diff --git a/orchestrator/api/api_v1/endpoints/search.py b/orchestrator/api/api_v1/endpoints/search.py index ac5fa4dcc..a1e0480dc 100644 --- a/orchestrator/api/api_v1/endpoints/search.py +++ b/orchestrator/api/api_v1/endpoints/search.py @@ -21,35 +21,32 @@ PathsResponse, SearchResultsSchema, ) +from orchestrator.schemas.search_requests import SearchRequest from orchestrator.search.core.exceptions import InvalidCursorError, QueryStateNotFoundError from orchestrator.search.core.types import EntityType, UIType -from orchestrator.search.filters.definitions import generate_definitions -from orchestrator.search.retrieval import SearchQueryState, execute_search, execute_search_for_export -from orchestrator.search.retrieval.builder import build_paths_query, create_path_autocomplete_lquery, process_path_rows +from orchestrator.search.filters.definitions import TypeDefinition, generate_definitions +from orchestrator.search.query import QueryState, engine +from orchestrator.search.query.builder import build_paths_query, create_path_autocomplete_lquery, process_path_rows +from orchestrator.search.query.queries import ExportQuery, SelectQuery +from orchestrator.search.query.results import SearchResult +from orchestrator.search.query.validation import is_lquery_syntactically_valid from orchestrator.search.retrieval.pagination import PageCursor, encode_next_page_cursor -from orchestrator.search.retrieval.validation import is_lquery_syntactically_valid -from orchestrator.search.schemas.parameters import ( - ProcessSearchParameters, - ProductSearchParameters, - SearchParameters, - SubscriptionSearchParameters, - WorkflowSearchParameters, -) -from orchestrator.search.schemas.results import SearchResult, TypeDefinition router = APIRouter() logger = structlog.get_logger(__name__) async def _perform_search_and_fetch( - search_params: SearchParameters | None = None, + entity_type: EntityType | None = None, + request: SearchRequest | None = None, cursor: str | None = None, query_id: str | None = None, ) -> SearchResultsSchema[SearchResult]: """Execute search with optional pagination. Args: - search_params: Search parameters for new search + entity_type: Entity type to search + request: Search request for new search cursor: Pagination cursor (loads saved query state) query_id: Saved query ID to retrieve and execute @@ -58,27 +55,31 @@ async def _perform_search_and_fetch( """ try: page_cursor: PageCursor | None = None + query: SelectQuery if cursor: page_cursor = PageCursor.decode(cursor) - query_state = SearchQueryState.load_from_id(page_cursor.query_id) + query_state = QueryState.load_from_id(page_cursor.query_id, SelectQuery) + query = query_state.query + elif query_id: - query_state = SearchQueryState.load_from_id(query_id) - elif search_params: - query_state = SearchQueryState(parameters=search_params, query_embedding=None) + query_state = QueryState.load_from_id(query_id, SelectQuery) + query = query_state.query + + elif request and entity_type: + query = request.to_query(entity_type) + query_state = QueryState(query=query, query_embedding=None) else: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Either search_params, cursor, or query_id must be provided", + detail="Either (request + entity_type), cursor, or query_id must be provided", ) - search_response = await execute_search( - query_state.parameters, db.session, page_cursor, query_state.query_embedding - ) + search_response = await engine.execute_search(query, db.session, page_cursor, query_state.query_embedding) if not search_response.results: return SearchResultsSchema(search_metadata=search_response.metadata) - next_page_cursor = encode_next_page_cursor(search_response, page_cursor, query_state.parameters) + next_page_cursor = encode_next_page_cursor(search_response, page_cursor, query) has_next_page = next_page_cursor is not None page_info = PageInfoSchema(has_next_page=has_next_page, next_page_cursor=next_page_cursor) @@ -98,34 +99,34 @@ async def _perform_search_and_fetch( @router.post("/subscriptions", response_model=SearchResultsSchema[SearchResult]) async def search_subscriptions( - search_params: SubscriptionSearchParameters, + request: SearchRequest, cursor: str | None = None, ) -> SearchResultsSchema[SearchResult]: - return await _perform_search_and_fetch(search_params, cursor) + return await _perform_search_and_fetch(EntityType.SUBSCRIPTION, request, cursor) @router.post("/workflows", response_model=SearchResultsSchema[SearchResult]) async def search_workflows( - search_params: WorkflowSearchParameters, + request: SearchRequest, cursor: str | None = None, ) -> SearchResultsSchema[SearchResult]: - return await _perform_search_and_fetch(search_params, cursor) + return await _perform_search_and_fetch(EntityType.WORKFLOW, request, cursor) @router.post("/products", response_model=SearchResultsSchema[SearchResult]) async def search_products( - search_params: ProductSearchParameters, + request: SearchRequest, cursor: str | None = None, ) -> SearchResultsSchema[SearchResult]: - return await _perform_search_and_fetch(search_params, cursor) + return await _perform_search_and_fetch(EntityType.PRODUCT, request, cursor) @router.post("/processes", response_model=SearchResultsSchema[SearchResult]) async def search_processes( - search_params: ProcessSearchParameters, + request: SearchRequest, cursor: str | None = None, ) -> SearchResultsSchema[SearchResult]: - return await _perform_search_and_fetch(search_params, cursor) + return await _perform_search_and_fetch(EntityType.PROCESS, request, cursor) @router.get( @@ -191,7 +192,7 @@ async def export_by_query_id(query_id: str) -> ExportResponse: as flattened records suitable for CSV download. Args: - query_id: Query UUID + query_id: QueryTypes UUID Returns: ExportResponse containing 'page' with an array of flattened entity records. @@ -200,8 +201,17 @@ async def export_by_query_id(query_id: str) -> ExportResponse: HTTPException: 404 if query not found, 400 if invalid data """ try: - query_state = SearchQueryState.load_from_id(query_id) - export_records = await execute_search_for_export(query_state, db.session) + # Load SelectQuery from the database (what gets saved during search) + query_state = QueryState.load_from_id(query_id, SelectQuery) + + # Convert to ExportQuery with export-appropriate limit + export_query = ExportQuery( + entity_type=query_state.query.entity_type, + filters=query_state.query.filters, + query_text=query_state.query.query_text, + ) + + export_records = await engine.execute_export(export_query, db.session, query_state.query_embedding) return ExportResponse(page=export_records) except ValueError as e: raise HTTPException(status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, detail=str(e)) diff --git a/orchestrator/search/retrieval/utils.py b/orchestrator/cli/search/display.py similarity index 72% rename from orchestrator/search/retrieval/utils.py rename to orchestrator/cli/search/display.py index ff84635a3..579d7f7dc 100644 --- a/orchestrator/search/retrieval/utils.py +++ b/orchestrator/cli/search/display.py @@ -12,7 +12,6 @@ # limitations under the License. import json -import re import structlog from sqlalchemy import and_ @@ -22,37 +21,13 @@ from orchestrator.db.models import AiSearchIndex from orchestrator.search.core.types import EntityType from orchestrator.search.indexing.registry import ENTITY_CONFIG_REGISTRY -from orchestrator.search.schemas.parameters import BaseSearchParameters -from orchestrator.search.schemas.results import SearchResult +from orchestrator.search.query.queries import BaseQuery +from orchestrator.search.query.results import SearchResult logger = structlog.get_logger(__name__) -def generate_highlight_indices(text: str, term: str) -> list[tuple[int, int]]: - """Finds all occurrences of individual words from the term, including both word boundary and substring matches.""" - if not text or not term: - return [] - - all_matches = [] - words = [w.strip() for w in term.split() if w.strip()] - - for word in words: - # First find word boundary matches - word_boundary_pattern = rf"\b{re.escape(word)}\b" - word_matches = list(re.finditer(word_boundary_pattern, text, re.IGNORECASE)) - all_matches.extend([(m.start(), m.end()) for m in word_matches]) - - # Then find all substring matches - substring_pattern = re.escape(word) - substring_matches = list(re.finditer(substring_pattern, text, re.IGNORECASE)) - all_matches.extend([(m.start(), m.end()) for m in substring_matches]) - - return sorted(set(all_matches)) - - -def display_filtered_paths_only( - results: list[SearchResult], search_params: BaseSearchParameters, db_session: WrappedSession -) -> None: +def display_filtered_paths_only(results: list[SearchResult], query: BaseQuery, db_session: WrappedSession) -> None: """Display only the paths that were searched for in the results.""" if not results: logger.info("No results found.") @@ -60,7 +35,7 @@ def display_filtered_paths_only( logger.info("--- Search Results ---") - searched_paths = search_params.filters.get_all_paths() if search_params.filters else [] + searched_paths = query.filters.get_all_paths() if query.filters else [] if not searched_paths: return diff --git a/orchestrator/cli/search/search_explore.py b/orchestrator/cli/search/search_explore.py index 98cbe6937..6b4768649 100644 --- a/orchestrator/cli/search/search_explore.py +++ b/orchestrator/cli/search/search_explore.py @@ -4,13 +4,13 @@ import typer from pydantic import ValidationError +from orchestrator.cli.search.display import display_filtered_paths_only, display_results from orchestrator.db import db from orchestrator.search.core.types import EntityType, FilterOp, UIType from orchestrator.search.filters import EqualityFilter, FilterTree, LtreeFilter, PathFilter -from orchestrator.search.retrieval import execute_search -from orchestrator.search.retrieval.utils import display_filtered_paths_only, display_results -from orchestrator.search.retrieval.validation import get_structured_filter_schema -from orchestrator.search.schemas.parameters import BaseSearchParameters +from orchestrator.search.query import engine +from orchestrator.search.query.queries import SelectQuery +from orchestrator.search.query.validation import get_structured_filter_schema app = typer.Typer(help="Experiment with the subscription search indexes.") @@ -31,16 +31,14 @@ def structured(path: str, value: str, entity_type: EntityType = EntityType.SUBSC ... """ path_filter = PathFilter(path=path, condition=EqualityFilter(op=FilterOp.EQ, value=value), value_kind=UIType.STRING) - search_params = BaseSearchParameters.create( - entity_type=entity_type, filters=FilterTree.from_flat_and([path_filter]), limit=limit - ) - search_response = asyncio.run(execute_search(search_params=search_params, db_session=db.session)) - display_filtered_paths_only(search_response.results, search_params, db.session) + query = SelectQuery(entity_type=entity_type, filters=FilterTree.from_flat_and([path_filter]), limit=limit) + search_response = asyncio.run(engine.execute_search(query=query, db_session=db.session)) + display_filtered_paths_only(search_response.results, query, db.session) display_results(search_response.results, db.session, "Match") @app.command() -def semantic(query: str, entity_type: EntityType = EntityType.SUBSCRIPTION, limit: int = 10) -> None: +def semantic(query_text: str, entity_type: EntityType = EntityType.SUBSCRIPTION, limit: int = 10) -> None: """Finds subscriptions that are conceptually most similar to the query text. Example: @@ -52,8 +50,8 @@ def semantic(query: str, entity_type: EntityType = EntityType.SUBSCRIPTION, limi }, ... """ - search_params = BaseSearchParameters.create(entity_type=entity_type, query=query, limit=limit) - search_response = asyncio.run(execute_search(search_params=search_params, db_session=db.session)) + query = SelectQuery(entity_type=entity_type, query_text=query_text, limit=limit) + search_response = asyncio.run(engine.execute_search(query=query, db_session=db.session)) display_results(search_response.results, db.session, "Distance") @@ -70,8 +68,8 @@ def fuzzy(term: str, entity_type: EntityType = EntityType.SUBSCRIPTION, limit: i }, ... """ - search_params = BaseSearchParameters.create(entity_type=entity_type, query=term, limit=limit) - search_response = asyncio.run(execute_search(search_params=search_params, db_session=db.session)) + query = SelectQuery(entity_type=entity_type, query_text=term, limit=limit) + search_response = asyncio.run(engine.execute_search(query=query, db_session=db.session)) display_results(search_response.results, db.session, "Similarity") @@ -79,7 +77,7 @@ def fuzzy(term: str, entity_type: EntityType = EntityType.SUBSCRIPTION, limit: i def hierarchical( op: str = typer.Argument(..., help="The hierarchical operation to perform."), path: str = typer.Argument(..., help="The ltree path or lquery pattern for the operation."), - query: str | None = typer.Option(None, "--query", "-f", help="An optional fuzzy term to rank the results."), + query_text: str | None = typer.Option(None, "--query", "-q", help="An optional fuzzy term to rank the results."), entity_type: EntityType = EntityType.SUBSCRIPTION, limit: int = 10, ) -> None: @@ -96,23 +94,23 @@ def hierarchical( path_filter = PathFilter(path="ltree_hierarchical_filter", condition=condition, value_kind=UIType.STRING) - search_params = BaseSearchParameters.create( - entity_type=entity_type, filters=FilterTree.from_flat_and([path_filter]), query=query, limit=limit + query = SelectQuery( + entity_type=entity_type, filters=FilterTree.from_flat_and([path_filter]), query_text=query_text, limit=limit ) - search_response = asyncio.run(execute_search(search_params=search_params, db_session=db.session)) + search_response = asyncio.run(engine.execute_search(query=query, db_session=db.session)) display_results(search_response.results, db.session, "Hierarchical Score") @app.command() -def hybrid(query: str, term: str, entity_type: EntityType = EntityType.SUBSCRIPTION, limit: int = 10) -> None: +def hybrid(query_text: str, term: str, entity_type: EntityType = EntityType.SUBSCRIPTION, limit: int = 10) -> None: """Performs a hybrid search, combining semantic and fuzzy matching. Example: dotenv run python main.py search hybrid "reptile store" "Kingswood" """ - search_params = BaseSearchParameters.create(entity_type=entity_type, query=query, limit=limit) - logger.info("Executing Hybrid Search", query=query, term=term) - search_response = asyncio.run(execute_search(search_params=search_params, db_session=db.session)) + query = SelectQuery(entity_type=entity_type, query_text=query_text, limit=limit) + logger.info("Executing Hybrid Search", query_text=query_text, term=term) + search_response = asyncio.run(engine.execute_search(query=query, db_session=db.session)) display_results(search_response.results, db.session, "Hybrid Score") @@ -198,8 +196,8 @@ def nested_demo(entity_type: EntityType = EntityType.SUBSCRIPTION, limit: int = } ) - params = BaseSearchParameters.create(entity_type=entity_type, filters=tree, limit=limit) - search_response = asyncio.run(execute_search(params, db.session)) + query = SelectQuery(entity_type=entity_type, filters=tree, limit=limit) + search_response = asyncio.run(engine.execute_search(query=query, db_session=db.session)) display_results(search_response.results, db.session, "Score") diff --git a/orchestrator/cli/search/speedtest.py b/orchestrator/cli/search/speedtest.py index 5c215c219..e0129e676 100644 --- a/orchestrator/cli/search/speedtest.py +++ b/orchestrator/cli/search/speedtest.py @@ -12,8 +12,8 @@ from orchestrator.search.core.embedding import QueryEmbedder from orchestrator.search.core.types import EntityType from orchestrator.search.core.validators import is_uuid -from orchestrator.search.retrieval.engine import execute_search -from orchestrator.search.schemas.parameters import BaseSearchParameters +from orchestrator.search.query import engine +from orchestrator.search.query.queries import SelectQuery logger = structlog.get_logger(__name__) console = Console() @@ -50,23 +50,25 @@ async def generate_embeddings_for_queries(queries: list[str]) -> dict[str, list[ return embedding_lookup -async def run_single_query(query: str, embedding_lookup: dict[str, list[float]]) -> dict[str, Any]: - search_params = BaseSearchParameters(entity_type=EntityType.SUBSCRIPTION, query=query, limit=30) +async def run_single_query(query_text: str, embedding_lookup: dict[str, list[float]]) -> dict[str, Any]: + query = SelectQuery(entity_type=EntityType.SUBSCRIPTION, query_text=query_text, limit=30) query_embedding = None - if is_uuid(query): - logger.debug("Using fuzzy-only ranking for full UUID", query=query) + if is_uuid(query_text): + logger.debug("Using fuzzy-only ranking for full UUID", query_text=query_text) else: - query_embedding = embedding_lookup[query] + query_embedding = embedding_lookup[query_text] with db.session as session: start_time = time.perf_counter() - response = await execute_search(search_params, session, cursor=None, query_embedding=query_embedding) + response = await engine.execute_search( + query=query, db_session=session, cursor=None, query_embedding=query_embedding + ) end_time = time.perf_counter() return { - "query": query, + "query": query_text, "time": end_time - start_time, "results": len(response.results), "search_type": response.metadata.search_type if hasattr(response, "metadata") else "unknown", diff --git a/orchestrator/db/models.py b/orchestrator/db/models.py index ef319fda3..28660386b 100644 --- a/orchestrator/db/models.py +++ b/orchestrator/db/models.py @@ -60,7 +60,7 @@ from orchestrator.version import GIT_COMMIT_HASH if TYPE_CHECKING: - from orchestrator.search.retrieval.query_state import SearchQueryState + from orchestrator.search.query.state import QueryState logger = structlog.get_logger(__name__) @@ -707,7 +707,7 @@ class SearchQueryTable(BaseModel): ) query_number = mapped_column(Integer, nullable=False) - # Search parameters as JSONB (maps to BaseSearchParameters subclasses) + # Search parameters as JSONB (maps to BaseQuery subclasses) parameters = mapped_column(pg.JSONB, nullable=False) # Query embedding for semantic search (pgvector) @@ -726,14 +726,14 @@ class SearchQueryTable(BaseModel): @classmethod def from_state( cls, - state: "SearchQueryState", + state: "QueryState", run_id: "UUID | None" = None, query_number: int = 1, ) -> "SearchQueryTable": - """Create a SearchQueryTable instance from a SearchQueryState. + """Create a SearchQueryTable instance from a QueryState. Args: - state: The search query state with parameters and embedding + state: QueryState wrapping the query and embedding run_id: Optional agent run ID (NULL for regular API searches) query_number: Query number within the run (default=1) @@ -743,7 +743,7 @@ def from_state( return cls( run_id=run_id, query_number=query_number, - parameters=state.parameters.model_dump(), + parameters=state.query.model_dump(), query_embedding=state.query_embedding, ) diff --git a/orchestrator/log_config.py b/orchestrator/log_config.py index 1dfe292c1..4bad8cfa0 100644 --- a/orchestrator/log_config.py +++ b/orchestrator/log_config.py @@ -41,9 +41,11 @@ def logger_config(name: str, default_level: str = "INFO") -> tuple[str, dict]: [ logger_config("asyncio"), logger_config("httpcore"), + logger_config("openai", default_level="WARNING"), logger_config("orchestrator.graphql.autoregistration"), logger_config("sqlalchemy.engine", default_level="WARNING"), logger_config("uvicorn"), logger_config("LiteLLM", default_level="WARNING"), + logger_config("pydantic_ai", default_level="DEBUG"), ] ) diff --git a/orchestrator/schemas/search.py b/orchestrator/schemas/search.py index d6f4a7f3e..ff2c398c3 100644 --- a/orchestrator/schemas/search.py +++ b/orchestrator/schemas/search.py @@ -16,7 +16,7 @@ from pydantic import BaseModel, ConfigDict, Field from orchestrator.search.core.types import SearchMetadata -from orchestrator.search.schemas.results import ComponentInfo, LeafInfo +from orchestrator.search.query.builder import ComponentInfo, LeafInfo T = TypeVar("T") diff --git a/orchestrator/schemas/search_requests.py b/orchestrator/schemas/search_requests.py new file mode 100644 index 000000000..892f60a9b --- /dev/null +++ b/orchestrator/schemas/search_requests.py @@ -0,0 +1,59 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from pydantic import BaseModel, ConfigDict, Field + +from orchestrator.search.core.types import EntityType +from orchestrator.search.filters import FilterTree +from orchestrator.search.query.queries import SelectQuery + + +class SearchRequest(BaseModel): + """API request model for search operations. + + Only supports SELECT action, used by search endpoints. + """ + + filters: FilterTree | None = Field( + default=None, + description="Structured filters to apply to the search.", + ) + query: str | None = Field( + default=None, + description="Text search query for semantic/fuzzy search.", + ) + limit: int = Field( + default=SelectQuery.DEFAULT_LIMIT, + ge=SelectQuery.MIN_LIMIT, + le=SelectQuery.MAX_LIMIT, + description="Maximum number of search results to return.", + ) + + model_config = ConfigDict(extra="forbid") + + def to_query(self, entity_type: EntityType) -> SelectQuery: + """Convert API request to SelectQuery domain model. + + Args: + entity_type: The entity type to search (provided by endpoint) + + Returns: + SelectQuery for search operation + """ + return SelectQuery( + entity_type=entity_type, + filters=self.filters, + query_text=self.query, + limit=self.limit, + ) diff --git a/orchestrator/search/agent/handlers.py b/orchestrator/search/agent/handlers.py new file mode 100644 index 000000000..729670c0b --- /dev/null +++ b/orchestrator/search/agent/handlers.py @@ -0,0 +1,129 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Handlers for search and aggregation execution with persistence.""" + +from uuid import UUID + +import structlog + +from orchestrator.db import AgentRunTable, SearchQueryTable +from orchestrator.db.database import WrappedSession +from orchestrator.search.query import engine +from orchestrator.search.query.queries import AggregateQuery, CountQuery, SelectQuery +from orchestrator.search.query.results import AggregationResponse, SearchResponse +from orchestrator.search.query.state import QueryState + +logger = structlog.get_logger(__name__) + + +async def execute_search_with_persistence( + query: SelectQuery, + db_session: WrappedSession, + run_id: UUID | None, +) -> tuple[SearchResponse, UUID, UUID]: + """Execute search, persist to DB, return response and IDs. + + Args: + query: SelectQuery for search operation + db_session: Database session + run_id: Existing run ID or None to create new one + + Returns: + Tuple of (search_response, run_id, query_id) + """ + # Create agent run + if not run_id: + agent_run = AgentRunTable(agent_type="search") + db_session.add(agent_run) + db_session.commit() + db_session.expire_all() + run_id = agent_run.run_id + logger.debug("Created new agent run", run_id=str(run_id)) + + if run_id is None: + raise ValueError("run_id should not be None here") + + # Execute search + search_response = await engine.execute_search(query, db_session) + + # Save to database + query_embedding = search_response.query_embedding + query_state = QueryState(query=query, query_embedding=query_embedding) + query_number = db_session.query(SearchQueryTable).filter_by(run_id=run_id).count() + 1 + search_query = SearchQueryTable.from_state( + state=query_state, + run_id=run_id, + query_number=query_number, + ) + db_session.add(search_query) + db_session.commit() + db_session.expire_all() + + logger.debug("Saved search query", query_id=str(search_query.query_id), query_number=query_number) + + logger.debug( + "Search results", + results=[r.model_dump() for r in search_response.results], + total_count=len(search_response.results), + search_type=search_response.metadata.search_type, + ) + + return search_response, run_id, search_query.query_id + + +async def execute_aggregation_with_persistence( + query: CountQuery | AggregateQuery, + db_session: WrappedSession, + run_id: UUID | None, +) -> tuple[AggregationResponse, UUID, UUID]: + """Execute aggregation, persist to DB, return response and IDs. + + Args: + query: CountQuery or AggregateQuery for aggregation operations + db_session: Database session + run_id: Existing run ID or None to create new one + + Returns: + Tuple of (aggregation_response, run_id, query_id) + """ + # Create agent run if needed + if not run_id: + agent_run = AgentRunTable(agent_type="search") + db_session.add(agent_run) + db_session.commit() + db_session.expire_all() + run_id = agent_run.run_id + logger.debug("Created new agent run", run_id=str(run_id)) + + if run_id is None: + raise ValueError("run_id should not be None here") + + # Execute aggregation + aggregation_response = await engine.execute_aggregation(query, db_session) + + # Save to database + query_state = QueryState(query=query, query_embedding=None) + query_number = db_session.query(SearchQueryTable).filter_by(run_id=run_id).count() + 1 + search_query = SearchQueryTable.from_state( + state=query_state, + run_id=run_id, + query_number=query_number, + ) + db_session.add(search_query) + db_session.commit() + db_session.expire_all() + + logger.debug("Saved aggregation query", query_id=str(search_query.query_id), query_number=query_number) + + return aggregation_response, run_id, search_query.query_id diff --git a/orchestrator/search/agent/prompts.py b/orchestrator/search/agent/prompts.py index e0567f4ab..5f2e4f30d 100644 --- a/orchestrator/search/agent/prompts.py +++ b/orchestrator/search/agent/prompts.py @@ -19,6 +19,7 @@ from pydantic_ai.ag_ui import StateDeps from orchestrator.search.agent.state import SearchState +from orchestrator.search.core.types import ActionType logger = structlog.get_logger(__name__) @@ -33,7 +34,6 @@ async def get_base_instructions() -> str: Your ultimate goal is to **find information** that answers the user's request. - To do this, you will perform either a broad search or a filtered search. For **filtered searches**, your primary method is to **construct a valid `FilterTree` object**. To do this correctly, you must infer the exact structure, operators, and nesting rules from the Pydantic schema of the `set_filter_tree` tool itself. @@ -48,15 +48,19 @@ async def get_base_instructions() -> str: --- ### 3. Execution Workflow - Follow these steps in strict order: + Follow these steps: - 1. **Set Context**: If the user is asking for a NEW search, call `start_new_search`. - 2. **Analyze for Filters**: Based on the user's request, decide if specific filters are necessary. - - **If filters ARE required**, follow these sub-steps: - a. **Gather Intel**: Identify all needed field names, then call `discover_filter_paths` and `get_valid_operators` **once each** to get all required information. - b. **Construct FilterTree**: Build the `FilterTree` object. - c. **Set Filters**: Call `set_filter_tree`. - 3. **Execute**: Call `run_search`. This is done for both filtered and non-filtered searches. + 1. **Set Context**: Call `start_new_search` with appropriate entity_type and action + 2. **Set Filters** (if needed): Discover paths, build FilterTree, call `set_filter_tree` + - IMPORTANT: Temporal constraints like "in 2025", "in January", "between X and Y" require filters on datetime fields + - Filters restrict WHICH records to include; grouping controls HOW to aggregate them + 3. **Set Grouping/Aggregations** (for COUNT/AGGREGATE): + - For temporal grouping (per month, per year, per day, etc.): Use `set_temporal_grouping` + - For regular grouping (by status, by name, etc.): Use `set_grouping` + - For aggregations: Use `set_aggregations` + 4. **Execute**: + - For SELECT action: Call `run_search()` + - For COUNT/AGGREGATE actions: Call `run_aggregation()` After search execution, follow the dynamic instructions based on the current state. @@ -73,31 +77,46 @@ async def get_base_instructions() -> str: async def get_dynamic_instructions(ctx: RunContext[StateDeps[SearchState]]) -> str: """Dynamically provides 'next step' coaching based on the current state.""" state = ctx.deps.state - param_state_str = json.dumps(state.parameters, indent=2, default=str) if state.parameters else "Not set." - results_count = state.results_data.total_count if state.results_data else 0 + query_state_str = json.dumps(state.query.model_dump(), indent=2, default=str) if state.query else "Not set." + results_count = state.results_count or 0 + action = state.action or ActionType.SELECT - if state.export_data: + if not state.query: next_step_guidance = ( - "INSTRUCTION: Export has been prepared successfully. " - "Simply confirm to the user that the export is ready for download. " - "DO NOT include or mention the download URL - the UI will display it automatically." - ) - elif not state.parameters or not state.parameters.get("entity_type"): - next_step_guidance = ( - "INSTRUCTION: The search context is not set. Your next action is to call `start_new_search`." + f"INSTRUCTION: The search context is not set. Your next action is to call `start_new_search`. " + f"For counting or aggregation queries, set action='{ActionType.COUNT.value}' or action='{ActionType.AGGREGATE.value}'." ) elif results_count > 0: - next_step_guidance = dedent( - f""" - INSTRUCTION: Search completed successfully. - Found {results_count} results containing only: entity_id, title, score. - - Choose your next action based on what the user requested: - 1. **Broad/generic search** (e.g., 'show me subscriptions'): Confirm search completed and report count. Do nothing else. - 2. **Question answerable with entity_id/title/score**: Answer directly using the current results. - 3. **Question requiring other details**: Call `fetch_entity_details` first, then answer with the detailed data. - 4. **Export request** (phrases like 'export', 'download', 'save as CSV'): Call `prepare_export` directly. - """ + if action in (ActionType.COUNT, ActionType.AGGREGATE): + # Aggregation completed + next_step_guidance = ( + "INSTRUCTION: Aggregation completed successfully. " + "The results are already displayed in the UI. " + "Simply confirm completion to the user in a brief sentence. " + "DO NOT repeat, summarize, or restate the aggregation data." + ) + else: + # Search completed + next_step_guidance = dedent( + f""" + INSTRUCTION: Search completed successfully. + Found {results_count} results containing only: entity_id, title, score. + + Choose your next action based on what the user requested: + 1. **Broad/generic search** (e.g., 'show me subscriptions'): Confirm search completed and report count. Do not repeat the results. + 2. **Question answerable with entity_id/title/score**: Answer directly using the current results. + 3. **Question requiring other details**: Call `fetch_entity_details` first, then answer with the detailed data. + 4. **Export request** (phrases like 'export', 'download', 'save as CSV'): Call `prepare_export` directly. Simply confirm the export is ready. Do not repeat the results. + """ + ) + elif action in (ActionType.COUNT, ActionType.AGGREGATE): + # COUNT or AGGREGATE action but no results yet + next_step_guidance = ( + "INSTRUCTION: Aggregation context is set. " + "For temporal queries (per month, per year, over time): call `set_temporal_grouping` with datetime field and period. " + "For regular grouping: call `set_grouping` with paths to group by. " + f"For {ActionType.AGGREGATE.value.upper()}: call `set_aggregations` with aggregation specs. " + "Then call `run_aggregation`." ) else: next_step_guidance = ( @@ -106,17 +125,19 @@ async def get_dynamic_instructions(ctx: RunContext[StateDeps[SearchState]]) -> s "If no specific filters are needed, you can proceed directly to `run_search`." ) + status_summary = f"Results: {results_count}" if results_count > 0 else "No results yet" + return dedent( f""" --- ## CURRENT STATE - **Current Search Parameters:** + **Current Query:** ```json - {param_state_str} + {query_state_str} ``` - **Current Results Count:** {results_count} + **Status:** {status_summary} --- ## NEXT ACTION REQUIRED diff --git a/orchestrator/search/agent/state.py b/orchestrator/search/agent/state.py index 075e3a192..a152b90a2 100644 --- a/orchestrator/search/agent/state.py +++ b/orchestrator/search/agent/state.py @@ -11,37 +11,22 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any from uuid import UUID from pydantic import BaseModel -from orchestrator.search.schemas.results import SearchResult +from orchestrator.search.core.types import ActionType +from orchestrator.search.query.queries import Query -class ExportData(BaseModel): - """Export metadata for download.""" - - action: str = "export" - query_id: str - download_url: str - message: str - - -class SearchResultsData(BaseModel): - """Search results data for frontend display and agent context.""" - - action: str = "view_results" - query_id: str - results_url: str - total_count: int - message: str - results: list[SearchResult] = [] +class SearchState(BaseModel): + """Agent state for search operations. + Tracks the current search context and execution status. + """ -class SearchState(BaseModel): run_id: UUID | None = None query_id: UUID | None = None - parameters: dict[str, Any] | None = None - results_data: SearchResultsData | None = None - export_data: ExportData | None = None + action: ActionType | None = None + query: Query | None = None + results_count: int | None = None # Number of results from last executed search/aggregation diff --git a/orchestrator/search/agent/tools.py b/orchestrator/search/agent/tools.py index 5a0e48154..908a1a73a 100644 --- a/orchestrator/search/agent/tools.py +++ b/orchestrator/search/agent/tools.py @@ -12,10 +12,10 @@ # limitations under the License. import json -from typing import Any +from typing import Any, cast import structlog -from ag_ui.core import EventType, StateDeltaEvent, StateSnapshotEvent +from ag_ui.core import EventType, StateSnapshotEvent from pydantic_ai import RunContext from pydantic_ai.ag_ui import StateDeps from pydantic_ai.exceptions import ModelRetry @@ -26,17 +26,28 @@ get_definitions, list_paths, ) -from orchestrator.db import AgentRunTable, SearchQueryTable, db -from orchestrator.search.agent.json_patch import JSONPatchOp -from orchestrator.search.agent.state import ExportData, SearchResultsData, SearchState +from orchestrator.db import db +from orchestrator.search.agent.handlers import ( + execute_aggregation_with_persistence, + execute_search_with_persistence, +) +from orchestrator.search.agent.state import SearchState +from orchestrator.search.agent.validation import require_action +from orchestrator.search.aggregations import Aggregation, FieldAggregation, TemporalGrouping from orchestrator.search.core.types import ActionType, EntityType, FilterOp -from orchestrator.search.export import fetch_export_data from orchestrator.search.filters import FilterTree -from orchestrator.search.retrieval.engine import execute_search -from orchestrator.search.retrieval.exceptions import FilterValidationError, PathNotFoundError -from orchestrator.search.retrieval.query_state import SearchQueryState -from orchestrator.search.retrieval.validation import validate_filter_tree -from orchestrator.search.schemas.parameters import BaseSearchParameters +from orchestrator.search.query import engine +from orchestrator.search.query.exceptions import PathNotFoundError, QueryValidationError +from orchestrator.search.query.export import fetch_export_data +from orchestrator.search.query.queries import AggregateQuery, CountQuery, Query, SelectQuery +from orchestrator.search.query.results import AggregationResponse, AggregationResult, ExportData, VisualizationType +from orchestrator.search.query.state import QueryState +from orchestrator.search.query.validation import ( + validate_aggregation_field, + validate_filter_path, + validate_filter_tree, + validate_temporal_grouping_field, +) from orchestrator.settings import app_settings logger = structlog.get_logger(__name__) @@ -53,27 +64,11 @@ def last_user_message(ctx: RunContext[StateDeps[SearchState]]) -> str | None: return None -def _set_parameters( - ctx: RunContext[StateDeps[SearchState]], - entity_type: EntityType, - action: str | ActionType, - query: str, - filters: Any | None, -) -> None: - """Internal helper to set parameters.""" - ctx.deps.state.parameters = { - "action": action, - "entity_type": entity_type, - "filters": filters, - "query": query, - } - - @search_toolset.tool async def start_new_search( ctx: RunContext[StateDeps[SearchState]], entity_type: EntityType, - action: str | ActionType = ActionType.SELECT, + action: ActionType = ActionType.SELECT, ) -> StateSnapshotEvent: """Starts a completely new search, clearing all previous state. @@ -90,13 +85,26 @@ async def start_new_search( ) # Clear all state - ctx.deps.state.results_data = None - ctx.deps.state.export_data = None - - # Set fresh parameters with no filters - _set_parameters(ctx, entity_type, action, final_query, None) + ctx.deps.state.results_count = None + ctx.deps.state.action = action + + # Create the appropriate query object based on action + if action == ActionType.SELECT: + ctx.deps.state.query = SelectQuery( + entity_type=entity_type, + query_text=final_query, + ) + elif action == ActionType.COUNT: + ctx.deps.state.query = CountQuery( + entity_type=entity_type, + ) + else: # ActionType.AGGREGATE + ctx.deps.state.query = AggregateQuery( + entity_type=entity_type, + aggregations=[], # Will be set by set_aggregations tool + ) - logger.debug("New search started", parameters=ctx.deps.state.parameters) + logger.debug("New search started", action=action.value, query_type=type(ctx.deps.state.query).__name__) return StateSnapshotEvent( type=EventType.STATE_SNAPSHOT, @@ -108,18 +116,15 @@ async def start_new_search( async def set_filter_tree( ctx: RunContext[StateDeps[SearchState]], filters: FilterTree | None, -) -> StateDeltaEvent: +) -> StateSnapshotEvent: """Replace current filters atomically with a full FilterTree, or clear with None. - Requirements: - - Root/group operators must be 'AND' or 'OR' (uppercase). - - Provide either PathFilters or nested groups under `children`. - - See the FilterTree schema examples for the exact shape. + See FilterTree model for structure, operators, and examples. """ - if ctx.deps.state.parameters is None: - raise ModelRetry("Search parameters are not initialized. Call start_new_search first.") + if ctx.deps.state.query is None: + raise ModelRetry("Search query is not initialized. Call start_new_search first.") - entity_type = EntityType(ctx.deps.state.parameters["entity_type"]) + entity_type = ctx.deps.state.query.entity_type logger.debug( "Setting filter tree", @@ -133,108 +138,112 @@ async def set_filter_tree( except PathNotFoundError as e: logger.debug(f"{PathNotFoundError.__name__}: {str(e)}") raise ModelRetry(f"{str(e)} Use discover_filter_paths tool to find valid paths.") - except FilterValidationError as e: + except QueryValidationError as e: # ModelRetry will trigger an agent retry, containing the specific validation error. - logger.debug(f"Filter validation failed: {str(e)}") + logger.debug(f"Query validation failed: {str(e)}") raise ModelRetry(str(e)) except Exception as e: logger.error("Unexpected Filter validation exception", error=str(e)) raise ModelRetry(f"Filter validation failed: {str(e)}. Please check your filter structure and try again.") - filter_data = None if filters is None else filters.model_dump(mode="json", by_alias=True) - filters_existed = "filters" in ctx.deps.state.parameters - ctx.deps.state.parameters["filters"] = filter_data - return StateDeltaEvent( - type=EventType.STATE_DELTA, - delta=[ - JSONPatchOp.upsert( - path="/parameters/filters", - value=filter_data, - existed=filters_existed, - ) - ], + ctx.deps.state.query = cast(Query, ctx.deps.state.query).model_copy(update={"filters": filters}) + + # Use snapshot to workaround state persistence issue + # TODO: Fix root cause; state tree may be empty on frontend when parameters are being set + return StateSnapshotEvent( + type=EventType.STATE_SNAPSHOT, + snapshot=ctx.deps.state.model_dump(), ) @search_toolset.tool +@require_action(ActionType.SELECT) async def run_search( ctx: RunContext[StateDeps[SearchState]], limit: int = 10, -) -> StateDeltaEvent: - """Execute the search with the current parameters and save to database.""" - if not ctx.deps.state.parameters: - raise ValueError("No search parameters set") +) -> AggregationResponse: + """Execute a search to find and rank entities. - params = BaseSearchParameters.create(**ctx.deps.state.parameters) - logger.debug( - "Executing database search", - search_entity_type=params.entity_type.value, - limit=limit, - has_filters=params.filters is not None, - query=params.query, - action=params.action, + Use this tool for SELECT action to find entities matching your criteria. + For counting or computing statistics, use run_aggregation instead. + """ + query = cast(SelectQuery, cast(Query, ctx.deps.state.query).model_copy(update={"limit": limit})) + + search_response, run_id, query_id = await execute_search_with_persistence(query, db.session, ctx.deps.state.run_id) + + ctx.deps.state.run_id = run_id + ctx.deps.state.query_id = query_id + ctx.deps.state.results_count = len(search_response.results) + + # Convert SearchResults to AggregationResults for consistent rendering + aggregation_results = [ + AggregationResult( + group_values={ + "entity_id": result.entity_id, + "title": result.entity_title, + "entity_type": result.entity_type.value, + }, + aggregations={"score": result.score}, + ) + for result in search_response.results + ] + + # For now use the default table visualization for search results + aggregation_response = AggregationResponse( + results=aggregation_results, + total_groups=len(aggregation_results), + metadata=search_response.metadata, + visualization_type=VisualizationType(type="table"), ) - if params.filters: - logger.debug("Search filters", filters=params.filters) - - params.limit = limit + logger.debug( + "Search completed", + total_count=ctx.deps.state.results_count, + query_id=str(query_id), + ) - changes: list[JSONPatchOp] = [] + return aggregation_response - if not ctx.deps.state.run_id: - agent_run = AgentRunTable(agent_type="search") - db.session.add(agent_run) - db.session.commit() - db.session.expire_all() # Release connection to prevent stacking while agent runs +@search_toolset.tool +@require_action(ActionType.COUNT, ActionType.AGGREGATE) +async def run_aggregation( + ctx: RunContext[StateDeps[SearchState]], + visualization_type: VisualizationType, +) -> AggregationResponse: + """Execute an aggregation to compute counts or statistics over entities. - ctx.deps.state.run_id = agent_run.run_id - logger.debug("Created new agent run", run_id=str(agent_run.run_id)) - changes.append(JSONPatchOp(op="add", path="/run_id", value=str(ctx.deps.state.run_id))) + Use this tool for COUNT or AGGREGATE actions after setting up: + - Grouping fields with set_grouping or set_temporal_grouping + - Aggregation functions with set_aggregations (for AGGREGATE action) + """ + query = cast(CountQuery | AggregateQuery, ctx.deps.state.query) - # Get query with embedding and save to DB - search_response = await execute_search(params, db.session) - query_embedding = search_response.query_embedding - query_state = SearchQueryState(parameters=params, query_embedding=query_embedding) - query_number = db.session.query(SearchQueryTable).filter_by(run_id=ctx.deps.state.run_id).count() + 1 - search_query = SearchQueryTable.from_state( - state=query_state, - run_id=ctx.deps.state.run_id, - query_number=query_number, + logger.debug( + "Executing aggregation", + search_entity_type=query.entity_type.value, + has_filters=query.filters is not None, + action=query.action, ) - db.session.add(search_query) - db.session.commit() - db.session.expire_all() - - query_id_existed = ctx.deps.state.query_id is not None - ctx.deps.state.query_id = search_query.query_id - logger.debug("Saved search query", query_id=str(search_query.query_id), query_number=query_number) - changes.append(JSONPatchOp.upsert(path="/query_id", value=str(ctx.deps.state.query_id), existed=query_id_existed)) - logger.debug( - "Search completed", - total_results=len(search_response.results), + aggregation_response, run_id, query_id = await execute_aggregation_with_persistence( + query, db.session, ctx.deps.state.run_id ) - # Store results data for both frontend display and agent context - results_url = f"{app_settings.BASE_URL}/api/search/queries/{ctx.deps.state.query_id}" + ctx.deps.state.run_id = run_id + ctx.deps.state.query_id = query_id + ctx.deps.state.results_count = aggregation_response.total_groups - results_data_existed = ctx.deps.state.results_data is not None - ctx.deps.state.results_data = SearchResultsData( - query_id=str(ctx.deps.state.query_id), - results_url=results_url, - total_count=len(search_response.results), - message=f"Found {len(search_response.results)} results.", - results=search_response.results, # Include actual results in state - ) - changes.append( - JSONPatchOp.upsert( - path="/results_data", value=ctx.deps.state.results_data.model_dump(), existed=results_data_existed - ) + aggregation_response.visualization_type = visualization_type + + logger.debug( + "Aggregation completed", + total_groups=aggregation_response.total_groups, + visualization_type=visualization_type.type, + query_id=str(query_id), ) - return StateDeltaEvent(type=EventType.STATE_DELTA, delta=changes) + return aggregation_response @search_toolset.tool @@ -248,10 +257,11 @@ async def discover_filter_paths( Returns a dictionary where each key is a field_name from the input list and the value is its discovery result. """ - if not entity_type and ctx.deps.state.parameters: - entity_type = EntityType(ctx.deps.state.parameters.get("entity_type")) if not entity_type: - entity_type = EntityType.SUBSCRIPTION + if ctx.deps.state.query: + entity_type = ctx.deps.state.query.entity_type + else: + raise ModelRetry("Entity type not specified and no query in state. Call start_new_search first.") all_results = {} for field_name in field_names: @@ -332,17 +342,21 @@ async def fetch_entity_details( JSON string containing detailed entity information. Raises: - ValueError: If no search results are available. + ModelRetry: If no search has been executed. """ - if not ctx.deps.state.results_data or not ctx.deps.state.results_data.results: - raise ValueError("No search results available. Run a search first before fetching entity details.") + if ctx.deps.state.query_id is None: + raise ModelRetry("No query_id found. Run a search first.") - if not ctx.deps.state.parameters: - raise ValueError("No search parameters found.") + # Load the saved query and re-execute it to get entity IDs + query_state = QueryState.load_from_id(ctx.deps.state.query_id, SelectQuery) + query = query_state.query.model_copy(update={"limit": limit}) + search_response = await engine.execute_search(query, db.session) + entity_ids = [r.entity_id for r in search_response.results] - entity_type = EntityType(ctx.deps.state.parameters["entity_type"]) + if not entity_ids: + return json.dumps({"message": "No entities found in search results."}) - entity_ids = [r.entity_id for r in ctx.deps.state.results_data.results[:limit]] + entity_type = query.entity_type logger.debug( "Fetching detailed entity data", @@ -356,23 +370,16 @@ async def fetch_entity_details( @search_toolset.tool +@require_action(ActionType.SELECT) async def prepare_export( ctx: RunContext[StateDeps[SearchState]], -) -> StateSnapshotEvent: - """Prepares export URL using the last executed search query.""" - if not ctx.deps.state.query_id or not ctx.deps.state.run_id: - raise ValueError("No search has been executed yet. Run a search first before exporting.") +) -> ExportData: + """Prepares export URL using the last executed search query. - if not ctx.deps.state.parameters: - raise ValueError("No search parameters found. Run a search first before exporting.") - - # Validate that export is only available for SELECT actions - action = ctx.deps.state.parameters.get("action", ActionType.SELECT) - if action != ActionType.SELECT: - raise ValueError( - f"Export is only available for SELECT actions. Current action is '{action}'. " - "Please run a SELECT search first." - ) + Returns export data which is displayed directly in the UI. + """ + if not ctx.deps.state.query_id or not ctx.deps.state.run_id: + raise ModelRetry("No search has been executed yet. Run a search first before exporting.") logger.debug( "Prepared query for export", @@ -381,16 +388,88 @@ async def prepare_export( download_url = f"{app_settings.BASE_URL}/api/search/queries/{ctx.deps.state.query_id}/export" - ctx.deps.state.export_data = ExportData( + export_data = ExportData( query_id=str(ctx.deps.state.query_id), download_url=download_url, message="Export ready for download.", ) - logger.debug("Export data set in state", export_data=ctx.deps.state.export_data.model_dump()) + logger.debug("Export prepared", query_id=export_data.query_id) + + return export_data + + +@search_toolset.tool(retries=2) +@require_action(ActionType.COUNT, ActionType.AGGREGATE) +async def set_grouping( + ctx: RunContext[StateDeps[SearchState]], + group_by_paths: list[str], +) -> StateSnapshotEvent: + """Set which field paths to group results by for aggregation. + + Only used with COUNT or AGGREGATE actions. Paths must exist in the schema; use discover_filter_paths to verify. + """ + for path in group_by_paths: + field_type = validate_filter_path(path) + if field_type is None: + raise ModelRetry( + f"Path '{path}' not found in database schema. " + f"Use discover_filter_paths(['{path.split('.')[-1]}']) to find valid paths." + ) + + ctx.deps.state.query = cast(Query, ctx.deps.state.query).model_copy(update={"group_by": group_by_paths}) + + return StateSnapshotEvent( + type=EventType.STATE_SNAPSHOT, + snapshot=ctx.deps.state.model_dump(), + ) + + +@search_toolset.tool(retries=2) +@require_action(ActionType.AGGREGATE) +async def set_aggregations( + ctx: RunContext[StateDeps[SearchState]], + aggregations: list[Aggregation], +) -> StateSnapshotEvent: + """Define what aggregations to compute over the matching records. + + Only used with AGGREGATE action. See Aggregation model (CountAggregation, FieldAggregation) for structure and field requirements. + """ + # Validate field paths for FieldAggregations + try: + for agg in aggregations: + if isinstance(agg, FieldAggregation): + validate_aggregation_field(agg.type, agg.field) + except ValueError as e: + raise ModelRetry(f"{str(e)} Use discover_filter_paths to find valid paths.") + + ctx.deps.state.query = cast(Query, ctx.deps.state.query).model_copy(update={"aggregations": aggregations}) + + return StateSnapshotEvent( + type=EventType.STATE_SNAPSHOT, + snapshot=ctx.deps.state.model_dump(), + ) + + +@search_toolset.tool(retries=2) +@require_action(ActionType.COUNT, ActionType.AGGREGATE) +async def set_temporal_grouping( + ctx: RunContext[StateDeps[SearchState]], + temporal_groups: list[TemporalGrouping], +) -> StateSnapshotEvent: + """Set temporal grouping to group datetime fields by time periods. + + Only used with COUNT or AGGREGATE actions. See TemporalGrouping model for structure, periods, and examples. + """ + # Validate that fields exist and are datetime types + try: + for tg in temporal_groups: + validate_temporal_grouping_field(tg.field) + except ValueError as e: + raise ModelRetry(f"{str(e)} Use discover_filter_paths to find datetime fields.") + + ctx.deps.state.query = cast(Query, ctx.deps.state.query).model_copy(update={"temporal_group_by": temporal_groups}) - # Should use StateDelta here? Use snapshot to workaround state persistence issue - # TODO: Fix root cause; state is empty on frontend when it should have data from run_search return StateSnapshotEvent( type=EventType.STATE_SNAPSHOT, snapshot=ctx.deps.state.model_dump(), diff --git a/orchestrator/search/agent/validation.py b/orchestrator/search/agent/validation.py new file mode 100644 index 000000000..9b69adffd --- /dev/null +++ b/orchestrator/search/agent/validation.py @@ -0,0 +1,80 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from functools import wraps +from typing import TYPE_CHECKING, Any, Callable + +import structlog +from pydantic_ai import RunContext +from pydantic_ai.ag_ui import StateDeps +from pydantic_ai.exceptions import ModelRetry + +from orchestrator.search.core.types import ActionType + +if TYPE_CHECKING: + from orchestrator.search.agent.state import SearchState + +logger = structlog.get_logger(__name__) + + +def require_action(*allowed_actions: ActionType) -> Callable: + """Validate that the current search action is one of the allowed types. + + This decorator is in preparation for a future finite state machine implementation + where we explicitly define which actions are valid in which states. + + Example: + @search_toolset.tool + @require_action(ActionType.SELECT) + async def run_search(ctx, ...): + # Only callable when action is SELECT + ... + + Args: + allowed_actions: One or more ActionType values that are valid for this tool. + + Returns: + Decorated function that validates action before execution. + + Raises: + ModelRetry: If current action is not in allowed_actions. + """ + + def decorator(func: Callable) -> Callable: + @wraps(func) + async def wrapper(ctx: "RunContext[StateDeps[SearchState]]", *args: Any, **kwargs: Any) -> Any: + if ctx.deps.state.action is None or ctx.deps.state.query is None: + logger.warning(f"Action validation failed for {func.__name__}: action or query is None") + raise ModelRetry("Search action and query are not initialized. Call start_new_search first.") + + current_action = ctx.deps.state.action + + if current_action not in allowed_actions: + allowed_names = ", ".join(a.value for a in allowed_actions) + logger.warning( + "Invalid action for tool", + tool=func.__name__, + allowed_actions=allowed_names, + current_action=current_action.value, + ) + raise ModelRetry( + f"{func.__name__} is only available for {allowed_names} action(s). " + f"Current action is '{current_action.value}'." + ) + + return await func(ctx, *args, **kwargs) + + return wrapper + + return decorator diff --git a/orchestrator/search/aggregations/__init__.py b/orchestrator/search/aggregations/__init__.py new file mode 100644 index 000000000..a05f3b343 --- /dev/null +++ b/orchestrator/search/aggregations/__init__.py @@ -0,0 +1,32 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .base import ( + Aggregation, + AggregationType, + BaseAggregation, + CountAggregation, + FieldAggregation, + TemporalGrouping, + TemporalPeriod, +) + +__all__ = [ + "Aggregation", + "AggregationType", + "BaseAggregation", + "CountAggregation", + "FieldAggregation", + "TemporalGrouping", + "TemporalPeriod", +] diff --git a/orchestrator/search/aggregations/base.py b/orchestrator/search/aggregations/base.py new file mode 100644 index 000000000..8aa2c941e --- /dev/null +++ b/orchestrator/search/aggregations/base.py @@ -0,0 +1,201 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from abc import abstractmethod +from enum import Enum +from typing import Annotated, Any, Literal, TypeAlias + +from pydantic import BaseModel, ConfigDict, Field +from sqlalchemy import Integer, cast, func +from sqlalchemy.sql.elements import ColumnElement, Label + + +class AggregationType(str, Enum): + """Types of aggregations that can be computed.""" + + COUNT = "count" + SUM = "sum" + AVG = "avg" + MIN = "min" + MAX = "max" + + +class TemporalPeriod(str, Enum): + """Time periods for temporal grouping.""" + + YEAR = "year" + QUARTER = "quarter" + MONTH = "month" + WEEK = "week" + DAY = "day" + HOUR = "hour" + + +class TemporalGrouping(BaseModel): + """Defines temporal grouping for date/time fields. + + Used to group query results by time periods (e.g., group subscriptions by start_date per month). + """ + + field: str = Field(description="The datetime field path to group by temporally.") + period: TemporalPeriod = Field(description="The time period to group by (year, quarter, month, week, day, hour).") + + model_config = ConfigDict( + extra="forbid", + json_schema_extra={ + "examples": [ + {"field": "subscription.start_date", "period": "month"}, + {"field": "subscription.end_date", "period": "year"}, + {"field": "process.created_at", "period": "day"}, + ] + }, + ) + + def get_pivot_fields(self) -> list[str]: + """Return fields that need to be pivoted for this temporal grouping.""" + return [self.field] + + def to_expression(self, pivot_cte_columns: Any) -> tuple[Label, Any, str]: + """Build SQLAlchemy expression for temporal grouping. + + Args: + pivot_cte_columns: The columns object from the pivot CTE + + Returns: + tuple: (select_column, group_by_column, column_name) + - select_column: Labeled column for SELECT + - group_by_column: Column expression for GROUP BY + - column_name: The label/name of the column in results + """ + from sqlalchemy import TIMESTAMP, cast, func + + field_alias = BaseAggregation.field_to_alias(self.field) + col = getattr(pivot_cte_columns, field_alias) + truncated_col = func.date_trunc(self.period.value, cast(col, TIMESTAMP(timezone=True))) + + # Column name without prefix + col_name = f"{field_alias}_{self.period.value}" + select_col = truncated_col.label(col_name) + return select_col, truncated_col, col_name + + +class BaseAggregation(BaseModel): + """Base class for all aggregation types.""" + + type: AggregationType = Field(description="The type of aggregation to perform.") + alias: str = Field(description="The name for this aggregation in the results.") + + @classmethod + def create(cls, data: dict) -> "Aggregation": + """Create the correct aggregation instance based on type field. + + Args: + data: Dictionary with aggregation data including 'type' discriminator + + Returns: + Validated aggregation instance (CountAggregation or FieldAggregation) + + Raises: + ValidationError: If data is invalid or type is unknown + """ + from pydantic import TypeAdapter + + adapter: TypeAdapter = TypeAdapter(Aggregation) + return adapter.validate_python(data) + + @staticmethod + def field_to_alias(field_path: str) -> str: + """Convert field path to SQL column alias. + + Examples: + 'subscription.name' -> 'subscription_name' + 'product.serial-number' -> 'product_serial_number' + """ + return field_path.replace(".", "_").replace("-", "_") + + def get_pivot_fields(self) -> list[str]: + """Return fields that need to be pivoted for this aggregation.""" + return [] + + @abstractmethod + def to_expression(self, *args: Any, **kwargs: Any) -> Label: + """Build SQLAlchemy expression for this aggregation. + + Returns: + Label: A labeled SQLAlchemy expression + """ + raise NotImplementedError + + +class CountAggregation(BaseAggregation): + """Count aggregation - counts number of entities.""" + + type: Literal[AggregationType.COUNT] + + def to_expression(self, entity_id_column: ColumnElement) -> Label: + """Build SQLAlchemy expression for count aggregation. + + Args: + entity_id_column: The entity_id column from the pivot CTE + + Returns: + Label: A labeled SQLAlchemy expression + """ + return func.count(entity_id_column).label(self.alias) + + +class FieldAggregation(BaseAggregation): + """Field-based aggregation (sum, avg, min, max).""" + + type: Literal[AggregationType.SUM, AggregationType.AVG, AggregationType.MIN, AggregationType.MAX] + field: str = Field(description="The field path to aggregate on.") + + def get_pivot_fields(self) -> list[str]: + """Return fields that need to be pivoted for this aggregation.""" + return [self.field] + + def to_expression(self, pivot_cte_columns: Any) -> Label: + """Build SQLAlchemy expression for field-based aggregation. + + Args: + pivot_cte_columns: The columns object from the pivot CTE + + Returns: + Label: A labeled SQLAlchemy expression + + Raises: + ValueError: If the field is not found in the pivot CTE + """ + field_alias = self.field_to_alias(self.field) + + if not hasattr(pivot_cte_columns, field_alias): + raise ValueError(f"Field '{self.field}' (alias: '{field_alias}') not found in pivot CTE columns") + + col = getattr(pivot_cte_columns, field_alias) + + numeric_col = cast(col, Integer) + + match self.type: + case AggregationType.SUM: + return func.sum(numeric_col).label(self.alias) + case AggregationType.AVG: + return func.avg(numeric_col).label(self.alias) + case AggregationType.MIN: + return func.min(numeric_col).label(self.alias) + case AggregationType.MAX: + return func.max(numeric_col).label(self.alias) + case _: + raise ValueError(f"Unsupported aggregation type: {self.type}") + + +Aggregation: TypeAlias = Annotated[CountAggregation | FieldAggregation, Field(discriminator="type")] diff --git a/orchestrator/search/core/types.py b/orchestrator/search/core/types.py index 01f7a4425..64e326d6d 100644 --- a/orchestrator/search/core/types.py +++ b/orchestrator/search/core/types.py @@ -105,7 +105,8 @@ class ActionType(str, Enum): """Defines the explicit, safe actions the agent can request.""" SELECT = "select" # Retrieve a list of matching records. - # COUNT = "count" # For phase1; the agent will not support this yet. + COUNT = "count" # Count matching records, optionally grouped. + AGGREGATE = "aggregate" # Compute aggregations (sum, avg, etc.) over matching records. class UIType(str, Enum): diff --git a/orchestrator/search/filters/__init__.py b/orchestrator/search/filters/__init__.py index 0f608c069..583a5abae 100644 --- a/orchestrator/search/filters/__init__.py +++ b/orchestrator/search/filters/__init__.py @@ -19,6 +19,7 @@ StringFilter, ) from .date_filters import DateFilter, DateRangeFilter, DateValueFilter +from .definitions import TypeDefinition, ValueSchema from .ltree_filters import LtreeFilter from .numeric_filter import NumericFilter, NumericRangeFilter, NumericValueFilter @@ -37,4 +38,7 @@ "DateFilter", "LtreeFilter", "NumericFilter", + # Schema types + "TypeDefinition", + "ValueSchema", ] diff --git a/orchestrator/search/filters/definitions.py b/orchestrator/search/filters/definitions.py index 6ffbeff2b..68d77d08f 100644 --- a/orchestrator/search/filters/definitions.py +++ b/orchestrator/search/filters/definitions.py @@ -11,8 +11,29 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Literal + +from pydantic import BaseModel, ConfigDict + from orchestrator.search.core.types import FieldType, FilterOp, UIType -from orchestrator.search.schemas.results import TypeDefinition, ValueSchema + + +class ValueSchema(BaseModel): + """Schema describing the expected value type for a filter operator.""" + + kind: UIType | Literal["none", "object"] = UIType.STRING + fields: dict[str, "ValueSchema"] | None = None + + model_config = ConfigDict(extra="forbid") + + +class TypeDefinition(BaseModel): + """Definition of available operators and their value schemas for a field type.""" + + operators: list[FilterOp] + value_schema: dict[FilterOp, ValueSchema] + + model_config = ConfigDict(use_enum_values=True) def operators_for(ft: FieldType) -> list[FilterOp]: diff --git a/orchestrator/search/llm_migration.py b/orchestrator/search/llm_migration.py index 19bd06cee..27db35115 100644 --- a/orchestrator/search/llm_migration.py +++ b/orchestrator/search/llm_migration.py @@ -13,6 +13,7 @@ """Simple search migration function that runs when SEARCH_ENABLED = True.""" +import pydantic_ai from sqlalchemy import text from sqlalchemy.engine import Connection from structlog import get_logger @@ -28,7 +29,7 @@ def run_migration(connection: Connection) -> None: """Run LLM migration with ON CONFLICT DO NOTHING pattern.""" - logger.info("Running LLM migration") + logger.info("Running LLM migration", pydantic_ai_version=pydantic_ai.__version__) try: # Test to see if the extenstion exists and then skip the migration; Needed for certain situations where db user diff --git a/orchestrator/search/query/__init__.py b/orchestrator/search/query/__init__.py new file mode 100644 index 000000000..e9dc0ac59 --- /dev/null +++ b/orchestrator/search/query/__init__.py @@ -0,0 +1,90 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Query building and execution module.""" + +from orchestrator.search.aggregations import TemporalGrouping + +from . import engine +from .builder import ( + ComponentInfo, + LeafInfo, + build_aggregation_query, + build_candidate_query, + build_paths_query, + process_path_rows, +) +from .exceptions import ( + EmptyFilterPathError, + IncompatibleAggregationTypeError, + IncompatibleFilterTypeError, + IncompatibleTemporalGroupingTypeError, + InvalidEntityPrefixError, + InvalidLtreePatternError, + PathNotFoundError, + QueryValidationError, +) +from .queries import AggregateQuery, CountQuery, ExportQuery, Query, SelectQuery +from .results import ( + AggregationResponse, + AggregationResult, + MatchingField, + SearchResponse, + SearchResult, + VisualizationType, + format_aggregation_response, + format_search_response, + generate_highlight_indices, +) +from .state import QueryState + +__all__ = [ + # Builder functions + "build_aggregation_query", + "build_candidate_query", + "build_paths_query", + "process_path_rows", + # Builder metadata + "ComponentInfo", + "LeafInfo", + # Engine + "engine", + # Exceptions + "EmptyFilterPathError", + "IncompatibleAggregationTypeError", + "IncompatibleFilterTypeError", + "IncompatibleTemporalGroupingTypeError", + "InvalidEntityPrefixError", + "InvalidLtreePatternError", + "PathNotFoundError", + "QueryValidationError", + # Query models + "AggregateQuery", + "CountQuery", + "ExportQuery", + "Query", + "SelectQuery", + "TemporalGrouping", + # Results + "AggregationResponse", + "AggregationResult", + "MatchingField", + "SearchResponse", + "SearchResult", + "VisualizationType", + "format_aggregation_response", + "format_search_response", + "generate_highlight_indices", + # State + "QueryState", +] diff --git a/orchestrator/search/query/builder.py b/orchestrator/search/query/builder.py new file mode 100644 index 000000000..f5c737e46 --- /dev/null +++ b/orchestrator/search/query/builder.py @@ -0,0 +1,285 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import defaultdict +from typing import Any, Sequence + +from pydantic import BaseModel, ConfigDict +from sqlalchemy import Select, String, case, cast, func, select +from sqlalchemy.engine import Row +from sqlalchemy.sql.elements import Label +from sqlalchemy.sql.selectable import CTE +from sqlalchemy_utils.types.ltree import Ltree + +from orchestrator.db.models import AiSearchIndex +from orchestrator.search.aggregations import AggregationType, BaseAggregation, CountAggregation +from orchestrator.search.core.types import EntityType, FieldType, FilterOp, UIType +from orchestrator.search.filters import LtreeFilter +from orchestrator.search.query.queries import AggregateQuery, CountQuery, Query + + +class LeafInfo(BaseModel): + """Information about a leaf (terminal field) in the entity schema.""" + + name: str + ui_types: list[UIType] + paths: list[str] + + model_config = ConfigDict( + extra="forbid", + use_enum_values=True, + ) + + +class ComponentInfo(BaseModel): + """Information about a component (nested object) in the entity schema.""" + + name: str + ui_types: list[UIType] + + model_config = ConfigDict( + extra="forbid", + use_enum_values=True, + ) + + +def create_path_autocomplete_lquery(prefix: str) -> str: + """Create the lquery pattern for a multi-level path autocomplete search.""" + return f"{prefix}*.*" + + +def build_candidate_query(query: Query) -> Select: + """Build the base query for retrieving candidate entities. + + Constructs a `SELECT` statement that retrieves distinct `entity_id` values + from the index table for the given entity type, applying any structured + filters from the provided query plan. + + Args: + query: Any query type (SelectQuery, CountQuery, AggregateQuery) containing entity type and optional filters. + + Returns: + Select: The SQLAlchemy `Select` object representing the query. + """ + + stmt = ( + select(AiSearchIndex.entity_id, AiSearchIndex.entity_title) + .where(AiSearchIndex.entity_type == query.entity_type.value) + .distinct() + ) + + if query.filters is not None: + entity_id_col = AiSearchIndex.entity_id + stmt = stmt.where( + query.filters.to_expression( + entity_id_col, + entity_type_value=query.entity_type.value, + ) + ) + + return stmt + + +def build_paths_query(entity_type: EntityType, prefix: str | None = None, q: str | None = None) -> Select: + """Build the query for retrieving paths and their value types for leaves/components processing.""" + stmt = select(AiSearchIndex.path, AiSearchIndex.value_type).where(AiSearchIndex.entity_type == entity_type.value) + + if prefix: + lquery_pattern = create_path_autocomplete_lquery(prefix) + ltree_filter = LtreeFilter(op=FilterOp.MATCHES_LQUERY, value=lquery_pattern) + stmt = stmt.where(ltree_filter.to_expression(AiSearchIndex.path, path="")) + + stmt = stmt.group_by(AiSearchIndex.path, AiSearchIndex.value_type) + + if q: + score = func.similarity(cast(AiSearchIndex.path, String), q) + stmt = stmt.order_by(score.desc(), AiSearchIndex.path) + else: + stmt = stmt.order_by(AiSearchIndex.path) + + return stmt + + +def process_path_rows(rows: Sequence[Row]) -> tuple[list[LeafInfo], list[ComponentInfo]]: + """Process query results to extract leaves and components information. + + Parameters + ---------- + rows : Sequence[Row] + Database rows containing path and value_type information + + Returns: + ------- + tuple[list[LeafInfo], list[ComponentInfo]] + Processed leaves and components + """ + leaves_dict: dict[str, set[UIType]] = defaultdict(set) + leaves_paths_dict: dict[str, set[str]] = defaultdict(set) + components_set: set[str] = set() + + for row in rows: + path, value_type = row + + path_str = str(path) + path_segments = path_str.split(".") + + # Remove numeric segments + clean_segments = [seg for seg in path_segments if not seg.isdigit()] + + if clean_segments: + # Last segment is a leaf + leaf_name = clean_segments[-1] + ui_type = UIType.from_field_type(FieldType(value_type)) + leaves_dict[leaf_name].add(ui_type) + leaves_paths_dict[leaf_name].add(path_str) + + # All segments except the first/last are components + for component in clean_segments[1:-1]: + components_set.add(component) + + leaves = [ + LeafInfo(name=leaf, ui_types=list(types), paths=sorted(leaves_paths_dict[leaf])) + for leaf, types in leaves_dict.items() + ] + components = [ComponentInfo(name=component, ui_types=[UIType.COMPONENT]) for component in sorted(components_set)] + + return leaves, components + + +def _build_pivot_cte(base_query: Select, pivot_fields: list[str]) -> CTE: + """Build CTE that pivots EAV rows into columns using CASE WHEN.""" + from orchestrator.search.aggregations import BaseAggregation + + pivot_columns = [AiSearchIndex.entity_id.label("entity_id")] + + for field_path in pivot_fields: + pivot_columns.append( + func.max(case((AiSearchIndex.path == Ltree(field_path), AiSearchIndex.value), else_=None)).label( + BaseAggregation.field_to_alias(field_path) + ) + ) + + return ( + select(*pivot_columns) + .where( + AiSearchIndex.entity_id.in_(select(base_query.c.entity_id)), + AiSearchIndex.path.in_([Ltree(p) for p in pivot_fields]), + ) + .group_by(AiSearchIndex.entity_id) + .cte("pivoted_entities") + ) + + +def _build_grouping_columns( + query: CountQuery | AggregateQuery, pivot_cte: CTE +) -> tuple[list[Any], list[Any], list[str]]: + """Build GROUP BY columns and their SELECT columns. + + Args: + query: CountQuery or AggregateQuery with group_by and temporal_group_by fields + pivot_cte: The pivoted CTE containing entity fields as columns + + Returns: + tuple: (select_columns, group_by_columns, group_column_names) + - select_columns: List of labeled columns for SELECT + - group_by_columns: List of columns for GROUP BY clause + - group_column_names: List of column names (labels) that are grouping columns + """ + + select_columns = [] + group_by_columns = [] + group_column_names = [] + + if query.group_by: + for group_field in query.group_by: + field_alias = BaseAggregation.field_to_alias(group_field) + col = getattr(pivot_cte.c, field_alias) + select_columns.append(col.label(field_alias)) + group_by_columns.append(col) + group_column_names.append(field_alias) + + if query.temporal_group_by: + for temp_group in query.temporal_group_by: + select_col, group_col, col_name = temp_group.to_expression(pivot_cte.c) + select_columns.append(select_col) + group_by_columns.append(group_col) + group_column_names.append(col_name) + + return select_columns, group_by_columns, group_column_names + + +def _build_aggregation_columns(query: CountQuery | AggregateQuery, pivot_cte: CTE) -> list[Label]: + """Build aggregation columns (COUNT, SUM, AVG, MIN, MAX). + + Args: + query: CountQuery or AggregateQuery + pivot_cte: The pivoted CTE containing entity fields as columns + + Returns: + List of labeled aggregation expressions + """ + + if isinstance(query, AggregateQuery): + # AGGREGATE query with custom aggregations + agg_columns = [] + for agg in query.aggregations: + if isinstance(agg, CountAggregation): + agg_columns.append(agg.to_expression(pivot_cte.c.entity_id)) + else: + agg_columns.append(agg.to_expression(pivot_cte.c)) + return agg_columns + + # CountQuery without aggregations + count_agg = CountAggregation(type=AggregationType.COUNT, alias="count") + return [count_agg.to_expression(pivot_cte.c.entity_id)] + + +def build_simple_count_query(base_query: Select) -> Select: + """Build a simple count query without grouping. + + Args: + base_query: Base candidate query with filters applied + + Returns: + Select statement that counts distinct entity IDs + """ + return select(func.count(func.distinct(base_query.c.entity_id)).label("total_count")).select_from( + base_query.subquery() + ) + + +def build_aggregation_query(query: CountQuery | AggregateQuery, base_query: Select) -> tuple[Select, list[str]]: + """Build aggregation query with GROUP BY and aggregation functions. + + Handles EAV storage by pivoting rows to columns, then applying SQL aggregations. + This function only handles grouped aggregations. Simple counts are handled directly + in the engine. + + Args: + query: CountQuery or AggregateQuery with group_by and optional aggregations + base_query: Base candidate query with filters applied + + Returns: + tuple: (query_stmt, group_column_names) + - query_stmt: SQLAlchemy Select statement for grouped aggregation + - group_column_names: List of column names that are grouping columns + """ + pivot_cte = _build_pivot_cte(base_query, query.get_pivot_fields()) + select_cols, group_cols, group_col_names = _build_grouping_columns(query, pivot_cte) + agg_cols = _build_aggregation_columns(query, pivot_cte) + + stmt = select(*(select_cols + agg_cols)).select_from(pivot_cte) + if group_cols: + stmt = stmt.group_by(*group_cols) + + return stmt, group_col_names diff --git a/orchestrator/search/query/engine.py b/orchestrator/search/query/engine.py new file mode 100644 index 000000000..9f4defa69 --- /dev/null +++ b/orchestrator/search/query/engine.py @@ -0,0 +1,152 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import structlog +from sqlalchemy.orm import Session + +from orchestrator.search.core.embedding import QueryEmbedder +from orchestrator.search.core.types import SearchMetadata +from orchestrator.search.query.results import ( + AggregationResponse, + SearchResponse, + format_aggregation_response, + format_search_response, +) +from orchestrator.search.retrieval.pagination import PageCursor +from orchestrator.search.retrieval.retrievers import Retriever + +from .builder import build_aggregation_query, build_candidate_query, build_simple_count_query +from .export import fetch_export_data +from .queries import AggregateQuery, CountQuery, ExportQuery, SelectQuery + +logger = structlog.get_logger(__name__) + + +async def _execute_search( + query: SelectQuery | ExportQuery, + db_session: Session, + limit: int, + cursor: PageCursor | None = None, + query_embedding: list[float] | None = None, +) -> SearchResponse: + """Internal implementation to execute search with specified query. + + Args: + query: The SELECT or EXPORT query with vector, fuzzy, or filter criteria. + db_session: The active SQLAlchemy session for executing the query. + limit: Maximum number of results to return. + cursor: Optional pagination cursor. + query_embedding: Optional pre-computed query embedding to use instead of generating a new one. + + Returns: + SearchResponse with results and embedding (for internal use). + """ + if not query.vector_query and not query.filters and not query.fuzzy_term: + logger.warning("No search criteria provided (vector_query, fuzzy_term, or filters).") + return SearchResponse(results=[], metadata=SearchMetadata.empty()) + + candidate_query = build_candidate_query(query) + + if query.vector_query and not query_embedding: + query_embedding = await QueryEmbedder.generate_for_text_async(query.vector_query) + + retriever = Retriever.route(query, cursor, query_embedding) + logger.debug("Using retriever", retriever_type=retriever.__class__.__name__) + + final_stmt = retriever.apply(candidate_query) + final_stmt = final_stmt.limit(limit) + logger.debug(final_stmt) + result = db_session.execute(final_stmt).mappings().all() + + response = format_search_response(result, query, retriever.metadata) + # Store embedding in response for agent to save to DB + response.query_embedding = query_embedding + return response + + +async def execute_search( + query: SelectQuery, + db_session: Session, + cursor: PageCursor | None = None, + query_embedding: list[float] | None = None, +) -> SearchResponse: + """Execute a SELECT search query. + + This executes a SELECT action search using vector/fuzzy/filter search with ranking. + + Args: + query: SelectQuery with search criteria + db_session: Database session + cursor: Optional pagination cursor + query_embedding: Optional pre-computed embedding + + Returns: + SearchResponse with ranked results + """ + return await _execute_search(query, db_session, query.limit, cursor, query_embedding) + + +async def execute_export( + query: ExportQuery, + db_session: Session, + query_embedding: list[float] | None = None, +) -> list[dict]: + """Execute a search and export flattened entity data. + + Args: + query: ExportQuery with search criteria + db_session: Database session + query_embedding: Optional pre-computed embedding + + Returns: + List of flattened entity records suitable for export. + """ + search_response = await _execute_search( + query=query, + db_session=db_session, + limit=query.limit, + query_embedding=query_embedding, + ) + + entity_ids = [res.entity_id for res in search_response.results] + return fetch_export_data(query.entity_type, entity_ids) + + +async def execute_aggregation( + query: CountQuery | AggregateQuery, + db_session: Session, +) -> AggregationResponse: + """Execute aggregation query and return formatted results. + + Args: + query: CountQuery or AggregateQuery + db_session: Database session + + Returns: + AggregationResponse with results and metadata + """ + candidate_query = build_candidate_query(query) + + if isinstance(query, CountQuery) and not query.group_by and not query.temporal_group_by: + # Simple count without grouping + agg_query = build_simple_count_query(candidate_query) + group_column_names: list[str] = [] + else: + # Grouped aggregation - needs pivoting + agg_query, group_column_names = build_aggregation_query(query, candidate_query) + + logger.debug("Executing aggregation query", sql=str(agg_query)) + + result_rows = db_session.execute(agg_query).mappings().all() + + return format_aggregation_response(result_rows, group_column_names, query) diff --git a/orchestrator/search/retrieval/exceptions.py b/orchestrator/search/query/exceptions.py similarity index 65% rename from orchestrator/search/retrieval/exceptions.py rename to orchestrator/search/query/exceptions.py index 6ee8cfe12..d289c37f5 100644 --- a/orchestrator/search/retrieval/exceptions.py +++ b/orchestrator/search/query/exceptions.py @@ -14,13 +14,13 @@ from orchestrator.search.core.types import FilterOp -class FilterValidationError(Exception): - """Base exception for filter validation errors.""" +class QueryValidationError(Exception): + """Base exception for all query validation errors.""" pass -class InvalidLtreePatternError(FilterValidationError): +class InvalidLtreePatternError(QueryValidationError): """Raised when an ltree pattern has invalid ltree query syntax.""" def __init__(self, pattern: str) -> None: @@ -28,7 +28,7 @@ def __init__(self, pattern: str) -> None: super().__init__(message) -class EmptyFilterPathError(FilterValidationError): +class EmptyFilterPathError(QueryValidationError): """Raised when a filter path is empty or contains only whitespace.""" def __init__(self) -> None: @@ -38,7 +38,7 @@ def __init__(self) -> None: super().__init__(message) -class PathNotFoundError(FilterValidationError): +class PathNotFoundError(QueryValidationError): """Raised when a filter path doesn't exist in the database schema. Examples: @@ -53,7 +53,7 @@ def __init__(self, path: str) -> None: super().__init__(message) -class IncompatibleFilterTypeError(FilterValidationError): +class IncompatibleFilterTypeError(QueryValidationError): """Raised when a filter operator is incompatible with the field's data type. Examples: @@ -75,7 +75,7 @@ def __init__(self, operator: str, field_type: str, path: str, expected_operators super().__init__(message) -class InvalidEntityPrefixError(FilterValidationError): +class InvalidEntityPrefixError(QueryValidationError): """Raised when a filter path doesn't have the correct entity type prefix. Examples: @@ -88,3 +88,34 @@ class InvalidEntityPrefixError(FilterValidationError): def __init__(self, path: str, expected_prefix: str, entity_type: str) -> None: message = f"Filter path '{path}' must start with '{expected_prefix}' for {entity_type} searches, or use '*' for wildcard paths." super().__init__(message) + + +class IncompatibleAggregationTypeError(QueryValidationError): + """Raised when an aggregation function is incompatible with the field's data type. + + Examples: + Using SUM on a string field: + + >>> print(IncompatibleAggregationTypeError('sum', 'string', 'subscription.name', ['integer', 'float'])) + Aggregation 'sum' requires numeric fields (integer, float), but 'subscription.name' has type 'string'. + """ + + def __init__(self, agg_type: str, field_type: str, path: str, expected_types: list[str]) -> None: + expected_types_str = ", ".join(expected_types) + message = f"Aggregation '{agg_type}' requires numeric fields ({expected_types_str}), but '{path}' has type '{field_type}'." + super().__init__(message) + + +class IncompatibleTemporalGroupingTypeError(QueryValidationError): + """Raised when temporal grouping is used on a non-datetime field. + + Examples: + Using temporal grouping on a string field: + + >>> print(IncompatibleTemporalGroupingTypeError('subscription.name', 'string')) + Temporal grouping requires a datetime field, but 'subscription.name' has type 'string'. + """ + + def __init__(self, path: str, field_type: str) -> None: + message = f"Temporal grouping requires a datetime field, but '{path}' has type '{field_type}'." + super().__init__(message) diff --git a/orchestrator/search/export.py b/orchestrator/search/query/export.py similarity index 100% rename from orchestrator/search/export.py rename to orchestrator/search/query/export.py diff --git a/orchestrator/search/query/mixins.py b/orchestrator/search/query/mixins.py new file mode 100644 index 000000000..551e623e8 --- /dev/null +++ b/orchestrator/search/query/mixins.py @@ -0,0 +1,95 @@ +import uuid + +from pydantic import BaseModel, Field + +from orchestrator.search.aggregations import Aggregation, TemporalGrouping + +__all__ = [ + "SearchMixin", + "GroupingMixin", + "AggregationMixin", +] + + +class SearchMixin(BaseModel): + """Mixin providing text search capability. + + Provides query text processing and derived properties for vector and fuzzy search. + """ + + query_text: str | None = Field(default=None, description="Text query for semantic/fuzzy search") + + @property + def vector_query(self) -> str | None: + """Extract vector query from query text. + + Returns None if query_text is empty or is a UUID (UUIDs are not vectorized). + This matches the original logic from BaseQuery. + """ + if not self.query_text: + return None + try: + uuid.UUID(self.query_text) + return None # It's a UUID, disable vector search + except ValueError: + return self.query_text + + @property + def fuzzy_term(self) -> str | None: + """Extract fuzzy term from query text. + + Only single-word queries are used for fuzzy search to avoid + the trigram operator filtering out too many results. + This matches the original logic from BaseQuery. + """ + if not self.query_text: + return None + words = self.query_text.split() + return self.query_text if len(words) == 1 else None + + +class GroupingMixin(BaseModel): + """Mixin providing grouping capability. + + Used by COUNT and AGGREGATE queries for grouping results. + """ + + group_by: list[str] | None = Field(default=None, description="Field paths to group by") + temporal_group_by: list[TemporalGrouping] | None = Field( + default=None, + description="Temporal grouping specifications (group by month, year, etc.)", + ) + + def get_pivot_fields(self) -> list[str]: + """Get all fields needed for EAV pivot from grouping. + + Returns deduplicated list maintaining insertion order. + This matches the original logic from BaseQuery.get_pivot_fields(). + """ + fields = list(self.group_by or []) + + # Collect from temporal groupings + if self.temporal_group_by: + for temp_group in self.temporal_group_by: + fields.extend(temp_group.get_pivot_fields()) + + return list(dict.fromkeys(fields)) + + +class AggregationMixin(BaseModel): + """Mixin providing aggregation computation capability. + + Used by AGGREGATE queries to define what statistics to compute. + """ + + aggregations: list[Aggregation] = Field(description="Aggregations to compute (SUM, AVG, MIN, MAX, COUNT)") + + def get_aggregation_pivot_fields(self) -> list[str]: + """Get fields needed for EAV pivot from aggregations. + + Returns deduplicated list maintaining insertion order. + """ + fields = [] + for agg in self.aggregations: + fields.extend(agg.get_pivot_fields()) + return list(dict.fromkeys(fields)) diff --git a/orchestrator/search/query/queries.py b/orchestrator/search/query/queries.py new file mode 100644 index 000000000..3d988dfd2 --- /dev/null +++ b/orchestrator/search/query/queries.py @@ -0,0 +1,129 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Annotated, Any, ClassVar, Literal, Self, Union + +from pydantic import BaseModel, ConfigDict, Discriminator, Field + +from orchestrator.search.core.types import ActionType, EntityType +from orchestrator.search.filters import FilterTree + +from .mixins import ( + AggregationMixin, + GroupingMixin, + SearchMixin, +) + + +class BaseQuery(BaseModel): + """Base model for all query types. + + Contains shared constants, properties, and utilities. + """ + + MIN_LIMIT: ClassVar[int] = 1 + DEFAULT_LIMIT: ClassVar[int] = 10 + MAX_LIMIT: ClassVar[int] = 30 + DEFAULT_EXPORT_LIMIT: ClassVar[int] = 1000 + MAX_EXPORT_LIMIT: ClassVar[int] = 10000 + + _action: ClassVar[ActionType] + + entity_type: EntityType + filters: FilterTree | None = Field(default=None, description="Structured filters to apply") + + model_config = ConfigDict(extra="forbid") + + @property + def action(self) -> ActionType: + return self._action + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> Self: + """Build query from a dictionary. + + Args: + data: Dictionary with query parameters + + Returns: + Query instance of the appropriate type + """ + return cls.model_validate(data) + + +class SelectQuery(BaseQuery, SearchMixin): + """Query for SELECT operations. + + Composes BaseQuery with SearchMixin for text search, with strict result limits. + """ + + query_type: Literal["select"] = "select" + _action: ClassVar[ActionType] = ActionType.SELECT + + limit: int = Field( + default=BaseQuery.DEFAULT_LIMIT, + ge=BaseQuery.MIN_LIMIT, + le=BaseQuery.MAX_LIMIT, + description="Maximum number of search results to return", + ) + + +class ExportQuery(BaseQuery, SearchMixin): + """Query for EXPORT operations . + + Similar to SelectQuery but with higher limits for bulk exports. + """ + + query_type: Literal["export"] = "export" + _action: ClassVar[ActionType] = ActionType.SELECT + + limit: int = Field( + default=BaseQuery.DEFAULT_EXPORT_LIMIT, + ge=BaseQuery.MIN_LIMIT, + le=BaseQuery.MAX_EXPORT_LIMIT, + description="Maximum number of results to export", + ) + + +class CountQuery(BaseQuery, GroupingMixin): + """Query for COUNT operations with optional grouping.""" + + query_type: Literal["count"] = "count" + _action: ClassVar[ActionType] = ActionType.COUNT + + +class AggregateQuery(BaseQuery, GroupingMixin, AggregationMixin): + """Query for AGGREGATE operations. + + Composes BaseQuery with GroupingMixin and AggregationMixin + to provide both grouping and aggregation capabilities. + """ + + query_type: Literal["aggregate"] = "aggregate" + _action: ClassVar[ActionType] = ActionType.AGGREGATE + + def get_pivot_fields(self) -> list[str]: + """Get all fields needed for EAV pivot including aggregation fields.""" + # Get grouping fields from GroupingMixin + fields = super().get_pivot_fields() + + # Add aggregation fields + fields.extend(self.get_aggregation_pivot_fields()) + + return list(dict.fromkeys(fields)) + + +Query = Annotated[ + Union[SelectQuery, ExportQuery, CountQuery, AggregateQuery], + Discriminator("query_type"), +] diff --git a/orchestrator/search/query/results.py b/orchestrator/search/query/results.py new file mode 100644 index 000000000..3b5fc0d4b --- /dev/null +++ b/orchestrator/search/query/results.py @@ -0,0 +1,251 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Query execution result models and formatting functions.""" + +from collections.abc import Sequence +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field +from sqlalchemy.engine.row import RowMapping + +from orchestrator.search.core.types import EntityType, FilterOp, SearchMetadata +from orchestrator.search.filters import FilterTree + +from .queries import AggregateQuery, CountQuery, ExportQuery, SelectQuery + + +class VisualizationType(BaseModel): + """Visualization type for aggregation results. + + Choose based on the query context: + - 'pie': For categorical distributions (e.g., subscriptions by status, products by type) + - 'line': For time-series data (e.g., subscriptions created per month, trends over time) + - 'table': For detailed data or multiple grouping dimensions (default) + """ + + type: Literal["pie", "line", "table"] = Field( + default="table", + description="Visualization render type: 'pie' for categorical distributions, 'line' for time-series, 'table' for detailed data", + ) + + +class MatchingField(BaseModel): + """Contains the field that contributed most to the (fuzzy) search result.""" + + text: str + path: str + highlight_indices: list[tuple[int, int]] | None = None + + +class SearchResult(BaseModel): + """Represents a single search result item.""" + + entity_id: str + entity_type: EntityType + entity_title: str + score: float + perfect_match: int = 0 + matching_field: MatchingField | None = None + + +class SearchResponse(BaseModel): + """Response containing search results and metadata.""" + + results: list[SearchResult] + metadata: SearchMetadata + query_embedding: list[float] | None = None + + +class AggregationResult(BaseModel): + """Represents a single aggregation result row.""" + + group_values: dict[str, str] = Field(default_factory=dict) # group_by field -> value + aggregations: dict[str, float | int] = Field(default_factory=dict) # alias -> computed value + + model_config = ConfigDict(extra="forbid") + + +class AggregationResponse(BaseModel): + """Response containing aggregation results.""" + + results: list[AggregationResult] + total_groups: int + metadata: SearchMetadata + visualization_type: VisualizationType = Field(default_factory=VisualizationType) + + model_config = ConfigDict(extra="forbid") + + +class ExportData(BaseModel): + """Export metadata for download.""" + + action: str = "export" + query_id: str + download_url: str + message: str + + +def format_aggregation_response( + result_rows: Sequence[RowMapping], + group_column_names: list[str], + query: CountQuery | AggregateQuery, +) -> AggregationResponse: + """Format raw aggregation query results into AggregationResponse. + + Args: + result_rows: Raw database result rows + group_column_names: List of column names that are grouping columns + query: Query plan for metadata + + Returns: + AggregationResponse with formatted results and metadata + """ + results = [] + for row in result_rows: + group_values = {} + aggregations = {} + + for key, value in row.items(): + if key in group_column_names: + # It's a grouping column + group_values[key] = str(value) if value is not None else "" + else: + # It's an aggregation result + aggregations[key] = value if value is not None else 0 + + results.append(AggregationResult(group_values=group_values, aggregations=aggregations)) + + metadata = SearchMetadata( + search_type="aggregation", + description=f"Aggregation query with {len(query.group_by or [])} grouping dimension(s)", + ) + + return AggregationResponse( + results=results, + total_groups=len(results), + metadata=metadata, + ) + + +def generate_highlight_indices(text: str, term: str) -> list[tuple[int, int]]: + """Finds all occurrences of individual words from the term, including both word boundary and substring matches.""" + import re + + if not text or not term: + return [] + + all_matches = [] + words = [w.strip() for w in term.split() if w.strip()] + + for word in words: + # First find word boundary matches + word_boundary_pattern = rf"\b{re.escape(word)}\b" + word_matches = list(re.finditer(word_boundary_pattern, text, re.IGNORECASE)) + all_matches.extend([(m.start(), m.end()) for m in word_matches]) + + # Then find all substring matches + substring_pattern = re.escape(word) + substring_matches = list(re.finditer(substring_pattern, text, re.IGNORECASE)) + all_matches.extend([(m.start(), m.end()) for m in substring_matches]) + + return sorted(set(all_matches)) + + +def format_search_response( + db_rows: Sequence[RowMapping], query: "SelectQuery | ExportQuery", metadata: SearchMetadata +) -> SearchResponse: + """Format database query results into a `SearchResponse`. + + Converts raw SQLAlchemy `RowMapping` objects into `SearchResult` instances, + including highlight metadata if present in the database results. + + Args: + db_rows: The rows returned from the executed SQLAlchemy query. + query: SelectQuery or ExportQuery with search criteria. + metadata: Metadata about the search execution. + + Returns: + SearchResponse: A list of `SearchResult` objects containing entity IDs, scores, + and optional highlight information. + """ + from orchestrator.search.retrieval.retrievers import Retriever + + if not db_rows: + return SearchResponse(results=[], metadata=metadata) + + user_query = query.query_text + + results = [] + for row in db_rows: + matching_field = None + + if ( + user_query + and (text := row.get(Retriever.HIGHLIGHT_TEXT_LABEL)) + and (path := row.get(Retriever.HIGHLIGHT_PATH_LABEL)) + ): + if not isinstance(text, str): + text = str(text) + if not isinstance(path, str): + path = str(path) + + highlight_indices = generate_highlight_indices(text, user_query) or None + matching_field = MatchingField(text=text, path=path, highlight_indices=highlight_indices) + + elif not user_query and query.filters and metadata.search_type == "structured": + # Structured search (filter-only) + matching_field = _extract_matching_field_from_filters(query.filters) + + entity_title = row.get("entity_title", "") + if not isinstance(entity_title, str): + entity_title = str(entity_title) if entity_title is not None else "" + + results.append( + SearchResult( + entity_id=str(row.entity_id), + entity_type=query.entity_type, + entity_title=entity_title, + score=row.score, + perfect_match=row.get("perfect_match", 0), + matching_field=matching_field, + ) + ) + return SearchResponse(results=results, metadata=metadata) + + +def _extract_matching_field_from_filters(filters: "FilterTree") -> MatchingField | None: + """Extract the first path filter to use as matching field for structured searches.""" + from orchestrator.search.filters import LtreeFilter + + leaves = filters.get_all_leaves() + if len(leaves) != 1: + return None + + pf = leaves[0] + + if isinstance(pf.condition, LtreeFilter): + op = pf.condition.op + # Prefer the original component/pattern (validator may set path="*" and move the value) + display = str(getattr(pf.condition, "value", "") or pf.path) + + # There can be no match for abscence. + if op == FilterOp.NOT_HAS_COMPONENT: + return None + + return MatchingField(text=display, path=display, highlight_indices=[(0, len(display))]) + + # Everything thats not Ltree + val = getattr(pf.condition, "value", "") + text = "" if val is None else str(val) + return MatchingField(text=text, path=pf.path, highlight_indices=[(0, len(text))]) diff --git a/orchestrator/search/retrieval/query_state.py b/orchestrator/search/query/state.py similarity index 51% rename from orchestrator/search/retrieval/query_state.py rename to orchestrator/search/query/state.py index 2dc80574b..0cdbd07aa 100644 --- a/orchestrator/search/retrieval/query_state.py +++ b/orchestrator/search/query/state.py @@ -11,39 +11,47 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Generic, TypeVar, cast from uuid import UUID +import structlog from pydantic import BaseModel, ConfigDict, Field from orchestrator.db import SearchQueryTable, db from orchestrator.search.core.exceptions import QueryStateNotFoundError -from orchestrator.search.schemas.parameters import SearchParameters +from orchestrator.search.query.queries import BaseQuery, Query +logger = structlog.get_logger(__name__) -class SearchQueryState(BaseModel): - """State of a search query including parameters and embedding. +T = TypeVar("T", bound=Query) - This model provides a complete snapshot of what was searched and how. - Used for both agent and regular API searches. + +class QueryState(BaseModel, Generic[T]): + """State of a query including parameters and embedding. + + Thin wrapper around SearchQueryTable that stores query as JSONB blob. + Generic over query type for type-safe loading. + Used for both agent and regular API queries. """ - parameters: SearchParameters = Field(discriminator="entity_type") + query: T query_embedding: list[float] | None = Field(default=None, description="The embedding vector for semantic search") model_config = ConfigDict(from_attributes=True) @classmethod - def load_from_id(cls, query_id: UUID | str) -> "SearchQueryState": - """Load query state from database by query_id. + def load_from_id(cls, query_id: UUID | str, expected_type: type[T]) -> "QueryState[T]": + """Load query state from database by query_id with type validation. Args: query_id: UUID or string UUID of the saved query + expected_type: Expected query type class (SelectQuery, ExportQuery, etc.) Returns: - SearchQueryState loaded from database + QueryState with validated query type Raises: - ValueError: If query_id format is invalid + ValueError: If query_id format is invalid or query type doesn't match expected QueryStateNotFoundError: If query not found in database """ if isinstance(query_id, UUID): @@ -58,4 +66,16 @@ def load_from_id(cls, query_id: UUID | str) -> "SearchQueryState": if not search_query: raise QueryStateNotFoundError(f"Query {query_uuid} not found in database") - return cls.model_validate(search_query) + # Clamp limit to valid range to handle legacy queries outside the current limits + if "limit" in search_query.parameters and search_query.parameters["limit"] > BaseQuery.MAX_LIMIT: + logger.warning( + "Loaded query limit exceeds maximum, clamping to MAX_LIMIT", + query_id=query_uuid, + original_limit=search_query.parameters["limit"], + clamped_to=BaseQuery.MAX_LIMIT, + ) + search_query.parameters["limit"] = BaseQuery.MAX_LIMIT + + query = cast(T, expected_type.from_dict(search_query.parameters)) + + return cls(query=query, query_embedding=search_query.query_embedding) diff --git a/orchestrator/search/retrieval/validation.py b/orchestrator/search/query/validation.py similarity index 70% rename from orchestrator/search/retrieval/validation.py rename to orchestrator/search/query/validation.py index 6f9d3a283..a15ff0ab1 100644 --- a/orchestrator/search/retrieval/validation.py +++ b/orchestrator/search/query/validation.py @@ -18,12 +18,15 @@ from orchestrator.db import db from orchestrator.db.database import WrappedSession from orchestrator.db.models import AiSearchIndex +from orchestrator.search.aggregations import AggregationType from orchestrator.search.core.types import EntityType, FieldType from orchestrator.search.filters import FilterCondition, FilterTree, LtreeFilter, PathFilter from orchestrator.search.filters.definitions import operators_for -from orchestrator.search.retrieval.exceptions import ( +from orchestrator.search.query.exceptions import ( EmptyFilterPathError, + IncompatibleAggregationTypeError, IncompatibleFilterTypeError, + IncompatibleTemporalGroupingTypeError, InvalidEntityPrefixError, InvalidLtreePatternError, PathNotFoundError, @@ -150,3 +153,57 @@ async def validate_filter_tree(filters: FilterTree | None, entity_type: EntityTy return for leaf in filters.get_all_leaves(): await complete_filter_validation(leaf, entity_type) + + +def validate_aggregation_field(agg_type: AggregationType, field_path: str) -> None: + """Validate that an aggregation field exists and is compatible with the aggregation type. + + Note: Only for FieldAggregations (SUM, AVG, MIN, MAX). COUNT does not require field validation. + + Args: + agg_type: The aggregation type enum + field_path: The field path to validate + + Raises: + PathNotFoundError: If the field doesn't exist in the database. + IncompatibleAggregationTypeError: If the field type is incompatible with the aggregation type. + """ + # Check if field exists in database + field_type_str = validate_filter_path(field_path) + if field_type_str is None: + raise PathNotFoundError(field_path) + + # Validate field type compatibility with aggregation type + if agg_type in (AggregationType.SUM, AggregationType.AVG): + if field_type_str not in (FieldType.INTEGER.value, FieldType.FLOAT.value): + raise IncompatibleAggregationTypeError( + agg_type.value, field_type_str, field_path, [FieldType.INTEGER.value, FieldType.FLOAT.value] + ) + elif agg_type in (AggregationType.MIN, AggregationType.MAX): + if field_type_str not in (FieldType.INTEGER.value, FieldType.FLOAT.value, FieldType.DATETIME.value): + raise IncompatibleAggregationTypeError( + agg_type.value, + field_type_str, + field_path, + [FieldType.INTEGER.value, FieldType.FLOAT.value, FieldType.DATETIME.value], + ) + + +def validate_temporal_grouping_field(field_path: str) -> None: + """Validate that a field exists and is a datetime type for temporal grouping. + + Args: + field_path: The field path to validate + + Raises: + PathNotFoundError: If the field doesn't exist in the database + IncompatibleTemporalGroupingTypeError: If the field is not a datetime type + """ + # Check if field exists in database + field_type_str = validate_filter_path(field_path) + if field_type_str is None: + raise PathNotFoundError(field_path) + + # Validate field type is datetime + if field_type_str != FieldType.DATETIME.value: + raise IncompatibleTemporalGroupingTypeError(field_path, field_type_str) diff --git a/orchestrator/search/retrieval/__init__.py b/orchestrator/search/retrieval/__init__.py index a6f505450..f316b38b8 100644 --- a/orchestrator/search/retrieval/__init__.py +++ b/orchestrator/search/retrieval/__init__.py @@ -10,8 +10,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - -from .engine import execute_search, execute_search_for_export -from .query_state import SearchQueryState - -__all__ = ["execute_search", "execute_search_for_export", "SearchQueryState"] diff --git a/orchestrator/search/retrieval/builder.py b/orchestrator/search/retrieval/builder.py deleted file mode 100644 index 7aabb36a9..000000000 --- a/orchestrator/search/retrieval/builder.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright 2019-2025 SURF, GÉANT. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from collections import defaultdict -from typing import Sequence - -from sqlalchemy import Select, String, cast, func, select -from sqlalchemy.engine import Row - -from orchestrator.db.models import AiSearchIndex -from orchestrator.search.core.types import EntityType, FieldType, FilterOp, UIType -from orchestrator.search.filters import LtreeFilter -from orchestrator.search.schemas.parameters import BaseSearchParameters -from orchestrator.search.schemas.results import ComponentInfo, LeafInfo - - -def create_path_autocomplete_lquery(prefix: str) -> str: - """Create the lquery pattern for a multi-level path autocomplete search.""" - return f"{prefix}*.*" - - -def build_candidate_query(params: BaseSearchParameters) -> Select: - """Build the base query for retrieving candidate entities. - - Constructs a `SELECT` statement that retrieves distinct `entity_id` values - from the index table for the given entity type, applying any structured - filters from the provided search parameters. - - Args: - params (BaseSearchParameters): The search parameters containing the entity type and optional filters. - - Returns: - Select: The SQLAlchemy `Select` object representing the query. - """ - - stmt = ( - select(AiSearchIndex.entity_id, AiSearchIndex.entity_title) - .where(AiSearchIndex.entity_type == params.entity_type.value) - .distinct() - ) - - if params.filters is not None: - entity_id_col = AiSearchIndex.entity_id - stmt = stmt.where( - params.filters.to_expression( - entity_id_col, - entity_type_value=params.entity_type.value, - ) - ) - - return stmt - - -def build_paths_query(entity_type: EntityType, prefix: str | None = None, q: str | None = None) -> Select: - """Build the query for retrieving paths and their value types for leaves/components processing.""" - stmt = select(AiSearchIndex.path, AiSearchIndex.value_type).where(AiSearchIndex.entity_type == entity_type.value) - - if prefix: - lquery_pattern = create_path_autocomplete_lquery(prefix) - ltree_filter = LtreeFilter(op=FilterOp.MATCHES_LQUERY, value=lquery_pattern) - stmt = stmt.where(ltree_filter.to_expression(AiSearchIndex.path, path="")) - - stmt = stmt.group_by(AiSearchIndex.path, AiSearchIndex.value_type) - - if q: - score = func.similarity(cast(AiSearchIndex.path, String), q) - stmt = stmt.order_by(score.desc(), AiSearchIndex.path) - else: - stmt = stmt.order_by(AiSearchIndex.path) - - return stmt - - -def process_path_rows(rows: Sequence[Row]) -> tuple[list[LeafInfo], list[ComponentInfo]]: - """Process query results to extract leaves and components information. - - Parameters - ---------- - rows : Sequence[Row] - Database rows containing path and value_type information - - Returns: - ------- - tuple[list[LeafInfo], list[ComponentInfo]] - Processed leaves and components - """ - leaves_dict: dict[str, set[UIType]] = defaultdict(set) - leaves_paths_dict: dict[str, set[str]] = defaultdict(set) - components_set: set[str] = set() - - for row in rows: - path, value_type = row - - path_str = str(path) - path_segments = path_str.split(".") - - # Remove numeric segments - clean_segments = [seg for seg in path_segments if not seg.isdigit()] - - if clean_segments: - # Last segment is a leaf - leaf_name = clean_segments[-1] - ui_type = UIType.from_field_type(FieldType(value_type)) - leaves_dict[leaf_name].add(ui_type) - leaves_paths_dict[leaf_name].add(path_str) - - # All segments except the first/last are components - for component in clean_segments[1:-1]: - components_set.add(component) - - leaves = [ - LeafInfo(name=leaf, ui_types=list(types), paths=sorted(leaves_paths_dict[leaf])) - for leaf, types in leaves_dict.items() - ] - components = [ComponentInfo(name=component, ui_types=[UIType.COMPONENT]) for component in sorted(components_set)] - - return leaves, components diff --git a/orchestrator/search/retrieval/engine.py b/orchestrator/search/retrieval/engine.py deleted file mode 100644 index df4b773dd..000000000 --- a/orchestrator/search/retrieval/engine.py +++ /dev/null @@ -1,197 +0,0 @@ -# Copyright 2019-2025 SURF, GÉANT. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from collections.abc import Sequence - -import structlog -from sqlalchemy.engine.row import RowMapping -from sqlalchemy.orm import Session - -from orchestrator.search.core.embedding import QueryEmbedder -from orchestrator.search.core.types import FilterOp, SearchMetadata -from orchestrator.search.filters import FilterTree, LtreeFilter -from orchestrator.search.schemas.parameters import BaseSearchParameters -from orchestrator.search.schemas.results import MatchingField, SearchResponse, SearchResult - -from .builder import build_candidate_query -from .pagination import PageCursor -from .query_state import SearchQueryState -from .retrievers import Retriever -from .utils import generate_highlight_indices - -logger = structlog.get_logger(__name__) - - -def _format_response( - db_rows: Sequence[RowMapping], search_params: BaseSearchParameters, metadata: SearchMetadata -) -> SearchResponse: - """Format database query results into a `SearchResponse`. - - Converts raw SQLAlchemy `RowMapping` objects into `SearchResult` instances, - including highlight metadata if present in the database results. - - Args: - db_rows (Sequence[RowMapping]): The rows returned from the executed SQLAlchemy query. - search_params (BaseSearchParameters): The search parameters, including query text and filters. - metadata (SearchMetadata): Metadata about the search execution. - - Returns: - SearchResponse: A list of `SearchResult` objects containing entity IDs, scores, - and optional highlight information. - """ - - if not db_rows: - return SearchResponse(results=[], metadata=metadata) - - user_query = search_params.query - - results = [] - for row in db_rows: - matching_field = None - - if ( - user_query - and (text := row.get(Retriever.HIGHLIGHT_TEXT_LABEL)) - and (path := row.get(Retriever.HIGHLIGHT_PATH_LABEL)) - ): - if not isinstance(text, str): - text = str(text) - if not isinstance(path, str): - path = str(path) - - highlight_indices = generate_highlight_indices(text, user_query) or None - matching_field = MatchingField(text=text, path=path, highlight_indices=highlight_indices) - - elif not user_query and search_params.filters and metadata.search_type == "structured": - # Structured search (filter-only) - matching_field = _extract_matching_field_from_filters(search_params.filters) - - entity_title = row.get("entity_title", "") - if not isinstance(entity_title, str): - entity_title = str(entity_title) if entity_title is not None else "" - - results.append( - SearchResult( - entity_id=str(row.entity_id), - entity_type=search_params.entity_type, - entity_title=entity_title, - score=row.score, - perfect_match=row.get("perfect_match", 0), - matching_field=matching_field, - ) - ) - return SearchResponse(results=results, metadata=metadata) - - -def _extract_matching_field_from_filters(filters: FilterTree) -> MatchingField | None: - """Extract the first path filter to use as matching field for structured searches.""" - leaves = filters.get_all_leaves() - if len(leaves) != 1: - return None - - pf = leaves[0] - - if isinstance(pf.condition, LtreeFilter): - op = pf.condition.op - # Prefer the original component/pattern (validator may set path="*" and move the value) - display = str(getattr(pf.condition, "value", "") or pf.path) - - # There can be no match for abscence. - if op == FilterOp.NOT_HAS_COMPONENT: - return None - - return MatchingField(text=display, path=display, highlight_indices=[(0, len(display))]) - - # Everything thats not Ltree - val = getattr(pf.condition, "value", "") - text = "" if val is None else str(val) - return MatchingField(text=text, path=pf.path, highlight_indices=[(0, len(text))]) - - -async def _execute_search_internal( - search_params: BaseSearchParameters, - db_session: Session, - limit: int, - cursor: PageCursor | None = None, - query_embedding: list[float] | None = None, -) -> SearchResponse: - """Internal function to execute search with specified parameters. - - Args: - search_params: The search parameters specifying vector, fuzzy, or filter criteria. - db_session: The active SQLAlchemy session for executing the query. - limit: Maximum number of results to return. - cursor: Optional pagination cursor. - query_embedding: Optional pre-computed query embedding to use instead of generating a new one. - - Returns: - SearchResponse with results and embedding (for internal use). - """ - if not search_params.vector_query and not search_params.filters and not search_params.fuzzy_term: - logger.warning("No search criteria provided (vector_query, fuzzy_term, or filters).") - return SearchResponse(results=[], metadata=SearchMetadata.empty()) - - candidate_query = build_candidate_query(search_params) - - if search_params.vector_query and not query_embedding: - - query_embedding = await QueryEmbedder.generate_for_text_async(search_params.vector_query) - - retriever = await Retriever.route(search_params, cursor, query_embedding) - logger.debug("Using retriever", retriever_type=retriever.__class__.__name__) - - final_stmt = retriever.apply(candidate_query) - final_stmt = final_stmt.limit(limit) - logger.debug(final_stmt) - result = db_session.execute(final_stmt).mappings().all() - - response = _format_response(result, search_params, retriever.metadata) - # Store embedding in response for agent to save to DB - response.query_embedding = query_embedding - return response - - -async def execute_search( - search_params: BaseSearchParameters, - db_session: Session, - cursor: PageCursor | None = None, - query_embedding: list[float] | None = None, -) -> SearchResponse: - """Execute a search and return ranked results.""" - return await _execute_search_internal(search_params, db_session, search_params.limit, cursor, query_embedding) - - -async def execute_search_for_export( - query_state: SearchQueryState, - db_session: Session, -) -> list[dict]: - """Execute a search for export and fetch flattened entity data. - - Args: - query_state: Query state containing parameters and query_embedding. - db_session: The active SQLAlchemy session for executing the query. - - Returns: - List of flattened entity records suitable for export. - """ - from orchestrator.search.export import fetch_export_data - - search_response = await _execute_search_internal( - search_params=query_state.parameters, - db_session=db_session, - limit=query_state.parameters.export_limit, - query_embedding=query_state.query_embedding, - ) - - entity_ids = [res.entity_id for res in search_response.results] - return fetch_export_data(query_state.parameters.entity_type, entity_ids) diff --git a/orchestrator/search/retrieval/pagination.py b/orchestrator/search/retrieval/pagination.py index a630bf149..7cb2ad9d9 100644 --- a/orchestrator/search/retrieval/pagination.py +++ b/orchestrator/search/retrieval/pagination.py @@ -18,8 +18,8 @@ from orchestrator.db import SearchQueryTable, db from orchestrator.search.core.exceptions import InvalidCursorError -from orchestrator.search.schemas.parameters import SearchParameters -from orchestrator.search.schemas.results import SearchResponse +from orchestrator.search.query.queries import SelectQuery +from orchestrator.search.query.results import SearchResponse class PageCursor(BaseModel): @@ -45,7 +45,7 @@ def decode(cls, cursor: str) -> "PageCursor": def encode_next_page_cursor( search_response: SearchResponse, cursor: PageCursor | None, - search_params: SearchParameters, + query: SelectQuery, ) -> str | None: """Create next page cursor if there are more results. @@ -55,20 +55,20 @@ def encode_next_page_cursor( Args: search_response: SearchResponse containing results and query_embedding cursor: Current page cursor (None for first page, PageCursor for subsequent pages) - search_params: Search parameters to save for pagination consistency + query: SelectQuery for search operation to save for pagination consistency Returns: Encoded cursor for next page, or None if no more results """ - from orchestrator.search.retrieval.query_state import SearchQueryState + from orchestrator.search.query.state import QueryState - has_next_page = len(search_response.results) == search_params.limit and search_params.limit > 0 + has_next_page = len(search_response.results) == query.limit and query.limit > 0 if not has_next_page: return None # If this is the first page, save query state to database if cursor is None: - query_state = SearchQueryState(parameters=search_params, query_embedding=search_response.query_embedding) + query_state = QueryState(query=query, query_embedding=search_response.query_embedding) search_query = SearchQueryTable.from_state(state=query_state) db.session.add(search_query) diff --git a/orchestrator/search/retrieval/retrievers/base.py b/orchestrator/search/retrieval/retrievers/base.py index e7da35596..58e433503 100644 --- a/orchestrator/search/retrieval/retrievers/base.py +++ b/orchestrator/search/retrieval/retrievers/base.py @@ -18,7 +18,7 @@ from sqlalchemy import BindParameter, Numeric, Select, literal from orchestrator.search.core.types import FieldType, SearchMetadata -from orchestrator.search.schemas.parameters import BaseSearchParameters +from orchestrator.search.query.queries import ExportQuery, SelectQuery from ..pagination import PageCursor @@ -41,13 +41,13 @@ class Retriever(ABC): ] @classmethod - async def route( + def route( cls, - params: BaseSearchParameters, + query: "SelectQuery | ExportQuery", cursor: PageCursor | None, query_embedding: list[float] | None = None, ) -> "Retriever": - """Route to the appropriate retriever instance based on search parameters. + """Route to the appropriate retriever instance based on query plan. Selects the retriever type based on available search criteria: - Hybrid: both embedding and fuzzy term available @@ -56,7 +56,7 @@ async def route( - Structured: only filters available Args: - params: Search parameters including vector queries, fuzzy terms, and filters + query: SelectQuery or ExportQuery with search criteria cursor: Pagination cursor for cursor-based paging query_embedding: Query embedding for semantic search, or None if not available @@ -68,11 +68,11 @@ async def route( from .semantic import SemanticRetriever from .structured import StructuredRetriever - fuzzy_term = params.fuzzy_term + fuzzy_term = query.fuzzy_term - # If vector_query exists but embedding generation failed, fall back to fuzzy search with full query - if query_embedding is None and params.vector_query is not None and params.query is not None: - fuzzy_term = params.query + # If vector_query exists but embedding generation failed, fall back to fuzzy search with full query text + if query_embedding is None and query.vector_query is not None and query.query_text is not None: + fuzzy_term = query.query_text # Select retriever based on available search criteria if query_embedding is not None and fuzzy_term is not None: diff --git a/orchestrator/search/schemas/parameters.py b/orchestrator/search/schemas/parameters.py deleted file mode 100644 index 0a006d9e1..000000000 --- a/orchestrator/search/schemas/parameters.py +++ /dev/null @@ -1,133 +0,0 @@ -# Copyright 2019-2025 SURF, GÉANT. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import uuid -from typing import Any, ClassVar, Literal - -from pydantic import BaseModel, ConfigDict, Field, TypeAdapter - -from orchestrator.search.core.types import ActionType, EntityType -from orchestrator.search.filters import FilterTree - - -class BaseSearchParameters(BaseModel): - """Base model with common search parameters.""" - - DEFAULT_EXPORT_LIMIT: ClassVar[int] = 1000 - MAX_EXPORT_LIMIT: ClassVar[int] = 10000 - - action: ActionType = Field(default=ActionType.SELECT, description="The action to perform.") - entity_type: EntityType - - filters: FilterTree | None = Field(default=None, description="A list of structured filters to apply to the search.") - - query: str | None = Field( - default=None, description="Unified search query - will be processed into vector_query and/or fuzzy_term" - ) - - limit: int = Field(default=10, ge=1, le=30, description="Maximum number of search results to return.") - export_limit: int = Field( - default=DEFAULT_EXPORT_LIMIT, ge=1, le=MAX_EXPORT_LIMIT, description="Maximum number of results to export." - ) - model_config = ConfigDict(extra="forbid") - - @classmethod - def create(cls, **kwargs: Any) -> "SearchParameters": - """Create the correct search parameter subclass instance based on entity_type.""" - from orchestrator.search.schemas.parameters import SearchParameters - - adapter: TypeAdapter = TypeAdapter(SearchParameters) - return adapter.validate_python(kwargs) - - @property - def vector_query(self) -> str | None: - """Extract vector query from unified query field.""" - if not self.query: - return None - try: - uuid.UUID(self.query) - return None # It's a UUID, so disable vector search. - except ValueError: - return self.query - - @property - def fuzzy_term(self) -> str | None: - """Extract fuzzy term from unified query field.""" - if not self.query: - return None - - words = self.query.split() - # Only use fuzzy for single words - # otherwise, trigram operator filters out too much. - return self.query if len(words) == 1 else None - - -class SubscriptionSearchParameters(BaseSearchParameters): - entity_type: Literal[EntityType.SUBSCRIPTION] = Field( - default=EntityType.SUBSCRIPTION, description="The type of entity to search." - ) - model_config = ConfigDict( - json_schema_extra={ - "title": "SearchSubscriptions", - "description": "Search subscriptions based on specific criteria.", - "examples": [ - { - "filters": { - "op": "AND", - "children": [ - {"path": "subscription.status", "condition": {"op": "eq", "value": "provisioning"}}, - {"path": "subscription.end_date", "condition": {"op": "gte", "value": "2025-01-01"}}, - ], - } - } - ], - } - ) - - -class ProductSearchParameters(BaseSearchParameters): - entity_type: Literal[EntityType.PRODUCT] = Field( - default=EntityType.PRODUCT, description="The type of entity to search." - ) - model_config = ConfigDict( - json_schema_extra={ - "title": "SearchProducts", - "description": "Search products based on specific criteria.", - "examples": [ - { - "filters": [ - {"path": "product.product_type", "condition": {"op": "eq", "value": "Shop"}}, - ] - } - ], - } - ) - - -class WorkflowSearchParameters(BaseSearchParameters): - entity_type: Literal[EntityType.WORKFLOW] = Field( - default=EntityType.WORKFLOW, description="The type of entity to search." - ) - - -class ProcessSearchParameters(BaseSearchParameters): - """Search parameters specifically for PROCESS entities.""" - - entity_type: Literal[EntityType.PROCESS] = Field( - default=EntityType.PROCESS, description="The type of entity to search." - ) - - -SearchParameters = ( - SubscriptionSearchParameters | ProductSearchParameters | WorkflowSearchParameters | ProcessSearchParameters -) diff --git a/orchestrator/search/schemas/results.py b/orchestrator/search/schemas/results.py deleted file mode 100644 index 7dcb36394..000000000 --- a/orchestrator/search/schemas/results.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright 2019-2025 SURF, GÉANT. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Literal - -from pydantic import BaseModel, ConfigDict - -from orchestrator.search.core.types import EntityType, FilterOp, SearchMetadata, UIType - - -class MatchingField(BaseModel): - """Contains the field that contributed most to the (fuzzy) search result.""" - - text: str - path: str - highlight_indices: list[tuple[int, int]] | None = None - - -class SearchResult(BaseModel): - """Represents a single search result item.""" - - entity_id: str - entity_type: EntityType - entity_title: str - score: float - perfect_match: int = 0 - matching_field: MatchingField | None = None - - -class SearchResponse(BaseModel): - """Response containing search results and metadata.""" - - results: list[SearchResult] - metadata: SearchMetadata - query_embedding: list[float] | None = None - - -class ValueSchema(BaseModel): - kind: UIType | Literal["none", "object"] = UIType.STRING - fields: dict[str, "ValueSchema"] | None = None - - model_config = ConfigDict(extra="forbid") - - -class LeafInfo(BaseModel): - name: str - ui_types: list[UIType] - paths: list[str] - - model_config = ConfigDict( - extra="forbid", - use_enum_values=True, - ) - - -class ComponentInfo(BaseModel): - name: str - ui_types: list[UIType] - - model_config = ConfigDict( - extra="forbid", - use_enum_values=True, - ) - - -class TypeDefinition(BaseModel): - operators: list[FilterOp] - value_schema: dict[FilterOp, ValueSchema] - - model_config = ConfigDict(use_enum_values=True) diff --git a/pyproject.toml b/pyproject.toml index a75827a6f..2dac48423 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,7 +83,7 @@ search = [ "litellm>=1.75.7", ] agent = [ - "pydantic-ai-slim >=1.3.0", + "pydantic-ai-slim >=1.9.0", "ag-ui-protocol>=0.1.8", "litellm>=1.75.7", ] diff --git a/test/unit_tests/search/filters/conftest.py b/test/unit_tests/search/filters/conftest.py new file mode 100644 index 000000000..b53866069 --- /dev/null +++ b/test/unit_tests/search/filters/conftest.py @@ -0,0 +1,58 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytest + +from orchestrator.search.core.types import BooleanOperator, FilterOp, UIType +from orchestrator.search.filters import EqualityFilter, FilterTree, PathFilter, StringFilter + + +@pytest.fixture +def path_filter_leaf() -> PathFilter: + """Basic PathFilter leaf for building trees.""" + return PathFilter( + path="subscription.status", + condition=EqualityFilter(op=FilterOp.EQ, value="active"), + value_kind=UIType.STRING, + ) + + +@pytest.fixture +def nested_filter_tree() -> FilterTree: + """Nested FilterTree with multiple levels for testing tree operations.""" + return FilterTree( + op=BooleanOperator.AND, + children=[ + PathFilter( + path="subscription.status", + condition=EqualityFilter(op=FilterOp.EQ, value="active"), + value_kind=UIType.STRING, + ), + FilterTree( + op=BooleanOperator.OR, + children=[ + PathFilter( + path="subscription.product.name", + condition=StringFilter(op=FilterOp.LIKE, value="%fiber%"), + value_kind=UIType.STRING, + ), + PathFilter( + path="subscription.customer_id", + condition=EqualityFilter(op=FilterOp.EQ, value="acme"), + value_kind=UIType.STRING, + ), + ], + ), + ], + ) diff --git a/test/unit_tests/search/filters/test_filters.py b/test/unit_tests/search/filters/test_filters.py new file mode 100644 index 000000000..88e1ce9c8 --- /dev/null +++ b/test/unit_tests/search/filters/test_filters.py @@ -0,0 +1,121 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytest +from pydantic import ValidationError + +from orchestrator.search.core.types import BooleanOperator, FilterOp +from orchestrator.search.filters import FilterTree, PathFilter, StringFilter + +pytestmark = pytest.mark.search + + +class TestStringFilterValidation: + """Test StringFilter LIKE pattern validation.""" + + def test_like_without_wildcard_raises_error(self): + """Test that LIKE operation without wildcard raises ValueError.""" + with pytest.raises(ValidationError) as exc: + StringFilter(op=FilterOp.LIKE, value="test") + assert "wildcard" in str(exc.value).lower() + + def test_like_with_percent_wildcard(self): + """Test LIKE operation with % wildcard succeeds.""" + filter = StringFilter(op=FilterOp.LIKE, value="%test%") + assert filter.value == "%test%" + + def test_like_with_underscore_wildcard(self): + """Test LIKE operation with _ wildcard succeeds.""" + filter = StringFilter(op=FilterOp.LIKE, value="test_") + assert filter.value == "test_" + + +class TestPathFilterTransformation: + """Test PathFilter path-to-value transformation for path-only operators.""" + + def test_has_component_without_value_transforms_path(self): + """Test has_component without value moves path to value and sets path to '*'.""" + data = { + "path": "subscription.product", + "condition": {"op": "has_component"}, + "value_kind": "component", + } + filter = PathFilter.model_validate(data) + + assert filter.path == "*" + assert filter.condition.value == "subscription.product" + + def test_has_component_with_value_no_transformation(self): + """Test has_component with explicit value doesn't transform.""" + data = { + "path": "subscription", + "condition": {"op": "has_component", "value": "product"}, + "value_kind": "component", + } + filter = PathFilter.model_validate(data) + + assert filter.path == "subscription" + assert filter.condition.value == "product" + + def test_not_has_component_without_value_transforms_path(self): + """Test not_has_component without value transforms path.""" + data = { + "path": "subscription.customer", + "condition": {"op": "not_has_component"}, + "value_kind": "component", + } + filter = PathFilter.model_validate(data) + + assert filter.path == "*" + assert filter.condition.value == "subscription.customer" + + +class TestFilterTreeDepthValidation: + """Test FilterTree depth validation.""" + + def test_depth_exceeds_max_raises_error(self, path_filter_leaf: PathFilter): + """Test that FilterTree depth > MAX_DEPTH raises ValueError.""" + # Build nested tree: depth = MAX_DEPTH + 1, validation happens during construction + with pytest.raises(ValidationError) as exc: + tree = FilterTree(op=BooleanOperator.AND, children=[path_filter_leaf]) + for _ in range(FilterTree.MAX_DEPTH - 1): + tree = FilterTree(op=BooleanOperator.AND, children=[tree]) + assert "MAX_DEPTH" in str(exc.value) + + def test_depth_at_max_succeeds(self, path_filter_leaf: PathFilter): + """Test that FilterTree at MAX_DEPTH succeeds.""" + # Build nested tree: depth = MAX_DEPTH + # Starting depth: tree with leaf = 2 + # Each iteration adds 1 to depth + tree = FilterTree(op=BooleanOperator.AND, children=[path_filter_leaf]) + for _ in range(FilterTree.MAX_DEPTH - 2): + tree = FilterTree(op=BooleanOperator.AND, children=[tree]) + + # Should not raise + validated = FilterTree.model_validate(tree.model_dump()) + assert validated is not None + + +class TestFilterTreeHelpers: + """Test FilterTree helper methods for traversing the tree.""" + + def test_get_all_paths_from_nested_tree(self, nested_filter_tree: FilterTree): + """Test get_all_paths returns all unique paths from nested tree.""" + paths = nested_filter_tree.get_all_paths() + assert paths == {"subscription.status", "subscription.product.name", "subscription.customer_id"} + + def test_get_all_leaves_from_nested_tree(self, nested_filter_tree: FilterTree): + """Test get_all_leaves returns all PathFilter leaves.""" + leaves = nested_filter_tree.get_all_leaves() + assert len(leaves) == 3 diff --git a/orchestrator/search/schemas/__init__.py b/test/unit_tests/search/query/__init__.py similarity index 100% rename from orchestrator/search/schemas/__init__.py rename to test/unit_tests/search/query/__init__.py diff --git a/test/unit_tests/search/query/conftest.py b/test/unit_tests/search/query/conftest.py new file mode 100644 index 000000000..c2fc13eee --- /dev/null +++ b/test/unit_tests/search/query/conftest.py @@ -0,0 +1,196 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytest + +from orchestrator.search.aggregations import ( + AggregationType, + CountAggregation, + FieldAggregation, + TemporalGrouping, + TemporalPeriod, +) +from orchestrator.search.core.types import BooleanOperator, EntityType, FilterOp, UIType +from orchestrator.search.filters import EqualityFilter, FilterTree, PathFilter +from orchestrator.search.query.queries import AggregateQuery, CountQuery, ExportQuery, SelectQuery + +# ============================================================================= +# Building Block Fixtures +# ============================================================================= + + +@pytest.fixture +def simple_filter_tree() -> FilterTree: + """Simple filter tree with status=active.""" + return FilterTree( + op=BooleanOperator.AND, + children=[ + PathFilter( + path="subscription.status", + condition=EqualityFilter(op=FilterOp.EQ, value="active"), + value_kind=UIType.STRING, + ) + ], + ) + + +@pytest.fixture +def temporal_grouping_month() -> TemporalGrouping: + """Temporal grouping by month on start_date.""" + return TemporalGrouping(field="subscription.start_date", period=TemporalPeriod.MONTH) + + +@pytest.fixture +def temporal_grouping_year() -> TemporalGrouping: + """Temporal grouping by year on start_date.""" + return TemporalGrouping(field="subscription.start_date", period=TemporalPeriod.YEAR) + + +@pytest.fixture +def count_aggregation() -> CountAggregation: + """Count aggregation with alias 'count'.""" + return CountAggregation(type=AggregationType.COUNT, alias="count") + + +@pytest.fixture +def sum_aggregation() -> FieldAggregation: + """Sum aggregation on subscription.price.""" + return FieldAggregation(type=AggregationType.SUM, field="subscription.price", alias="total") + + +@pytest.fixture +def avg_aggregation() -> FieldAggregation: + """Average aggregation on subscription.price.""" + return FieldAggregation(type=AggregationType.AVG, field="subscription.price", alias="average") + + +# ============================================================================= +# Query Fixtures +# ============================================================================= + + +@pytest.fixture +def select_query_minimal() -> SelectQuery: + """Minimal SelectQuery with no filters or search text.""" + return SelectQuery(entity_type=EntityType.SUBSCRIPTION) + + +@pytest.fixture +def select_query_with_text() -> SelectQuery: + """SelectQuery with text search.""" + return SelectQuery(entity_type=EntityType.SUBSCRIPTION, query_text="fiber connection") + + +@pytest.fixture +def select_query_with_filters(simple_filter_tree: FilterTree) -> SelectQuery: + """SelectQuery with structured filters.""" + return SelectQuery(entity_type=EntityType.SUBSCRIPTION, filters=simple_filter_tree) + + +@pytest.fixture +def select_query_full(simple_filter_tree: FilterTree) -> SelectQuery: + """Fully-specified SelectQuery with text search, filters, and custom limit.""" + return SelectQuery( + entity_type=EntityType.SUBSCRIPTION, + query_text="network service", + filters=simple_filter_tree, + limit=20, + ) + + +@pytest.fixture +def export_query_minimal() -> ExportQuery: + """Minimal ExportQuery with default limit.""" + return ExportQuery(entity_type=EntityType.SUBSCRIPTION) + + +@pytest.fixture +def export_query_with_filters(simple_filter_tree: FilterTree) -> ExportQuery: + """ExportQuery with filters for bulk export.""" + return ExportQuery( + entity_type=EntityType.SUBSCRIPTION, + filters=simple_filter_tree, + limit=5000, + ) + + +@pytest.fixture +def count_query_simple() -> CountQuery: + """Simple CountQuery without grouping.""" + return CountQuery(entity_type=EntityType.SUBSCRIPTION) + + +@pytest.fixture +def count_query_grouped() -> CountQuery: + """CountQuery with group_by fields.""" + return CountQuery( + entity_type=EntityType.SUBSCRIPTION, + group_by=["subscription.status", "subscription.product.name"], + ) + + +@pytest.fixture +def count_query_temporal(temporal_grouping_month: TemporalGrouping) -> CountQuery: + """CountQuery with temporal grouping.""" + return CountQuery( + entity_type=EntityType.SUBSCRIPTION, + temporal_group_by=[temporal_grouping_month], + ) + + +@pytest.fixture +def count_query_full(simple_filter_tree: FilterTree, temporal_grouping_year: TemporalGrouping) -> CountQuery: + """CountQuery with grouping, temporal grouping, and filters.""" + return CountQuery( + entity_type=EntityType.SUBSCRIPTION, + group_by=["subscription.status"], + temporal_group_by=[temporal_grouping_year], + filters=simple_filter_tree, + ) + + +@pytest.fixture +def aggregate_query_simple(count_aggregation: CountAggregation) -> AggregateQuery: + """Simple AggregateQuery with count aggregation.""" + return AggregateQuery( + entity_type=EntityType.SUBSCRIPTION, + aggregations=[count_aggregation], + ) + + +@pytest.fixture +def aggregate_query_with_grouping(count_aggregation: CountAggregation) -> AggregateQuery: + """AggregateQuery with aggregations and grouping.""" + return AggregateQuery( + entity_type=EntityType.SUBSCRIPTION, + aggregations=[count_aggregation], + group_by=["subscription.status"], + ) + + +@pytest.fixture +def aggregate_query_full( + count_aggregation: CountAggregation, + sum_aggregation: FieldAggregation, + temporal_grouping_month: TemporalGrouping, + simple_filter_tree: FilterTree, +) -> AggregateQuery: + """AggregateQuery with multiple aggregations, grouping, and filters.""" + return AggregateQuery( + entity_type=EntityType.SUBSCRIPTION, + aggregations=[count_aggregation, sum_aggregation], + group_by=["subscription.status", "subscription.product.name"], + temporal_group_by=[temporal_grouping_month], + filters=simple_filter_tree, + ) diff --git a/test/unit_tests/search/query/test_queries.py b/test/unit_tests/search/query/test_queries.py new file mode 100644 index 000000000..995c5e74b --- /dev/null +++ b/test/unit_tests/search/query/test_queries.py @@ -0,0 +1,245 @@ +# Copyright 2019-2025 SURF, GÉANT. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import pytest +from pydantic import ValidationError + +from orchestrator.search.aggregations import ( + AggregationType, + CountAggregation, + FieldAggregation, + TemporalGrouping, + TemporalPeriod, +) +from orchestrator.search.core.types import ActionType, EntityType +from orchestrator.search.filters import FilterTree +from orchestrator.search.query.queries import AggregateQuery, CountQuery, ExportQuery, Query, SelectQuery + +pytestmark = pytest.mark.search + + +class TestSelectQueryConstruction: + """Test SelectQuery construction with various combinations.""" + + def test_minimal_select_query(self, select_query_minimal: SelectQuery): + """Test creating a minimal SelectQuery with only required fields.""" + assert select_query_minimal.action == ActionType.SELECT + assert select_query_minimal.limit == 10 # default + assert select_query_minimal.query_text is None + assert select_query_minimal.filters is None + + def test_select_query_with_text_search(self): + """Test SelectQuery with single word text search.""" + query = SelectQuery(entity_type=EntityType.SUBSCRIPTION, query_text="test") + + assert query.vector_query is not None + assert query.fuzzy_term is not None # single word gets fuzzy + + def test_select_query_with_multi_word_text(self): + """Test SelectQuery with multi-word text (no fuzzy search).""" + query = SelectQuery(entity_type=EntityType.SUBSCRIPTION, query_text="multiple word search") + + assert query.vector_query is not None + assert query.fuzzy_term is None # only single words get fuzzy + + def test_select_query_with_uuid_text(self): + """Test SelectQuery with UUID (no vector search).""" + uuid_str = "12345678-1234-1234-1234-123456789abc" + query = SelectQuery(entity_type=EntityType.SUBSCRIPTION, query_text=uuid_str) + + assert query.vector_query is None # UUIDs don't get vectorized + assert query.fuzzy_term is not None # but can use fuzzy + + def test_select_query_with_filters(self, select_query_with_filters: SelectQuery): + """Test SelectQuery with structured filters.""" + assert select_query_with_filters.filters is not None + + +class TestExportQueryConstruction: + """Test ExportQuery construction with higher limits.""" + + def test_export_query_limits(self, export_query_minimal: ExportQuery): + """Test ExportQuery has higher limits than SelectQuery for bulk exports.""" + # Default export limit (1000 vs SelectQuery's 10) + assert export_query_minimal.limit == 1000 + + # Max export limit (10000 vs SelectQuery's 30) + query_max = ExportQuery(entity_type=EntityType.SUBSCRIPTION, limit=10000) + assert query_max.limit == 10000 + + +class TestCountQueryConstruction: + """Test CountQuery construction with grouping options.""" + + def test_minimal_count_query(self, count_query_simple: CountQuery): + """Test creating a minimal CountQuery (simple count).""" + assert count_query_simple.action == ActionType.COUNT + assert count_query_simple.group_by is None + assert count_query_simple.temporal_group_by is None + + def test_count_query_with_multiple_group_by(self, count_query_grouped: CountQuery): + """Test CountQuery with multiple group_by fields.""" + assert count_query_grouped.group_by is not None + assert count_query_grouped.get_pivot_fields() == ["subscription.status", "subscription.product.name"] + + def test_count_query_with_temporal_grouping(self, count_query_temporal: CountQuery): + """Test CountQuery with temporal grouping.""" + assert count_query_temporal.temporal_group_by is not None + + def test_count_query_with_both_groupings(self, temporal_grouping_year: TemporalGrouping): + """Test CountQuery with both regular and temporal grouping.""" + query = CountQuery( + entity_type=EntityType.SUBSCRIPTION, + group_by=["subscription.status"], + temporal_group_by=[temporal_grouping_year], + ) + + pivot_fields = query.get_pivot_fields() + assert "subscription.status" in pivot_fields + assert "subscription.start_date" in pivot_fields + + def test_count_query_pivot_fields_deduplication(self, temporal_grouping_month: TemporalGrouping): + """Test that get_pivot_fields deduplicates fields.""" + query = CountQuery( + entity_type=EntityType.SUBSCRIPTION, + group_by=["subscription.status", "subscription.start_date"], + temporal_group_by=[temporal_grouping_month], + ) + + pivot_fields = query.get_pivot_fields() + # Should deduplicate subscription.start_date + assert pivot_fields.count("subscription.start_date") == 1 + + def test_count_query_with_filters(self, simple_filter_tree: FilterTree): + """Test CountQuery with filters.""" + query = CountQuery( + entity_type=EntityType.SUBSCRIPTION, + filters=simple_filter_tree, + group_by=["subscription.product.name"], + ) + + assert query.filters is not None + assert query.group_by is not None + + +class TestAggregateQueryConstruction: + """Test AggregateQuery construction with aggregations and grouping.""" + + def test_minimal_aggregate_query(self): + """Test creating an AggregateQuery with minimal aggregations.""" + agg = CountAggregation(type=AggregationType.COUNT, alias="total") + query = AggregateQuery(entity_type=EntityType.SUBSCRIPTION, aggregations=[agg]) + + assert query.action == ActionType.AGGREGATE + assert query.aggregations is not None + + def test_aggregate_query_with_grouping(self, aggregate_query_with_grouping: AggregateQuery): + """Test AggregateQuery with grouping.""" + assert aggregate_query_with_grouping.group_by is not None + pivot_fields = aggregate_query_with_grouping.get_pivot_fields() + assert "subscription.status" in pivot_fields + + def test_aggregate_query_pivot_fields_includes_aggregation_fields(self, sum_aggregation: FieldAggregation): + """Test that get_pivot_fields includes both grouping and aggregation fields.""" + query = AggregateQuery( + entity_type=EntityType.SUBSCRIPTION, + aggregations=[sum_aggregation], + group_by=["subscription.status"], + ) + + pivot_fields = query.get_pivot_fields() + assert "subscription.status" in pivot_fields + assert "subscription.price" in pivot_fields + + def test_aggregate_query_with_temporal_grouping( + self, count_aggregation: CountAggregation, temporal_grouping_month: TemporalGrouping + ): + """Test AggregateQuery with temporal grouping.""" + query = AggregateQuery( + entity_type=EntityType.SUBSCRIPTION, + aggregations=[count_aggregation], + temporal_group_by=[temporal_grouping_month], + ) + + assert query.temporal_group_by is not None + pivot_fields = query.get_pivot_fields() + assert "subscription.start_date" in pivot_fields + + def test_aggregate_query_complex_combination( + self, + count_aggregation: CountAggregation, + sum_aggregation: FieldAggregation, + avg_aggregation: FieldAggregation, + simple_filter_tree: FilterTree, + ): + """Test AggregateQuery with complex combination of features.""" + temporal = TemporalGrouping(field="subscription.start_date", period=TemporalPeriod.QUARTER) + + query = AggregateQuery( + entity_type=EntityType.SUBSCRIPTION, + aggregations=[count_aggregation, sum_aggregation, avg_aggregation], + group_by=["subscription.status", "subscription.product.name"], + temporal_group_by=[temporal], + filters=simple_filter_tree, + ) + + assert query.aggregations is not None + assert query.group_by is not None + assert query.filters is not None + pivot_fields = query.get_pivot_fields() + # Should include group_by fields, temporal fields, and aggregation fields + assert "subscription.status" in pivot_fields + assert "subscription.product.name" in pivot_fields + assert "subscription.start_date" in pivot_fields + assert "subscription.price" in pivot_fields + + def test_aggregate_query_multiple_temporal_groupings( + self, count_aggregation: CountAggregation, temporal_grouping_year: TemporalGrouping + ): + """Test AggregateQuery with multiple temporal groupings.""" + temporal2 = TemporalGrouping(field="subscription.end_date", period=TemporalPeriod.MONTH) + + query = AggregateQuery( + entity_type=EntityType.SUBSCRIPTION, + aggregations=[count_aggregation], + temporal_group_by=[temporal_grouping_year, temporal2], + ) + + pivot_fields = query.get_pivot_fields() + assert "subscription.start_date" in pivot_fields + assert "subscription.end_date" in pivot_fields + + +class TestQueryDiscriminator: + """Test Pydantic discriminated union for Query type.""" + + def test_discriminator_routes_to_select(self): + """Test discriminator correctly creates SelectQuery.""" + data = {"query_type": "select", "entity_type": "SUBSCRIPTION"} + from pydantic import TypeAdapter + + adapter = TypeAdapter(Query) + query = adapter.validate_python(data) + + assert isinstance(query, SelectQuery) + + def test_discriminator_invalid_query_type(self): + """Test that invalid query_type raises validation error.""" + data = {"query_type": "invalid", "entity_type": "subscription"} + from pydantic import TypeAdapter + + adapter = TypeAdapter(Query) + with pytest.raises(ValidationError) as exc: + adapter.validate_python(data) + assert "query_type" in str(exc.value) diff --git a/test/unit_tests/search/retrieval/test_utils.py b/test/unit_tests/search/retrieval/test_utils.py index 87ce5563d..d52994472 100644 --- a/test/unit_tests/search/retrieval/test_utils.py +++ b/test/unit_tests/search/retrieval/test_utils.py @@ -11,7 +11,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from orchestrator.search.retrieval.utils import generate_highlight_indices +from orchestrator.search.query.results import generate_highlight_indices class TestGenerateHighlightIndices: diff --git a/test/unit_tests/search/retrieval/test_validation_exceptions.py b/test/unit_tests/search/retrieval/test_validation_exceptions.py index 55fc9e8e3..57ad9e8d6 100644 --- a/test/unit_tests/search/retrieval/test_validation_exceptions.py +++ b/test/unit_tests/search/retrieval/test_validation_exceptions.py @@ -25,14 +25,14 @@ PathFilter, StringFilter, ) -from orchestrator.search.retrieval.exceptions import ( +from orchestrator.search.query.exceptions import ( EmptyFilterPathError, IncompatibleFilterTypeError, InvalidEntityPrefixError, InvalidLtreePatternError, PathNotFoundError, ) -from orchestrator.search.retrieval.validation import ( +from orchestrator.search.query.validation import ( complete_filter_validation, is_filter_compatible_with_field_type, validate_filter_tree, @@ -54,7 +54,7 @@ async def test_empty_filter_path_error(self, path): await complete_filter_validation(filter_with_empty_path, EntityType.SUBSCRIPTION) @pytest.mark.asyncio - @patch("orchestrator.search.retrieval.validation.validate_filter_path") + @patch("orchestrator.search.query.validation.validate_filter_path") async def test_path_not_found_error(self, mock_validate_path): """Test that non-existent paths raise PathNotFoundError.""" mock_validate_path.return_value = None @@ -69,7 +69,7 @@ async def test_path_not_found_error(self, mock_validate_path): await complete_filter_validation(filter_with_invalid_path, EntityType.SUBSCRIPTION) @pytest.mark.asyncio - @patch("orchestrator.search.retrieval.validation.validate_filter_path") + @patch("orchestrator.search.query.validation.validate_filter_path") async def test_incompatible_filter_type_error(self, mock_validate_path): """Test that incompatible filter types raise IncompatibleFilterTypeError.""" mock_validate_path.return_value = FieldType.STRING.value @@ -84,7 +84,7 @@ async def test_incompatible_filter_type_error(self, mock_validate_path): await complete_filter_validation(filter_with_wrong_type, EntityType.SUBSCRIPTION) @pytest.mark.asyncio - @patch("orchestrator.search.retrieval.validation.validate_filter_path") + @patch("orchestrator.search.query.validation.validate_filter_path") async def test_invalid_entity_prefix_error(self, mock_validate_path): """Test that wrong entity prefixes raise InvalidEntityPrefixError.""" mock_validate_path.return_value = FieldType.STRING.value @@ -97,7 +97,7 @@ async def test_invalid_entity_prefix_error(self, mock_validate_path): await complete_filter_validation(filter_with_wrong_prefix, EntityType.SUBSCRIPTION) @pytest.mark.asyncio - @patch("orchestrator.search.retrieval.validation.is_lquery_syntactically_valid") + @patch("orchestrator.search.query.validation.is_lquery_syntactically_valid") async def test_invalid_ltree_pattern_error(self, mock_is_valid): """Test that invalid ltree patterns raise InvalidLtreePatternError.""" mock_is_valid.return_value = False @@ -121,7 +121,7 @@ async def test_validate_filter_tree_with_none(self): await validate_filter_tree(None, EntityType.SUBSCRIPTION) @pytest.mark.asyncio - @patch("orchestrator.search.retrieval.validation.validate_filter_path") + @patch("orchestrator.search.query.validation.validate_filter_path") async def test_validate_filter_tree_propagates_exceptions(self, mock_validate_path): """Test that validate_filter_tree propagates specific exceptions from individual filters.""" mock_validate_path.return_value = None @@ -141,7 +141,7 @@ class TestFilterCompatibilityPaths: """Test successful validation paths for different filter types.""" @pytest.mark.asyncio - @patch("orchestrator.search.retrieval.validation.validate_filter_path") + @patch("orchestrator.search.query.validation.validate_filter_path") @pytest.mark.parametrize( "field_type,filter_condition,value_kind", [ @@ -163,7 +163,7 @@ async def test_successful_filter_validation(self, mock_validate_path, field_type await complete_filter_validation(path_filter, EntityType.SUBSCRIPTION) @pytest.mark.asyncio - @patch("orchestrator.search.retrieval.validation.validate_filter_path") + @patch("orchestrator.search.query.validation.validate_filter_path") async def test_wildcard_path_bypasses_entity_prefix_check(self, mock_validate_path): """Test that wildcard paths ('*') bypass entity prefix validation.""" mock_validate_path.return_value = FieldType.STRING.value @@ -176,7 +176,7 @@ async def test_wildcard_path_bypasses_entity_prefix_check(self, mock_validate_pa mock_validate_path.assert_called_once_with("*.name") @pytest.mark.asyncio - 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