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691 lines
25 KiB
Python
691 lines
25 KiB
Python
from __future__ import annotations
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import base64
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import json
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import logging
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import uuid
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Dict,
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Iterable,
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List,
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Optional,
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Tuple,
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Type,
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Union,
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)
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import numpy as np
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from langchain_core.callbacks import (
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AsyncCallbackManagerForRetrieverRun,
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CallbackManagerForRetrieverRun,
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)
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import root_validator
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from langchain_core.retrievers import BaseRetriever
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from langchain_core.utils import get_from_env
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from langchain_core.vectorstores import VectorStore
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logger = logging.getLogger()
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if TYPE_CHECKING:
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from azure.search.documents import SearchClient
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from azure.search.documents.indexes.models import (
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CorsOptions,
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ScoringProfile,
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SearchField,
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SemanticConfiguration,
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VectorSearch,
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)
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# Allow overriding field names for Azure Search
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FIELDS_ID = get_from_env(
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key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id"
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)
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FIELDS_CONTENT = get_from_env(
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key="AZURESEARCH_FIELDS_CONTENT",
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env_key="AZURESEARCH_FIELDS_CONTENT",
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default="content",
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)
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FIELDS_CONTENT_VECTOR = get_from_env(
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key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
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env_key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
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default="content_vector",
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)
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FIELDS_METADATA = get_from_env(
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key="AZURESEARCH_FIELDS_TAG", env_key="AZURESEARCH_FIELDS_TAG", default="metadata"
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)
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MAX_UPLOAD_BATCH_SIZE = 1000
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def _get_search_client(
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endpoint: str,
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key: str,
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index_name: str,
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semantic_configuration_name: Optional[str] = None,
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fields: Optional[List[SearchField]] = None,
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vector_search: Optional[VectorSearch] = None,
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semantic_configurations: Optional[SemanticConfiguration] = None,
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scoring_profiles: Optional[List[ScoringProfile]] = None,
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default_scoring_profile: Optional[str] = None,
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default_fields: Optional[List[SearchField]] = None,
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user_agent: Optional[str] = "langchain",
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cors_options: Optional[CorsOptions] = None,
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) -> SearchClient:
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from azure.core.credentials import AzureKeyCredential
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from azure.core.exceptions import ResourceNotFoundError
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from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
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from azure.search.documents import SearchClient
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from azure.search.documents.indexes import SearchIndexClient
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from azure.search.documents.indexes.models import (
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ExhaustiveKnnAlgorithmConfiguration,
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ExhaustiveKnnParameters,
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HnswAlgorithmConfiguration,
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HnswParameters,
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SearchIndex,
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SemanticConfiguration,
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SemanticField,
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SemanticPrioritizedFields,
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SemanticSearch,
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VectorSearchAlgorithmKind,
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VectorSearchAlgorithmMetric,
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VectorSearchProfile,
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)
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default_fields = default_fields or []
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if key is None:
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credential = DefaultAzureCredential()
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elif key.upper() == "INTERACTIVE":
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credential = InteractiveBrowserCredential()
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credential.get_token("https://search.azure.com/.default")
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else:
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credential = AzureKeyCredential(key)
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index_client: SearchIndexClient = SearchIndexClient(
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endpoint=endpoint, credential=credential, user_agent=user_agent
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)
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try:
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index_client.get_index(name=index_name)
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except ResourceNotFoundError:
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# Fields configuration
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if fields is not None:
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# Check mandatory fields
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fields_types = {f.name: f.type for f in fields}
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mandatory_fields = {df.name: df.type for df in default_fields}
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# Check for missing keys
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missing_fields = {
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key: mandatory_fields[key]
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for key, value in set(mandatory_fields.items())
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- set(fields_types.items())
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}
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if len(missing_fields) > 0:
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# Helper for formatting field information for each missing field.
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def fmt_err(x: str) -> str:
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return (
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f"{x} current type: '{fields_types.get(x, 'MISSING')}'. "
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f"It has to be '{mandatory_fields.get(x)}' or you can point "
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f"to a different '{mandatory_fields.get(x)}' field name by "
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f"using the env variable 'AZURESEARCH_FIELDS_{x.upper()}'"
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)
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error = "\n".join([fmt_err(x) for x in missing_fields])
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raise ValueError(
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f"You need to specify at least the following fields "
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f"{missing_fields} or provide alternative field names in the env "
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f"variables.\n\n{error}"
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)
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else:
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fields = default_fields
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# Vector search configuration
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if vector_search is None:
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vector_search = VectorSearch(
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algorithms=[
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HnswAlgorithmConfiguration(
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name="default",
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kind=VectorSearchAlgorithmKind.HNSW,
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parameters=HnswParameters(
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m=4,
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ef_construction=400,
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ef_search=500,
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metric=VectorSearchAlgorithmMetric.COSINE,
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),
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),
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ExhaustiveKnnAlgorithmConfiguration(
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name="default_exhaustive_knn",
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kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
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parameters=ExhaustiveKnnParameters(
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metric=VectorSearchAlgorithmMetric.COSINE
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),
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),
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],
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profiles=[
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VectorSearchProfile(
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name="myHnswProfile",
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algorithm_configuration_name="default",
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),
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VectorSearchProfile(
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name="myExhaustiveKnnProfile",
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algorithm_configuration_name="default_exhaustive_knn",
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),
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],
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)
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# Create the semantic settings with the configuration
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semantic_search = None
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if semantic_configurations is None and semantic_configuration_name is not None:
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semantic_configuration = SemanticConfiguration(
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name=semantic_configuration_name,
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prioritized_fields=SemanticPrioritizedFields(
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content_fields=[SemanticField(field_name=FIELDS_CONTENT)],
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),
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)
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semantic_search = SemanticSearch(configurations=[semantic_configuration])
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# Create the search index with the semantic settings and vector search
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index = SearchIndex(
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name=index_name,
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fields=fields,
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vector_search=vector_search,
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semantic_search=semantic_search,
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scoring_profiles=scoring_profiles,
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default_scoring_profile=default_scoring_profile,
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cors_options=cors_options,
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)
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index_client.create_index(index)
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# Create the search client
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return SearchClient(
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endpoint=endpoint,
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index_name=index_name,
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credential=credential,
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user_agent=user_agent,
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)
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class AzureSearch(VectorStore):
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"""`Azure Cognitive Search` vector store."""
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def __init__(
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self,
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azure_search_endpoint: str,
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azure_search_key: str,
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index_name: str,
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embedding_function: Union[Callable, Embeddings],
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search_type: str = "hybrid",
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semantic_configuration_name: Optional[str] = None,
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fields: Optional[List[SearchField]] = None,
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vector_search: Optional[VectorSearch] = None,
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semantic_configurations: Optional[SemanticConfiguration] = None,
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scoring_profiles: Optional[List[ScoringProfile]] = None,
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default_scoring_profile: Optional[str] = None,
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cors_options: Optional[CorsOptions] = None,
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**kwargs: Any,
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):
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from azure.search.documents.indexes.models import (
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SearchableField,
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SearchField,
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SearchFieldDataType,
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SimpleField,
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)
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"""Initialize with necessary components."""
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# Initialize base class
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self.embedding_function = embedding_function
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if isinstance(self.embedding_function, Embeddings):
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self.embed_query = self.embedding_function.embed_query
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else:
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self.embed_query = self.embedding_function
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default_fields = [
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SimpleField(
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name=FIELDS_ID,
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type=SearchFieldDataType.String,
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key=True,
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filterable=True,
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),
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SearchableField(
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name=FIELDS_CONTENT,
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type=SearchFieldDataType.String,
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),
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SearchField(
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name=FIELDS_CONTENT_VECTOR,
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type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
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searchable=True,
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vector_search_dimensions=len(self.embed_query("Text")),
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vector_search_configuration="default",
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),
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SearchableField(
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name=FIELDS_METADATA,
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type=SearchFieldDataType.String,
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),
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]
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user_agent = "langchain"
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if "user_agent" in kwargs and kwargs["user_agent"]:
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user_agent += " " + kwargs["user_agent"]
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self.client = _get_search_client(
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azure_search_endpoint,
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azure_search_key,
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index_name,
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semantic_configuration_name=semantic_configuration_name,
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fields=fields,
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vector_search=vector_search,
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semantic_configurations=semantic_configurations,
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scoring_profiles=scoring_profiles,
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default_scoring_profile=default_scoring_profile,
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default_fields=default_fields,
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user_agent=user_agent,
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cors_options=cors_options,
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)
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self.search_type = search_type
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self.semantic_configuration_name = semantic_configuration_name
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self.fields = fields if fields else default_fields
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@property
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def embeddings(self) -> Optional[Embeddings]:
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# TODO: Support embedding object directly
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return None
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Add texts data to an existing index."""
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keys = kwargs.get("keys")
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ids = []
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# batching support if embedding function is an Embeddings object
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if isinstance(self.embedding_function, Embeddings):
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try:
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embeddings = self.embedding_function.embed_documents(texts) # type: ignore[arg-type]
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except NotImplementedError:
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embeddings = [self.embedding_function.embed_query(x) for x in texts]
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else:
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embeddings = [self.embedding_function(x) for x in texts]
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if len(embeddings) == 0:
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logger.debug("Nothing to insert, skipping.")
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return []
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# Write data to index
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data = []
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for i, text in enumerate(texts):
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# Use provided key otherwise use default key
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key = keys[i] if keys else str(uuid.uuid4())
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# Encoding key for Azure Search valid characters
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key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
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metadata = metadatas[i] if metadatas else {}
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# Add data to index
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# Additional metadata to fields mapping
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doc = {
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"@search.action": "upload",
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FIELDS_ID: key,
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FIELDS_CONTENT: text,
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FIELDS_CONTENT_VECTOR: np.array(
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embeddings[i], dtype=np.float32
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).tolist(),
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FIELDS_METADATA: json.dumps(metadata),
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}
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if metadata:
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additional_fields = {
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k: v
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for k, v in metadata.items()
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if k in [x.name for x in self.fields]
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}
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doc.update(additional_fields)
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data.append(doc)
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ids.append(key)
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# Upload data in batches
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if len(data) == MAX_UPLOAD_BATCH_SIZE:
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response = self.client.upload_documents(documents=data)
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# Check if all documents were successfully uploaded
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if not all([r.succeeded for r in response]):
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raise Exception(response)
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# Reset data
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data = []
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# Considering case where data is an exact multiple of batch-size entries
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if len(data) == 0:
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return ids
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# Upload data to index
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response = self.client.upload_documents(documents=data)
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# Check if all documents were successfully uploaded
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if all([r.succeeded for r in response]):
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return ids
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else:
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raise Exception(response)
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def similarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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search_type = kwargs.get("search_type", self.search_type)
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if search_type == "similarity":
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docs = self.vector_search(query, k=k, **kwargs)
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elif search_type == "hybrid":
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docs = self.hybrid_search(query, k=k, **kwargs)
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elif search_type == "semantic_hybrid":
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docs = self.semantic_hybrid_search(query, k=k, **kwargs)
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else:
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raise ValueError(f"search_type of {search_type} not allowed.")
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return docs
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def similarity_search_with_relevance_scores(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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score_threshold = kwargs.pop("score_threshold", None)
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result = self.vector_search_with_score(query, k=k, **kwargs)
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return (
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result
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if score_threshold is None
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else [r for r in result if r[1] >= score_threshold]
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)
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def vector_search(self, query: str, k: int = 4, **kwargs: Any) -> List[Document]:
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"""
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Returns the most similar indexed documents to the query text.
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Args:
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query (str): The query text for which to find similar documents.
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k (int): The number of documents to return. Default is 4.
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Returns:
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List[Document]: A list of documents that are most similar to the query text.
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"""
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docs_and_scores = self.vector_search_with_score(
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query, k=k, filters=kwargs.get("filters", None)
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)
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return [doc for doc, _ in docs_and_scores]
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def vector_search_with_score(
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self, query: str, k: int = 4, filters: Optional[str] = None
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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from azure.search.documents.models import VectorizedQuery
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results = self.client.search(
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search_text="",
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vector_queries=[
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VectorizedQuery(
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vector=np.array(self.embed_query(query), dtype=np.float32).tolist(),
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k_nearest_neighbors=k,
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fields=FIELDS_CONTENT_VECTOR,
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)
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],
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filter=filters,
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top=k,
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)
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# Convert results to Document objects
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docs = [
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(
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Document(
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page_content=result.pop(FIELDS_CONTENT),
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metadata=json.loads(result[FIELDS_METADATA])
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if FIELDS_METADATA in result
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else {
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k: v for k, v in result.items() if k != FIELDS_CONTENT_VECTOR
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},
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),
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float(result["@search.score"]),
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)
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for result in results
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]
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return docs
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|
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def hybrid_search(self, query: str, k: int = 4, **kwargs: Any) -> List[Document]:
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"""
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Returns the most similar indexed documents to the query text.
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Args:
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query (str): The query text for which to find similar documents.
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k (int): The number of documents to return. Default is 4.
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|
|
Returns:
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List[Document]: A list of documents that are most similar to the query text.
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"""
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docs_and_scores = self.hybrid_search_with_score(
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query, k=k, filters=kwargs.get("filters", None)
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)
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return [doc for doc, _ in docs_and_scores]
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|
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def hybrid_search_with_score(
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self, query: str, k: int = 4, filters: Optional[str] = None
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query with an hybrid query.
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|
Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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|
Returns:
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List of Documents most similar to the query and score for each
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"""
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from azure.search.documents.models import VectorizedQuery
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|
|
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results = self.client.search(
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search_text=query,
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vector_queries=[
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VectorizedQuery(
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vector=np.array(self.embed_query(query), dtype=np.float32).tolist(),
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k_nearest_neighbors=k,
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fields=FIELDS_CONTENT_VECTOR,
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)
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],
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filter=filters,
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top=k,
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)
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# Convert results to Document objects
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docs = [
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(
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Document(
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page_content=result.pop(FIELDS_CONTENT),
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metadata=json.loads(result[FIELDS_METADATA])
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if FIELDS_METADATA in result
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else {
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k: v for k, v in result.items() if k != FIELDS_CONTENT_VECTOR
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},
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),
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float(result["@search.score"]),
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)
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for result in results
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]
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return docs
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|
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def semantic_hybrid_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""
|
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Returns the most similar indexed documents to the query text.
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Args:
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query (str): The query text for which to find similar documents.
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k (int): The number of documents to return. Default is 4.
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|
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Returns:
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List[Document]: A list of documents that are most similar to the query text.
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"""
|
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docs_and_scores = self.semantic_hybrid_search_with_score_and_rerank(
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query, k=k, filters=kwargs.get("filters", None)
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)
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return [doc for doc, _, _ in docs_and_scores]
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|
|
def semantic_hybrid_search_with_score(
|
|
self, query: str, k: int = 4, **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.
|
|
|
|
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, filters=kwargs.get("filters", None)
|
|
)
|
|
return [(doc, score) for doc, score, _ in docs_and_scores]
|
|
|
|
def semantic_hybrid_search_with_score_and_rerank(
|
|
self, query: str, k: int = 4, filters: Optional[str] = None
|
|
) -> List[Tuple[Document, float, 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
|
|
"""
|
|
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,
|
|
)
|
|
# 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(
|
|
json.loads(result["metadata"]).get("key"),
|
|
"",
|
|
),
|
|
},
|
|
},
|
|
),
|
|
float(result["@search.score"]),
|
|
float(result["@search.reranker_score"]),
|
|
)
|
|
for result in results
|
|
]
|
|
return 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,
|
|
)
|
|
azure_search.add_texts(texts, metadatas, **kwargs)
|
|
return azure_search
|
|
|
|
|
|
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"."""
|
|
k: int = 4
|
|
"""Number of documents to return."""
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
arbitrary_types_allowed = True
|
|
|
|
@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,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
if self.search_type == "similarity":
|
|
docs = self.vectorstore.vector_search(query, k=self.k, **kwargs)
|
|
elif self.search_type == "hybrid":
|
|
docs = self.vectorstore.hybrid_search(query, k=self.k, **kwargs)
|
|
elif self.search_type == "semantic_hybrid":
|
|
docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **kwargs)
|
|
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"
|
|
)
|