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508 lines
17 KiB
Python
508 lines
17 KiB
Python
"""Wrapper around Azure Cognitive Search."""
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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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)
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import numpy as np
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from pydantic import BaseModel, root_validator
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.schema import BaseRetriever
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from langchain.utils import get_from_env
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from langchain.vectorstores.base 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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# 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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embedding_function: Callable,
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semantic_configuration_name: Optional[str] = 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
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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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PrioritizedFields,
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SearchableField,
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SearchField,
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SearchFieldDataType,
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SearchIndex,
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SemanticConfiguration,
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SemanticField,
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SemanticSettings,
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SimpleField,
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VectorSearch,
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VectorSearchAlgorithmConfiguration,
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)
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if key is None:
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credential = DefaultAzureCredential()
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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
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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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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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searchable=True,
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retrievable=True,
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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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dimensions=len(embedding_function("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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searchable=True,
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retrievable=True,
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),
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]
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# Vector search configuration
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vector_search = VectorSearch(
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algorithm_configurations=[
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VectorSearchAlgorithmConfiguration(
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name="default",
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kind="hnsw",
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hnsw_parameters={
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"m": 4,
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"efConstruction": 400,
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"efSearch": 500,
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"metric": "cosine",
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},
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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_settings = (
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None
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if semantic_configuration_name is None
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else SemanticSettings(
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configurations=[
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SemanticConfiguration(
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name=semantic_configuration_name,
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prioritized_fields=PrioritizedFields(
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prioritized_content_fields=[
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SemanticField(field_name=FIELDS_CONTENT)
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],
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),
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)
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]
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)
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)
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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_settings=semantic_settings,
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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(endpoint=endpoint, index_name=index_name, credential=credential)
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class AzureSearch(VectorStore):
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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: Callable,
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search_type: str = "hybrid",
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semantic_configuration_name: Optional[str] = None,
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semantic_query_language: str = "en-us",
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**kwargs: Any,
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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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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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embedding_function,
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semantic_configuration_name,
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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.semantic_query_language = semantic_query_language
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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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# 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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data.append(
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{
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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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self.embedding_function(text), 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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)
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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)
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elif search_type == "hybrid":
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docs = self.hybrid_search(query, k=k)
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elif search_type == "semantic_hybrid":
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docs = self.semantic_hybrid_search(query, k=k)
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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 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 Vector
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results = self.client.search(
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search_text="",
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vector=Vector(
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value=np.array(
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self.embedding_function(query), dtype=np.float32
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).tolist(),
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k=k,
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fields=FIELDS_CONTENT_VECTOR,
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),
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select=[f"{FIELDS_ID},{FIELDS_CONTENT},{FIELDS_METADATA}"],
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filter=filters,
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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[FIELDS_CONTENT],
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metadata=json.loads(result[FIELDS_METADATA]),
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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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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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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 Vector
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results = self.client.search(
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search_text=query,
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vector=Vector(
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value=np.array(
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self.embedding_function(query), dtype=np.float32
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).tolist(),
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k=k,
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fields=FIELDS_CONTENT_VECTOR,
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),
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select=[f"{FIELDS_ID},{FIELDS_CONTENT},{FIELDS_METADATA}"],
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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[FIELDS_CONTENT],
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metadata=json.loads(result[FIELDS_METADATA]),
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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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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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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(
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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(
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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 Vector
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results = self.client.search(
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search_text=query,
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vector=Vector(
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value=np.array(
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self.embedding_function(query), dtype=np.float32
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).tolist(),
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k=50, # Hardcoded value to maximize L2 retrieval
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fields=FIELDS_CONTENT_VECTOR,
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),
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select=[f"{FIELDS_ID},{FIELDS_CONTENT},{FIELDS_METADATA}"],
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filter=filters,
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query_type="semantic",
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query_language=self.semantic_query_language,
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semantic_configuration_name=self.semantic_configuration_name,
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query_caption="extractive",
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query_answer="extractive",
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top=k,
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)
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# Get Semantic Answers
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semantic_answers = results.get_answers()
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semantic_answers_dict = {}
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for semantic_answer in semantic_answers:
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semantic_answers_dict[semantic_answer.key] = {
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"text": semantic_answer.text,
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"highlights": semantic_answer.highlights,
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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["content"],
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metadata={
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**json.loads(result["metadata"]),
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**{
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"captions": {
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"text": result.get("@search.captions", [{}])[0].text,
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"highlights": result.get("@search.captions", [{}])[
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0
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].highlights,
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}
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if result.get("@search.captions")
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else {},
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"answers": semantic_answers_dict.get(
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json.loads(result["metadata"]).get("key"), ""
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),
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},
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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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@classmethod
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def from_texts(
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cls: Type[AzureSearch],
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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azure_search_endpoint: str = "",
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azure_search_key: str = "",
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index_name: str = "langchain-index",
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**kwargs: Any,
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) -> AzureSearch:
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# Creating a new Azure Search instance
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azure_search = cls(
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azure_search_endpoint,
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azure_search_key,
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index_name,
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embedding.embed_query,
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)
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azure_search.add_texts(texts, metadatas, **kwargs)
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return azure_search
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|
|
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class AzureSearchVectorStoreRetriever(BaseRetriever, BaseModel):
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vectorstore: AzureSearch
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search_type: str = "hybrid"
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k: int = 4
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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|
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@root_validator()
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def validate_search_type(cls, values: Dict) -> Dict:
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"""Validate search type."""
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if "search_type" in values:
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search_type = values["search_type"]
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if search_type not in ("similarity", "hybrid", "semantic_hybrid"):
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raise ValueError(f"search_type of {search_type} not allowed.")
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return values
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def get_relevant_documents(self, query: str) -> List[Document]:
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if self.search_type == "similarity":
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docs = self.vectorstore.vector_search(query, k=self.k)
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elif self.search_type == "hybrid":
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docs = self.vectorstore.hybrid_search(query, k=self.k)
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elif self.search_type == "semantic_hybrid":
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docs = self.vectorstore.semantic_hybrid_search(query, k=self.k)
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else:
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raise ValueError(f"search_type of {self.search_type} not allowed.")
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return docs
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async def aget_relevant_documents(self, query: str) -> List[Document]:
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raise NotImplementedError(
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"AzureSearchVectorStoreRetriever does not support async"
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)
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