added a multiturn search based on Vertex AI Search (#11885)

Replace this entire comment with:
- **Description:** Added a retriever based on multi-turn Vertex AI
Search
  - **Twitter handle:** lkuligin

docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb

@@ -161,7 +161,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.retrievers import GoogleVertexAISearchRetriever\n",
+    "from langchain.retrievers import GoogleVertexAISearchRetriever, GoogleVertexAIMultiTurnSearchRetriever\n",
     "\n",
     "PROJECT_ID = \"<YOUR PROJECT ID>\"  # Set to your Project ID\n",
     "LOCATION_ID = \"<YOUR LOCATION>\"  # Set to your data store location\n",
@@ -247,6 +247,37 @@
     "for doc in result:\n",
     "    print(doc)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure and use the retrieve for multi-turn search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Search with follow-ups is [based](https://cloud.google.com/generative-ai-app-builder/docs/multi-turn-search) on generative AI models and it is different from the regular unstructured data search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = GoogleVertexAIMultiTurnSearchRetriever(\n",
+    "    project_id=PROJECT_ID,\n",
+    "    location_id=LOCATION_ID,\n",
+    "    data_store_id=DATA_STORE_ID\n",
+    ")\n",
+    "\n",
+    "result = retriever.get_relevant_documents(query)\n",
+    "for doc in result:\n",
+    "    print(doc)"
+   ]
   }
  ],
  "metadata": {

libs/langchain/langchain/retrievers/__init__.py

@@ -35,6 +35,7 @@ from langchain.retrievers.google_cloud_enterprise_search import (
     GoogleCloudEnterpriseSearchRetriever,
 )
 from langchain.retrievers.google_vertex_ai_search import (
+    GoogleVertexAIMultiTurnSearchRetriever,
     GoogleVertexAISearchRetriever,
 )
 from langchain.retrievers.kay import KayAiRetriever
@@ -79,6 +80,7 @@ __all__ = [
     "ElasticSearchBM25Retriever",
     "GoogleDocumentAIWarehouseRetriever",
     "GoogleCloudEnterpriseSearchRetriever",
+    "GoogleVertexAIMultiTurnSearchRetriever",
     "GoogleVertexAISearchRetriever",
     "KayAiRetriever",
     "KNNRetriever",

libs/langchain/langchain/retrievers/google_vertex_ai_search.py

@@ -4,88 +4,32 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence
 
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
-from langchain.pydantic_v1 import Extra, Field, root_validator
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.schema import BaseRetriever, Document
 from langchain.utils import get_from_dict_or_env
 
 if TYPE_CHECKING:
+    from google.api_core.client_options import ClientOptions
     from google.cloud.discoveryengine_v1beta import (
+        ConversationalSearchServiceClient,
         SearchRequest,
         SearchResult,
         SearchServiceClient,
     )
 
 
-class GoogleVertexAISearchRetriever(BaseRetriever):
+class _BaseGoogleVertexAISearchRetriever(BaseModel):
-    """`Google Vertex AI Search` retriever.
-
-    For a detailed explanation of the Vertex AI Search concepts
-    and configuration parameters, refer to the product documentation.
-    https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction
-    """
-
     project_id: str
     """Google Cloud Project ID."""
     data_store_id: str
     """Vertex AI Search data store ID."""
-    serving_config_id: str = "default_config"
-    """Vertex AI Search serving config ID."""
     location_id: str = "global"
     """Vertex AI Search data store location."""
-    filter: Optional[str] = None
-    """Filter expression."""
-    get_extractive_answers: bool = False
-    """If True return Extractive Answers, otherwise return Extractive Segments."""
-    max_documents: int = Field(default=5, ge=1, le=100)
-    """The maximum number of documents to return."""
-    max_extractive_answer_count: int = Field(default=1, ge=1, le=5)
-    """The maximum number of extractive answers returned in each search result.
-    At most 5 answers will be returned for each SearchResult.
-    """
-    max_extractive_segment_count: int = Field(default=1, ge=1, le=1)
-    """The maximum number of extractive segments returned in each search result.
-    Currently one segment will be returned for each SearchResult.
-    """
-    query_expansion_condition: int = Field(default=1, ge=0, le=2)
-    """Specification to determine under which conditions query expansion should occur.
-    0 - Unspecified query expansion condition. In this case, server behavior defaults 
-        to disabled
-    1 - Disabled query expansion. Only the exact search query is used, even if 
-        SearchResponse.total_size is zero.
-    2 - Automatic query expansion built by the Search API.
-    """
-    spell_correction_mode: int = Field(default=2, ge=0, le=2)
-    """Specification to determine under which conditions query expansion should occur.
-    0 - Unspecified spell correction mode. In this case, server behavior defaults 
-        to auto.
-    1 - Suggestion only. Search API will try to find a spell suggestion if there is any
-        and put in the `SearchResponse.corrected_query`.
-        The spell suggestion will not be used as the search query.
-    2 - Automatic spell correction built by the Search API.
-        Search will be based on the corrected query if found.
-    """
     credentials: Any = None
     """The default custom credentials (google.auth.credentials.Credentials) to use
     when making API calls. If not provided, credentials will be ascertained from
     the environment."""
 
-    # TODO: Add extra data type handling for type website
-    engine_data_type: int = Field(default=0, ge=0, le=1)
-    """ Defines the Vertex AI Search data type
-    0 - Unstructured data 
-    1 - Structured data
-    """
-
-    _client: SearchServiceClient
-    _serving_config: str
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.ignore
-        arbitrary_types_allowed = True
-        underscore_attrs_are_private = True
-
     @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validates the environment."""
@@ -94,9 +38,9 @@ class GoogleVertexAISearchRetriever(BaseRetriever):
         except ImportError as exc:
             raise ImportError(
                 "google.cloud.discoveryengine is not installed."
-                "Please install it with pip install google-cloud-discoveryengine"
+                "Please install it with pip install "
+                "google-cloud-discoveryengine>=0.11.0"
             ) from exc
-
         try:
             from google.api_core.exceptions import InvalidArgument  # noqa: F401
         except ImportError as exc:
@@ -130,87 +74,16 @@ class GoogleVertexAISearchRetriever(BaseRetriever):
 
         return values
 
-    def __init__(self, **data: Any) -> None:
+    @property
-        """Initializes private fields."""
+    def client_options(self) -> "ClientOptions":
-        try:
+        from google.api_core.client_options import ClientOptions
-            from google.cloud.discoveryengine_v1beta import SearchServiceClient
-        except ImportError as exc:
-            raise ImportError(
-                "google.cloud.discoveryengine is not installed."
-                "Please install it with pip install google-cloud-discoveryengine"
-            ) from exc
-        try:
-            from google.api_core.client_options import ClientOptions
-        except ImportError as exc:
-            raise ImportError(
-                "google.api_core.client_options is not installed."
-                "Please install it with pip install google-api-core"
-            ) from exc
 
-        super().__init__(**data)
+        return ClientOptions(
-
+            api_endpoint=f"{self.location_id}-discoveryengine.googleapis.com"
-        #  For more information, refer to:
+            if self.location_id != "global"
-        # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
+            else None
-        api_endpoint = (
-            "discoveryengine.googleapis.com"
-            if self.location_id == "global"
-            else f"{self.location_id}-discoveryengine.googleapis.com"
         )
 
-        self._client = SearchServiceClient(
-            credentials=self.credentials,
-            client_options=ClientOptions(api_endpoint=api_endpoint),
-        )
-
-        self._serving_config = self._client.serving_config_path(
-            project=self.project_id,
-            location=self.location_id,
-            data_store=self.data_store_id,
-            serving_config=self.serving_config_id,
-        )
-
-    def _convert_unstructured_search_response(
-        self, results: Sequence[SearchResult]
-    ) -> List[Document]:
-        """Converts a sequence of search results to a list of LangChain documents."""
-        from google.protobuf.json_format import MessageToDict
-
-        documents: List[Document] = []
-
-        for result in results:
-            document_dict = MessageToDict(
-                result.document._pb, preserving_proto_field_name=True
-            )
-            derived_struct_data = document_dict.get("derived_struct_data")
-            if not derived_struct_data:
-                continue
-
-            doc_metadata = document_dict.get("struct_data", {})
-            doc_metadata["id"] = document_dict["id"]
-
-            chunk_type = (
-                "extractive_answers"
-                if self.get_extractive_answers
-                else "extractive_segments"
-            )
-
-            if chunk_type not in derived_struct_data:
-                continue
-
-            for chunk in derived_struct_data[chunk_type]:
-                doc_metadata["source"] = derived_struct_data.get("link", "")
-
-                if chunk_type == "extractive_answers":
-                    doc_metadata["source"] += f":{chunk.get('pageNumber', '')}"
-
-                documents.append(
-                    Document(
-                        page_content=chunk.get("content", ""), metadata=doc_metadata
-                    )
-                )
-
-        return documents
-
     def _convert_structured_search_response(
         self, results: Sequence[SearchResult]
     ) -> List[Document]:
@@ -235,6 +108,128 @@ class GoogleVertexAISearchRetriever(BaseRetriever):
 
         return documents
 
+    def _convert_unstructured_search_response(
+        self, results: Sequence[SearchResult], chunk_type: str
+    ) -> List[Document]:
+        """Converts a sequence of search results to a list of LangChain documents."""
+        from google.protobuf.json_format import MessageToDict
+
+        documents: List[Document] = []
+
+        for result in results:
+            document_dict = MessageToDict(
+                result.document._pb, preserving_proto_field_name=True
+            )
+            derived_struct_data = document_dict.get("derived_struct_data")
+            if not derived_struct_data:
+                continue
+
+            doc_metadata = document_dict.get("struct_data", {})
+            doc_metadata["id"] = document_dict["id"]
+
+            if chunk_type not in derived_struct_data:
+                continue
+
+            for chunk in derived_struct_data[chunk_type]:
+                doc_metadata["source"] = derived_struct_data.get("link", "")
+
+                if chunk_type == "extractive_answers":
+                    doc_metadata["source"] += f":{chunk.get('pageNumber', '')}"
+
+                documents.append(
+                    Document(
+                        page_content=chunk.get("content", ""), metadata=doc_metadata
+                    )
+                )
+
+        return documents
+
+
+class GoogleVertexAISearchRetriever(BaseRetriever, _BaseGoogleVertexAISearchRetriever):
+    """`Google Vertex AI Search` retriever.
+
+    For a detailed explanation of the Vertex AI Search concepts
+    and configuration parameters, refer to the product documentation.
+    https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction
+    """
+
+    serving_config_id: str = "default_config"
+    """Vertex AI Search serving config ID."""
+    filter: Optional[str] = None
+    """Filter expression."""
+    get_extractive_answers: bool = False
+    """If True return Extractive Answers, otherwise return Extractive Segments."""
+    max_documents: int = Field(default=5, ge=1, le=100)
+    """The maximum number of documents to return."""
+    max_extractive_answer_count: int = Field(default=1, ge=1, le=5)
+    """The maximum number of extractive answers returned in each search result.
+    At most 5 answers will be returned for each SearchResult.
+    """
+    max_extractive_segment_count: int = Field(default=1, ge=1, le=1)
+    """The maximum number of extractive segments returned in each search result.
+    Currently one segment will be returned for each SearchResult.
+    """
+    query_expansion_condition: int = Field(default=1, ge=0, le=2)
+    """Specification to determine under which conditions query expansion should occur.
+    0 - Unspecified query expansion condition. In this case, server behavior defaults 
+        to disabled
+    1 - Disabled query expansion. Only the exact search query is used, even if 
+        SearchResponse.total_size is zero.
+    2 - Automatic query expansion built by the Search API.
+    """
+    spell_correction_mode: int = Field(default=2, ge=0, le=2)
+    """Specification to determine under which conditions query expansion should occur.
+    0 - Unspecified spell correction mode. In this case, server behavior defaults 
+        to auto.
+    1 - Suggestion only. Search API will try to find a spell suggestion if there is any
+        and put in the `SearchResponse.corrected_query`.
+        The spell suggestion will not be used as the search query.
+    2 - Automatic spell correction built by the Search API.
+        Search will be based on the corrected query if found.
+    """
+
+    # TODO: Add extra data type handling for type website
+    engine_data_type: int = Field(default=0, ge=0, le=1)
+    """ Defines the Vertex AI Search data type
+    0 - Unstructured data 
+    1 - Structured data
+    """
+
+    _client: SearchServiceClient
+    _serving_config: str
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.ignore
+        arbitrary_types_allowed = True
+        underscore_attrs_are_private = True
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initializes private fields."""
+        try:
+            from google.cloud.discoveryengine_v1beta import SearchServiceClient
+        except ImportError as exc:
+            raise ImportError(
+                "google.cloud.discoveryengine is not installed."
+                "Please install it with pip install google-cloud-discoveryengine"
+            ) from exc
+
+        super().__init__(**kwargs)
+
+        #  For more information, refer to:
+        # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
+        self._client = SearchServiceClient(
+            credentials=self.credentials, client_options=self.client_options
+        )
+
+        self._serving_config = self._client.serving_config_path(
+            project=self.project_id,
+            location=self.location_id,
+            data_store=self.data_store_id,
+            serving_config=self.serving_config_id,
+        )
+
     def _create_search_request(self, query: str) -> SearchRequest:
         """Prepares a SearchRequest object."""
         from google.cloud.discoveryengine_v1beta import SearchRequest
@@ -300,7 +295,14 @@ class GoogleVertexAISearchRetriever(BaseRetriever):
             )
 
         if self.engine_data_type == 0:
-            documents = self._convert_unstructured_search_response(response.results)
+            chunk_type = (
+                "extractive_answers"
+                if self.get_extractive_answers
+                else "extractive_segments"
+            )
+            documents = self._convert_unstructured_search_response(
+                response.results, chunk_type
+            )
         elif self.engine_data_type == 1:
             documents = self._convert_structured_search_response(response.results)
         else:
@@ -312,3 +314,46 @@ class GoogleVertexAISearchRetriever(BaseRetriever):
             )
 
         return documents
+
+
+class GoogleVertexAIMultiTurnSearchRetriever(
+    BaseRetriever, _BaseGoogleVertexAISearchRetriever
+):
+    _client: ConversationalSearchServiceClient
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.ignore
+        arbitrary_types_allowed = True
+        underscore_attrs_are_private = True
+
+    def __init__(self, **kwargs: Any):
+        super().__init__(**kwargs)
+        from google.cloud.discoveryengine_v1beta import (
+            ConversationalSearchServiceClient,
+        )
+
+        self._client = ConversationalSearchServiceClient(
+            credentials=self.credentials, client_options=self.client_options
+        )
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        """Get documents relevant for a query."""
+        from google.cloud.discoveryengine_v1beta import (
+            ConverseConversationRequest,
+            TextInput,
+        )
+
+        request = ConverseConversationRequest(
+            name=self._client.conversation_path(
+                self.project_id, self.location_id, self.data_store_id, "-"
+            ),
+            query=TextInput(input=query),
+        )
+        response = self._client.converse_conversation(request)
+        return self._convert_unstructured_search_response(
+            response.search_results, "extractive_answers"
+        )

libs/langchain/tests/integration_tests/retrievers/test_google_vertex_ai_search.py

@@ -7,8 +7,8 @@ google_vertex_ai_search.ipynb
 to set up the app and configure authentication.
 
 Set the following environment variables before the tests:
-PROJECT_ID - set to your Google Cloud project ID
+export PROJECT_ID=... - set to your Google Cloud project ID
-DATA_STORE_ID - the ID of the search engine to use for the test
+export DATA_STORE_ID=... - the ID of the search engine to use for the test
 """
 
 import os
@@ -18,7 +18,10 @@ import pytest
 from langchain.retrievers.google_cloud_enterprise_search import (
     GoogleCloudEnterpriseSearchRetriever,
 )
-from langchain.retrievers.google_vertex_ai_search import GoogleVertexAISearchRetriever
+from langchain.retrievers.google_vertex_ai_search import (
+    GoogleVertexAIMultiTurnSearchRetriever,
+    GoogleVertexAISearchRetriever,
+)
 from langchain.schema import Document
 
 
@@ -35,6 +38,19 @@ def test_google_vertex_ai_search_get_relevant_documents() -> None:
         assert doc.metadata["source"]
 
 
+@pytest.mark.requires("google_api_core")
+def test_google_vertex_ai_multiturnsearch_get_relevant_documents() -> None:
+    """Test the get_relevant_documents() method."""
+    retriever = GoogleVertexAIMultiTurnSearchRetriever()
+    documents = retriever.get_relevant_documents("What are Alphabet's Other Bets?")
+    assert len(documents) > 0
+    for doc in documents:
+        assert isinstance(doc, Document)
+        assert doc.page_content
+        assert doc.metadata["id"]
+        assert doc.metadata["source"]
+
+
 @pytest.mark.requires("google_api_core")
 def test_google_vertex_ai_search_enterprise_search_deprecation() -> None:
     """Test the deprecation of GoogleCloudEnterpriseSearchRetriever."""