]], Dict[str, str]
] = PrivateAttr()
_metadata_to_index_field_map: Dict[
str, Tuple[str, MetadataIndexFieldType]
] = PrivateAttr()
_vector_profile_name: str = PrivateAttr()
_compression_type: str = PrivateAttr()
def _normalise_metadata_to_index_fields(
self,
filterable_metadata_field_keys: Union[
List[str],
Dict[str, str],
Dict[str, Tuple[str, MetadataIndexFieldType]],
None,
] = [],
) -> Dict[str, Tuple[str, MetadataIndexFieldType]]:
index_field_spec: Dict[str, Tuple[str, MetadataIndexFieldType]] = {}
if isinstance(filterable_metadata_field_keys, List):
for field in filterable_metadata_field_keys:
# Index field name and the metadata field name are the same
# Use String as the default index field type
index_field_spec[field] = (field, MetadataIndexFieldType.STRING)
elif isinstance(filterable_metadata_field_keys, dict):
for k, v in filterable_metadata_field_keys.items():
if isinstance(v, tuple):
# Index field name and metadata field name may differ
# The index field type used is as supplied
index_field_spec[k] = v
elif isinstance(v, list):
# Handle list types as COLLECTION
index_field_spec[k] = (k, MetadataIndexFieldType.COLLECTION)
elif isinstance(v, bool):
index_field_spec[k] = (k, MetadataIndexFieldType.BOOLEAN)
elif isinstance(v, int):
index_field_spec[k] = (k, MetadataIndexFieldType.INT32)
elif isinstance(v, float):
index_field_spec[k] = (k, MetadataIndexFieldType.DOUBLE)
elif isinstance(v, str):
index_field_spec[k] = (k, MetadataIndexFieldType.STRING)
else:
# Index field name and metadata field name may differ
# Use String as the default index field type
index_field_spec[k] = (v, MetadataIndexFieldType.STRING)
return index_field_spec
def _index_exists(self, index_name: str) -> bool:
return index_name in self._index_client.list_index_names()
async def _aindex_exists(self, index_name: str) -> bool:
return index_name in [
name async for name in self._async_index_client.list_index_names()
]
def _create_index_if_not_exists(self, index_name: str) -> None:
if not self._index_exists(index_name):
logger.info(
f"Index {index_name} does not exist in Azure AI Search, creating index"
)
self._create_index(index_name)
async def _acreate_index_if_not_exists(self, index_name: str) -> None:
if not await self._aindex_exists(index_name):
logger.info(
f"Index {index_name} does not exist in Azure AI Search, creating index"
)
await self._acreate_index(index_name)
def _create_metadata_index_fields(self) -> List[Any]:
"""Create a list of index fields for storing metadata values."""
from azure.search.documents.indexes.models import SimpleField
index_fields = []
# create search fields
for v in self._metadata_to_index_field_map.values():
field_name, field_type = v
# Skip if the field is already mapped
if field_name in self._field_mapping.values():
continue
if field_type == MetadataIndexFieldType.STRING:
index_field_type = "Edm.String"
elif field_type == MetadataIndexFieldType.INT32:
index_field_type = "Edm.Int32"
elif field_type == MetadataIndexFieldType.INT64:
index_field_type = "Edm.Int64"
elif field_type == MetadataIndexFieldType.DOUBLE:
index_field_type = "Edm.Double"
elif field_type == MetadataIndexFieldType.BOOLEAN:
index_field_type = "Edm.Boolean"
elif field_type == MetadataIndexFieldType.COLLECTION:
index_field_type = "Collection(Edm.String)"
field = SimpleField(name=field_name, type=index_field_type, filterable=True)
index_fields.append(field)
return index_fields
def _get_compressions(self) -> List[Any]:
"""Get the compressions for the vector search."""
from azure.search.documents.indexes.models import (
BinaryQuantizationCompression,
ScalarQuantizationCompression,
)
compressions = []
if self._compression_type == "binary":
compressions.append(
BinaryQuantizationCompression(compression_name="myBinaryCompression")
)
elif self._compression_type == "scalar":
compressions.append(
ScalarQuantizationCompression(compression_name="myScalarCompression")
)
return compressions
def _create_index(self, index_name: Optional[str]) -> None:
"""
Creates a default index based on the supplied index name, key field names and
metadata filtering keys.
"""
from azure.search.documents.indexes.models import (
ExhaustiveKnnAlgorithmConfiguration,
ExhaustiveKnnParameters,
HnswAlgorithmConfiguration,
HnswParameters,
SearchableField,
SearchField,
SearchFieldDataType,
SearchIndex,
SemanticConfiguration,
SemanticField,
SemanticPrioritizedFields,
SemanticSearch,
SimpleField,
VectorSearch,
VectorSearchAlgorithmKind,
VectorSearchAlgorithmMetric,
VectorSearchProfile,
)
logger.info(f"Configuring {index_name} fields for Azure AI Search")
fields = [
SimpleField(name=self._field_mapping["id"], type="Edm.String", key=True),
SearchableField(
name=self._field_mapping["chunk"],
type="Edm.String",
analyzer_name=self._language_analyzer,
),
SearchField(
name=self._field_mapping["embedding"],
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=self._embedding_dimensionality,
vector_search_profile_name=self._vector_profile_name,
),
SimpleField(name=self._field_mapping["metadata"], type="Edm.String"),
SimpleField(
name=self._field_mapping["doc_id"], type="Edm.String", filterable=True
),
]
logger.info(f"Configuring {index_name} metadata fields")
metadata_index_fields = self._create_metadata_index_fields()
fields.extend(metadata_index_fields)
logger.info(f"Configuring {index_name} vector search")
# Determine the compression type
compressions = self._get_compressions()
logger.info(
f"Configuring {index_name} vector search with {self._compression_type} compression"
)
# Configure the vector search algorithms and profiles
vector_search = VectorSearch(
algorithms=[
HnswAlgorithmConfiguration(
name="myHnsw",
kind=VectorSearchAlgorithmKind.HNSW,
parameters=HnswParameters(
m=4,
ef_construction=400,
ef_search=500,
metric=VectorSearchAlgorithmMetric.COSINE,
),
),
ExhaustiveKnnAlgorithmConfiguration(
name="myExhaustiveKnn",
kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
parameters=ExhaustiveKnnParameters(
metric=VectorSearchAlgorithmMetric.COSINE,
),
),
],
compressions=compressions,
profiles=[
VectorSearchProfile(
name="myHnswProfile",
algorithm_configuration_name="myHnsw",
compression_name=(
compressions[0].compression_name if compressions else None
),
),
VectorSearchProfile(
name="myExhaustiveKnnProfile",
algorithm_configuration_name="myExhaustiveKnn",
compression_name=None, # Exhaustive KNN doesn't support compression at the moment
),
],
)
logger.info(f"Configuring {index_name} semantic search")
semantic_config = SemanticConfiguration(
name="mySemanticConfig",
prioritized_fields=SemanticPrioritizedFields(
content_fields=[SemanticField(field_name=self._field_mapping["chunk"])],
),
)
semantic_search = SemanticSearch(configurations=[semantic_config])
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search,
semantic_search=semantic_search,
)
logger.debug(f"Creating {index_name} search index")
self._index_client.create_index(index)
async def _acreate_index(self, index_name: Optional[str]) -> None:
"""
Asynchronous version of index creation with optional compression.
Creates a default index based on the supplied index name, key field names, and metadata filtering keys.
"""
from azure.search.documents.indexes.models import (
ExhaustiveKnnAlgorithmConfiguration,
ExhaustiveKnnParameters,
HnswAlgorithmConfiguration,
HnswParameters,
SearchField,
SearchFieldDataType,
SearchIndex,
SearchableField,
SemanticConfiguration,
SemanticField,
SemanticPrioritizedFields,
SemanticSearch,
SimpleField,
VectorSearch,
VectorSearchAlgorithmKind,
VectorSearchAlgorithmMetric,
VectorSearchProfile,
)
logger.info(f"Configuring {index_name} fields for Azure AI Search")
fields = [
SimpleField(name=self._field_mapping["id"], type="Edm.String", key=True),
SearchableField(
name=self._field_mapping["chunk"],
type="Edm.String",
analyzer_name=self._language_analyzer,
),
SearchField(
name=self._field_mapping["embedding"],
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=self._embedding_dimensionality,
vector_search_profile_name=self._vector_profile_name,
),
SimpleField(name=self._field_mapping["metadata"], type="Edm.String"),
SimpleField(
name=self._field_mapping["doc_id"], type="Edm.String", filterable=True
),
]
logger.info(f"Configuring {index_name} metadata fields")
metadata_index_fields = self._create_metadata_index_fields()
fields.extend(metadata_index_fields)
# Determine the compression type
compressions = self._get_compressions()
logger.info(
f"Configuring {index_name} vector search with {self._compression_type} compression"
)
# Configure the vector search algorithms and profiles
vector_search = VectorSearch(
algorithms=[
HnswAlgorithmConfiguration(
name="myHnsw",
kind=VectorSearchAlgorithmKind.HNSW,
# For more information on HNSw parameters, visit https://learn.microsoft.com//azure/search/vector-search-ranking#creating-the-hnsw-graph
parameters=HnswParameters(
m=4,
ef_construction=400,
ef_search=500,
metric=VectorSearchAlgorithmMetric.COSINE,
),
),
ExhaustiveKnnAlgorithmConfiguration(
name="myExhaustiveKnn",
kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
parameters=ExhaustiveKnnParameters(
metric=VectorSearchAlgorithmMetric.COSINE,
),
),
],
compressions=compressions,
profiles=[
VectorSearchProfile(
name="myHnswProfile",
algorithm_configuration_name="myHnsw",
compression_name=(
compressions[0].compression_name if compressions else None
),
),
VectorSearchProfile(
name="myExhaustiveKnnProfile",
algorithm_configuration_name="myExhaustiveKnn",
compression_name=None, # Exhaustive KNN doesn't support compression at the moment
),
],
)
logger.info(f"Configuring {index_name} semantic search")
semantic_config = SemanticConfiguration(
name="mySemanticConfig",
prioritized_fields=SemanticPrioritizedFields(
content_fields=[SemanticField(field_name=self._field_mapping["chunk"])],
),
)
semantic_search = SemanticSearch(configurations=[semantic_config])
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search,
semantic_search=semantic_search,
)
logger.debug(f"Creating {index_name} search index")
await self._async_index_client.create_index(index)
def _validate_index(self, index_name: Optional[str]) -> None:
if self._index_client and index_name and not self._index_exists(index_name):
raise ValueError(f"Validation failed, index {index_name} does not exist.")
async def _avalidate_index(self, index_name: Optional[str]) -> None:
if (
self._async_index_client
and index_name
and not await self._aindex_exists(index_name)
):
raise ValueError(f"Validation failed, index {index_name} does not exist.")
def __init__(
self,
search_or_index_client: Any,
id_field_key: str,
chunk_field_key: str,
embedding_field_key: str,
metadata_string_field_key: str,
doc_id_field_key: str,
filterable_metadata_field_keys: Optional[
Union[
List[str],
Dict[str, str],
Dict[str, Tuple[str, MetadataIndexFieldType]],
]
] = None,
index_name: Optional[str] = None,
index_mapping: Optional[
Callable[[Dict[str, str], Dict[str, Any]], Dict[str, str]]
] = None,
index_management: IndexManagement = IndexManagement.NO_VALIDATION,
embedding_dimensionality: int = 1536,
vector_algorithm_type: str = "exhaustiveKnn",
# If we have content in other languages, it is better to enable the language analyzer to be adjusted in searchable fields.
# https://learn.microsoft.com/en-us/azure/search/index-add-language-analyzers
language_analyzer: str = "en.lucene",
compression_type: str = "none",
**kwargs: Any,
) -> None:
# ruff: noqa: E501
"""
Embeddings and documents are stored in an Azure AI Search index,
a merge or upload approach is used when adding embeddings.
When adding multiple embeddings the index is updated by this vector store
in batches of 10 documents, very large nodes may result in failure due to
the batch byte size being exceeded.
Args:
search_client (azure.search.documents.SearchClient):
Client for index to populated / queried.
id_field_key (str): Index field storing the id
chunk_field_key (str): Index field storing the node text
embedding_field_key (str): Index field storing the embedding vector
metadata_string_field_key (str):
Index field storing node metadata as a json string.
Schema is arbitrary, to filter on metadata values they must be stored
as separate fields in the index, use filterable_metadata_field_keys
to specify the metadata values that should be stored in these filterable fields
doc_id_field_key (str): Index field storing doc_id
index_mapping:
Optional function with definition
(enriched_doc: Dict[str, str], metadata: Dict[str, Any]): Dict[str,str]
used to map document fields to the AI search index fields
(return value of function).
If none is specified a default mapping is provided which uses
the field keys. The keys in the enriched_doc are
["id", "chunk", "embedding", "metadata"]
The default mapping is:
- "id" to id_field_key
- "chunk" to chunk_field_key
- "embedding" to embedding_field_key
- "metadata" to metadata_field_key
*kwargs (Any): Additional keyword arguments.
Raises:
ImportError: Unable to import `azure-search-documents`
ValueError: If `search_or_index_client` is not provided
ValueError: If `index_name` is not provided and `search_or_index_client`
is of type azure.search.documents.SearchIndexClient
ValueError: If `index_name` is provided and `search_or_index_client`
is of type azure.search.documents.SearchClient
ValueError: If `create_index_if_not_exists` is true and
`search_or_index_client` is of type azure.search.documents.SearchClient
"""
import_err_msg = (
"`azure-search-documents` package not found, please run "
"`pip install azure-search-documents==11.4.0`"
)
try:
import azure.search.documents # noqa
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
except ImportError:
raise ImportError(import_err_msg)
super().__init__()
self._index_client: SearchIndexClient = cast(SearchIndexClient, None)
self._async_index_client: AsyncSearchIndexClient = cast(
AsyncSearchIndexClient, None
)
self._search_client: SearchClient = cast(SearchClient, None)
self._async_search_client: AsyncSearchClient = cast(AsyncSearchClient, None)
self._embedding_dimensionality = embedding_dimensionality
self._index_name = index_name
if vector_algorithm_type == "exhaustiveKnn":
self._vector_profile_name = "myExhaustiveKnnProfile"
elif vector_algorithm_type == "hnsw":
self._vector_profile_name = "myHnswProfile"
else:
raise ValueError(
"Only 'exhaustiveKnn' and 'hnsw' are supported for vector_algorithm_type"
)
self._language_analyzer = language_analyzer
self._compression_type = compression_type.lower()
# Validate search_or_index_client
if search_or_index_client is not None:
if isinstance(search_or_index_client, SearchIndexClient):
# If SearchIndexClient is supplied so must index_name
self._index_client = cast(SearchIndexClient, search_or_index_client)
if not index_name:
raise ValueError(
"index_name must be supplied if search_or_index_client is of "
"type azure.search.documents.SearchIndexClient"
)
self._search_client = self._index_client.get_search_client(
index_name=index_name
)
elif isinstance(search_or_index_client, AsyncSearchIndexClient):
# If SearchIndexClient is supplied so must index_name
self._async_index_client = cast(
AsyncSearchIndexClient, search_or_index_client
)
if not index_name:
raise ValueError(
"index_name must be supplied if search_or_index_client is of "
"type azure.search.documents.aio.SearchIndexClient"
)
self._async_search_client = self._async_index_client.get_search_client(
index_name=index_name
)
elif isinstance(search_or_index_client, SearchClient):
self._search_client = cast(SearchClient, search_or_index_client)
# Validate index_name
if index_name:
raise ValueError(
"index_name cannot be supplied if search_or_index_client "
"is of type azure.search.documents.SearchClient"
)
elif isinstance(search_or_index_client, AsyncSearchClient):
self._async_search_client = cast(
AsyncSearchClient, search_or_index_client
)
# Validate index_name
if index_name:
raise ValueError(
"index_name cannot be supplied if search_or_index_client "
"is of type azure.search.documents.SearchClient"
)
if isinstance(search_or_index_client, AsyncSearchIndexClient):
if not self._async_index_client and not self._async_search_client:
raise ValueError(
"search_or_index_client must be of type "
"azure.search.documents.SearchIndexClient or "
"azure.search.documents.SearchClient"
)
if isinstance(search_or_index_client, SearchIndexClient):
if not self._index_client and not self._search_client:
raise ValueError(
"search_or_index_client must be of type "
"azure.search.documents.SearchIndexClient or "
"azure.search.documents.SearchClient"
)
else:
raise ValueError("search_or_index_client not specified")
if index_management == IndexManagement.CREATE_IF_NOT_EXISTS and not (
self._index_client or self._async_index_client
):
raise ValueError(
"index_management has value of IndexManagement.CREATE_IF_NOT_EXISTS "
"but search_or_index_client is not of type "
"azure.search.documents.SearchIndexClient or azure.search.documents.aio.SearchIndexClient "
)
self._index_management = index_management
# Default field mapping
field_mapping = {
"id": id_field_key,
"chunk": chunk_field_key,
"embedding": embedding_field_key,
"metadata": metadata_string_field_key,
"doc_id": doc_id_field_key,
}
self._field_mapping = field_mapping
self._index_mapping = (
self._default_index_mapping if index_mapping is None else index_mapping
)
# self._filterable_metadata_field_keys = filterable_metadata_field_keys
self._metadata_to_index_field_map = self._normalise_metadata_to_index_fields(
filterable_metadata_field_keys
)
# need to do lazy init for async client
if not isinstance(search_or_index_client, AsyncSearchIndexClient):
if self._index_management == IndexManagement.CREATE_IF_NOT_EXISTS:
if index_name:
self._create_index_if_not_exists(index_name)
if self._index_management == IndexManagement.VALIDATE_INDEX:
self._validate_index(index_name)
@property
def client(self) -> Any:
"""Get client."""
return self._search_client
@property
def aclient(self) -> Any:
"""Get async client."""
return self._async_search_client
def _default_index_mapping(
self, enriched_doc: Dict[str, str], metadata: Dict[str, Any]
) -> Dict[str, str]:
index_doc: Dict[str, str] = {}
for field in self._field_mapping:
index_doc[self._field_mapping[field]] = enriched_doc[field]
for metadata_field_name, (
index_field_name,
_,
) in self._metadata_to_index_field_map.items():
metadata_value = metadata.get(metadata_field_name)
if metadata_value:
index_doc[index_field_name] = metadata_value
return index_doc
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
"""
Add nodes to index associated with the configured search client.
Args:
nodes: List[BaseNode]: nodes with embeddings
"""
from azure.search.documents import IndexDocumentsBatch
if not self._search_client:
raise ValueError("Search client not initialized")
accumulator = IndexDocumentsBatch()
documents = []
ids = []
accumulated_size = 0
max_size = DEFAULT_MAX_MB_SIZE # 16MB in bytes
max_docs = DEFAULT_MAX_BATCH_SIZE
for node in nodes:
logger.debug(f"Processing embedding: {node.node_id}")
ids.append(node.node_id)
index_document = self._create_index_document(node)
document_size = len(json.dumps(index_document).encode("utf-8"))
documents.append(index_document)
accumulated_size += document_size
accumulator.add_upload_actions(index_document)
if len(documents) >= max_docs or accumulated_size >= max_size:
logger.info(
f"Uploading batch of size {len(documents)}, "
f"current progress {len(ids)} of {len(nodes)}, "
f"accumulated size {accumulated_size / (1024 * 1024):.2f} MB"
)
self._search_client.index_documents(accumulator)
accumulator.dequeue_actions()
documents = []
accumulated_size = 0
# Upload remaining batch
if documents:
logger.info(
f"Uploading remaining batch of size {len(documents)}, "
f"current progress {len(ids)} of {len(nodes)}, "
f"accumulated size {accumulated_size / (1024 * 1024):.2f} MB"
)
self._search_client.index_documents(accumulator)
return ids
async def async_add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
"""
Add nodes to index associated with the configured search client.
Args:
nodes: List[BaseNode]: nodes with embeddings
"""
from azure.search.documents import IndexDocumentsBatch
if not self._async_search_client:
raise ValueError("Async Search client not initialized")
if len(nodes) > 0:
if self._index_management == IndexManagement.CREATE_IF_NOT_EXISTS:
if self._index_name:
await self._acreate_index_if_not_exists(self._index_name)
if self._index_management == IndexManagement.VALIDATE_INDEX:
await self._avalidate_index(self._index_name)
accumulator = IndexDocumentsBatch()
documents = []
ids = []
accumulated_size = 0
max_size = DEFAULT_MAX_MB_SIZE # 16MB in bytes
max_docs = DEFAULT_MAX_BATCH_SIZE
for node in nodes:
logger.debug(f"Processing embedding: {node.node_id}")
ids.append(node.node_id)
index_document = self._create_index_document(node)
document_size = len(json.dumps(index_document).encode("utf-8"))
documents.append(index_document)
accumulated_size += document_size
accumulator.add_upload_actions(index_document)
if len(documents) >= max_docs or accumulated_size >= max_size:
logger.info(
f"Uploading batch of size {len(documents)}, "
f"current progress {len(ids)} of {len(nodes)}, "
f"accumulated size {accumulated_size / (1024 * 1024):.2f} MB"
)
await self._async_search_client.index_documents(accumulator)
accumulator.dequeue_actions()
documents = []
accumulated_size = 0
# Upload remaining batch
if documents:
logger.info(
f"Uploading remaining batch of size {len(documents)}, "
f"current progress {len(ids)} of {len(nodes)}, "
f"accumulated size {accumulated_size / (1024 * 1024):.2f} MB"
)
await self._async_search_client.index_documents(accumulator)
return ids
def _create_index_document(self, node: BaseNode) -> Dict[str, Any]:
"""Create AI Search index document from embedding result."""
doc: Dict[str, Any] = {}
doc["id"] = node.node_id
doc["chunk"] = node.get_content(metadata_mode=MetadataMode.NONE) or ""
doc["embedding"] = node.get_embedding()
doc["doc_id"] = node.ref_doc_id
node_metadata = node_to_metadata_dict(
node,
remove_text=True,
flat_metadata=self.flat_metadata,
)
doc["metadata"] = json.dumps(node_metadata)
return self._index_mapping(doc, node_metadata)
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete documents from the AI Search Index
with doc_id_field_key field equal to ref_doc_id.
"""
if not self._index_exists(self._index_name):
return
# Locate documents to delete
filter = f'{self._field_mapping["doc_id"]} eq \'{ref_doc_id}\''
batch_size = 1000
while True:
results = self._search_client.search(
search_text="*",
filter=filter,
top=batch_size,
)
logger.debug(f"Searching with filter {filter}")
docs_to_delete = [
{"id": result[self._field_mapping["id"]]} for result in results
]
if docs_to_delete:
logger.debug(f"Deleting {len(docs_to_delete)} documents")
self._search_client.delete_documents(docs_to_delete)
else:
break
async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete documents from the AI Search Index
with doc_id_field_key field equal to ref_doc_id.
"""
if not await self._aindex_exists(self._index_name):
return
# Locate documents to delete
filter = f'{self._field_mapping["doc_id"]} eq \'{ref_doc_id}\''
batch_size = 1000
while True:
results = await self._async_search_client.search(
search_text="*",
filter=filter,
top=batch_size,
)
logger.debug(f"Searching with filter {filter}")
docs_to_delete = [
{"id": result[self._field_mapping["id"]]} async for result in results
]
if docs_to_delete:
logger.debug(f"Deleting {len(docs_to_delete)} documents")
await self._async_search_client.delete_documents(docs_to_delete)
else:
break
def delete_nodes(
self,
node_ids: Optional[List[str]] = None,
filters: Optional[MetadataFilters] = None,
**delete_kwargs: Any,
) -> None:
"""
Delete documents from the AI Search Index.
"""
if node_ids is None and filters is None:
raise ValueError("Either node_ids or filters must be provided")
filter = self._build_filter_delete_query(node_ids, filters)
batch_size = 1000
while True:
results = self._search_client.search(
search_text="*",
filter=filter,
top=batch_size,
)
logger.debug(f"Searching with filter {filter}")
docs_to_delete = [
{"id": result[self._field_mapping["id"]]} for result in results
]
if docs_to_delete:
logger.debug(f"Deleting {len(docs_to_delete)} documents")
self._search_client.delete_documents(docs_to_delete)
else:
break
async def adelete_nodes(
self,
node_ids: Optional[List[str]] = None,
filters: Optional[MetadataFilters] = None,
**delete_kwargs: Any,
) -> None:
"""
Delete documents from the AI Search Index.
"""
if node_ids is None and filters is None:
raise ValueError("Either node_ids or filters must be provided")
filter = self._build_filter_delete_query(node_ids, filters)
batch_size = 1000
while True:
results = await self._async_search_client.search(
search_text="*",
filter=filter,
top=batch_size,
)
logger.debug(f"Searching with filter {filter}")
docs_to_delete = [
{"id": result[self._field_mapping["id"]]} async for result in results
]
if docs_to_delete:
logger.debug(f"Deleting {len(docs_to_delete)} documents")
await self._async_search_client.delete_documents(docs_to_delete)
else:
break
def _build_filter_delete_query(
self,
node_ids: Optional[List[str]] = None,
filters: Optional[MetadataFilters] = None,
) -> str:
"""Build the OData filter query for the deletion process."""
if node_ids:
return " or ".join(
[
f'{self._field_mapping["id"]} eq \'{node_id}\''
for node_id in node_ids
]
)
if filters and filters.filters:
# Find the filter with key doc_ids
doc_ids_filter = next(
(f for f in filters.filters if f.key == "doc_id"), None
)
if doc_ids_filter and doc_ids_filter.operator == FilterOperator.IN:
# use search.in to filter on multiple values
doc_ids_str = ",".join(doc_ids_filter.value)
return (
f"search.in({self._field_mapping['doc_id']}, '{doc_ids_str}', ',')"
)
return self._create_odata_filter(filters)
raise ValueError("Invalid filter configuration")
def _create_odata_filter(self, metadata_filters: MetadataFilters) -> str:
"""Generate an OData filter string using supplied metadata filters."""
odata_filter: List[str] = []
for subfilter in metadata_filters.filters:
if isinstance(subfilter, MetadataFilters):
nested_filter = self._create_odata_filter(subfilter)
odata_filter.append(f"({nested_filter})")
continue
# Join values with ' or ' to create an OR condition inside the any function
metadata_mapping = self._metadata_to_index_field_map.get(subfilter.key)
index_field = metadata_mapping[0]
if not metadata_mapping:
raise ValueError(
f"Metadata field '{subfilter.key}' is missing a mapping to an index field, "
"provide entry in 'filterable_metadata_field_keys' for this "
"vector store"
)
if subfilter.operator == FilterOperator.IN:
value_str = " or ".join(
[
f"t eq '{value}'" if isinstance(value, str) else f"t eq {value}"
for value in subfilter.value
]
)
odata_filter.append(f"{index_field}/any(t: {value_str})")
elif subfilter.operator == FilterOperator.EQ:
if isinstance(subfilter.value, str):
escaped_value = "".join(
[("''" if s == "'" else s) for s in subfilter.value]
)
odata_filter.append(f"{index_field} eq '{escaped_value}'")
else:
odata_filter.append(f"{index_field} eq {subfilter.value}")
else:
raise ValueError(f"Unsupported filter operator {subfilter.operator}")
if metadata_filters.condition == FilterCondition.AND:
odata_expr = " and ".join(odata_filter)
elif metadata_filters.condition == FilterCondition.OR:
odata_expr = " or ".join(odata_filter)
else:
raise ValueError(
f"Unsupported filter condition {metadata_filters.condition}"
)
logger.info(f"Odata filter: {odata_expr}")
return odata_expr
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
odata_filter = None
if query.filters is not None:
odata_filter = self._create_odata_filter(query.filters)
azure_query_result_search: AzureQueryResultSearchBase = (
AzureQueryResultSearchDefault(
query, self._field_mapping, odata_filter, self._search_client
)
)
if query.mode == VectorStoreQueryMode.SPARSE:
azure_query_result_search = AzureQueryResultSearchSparse(
query, self._field_mapping, odata_filter, self._search_client
)
elif query.mode == VectorStoreQueryMode.HYBRID:
azure_query_result_search = AzureQueryResultSearchHybrid(
query, self._field_mapping, odata_filter, self._search_client
)
elif query.mode == VectorStoreQueryMode.SEMANTIC_HYBRID:
azure_query_result_search = AzureQueryResultSearchSemanticHybrid(
query, self._field_mapping, odata_filter, self._search_client
)
return azure_query_result_search.search()
async def aquery(
self, query: VectorStoreQuery, **kwargs: Any
) -> VectorStoreQueryResult:
odata_filter = None
# NOTE: users can provide odata_filters directly to the query
odata_filters = kwargs.get("odata_filters")
if odata_filters is not None:
odata_filter = odata_filter
else:
if query.filters is not None:
odata_filter = self._create_odata_filter(query.filters)
azure_query_result_search: AzureQueryResultSearchBase = (
AzureQueryResultSearchDefault(
query, self._field_mapping, odata_filter, self._async_search_client
)
)
if query.mode == VectorStoreQueryMode.SPARSE:
azure_query_result_search = AzureQueryResultSearchSparse(
query, self._field_mapping, odata_filter, self._async_search_client
)
elif query.mode == VectorStoreQueryMode.HYBRID:
azure_query_result_search = AzureQueryResultSearchHybrid(
query, self._field_mapping, odata_filter, self._async_search_client
)
elif query.mode == VectorStoreQueryMode.SEMANTIC_HYBRID:
azure_query_result_search = AzureQueryResultSearchSemanticHybrid(
query, self._field_mapping, odata_filter, self._async_search_client
)
return await azure_query_result_search.asearch()
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