from __future__ import annotations
import asyncio
import base64
import itertools
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Collection,
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Type,
Union,
cast,
)
import numpy as np
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.exceptions import LangChainException
from langchain_core.pydantic_v1 import root_validator
from langchain_core.retrievers import BaseRetriever
from langchain_core.utils import get_from_env
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger()
if TYPE_CHECKING:
from azure.search.documents import SearchClient, SearchItemPaged
from azure.search.documents.aio import (
AsyncSearchItemPaged,
)
from azure.search.documents.aio import (
SearchClient as AsyncSearchClient,
)
from azure.search.documents.indexes.models import (
CorsOptions,
ScoringProfile,
SearchField,
SemanticConfiguration,
VectorSearch,
)
# Allow overriding field names for Azure Search
FIELDS_ID = get_from_env(
key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id"
)
FIELDS_CONTENT = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT",
env_key="AZURESEARCH_FIELDS_CONTENT",
default="content",
)
FIELDS_CONTENT_VECTOR = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
env_key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
default="content_vector",
)
FIELDS_METADATA = get_from_env(
key="AZURESEARCH_FIELDS_TAG", env_key="AZURESEARCH_FIELDS_TAG", default="metadata"
)
MAX_UPLOAD_BATCH_SIZE = 1000
def _get_search_client(
endpoint: str,
key: str,
index_name: str,
semantic_configuration_name: Optional[str] = None,
fields: Optional[List[SearchField]] = None,
vector_search: Optional[VectorSearch] = None,
semantic_configurations: Optional[
Union[SemanticConfiguration, List[SemanticConfiguration]]
] = None,
scoring_profiles: Optional[List[ScoringProfile]] = None,
default_scoring_profile: Optional[str] = None,
default_fields: Optional[List[SearchField]] = None,
user_agent: Optional[str] = "langchain",
cors_options: Optional[CorsOptions] = None,
async_: bool = False,
additional_search_client_options: Optional[Dict[str, Any]] = None,
) -> Union[SearchClient, AsyncSearchClient]:
from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
from azure.search.documents import SearchClient
from azure.search.documents.aio import SearchClient as AsyncSearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
ExhaustiveKnnAlgorithmConfiguration,
ExhaustiveKnnParameters,
HnswAlgorithmConfiguration,
HnswParameters,
SearchIndex,
SemanticConfiguration,
SemanticField,
SemanticPrioritizedFields,
SemanticSearch,
VectorSearch,
VectorSearchAlgorithmKind,
VectorSearchAlgorithmMetric,
VectorSearchProfile,
)
additional_search_client_options = additional_search_client_options or {}
default_fields = default_fields or []
if key is None:
credential = DefaultAzureCredential()
elif key.upper() == "INTERACTIVE":
credential = InteractiveBrowserCredential()
credential.get_token("https://search.azure.com/.default")
else:
credential = AzureKeyCredential(key)
index_client: SearchIndexClient = SearchIndexClient(
endpoint=endpoint, credential=credential, user_agent=user_agent
)
try:
index_client.get_index(name=index_name)
except ResourceNotFoundError:
# Fields configuration
if fields is not None:
# Check mandatory fields
fields_types = {f.name: f.type for f in fields}
mandatory_fields = {df.name: df.type for df in default_fields}
# Check for missing keys
missing_fields = {
key: mandatory_fields[key]
for key, value in set(mandatory_fields.items())
- set(fields_types.items())
}
if len(missing_fields) > 0:
# Helper for formatting field information for each missing field.
def fmt_err(x: str) -> str:
return (
f"{x} current type: '{fields_types.get(x, 'MISSING')}'. "
f"It has to be '{mandatory_fields.get(x)}' or you can point "
f"to a different '{mandatory_fields.get(x)}' field name by "
f"using the env variable 'AZURESEARCH_FIELDS_{x.upper()}'"
)
error = "\n".join([fmt_err(x) for x in missing_fields])
raise ValueError(
f"You need to specify at least the following fields "
f"{missing_fields} or provide alternative field names in the env "
f"variables.\n\n{error}"
)
else:
fields = default_fields
# Vector search configuration
if vector_search is None:
vector_search = VectorSearch(
algorithms=[
HnswAlgorithmConfiguration(
name="default",
kind=VectorSearchAlgorithmKind.HNSW,
parameters=HnswParameters(
m=4,
ef_construction=400,
ef_search=500,
metric=VectorSearchAlgorithmMetric.COSINE,
),
),
ExhaustiveKnnAlgorithmConfiguration(
name="default_exhaustive_knn",
kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
parameters=ExhaustiveKnnParameters(
metric=VectorSearchAlgorithmMetric.COSINE
),
),
],
profiles=[
VectorSearchProfile(
name="myHnswProfile",
algorithm_configuration_name="default",
),
VectorSearchProfile(
name="myExhaustiveKnnProfile",
algorithm_configuration_name="default_exhaustive_knn",
),
],
)
# Create the semantic settings with the configuration
if semantic_configurations:
if not isinstance(semantic_configurations, list):
semantic_configurations = [semantic_configurations]
semantic_search = SemanticSearch(
configurations=semantic_configurations,
default_configuration_name=semantic_configuration_name,
)
elif semantic_configuration_name:
# use default semantic configuration
semantic_configuration = SemanticConfiguration(
name=semantic_configuration_name,
prioritized_fields=SemanticPrioritizedFields(
content_fields=[SemanticField(field_name=FIELDS_CONTENT)],
),
)
semantic_search = SemanticSearch(configurations=[semantic_configuration])
else:
# don't use semantic search
semantic_search = None
# Create the search index with the semantic settings and vector search
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search,
semantic_search=semantic_search,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
cors_options=cors_options,
)
index_client.create_index(index)
# Create the search client
if not async_:
return SearchClient(
endpoint=endpoint,
index_name=index_name,
credential=credential,
user_agent=user_agent,
**additional_search_client_options,
)
else:
return AsyncSearchClient(
endpoint=endpoint,
index_name=index_name,
credential=credential,
user_agent=user_agent,
**additional_search_client_options,
)
[docs]class AzureSearch(VectorStore):
"""`Azure Cognitive Search` vector store."""
[docs] def __init__(
self,
azure_search_endpoint: str,
azure_search_key: str,
index_name: str,
embedding_function: Union[Callable, Embeddings],
search_type: str = "hybrid",
semantic_configuration_name: Optional[str] = None,
fields: Optional[List[SearchField]] = None,
vector_search: Optional[VectorSearch] = None,
semantic_configurations: Optional[
Union[SemanticConfiguration, List[SemanticConfiguration]]
] = None,
scoring_profiles: Optional[List[ScoringProfile]] = None,
default_scoring_profile: Optional[str] = None,
cors_options: Optional[CorsOptions] = None,
*,
vector_search_dimensions: Optional[int] = None,
additional_search_client_options: Optional[Dict[str, Any]] = None,
**kwargs: Any,
):
try:
from azure.search.documents.indexes.models import (
SearchableField,
SearchField,
SearchFieldDataType,
SimpleField,
)
except ImportError as e:
raise ImportError(
"Unable to import azure.search.documents. Please install with "
"`pip install -U azure-search-documents`."
) from e
"""Initialize with necessary components."""
# Initialize base class
self.embedding_function = embedding_function
if isinstance(self.embedding_function, Embeddings):
self.embed_query = self.embedding_function.embed_query
else:
self.embed_query = self.embedding_function
default_fields = [
SimpleField(
name=FIELDS_ID,
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name=FIELDS_CONTENT,
type=SearchFieldDataType.String,
),
SearchField(
name=FIELDS_CONTENT_VECTOR,
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=vector_search_dimensions
or len(self.embed_query("Text")),
vector_search_profile_name="myHnswProfile",
),
SearchableField(
name=FIELDS_METADATA,
type=SearchFieldDataType.String,
),
]
user_agent = "langchain"
if "user_agent" in kwargs and kwargs["user_agent"]:
user_agent += " " + kwargs["user_agent"]
self.client = _get_search_client(
azure_search_endpoint,
azure_search_key,
index_name,
semantic_configuration_name=semantic_configuration_name,
fields=fields,
vector_search=vector_search,
semantic_configurations=semantic_configurations,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
default_fields=default_fields,
user_agent=user_agent,
cors_options=cors_options,
additional_search_client_options=additional_search_client_options,
)
self.async_client = _get_search_client(
azure_search_endpoint,
azure_search_key,
index_name,
semantic_configuration_name=semantic_configuration_name,
fields=fields,
vector_search=vector_search,
semantic_configurations=semantic_configurations,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
default_fields=default_fields,
user_agent=user_agent,
cors_options=cors_options,
async_=True,
)
self.search_type = search_type
self.semantic_configuration_name = semantic_configuration_name
self.fields = fields if fields else default_fields
self._azure_search_endpoint = azure_search_endpoint
self._azure_search_key = azure_search_key
self._index_name = index_name
self._semantic_configuration_name = semantic_configuration_name
self._fields = fields
self._vector_search = vector_search
self._semantic_configurations = semantic_configurations
self._scoring_profiles = scoring_profiles
self._default_scoring_profile = default_scoring_profile
self._default_fields = default_fields
self._user_agent = user_agent
self._cors_options = cors_options
def __del__(self) -> None:
# Close the sync client
if hasattr(self, "client") and self.client:
self.client.close()
# Close the async client
if hasattr(self, "async_client") and self.async_client:
# Check if we're in an existing event loop
try:
loop = asyncio.get_event_loop()
if loop.is_running():
# Schedule the coroutine to close the async client
loop.create_task(self.async_client.close())
else:
# If no event loop is running, run the coroutine directly
loop.run_until_complete(self.async_client.close())
except RuntimeError:
# Handle the case where there's no event loop
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
loop.run_until_complete(self.async_client.close())
finally:
loop.close()
@property
def embeddings(self) -> Optional[Embeddings]:
# TODO: Support embedding object directly
return (
self.embedding_function
if isinstance(self.embedding_function, Embeddings)
else None
)
async def _aembed_query(self, text: str) -> List[float]:
if self.embeddings:
return await self.embeddings.aembed_query(text)
else:
return cast(Callable, self.embedding_function)(text)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
*,
keys: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Add texts data to an existing index."""
# batching support if embedding function is an Embeddings object
if isinstance(self.embedding_function, Embeddings):
try:
embeddings = self.embedding_function.embed_documents(list(texts))
except NotImplementedError:
embeddings = [self.embedding_function.embed_query(x) for x in texts]
else:
embeddings = [self.embedding_function(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
return self.add_embeddings(zip(texts, embeddings), metadatas, keys=keys)
[docs] async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
*,
keys: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
if isinstance(self.embedding_function, Embeddings):
try:
embeddings = await self.embedding_function.aembed_documents(list(texts))
except NotImplementedError:
embeddings = [
await self.embedding_function.aembed_query(x) for x in texts
]
else:
embeddings = [self.embedding_function(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
return await self.aadd_embeddings(zip(texts, embeddings), metadatas, keys=keys)
[docs] def add_embeddings(
self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
*,
keys: Optional[List[str]] = None,
) -> List[str]:
"""Add embeddings to an existing index."""
ids = []
# Write data to index
data = []
for i, (text, embedding) in enumerate(text_embeddings):
# Use provided key otherwise use default key
key = keys[i] if keys else str(uuid.uuid4())
# Encoding key for Azure Search valid characters
key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
metadata = metadatas[i] if metadatas else {}
# Add data to index
# Additional metadata to fields mapping
doc = {
"@search.action": "upload",
FIELDS_ID: key,
FIELDS_CONTENT: text,
FIELDS_CONTENT_VECTOR: np.array(embedding, dtype=np.float32).tolist(),
FIELDS_METADATA: json.dumps(metadata),
}
if metadata:
additional_fields = {
k: v
for k, v in metadata.items()
if k in [x.name for x in self.fields]
}
doc.update(additional_fields)
data.append(doc)
ids.append(key)
# Upload data in batches
if len(data) == MAX_UPLOAD_BATCH_SIZE:
response = self.client.upload_documents(documents=data)
# Check if all documents were successfully uploaded
if not all(r.succeeded for r in response):
raise LangChainException(response)
# Reset data
data = []
# Considering case where data is an exact multiple of batch-size entries
if len(data) == 0:
return ids
# Upload data to index
response = self.client.upload_documents(documents=data)
# Check if all documents were successfully uploaded
if all(r.succeeded for r in response):
return ids
else:
raise LangChainException(response)
[docs] async def aadd_embeddings(
self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
*,
keys: Optional[List[str]] = None,
) -> List[str]:
"""Add embeddings to an existing index."""
ids = []
# Write data to index
data = []
for i, (text, embedding) in enumerate(text_embeddings):
# Use provided key otherwise use default key
key = keys[i] if keys else str(uuid.uuid4())
# Encoding key for Azure Search valid characters
key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
metadata = metadatas[i] if metadatas else {}
# Add data to index
# Additional metadata to fields mapping
doc = {
"@search.action": "upload",
FIELDS_ID: key,
FIELDS_CONTENT: text,
FIELDS_CONTENT_VECTOR: np.array(embedding, dtype=np.float32).tolist(),
FIELDS_METADATA: json.dumps(metadata),
}
if metadata:
additional_fields = {
k: v
for k, v in metadata.items()
if k in [x.name for x in self.fields]
}
doc.update(additional_fields)
data.append(doc)
ids.append(key)
# Upload data in batches
if len(data) == MAX_UPLOAD_BATCH_SIZE:
response = await self.async_client.upload_documents(documents=data)
# Check if all documents were successfully uploaded
if not all(r.succeeded for r in response):
raise LangChainException(response)
# Reset data
data = []
# Considering case where data is an exact multiple of batch-size entries
if len(data) == 0:
return ids
# Upload data to index
response = await self.async_client.upload_documents(documents=data)
# Check if all documents were successfully uploaded
if all(r.succeeded for r in response):
return ids
else:
raise LangChainException(response)
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
"""Delete by vector ID.
Args:
ids: List of ids to delete.
Returns:
bool: True if deletion is successful,
False otherwise.
"""
if ids:
res = self.client.delete_documents([{FIELDS_ID: i} for i in ids])
return len(res) > 0
else:
return False
[docs] async def adelete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
"""Delete by vector ID.
Args:
ids: List of ids to delete.
Returns:
bool: True if deletion is successful,
False otherwise.
"""
if ids:
res = await self.async_client.delete_documents([{"id": i} for i in ids])
return len(res) > 0
else:
return False
[docs] def similarity_search(
self,
query: str,
k: int = 4,
*,
search_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
search_type = search_type or self.search_type
if search_type == "similarity":
docs = self.vector_search(query, k=k, **kwargs)
elif search_type == "hybrid":
docs = self.hybrid_search(query, k=k, **kwargs)
elif search_type == "semantic_hybrid":
docs = self.semantic_hybrid_search(query, k=k, **kwargs)
else:
raise ValueError(f"search_type of {search_type} not allowed.")
return docs
[docs] def similarity_search_with_score(
self, query: str, *, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance."""
search_type = kwargs.get("search_type", self.search_type)
if search_type == "similarity":
return self.vector_search_with_score(query, k=k, **kwargs)
elif search_type == "hybrid":
return self.hybrid_search_with_score(query, k=k, **kwargs)
elif search_type == "semantic_hybrid":
return self.semantic_hybrid_search_with_score(query, k=k, **kwargs)
else:
raise ValueError(f"search_type of {search_type} not allowed.")
[docs] async def asimilarity_search(
self,
query: str,
k: int = 4,
*,
search_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
search_type = search_type or self.search_type
if search_type == "similarity":
docs = await self.avector_search(query, k=k, **kwargs)
elif search_type == "hybrid":
docs = await self.ahybrid_search(query, k=k, **kwargs)
elif search_type == "semantic_hybrid":
docs = await self.asemantic_hybrid_search(query, k=k, **kwargs)
else:
raise ValueError(f"search_type of {search_type} not allowed.")
return docs
[docs] async def asimilarity_search_with_score(
self, query: str, *, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance."""
search_type = kwargs.get("search_type", self.search_type)
if search_type == "similarity":
return await self.avector_search_with_score(query, k=k, **kwargs)
elif search_type == "hybrid":
return await self.ahybrid_search_with_score(query, k=k, **kwargs)
elif search_type == "semantic_hybrid":
return await self.asemantic_hybrid_search_with_score(query, k=k, **kwargs)
else:
raise ValueError(f"search_type of {search_type} not allowed.")
[docs] def similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
*,
score_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
result = self.vector_search_with_score(query, k=k, **kwargs)
return (
result
if score_threshold is None
else [r for r in result if r[1] >= score_threshold]
)
[docs] async def asimilarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
*,
score_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
result = await self.avector_search_with_score(query, k=k, **kwargs)
return (
result
if score_threshold is None
else [r for r in result if r[1] >= score_threshold]
)
[docs] def vector_search(
self, query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.vector_search_with_score(query, k=k, filters=filters)
return [doc for doc, _ in docs_and_scores]
[docs] async def avector_search(
self, query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = await self.avector_search_with_score(
query, k=k, filters=filters
)
return [doc for doc, _ in docs_and_scores]
[docs] def vector_search_with_score(
self,
query: str,
k: int = 4,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query (str): Text to look up documents similar to.
k (int, optional): Number of Documents to return. Defaults to 4.
filters (str, optional): Filtering expression. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents most similar
to the query and score for each
"""
embedding = self.embed_query(query)
results = self._simple_search(embedding, "", k, filters=filters, **kwargs)
return _results_to_documents(results)
[docs] async def avector_search_with_score(
self,
query: str,
k: int = 4,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query (str): Text to look up documents similar to.
k (int, optional): Number of Documents to return. Defaults to 4.
filters (str, optional): Filtering expression. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents most similar
to the query and score for each
"""
embedding = await self._aembed_query(query)
results = await self._asimple_search(
embedding, "", k, filters=filters, **kwargs
)
return await _aresults_to_documents(results)
[docs] def max_marginal_relevance_search_with_score(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
*,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Perform a search and return results that are reordered by MMR.
Args:
query (str): Text to look up documents similar to.
k (int, optional): How many results to give. Defaults to 4.
fetch_k (int, optional): Total results to select k from.
Defaults to 20.
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
filters (str, optional): Filtering expression. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents most similar
to the query and score for each
"""
embedding = self.embed_query(query)
results = self._simple_search(embedding, "", fetch_k, filters=filters, **kwargs)
return _reorder_results_with_maximal_marginal_relevance(
results, query_embedding=np.array(embedding), lambda_mult=lambda_mult, k=k
)
[docs] async def amax_marginal_relevance_search_with_score(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
*,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Perform a search and return results that are reordered by MMR.
Args:
query (str): Text to look up documents similar to.
k (int, optional): How many results to give. Defaults to 4.
fetch_k (int, optional): Total results to select k from.
Defaults to 20.
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
filters (str, optional): Filtering expression. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents most similar
to the query and score for each
"""
embedding = await self._aembed_query(query)
results = await self._asimple_search(
embedding, "", fetch_k, filters=filters, **kwargs
)
return await _areorder_results_with_maximal_marginal_relevance(
results,
query_embedding=np.array(embedding),
lambda_mult=lambda_mult,
k=k,
)
[docs] def hybrid_search(self, query: str, k: int = 4, **kwargs: Any) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.hybrid_search_with_score(query, k=k, **kwargs)
return [doc for doc, _ in docs_and_scores]
[docs] async def ahybrid_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = await self.ahybrid_search_with_score(query, k=k, **kwargs)
return [doc for doc, _ in docs_and_scores]
[docs] def hybrid_search_with_score(
self,
query: str,
k: int = 4,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with a hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embed_query(query)
results = self._simple_search(embedding, query, k, filters=filters, **kwargs)
return _results_to_documents(results)
[docs] async def ahybrid_search_with_score(
self,
query: str,
k: int = 4,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with a hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = await self._aembed_query(query)
results = await self._asimple_search(
embedding, query, k, filters=filters, **kwargs
)
return await _aresults_to_documents(results)
[docs] def hybrid_search_with_relevance_scores(
self,
query: str,
k: int = 4,
*,
score_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
result = self.hybrid_search_with_score(query, k=k, **kwargs)
return (
result
if score_threshold is None
else [r for r in result if r[1] >= score_threshold]
)
[docs] async def ahybrid_search_with_relevance_scores(
self,
query: str,
k: int = 4,
*,
score_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
result = await self.ahybrid_search_with_score(query, k=k, **kwargs)
return (
result
if score_threshold is None
else [r for r in result if r[1] >= score_threshold]
)
[docs] def hybrid_max_marginal_relevance_search_with_score(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
*,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with a hybrid query
and reorder results by MMR.
Args:
query (str): Text to look up documents similar to.
k (int, optional): Number of Documents to return. Defaults to 4.
fetch_k (int, optional): Total results to select k from.
Defaults to 20.
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
filters (str, optional): Filtering expression. Defaults to None.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embed_query(query)
results = self._simple_search(
embedding, query, fetch_k, filters=filters, **kwargs
)
return _reorder_results_with_maximal_marginal_relevance(
results, query_embedding=np.array(embedding), lambda_mult=lambda_mult, k=k
)
[docs] async def ahybrid_max_marginal_relevance_search_with_score(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
*,
filters: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with a hybrid query
and reorder results by MMR.
Args:
query (str): Text to look up documents similar to.
k (int, optional): Number of Documents to return. Defaults to 4.
fetch_k (int, optional): Total results to select k from.
Defaults to 20.
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
filters (str, optional): Filtering expression. Defaults to None.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = await self._aembed_query(query)
results = await self._asimple_search(
embedding, query, fetch_k, filters=filters, **kwargs
)
return await _areorder_results_with_maximal_marginal_relevance(
results,
query_embedding=np.array(embedding),
lambda_mult=lambda_mult,
k=k,
)
def _simple_search(
self,
embedding: List[float],
text_query: str,
k: int,
*,
filters: Optional[str] = None,
**kwargs: Any,
) -> SearchItemPaged[dict]:
"""Perform vector or hybrid search in the Azure search index.
Args:
embedding: A vector embedding to search in the vector space.
text_query: A full-text search query expression;
Use "*" or omit this parameter to perform only vector search.
k: Number of documents to return.
filters: Filtering expression.
Returns:
Search items
"""
from azure.search.documents.models import VectorizedQuery
return self.client.search(
search_text=text_query,
vector_queries=[
VectorizedQuery(
vector=np.array(embedding, dtype=np.float32).tolist(),
k_nearest_neighbors=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
filter=filters,
top=k,
**kwargs,
)
async def _asimple_search(
self,
embedding: List[float],
text_query: str,
k: int,
*,
filters: Optional[str] = None,
**kwargs: Any,
) -> AsyncSearchItemPaged[dict]:
"""Perform vector or hybrid search in the Azure search index.
Args:
embedding: A vector embedding to search in the vector space.
text_query: A full-text search query expression;
Use "*" or omit this parameter to perform only vector search.
k: Number of documents to return.
filters: Filtering expression.
Returns:
Search items
"""
from azure.search.documents.models import VectorizedQuery
return await self.async_client.search(
search_text=text_query,
vector_queries=[
VectorizedQuery(
vector=np.array(embedding, dtype=np.float32).tolist(),
k_nearest_neighbors=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
filter=filters,
top=k,
**kwargs,
)
[docs] def semantic_hybrid_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
filters: Filtering expression.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.semantic_hybrid_search_with_score_and_rerank(
query, k=k, **kwargs
)
return [doc for doc, _, _ in docs_and_scores]
[docs] async def asemantic_hybrid_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
filters: Filtering expression.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = await self.asemantic_hybrid_search_with_score_and_rerank(
query, k=k, **kwargs
)
return [doc for doc, _, _ in docs_and_scores]
[docs] def semantic_hybrid_search_with_score(
self,
query: str,
k: int = 4,
score_type: Literal["score", "reranker_score"] = "score",
*,
score_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
score_type: Must either be "score" or "reranker_score".
Defaulted to "score".
filters: Filtering expression.
Returns:
List[Tuple[Document, float]]: A list of documents and their
corresponding scores.
"""
docs_and_scores = self.semantic_hybrid_search_with_score_and_rerank(
query, k=k, **kwargs
)
if score_type == "score":
return [
(doc, score)
for doc, score, _ in docs_and_scores
if score_threshold is None or score >= score_threshold
]
elif score_type == "reranker_score":
return [
(doc, reranker_score)
for doc, _, reranker_score in docs_and_scores
if score_threshold is None or reranker_score >= score_threshold
]
[docs] async def asemantic_hybrid_search_with_score(
self,
query: str,
k: int = 4,
score_type: Literal["score", "reranker_score"] = "score",
*,
score_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
score_type: Must either be "score" or "reranker_score".
Defaulted to "score".
filters: Filtering expression.
Returns:
List[Tuple[Document, float]]: A list of documents and their
corresponding scores.
"""
docs_and_scores = await self.asemantic_hybrid_search_with_score_and_rerank(
query, k=k, **kwargs
)
if score_type == "score":
return [
(doc, score)
for doc, score, _ in docs_and_scores
if score_threshold is None or score >= score_threshold
]
elif score_type == "reranker_score":
return [
(doc, reranker_score)
for doc, _, reranker_score in docs_and_scores
if score_threshold is None or reranker_score >= score_threshold
]
[docs] def semantic_hybrid_search_with_score_and_rerank(
self, query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float, float]]:
"""Return docs most similar to query with a hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filters: Filtering expression.
Returns:
List of Documents most similar to the query and score for each
"""
from azure.search.documents.models import VectorizedQuery
results = self.client.search(
search_text=query,
vector_queries=[
VectorizedQuery(
vector=np.array(self.embed_query(query), dtype=np.float32).tolist(),
k_nearest_neighbors=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
filter=filters,
query_type="semantic",
semantic_configuration_name=self.semantic_configuration_name,
query_caption="extractive",
query_answer="extractive",
top=k,
**kwargs,
)
# Get Semantic Answers
semantic_answers = results.get_answers() or []
semantic_answers_dict: Dict = {}
for semantic_answer in semantic_answers:
semantic_answers_dict[semantic_answer.key] = {
"text": semantic_answer.text,
"highlights": semantic_answer.highlights,
}
# Convert results to Document objects
docs = [
(
Document(
page_content=result.pop(FIELDS_CONTENT),
metadata={
**(
json.loads(result[FIELDS_METADATA])
if FIELDS_METADATA in result
else {
k: v
for k, v in result.items()
if k != FIELDS_CONTENT_VECTOR
}
),
**{
"captions": {
"text": result.get("@search.captions", [{}])[0].text,
"highlights": result.get("@search.captions", [{}])[
0
].highlights,
}
if result.get("@search.captions")
else {},
"answers": semantic_answers_dict.get(
result.get(FIELDS_ID, ""),
"",
),
},
},
),
float(result["@search.score"]),
float(result["@search.reranker_score"]),
)
for result in results
]
return docs
[docs] async def asemantic_hybrid_search_with_score_and_rerank(
self, query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float, float]]:
"""Return docs most similar to query with a hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filters: Filtering expression.
Returns:
List of Documents most similar to the query and score for each
"""
from azure.search.documents.models import VectorizedQuery
vector = await self._aembed_query(query)
results = await self.async_client.search(
search_text=query,
vector_queries=[
VectorizedQuery(
vector=np.array(vector, dtype=np.float32).tolist(),
k_nearest_neighbors=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
filter=filters,
query_type="semantic",
semantic_configuration_name=self.semantic_configuration_name,
query_caption="extractive",
query_answer="extractive",
top=k,
**kwargs,
)
# Get Semantic Answers
semantic_answers = (await results.get_answers()) or []
semantic_answers_dict: Dict = {}
for semantic_answer in semantic_answers:
semantic_answers_dict[semantic_answer.key] = {
"text": semantic_answer.text,
"highlights": semantic_answer.highlights,
}
# Convert results to Document objects
docs = [
(
Document(
page_content=result.pop(FIELDS_CONTENT),
metadata={
**(
json.loads(result[FIELDS_METADATA])
if FIELDS_METADATA in result
else {
k: v
for k, v in result.items()
if k != FIELDS_CONTENT_VECTOR
}
),
**{
"captions": {
"text": result.get("@search.captions", [{}])[0].text,
"highlights": result.get("@search.captions", [{}])[
0
].highlights,
}
if result.get("@search.captions")
else {},
"answers": semantic_answers_dict.get(
result.get(FIELDS_ID, ""),
"",
),
},
},
),
float(result["@search.score"]),
float(result["@search.reranker_score"]),
)
async for result in results
]
return docs
[docs] @classmethod
def from_texts(
cls: Type[AzureSearch],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
azure_search_endpoint: str = "",
azure_search_key: str = "",
index_name: str = "langchain-index",
fields: Optional[List[SearchField]] = None,
**kwargs: Any,
) -> AzureSearch:
# Creating a new Azure Search instance
azure_search = cls(
azure_search_endpoint,
azure_search_key,
index_name,
embedding,
fields=fields,
**kwargs,
)
azure_search.add_texts(texts, metadatas, **kwargs)
return azure_search
[docs] @classmethod
async def afrom_texts(
cls: Type[AzureSearch],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
azure_search_endpoint: str = "",
azure_search_key: str = "",
index_name: str = "langchain-index",
fields: Optional[List[SearchField]] = None,
**kwargs: Any,
) -> AzureSearch:
# Creating a new Azure Search instance
azure_search = cls(
azure_search_endpoint,
azure_search_key,
index_name,
embedding,
fields=fields,
**kwargs,
)
await azure_search.aadd_texts(texts, metadatas, **kwargs)
return azure_search
[docs] @classmethod
async def afrom_embeddings(
cls: Type[AzureSearch],
text_embeddings: Iterable[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
*,
azure_search_endpoint: str = "",
azure_search_key: str = "",
index_name: str = "langchain-index",
fields: Optional[List[SearchField]] = None,
**kwargs: Any,
) -> AzureSearch:
text_embeddings, first_text_embedding = _peek(text_embeddings)
if first_text_embedding is None:
raise ValueError("Cannot create AzureSearch from empty embeddings.")
vector_search_dimensions = len(first_text_embedding[1])
azure_search = cls(
azure_search_endpoint=azure_search_endpoint,
azure_search_key=azure_search_key,
index_name=index_name,
embedding_function=embedding,
fields=fields,
vector_search_dimensions=vector_search_dimensions,
**kwargs,
)
await azure_search.aadd_embeddings(text_embeddings, metadatas, **kwargs)
return azure_search
[docs] @classmethod
def from_embeddings(
cls: Type[AzureSearch],
text_embeddings: Iterable[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
*,
azure_search_endpoint: str = "",
azure_search_key: str = "",
index_name: str = "langchain-index",
fields: Optional[List[SearchField]] = None,
**kwargs: Any,
) -> AzureSearch:
# Creating a new Azure Search instance
text_embeddings, first_text_embedding = _peek(text_embeddings)
if first_text_embedding is None:
raise ValueError("Cannot create AzureSearch from empty embeddings.")
vector_search_dimensions = len(first_text_embedding[1])
azure_search = cls(
azure_search_endpoint=azure_search_endpoint,
azure_search_key=azure_search_key,
index_name=index_name,
embedding_function=embedding,
fields=fields,
vector_search_dimensions=vector_search_dimensions,
**kwargs,
)
azure_search.add_embeddings(text_embeddings, metadatas, **kwargs)
return azure_search
[docs] def as_retriever(self, **kwargs: Any) -> AzureSearchVectorStoreRetriever: # type: ignore
"""Return AzureSearchVectorStoreRetriever initialized from this VectorStore.
Args:
search_type (Optional[str]): Defines the type of search that
the Retriever should perform.
Can be "similarity" (default), "hybrid", or
"semantic_hybrid".
search_kwargs (Optional[Dict]): Keyword arguments to pass to the
search function. Can include things like:
score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns:
AzureSearchVectorStoreRetriever: Retriever class for VectorStore.
"""
tags = kwargs.pop("tags", None) or []
tags.extend(self._get_retriever_tags())
return AzureSearchVectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
[docs]class AzureSearchVectorStoreRetriever(BaseRetriever):
"""Retriever that uses `Azure Cognitive Search`."""
vectorstore: AzureSearch
"""Azure Search instance used to find similar documents."""
search_type: str = "hybrid"
"""Type of search to perform. Options are "similarity", "hybrid",
"semantic_hybrid", "similarity_score_threshold", "hybrid_score_threshold",
or "semantic_hybrid_score_threshold"."""
k: int = 4
"""Number of documents to return."""
search_kwargs: dict = {}
"""Search params.
score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
"""
allowed_search_types: ClassVar[Collection[str]] = (
"similarity",
"similarity_score_threshold",
"hybrid",
"hybrid_score_threshold",
"semantic_hybrid",
"semantic_hybrid_score_threshold",
)
class Config:
arbitrary_types_allowed = True
@root_validator(pre=True)
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
search_type = values["search_type"]
if search_type not in cls.allowed_search_types:
raise ValueError(
f"search_type of {search_type} not allowed. Valid values are: "
f"{cls.allowed_search_types}"
)
return values
def _get_relevant_documents(
self,
query: str,
run_manager: CallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
params = {**self.search_kwargs, **kwargs}
if self.search_type == "similarity":
docs = self.vectorstore.vector_search(query, k=self.k, **params)
elif self.search_type == "similarity_score_threshold":
docs = [
doc
for doc, _ in self.vectorstore.similarity_search_with_relevance_scores(
query, k=self.k, **params
)
]
elif self.search_type == "hybrid":
docs = self.vectorstore.hybrid_search(query, k=self.k, **params)
elif self.search_type == "hybrid_score_threshold":
docs = [
doc
for doc, _ in self.vectorstore.hybrid_search_with_relevance_scores(
query, k=self.k, **params
)
]
elif self.search_type == "semantic_hybrid":
docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **params)
elif self.search_type == "semantic_hybrid_score_threshold":
docs = [
doc
for doc, _ in self.vectorstore.semantic_hybrid_search_with_score(
query, k=self.k, **params
)
]
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
params = {**self.search_kwargs, **kwargs}
if self.search_type == "similarity":
docs = await self.vectorstore.avector_search(query, k=self.k, **params)
elif self.search_type == "similarity_score_threshold":
docs_and_scores = (
await self.vectorstore.asimilarity_search_with_relevance_scores(
query, k=self.k, **params
)
)
docs = [doc for doc, _ in docs_and_scores]
elif self.search_type == "hybrid":
docs = await self.vectorstore.ahybrid_search(query, k=self.k, **params)
elif self.search_type == "hybrid_score_threshold":
docs_and_scores = (
await self.vectorstore.ahybrid_search_with_relevance_scores(
query, k=self.k, **params
)
)
docs = [doc for doc, _ in docs_and_scores]
elif self.search_type == "semantic_hybrid":
docs = await self.vectorstore.asemantic_hybrid_search(
query, k=self.k, **params
)
elif self.search_type == "semantic_hybrid_score_threshold":
docs = [
doc
for doc, _ in await self.vectorstore.asemantic_hybrid_search_with_score(
query, k=self.k, **params
)
]
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
def _results_to_documents(
results: SearchItemPaged[Dict],
) -> List[Tuple[Document, float]]:
docs = [
(
_result_to_document(result),
float(result["@search.score"]),
)
for result in results
]
return docs
async def _aresults_to_documents(
results: AsyncSearchItemPaged[Dict],
) -> List[Tuple[Document, float]]:
docs = [
(
_result_to_document(result),
float(result["@search.score"]),
)
async for result in results
]
return docs
async def _areorder_results_with_maximal_marginal_relevance(
results: SearchItemPaged[Dict],
query_embedding: np.ndarray,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[Tuple[Document, float]]:
# Convert results to Document objects
docs = [
(
_result_to_document(result),
float(result["@search.score"]),
result[FIELDS_CONTENT_VECTOR],
)
async for result in results
]
documents, scores, vectors = map(list, zip(*docs))
# Get the new order of results.
new_ordering = maximal_marginal_relevance(
query_embedding, vectors, k=k, lambda_mult=lambda_mult
)
# Reorder the values and return.
ret: List[Tuple[Document, float]] = []
for x in new_ordering:
# Function can return -1 index
if x == -1:
break
ret.append((documents[x], scores[x])) # type: ignore
return ret
def _reorder_results_with_maximal_marginal_relevance(
results: SearchItemPaged[Dict],
query_embedding: np.ndarray,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[Tuple[Document, float]]:
# Convert results to Document objects
docs = [
(
_result_to_document(result),
float(result["@search.score"]),
result[FIELDS_CONTENT_VECTOR],
)
for result in results
]
documents, scores, vectors = map(list, zip(*docs))
# Get the new order of results.
new_ordering = maximal_marginal_relevance(
query_embedding, vectors, k=k, lambda_mult=lambda_mult
)
# Reorder the values and return.
ret: List[Tuple[Document, float]] = []
for x in new_ordering:
# Function can return -1 index
if x == -1:
break
ret.append((documents[x], scores[x])) # type: ignore
return ret
def _result_to_document(result: Dict) -> Document:
return Document(
page_content=result.pop(FIELDS_CONTENT),
metadata=json.loads(result[FIELDS_METADATA])
if FIELDS_METADATA in result
else {
key: value for key, value in result.items() if key != FIELDS_CONTENT_VECTOR
},
)
def _peek(iterable: Iterable, default: Optional[Any] = None) -> Tuple[Iterable, Any]:
try:
iterator = iter(iterable)
value = next(iterator)
iterable = itertools.chain([value], iterator)
return iterable, value
except StopIteration:
return iterable, default
