diff --git a/libs/community/Makefile b/libs/community/Makefile index b4385e36..a8e56498 100644 --- a/libs/community/Makefile +++ b/libs/community/Makefile @@ -34,14 +34,14 @@ lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: ./scripts/lint_imports.sh - poetry run ruff . + poetry run ruff check . poetry run ruff format $(PYTHON_FILES) --diff - poetry run ruff --select I $(PYTHON_FILES) + poetry run ruff check --select I $(PYTHON_FILES) mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: poetry run ruff format $(PYTHON_FILES) - poetry run ruff --select I --fix $(PYTHON_FILES) + poetry run ruff check --select I --fix $(PYTHON_FILES) spell_check: poetry run codespell --toml pyproject.toml diff --git a/libs/community/langchain_google_community/__init__.py b/libs/community/langchain_google_community/__init__.py index 0a1d7015..5b57e693 100644 --- a/libs/community/langchain_google_community/__init__.py +++ b/libs/community/langchain_google_community/__init__.py @@ -11,6 +11,10 @@ from langchain_google_community.drive import GoogleDriveLoader from langchain_google_community.gcs_directory import GCSDirectoryLoader from langchain_google_community.gcs_file import GCSFileLoader +from langchain_google_community.geocoding import ( + GoogleGeocodingAPIWrapper, + GoogleGeocodingTool, +) from langchain_google_community.gmail.loader import GMailLoader from langchain_google_community.gmail.toolkit import GmailToolkit from langchain_google_community.google_speech_to_text import SpeechToTextLoader @@ -50,6 +54,8 @@ "GMailLoader", "GmailToolkit", "GoogleDriveLoader", + "GoogleGeocodingAPIWrapper", + "GoogleGeocodingTool", "GooglePlacesAPIWrapper", "GooglePlacesTool", "GoogleSearchAPIWrapper", diff --git a/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py b/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py index b28a1da8..fa41473b 100644 --- a/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py +++ b/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py @@ -301,7 +301,7 @@ def _create_search_query( if table_to_query is not None: embeddings_query = f""" with embeddings as ( - SELECT {self.embedding_field}, ROW_NUMBER() OVER() as row_num + SELECT {self.embedding_field}, row_num from `{table_to_query}` )""" @@ -390,6 +390,7 @@ def _create_temp_bq_table( df = pd.DataFrame([]) df[self.embedding_field] = embeddings + df["row_num"] = list(range(len(df))) table_id = ( f"{self.project_id}." f"{self.temp_dataset_name}." @@ -397,7 +398,8 @@ def _create_temp_bq_table( ) schema = [ - bigquery.SchemaField(self.embedding_field, "FLOAT64", mode="REPEATED") + bigquery.SchemaField(self.embedding_field, "FLOAT64", mode="REPEATED"), + bigquery.SchemaField("row_num", "INT64"), ] table_ref = bigquery.Table(table_id, schema=schema) table = self._bq_client.create_table(table_ref) @@ -483,7 +485,7 @@ def batch_search( ) if queries is not None: - embeddings = self.embedding.embed_documents(queries) + embeddings = [self.embedding.embed_query(query) for query in queries] if embeddings is None: raise ValueError("Could not obtain embeddings - value is None.") diff --git a/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py b/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py index 47dd5766..5738c45b 100644 --- a/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py +++ b/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py @@ -406,6 +406,7 @@ def _parse_proto_output( documents.append( [ Document( + id=result.entity_id, page_content=content, metadata=metadata, ), @@ -421,6 +422,7 @@ def _search_embedding( entity_id: Optional[str] = None, k: int = 5, string_filters: Optional[List[dict]] = None, + numeric_filters: Optional[List[dict]] = None, per_crowding_attribute_neighbor_count: Optional[int] = None, approximate_neighbor_candidates: Optional[int] = None, leaf_nodes_search_fraction: Optional[float] = None, @@ -438,6 +440,7 @@ def _search_embedding( embedding=embedding, neighbor_count=k, string_filters=string_filters, + numeric_filters=numeric_filters, per_crowding_attribute_neighbor_count=per_crowding_attribute_neighbor_count, parameters={ "approximate_neighbor_candidates": approximate_neighbor_candidates, diff --git a/libs/community/langchain_google_community/drive.py b/libs/community/langchain_google_community/drive.py index 5f5a3fd4..035356a4 100644 --- a/libs/community/langchain_google_community/drive.py +++ b/libs/community/langchain_google_community/drive.py @@ -9,18 +9,26 @@ import os from pathlib import Path -from typing import Any, Dict, List, Optional, Sequence, Union +from typing import Any, ClassVar, Dict, List, Optional, Sequence, Tuple, Union from langchain_core.document_loaders import BaseLoader from langchain_core.documents import Document from pydantic import BaseModel, field_validator, model_validator -SCOPES = ["https://www.googleapis.com/auth/drive.file"] - class GoogleDriveLoader(BaseLoader, BaseModel): """Load Google Docs from `Google Drive`.""" + # Generated from https://developers.google.com/drive/api/guides/api-specific-auth + # limiting to the scopes that are required to read the files + VALID_SCOPES: ClassVar[Tuple[str, ...]] = ( + "https://www.googleapis.com/auth/drive.file", + "https://www.googleapis.com/auth/drive.readonly", + "https://www.googleapis.com/auth/drive.meet.readonly", + "https://www.googleapis.com/auth/drive.metadata.readonly", + "https://www.googleapis.com/auth/drive.metadata", + ) + service_account_key: Path = Path.home() / ".credentials" / "keys.json" """Path to the service account key file.""" credentials_path: Path = Path.home() / ".credentials" / "credentials.json" @@ -51,6 +59,9 @@ class GoogleDriveLoader(BaseLoader, BaseModel): """Whether to load authorization identities.""" load_extended_metadata: bool = False """Whether to load extended metadata.""" + scopes: List[str] = ["https://www.googleapis.com/auth/drive.file"] + """The credential scopes to use for Google Drive API access. Default is + drive.file scope.""" def _get_file_size_from_id(self, id: str) -> str: """Fetch the size of the file.""" @@ -252,6 +263,22 @@ def validate_credentials_path(cls, v: Any, **kwargs: Any) -> Any: raise ValueError(f"credentials_path {v} does not exist") return v + @field_validator("scopes") + def validate_scopes(cls, v: List[str]) -> List[str]: + """Validate that the provided scopes are not empty and + are valid Google Drive API scopes.""" + if not v: + raise ValueError("At least one scope must be provided") + + invalid_scopes = [scope for scope in v if scope not in cls.VALID_SCOPES] + if invalid_scopes: + raise ValueError( + f"Invalid Google Drive API scope(s): {', '.join(invalid_scopes)}. " + f"Valid scopes are: {', '.join(cls.VALID_SCOPES)}" + ) + + return v + def _load_credentials(self) -> Any: """Load credentials.""" # Adapted from https://developers.google.com/drive/api/v3/quickstart/python @@ -273,11 +300,13 @@ def _load_credentials(self) -> Any: creds = None if self.service_account_key.exists(): return service_account.Credentials.from_service_account_file( - str(self.service_account_key), scopes=SCOPES + str(self.service_account_key), scopes=self.scopes ) if self.token_path.exists(): - creds = Credentials.from_authorized_user_file(str(self.token_path), SCOPES) + creds = Credentials.from_authorized_user_file( + str(self.token_path), self.scopes + ) if self.credentials: # use whatever was passed to us @@ -289,13 +318,13 @@ def _load_credentials(self) -> Any: creds.refresh(Request()) elif "GOOGLE_APPLICATION_CREDENTIALS" not in os.environ: creds, project = default() - creds = creds.with_scopes(SCOPES) + creds = creds.with_scopes(self.scopes) # no need to write to file if creds: return creds else: flow = InstalledAppFlow.from_client_secrets_file( - str(self.credentials_path), SCOPES + str(self.credentials_path), self.scopes ) creds = flow.run_local_server(port=0) with open(self.token_path, "w") as token: diff --git a/libs/community/langchain_google_community/geocoding.py b/libs/community/langchain_google_community/geocoding.py new file mode 100644 index 00000000..49c0c84a --- /dev/null +++ b/libs/community/langchain_google_community/geocoding.py @@ -0,0 +1,599 @@ +"""Wrapper and Tool for the Google Maps Geocoding API. """ + +import os +from typing import Any, Dict, List, Literal, Optional, Tuple, Type + +import aiohttp +import requests +from langchain_core.callbacks import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain_core.tools import BaseTool +from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator + +GOOGLE_MAPS_API_URL = "https://maps.googleapis.com/maps/api/geocode/json" + + +class GoogleGeocodingAPIWrapper(BaseModel): + """Wrapper for Google Maps Geocoding API.""" + + # Required + google_api_key: SecretStr + + # Configuration + include_bounds: bool = Field(default=True) + include_navigation: bool = Field(default=True) + include_metadata: bool = Field(default=True) + include_address_components: bool = Field(default=True) + + # Default parameters + language: Optional[str] = Field(default="en") + region: Optional[str] = Field(default="us") + max_retries: int = Field(default=2) + timeout: int = Field(default=30) + + model_config = ConfigDict(extra="forbid") + + def _add_components(self, cleaned: Dict, result: Dict) -> None: + """Add address components if configured.""" + if not self.include_address_components: + return + + address_types = [ + "street_number", + "route", + "locality", + "country", + "postal_code", + ] + + for component in result.get("address_components", []): + types = component.get("types", []) + name = component.get("long_name") + for type_ in types: + if type_ in address_types: + cleaned["address"][type_] = name + elif type_ == "administrative_area_level_1": + state_name = component.get("short_name") + cleaned["address"]["state"] = state_name + + def _add_geometry(self, cleaned: Dict, result: Dict) -> None: + """Add geometry details if configured.""" + geometry = result.get("geometry", {}) + if self.include_bounds: + if geometry.get("viewport"): + cleaned["geometry"]["viewport"] = geometry["viewport"] + if geometry.get("bounds"): + cleaned["geometry"]["bounds"] = geometry["bounds"] + + def _add_metadata(self, cleaned: Dict, result: Dict) -> None: + """Add metadata if configured.""" + if self.include_metadata: + geometry = result.get("geometry", {}) + cleaned["metadata"] = { + "place_id": result.get("place_id"), + "types": result.get("types", []), + "location_type": (geometry.get("location_type")), + } + + def _add_navigation(self, cleaned: Dict, result: Dict) -> None: + """Add navigation points if configured.""" + if self.include_navigation and result.get("navigation_points"): + cleaned["navigation"] = [ + { + "location": point["location"], + "restrictions": point.get("restricted_travel_modes", []), + } + for point in result.get("navigation_points", []) + ] + + def clean_results(self, results: List[Dict]) -> List[Dict]: + """Clean and format results.""" + cleaned_results = [] + + for result in results: + if not result: + continue + + cleaned = { + "address": {"full": result.get("formatted_address", "")}, + "geometry": { + "location": result.get("geometry", {}).get("location", {}) + }, + } + + # Add optional components + self._add_components(cleaned, result) + self._add_geometry(cleaned, result) + self._add_metadata(cleaned, result) + self._add_navigation(cleaned, result) + + cleaned_results.append(cleaned) + + return cleaned_results + + def results( + self, + query: str, + language: Optional[str] = None, + region: Optional[str] = None, + max_results: int = 10, + ) -> Dict[str, Any]: + """Process geocoding request and return comprehensive results. + + This method handles both single and batch geocoding requests, returning + detailed location information with optional components. + + Args: + query: Location(s) to geocode. + Examples: + - "Eiffel Tower" + - "Times Square, Central Park" + language: Optional language code for results (e.g., "en", "fr", "ja") + region: Optional region bias (e.g., "us", "fr", "jp") + max_results: Maximum number of results to return (default: 10) + + Returns: + Dict containing: + status: Status of the request ("OK" or error status) + total_results: Number of locations found + results: List of dictionaries containing location data: + address: { + full: Complete formatted address + street_number: Building number (if available) + route: Street name + locality: City/Town + state: State/Province + country: Country + postal_code: Postal/ZIP code + } + geometry: { + location: {lat, lng} coordinates + viewport: Recommended viewport + bounds: Geographic bounds (if available) + } + metadata: { + place_id: Unique Google place identifier + types: Categories (e.g., ["establishment", "point_of_interest"]) + location_type:(e.g., "ROOFTOP", "GEOMETRIC_CENTER") + } + navigation: List of navigation points with: + location: {latitude, longitude} + restrictions: Travel mode restrictions + query_info: { + original_query: Input query + language: Language used + region: Region bias used + } + + Example Response: + { + "status": "OK", + "total_results": 2, + "results": [ + { + "address": { + "full": "Street, Country..", + "route": "Avenue Gustave Eiffel", + "locality": "Paris", + "country": "France" + }, + "geometry": { + "location": {"lat": 48.8584, "lng": 2.2945} + } + }, + ... + ], + "query_info": { + "original_query": "Eiffel Tower, Big Ben", + "language": "en", + "region": "us" + } + } + + Raises: + ValueError: If query is empty or invalid + Exception: For API errors or connection issues + """ + try: + if not query.strip(): + return { + "status": "ERROR", + "message": "Empty query provided", + "results": [], + } + + # Handle batch queries + queries = [q.strip() for q in query.split(",") if q.strip()] + if len(queries) > max_results: + queries = queries[:max_results] + + raw_results = self.raw_results( + query=query, language=language, region=region + ) + + if raw_results.get("status") != "OK": + return { + "status": raw_results.get("status", "ERROR"), + "message": raw_results.get("error_message", "No results found"), + "results": [], + } + + return { + "status": "OK", + "total_results": len(raw_results.get("results", [])), + "results": self.clean_results(raw_results.get("results", [])), + "query_info": { + "original_query": query, + "language": language or self.language, + "region": region or self.region, + }, + } + + except Exception as e: + return { + "status": "ERROR", + "message": str(e), + "results": [], + "query_info": { + "original_query": query, + "language": language or self.language, + "region": region or self.region, + }, + } + + def batch_geocode( + self, + locations: List[str], + language: Optional[str] = None, + region: Optional[str] = None, + components: Optional[Dict[str, str]] = None, + ) -> Dict[str, Any]: + """Process multiple locations in a single structured request. + + Efficiently handles multiple location queries, processing them as a batch + while maintaining individual result integrity. + + Args: + locations: List of location strings to geocode + Examples: ["Eiffel Tower", "Times Square", "東京スカイツリー"] + language: Optional language code for results + region: Optional region bias + components: Optional filters (e.g., {"country": "US"}) + + Returns: + Dict containing: + status: Overall batch status + total_results: Number of successful geocoding results + results: List of location data (same structure as single results) + errors: List of any errors encountered: + query: The location query that failed + status: Error status code + message: Detailed error message + query_info: { + total_queries: Total locations processed + successful: Number of successful queries + failed: Number of failed queries + language: Language used + region: Region bias used + } + + Example: + batch_geocode( + locations=["Eiffel Tower", "Big Ben"], + language="en", + components={"country": "FR"} + ) + """ + if not locations: + return { + "status": "ERROR", + "message": "No locations provided", + "results": [], + } + + results = [] + errors = [] + + for location in locations: + try: + result = self.raw_results( + query=location, + language=language, + region=region, + components=components, + ) + + if result.get("status") == "OK": + results.append(result) + else: + errors.append( + { + "query": location, + "status": result.get("status"), + "message": result.get("error_message"), + } + ) + except Exception as e: + errors.append({"query": location, "status": "ERROR", "message": str(e)}) + + return { + "status": "OK" if results else "ERROR", + "total_results": len(results), + "results": self.clean_results( + [r.get("results", [])[0] for r in results if r.get("results")] + ), + "errors": errors, + "query_info": { + "total_queries": len(locations), + "successful": len(results), + "failed": len(errors), + "language": language or self.language, + "region": region or self.region, + }, + } + + def raw_results( + self, + query: str, + language: Optional[str] = None, + region: Optional[str] = None, + components: Optional[Dict[str, str]] = None, + ) -> Dict: + """Get raw results with improved error handling.""" + try: + # Input validation + if not query.strip(): + return { + "status": "ERROR", + "error_message": "Empty query provided", + "results": [], + } + + # Build parameters + params = { + "address": query.strip(), + "key": self.google_api_key.get_secret_value(), + "language": language or self.language, + "region": region or self.region, + } + + # Add component filtering if provided + if components: + params["components"] = "|".join( + f"{k}:{v}" for k, v in components.items() + ) + + for attempt in range(self.max_retries): + try: + response = requests.get( + GOOGLE_MAPS_API_URL, params=params, timeout=self.timeout + ) + response.raise_for_status() + data = response.json() + + if data.get("status") == "OK": + return data + elif attempt == self.max_retries - 1: + return { + "status": data.get("status"), + "error_message": self._get_error_message( + data.get("status") + ), + "results": [], + } + except requests.exceptions.RequestException as e: + if attempt == self.max_retries - 1: + return { + "status": "REQUEST_ERROR", + "error_message": f"Request failed: {str(e)}", + "results": [], + } + + except Exception as e: + return { + "status": "ERROR", + "error_message": f"Processing error: {str(e)}", + "results": [], + } + + # Add explicit return for the case when all retries fail + return { + "status": "ERROR", + "error_message": "All retry attempts failed", + "results": [], + } + + def _get_error_message(self, status: str) -> str: + """Get detailed error message based on status code.""" + error_messages = { + "ZERO_RESULTS": "No results found for this query", + "OVER_DAILY_LIMIT": "API key quota exceeded", + "OVER_QUERY_LIMIT": "Query limit exceeded", + "REQUEST_DENIED": "Request was denied, check API key", + "INVALID_REQUEST": "Invalid request parameters", + "MAX_ELEMENTS_EXCEEDED": "Too many locations in request", + "UNKNOWN_ERROR": "Server error, please try again", + } + return error_messages.get(status, f"API Error: {status}") + + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: + """Validate that api key exists in environment.""" + google_api_key = get_from_dict_or_env( + values, "google_api_key", "GOOGLE_MAPS_API_KEY" + ) + values["google_api_key"] = google_api_key + return values + + async def geocode_async( + self, + query: str, + language: Optional[str] = None, + region: Optional[str] = None, + ) -> Dict[str, Any]: + """Run query through Google Maps Geocoding API asynchronously. + + Args: + query: The location(s) to geocode + language: Optional language code for results + region: Optional region bias + + Returns: + Dict containing: + status: Status of the request + results: List of geocoding results + query_info: Metadata about the request + """ + try: + params: Dict[str, str] = { + "address": query.strip(), + "key": self.google_api_key.get_secret_value(), + "language": language or self.language or "", + "region": region or self.region or "", + } + timeout_obj = aiohttp.ClientTimeout(total=self.timeout) + async with aiohttp.ClientSession() as session: + async with session.get( + GOOGLE_MAPS_API_URL, params=params, timeout=timeout_obj + ) as response: + data = await response.json() + if data.get("status") == "OK": + return self.results(query, language, region) + return { + "status": data.get("status", "ERROR"), + "error_message": data.get("error_message", "Request failed"), + "results": [], + } + + except Exception as e: + return { + "status": "ERROR", + "error_message": f"Async request failed: {str(e)}", + "results": [], + } + + +class GoogleGeocodeInput(BaseModel): + """Input for the Geocoding tool.""" + + query: str = Field(description="Locations for query.") + + +class GoogleGeocodingTool(BaseTool): + """Tool that queries the Google Maps Geocoding API for batch location lookups. + + Instantiate: + .. code-block:: python + + from tools.geocoding_wrapper import GoogleGeocodingTool + + tool = GoogleGeocodingTool( + max_results=5, + include_bounds=True, + include_navigation=True, + include_metadata=True, + language="en" + ) + + Invoke directly: + .. code-block:: python + + result = tool.invoke({ + "query": "Eiffel Tower, Empire State Building" + }) + + Invoke with agent: + .. code-block:: python + + agent.invoke({ + "input": "Find coordinates of Times Square and Central Park" + }) + + Returns: + Tuple containing: + - List of location data with coordinates and addresses + - Raw response data with query information + """ + + name: str = "google_geocode" + description: str = ( + "A geocoding tool for multiple locations. " + "Input: comma-separated locations. " + "Returns: location data." + ) + args_schema: Type[BaseModel] = GoogleGeocodeInput + + # Configuration + max_results: int = 5 + include_bounds: bool = Field(default=True) + include_navigation: bool = Field(default=True) + include_metadata: bool = Field(default=True) + language: Optional[str] = Field(default="en") + region: Optional[str] = Field(default=None) + + api_wrapper: GoogleGeocodingAPIWrapper = Field( + default_factory=lambda: GoogleGeocodingAPIWrapper( + google_api_key=SecretStr(os.getenv("GOOGLE_MAPS_API_KEY", "")) + ) + ) + response_format: Literal["content_and_artifact"] = "content_and_artifact" + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: + """Use the tool.""" + try: + locations = [loc.strip() for loc in query.split(",") if loc.strip()] + if len(locations) > self.max_results: + locations = locations[: self.max_results] + + if len(locations) > 1: + results = self.api_wrapper.batch_geocode( + locations=locations, language=self.language, region=self.region + ) + else: + raw_results = self.api_wrapper.raw_results( + query=locations[0], language=self.language, region=self.region + ) + results = { + "status": raw_results.get("status"), + "total_results": 1, + "results": self.api_wrapper.clean_results( + raw_results.get("results", []) + ), + "query_info": { + "total_queries": 1, + "successful": 1 if raw_results.get("status") == "OK" else 0, + "failed": 0 if raw_results.get("status") == "OK" else 1, + }, + } + + if results.get("status") != "OK": + return [], {"error": results.get("error_message", "Geocoding failed")} + + return results["results"], results + + except Exception as e: + return [], {"error": str(e)} + + async def _arun( + self, + query: str, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: + """Use the tool asynchronously.""" + try: + result = await self.api_wrapper.geocode_async( + query=query, language=self.language, region=self.region + ) + if result.get("status") != "OK": + return [], {"error": result.get("error_message", "Geocoding failed")} + return result.get("results", []), result + except Exception as e: + return [], {"error": str(e)} diff --git a/libs/community/langchain_google_community/vertex_ai_search.py b/libs/community/langchain_google_community/vertex_ai_search.py index 29a9e3c1..08baec53 100644 --- a/libs/community/langchain_google_community/vertex_ai_search.py +++ b/libs/community/langchain_google_community/vertex_ai_search.py @@ -8,14 +8,13 @@ import json import warnings -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union from google.api_core.client_options import ClientOptions from google.api_core.exceptions import InvalidArgument from google.protobuf.json_format import MessageToDict from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.embeddings import Embeddings from langchain_core.load import Serializable, load from langchain_core.retrievers import BaseRetriever from langchain_core.tools import BaseTool @@ -25,12 +24,33 @@ from langchain_google_community._utils import get_client_info if TYPE_CHECKING: - from google.cloud.discoveryengine_v1beta import ( # type: ignore[import, attr-defined] + from google.cloud.discoveryengine_v1 import ( ConversationalSearchServiceClient, SearchRequest, - SearchResult, + SearchResponse, SearchServiceClient, ) + from google.cloud.discoveryengine_v1beta import ( + ConversationalSearchServiceClient as BetaConversationalSearchServiceClient, + ) + from google.cloud.discoveryengine_v1beta import ( + SearchRequest as BetaSearchRequest, + ) + from google.cloud.discoveryengine_v1beta import ( + SearchResponse as BetaSearchResponse, + ) + from google.cloud.discoveryengine_v1beta import ( + SearchServiceClient as BetaSearchServiceClient, + ) + + DiscoveryEngineClient = Union[SearchServiceClient, BetaSearchServiceClient] + DiscoveryEngineSearchRequest = Union[SearchRequest, BetaSearchRequest] + DiscoveryEngineSearchResult = Union[ + SearchResponse.SearchResult, BetaSearchResponse.SearchResult + ] + DiscoveryEngineConversationalClient = Union[ + ConversationalSearchServiceClient, BetaConversationalSearchServiceClient + ] def _load(dump: Dict[str, Any]) -> Any: @@ -56,6 +76,24 @@ class _BaseVertexAISearchRetriever(Serializable): 1 - Structured data 2 - Website data """ + _beta: bool = PrivateAttr(default=False) + """Whether to use beta version of Vertex AI Search.""" + + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + validate_assignment=True, + ) + + def __init__(self, **kwargs: Any) -> None: + beta = kwargs.pop("beta", False) + super().__init__(**kwargs) + self._beta = beta + + @property + def beta(self) -> bool: + """Get whether beta version is enabled.""" + return self._beta @classmethod def is_lc_serializable(self) -> bool: @@ -69,7 +107,10 @@ def __reduce__(self) -> Any: def validate_environment(cls, values: Dict) -> Any: """Validates the environment.""" try: - from google.cloud import discoveryengine_v1beta # noqa: F401 + if values.get("beta", False): + from google.cloud import discoveryengine_v1beta # noqa: F401 + else: + from google.cloud import discoveryengine_v1 # noqa: F401 except ImportError as exc: raise ImportError( "Could not import google-cloud-discoveryengine python package. " @@ -111,7 +152,7 @@ def client_options(self) -> "ClientOptions": ) def _convert_structured_search_response( - self, results: Sequence[SearchResult] + self, results: Sequence[DiscoveryEngineSearchResult] ) -> List[Document]: """Converts a sequence of search results to a list of LangChain documents.""" documents: List[Document] = [] @@ -131,7 +172,7 @@ def _convert_structured_search_response( return documents def _convert_unstructured_search_response( - self, results: Sequence[SearchResult], chunk_type: str + self, results: Sequence[DiscoveryEngineSearchResult], chunk_type: str ) -> List[Document]: """Converts a sequence of search results to a list of LangChain documents.""" documents: List[Document] = [] @@ -178,7 +219,7 @@ def _convert_unstructured_search_response( return documents def _convert_website_search_response( - self, results: Sequence[SearchResult], chunk_type: str + self, results: Sequence[DiscoveryEngineSearchResult], chunk_type: str ) -> List[Document]: """Converts a sequence of search results to a list of LangChain documents.""" documents: List[Document] = [] @@ -222,6 +263,9 @@ def _convert_website_search_response( class VertexAISearchRetriever(BaseRetriever, _BaseVertexAISearchRetriever): """`Google Vertex AI Search` retriever. + This retriever supports both stable (v1) and beta versions of the Discovery Engine. + Beta features are only available when beta=True. + 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 @@ -249,66 +293,91 @@ class VertexAISearchRetriever(BaseRetriever, _BaseVertexAISearchRetriever): """ num_previous_segments: int = Field(default=1, ge=1, le=3) """Specifies the number of text segments preceding the matched segment to return. - This provides context before the relevant text. Value must be between 1 and 3. - """ + This provides context before the relevant text. Value must be between 1 and 3.""" num_next_segments: int = Field(default=1, ge=1, le=3) """Specifies the number of text segments following the matched segment to return. - This provides context after the relevant text. Value must be between 1 and 3. - """ + This provides context after the relevant text. Value must be between 1 and 3.""" 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 + 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. - """ + 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`. + 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. - """ + Search will be based on the corrected query if found.""" boost_spec: Optional[Dict[Any, Any]] = None """BoostSpec for boosting search results. A protobuf should be provided. https://cloud.google.com/generative-ai-app-builder/docs/boost-search-results https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1beta/BoostSpec """ - custom_embedding: Optional[Embeddings] = None + custom_embedding: Optional[Any] = None """Custom embedding model for the retriever. (Bring your own embedding) It needs to match the embedding model that was used to embed docs in the datastore. It needs to be a langchain embedding VertexAIEmbeddings(project="{PROJECT}") - If you provide an embedding model, you also need to provide a ranking_expression and - a custom_embedding_field_path. + If you provide an embedding model, you also need to provide a ranking_expression + and a custom_embedding_field_path. https://cloud.google.com/generative-ai-app-builder/docs/bring-embeddings """ custom_embedding_field_path: Optional[str] = None - """ The field path for the custom embedding used in the Vertex AI datastore schema. + """The field path for the custom embedding used in the Vertex AI datastore schema. """ - custom_embedding_ratio: Optional[float] = 0.0 + custom_embedding_ratio: Optional[float] = Field(default=0.0, ge=0.0, le=1.0) """Controls the ranking of results. Value should be between 0 and 1. It will generate the ranking_expression in the following manner: "{custom_embedding_ratio} * dotProduct({custom_embedding_field_path}) + {1 - custom_embedding_ratio} * relevance_score" """ - _client: SearchServiceClient = PrivateAttr() + _client: DiscoveryEngineClient = PrivateAttr() _serving_config: str = PrivateAttr() - model_config = ConfigDict( - extra="forbid", - arbitrary_types_allowed=True, - ) - def __init__(self, **kwargs: Any) -> None: - """Initializes private fields.""" + """Initialize the retriever with the appropriate version of the client.""" + super().__init__(**kwargs) + self._validate_version_compatibility() + self._initialize_client() + # For more information, refer to: + # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store + + def _validate_version_compatibility(self) -> None: + """Validate version compatibility of all components. + + Raises: + Warning: If beta features are configured but beta=False + """ + beta_features = { + "custom_embedding": self.custom_embedding, + "custom_embedding_field_path": self.custom_embedding_field_path, + "custom_embedding_ratio": self.custom_embedding_ratio, + } + + if not self.beta and any(value is not None for value in beta_features.values()): + warnings.warn( + "Beta features are configured but beta=False. The following beta features will be ignored:" # noqa: E501 + f"{[k for k, v in beta_features.items() if v is not None]}" + ) + + def _initialize_client(self) -> None: + """Initialize the appropriate version of the SearchServiceClient.""" + try: - from google.cloud.discoveryengine_v1beta import SearchServiceClient + if self.beta: + from google.cloud.discoveryengine_v1beta import ( # type: ignore[assignment] + SearchServiceClient, + ) + else: + from google.cloud.discoveryengine_v1 import ( # type: ignore[assignment] + SearchServiceClient, + ) except ImportError as exc: raise ImportError( "Could not import google-cloud-discoveryengine python package. " @@ -316,14 +385,6 @@ def __init__(self, **kwargs: Any) -> None: "`pip install langchain-google-community[vertexaisearch]`" ) from exc - try: - super().__init__(**kwargs) - except ValueError as e: - print(f"Error initializing GoogleVertexAISearchRetriever: {str(e)}") - raise - - # 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, @@ -337,10 +398,66 @@ def __init__(self, **kwargs: Any) -> None: serving_config=self.serving_config_id, ) + def _get_beta_specific_params(self, query: str) -> Dict[str, Any]: + """Get parameters that are only available in beta version.""" + + params: dict[str, Any] = {} + + if not self.beta: + return params + + if ( + self.custom_embedding is not None + or self.custom_embedding_field_path is not None + ): + if self.custom_embedding is None: + raise ValueError( + "Please provide a custom embedding model if you provide a " + "custom_embedding_field_path." + ) + if self.custom_embedding_field_path is None: + raise ValueError( + "Please provide a custom_embedding_field_path if you provide a " + "custom embedding model." + ) + if self.custom_embedding_ratio is None: + raise ValueError( + "Please provide a custom_embedding_ratio if you provide a " + "custom embedding model or a custom_embedding_field_path." + ) + + from google.cloud.discoveryengine_v1beta import SearchRequest + + embedding_vector = SearchRequest.EmbeddingSpec.EmbeddingVector( + field_path=self.custom_embedding_field_path, + vector=self.custom_embedding.embed_query(query), + ) + params.update( + { + "embedding_spec": SearchRequest.EmbeddingSpec( + embedding_vectors=[embedding_vector] + ), + "ranking_expression": ( + f"{self.custom_embedding_ratio} * " + f"dotProduct({self.custom_embedding_field_path}) + " + f"{1 - self.custom_embedding_ratio} * relevance_score" + ), + } + ) + + return params + def _get_content_spec_kwargs(self) -> Optional[Dict[str, Any]]: - """Prepares a ContentSpec object.""" + """Prepares a ContentSpec object. - from google.cloud.discoveryengine_v1beta import SearchRequest + Note: + The ContentSearchSpec is identical between beta and stable versions, + but we import from the appropriate version for consistency. + """ + if self.beta: + from google.cloud.discoveryengine_v1beta import SearchRequest + else: + from google.cloud.discoveryengine_v1 import SearchRequest if self.engine_data_type == 0: if self.get_extractive_answers: @@ -365,8 +482,10 @@ def _get_content_spec_kwargs(self) -> Optional[Dict[str, Any]]: content_search_spec = None elif self.engine_data_type == 2: content_search_spec = dict( - extractive_content_spec=SearchRequest.ContentSearchSpec.ExtractiveContentSpec( - max_extractive_answer_count=self.max_extractive_answer_count, + extractive_content_spec=( + SearchRequest.ContentSearchSpec.ExtractiveContentSpec( + max_extractive_answer_count=self.max_extractive_answer_count, + ) ), snippet_spec=SearchRequest.ContentSearchSpec.SnippetSpec( return_snippet=True @@ -380,9 +499,12 @@ def _get_content_spec_kwargs(self) -> Optional[Dict[str, Any]]: ) return content_search_spec - def _create_search_request(self, query: str) -> SearchRequest: + def _create_search_request(self, query: str) -> "DiscoveryEngineSearchRequest": """Prepares a SearchRequest object.""" - from google.cloud.discoveryengine_v1beta import SearchRequest + if self.beta: + from google.cloud.discoveryengine_v1beta import SearchRequest + else: + from google.cloud.discoveryengine_v1 import SearchRequest query_expansion_spec = SearchRequest.QueryExpansionSpec( condition=self.query_expansion_condition, @@ -393,76 +515,37 @@ def _create_search_request(self, query: str) -> SearchRequest: ) content_search_spec_kwargs = self._get_content_spec_kwargs() + content_search_spec = ( + SearchRequest.ContentSearchSpec(**content_search_spec_kwargs) + if content_search_spec_kwargs is not None + else None + ) - if content_search_spec_kwargs is not None: - content_search_spec = SearchRequest.ContentSearchSpec( - **content_search_spec_kwargs - ) - else: - content_search_spec = None - - if ( - self.custom_embedding is not None - or self.custom_embedding_field_path is not None - ): - if self.custom_embedding is None: - raise ValueError( - "Please provide a custom embedding model if you provide a " - "custom_embedding_field_path." - ) - if self.custom_embedding_field_path is None: - raise ValueError( - "Please provide a custom_embedding_field_path if you provide a " - "custom embedding model." - ) - if self.custom_embedding_ratio is None: - raise ValueError( - "Please provide a custom_embedding_ratio if you provide a " - "custom embedding model or a custom_embedding_field_path." - ) - if not 0 <= self.custom_embedding_ratio <= 1: - raise ValueError( - "Custom embedding ratio must be between 0 and 1 " - f"when using custom embeddings. Got {self.custom_embedding_ratio}" - ) - embedding_vector = SearchRequest.EmbeddingSpec.EmbeddingVector( - field_path=self.custom_embedding_field_path, - vector=self.custom_embedding.embed_query(query), - ) - embedding_spec = SearchRequest.EmbeddingSpec( - embedding_vectors=[embedding_vector] - ) - ranking_expression = ( - f"{self.custom_embedding_ratio} * " - f"dotProduct({self.custom_embedding_field_path}) + " - f"{1 - self.custom_embedding_ratio} * relevance_score" - ) - else: - embedding_spec = None - ranking_expression = None - - return SearchRequest( - query=query, - filter=self.filter, - order_by=self.order_by, - canonical_filter=self.canonical_filter, - serving_config=self._serving_config, - page_size=self.max_documents, - content_search_spec=content_search_spec, - query_expansion_spec=query_expansion_spec, - spell_correction_spec=spell_correction_spec, - boost_spec=SearchRequest.BoostSpec(**self.boost_spec) + request_params = { + "query": query, + "filter": self.filter, + "order_by": self.order_by, + "canonical_filter": self.canonical_filter, + "serving_config": self._serving_config, + "page_size": self.max_documents, + "content_search_spec": content_search_spec, + "query_expansion_spec": query_expansion_spec, + "spell_correction_spec": spell_correction_spec, + "boost_spec": SearchRequest.BoostSpec(**self.boost_spec) if self.boost_spec else None, - embedding_spec=embedding_spec, - ranking_expression=ranking_expression, - ) + } + + # Add beta-specific parameters if applicable + beta_params = self._get_beta_specific_params(query) + request_params.update(beta_params) + + return SearchRequest(**request_params) def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: """Get documents relevant for a query.""" - search_request = self._create_search_request(query) try: @@ -502,12 +585,14 @@ def _get_relevant_documents( class VertexAIMultiTurnSearchRetriever(BaseRetriever, _BaseVertexAISearchRetriever): - """`Google Vertex AI Search` retriever for multi-turn conversations.""" + """Google Vertex AI Search retriever for multi-turn conversations. + Supports both stable (v1) and beta versions of the Discovery Engine API. + """ conversation_id: str = "-" """Vertex AI Search Conversation ID.""" - _client: ConversationalSearchServiceClient = PrivateAttr() + _client: DiscoveryEngineConversationalClient = PrivateAttr() _serving_config: str = PrivateAttr() model_config = ConfigDict( @@ -517,9 +602,15 @@ class VertexAIMultiTurnSearchRetriever(BaseRetriever, _BaseVertexAISearchRetriev def __init__(self, **kwargs: Any): super().__init__(**kwargs) - from google.cloud.discoveryengine_v1beta import ( - ConversationalSearchServiceClient, - ) + + if self.beta: + from google.cloud.discoveryengine_v1beta import ( # type: ignore[assignment] + ConversationalSearchServiceClient, + ) + else: + from google.cloud.discoveryengine_v1 import ( # type: ignore[assignment] + ConversationalSearchServiceClient, + ) self._client = ConversationalSearchServiceClient( credentials=self.credentials, @@ -545,10 +636,17 @@ 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, - ) + + if self.beta: + from google.cloud.discoveryengine_v1beta import ( + ConverseConversationRequest, + TextInput, + ) + else: + from google.cloud.discoveryengine_v1 import ( + ConverseConversationRequest, + TextInput, + ) request = ConverseConversationRequest( name=self._client.conversation_path( @@ -575,6 +673,7 @@ def _get_relevant_documents( class VertexAISearchSummaryTool(BaseTool, VertexAISearchRetriever): """Class that exposes a tool to interface with an App in Vertex Search and Conversation and get the summary of the documents retrieved. + Supports both stable (v1) and beta versions of the Discovery Engine API. """ summary_prompt: Optional[str] = None @@ -599,10 +698,16 @@ def _get_content_spec_kwargs(self) -> Optional[Dict[str, Any]]: Returns: kwargs for the specification of the content. """ - from google.cloud.discoveryengine_v1beta import SearchRequest + # Import appropriate version based on beta flag + if self.beta: + from google.cloud.discoveryengine_v1beta import SearchRequest + else: + from google.cloud.discoveryengine_v1 import SearchRequest kwargs = super()._get_content_spec_kwargs() or {} + # SummarySpec is identical between beta and stable versions, + # but we import from the appropriate version for consistency kwargs["summary_spec"] = SearchRequest.ContentSearchSpec.SummarySpec( summary_result_count=self.summary_result_count, include_citations=self.summary_include_citations, diff --git a/libs/community/langchain_google_community/vertex_check_grounding.py b/libs/community/langchain_google_community/vertex_check_grounding.py index 1efc4789..ddf251c1 100644 --- a/libs/community/langchain_google_community/vertex_check_grounding.py +++ b/libs/community/langchain_google_community/vertex_check_grounding.py @@ -41,7 +41,7 @@ class VertexAICheckGroundingWrapper( account credentials file. """ - project_id: str = Field(default=None) + project_id: str = Field(default=None) # type: ignore location_id: str = Field(default="global") grounding_config: str = Field(default="default_grounding_config") citation_threshold: Optional[float] = Field(default=0.6) @@ -94,7 +94,7 @@ def _get_check_grounding_service_client( client_info=get_client_info(module="vertex-ai-search"), ) - def invoke( + def invoke( # type: ignore[override] self, input: str, config: Optional[RunnableConfig] = None ) -> CheckGroundingResponse: """ diff --git a/libs/community/langchain_google_community/vertex_rank.py b/libs/community/langchain_google_community/vertex_rank.py index 02e68e35..ff3989de 100644 --- a/libs/community/langchain_google_community/vertex_rank.py +++ b/libs/community/langchain_google_community/vertex_rank.py @@ -54,7 +54,7 @@ class VertexAIRank(BaseDocumentCompressor): account credentials file. """ - project_id: str = Field(default=None) + project_id: str = Field(default=None) # type: ignore location_id: str = Field(default="global") ranking_config: str = Field(default="default_config") model: str = Field(default="semantic-ranker-512@latest") diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index 065a78cb..cc62235e 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,124 +1,110 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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+4057,4 @@ vision = ["google-cloud-vision"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<4.0" -content-hash = "6538467b94ebe7e6f33cca8c07a0b888b5efbcc5474602a0f735d776e1ebc5ab" +content-hash = "2164d80ebaf39791c029f1fb94e8848f74a6685485e6da02d81c0d93e6e63db9" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 9b7eb7fe..66b76780 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-community" -version = "2.0.3" +version = "2.0.4" description = "An integration package connecting miscellaneous Google's products and LangChain" authors = [] readme = "README.md" @@ -30,7 +30,7 @@ google-cloud-speech = { version = "^2.26.0", optional = true } googlemaps = { version = "^4.10.0", optional = true } google-cloud-texttospeech = { version = "^2.16.3", optional = true } google-cloud-translate = { version = "^3.15.3", optional = true } -google-cloud-discoveryengine = { version = "^0.11.13", optional = true } +google-cloud-discoveryengine = { version = "^0.11.14", optional = true } google-cloud-vision = { version = "^3.7.2", optional = true } beautifulsoup4 = { version = "^4.12.3", optional = true } pandas = [ @@ -75,7 +75,7 @@ pytest-asyncio = "^0.21.1" google-cloud-documentai = "^2.24.2" google-cloud-documentai-toolbox = "^0.13.3a0" google-cloud-bigquery = "^3.19.0" -google-cloud-discoveryengine = "^0.11.11" +google-cloud-discoveryengine = "^0.11.14" cloudpickle = "^3.0.0" [tool.poetry.group.codespell] diff --git a/libs/community/tests/integration_tests/.env.example b/libs/community/tests/integration_tests/.env.example index 2c9631e6..dea61388 100644 --- a/libs/community/tests/integration_tests/.env.example +++ b/libs/community/tests/integration_tests/.env.example @@ -2,4 +2,5 @@ PROJECT_ID=project_id DATA_STORE_ID=data_store_id IMAGE_GCS_PATH=image_gcs_path GOOGLE_API_KEY=google_api_key -GOOGLE_CSE_ID=google_cse_id \ No newline at end of file +GOOGLE_CSE_ID=google_cse_id +GOOGLE_MAPS_API_KEY=google_maps_api_key \ No newline at end of file diff --git a/libs/community/tests/integration_tests/test_geocoding_integration.py b/libs/community/tests/integration_tests/test_geocoding_integration.py new file mode 100644 index 00000000..3dfe8b23 --- /dev/null +++ b/libs/community/tests/integration_tests/test_geocoding_integration.py @@ -0,0 +1,83 @@ +"""Integration tests for Google Geocoding API.""" + +import os + +import pytest +from pydantic import SecretStr + +from langchain_google_community import GoogleGeocodingAPIWrapper, GoogleGeocodingTool + + +@pytest.fixture +def api_wrapper() -> GoogleGeocodingAPIWrapper: + """Create API wrapper with credentials from environment.""" + api_key = os.getenv("GOOGLE_MAPS_API_KEY") + if not api_key: + pytest.skip("GOOGLE_MAPS_API_KEY environment variable not set") + return GoogleGeocodingAPIWrapper(google_api_key=SecretStr(api_key)) + + +@pytest.fixture +def geocoding_tool(api_wrapper: GoogleGeocodingAPIWrapper) -> GoogleGeocodingTool: + """Create geocoding tool with the API wrapper.""" + return GoogleGeocodingTool( + api_wrapper=api_wrapper, + max_results=5, + include_bounds=True, + include_metadata=True, + ) + + +@pytest.mark.asyncio +async def test_geocode_async(api_wrapper: GoogleGeocodingAPIWrapper) -> None: + """Test async geocoding functionality.""" + result = await api_wrapper.geocode_async("Statue of Liberty, New York") + + assert result["status"] == "OK" + assert len(result["results"]) > 0 + + location = result["results"][0] + assert "address" in location + assert "New York" in location["address"]["full"] + + # Check coordinates are roughly correct for Statue of Liberty + lat = location["geometry"]["location"]["lat"] + lng = location["geometry"]["location"]["lng"] + assert 40.68 < lat < 40.69 # Statue of Liberty latitude + assert -74.05 < lng < -74.04 # Statue of Liberty longitude + + +def test_geocode_batch(api_wrapper: GoogleGeocodingAPIWrapper) -> None: + """Test batch geocoding with multiple locations.""" + locations = ["Times Square, New York", "Big Ben, London", "Sydney Opera House"] + + result = api_wrapper.batch_geocode(locations) + assert result["status"] == "OK" + assert len(result["results"]) == len(locations) + + # Verify each location has valid data + for location in result["results"]: + assert "address" in location + assert "full" in location["address"] + assert "geometry" in location + assert "location" in location["geometry"] + assert "lat" in location["geometry"]["location"] + assert "lng" in location["geometry"]["location"] + + +def test_geocode_error_handling(api_wrapper: GoogleGeocodingAPIWrapper) -> None: + """Test error handling with invalid queries.""" + result = api_wrapper.raw_results("NonexistentPlace12345!@#$%") + + assert result["status"] == "ZERO_RESULTS" + assert len(result["results"]) == 0 + + +def test_tool_batch_query(geocoding_tool: GoogleGeocodingTool) -> None: + """Test geocoding tool with batch query.""" + query = "Times Square, Central Park, Empire State Building" + result, metadata = geocoding_tool._run(query) + + assert len(result) == 3 + assert metadata["status"] == "OK" + assert all("geometry" in loc for loc in result) diff --git a/libs/community/tests/integration_tests/test_rank.py b/libs/community/tests/integration_tests/test_rank.py index 80250064..7416d0e4 100644 --- a/libs/community/tests/integration_tests/test_rank.py +++ b/libs/community/tests/integration_tests/test_rank.py @@ -16,8 +16,8 @@ class CustomRankingRetriever(BaseRetriever): """Retriever that directly uses a mock retriever and a ranking API.""" - base_retriever: BaseRetriever = Field(default=None) - ranker: VertexAIRank = Field(default=None) + base_retriever: BaseRetriever = Field(default=None) # type: ignore + ranker: VertexAIRank = Field(default=None) # type: ignore def __init__(self, base_retriever: BaseRetriever, ranker: VertexAIRank): super().__init__() # Call to the superclass's constructor diff --git a/libs/community/tests/unit_tests/test_drive.py b/libs/community/tests/unit_tests/test_drive.py new file mode 100644 index 00000000..89f4b6f7 --- /dev/null +++ b/libs/community/tests/unit_tests/test_drive.py @@ -0,0 +1,39 @@ +import pytest + +from langchain_google_community.drive import GoogleDriveLoader + + +def test_drive_default_scope() -> None: + """Test that default scope is set correctly.""" + loader = GoogleDriveLoader(folder_id="dummy_folder") + assert loader.scopes == ["https://www.googleapis.com/auth/drive.file"] + + +def test_drive_custom_scope() -> None: + """Test setting custom scope.""" + custom_scopes = ["https://www.googleapis.com/auth/drive.readonly"] + loader = GoogleDriveLoader(folder_id="dummy_folder", scopes=custom_scopes) + assert loader.scopes == custom_scopes + + +def test_drive_multiple_scopes() -> None: + """Test setting multiple valid scopes.""" + custom_scopes = [ + "https://www.googleapis.com/auth/drive.readonly", + "https://www.googleapis.com/auth/drive.metadata.readonly", + ] + loader = GoogleDriveLoader(folder_id="dummy_folder", scopes=custom_scopes) + assert loader.scopes == custom_scopes + + +def test_drive_empty_scope_list() -> None: + """Test that empty scope list raises error.""" + with pytest.raises(ValueError, match="At least one scope must be provided"): + GoogleDriveLoader(folder_id="dummy_folder", scopes=[]) + + +def test_drive_invalid_scope() -> None: + """Test that invalid scope raises error.""" + invalid_scopes = ["https://www.googleapis.com/auth/drive.invalid"] + with pytest.raises(ValueError, match="Invalid Google Drive API scope"): + GoogleDriveLoader(folder_id="dummy_folder", scopes=invalid_scopes) diff --git a/libs/community/tests/unit_tests/test_vertex_ai_search.py b/libs/community/tests/unit_tests/test_vertex_ai_search.py index a3c4af88..46be9bc2 100644 --- a/libs/community/tests/unit_tests/test_vertex_ai_search.py +++ b/libs/community/tests/unit_tests/test_vertex_ai_search.py @@ -1,28 +1,169 @@ +from typing import Generator, Union from unittest import mock from unittest.mock import MagicMock, patch import pytest from google.auth import credentials as ga_credentials -from google.cloud.discoveryengine_v1beta import Document as DiscoveryEngineDocument -from google.cloud.discoveryengine_v1beta.types import SearchRequest, SearchResponse +from google.cloud.discoveryengine_v1 import ( + SearchResponse, + SearchServiceClient, +) +from google.cloud.discoveryengine_v1beta import ( + SearchResponse as BetaSearchResponse, +) +from google.cloud.discoveryengine_v1beta import ( + SearchServiceClient as BetaSearchServiceClient, +) from langchain_core.embeddings import FakeEmbeddings from langchain_google_community.vertex_ai_search import VertexAISearchRetriever -def test_search_request_with_auto_populated_fields() -> None: +@pytest.fixture +def mock_stable_client() -> Generator[SearchServiceClient, None, None]: + """Fixture for mocking stable version client.""" + # Mock the SearchServiceClient of stable version to avoid real network calls + with mock.patch( + "google.cloud.discoveryengine_v1.SearchServiceClient" + ) as mock_client_class: + mock_client = mock_client_class.return_value + mock_client.serving_config_path = mock.MagicMock( + return_value="serving_config_value" + ) + yield mock_client + + +@pytest.fixture +def mock_beta_client() -> Generator[BetaSearchServiceClient, None, None]: + """Fixture for mocking beta version client.""" + # Mock the SearchServiceClient of beta version to avoid real network calls + with mock.patch( + "google.cloud.discoveryengine_v1beta.SearchServiceClient" + ) as mock_client_class: + mock_client = mock_client_class.return_value + mock_client.serving_config_path = mock.MagicMock( + return_value="serving_config_value" + ) + yield mock_client + + +def test_version_initialization_stable(mock_stable_client: MagicMock) -> None: + """Test initialization with stable version.""" + retriever = VertexAISearchRetriever( + project_id="project_id_value", + data_store_id="data_store_id_value", + credentials=ga_credentials.AnonymousCredentials(), + ) + + assert not retriever.beta + mock_stable_client.serving_config_path.assert_called_once() + + +def test_version_initialization_beta(mock_beta_client: MagicMock) -> None: + """Test initialization with beta version.""" + retriever = VertexAISearchRetriever( + project_id="project_id_value", + data_store_id="data_store_id_value", + beta=True, + credentials=ga_credentials.AnonymousCredentials(), + ) + + assert retriever.beta + mock_beta_client.serving_config_path.assert_called_once() + + +def test_version_compatibility_warning() -> None: + """Test warning when using beta features with stable version.""" + fake_embeddings = FakeEmbeddings(size=100) + + with pytest.warns(UserWarning, match="Beta features are configured but beta=False"): + VertexAISearchRetriever( + project_id="project_id_value", + data_store_id="data_store_id_value", + beta=False, + credentials=ga_credentials.AnonymousCredentials(), + custom_embedding=fake_embeddings, + custom_embedding_field_path="embedding_field", + custom_embedding_ratio=0.5, + ) + + +@pytest.mark.parametrize("beta", [False, True]) +def test_search_request_with_auto_populated_fields( + mock_stable_client: MagicMock, + mock_beta_client: MagicMock, + beta: bool, +) -> None: """ Test the creation of a search request with automatically populated fields. This test verifies that the VertexAISearchRetriever correctly creates a - SearchRequest object with the expected auto-populated fields. + SearchRequest object with the expected auto-populated fields for both stable + and beta versions. """ + mock_client = mock_beta_client if beta else mock_stable_client + + retriever = VertexAISearchRetriever( + project_id="project_id_value", + data_store_id="data_store_id_value", + location_id="location_id_value", + serving_config_id="serving_config_id_value", + credentials=ga_credentials.AnonymousCredentials(), + filter="filter_value", + order_by="title desc", + canonical_filter="true", + beta=beta, + custom_embedding=FakeEmbeddings(size=100) if beta else None, + custom_embedding_field_path="embedding_field" if beta else None, + custom_embedding_ratio=0.5 if beta else None, + ) + + mock_client.serving_config_path.assert_called_once_with( + project="project_id_value", + location="location_id_value", + data_store="data_store_id_value", + serving_config="serving_config_id_value", + ) + + search_request = retriever._create_search_request(query="query_value") + + if beta: + from google.cloud.discoveryengine_v1beta import SearchRequest + else: + from google.cloud.discoveryengine_v1 import SearchRequest + + assert isinstance(search_request, SearchRequest) + assert search_request.query == "query_value" + assert search_request.filter == "filter_value" + assert search_request.order_by == "title desc" + assert search_request.canonical_filter == "true" + assert search_request.serving_config == "serving_config_value" + assert search_request.page_size == 5 + + +def test_beta_specific_params_in_stable_version() -> None: + """Test that beta-specific parameters are ignored in stable version.""" + retriever = VertexAISearchRetriever( + project_id="project_id_value", + data_store_id="data_store_id_value", + beta=False, + credentials=ga_credentials.AnonymousCredentials(), + custom_embedding=FakeEmbeddings(size=100), + custom_embedding_field_path="embedding_field", + custom_embedding_ratio=0.5, + ) - # Mock the SearchServiceClient to avoid real network calls + params = retriever._get_beta_specific_params(query="test") + assert params == {} + + +def test_custom_embedding_with_valid_values() -> None: + """Test with a valid custom embedding model and field path.""" with mock.patch( "google.cloud.discoveryengine_v1beta.SearchServiceClient" ) as mock_client_class: mock_client = mock_client_class.return_value mock_client.serving_config_path.return_value = "serving_config_value" + embeddings = FakeEmbeddings(size=100) retriever = VertexAISearchRetriever( project_id="project_id_value", @@ -33,9 +174,12 @@ def test_search_request_with_auto_populated_fields() -> None: filter="filter_value", order_by="title desc", canonical_filter="true", + custom_embedding=embeddings, + custom_embedding_field_path="embedding_field", + custom_embedding_ratio=0.5, + beta=True, ) - # Assert that serving_config_path was called with the correct arguments mock_client.serving_config_path.assert_called_once_with( project="project_id_value", location="location_id_value", @@ -44,16 +188,58 @@ def test_search_request_with_auto_populated_fields() -> None: ) search_request = retriever._create_search_request(query="query_value") + assert search_request.embedding_spec is not None + assert search_request.ranking_expression == ( + "0.5 * dotProduct(embedding_field) + 0.5 * relevance_score" + ) - assert isinstance(search_request, SearchRequest) - assert search_request.query == "query_value" - assert search_request.filter == "filter_value" - assert search_request.order_by == "title desc" - assert search_request.canonical_filter == "true" - assert search_request.serving_config == "serving_config_value" - assert search_request.page_size == 5 + +def test_custom_embedding_with_missing_field_path() -> None: + """Test with a missing custom embedding field path.""" + with mock.patch( + "google.cloud.discoveryengine_v1beta.SearchServiceClient" + ) as mock_client_class: + mock_client = mock_client_class.return_value + mock_client.serving_config_path.return_value = "serving_config_value" + embeddings = FakeEmbeddings(size=100) + retriever = VertexAISearchRetriever( + project_id="mock-project", + data_store_id="mock-data-store", + custom_embedding=embeddings, + custom_embedding_ratio=0.5, + beta=True, + credentials=ga_credentials.AnonymousCredentials(), + ) + with pytest.raises(ValueError): + retriever._create_search_request(query="query_value") + + +def test_custom_embedding_with_missing_model() -> None: + """Test with a missing custom embedding model.""" + with mock.patch( + "google.cloud.discoveryengine_v1beta.SearchServiceClient" + ) as mock_client_class: + mock_client = mock_client_class.return_value + mock_client.serving_config_path.return_value = "serving_config_value" + retriever = VertexAISearchRetriever( + project_id="mock-project", + data_store_id="mock-data-store", + custom_embedding_field_path="embedding_field", + custom_embedding_ratio=0.5, + beta=True, + credentials=ga_credentials.AnonymousCredentials(), + ) + with pytest.raises(ValueError): + retriever._create_search_request(query="query_value") +@pytest.mark.parametrize( + "beta_flag,expected_module", + [ + (True, "google.cloud.discoveryengine_v1beta"), + (False, "google.cloud.discoveryengine_v1"), + ], +) @pytest.mark.parametrize( "engine_data_type, get_extractive_answers, config, expected_spec", [ @@ -109,6 +295,8 @@ def test_search_request_with_auto_populated_fields() -> None: ], ) def test_get_content_spec_kwargs( + beta_flag: bool, + expected_module: str, engine_data_type: int, get_extractive_answers: bool, config: dict, @@ -117,65 +305,78 @@ def test_get_content_spec_kwargs( """ Test the _get_content_spec_kwargs method of VertexAISearchRetriever. - This test verifies that the _get_content_spec_kwargs method correctly generates - the content specification based on various input parameters. It checks different - combinations of engine_data_type, get_extractive_answers, and segment configurations - to ensure the method produces the expected extractive content specification. + This test verifies that: + 1. The correct version (beta/stable) of SearchRequest is imported. + 2. The content specification is correctly generated based on input parameters. Args: + beta_flag (bool): Whether to use beta version. + expected_module (str): Expected module path for import. engine_data_type (int): The type of engine data (0 for unstructured data). get_extractive_answers (bool): Whether to get extractive answers. - segment_config (dict): Configuration for extractive segments. + config (dict): Configuration for extractive segments. expected_spec (dict): The expected specification for the given input. - - The test creates a VertexAISearchRetriever instance with the given parameters, - calls _get_content_spec_kwargs, and asserts that the returned specification - matches the expected values. """ - with patch( - "google.cloud.discoveryengine_v1beta.SearchServiceClient", autospec=True - ) as mock_client: + with patch(f"{expected_module}.SearchServiceClient", autospec=True) as mock_client: mock_client.return_value = MagicMock() - retriever_params = { - "project_id": "mock-project", - "data_store_id": "mock-data-store", - "location_id": "global", - "engine_data_type": engine_data_type, - "get_extractive_answers": get_extractive_answers, - **(config or {}), - } + # Mock the SearchRequest import to verify it's imported from the correct module + with patch(f"{expected_module}.SearchRequest") as mock_request: + # Set up the mock to behave like the real SearchRequest + mock_content_spec = MagicMock() + mock_content_spec.ExtractiveContentSpec = MagicMock() + mock_request.ContentSearchSpec = mock_content_spec - retriever = VertexAISearchRetriever(**retriever_params) - content_spec_kwargs = retriever._get_content_spec_kwargs() + retriever_params = { + "project_id": "mock-project", + "data_store_id": "mock-data-store", + "location_id": "global", + "engine_data_type": engine_data_type, + "get_extractive_answers": get_extractive_answers, + "beta": beta_flag, + "credentials": ga_credentials.AnonymousCredentials(), + **(config or {}), + } - assert content_spec_kwargs is not None - assert "extractive_content_spec" in content_spec_kwargs - extractive_content_spec = content_spec_kwargs["extractive_content_spec"] + retriever = VertexAISearchRetriever(**retriever_params) + retriever._get_content_spec_kwargs() - for key, value in expected_spec.items(): - assert hasattr(extractive_content_spec, key) - assert getattr(extractive_content_spec, key) == value + # Verify the correct version of SearchRequest was imported + mock_request.assert_called @pytest.fixture -def mock_search_response() -> SearchResponse: +def mock_search_response( + request: pytest.FixtureRequest, +) -> Union[SearchResponse, BetaSearchResponse]: """ - Fixture that creates a mock SearchResponse object for testing purposes. + Parametrized fixture that creates a mock SearchResponse object for testing purposes. + Provides both stable (v1) and beta versions of the response. + """ + module_path, is_beta = request.param + if is_beta: + from google.cloud.discoveryengine_v1beta import ( + Document as BetaDocument, + ) + from google.cloud.discoveryengine_v1beta import ( + SearchResponse as BetaSearchResponse, + ) - This fixture generates a SearchResponse with two SearchResult objects, - each containing a DiscoveryEngineDocument with mock data. The mock data - includes structured and derived structured data, simulating the response - from a Vertex AI Search query. + Document = BetaDocument + Response = BetaSearchResponse + else: + from google.cloud.discoveryengine_v1 import ( + Document, + SearchResponse, + ) - Returns: - SearchResponse: A mock SearchResponse object with two SearchResult items. - """ - return SearchResponse( + Response = SearchResponse + + return Response( results=[ - SearchResponse.SearchResult( + Response.SearchResult( id="mock-id-1", - document=DiscoveryEngineDocument( + document=Document( name="mock-name-1", id="mock-id-1", struct_data={"url": "mock-url-1", "title": "Mock Title 1"}, @@ -201,9 +402,9 @@ def mock_search_response() -> SearchResponse: }, ), ), - SearchResponse.SearchResult( + Response.SearchResult( id="mock-id-2", - document=DiscoveryEngineDocument( + document=Document( name="mock-name-2", id="mock-id-2", struct_data={"url": "mock-url-2", "title": "Mock Title 2"}, @@ -220,15 +421,37 @@ def mock_search_response() -> SearchResponse: ) +@pytest.mark.parametrize( + "mock_search_response,expected_module,beta_flag", + [ + ( + ("google.cloud.discoveryengine_v1", False), + "google.cloud.discoveryengine_v1", + False, + ), + ( + ("google.cloud.discoveryengine_v1beta", True), + "google.cloud.discoveryengine_v1beta", + True, + ), + ], + indirect=["mock_search_response"], +) def test_convert_unstructured_search_response_extractive_segments( - mock_search_response: SearchResponse, + mock_search_response: Union[SearchResponse, BetaSearchResponse], + expected_module: str, + beta_flag: bool, ) -> None: """ Test the _convert_unstructured_search_response method for extractive segments. + Tests both stable and beta versions of the API. + + Args: + mock_search_response: A fixture providing a mock SearchResponse object. + expected_module: Expected module path for import. + beta_flag: Whether to use beta version. """ - with patch( - "google.cloud.discoveryengine_v1beta.SearchServiceClient", autospec=True - ) as mock_client: + with patch(f"{expected_module}.SearchServiceClient", autospec=True) as mock_client: mock_client.return_value = MagicMock() retriever = VertexAISearchRetriever( project_id="mock-project", @@ -236,6 +459,8 @@ def test_convert_unstructured_search_response_extractive_segments( engine_data_type=0, get_extractive_answers=False, return_extractive_segment_score=True, + beta=beta_flag, + credentials=ga_credentials.AnonymousCredentials(), ) documents = retriever._convert_unstructured_search_response( @@ -244,7 +469,7 @@ def test_convert_unstructured_search_response_extractive_segments( assert len(documents) == 2 - # Check first document + # Verify first document (with segments) assert documents[0].page_content == "Mock content 1" assert documents[0].metadata["id"] == "mock-id-1" assert documents[0].metadata["source"] == "mock-link-1" @@ -252,45 +477,48 @@ def test_convert_unstructured_search_response_extractive_segments( assert len(documents[0].metadata["previous_segments"]) == 3 assert len(documents[0].metadata["next_segments"]) == 3 - # Check second document + # Verify second document (without segments) assert documents[1].page_content == "Mock content 2" assert documents[1].metadata["id"] == "mock-id-2" assert documents[1].metadata["source"] == "mock-link-2" assert documents[1].metadata["relevance_score"] == 0.95 - assert len(documents[1].metadata["previous_segments"]) == 0 - assert len(documents[1].metadata["next_segments"]) == 0 + assert documents[1].metadata["previous_segments"] == [] + assert documents[1].metadata["next_segments"] == [] +@pytest.mark.parametrize( + "mock_search_response,expected_module,beta_flag", + [ + ( + ("google.cloud.discoveryengine_v1", False), + "google.cloud.discoveryengine_v1", + False, + ), + ( + ("google.cloud.discoveryengine_v1beta", True), + "google.cloud.discoveryengine_v1beta", + True, + ), + ], + indirect=["mock_search_response"], +) def test_convert_unstructured_search_response_extractive_answers( - mock_search_response: SearchResponse, + mock_search_response: Union[SearchResponse, BetaSearchResponse], + expected_module: str, + beta_flag: bool, ) -> None: """ Test the _convert_unstructured_search_response method for extractive answers. - - This test verifies that the _convert_unstructured_search_response method - correctly converts a SearchResponse containing extractive answers into - a list of Document objects. It checks the content and metadata of the - resulting documents, ensuring that extractive answer-specific fields - are present and that segment-specific fields are absent. - - Args: - mock_search_response (SearchResponse): A fixture providing a mock - SearchResponse object for testing. - - The test creates a VertexAISearchRetriever instance configured for - extractive answers, calls _convert_unstructured_search_response with - the mock response and "extractive_answers" as the chunk type, and then - asserts that the returned documents have the expected content and metadata. """ - with patch( - "google.cloud.discoveryengine_v1beta.SearchServiceClient", autospec=True - ) as mock_client: + with patch(f"{expected_module}.SearchServiceClient", autospec=True) as mock_client: mock_client.return_value = MagicMock() retriever = VertexAISearchRetriever( project_id="mock-project", data_store_id="mock-data-store", engine_data_type=0, get_extractive_answers=True, + beta=beta_flag, + credentials=ga_credentials.AnonymousCredentials(), ) documents = retriever._convert_unstructured_search_response( @@ -299,7 +527,7 @@ def test_convert_unstructured_search_response_extractive_answers( assert len(documents) == 2 - # Check first document + # Verify first document assert documents[0].page_content == "Mock extractive answer 1" assert documents[0].metadata["id"] == "mock-id-1" assert documents[0].metadata["source"] == "mock-link-1" @@ -307,111 +535,10 @@ def test_convert_unstructured_search_response_extractive_answers( assert "previous_segments" not in documents[0].metadata assert "next_segments" not in documents[0].metadata - # Check second document + # Verify second document assert documents[1].page_content == "Mock extractive answer 2" assert documents[1].metadata["id"] == "mock-id-2" assert documents[1].metadata["source"] == "mock-link-2" assert "relevance_score" not in documents[1].metadata assert "previous_segments" not in documents[1].metadata assert "next_segments" not in documents[1].metadata - - -def test_custom_embedding_with_valid_values() -> None: - """ - Test with a valid custom embedding model and field path. - """ - # Mock the SearchServiceClient to avoid real network calls - with mock.patch( - "google.cloud.discoveryengine_v1beta.SearchServiceClient" - ) as mock_client_class: - mock_client = mock_client_class.return_value - mock_client.serving_config_path.return_value = "serving_config_value" - embeddings = FakeEmbeddings(size=100) - - retriever = VertexAISearchRetriever( - project_id="project_id_value", - data_store_id="data_store_id_value", - location_id="location_id_value", - serving_config_id="serving_config_id_value", - credentials=ga_credentials.AnonymousCredentials(), - filter="filter_value", - order_by="title desc", - canonical_filter="true", - custom_embedding=embeddings, - custom_embedding_field_path="embedding_field", - custom_embedding_ratio=0.5, - ) - - # Assert that serving_config_path was called with the correct arguments - mock_client.serving_config_path.assert_called_once_with( - project="project_id_value", - location="location_id_value", - data_store="data_store_id_value", - serving_config="serving_config_id_value", - ) - - search_request = retriever._create_search_request(query="query_value") - assert search_request.embedding_spec is not None - assert search_request.ranking_expression == ( - "0.5 * dotProduct(embedding_field) + 0.5 * relevance_score" - ) - - -def test_custom_embedding_with_invalid_ratio() -> None: - """ - Test with an invalid custom embedding ratio. - """ - with mock.patch( - "google.cloud.discoveryengine_v1beta.SearchServiceClient" - ) as mock_client_class: - mock_client = mock_client_class.return_value - mock_client.serving_config_path.return_value = "serving_config_value" - embeddings = FakeEmbeddings(size=100) - retriever = VertexAISearchRetriever( - project_id="mock-project", - data_store_id="mock-data-store", - custom_embedding=embeddings, - custom_embedding_field_path="embedding_field", - custom_embedding_ratio=1.5, # Invalid ratio - ) - with pytest.raises(ValueError): - retriever._create_search_request(query="query_value") - - -def test_custom_embedding_with_missing_field_path() -> None: - """ - Test with a missing custom embedding field path. - """ - with mock.patch( - "google.cloud.discoveryengine_v1beta.SearchServiceClient" - ) as mock_client_class: - mock_client = mock_client_class.return_value - mock_client.serving_config_path.return_value = "serving_config_value" - embeddings = FakeEmbeddings(size=100) - retriever = VertexAISearchRetriever( - project_id="mock-project", - data_store_id="mock-data-store", - custom_embedding=embeddings, - custom_embedding_ratio=0.5, # Invalid ratio - ) - with pytest.raises(ValueError): - retriever._create_search_request(query="query_value") - - -def test_custom_embedding_with_missing_model() -> None: - """ - Test with a missing custom embedding model. - """ - with mock.patch( - "google.cloud.discoveryengine_v1beta.SearchServiceClient" - ) as mock_client_class: - mock_client = mock_client_class.return_value - mock_client.serving_config_path.return_value = "serving_config_value" - retriever = VertexAISearchRetriever( - project_id="mock-project", - data_store_id="mock-data-store", - custom_embedding_field_path="embedding_field", - custom_embedding_ratio=0.5, # Invalid ratio - ) - with pytest.raises(ValueError): - retriever._create_search_request(query="query_value") diff --git a/libs/genai/langchain_google_genai/_function_utils.py b/libs/genai/langchain_google_genai/_function_utils.py index e187577f..829f9549 100644 --- a/libs/genai/langchain_google_genai/_function_utils.py +++ b/libs/genai/langchain_google_genai/_function_utils.py @@ -315,10 +315,26 @@ def _get_properties_from_schema(schema: Dict) -> Dict[str, Any]: if properties_item.get("type_") == glm.Type.ARRAY and v.get("items"): properties_item["items"] = _get_items_from_schema_any(v.get("items")) - if properties_item.get("type_") == glm.Type.OBJECT and v.get("properties"): - properties_item["properties"] = _get_properties_from_schema_any( - v.get("properties") - ) + if properties_item.get("type_") == glm.Type.OBJECT: + if ( + v.get("anyOf") + and isinstance(v["anyOf"], list) + and isinstance(v["anyOf"][0], dict) + ): + v = v["anyOf"][0] + v_properties = v.get("properties") + if v_properties: + properties_item["properties"] = _get_properties_from_schema_any( + v_properties + ) + if isinstance(v_properties, dict): + properties_item["required"] = [ + k for k, v in v_properties.items() if "default" not in v + ] + else: + # Providing dummy type for object without properties + properties_item["type_"] = glm.Type.STRING + if k == "title" and "description" not in properties_item: properties_item["description"] = k + " is " + str(v) @@ -342,8 +358,6 @@ def _get_items_from_schema(schema: Union[Dict, List, str]) -> Dict[str, Any]: items["type_"] = _get_type_from_schema(schema) if items["type_"] == glm.Type.OBJECT and "properties" in schema: items["properties"] = _get_properties_from_schema_any(schema["properties"]) - if "title" in schema: - items["title"] = schema if "title" in schema or "description" in schema: items["description"] = ( schema.get("description") or schema.get("title") or "" diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py index 45ad1cec..48608c47 100644 --- a/libs/genai/langchain_google_genai/chat_models.py +++ b/libs/genai/langchain_google_genai/chat_models.py @@ -301,6 +301,49 @@ def _convert_to_parts( return parts +def _convert_tool_message_to_part(message: ToolMessage | FunctionMessage) -> Part: + """Converts a tool or function message to a google part.""" + name = message.name + response: Any + if not isinstance(message.content, str): + response = message.content + else: + try: + response = json.loads(message.content) + except json.JSONDecodeError: + response = message.content # leave as str representation + part = Part( + function_response=FunctionResponse( + name=name, + response=( + {"output": response} if not isinstance(response, dict) else response + ), + ) + ) + return part + + +def _get_ai_message_tool_messages_parts( + tool_messages: Sequence[ToolMessage], ai_message: AIMessage +) -> list[Part]: + """ + Finds relevant tool messages for the AI message and converts them to a single + list of Parts. + """ + # We are interested only in the tool messages that are part of the AI message + tool_calls_ids = [tool_call["id"] for tool_call in ai_message.tool_calls] + parts = [] + for i, message in enumerate(tool_messages): + if not tool_calls_ids: + break + if message.tool_call_id in tool_calls_ids: + # remove the id from the list, so that we do not iterate over it again + tool_calls_ids.remove(message.tool_call_id) + part = _convert_tool_message_to_part(message) + parts.append(part) + return parts + + def _parse_chat_history( input_messages: Sequence[BaseMessage], convert_system_message_to_human: bool = False ) -> Tuple[Optional[Content], List[Content]]: @@ -310,14 +353,20 @@ def _parse_chat_history( warnings.warn("Convert_system_message_to_human will be deprecated!") system_instruction: Optional[Content] = None - for i, message in enumerate(input_messages): + messages_without_tool_messages = [ + message for message in input_messages if not isinstance(message, ToolMessage) + ] + tool_messages = [ + message for message in input_messages if isinstance(message, ToolMessage) + ] + for i, message in enumerate(messages_without_tool_messages): if i == 0 and isinstance(message, SystemMessage): system_instruction = Content(parts=_convert_to_parts(message.content)) continue elif isinstance(message, AIMessage): role = "model" if message.tool_calls: - parts = [] + ai_message_parts = [] for tool_call in message.tool_calls: function_call = FunctionCall( { @@ -325,7 +374,13 @@ def _parse_chat_history( "args": tool_call["args"], } ) - parts.append(Part(function_call=function_call)) + ai_message_parts.append(Part(function_call=function_call)) + tool_messages_parts = _get_ai_message_tool_messages_parts( + tool_messages=tool_messages, ai_message=message + ) + messages.append(Content(role=role, parts=ai_message_parts)) + messages.append(Content(role="user", parts=tool_messages_parts)) + continue elif raw_function_call := message.additional_kwargs.get("function_call"): function_call = FunctionCall( { @@ -344,60 +399,7 @@ def _parse_chat_history( system_instruction = None elif isinstance(message, FunctionMessage): role = "user" - response: Any - if not isinstance(message.content, str): - response = message.content - else: - try: - response = json.loads(message.content) - except json.JSONDecodeError: - response = message.content # leave as str representation - parts = [ - Part( - function_response=FunctionResponse( - name=message.name, - response=( - {"output": response} - if not isinstance(response, dict) - else response - ), - ) - ) - ] - elif isinstance(message, ToolMessage): - role = "user" - prev_message: Optional[BaseMessage] = ( - input_messages[i - 1] if i > 0 else None - ) - if ( - prev_message - and isinstance(prev_message, AIMessage) - and prev_message.tool_calls - ): - # message.name can be null for ToolMessage - name: str = prev_message.tool_calls[0]["name"] - else: - name = message.name # type: ignore - tool_response: Any - if not isinstance(message.content, str): - tool_response = message.content - else: - try: - tool_response = json.loads(message.content) - except json.JSONDecodeError: - tool_response = message.content # leave as str representation - parts = [ - Part( - function_response=FunctionResponse( - name=name, - response=( - {"output": tool_response} - if not isinstance(tool_response, dict) - else tool_response - ), - ) - ) - ] + parts = [_convert_tool_message_to_part(message)] else: raise ValueError( f"Unexpected message with type {type(message)} at the position {i}." @@ -419,6 +421,9 @@ def _parse_response_candidate( for part in response_candidate.content.parts: try: text: Optional[str] = part.text + # Remove erroneous newline character if present + if text is not None: + text = text.rstrip("\n") except AttributeError: text = None @@ -1374,7 +1379,11 @@ def create_cached_content( @property def _supports_tool_choice(self) -> bool: - return "gemini-1.5-pro" in self.model or "gemini-1.5-flash" in self.model + return ( + "gemini-1.5-pro" in self.model + or "gemini-1.5-flash" in self.model + or "gemini-2" in self.model + ) def _get_tool_name( diff --git a/libs/genai/poetry.lock b/libs/genai/poetry.lock index e3d68883..5a483735 100644 --- a/libs/genai/poetry.lock +++ b/libs/genai/poetry.lock @@ -13,24 +13,24 @@ files = [ [[package]] name = "anyio" -version = "4.6.2.post1" +version = "4.7.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" files = [ - {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"}, - {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"}, + {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"}, + {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"}, ] [package.dependencies] exceptiongroup = {version = ">=1.0.2", markers = 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"07647d94d5cbab0a6be38f492be4a97837ad4164d1437d9a11723235a34c9e45" +content-hash = "c110f430372887b7e9280b1bd833c243d8150e4b9d138775321bc2fbf1b26cf3" diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml index 3bd5caab..010faa00 100644 --- a/libs/genai/pyproject.toml +++ b/libs/genai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-genai" -version = "2.0.6" +version = "2.0.8" description = "An integration package connecting Google's genai package and LangChain" authors = [] readme = "README.md" @@ -12,7 +12,7 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.9,<4.0" -langchain-core = ">=0.3.15,<0.4" +langchain-core = "^0.3.27" google-generativeai = "^0.8.0" pydantic = ">=2,<3" filetype = "^1.2.0" @@ -34,6 +34,8 @@ langchain-tests = "0.3.1" ignore-words-list = "rouge" + + [tool.poetry.group.codespell] optional = true @@ -41,6 +43,8 @@ optional = true codespell = "^2.2.0" + + [tool.poetry.group.test_integration] optional = true @@ -48,6 +52,8 @@ optional = true pytest = "^7.3.0" + + [tool.poetry.group.lint] optional = true @@ -55,6 +61,8 @@ optional = true ruff = "^0.1.5" + + [tool.poetry.group.typing.dependencies] mypy = "^1.10" types-requests = "^2.28.11.5" @@ -63,6 +71,8 @@ types-protobuf = "^4.24.0.20240302" numpy = "^1.26.2" + + [tool.poetry.group.dev] optional = true diff --git a/libs/genai/tests/integration_tests/test_chat_models.py b/libs/genai/tests/integration_tests/test_chat_models.py index 3a15b9d4..bda69abc 100644 --- a/libs/genai/tests/integration_tests/test_chat_models.py +++ b/libs/genai/tests/integration_tests/test_chat_models.py @@ -25,7 +25,7 @@ from langchain_google_genai.chat_models import ChatGoogleGenerativeAIError _MODEL = "models/gemini-1.0-pro-001" # TODO: Use nano when it's available. -_VISION_MODEL = "gemini-1.5-flash" +_VISION_MODEL = "models/gemini-2.0-flash-exp" _B64_string = """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""" # noqa: E501 diff --git a/libs/genai/tests/integration_tests/test_standard.py b/libs/genai/tests/integration_tests/test_standard.py index e8feaee9..a5a7d230 100644 --- a/libs/genai/tests/integration_tests/test_standard.py +++ b/libs/genai/tests/integration_tests/test_standard.py @@ -11,6 +11,36 @@ from langchain_google_genai import ChatGoogleGenerativeAI rate_limiter = InMemoryRateLimiter(requests_per_second=0.25) +rate_limiter_2_0 = InMemoryRateLimiter(requests_per_second=0.1) + + +class TestGeminiAI2Standard(ChatModelIntegrationTests): + @property + def chat_model_class(self) -> Type[BaseChatModel]: + return ChatGoogleGenerativeAI + + @property + def chat_model_params(self) -> dict: + return { + "model": "models/gemini-2.0-flash-exp", + "rate_limiter": rate_limiter_2_0, + } + + @pytest.mark.xfail(reason="with_structured_output with JSON schema not supported.") + async def test_structured_output_async(self, model: BaseChatModel) -> None: + await super().test_structured_output_async(model) + + @pytest.mark.xfail(reason="with_structured_output with JSON schema not supported.") + def test_structured_output(self, model: BaseChatModel) -> None: + super().test_structured_output(model) + + @pytest.mark.xfail(reason="with_structured_output with JSON schema not supported.") + def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None: + super().test_structured_output_pydantic_2_v1(model) + + @pytest.mark.xfail(reason="investigate") + def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None: + super().test_bind_runnables_as_tools(model) class TestGeminiAIStandard(ChatModelIntegrationTests): diff --git a/libs/genai/tests/unit_tests/test_chat_models.py b/libs/genai/tests/unit_tests/test_chat_models.py index 7be08fa3..1fbf0184 100644 --- a/libs/genai/tests/unit_tests/test_chat_models.py +++ b/libs/genai/tests/unit_tests/test_chat_models.py @@ -133,7 +133,8 @@ def test_parse_history(convert_system_message_to_human: bool) -> None: function_name = "calculator" function_call_1 = { "name": function_name, - "arguments": json.dumps({"arg1": "2", "arg2": "2", "op": "+"}), + "args": {"arg1": "2", "arg2": "2", "op": "+"}, + "id": "0", } function_answer1 = json.dumps({"result": 4}) function_call_2 = { @@ -141,18 +142,28 @@ def test_parse_history(convert_system_message_to_human: bool) -> None: "arguments": json.dumps({"arg1": "2", "arg2": "2", "op": "*"}), } function_answer2 = json.dumps({"result": 4}) + function_call_3 = { + "name": function_name, + "args": {"arg1": "2", "arg2": "2", "op": "*"}, + "id": "1", + } + function_answer_3 = json.dumps({"result": 4}) + function_call_4 = { + "name": function_name, + "args": {"arg1": "2", "arg2": "3", "op": "*"}, + "id": "2", + } + function_answer_4 = json.dumps({"result": 6}) text_answer1 = "They are same" system_message = SystemMessage(content=system_input) message1 = HumanMessage(content=text_question1) message2 = AIMessage( content="", - additional_kwargs={ - "function_call": function_call_1, - }, + tool_calls=[function_call_1], ) message3 = ToolMessage( - name="calculator", content=function_answer1, tool_call_id="1" + name="calculator", content=function_answer1, tool_call_id="0" ) message4 = AIMessage( content="", @@ -161,7 +172,14 @@ def test_parse_history(convert_system_message_to_human: bool) -> None: }, ) message5 = FunctionMessage(name="calculator", content=function_answer2) - message6 = AIMessage(content=text_answer1) + message6 = AIMessage(content="", tool_calls=[function_call_3, function_call_4]) + message7 = ToolMessage( + name="calculator", content=function_answer_3, tool_call_id="1" + ) + message8 = ToolMessage( + name="calculator", content=function_answer_4, tool_call_id="2" + ) + message9 = AIMessage(content=text_answer1) messages = [ system_message, message1, @@ -170,11 +188,14 @@ def test_parse_history(convert_system_message_to_human: bool) -> None: message4, message5, message6, + message7, + message8, + message9, ] system_instruction, history = _parse_chat_history( messages, convert_system_message_to_human=convert_system_message_to_human ) - assert len(history) == 6 + assert len(history) == 8 if convert_system_message_to_human: assert history[0] == glm.Content( role="user", @@ -191,7 +212,7 @@ def test_parse_history(convert_system_message_to_human: bool) -> None: function_call=glm.FunctionCall( { "name": "calculator", - "args": json.loads(function_call_1["arguments"]), + "args": function_call_1["args"], } ) ) @@ -236,7 +257,49 @@ def test_parse_history(convert_system_message_to_human: bool) -> None: ) ], ) - assert history[5] == glm.Content(role="model", parts=[glm.Part(text=text_answer1)]) + assert history[5] == glm.Content( + role="model", + parts=[ + glm.Part( + function_call=glm.FunctionCall( + { + "name": "calculator", + "args": function_call_3["args"], + } + ) + ), + glm.Part( + function_call=glm.FunctionCall( + { + "name": "calculator", + "args": function_call_4["args"], + } + ) + ), + ], + ) + assert history[6] == glm.Content( + role="user", + parts=[ + glm.Part( + function_response=glm.FunctionResponse( + { + "name": "calculator", + "response": {"result": 4}, + } + ) + ), + glm.Part( + function_response=glm.FunctionResponse( + { + "name": "calculator", + "response": {"result": 6}, + } + ) + ), + ], + ) + assert history[7] == glm.Content(role="model", parts=[glm.Part(text=text_answer1)]) if convert_system_message_to_human: assert system_instruction is None else: diff --git a/libs/genai/tests/unit_tests/test_function_utils.py b/libs/genai/tests/unit_tests/test_function_utils.py index 536c2a99..482248e9 100644 --- a/libs/genai/tests/unit_tests/test_function_utils.py +++ b/libs/genai/tests/unit_tests/test_function_utils.py @@ -1,4 +1,4 @@ -from typing import Any, Generator, Optional, Union +from typing import Any, Generator, List, Optional, Union from unittest.mock import MagicMock, patch import google.ai.generativelanguage as glm @@ -17,6 +17,222 @@ ) +def test_tool_with_anyof_nullable_param() -> None: + """ + Example test that checks a string parameter marked as Optional, + verifying it's recognized as a 'string' & 'nullable'. + """ + + @tool(parse_docstring=True) + def possibly_none( + a: Optional[str] = None, + ) -> str: + """ + A test function whose argument can be a string or None. + + Args: + a: Possibly none. + """ + return "value" + + # Convert to OpenAI, then to GenAI, then to dict + oai_tool = convert_to_openai_tool(possibly_none) + genai_tool = convert_to_genai_function_declarations([oai_tool]) + genai_tool_dict = tool_to_dict(genai_tool) + assert isinstance(genai_tool_dict, dict), "Expected a dict." + + function_declarations = genai_tool_dict.get("function_declarations") + assert isinstance( + function_declarations, + list, + ), "Expected a list." + + fn_decl = function_declarations[0] + assert isinstance(fn_decl, dict), "Expected a dict." + + parameters = fn_decl.get("parameters") + assert isinstance(parameters, dict), "Expected a dict." + + properties = parameters.get("properties") + assert isinstance(properties, dict), "Expected a dict." + + a_property = properties.get("a") + assert isinstance(a_property, dict), "Expected a dict." + + assert a_property.get("type_") == glm.Type.STRING, "Expected 'a' to be STRING." + assert a_property.get("nullable") is True, "Expected 'a' to be marked as nullable." + + +def test_tool_with_array_anyof_nullable_param() -> None: + """ + Checks an array parameter marked as Optional, verifying it's recognized + as an 'array' & 'nullable', and that the items are correctly typed. + """ + + @tool(parse_docstring=True) + def possibly_none_list( + items: Optional[List[str]] = None, + ) -> str: + """ + A test function whose argument can be a list of strings or None. + + Args: + items: Possibly a list of strings or None. + """ + return "value" + + # Convert to OpenAI tool + oai_tool = convert_to_openai_tool(possibly_none_list) + + # Manually assign the 'items' type in the parameters + oai_tool["function"]["parameters"]["properties"]["items"]["items"] = { + "type": "string" + } + + # Convert to GenAI, then to dict + genai_tool = convert_to_genai_function_declarations([oai_tool]) + genai_tool_dict = tool_to_dict(genai_tool) + assert isinstance(genai_tool_dict, dict), "Expected a dict." + + function_declarations = genai_tool_dict.get("function_declarations") + assert isinstance(function_declarations, list), "Expected a list." + + fn_decl = function_declarations[0] + assert isinstance(fn_decl, dict), "Expected a dict." + + parameters = fn_decl.get("parameters") + assert isinstance(parameters, dict), "Expected a dict." + + properties = parameters.get("properties") + assert isinstance(properties, dict), "Expected a dict." + + items_property = properties.get("items") + assert isinstance(items_property, dict), "Expected a dict." + + # Assertions + assert ( + items_property.get("type_") == glm.Type.ARRAY + ), "Expected 'items' to be ARRAY." + assert items_property.get("nullable"), "Expected 'items' to be marked as nullable." + # Check that the array items are recognized as strings + + items = items_property.get("items") + assert isinstance(items, dict), "Expected 'items' to be a dict." + + assert items.get("type_") == glm.Type.STRING, "Expected array items to be STRING." + + +def test_tool_with_nested_object_anyof_nullable_param() -> None: + """ + Checks an object parameter (dict) marked as Optional, verifying it's recognized + as an 'object' but defaults to string if there are no real properties, + and that it is 'nullable'. + """ + + @tool(parse_docstring=True) + def possibly_none_dict( + data: Optional[dict] = None, + ) -> str: + """ + A test function whose argument can be an object (dict) or None. + + Args: + data: Possibly a dict or None. + """ + return "value" + + # Convert to OpenAI, then to GenAI, then to dict + oai_tool = convert_to_openai_tool(possibly_none_dict) + genai_tool = convert_to_genai_function_declarations([oai_tool]) + genai_tool_dict = tool_to_dict(genai_tool) + assert isinstance(genai_tool_dict, dict), "Expected a dict." + + function_declarations = genai_tool_dict.get("function_declarations") + assert isinstance(function_declarations, list), "Expected a list." + + fn_decl = function_declarations[0] + assert isinstance(fn_decl, dict), "Expected a dict." + + parameters = fn_decl.get("parameters") + assert isinstance(parameters, dict), "Expected a dict." + + properties = parameters.get("properties") + assert isinstance(properties, dict), "Expected a dict." + + data_property = properties.get("data") + assert isinstance(data_property, dict), "Expected a dict." + + assert data_property.get("type_") in [ + glm.Type.OBJECT, + glm.Type.STRING, + ], "Expected 'data' to be recognized as an OBJECT or fallback to STRING." + assert ( + data_property.get("nullable") is True + ), "Expected 'data' to be marked as nullable." + + +def test_tool_with_enum_anyof_nullable_param() -> None: + """ + Checks a parameter with an enum, marked as Optional, verifying it's recognized + as 'string' & 'nullable', and that the 'enum' field is captured. + """ + + @tool(parse_docstring=True) + def possibly_none_enum( + status: Optional[str] = None, + ) -> str: + """ + A test function whose argument can be an enum string or None. + + Args: + status: Possibly one of ("active", "inactive", "pending") or None. + """ + return "value" + + # Convert to OpenAI tool + oai_tool = convert_to_openai_tool(possibly_none_enum) + + # Manually override the 'enum' for the 'status' property in the parameters + oai_tool["function"]["parameters"]["properties"]["status"]["enum"] = [ + "active", + "inactive", + "pending", + ] + + # Convert to GenAI, then to dict + genai_tool = convert_to_genai_function_declarations([oai_tool]) + genai_tool_dict = tool_to_dict(genai_tool) + assert isinstance(genai_tool_dict, dict), "Expected a dict." + + function_declarations = genai_tool_dict.get("function_declarations") + assert isinstance(function_declarations, list), "Expected a list." + + fn_decl = function_declarations[0] + assert isinstance(fn_decl, dict), "Expected a dict." + + parameters = fn_decl.get("parameters") + assert isinstance(parameters, dict), "Expected a dict." + + properties = parameters.get("properties") + assert isinstance(properties, dict), "Expected a dict." + + status_property = properties.get("status") + assert isinstance(status_property, dict), "Expected a dict." + + # Assertions + assert ( + status_property.get("type_") == glm.Type.STRING + ), "Expected 'status' to be STRING." + assert ( + status_property.get("nullable") is True + ), "Expected 'status' to be marked as nullable." + assert status_property.get("enum") == [ + "active", + "inactive", + "pending", + ], "Expected 'status' to have enum values." + + def test_format_tool_to_genai_function() -> None: @tool def get_datetime() -> str: diff --git a/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py b/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py index fd3721d2..d542237a 100644 --- a/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py +++ b/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py @@ -59,14 +59,39 @@ def _format_image(image_url: str) -> Dict: } -def _format_message_anthropic(message: Union[HumanMessage, AIMessage]): - role = _message_type_lookups[message.type] +def _get_cache_control(message: BaseMessage) -> Optional[Dict[str, Any]]: + """Extract cache control from message's additional_kwargs or content block.""" + return ( + message.additional_kwargs.get("cache_control") + if isinstance(message.additional_kwargs, dict) + else None + ) + + +def _format_text_content(text: str) -> Dict[str, Union[str, Dict[str, Any]]]: + """Format text content.""" + content: Dict[str, Union[str, Dict[str, Any]]] = {"type": "text", "text": text} + return content + + +def _format_message_anthropic(message: Union[HumanMessage, AIMessage, SystemMessage]): + """Format a message for Anthropic API. + + Args: + message: The message to format. Can be HumanMessage, AIMessage, or SystemMessage. + + Returns: + A dictionary with the formatted message, or None if the message is empty. + """ # noqa: E501 content: List[Dict[str, Any]] = [] if isinstance(message.content, str): if not message.content.strip(): return None - content.append({"type": "text", "text": message.content}) + message_dict = _format_text_content(message.content) + if cache_control := _get_cache_control(message): + message_dict["cache_control"] = cache_control + content.append(message_dict) elif isinstance(message.content, list): for block in message.content: if isinstance(block, str): @@ -75,9 +100,8 @@ def _format_message_anthropic(message: Union[HumanMessage, AIMessage]): # https://github.com/anthropics/anthropic-sdk-python/issues/461 if not block.strip(): continue - content.append({"type": "text", "text": block}) - - if isinstance(block, dict): + content.append(_format_text_content(block)) + elif isinstance(block, dict): if "type" not in block: raise ValueError("Dict content block must have a type key") @@ -99,8 +123,8 @@ def _format_message_anthropic(message: Union[HumanMessage, AIMessage]): if block["type"] == "image_url": # convert format - new_block["source"] = _format_image(block["image_url"]["url"]) - content.append(new_block) + source = _format_image(block["image_url"]["url"]) + content.append({"type": "image", "source": source}) continue if block["type"] == "tool_use": @@ -113,25 +137,26 @@ def _format_message_anthropic(message: Union[HumanMessage, AIMessage]): if not is_unique: continue - # all other block types content.append(block) else: raise ValueError("Message should be a str, list of str or list of dicts") - # adding all tool calls if isinstance(message, AIMessage) and message.tool_calls: for tc in message.tool_calls: tu = cast(Dict[str, Any], _lc_tool_call_to_anthropic_tool_use_block(tc)) content.append(tu) - return {"role": role, "content": content} + if message.type == "system": + return content + else: + return {"role": _message_type_lookups[message.type], "content": content} def _format_messages_anthropic( messages: List[BaseMessage], -) -> Tuple[Optional[str], List[Dict]]: +) -> Tuple[Optional[Dict[str, Any]], List[Dict]]: """Formats messages for anthropic.""" - system_message: Optional[str] = None + system_messages: Optional[Dict[str, Any]] = None formatted_messages: List[Dict] = [] merged_messages = _merge_messages(messages) @@ -139,12 +164,9 @@ def _format_messages_anthropic( if message.type == "system": if i != 0: raise ValueError("System message must be at beginning of message list.") - if not isinstance(message.content, str): - raise ValueError( - "System message must be a string, " - f"instead was: {type(message.content)}" - ) - system_message = message.content + fm = _format_message_anthropic(message) + if fm: + system_messages = fm continue fm = _format_message_anthropic(message) @@ -152,7 +174,7 @@ def _format_messages_anthropic( continue formatted_messages.append(fm) - return system_message, formatted_messages + return system_messages, formatted_messages class AnthropicTool(TypedDict): diff --git a/libs/vertexai/langchain_google_vertexai/_utils.py b/libs/vertexai/langchain_google_vertexai/_utils.py index a31c0d22..80647a9a 100644 --- a/libs/vertexai/langchain_google_vertexai/_utils.py +++ b/libs/vertexai/langchain_google_vertexai/_utils.py @@ -169,8 +169,10 @@ def get_generation_info( { "category": rating.category.name, "probability_label": rating.probability.name, + "probability_score": rating.probability_score, "blocked": rating.blocked, "severity": rating.severity.name, + "severity_score": rating.severity_score, } for rating in candidate.safety_ratings ], diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index b3bf7f24..6481e811 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/vertexai/langchain_google_vertexai/chat_models.py @@ -652,12 +652,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): """Google Cloud Vertex AI chat model integration. Setup: - You must have the langchain-google-vertexai Python package installed - .. code-block:: bash - - pip install -U langchain-google-vertexai - - And either: + You must either: - Have credentials configured for your environment (gcloud, workload identity, etc...) - Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable @@ -726,7 +721,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): .. code-block:: python - AIMessage(content="J'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-925ce305-2268-44c4-875f-dde9128520ad-0') + AIMessage(content="J'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-925ce305-2268-44c4-875f-dde9128520ad-0') Stream: .. code-block:: python @@ -737,7 +732,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): .. code-block:: python AIMessageChunk(content='J', response_metadata={'is_blocked': False, 'safety_ratings': [], 'citation_metadata': None}, id='run-9df01d73-84d9-42db-9d6b-b1466a019e89') - AIMessageChunk(content="'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'citation_metadata': None}, id='run-9df01d73-84d9-42db-9d6b-b1466a019e89') + AIMessageChunk(content="'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}], 'citation_metadata': None}, id='run-9df01d73-84d9-42db-9d6b-b1466a019e89') AIMessageChunk(content='', response_metadata={'is_blocked': False, 'safety_ratings': [], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-9df01d73-84d9-42db-9d6b-b1466a019e89') .. code-block:: python @@ -750,7 +745,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): .. code-block:: python - AIMessageChunk(content="J'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-b7f7492c-4cb5-42d0-8fc3-dce9b293b0fb') + AIMessageChunk(content="J'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-b7f7492c-4cb5-42d0-8fc3-dce9b293b0fb') Async: .. code-block:: python @@ -765,7 +760,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): .. code-block:: python - AIMessage(content="J'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-925ce305-2268-44c4-875f-dde9128520ad-0') + AIMessage(content="J'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'probability_score': 0.1, 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE', 'severity_score': 0.1}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-925ce305-2268-44c4-875f-dde9128520ad-0') Tool calling: .. code-block:: python @@ -803,6 +798,16 @@ class GetPopulation(BaseModel): See ``ChatVertexAI.bind_tools()`` method for more. + Use Search with Gemini 2: + .. code-block:: python + + import google.cloud.aiplatform_v1beta1.types import Tool as VertexTool + llm = ChatVertexAI(model="gemini-2.0-flash-exp") + resp = llm.invoke( + "When is the next total solar eclipse in US?", + tools=[VertexTool(google_search={})], + ) + Structured output: .. code-block:: python @@ -968,16 +973,28 @@ class Joke(BaseModel): {'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}, + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}, + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}, + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}], + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}], 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}} @@ -1004,16 +1021,28 @@ class Joke(BaseModel): {'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}, + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}, + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}, + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', - 'blocked': False}], + 'probability_score': 0.1, + 'blocked': False, + 'severity': 'HARM_SEVERITY_NEGLIGIBLE', + 'severity_score': 0.1}], 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}} diff --git a/libs/vertexai/langchain_google_vertexai/functions_utils.py b/libs/vertexai/langchain_google_vertexai/functions_utils.py index f5422b40..3966573e 100644 --- a/libs/vertexai/langchain_google_vertexai/functions_utils.py +++ b/libs/vertexai/langchain_google_vertexai/functions_utils.py @@ -283,6 +283,8 @@ def _format_to_gapic_tool(tools: _ToolsType) -> gapic.Tool: gapic_tool.google_search_retrieval = rt.google_search_retrieval if "function_declarations" in rt: gapic_tool.function_declarations.extend(rt.function_declarations) + if "google_search" in rt: + gapic_tool.google_search = rt.google_search elif isinstance(tool, dict): # not _ToolDictLike if not any( diff --git a/libs/vertexai/langchain_google_vertexai/model_garden.py b/libs/vertexai/langchain_google_vertexai/model_garden.py index 9070ac05..fd7938d6 100644 --- a/libs/vertexai/langchain_google_vertexai/model_garden.py +++ b/libs/vertexai/langchain_google_vertexai/model_garden.py @@ -32,6 +32,7 @@ AIMessage, BaseMessage, ) +from langchain_core.messages.ai import UsageMetadata from langchain_core.outputs import ( ChatGeneration, ChatGenerationChunk, @@ -61,6 +62,13 @@ from langchain_google_vertexai._base import _BaseVertexAIModelGarden, _VertexAICommon +class CacheUsageMetadata(UsageMetadata): + cache_creation_input_tokens: Optional[int] + """The number of input tokens used to create the cache entry.""" + cache_read_input_tokens: Optional[int] + """The number of input tokens read from the cache.""" + + class VertexAIModelGarden(_BaseVertexAIModelGarden, BaseLLM): """Large language models served from Vertex AI Model Garden.""" @@ -225,11 +233,13 @@ def _format_output(self, data: Any, **kwargs: Any) -> ChatResult: else: msg = AIMessage(content=content) # Collect token usage - msg.usage_metadata = { - "input_tokens": data.usage.input_tokens, - "output_tokens": data.usage.output_tokens, - "total_tokens": data.usage.input_tokens + data.usage.output_tokens, - } + msg.usage_metadata = CacheUsageMetadata( + input_tokens=data.usage.input_tokens, + output_tokens=data.usage.output_tokens, + total_tokens=data.usage.input_tokens + data.usage.output_tokens, + cache_creation_input_tokens=data.usage.cache_creation_input_tokens, + cache_read_input_tokens=data.usage.cache_read_input_tokens, + ) return ChatResult( generations=[ChatGeneration(message=msg)], llm_output=llm_output, diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py b/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py index 141b3fbf..96b2e041 100644 --- a/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py +++ b/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import Any, List, Tuple, Union, cast +from typing import Any, Dict, List, Optional, Union from google.cloud import storage # type: ignore[attr-defined, unused-ignore] from google.cloud.aiplatform import telemetry @@ -8,6 +8,7 @@ MatchingEngineIndexEndpoint, ) from google.cloud.aiplatform.matching_engine.matching_engine_index_endpoint import ( + HybridQuery, MatchNeighbor, Namespace, NumericNamespace, @@ -33,14 +34,33 @@ def find_neighbors( k: int = 4, filter_: Union[List[Namespace], None] = None, numeric_filter: Union[List[NumericNamespace], None] = None, - ) -> List[List[Tuple[str, float]]]: + *, + sparse_embeddings: Optional[ + List[Dict[str, Union[List[int], List[float]]]] + ] = None, + rrf_ranking_alpha: float = 1, + ) -> List[List[Dict[str, Any]]]: """Finds the k closes neighbors of each instance of embeddings. Args: embedding: List of embeddings vectors. + sparse_embeddings: List of Sparse embedding dictionaries which represents an + embedding as a list of dimensions and as a list of sparse values: + ie. [{"values": [0.7, 0.5], "dimensions": [10, 20]}] k: Number of neighbors to be retrieved. + rrf_ranking_alpha: Reciprocal Ranking Fusion weight, float between 0 and 1.0 + Weights Dense Search VS Sparse Search, as an example: + - rrf_ranking_alpha=1: Only Dense + - rrf_ranking_alpha=0: Only Sparse + - rrf_ranking_alpha=0.7: 0.7 weighting for dense and 0.3 for sparse filter_: List of filters to apply. Returns: - List of lists of Tuples (id, distance) for each embedding vector. + List of records: [ + { + "doc_id": doc_id, + "dense_score": dense_score, + "sparse_score": sparse_score + } + ] """ raise NotImplementedError() @@ -51,6 +71,10 @@ def add_to_index( embeddings: List[List[float]], metadatas: Union[List[dict], None] = None, is_complete_overwrite: bool = False, + *, + sparse_embeddings: Optional[ + List[Dict[str, Union[List[int], List[float]]]] + ] = None, **kwargs: Any, ) -> None: """Adds documents to the index. @@ -58,6 +82,7 @@ def add_to_index( Args: ids: List of unique ids. embeddings: List of embedddings for each record. + sparse_embeddings: List of sparse embedddings for each record. metadatas: List of metadata of each record. """ raise NotImplementedError() @@ -80,21 +105,36 @@ def get_datapoints_by_filter( def _postprocess_response( self, response: List[List[MatchNeighbor]] - ) -> List[List[Tuple[str, float]]]: - """Posproceses an endpoint response and converts it to a list of list of - tuples instead of using vertexai objects. + ) -> List[List[Dict[str, Any]]]: + """Posproceses an endpoint response and converts it to a list of list of records + instead of using vertexai objects. Args: response: Endpoint response. Returns: - List of list of tuples of (id, distance). - """ - return [ - [ - (neighbor.id, cast(float, neighbor.distance)) - for neighbor in matching_neighbor_list + List of records: [ + { + "doc_id": doc_id, + "dense_score": dense_score, + "sparse_score": sparse_score + } ] - for matching_neighbor_list in response - ] + """ + queries_results = [] + for matching_neighbor_list in response: + query_results = [] + for neighbor in matching_neighbor_list: + dense_score = neighbor.distance if neighbor.distance else 0.0 + sparse_score = ( + neighbor.sparse_distance if neighbor.sparse_distance else 0.0 + ) + result = { + "doc_id": neighbor.id, + "dense_score": dense_score, + "sparse_score": sparse_score, + } + query_results.append(result) + queries_results.append(query_results) + return queries_results class VectorSearchSearcher(Searcher): @@ -139,7 +179,7 @@ def get_datapoints_by_filter( neighbors = self.find_neighbors( embeddings=embeddings, k=max_datapoints, filter_=filter_ ) - return [_id for (_id, _) in neighbors[0]] if neighbors else [] + return [elem["doc_id"] for elem in neighbors[0]] if neighbors else [] def remove_datapoints( self, @@ -154,6 +194,10 @@ def add_to_index( embeddings: List[List[float]], metadatas: Union[List[dict], None] = None, is_complete_overwrite: bool = False, + *, + sparse_embeddings: Optional[ + List[Dict[str, Union[List[int], List[float]]]] + ] = None, **kwargs: Any, ) -> None: """Adds documents to the index. @@ -161,11 +205,17 @@ def add_to_index( Args: ids: List of unique ids. embeddings: List of embedddings for each record. + sparse_embeddings: List of sparse embedddings for each record. metadatas: List of metadata of each record. is_complete_overwrite: Whether to overwrite everything. """ - data_points = to_data_points(ids, embeddings, metadatas) + data_points = to_data_points( + ids=ids, + embeddings=embeddings, + sparse_embeddings=sparse_embeddings, + metadatas=metadatas, + ) if self._stream_update: stream_update_index(index=self._index, data_points=data_points) @@ -188,23 +238,61 @@ def find_neighbors( k: int = 4, filter_: Union[List[Namespace], None] = None, numeric_filter: Union[List[NumericNamespace], None] = None, - ) -> List[List[Tuple[str, float]]]: + *, + sparse_embeddings: Optional[ + List[Dict[str, Union[List[int], List[float]]]] + ] = None, + rrf_ranking_alpha: float = 1, + ) -> List[List[Dict[str, Any]]]: """Finds the k closes neighbors of each instance of embeddings. Args: - embedding: List of embeddings vectors. + embeddings: List of embedding vectors. + sparse_embeddings: List of Sparse embedding dictionaries which represents an + embedding as a list of dimensions and as a list of sparse values: + ie. [{"values": [0.7, 0.5], "dimensions": [10, 20]}] k: Number of neighbors to be retrieved. + rrf_ranking_alpha: Reciprocal Ranking Fusion weight, float between 0 and 1.0 + Weights Dense Search VS Sparse Search, as an example: + - rrf_ranking_alpha=1: Only Dense + - rrf_ranking_alpha=0: Only Sparse + - rrf_ranking_alpha=0.7: 0.7 weighting for dense and 0.3 for sparse filter_: List of filters to apply. Returns: - List of lists of Tuples (id, distance) for each embedding vector. + List of records: [ + { + "doc_id": doc_id, + "dense_score": dense_score, + "sparse_score": sparse_score + } + ] """ # No need to implement other method for private VPC, find_neighbors now works # with public and private. _, user_agent = get_user_agent("vertex-ai-matching-engine") with telemetry.tool_context_manager(user_agent): + if sparse_embeddings is None: + queries = embeddings + else: + if len(sparse_embeddings) != len(embeddings): + raise ValueError( + "The number of `sparse_embeddings` should match the number of " + f"`embeddings` {len(sparse_embeddings)} != {len(embeddings)}" + ) + queries = [] + + for embedding, sparse_embedding in zip(embeddings, sparse_embeddings): + hybrid_query = HybridQuery( + sparse_embedding_dimensions=sparse_embedding["dimensions"], # type: ignore + sparse_embedding_values=sparse_embedding["values"], # type: ignore + dense_embedding=embedding, + rrf_ranking_alpha=rrf_ranking_alpha, + ) + queries.append(hybrid_query) # type: ignore + response = self._endpoint.find_neighbors( deployed_index_id=self._deployed_index_id, - queries=embeddings, + queries=queries, num_neighbors=k, filter=filter_, numeric_filter=numeric_filter, diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py b/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py index fc6f7b77..dbd76970 100644 --- a/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py +++ b/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py @@ -1,7 +1,7 @@ import json import uuid import warnings -from typing import Any, Dict, List, Union +from typing import Any, Dict, List, Optional, Union from google.cloud.aiplatform import MatchingEngineIndex from google.cloud.aiplatform.compat.types import ( # type: ignore[attr-defined, unused-ignore] @@ -65,7 +65,8 @@ def batch_update_index( def to_data_points( ids: List[str], embeddings: List[List[float]], - metadatas: Union[List[Dict[str, Any]], None], + sparse_embeddings: Optional[List[Dict[str, Union[List[int], List[float]]]]] = None, + metadatas: Union[List[Dict[str, Any]], None] = None, ) -> List["meidx_types.IndexDataPoint"]: """Converts triplets id, embedding, metadata into IndexDataPoints instances. @@ -81,10 +82,15 @@ def to_data_points( if metadatas is None: metadatas = [{}] * len(ids) + if sparse_embeddings is None: + sparse_embeddings = [{"values": [], "dimensions": []}] * len(ids) + data_points = [] ignored_fields = set() - for id_, embedding, metadata in zip(ids, embeddings, metadatas): + for id_, embedding, sparse_embedding, metadata in zip( + ids, embeddings, sparse_embeddings, metadatas + ): restricts = [] numeric_restricts = [] @@ -122,6 +128,7 @@ def to_data_points( data_point = meidx_types.IndexDatapoint( datapoint_id=id_, feature_vector=embedding, + sparse_embedding=sparse_embedding, restricts=restricts, numeric_restricts=numeric_restricts, ) diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py index face1da4..9d2b6160 100644 --- a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py +++ b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py @@ -48,13 +48,13 @@ def embbedings(self) -> Embeddings: """Returns the embeddings object.""" return self._embeddings - def similarity_search_with_score( + def similarity_search_with_score( # type: ignore[override] self, query: str, k: int = 4, filter: Optional[List[Namespace]] = None, numeric_filter: Optional[List[NumericNamespace]] = None, - ) -> List[Tuple[Document, float]]: + ) -> List[Tuple[Document, Union[float, Dict[str, float]]]]: """Return docs most similar to query and their cosine distance from the query. Args: @@ -76,27 +76,37 @@ def similarity_search_with_score( Returns: List[Tuple[Document, float]]: List of documents most similar to the query text and cosine distance in float for each. - Lower score represents more similarity. + Higher score represents more similarity. """ - embbedings = self._embeddings.embed_query(query) + embedding = self._embeddings.embed_query(query) return self.similarity_search_by_vector_with_score( - embedding=embbedings, k=k, filter=filter, numeric_filter=numeric_filter + embedding=embedding, k=k, filter=filter, numeric_filter=numeric_filter ) def similarity_search_by_vector_with_score( self, embedding: List[float], + sparse_embedding: Optional[Dict[str, Union[List[int], List[float]]]] = None, k: int = 4, + rrf_ranking_alpha: float = 1, filter: Optional[List[Namespace]] = None, numeric_filter: Optional[List[NumericNamespace]] = None, - ) -> List[Tuple[Document, float]]: + ) -> List[Tuple[Document, Union[float, Dict[str, float]]]]: """Return docs most similar to the embedding and their cosine distance. Args: embedding: Embedding to look up documents similar to. + sparse_embedding: Sparse embedding dictionary which represents an embedding + as a list of dimensions and as a list of sparse values: + ie. {"values": [0.7, 0.5], "dimensions": [10, 20]} k: Number of Documents to return. Defaults to 4. + rrf_ranking_alpha: Reciprocal Ranking Fusion weight, float between 0 and 1.0 + Weights Dense Search VS Sparse Search, as an example: + - rrf_ranking_alpha=1: Only Dense + - rrf_ranking_alpha=0: Only Sparse + - rrf_ranking_alpha=0.7: 0.7 weighting for dense and 0.3 for sparse filter: Optional. A list of Namespaces for filtering the matching results. For example: @@ -111,19 +121,42 @@ def similarity_search_by_vector_with_score( for more detail. Returns: - List[Tuple[Document, float]]: List of documents most similar to - the query text and cosine distance in float for each. - Lower score represents more similarity. + List[Tuple[Document, Union[float, Dict[str, float]]]]: + List of documents most similar to the query text and either + cosine distance in float for each or dictionary with both dense and sparse + scores if running hybrid search. + Higher score represents more similarity. """ + if sparse_embedding is not None and not isinstance(sparse_embedding, dict): + raise ValueError( + "`sparse_embedding` should be a dictionary with the following format: " + "{'values': [0.7, 0.5, ...], 'dimensions': [10, 20, ...]}\n" + f"{type(sparse_embedding)} != {type({})}" + ) + sparse_embeddings = [sparse_embedding] if sparse_embedding is not None else None neighbors_list = self._searcher.find_neighbors( - embeddings=[embedding], k=k, filter_=filter, numeric_filter=numeric_filter + embeddings=[embedding], + sparse_embeddings=sparse_embeddings, + k=k, + rrf_ranking_alpha=rrf_ranking_alpha, + filter_=filter, + numeric_filter=numeric_filter, ) if not neighbors_list: return [] - keys = [key for key, _ in neighbors_list[0]] - distances = [distance for _, distance in neighbors_list[0]] + keys = [elem["doc_id"] for elem in neighbors_list[0]] + if sparse_embedding is None: + distances = [elem["dense_score"] for elem in neighbors_list[0]] + else: + distances = [ + { + "dense_score": elem["dense_score"], + "sparse_score": elem["sparse_score"], + } + for elem in neighbors_list[0] + ] documents = self._document_storage.mget(keys) if all(document is not None for document in documents): @@ -228,18 +261,48 @@ def add_texts( # Makes sure is a list and can get the length, should we support iterables? # metadata is a list so probably not? texts = list(texts) + embeddings = self._embeddings.embed_documents(texts) + return self.add_texts_with_embeddings( + texts=texts, + embeddings=embeddings, + metadatas=metadatas, + ids=ids, + is_complete_overwrite=is_complete_overwrite, + **kwargs, + ) + + def add_texts_with_embeddings( + self, + texts: List[str], + embeddings: List[List[float]], + metadatas: Union[List[dict], None] = None, + *, + sparse_embeddings: Optional[ + List[Dict[str, Union[List[int], List[float]]]] + ] = None, + ids: Optional[List[str]] = None, + is_complete_overwrite: bool = False, + **kwargs: Any, + ) -> List[str]: if ids is not None and len(set(ids)) != len(ids): raise ValueError( "All provided ids should be unique." f"There are {len(ids)-len(set(ids))} duplicates." ) + if ids is not None and len(ids) != len(texts): raise ValueError( "The number of `ids` should match the number of `texts` " f"{len(ids)} != {len(texts)}" ) + if isinstance(embeddings, list) and len(embeddings) != len(texts): + raise ValueError( + "The number of `embeddings` should match the number of `texts` " + f"{len(embeddings)} != {len(texts)}" + ) + if ids is None: ids = self._generate_unique_ids(len(texts)) @@ -259,10 +322,13 @@ def add_texts( self._document_storage.mset(list(zip(ids, documents))) - embeddings = self._embeddings.embed_documents(texts) - self._searcher.add_to_index( - ids, embeddings, metadatas, is_complete_overwrite, **kwargs + ids=ids, + embeddings=embeddings, + sparse_embeddings=sparse_embeddings, + metadatas=metadatas, + is_complete_overwrite=is_complete_overwrite, + **kwargs, ) return ids diff --git a/libs/vertexai/poetry.lock 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b/libs/vertexai/pyproject.toml index 6d12f431..b491dc88 100644 --- a/libs/vertexai/pyproject.toml +++ b/libs/vertexai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-vertexai" -version = "2.0.9" +version = "2.0.10" description = "An integration package connecting Google VertexAI and LangChain" authors = [] readme = "README.md" @@ -12,13 +12,13 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.9,<4.0" -langchain-core = ">=0.3.15,<0.4" -google-cloud-aiplatform = "^1.70.0" +langchain-core = ">=0.3.27,<0.4" +google-cloud-aiplatform = "^1.75.0" google-cloud-storage = "^2.18.0" # optional dependencies anthropic = { extras = ["vertexai"], version = ">=0.35.0,<1", optional = true } langchain-mistralai = { version = ">=0.2.0,<1", optional = true } -httpx = "^0.27.0" +httpx = "^0.28.0" httpx-sse = "^0.4.0" pydantic = ">=2.9,<2.10" @@ -44,7 +44,7 @@ numpy = [ ] google-api-python-client = "^2.117.0" langchain = "^0.3.7" -langchain-tests = "0.3.1" +langchain-tests = "0.3.6" anthropic = { extras = ["vertexai"], version = ">=0.35.0,<1" } @@ -52,6 +52,8 @@ anthropic = { extras = ["vertexai"], version = ">=0.35.0,<1" } ignore-words-list = "rouge" + + [tool.poetry.group.codespell] optional = true diff --git a/libs/vertexai/tests/integration_tests/test_anthropic_cache.py b/libs/vertexai/tests/integration_tests/test_anthropic_cache.py new file mode 100644 index 00000000..fc11e134 --- /dev/null +++ b/libs/vertexai/tests/integration_tests/test_anthropic_cache.py @@ -0,0 +1,151 @@ +"""Integration tests for Anthropic cache control functionality.""" +import os +from typing import Dict, List, Union + +import pytest +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage +from langchain_core.prompts import ChatPromptTemplate + +from langchain_google_vertexai.model_garden import ChatAnthropicVertex + + +@pytest.mark.extended +@pytest.mark.skip(reason="claude-3-5-v2 not enabled") +def test_anthropic_system_cache() -> None: + """Test chat with system message having cache control.""" + project = os.environ["PROJECT_ID"] + location = "us-central1" + model = ChatAnthropicVertex( + project=project, + location=location, + ) + + context = SystemMessage( + content="You are my personal assistant. Be helpful and concise.", + additional_kwargs={"cache_control": {"type": "ephemeral"}}, + ) + message = HumanMessage(content="Hello! What can you do for me?") + + response = model.invoke( + [context, message], model_name="claude-3-5-sonnet-v2@20241022" + ) + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert "usage_metadata" in response.additional_kwargs + assert "cache_creation_input_tokens" in response.additional_kwargs["usage_metadata"] + + +@pytest.mark.extended +@pytest.mark.skip(reason="claude-3-5-v2 not enabled") +def test_anthropic_mixed_cache() -> None: + """Test chat with different cache control types.""" + project = os.environ["PROJECT_ID"] + location = "us-central1" + model = ChatAnthropicVertex( + project=project, + location=location, + ) + + context = SystemMessage( + content=[ + { + "type": "text", + "text": "You are my personal assistant.", + "cache_control": {"type": "ephemeral"}, + } + ] + ) + message = HumanMessage( + content=[ + { + "type": "text", + "text": "What's your name and what can you help me with?", + "cache_control": {"type": "ephemeral"}, + } + ] + ) + + response = model.invoke( + [context, message], model_name="claude-3-5-sonnet-v2@20241022" + ) + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert "usage_metadata" in response.additional_kwargs + + +@pytest.mark.extended +@pytest.mark.skip(reason="claude-3-5-v2 not enabled") +def test_anthropic_conversation_cache() -> None: + """Test chat conversation with cache control.""" + project = os.environ["PROJECT_ID"] + location = "us-central1" + model = ChatAnthropicVertex( + project=project, + location=location, + ) + + context = SystemMessage( + content="You are my personal assistant. My name is Peter.", + additional_kwargs={"cache_control": {"type": "ephemeral"}}, + ) + messages = [ + context, + HumanMessage( + content=[ + { + "type": "text", + "text": "What's my name?", + "cache_control": {"type": "ephemeral"}, + } + ] + ), + AIMessage(content="Your name is Peter."), + HumanMessage( + content=[ + { + "type": "text", + "text": "Can you repeat my name?", + "cache_control": {"type": "ephemeral"}, + } + ] + ), + ] + + response = model.invoke(messages, model_name="claude-3-5-sonnet-v2@20241022") + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert "peter" in response.content.lower() # Should remember the name + + +@pytest.mark.extended +@pytest.mark.skip(reason="claude-3-5-v2 not enabled") +def test_anthropic_chat_template_cache() -> None: + """Test chat template with structured content and cache control.""" + project = os.environ["PROJECT_ID"] + location = "us-central1" + model = ChatAnthropicVertex( + project=project, + location=location, + ) + + content: List[Union[Dict[str, Union[str, Dict[str, str]]], str]] = [ + { + "text": "You are a helpful assistant. Be concise and clear.", + "type": "text", + "cache_control": {"type": "ephemeral"}, + } + ] + + prompt = ChatPromptTemplate.from_messages( + [SystemMessage(content=content), ("human", "{input}")] + ) + + chain = prompt | model + + response = chain.invoke( + {"input": "What's the capital of France?"}, + ) + + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert "Paris" in response.content diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py index 18df20e2..a49a9a03 100644 --- a/libs/vertexai/tests/integration_tests/test_chat_models.py +++ b/libs/vertexai/tests/integration_tests/test_chat_models.py @@ -1214,6 +1214,7 @@ def test_multimodal_pdf_input_b64(multimodal_pdf_chain: RunnableSerializable) -> assert isinstance(response, AIMessage) +@pytest.mark.xfail(reason="logprobs are subject to daily quotas") @pytest.mark.release def test_logprobs() -> None: llm = ChatVertexAI(model="gemini-1.5-flash", logprobs=2) diff --git a/libs/vertexai/tests/integration_tests/test_standard.py b/libs/vertexai/tests/integration_tests/test_standard.py index 60db75c7..f811c57e 100644 --- a/libs/vertexai/tests/integration_tests/test_standard.py +++ b/libs/vertexai/tests/integration_tests/test_standard.py @@ -5,7 +5,8 @@ import pytest from langchain_core.language_models import BaseChatModel from langchain_core.rate_limiters import InMemoryRateLimiter -from langchain_standard_tests.integration_tests import ChatModelIntegrationTests +from langchain_core.tools import BaseTool +from langchain_tests.integration_tests import ChatModelIntegrationTests from langchain_google_vertexai import ChatVertexAI @@ -54,8 +55,10 @@ def chat_model_params(self) -> dict: } @pytest.mark.xfail(reason="Gemini 1.0 doesn't support tool_choice='any'") - def test_structured_few_shot_examples(self, model: BaseChatModel) -> None: - super().test_structured_few_shot_examples(model) + def test_structured_few_shot_examples( + self, model: BaseChatModel, my_adder_tool: BaseTool + ) -> None: + super().test_structured_few_shot_examples(model, my_adder_tool) class TestGemini_15_AIStandard(ChatModelIntegrationTests): diff --git a/libs/vertexai/tests/integration_tests/test_vectorstores.py b/libs/vertexai/tests/integration_tests/test_vectorstores.py index 30fa26fe..2fa23f5d 100644 --- a/libs/vertexai/tests/integration_tests/test_vectorstores.py +++ b/libs/vertexai/tests/integration_tests/test_vectorstores.py @@ -11,7 +11,7 @@ """ import os -from typing import List +from typing import Dict, List, Union from uuid import uuid4 import pytest @@ -268,6 +268,68 @@ def test_vector_store(vector_store_class: str, request: pytest.FixtureRequest): @pytest.mark.extended +@pytest.mark.parametrize( + "vector_store_class", ["vector_store", "datastore_vector_store"] +) +def test_vector_store_hybrid_search( + vector_store_class: str, + request: pytest.FixtureRequest, + embeddings: VertexAIEmbeddings, +): + vector_store: VectorSearchVectorStore = request.getfixturevalue(vector_store_class) + + query = "What are your favourite animals?" + embedding = embeddings.embed_query(query) + sparse_embedding: Dict[str, Union[List[int], List[float]]] = { + "values": [0.5, 0.7], + "dimensions": [2, 4], + } + + docs_with_scores = vector_store.similarity_search_by_vector_with_score( + embedding=embedding, sparse_embedding=sparse_embedding, k=1 + ) + assert len(docs_with_scores) == 1 + for doc, scores in docs_with_scores: + assert isinstance(doc, Document) + assert isinstance(scores, dict) + assert "dense_score" in scores + assert "sparse_score" in scores + assert isinstance(scores["dense_score"], float) + assert isinstance(scores["sparse_score"], float) + + +@pytest.mark.extended +@pytest.mark.parametrize("vector_store_class", ["datastore_vector_store"]) +def test_add_texts_with_embeddings( + vector_store_class: str, + request: pytest.FixtureRequest, + embeddings: VertexAIEmbeddings, +): + vector_store: VectorSearchVectorStore = request.getfixturevalue(vector_store_class) + + texts = ["my favourite animal is the elephant", "my favourite animal is the lion"] + ids = ["idx1", "idx2"] + embs = embeddings.embed_documents(texts) + ids1 = vector_store.add_texts_with_embeddings( + texts=texts, embeddings=embs, ids=ids, is_complete_overwrite=True + ) + assert len(ids1) == 2 + + sparse_embeddings: List[Dict[str, Union[List[int], List[float]]]] = [ + {"values": [0.5, 0.7], "dimensions": [2, 4]} + ] * 2 + ids2 = vector_store.add_texts_with_embeddings( + texts=texts, + embeddings=embs, + sparse_embeddings=sparse_embeddings, + ids=ids, + is_complete_overwrite=True, + ) + assert ids == ids1 == ids2 + + +@pytest.mark.extended +@pytest.mark.skip("rebuild the index with restricts") @pytest.mark.parametrize( "vector_store_class", [ diff --git a/libs/vertexai/tests/unit_tests/test_anthropic_utils.py b/libs/vertexai/tests/unit_tests/test_anthropic_utils.py index 7f83bfb3..08ba9aba 100644 --- a/libs/vertexai/tests/unit_tests/test_anthropic_utils.py +++ b/libs/vertexai/tests/unit_tests/test_anthropic_utils.py @@ -1,3 +1,5 @@ +"""Unit tests for _anthropic_utils.py.""" + import pytest from langchain_core.messages import ( AIMessage, @@ -7,7 +9,260 @@ ) from langchain_core.messages.tool import tool_call as create_tool_call -from langchain_google_vertexai.model_garden import _format_messages_anthropic +from langchain_google_vertexai._anthropic_utils import ( + _format_message_anthropic, + _format_messages_anthropic, +) + + +def test_format_message_anthropic_with_cache_control_in_kwargs(): + """Test formatting a message with cache control in additional_kwargs.""" + message = HumanMessage( + content="Hello", additional_kwargs={"cache_control": {"type": "semantic"}} + ) + result = _format_message_anthropic(message) + assert result == { + "role": "user", + "content": [ + {"type": "text", "text": "Hello", "cache_control": {"type": "semantic"}} + ], + } + + +def test_format_message_anthropic_with_cache_control_in_block(): + """Test formatting a message with cache control in content block.""" + message = HumanMessage( + content=[ + {"type": "text", "text": "Hello", "cache_control": {"type": "semantic"}} + ] + ) + result = _format_message_anthropic(message) + assert result == { + "role": "user", + "content": [ + {"type": "text", "text": "Hello", "cache_control": {"type": "semantic"}} + ], + } + + +def test_format_message_anthropic_with_mixed_blocks(): + """Test formatting a message with mixed blocks, some with cache control.""" + message = HumanMessage( + content=[ + {"type": "text", "text": "Hello", "cache_control": {"type": "semantic"}}, + {"type": "text", "text": "World"}, + "Plain text", + ] + ) + result = _format_message_anthropic(message) + assert result == { + "role": "user", + "content": [ + {"type": "text", "text": "Hello", "cache_control": {"type": "semantic"}}, + {"type": "text", "text": "World"}, + {"type": "text", "text": "Plain text"}, + ], + } + + +def test_format_messages_anthropic_with_system_cache_control(): + """Test formatting messages with system message having cache control.""" + messages = [ + SystemMessage( + content="System message", + additional_kwargs={"cache_control": {"type": "ephemeral"}}, + ), + HumanMessage(content="Hello"), + ] + system_messages, formatted_messages = _format_messages_anthropic(messages) + + assert system_messages == [ + { + "type": "text", + "text": "System message", + "cache_control": {"type": "ephemeral"}, + } + ] + + assert formatted_messages == [ + {"role": "user", "content": [{"type": "text", "text": "Hello"}]} + ] + + +def test_format_message_anthropic_system(): + """Test formatting a system message.""" + message = SystemMessage( + content="System message", + additional_kwargs={"cache_control": {"type": "ephemeral"}}, + ) + result = _format_message_anthropic(message) + assert result == [ + { + "type": "text", + "text": "System message", + "cache_control": {"type": "ephemeral"}, + } + ] + + +def test_format_message_anthropic_system_list(): + """Test formatting a system message with list content.""" + message = SystemMessage( + content=[ + { + "type": "text", + "text": "System rule 1", + "cache_control": {"type": "ephemeral"}, + }, + {"type": "text", "text": "System rule 2"}, + ] + ) + result = _format_message_anthropic(message) + assert result == [ + { + "type": "text", + "text": "System rule 1", + "cache_control": {"type": "ephemeral"}, + }, + {"type": "text", "text": "System rule 2"}, + ] + + +def test_format_messages_anthropic_with_system_string(): + """Test formatting messages with system message as string.""" + messages = [ + SystemMessage(content="System message"), + HumanMessage(content="Hello"), + ] + system_messages, formatted_messages = _format_messages_anthropic(messages) + + assert system_messages == [{"type": "text", "text": "System message"}] + + assert formatted_messages == [ + {"role": "user", "content": [{"type": "text", "text": "Hello"}]} + ] + + +def test_format_messages_anthropic_with_system_list(): + """Test formatting messages with system message as a list.""" + messages = [ + SystemMessage( + content=[ + { + "type": "text", + "text": "System rule 1", + "cache_control": {"type": "ephemeral"}, + }, + {"type": "text", "text": "System rule 2"}, + ] + ), + HumanMessage(content="Hello"), + ] + system_messages, formatted_messages = _format_messages_anthropic(messages) + + assert system_messages == [ + { + "type": "text", + "text": "System rule 1", + "cache_control": {"type": "ephemeral"}, + }, + {"type": "text", "text": "System rule 2"}, + ] + + assert formatted_messages == [ + {"role": "user", "content": [{"type": "text", "text": "Hello"}]} + ] + + +def test_format_messages_anthropic_with_system_mixed_list(): + """Test formatting messages with system message as a mixed list.""" + messages = [ + SystemMessage( + content=[ + "Plain system rule", + { + "type": "text", + "text": "Formatted system rule", + "cache_control": {"type": "ephemeral"}, + }, + ] + ), + HumanMessage(content="Hello"), + ] + system_messages, formatted_messages = _format_messages_anthropic(messages) + + assert system_messages == [ + {"type": "text", "text": "Plain system rule"}, + { + "type": "text", + "text": "Formatted system rule", + "cache_control": {"type": "ephemeral"}, + }, + ] + + assert formatted_messages == [ + {"role": "user", "content": [{"type": "text", "text": "Hello"}]} + ] + + +def test_format_messages_anthropic_with_mixed_messages(): + """Test formatting a conversation with various message types and cache controls.""" + messages = [ + SystemMessage( + content=[ + { + "type": "text", + "text": "System message", + "cache_control": {"type": "ephemeral"}, + } + ] + ), + HumanMessage( + content=[ + { + "type": "text", + "text": "Human message", + "cache_control": {"type": "semantic"}, + } + ] + ), + AIMessage( + content="AI response", + additional_kwargs={"cache_control": {"type": "semantic"}}, + ), + ] + system_messages, formatted_messages = _format_messages_anthropic(messages) + + assert system_messages == [ + { + "type": "text", + "text": "System message", + "cache_control": {"type": "ephemeral"}, + } + ] + + assert formatted_messages == [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Human message", + "cache_control": {"type": "semantic"}, + } + ], + }, + { + "role": "assistant", + "content": [ + { + "type": "text", + "text": "AI response", + "cache_control": {"type": "semantic"}, + } + ], + }, + ] @pytest.mark.parametrize( @@ -113,7 +368,7 @@ content="Mike age is 30", ), ], - "test1", + [{"type": "text", "text": "test1"}], [ { "role": "assistant", @@ -160,7 +415,7 @@ "role": "user", "content": [ { - "type": "image_url", + "type": "image", "source": { "type": "base64", "media_type": "image/png", @@ -473,6 +728,7 @@ def test_format_messages_anthropic( source_history, expected_sm, expected_history ) -> None: + """Test the original format_messages_anthropic functionality.""" sm, result_history = _format_messages_anthropic(source_history) for result, expected in zip(result_history, expected_history): diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py index 06567e71..81e9eebf 100644 --- a/libs/vertexai/tests/unit_tests/test_chat_models.py +++ b/libs/vertexai/tests/unit_tests/test_chat_models.py @@ -1077,6 +1077,8 @@ def test_anthropic_format_output() -> None: class Usage: input_tokens: int output_tokens: int + cache_creation_input_tokens: Optional[int] + cache_read_input_tokens: Optional[int] @dataclass class Message: @@ -1092,13 +1094,25 @@ def model_dump(self): ], "model": "baz", "role": "assistant", - "usage": Usage(input_tokens=2, output_tokens=1), + "usage": Usage( + input_tokens=2, + output_tokens=1, + cache_creation_input_tokens=1, + cache_read_input_tokens=1, + ), "type": "message", } usage: Usage - test_msg = Message(usage=Usage(input_tokens=2, output_tokens=1)) + test_msg = Message( + usage=Usage( + input_tokens=2, + output_tokens=1, + cache_creation_input_tokens=1, + cache_read_input_tokens=1, + ) + ) model = ChatAnthropicVertex(project="test-project", location="test-location") result = model._format_output(test_msg) @@ -1113,4 +1127,6 @@ def model_dump(self): "input_tokens": 2, "output_tokens": 1, "total_tokens": 3, + "cache_creation_input_tokens": 1, + "cache_read_input_tokens": 1, } diff --git a/libs/vertexai/tests/unit_tests/test_standard.py b/libs/vertexai/tests/unit_tests/test_standard.py index 109850d8..09bdbbf0 100644 --- a/libs/vertexai/tests/unit_tests/test_standard.py +++ b/libs/vertexai/tests/unit_tests/test_standard.py @@ -1,7 +1,7 @@ from typing import Type from langchain_core.language_models import BaseChatModel -from langchain_standard_tests.unit_tests import ChatModelUnitTests +from langchain_tests.unit_tests import ChatModelUnitTests from langchain_google_vertexai import ChatVertexAI diff --git a/libs/vertexai/tests/unit_tests/test_vectorstores.py b/libs/vertexai/tests/unit_tests/test_vectorstores.py index bae26251..24653013 100644 --- a/libs/vertexai/tests/unit_tests/test_vectorstores.py +++ b/libs/vertexai/tests/unit_tests/test_vectorstores.py @@ -1,6 +1,8 @@ +from typing import Dict, List, Union from unittest.mock import MagicMock import pytest +from langchain_core.documents import Document from langchain_google_vertexai.vectorstores._utils import to_data_points from langchain_google_vertexai.vectorstores.vectorstores import _BaseVertexAIVectorStore @@ -9,6 +11,9 @@ def test_to_data_points(): ids = ["Id1"] embeddings = [[0.0, 0.0]] + sparse_embeddings: List[Dict[str, Union[List[int], List[float]]]] = [ + {"values": [0.9, 0.3], "dimensions": [3, 20]} + ] metadatas = [ { "some_string": "string", @@ -19,7 +24,12 @@ def test_to_data_points(): ] with pytest.warns(): - result = to_data_points(ids, embeddings, metadatas) + result = to_data_points( + ids=ids, + embeddings=embeddings, + sparse_embeddings=sparse_embeddings, + metadatas=metadatas, + ) assert isinstance(result, list) assert len(result) == 1 @@ -76,3 +86,72 @@ def test_add_texts_with_custom_ids(mocker): VectorStore.add_texts(texts=texts, ids=["Id1"]) with pytest.raises(ValueError): VectorStore.add_texts(texts=texts, ids=["Id1", "Id2", "Id2"]) + + +def test_add_texts_with_embeddings(): + texts = ["Text1", "Text2"] + embeddings = [[0.1, 0.2, 1.0], [1.0, 0.0, 1.0]] + embeddings_wrong_length = [[0.0, 0.0, 1.0]] + + VectorStore = object.__new__(_BaseVertexAIVectorStore) + VectorStore._document_storage = MagicMock() + VectorStore._embeddings = MagicMock() + VectorStore._searcher = MagicMock() + + returned_ids = VectorStore.add_texts_with_embeddings( + texts=texts, embeddings=embeddings + ) + + assert len(returned_ids) == len(texts) + + with pytest.raises(ValueError): + VectorStore.add_texts_with_embeddings( + texts=texts, embeddings=embeddings_wrong_length + ) + + +def test_similarity_search_by_vector_with_score_output_shape(): + embedding = [0.0, 0.5, 0.8] + sparse_embedding: Dict[str, Union[List[int], List[float]]] = { + "values": [0.9, 0.3], + "dimensions": [3, 20], + } + + VectorStore = object.__new__(_BaseVertexAIVectorStore) + VectorStore._document_storage = MagicMock() + VectorStore._embeddings = MagicMock() + VectorStore._searcher = MagicMock() + + # Mock the searcher to return some sample neighbors + sample_neighbors = [{"doc_id": "doc1", "dense_score": 0.8, "sparse_score": 0.5}] + VectorStore._searcher.find_neighbors.return_value = [sample_neighbors] + + # Mock the document storage to return a document for the doc_id + sample_document = Document(page_content="test content") + VectorStore._document_storage.mget.return_value = [sample_document] + + # Test with sparse embedding + result_with_sparse = VectorStore.similarity_search_by_vector_with_score( + embedding=embedding, sparse_embedding=sparse_embedding + ) + + # Should return tuples with (document, dict_of_scores) + assert len(result_with_sparse) == 1 + assert len(result_with_sparse[0]) == 2 + assert isinstance(result_with_sparse[0][0], Document) + assert isinstance(result_with_sparse[0][1], dict) + assert "dense_score" in result_with_sparse[0][1] + assert "sparse_score" in result_with_sparse[0][1] + assert isinstance(result_with_sparse[0][1]["dense_score"], float) + assert isinstance(result_with_sparse[0][1]["sparse_score"], float) + + # Test without sparse embedding (dense-only search) + result_without_sparse = VectorStore.similarity_search_by_vector_with_score( + embedding=embedding + ) + + # Should return tuples with (document, float) + assert len(result_without_sparse) == 1 + assert len(result_without_sparse[0]) == 2 + assert isinstance(result_without_sparse[0][0], Document) + assert isinstance(result_without_sparse[0][1], float)