"""Wrapper around Azure Cognitive Search."""
from __future__ import annotations
import base64
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger()
if TYPE_CHECKING:
from azure.search.documents import SearchClient
from azure.search.documents.indexes.models import (
ScoringProfile,
SearchField,
SemanticSettings,
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_settings: Optional[SemanticSettings] = None,
scoring_profiles: Optional[List[ScoringProfile]] = None,
default_scoring_profile: Optional[str] = None,
default_fields: Optional[List[SearchField]] = None,
) -> SearchClient:
from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
PrioritizedFields,
SearchIndex,
SemanticConfiguration,
SemanticField,
SemanticSettings,
VectorSearch,
VectorSearchAlgorithmConfiguration,
)
default_fields = default_fields or []
if key is None:
credential = DefaultAzureCredential()
else:
credential = AzureKeyCredential(key)
index_client: SearchIndexClient = SearchIndexClient(
endpoint=endpoint, credential=credential, user_agent="langchain"
)
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:
fmt_err = lambda x: ( # noqa: E731
f"{x} current type: '{fields_types.get(x, 'MISSING')}'. It has to "
f"be '{mandatory_fields.get(x)}' or you can point to a different "
f"'{mandatory_fields.get(x)}' field name by using the env variable "
f"'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(
algorithm_configurations=[
VectorSearchAlgorithmConfiguration(
name="default",
kind="hnsw",
hnsw_parameters={ # type: ignore
"m": 4,
"efConstruction": 400,
"efSearch": 500,
"metric": "cosine",
},
)
]
)
# Create the semantic settings with the configuration
if semantic_settings is None and semantic_configuration_name is not None:
semantic_settings = SemanticSettings(
configurations=[
SemanticConfiguration(
name=semantic_configuration_name,
prioritized_fields=PrioritizedFields(
prioritized_content_fields=[
SemanticField(field_name=FIELDS_CONTENT)
],
),
)
]
)
# Create the search index with the semantic settings and vector search
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search,
semantic_settings=semantic_settings,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
)
index_client.create_index(index)
# Create the search client
return SearchClient(
endpoint=endpoint,
index_name=index_name,
credential=credential,
user_agent="langchain",
)
[docs]class AzureSearch(VectorStore):
"""Azure Cognitive Search vector store."""
def __init__(
self,
azure_search_endpoint: str,
azure_search_key: str,
index_name: str,
embedding_function: Callable,
search_type: str = "hybrid",
semantic_configuration_name: Optional[str] = None,
semantic_query_language: str = "en-us",
fields: Optional[List[SearchField]] = None,
vector_search: Optional[VectorSearch] = None,
semantic_settings: Optional[SemanticSettings] = None,
scoring_profiles: Optional[List[ScoringProfile]] = None,
default_scoring_profile: Optional[str] = None,
**kwargs: Any,
):
from azure.search.documents.indexes.models import (
SearchableField,
SearchField,
SearchFieldDataType,
SimpleField,
)
"""Initialize with necessary components."""
# Initialize base class
self.embedding_function = 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=len(embedding_function("Text")),
vector_search_configuration="default",
),
SearchableField(
name=FIELDS_METADATA,
type=SearchFieldDataType.String,
),
]
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_settings=semantic_settings,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
default_fields=default_fields,
)
self.search_type = search_type
self.semantic_configuration_name = semantic_configuration_name
self.semantic_query_language = semantic_query_language
self.fields = fields if fields else default_fields
@property
def embeddings(self) -> Optional[Embeddings]:
# TODO: Support embedding object directly
return None
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Add texts data to an existing index."""
keys = kwargs.get("keys")
ids = []
# Write data to index
data = []
for i, text in enumerate(texts):
# 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(
self.embedding_function(text), 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 Exception(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 Exception(response)
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
search_type = kwargs.get("search_type", 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 vector_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.vector_search_with_score(
query, k=k, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
[docs] def vector_search_with_score(
self, query: str, k: int = 4, filters: Optional[str] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to 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
"""
results = self.client.search(
search_text="",
vector=np.array(self.embedding_function(query), dtype=np.float32).tolist(),
top_k=k,
vector_fields=FIELDS_CONTENT_VECTOR,
select=[FIELDS_ID, FIELDS_CONTENT, FIELDS_METADATA],
filter=filters,
)
# Convert results to Document objects
docs = [
(
Document(
page_content=result[FIELDS_CONTENT],
metadata=json.loads(result[FIELDS_METADATA]),
),
float(result["@search.score"]),
)
for result in results
]
return docs
[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, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
[docs] def hybrid_search_with_score(
self, query: str, k: int = 4, filters: Optional[str] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with an 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
"""
results = self.client.search(
search_text=query,
vector=np.array(self.embedding_function(query), dtype=np.float32).tolist(),
top_k=k,
vector_fields=FIELDS_CONTENT_VECTOR,
select=[FIELDS_ID, FIELDS_CONTENT, FIELDS_METADATA],
filter=filters,
top=k,
)
# Convert results to Document objects
docs = [
(
Document(
page_content=result[FIELDS_CONTENT],
metadata=json.loads(result[FIELDS_METADATA]),
),
float(result["@search.score"]),
)
for result in results
]
return docs
[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.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
docs_and_scores = self.semantic_hybrid_search_with_score(
query, k=k, filters=kwargs.get("filters", None)
)
return [doc for doc, _ in docs_and_scores]
[docs] def semantic_hybrid_search_with_score(
self, query: str, k: int = 4, filters: Optional[str] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with an 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
"""
results = self.client.search(
search_text=query,
vector=np.array(self.embedding_function(query), dtype=np.float32).tolist(),
top_k=50, # Hardcoded value to maximize L2 retrieval
vector_fields=FIELDS_CONTENT_VECTOR,
select=[FIELDS_ID, FIELDS_CONTENT, FIELDS_METADATA],
filter=filters,
query_type="semantic",
query_language=self.semantic_query_language,
semantic_configuration_name=self.semantic_configuration_name,
query_caption="extractive",
query_answer="extractive",
top=k,
)
# 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["content"],
metadata={
**json.loads(result["metadata"]),
**{
"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(
json.loads(result["metadata"]).get("key"), ""
),
},
},
),
float(result["@search.score"]),
)
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",
**kwargs: Any,
) -> AzureSearch:
# Creating a new Azure Search instance
azure_search = cls(
azure_search_endpoint,
azure_search_key,
index_name,
embedding.embed_query,
)
azure_search.add_texts(texts, metadatas, **kwargs)
return azure_search
[docs]class AzureSearchVectorStoreRetriever(BaseRetriever):
"""Retriever that uses Azure Search to find similar documents."""
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"."""
k: int = 4
"""Number of documents to return."""
[docs] class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] @root_validator()
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 ("similarity", "hybrid", "semantic_hybrid"):
raise ValueError(f"search_type of {search_type} not allowed.")
return values
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.vector_search(query, k=self.k)
elif self.search_type == "hybrid":
docs = self.vectorstore.hybrid_search(query, k=self.k)
elif self.search_type == "semantic_hybrid":
docs = self.vectorstore.semantic_hybrid_search(query, k=self.k)
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,
) -> List[Document]:
raise NotImplementedError(
"AzureSearchVectorStoreRetriever does not support async"
)