diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 0c786f3..3b47910 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -8,9 +8,7 @@ updates: - package-ecosystem: "uv" multi-ecosystem-group: "risk-analyzer-service" directory: "/" - patterns: ["bigdata-client", "bigdata-research-tools"] - allow: - - dependency-name: "bigdata-client" + patterns: ["*"] - package-ecosystem: "docker" multi-ecosystem-group: "risk-analyzer-service" directory: "/" diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index fcad8c2..dc5951a 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -17,7 +17,7 @@ jobs: - name: Install uv uses: astral-sh/setup-uv@v6 - name: Install dependencies - run: uv sync --locked --dev + run: uv sync --dev - name: Type check the code run: make type-check - name: Lint the code @@ -42,6 +42,6 @@ jobs: python-version: ${{ matrix.python-version }} enable-cache: true - name: Install dependencies - run: uv sync --locked --dev + run: uv sync --dev - name: Test with pytest run: make tests diff --git a/CHANGELOG.md b/CHANGELOG.md index e8ddbef..82f90ed 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,21 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [3.0.0] - 25-08-2026 + +### Changed +- Migrated off the deprecated `bigdata-client` / `bigdata-research-tools` SDKs onto the Bigdata.com REST API (`X-API-KEY` / `https://api.bigdata.com`), `bigdata-smart-batching` for search, and direct OpenAI calls for taxonomy generation, chunk labeling, and company summaries. +- Company universes are now provided as a list of RP entity IDs or an uploaded CSV (`RP_ENTITY_ID`/`RP_COMPANY_ID` + `COMPANY_NAME`, optionally `TICKER`/`SECTOR`/`INDUSTRY`/`COUNTRY`). **Watchlists are no longer supported.** +- `document_type`, `fiscal_year`, `control_entities`, `frequency`, `batch_size`, and `document_limit` request fields were dropped (no equivalent in the new stack); `document_limit`/`batch_size`/`frequency` are replaced by a single `chunk_percentage` retrieval-volume knob. Added `max_leaf_labels` to cap generated taxonomy size, and renamed `llm_model_config` to `llm_model` (plain string, default `gpt-5.6-luna`). +- **The report storage schema changed and is not backwards compatible.** Databases created by 2.x keep their old columns and must be removed (or `DB_STRING` pointed at a new database) before starting 3.0; the service now fails at startup with an explicit message instead of returning 500s from `/status/{request_id}`. + +### Added +- `POST /risk-analysis/upload`: submit a risk analysis with a universe CSV instead of a list of RP entity IDs. + +### Fixed +- API keys supplied through a `.env` file are now picked up again. `bigdata-client` used to call `load_dotenv()` on import; without it the service started with unset credentials and failed mid-analysis. Settings read `.env` explicitly and the Bigdata.com and OpenAI clients are constructed with the configured keys instead of relying on `os.environ`. +- Retrieved chunks are attributed to every company from the universe detected in them, rather than to whichever entity ID happened to sort first. + ## [2.3.11] - 09-06-2026 ### Fixed diff --git a/README.md b/README.md index af02984..802fa99 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Risk Analyzer with Bigdata.com -This repository contains a docker image for running a risk analyzing service using Bigdata.com SDK. You can read more on our [docs](https://docs.bigdata.com/use-cases/docker-services/risk-analyzer). +This repository contains a docker image for running a risk analyzing service using the Bigdata.com REST API (`https://api.bigdata.com`, `X-API-KEY` auth) and OpenAI. You can read more on our [docs](https://docs.bigdata.com/use-cases/docker-services/risk-analyzer). # How to use? The risk analyzing service will allow you to assess and quantify risks related to a specific theme, such as US-China trade relations or supply chain disruptions. It will screen your trading universe and quantify the potential impact of identified risks for each company in the universe. @@ -10,9 +10,8 @@ The risk analyzing service will allow you to assess and quantify risks related t - For more information on how to get an API key, refer to the [Bigdata.com documentation](https://docs.bigdata.com/api-reference/introduction#api-key-beta). # Quickstart -To quickly get started, you have two options: -1. **Build and run locally:** +**Build and run locally:** You need to build the docker image first and then run it: ```bash @@ -32,16 +31,6 @@ docker run -d \ bigdata_risk_analyzer ``` -2. **Run directly from GitHub Container Registry:** - -```bash -docker run -d \ - --name bigdata_risk_analyzer \ - -p 8000:8000 \ - -e BIGDATA_API_KEY= \ - -e OPENAI_API_KEY= \ - ghcr.io/bigdata-com/bigdata-risk-analyzer:latest -``` This will start the risk analyzer service locally on port 8000. You can then access the service @ `http://localhost:8000/` and the documentation for the API @ `http://localhost:8000/docs`. @@ -55,7 +44,9 @@ We perform a pre-release security scan on our container images to detect vulnera ## How to analyse a set of companies? -A risk analysis report provides an executive summary of financially relevant information about a set of companies that form your watchlist. You can generate a report either using the UI or programmatically, allowing you to build custom workflows on top of this service. +A risk analysis report provides an executive summary of financially relevant information about a set of companies in your universe. You can generate a report either using the UI or programmatically, allowing you to build custom workflows on top of this service. + +The company universe is provided either as a list of RavenPack (RP) entity IDs, or as an uploaded CSV. **Watchlists (watchlist IDs) are not supported.** ### Using the UI There is a very simple UI available @ `http://localhost:8000/` where you can set your parameters and receive an easy-to-read summary of the analysis. @@ -64,7 +55,8 @@ There is a very simple UI available @ `http://localhost:8000/` where you can set The risk analysis API works asynchronously. You first submit a request to start the analysis, then check the status periodically until completion. #### Step 1: Submit Risk Analysis Request -Send a POST request to the `/risk-analysis` endpoint with the required parameters. This will return a `request_id` and queue the analysis for processing: + +**Option A — a list of RP entity IDs**, via `POST /risk-analysis`: ```bash curl -X 'POST' \ @@ -74,24 +66,26 @@ curl -X 'POST' \ -d '{ "main_theme": "US Import Tariffs against China", "focus": "Provide a detailed taxonomy of risks describing how new American import tariffs against China will impact US companies, their operations and strategy. Cover trade-relations risks, foreign market access risks, supply chain risks, US market sales and revenue risks (including price impacts), and intellectual property risks, provide at least 4 sub-scenarios for each risk factor.", - "companies": "44118802-9104-4265-b97a-2e6d88d74893", - "control_entities": { - "place": [ - "China" - ] - }, + "companies": ["D8442A", "228D42", "4A6F00"], "start_date": "2024-01-01", "end_date": "2024-12-31", - "keywords": [ - "Tariffs" - ], - "document_type": "TRANSCRIPTS", - "fiscal_year": 2024, - "frequency": "M" + "keywords": ["Tariffs"], + "chunk_percentage": 0.05, + "max_leaf_labels": 15 }' ``` -This will return a response like: +**Option B — a universe CSV**, via `POST /risk-analysis/upload` (multipart, same fields minus `companies`, sent as a JSON string in the `request` form field). The CSV needs `RP_ENTITY_ID` (alias `RP_COMPANY_ID`) and `COMPANY_NAME` columns; `TICKER`/`SECTOR`/`INDUSTRY`/`COUNTRY` are optional enrichment columns: + +```bash +curl -X 'POST' \ + 'http://localhost:8000/risk-analysis/upload' \ + -H 'accept: application/json' \ + -F 'file=@Internal/mag7.csv;type=text/csv' \ + -F 'request={"main_theme": "US Import Tariffs against China", "focus": "Provide a detailed taxonomy of risks describing how new American import tariffs against China will impact US companies.", "start_date": "2024-01-01", "end_date": "2024-12-31"};type=application/json' +``` + +Both endpoints return a response like: ```json { "request_id": "550e8400-e29b-41d4-a716-446655440000", @@ -139,20 +133,10 @@ curl -X 'POST' \ -d '{ "main_theme": "US Import Tariffs against China", "focus": "Provide a detailed taxonomy of risks describing how new American import tariffs against China will impact US companies, their operations and strategy. Cover trade-relations risks, foreign market access risks, supply chain risks, US market sales and revenue risks (including price impacts), and intellectual property risks, provide at least 4 sub-scenarios for each risk factor.", - "companies": "44118802-9104-4265-b97a-2e6d88d74893", - "control_entities": { - "place": [ - "China" - ] - }, + "companies": ["D8442A", "228D42", "4A6F00"], "start_date": "2024-01-01", "end_date": "2024-12-31", - "keywords": [ - "Tariffs" - ], - "document_type": "TRANSCRIPTS", - "fiscal_year": 2024, - "frequency": "M" + "keywords": ["Tariffs"] }' # Check status using the returned request_id diff --git a/bigdata_risk_analyzer/api/app.py b/bigdata_risk_analyzer/api/app.py index 213db58..c11b1c4 100644 --- a/bigdata_risk_analyzer/api/app.py +++ b/bigdata_risk_analyzer/api/app.py @@ -2,17 +2,28 @@ from typing import Annotated from uuid import UUID, uuid4 -from bigdata_client import Bigdata -from bigdata_client.models.search import DocumentType -from fastapi import BackgroundTasks, Body, Depends, FastAPI, HTTPException, Security +from fastapi import ( + BackgroundTasks, + Body, + Depends, + FastAPI, + File, + Form, + HTTPException, + Security, + UploadFile, +) from fastapi.responses import HTMLResponse, JSONResponse from fastapi.staticfiles import StaticFiles +from pydantic import ValidationError +from sqlalchemy import inspect from sqlmodel import Session, SQLModel, create_engine from bigdata_risk_analyzer import LOG_LEVEL, __version__, logger from bigdata_risk_analyzer.api.models import ( - ExampleWatchlists, + EXAMPLE_COMPANY_LISTS, RiskAnalysisRequest, + RiskAnalysisRequestBase, RiskAnalyzerAcceptedResponse, RiskAnalyzerStatusResponse, WorkflowStatus, @@ -22,17 +33,44 @@ from bigdata_risk_analyzer.api.utils import get_example_values_from_schema from bigdata_risk_analyzer.models import RiskAnalysisResponse from bigdata_risk_analyzer.service import process_request -from bigdata_risk_analyzer.settings import UNSET, settings +from bigdata_risk_analyzer.settings import settings from bigdata_risk_analyzer.templates import loader -from bigdata_risk_analyzer.traces import TraceEventName, send_trace +from bigdata_risk_analyzer.universe import ( + WATCHLIST_REJECTED_MESSAGE, + build_universe_from_ids, + load_universe_csv, +) -BIGDATA: Bigdata | None = None engine = create_engine(settings.DB_STRING, echo=LOG_LEVEL == "DEBUG") +def check_storage_schema(): + """Fail fast on a database created by an incompatible earlier release. + + ``create_all`` only creates missing tables, so a database left over from + 2.x keeps its old columns and every read fails later with an opaque 500. + """ + inspector = inspect(engine) + for table in SQLModel.metadata.sorted_tables: + if not inspector.has_table(table.name): + continue + existing_columns = { + column["name"] for column in inspector.get_columns(table.name) + } + missing_columns = {column.name for column in table.columns} - existing_columns + if missing_columns: + raise RuntimeError( + f"Table '{table.name}' in {settings.DB_STRING} is missing columns " + f"{sorted(missing_columns)}. The 3.0 schema is not compatible with " + "databases created by earlier releases. Remove the old database file " + "or point DB_STRING at a new one." + ) + + def create_db_and_tables(): logger.info("Setting up data storage", db_string=settings.DB_STRING) SQLModel.metadata.create_all(engine) + check_storage_schema() def get_session(): @@ -45,23 +83,8 @@ def get_storage_manager(session: Session = Depends(get_session)) -> StorageManag def lifespan(app: FastAPI): - global BIGDATA logger.info("Starting Risk Analyzer service") - - # Instantiate Bigdata client - BIGDATA = Bigdata(api_key=settings.BIGDATA_API_KEY) - - if settings.BIGDATA_API_KEY != UNSET: - send_trace( - BIGDATA, - event_name=TraceEventName.SERVICE_START, - trace={ - "version": __version__, - }, - ) - create_db_and_tables() - yield @@ -91,7 +114,7 @@ def health_check(): async def sample_frontend(_: str = Security(query_scheme)) -> HTMLResponse: # Get example values from the schema for all fields template_values = get_example_values_from_schema(RiskAnalysisRequest) - template_values["example_watchlists"] = list(dict(ExampleWatchlists).values()) + template_values["example_companies"] = EXAMPLE_COMPANY_LISTS template_values["demo_mode"] = settings.DEMO_MODE template_values["version"] = f"v{__version__}" @@ -101,31 +124,20 @@ async def sample_frontend(_: str = Security(query_scheme)) -> HTMLResponse: ) -@app.post("/risk-analysis", response_model=RiskAnalysisResponse) -def analyze_risk( - request: Annotated[RiskAnalysisRequest, Body()], +def _queue_analysis( + request: RiskAnalysisRequestBase, + universe_df, background_tasks: BackgroundTasks, - storage_manager: StorageManager = Depends(get_storage_manager), - _: str = Security(query_scheme), + storage_manager: StorageManager, ) -> JSONResponse: - """This endpoints starts the generation of therisk analyzer workflow on the background - and will return a request_id that can be used to check the status of the request in the - `/status/{request_id}` endpoint. - Note: for now, it only supports news as document type. - """ - # While we improve the UX of working with several document types with different sets of parameters - # we will limit the document type to news - DOCUMENT_TYPE = DocumentType.NEWS - request.document_type = DOCUMENT_TYPE - request_id = uuid4() - + request_id: UUID = uuid4() storage_manager.update_status(request_id, WorkflowStatus.QUEUED) background_tasks.add_task( partial( process_request, request, - bigdata=BIGDATA, + universe_df=universe_df, request_id=request_id, storage_manager=storage_manager, ) @@ -138,6 +150,68 @@ def analyze_risk( ) +@app.post("/risk-analysis", response_model=RiskAnalysisResponse) +def analyze_risk( + request: Annotated[RiskAnalysisRequest, Body()], + background_tasks: BackgroundTasks, + storage_manager: StorageManager = Depends(get_storage_manager), + _: str = Security(query_scheme), +) -> JSONResponse: + """This endpoint starts the generation of the risk analyzer workflow on the background + and will return a request_id that can be used to check the status of the request in the + `/status/{request_id}` endpoint. + + `companies` must be a list of RavenPack entity IDs. Watchlists are not supported; upload + a CSV via `/risk-analysis/upload` for larger or metadata-rich universes. + """ + companies = request.companies + if isinstance(companies, str): + raise HTTPException(status_code=400, detail=WATCHLIST_REJECTED_MESSAGE) + try: + universe_df = build_universe_from_ids( + companies, api_key=settings.BIGDATA_API_KEY + ) + except ValueError as e: + raise HTTPException(status_code=400, detail=str(e)) + + return _queue_analysis(request, universe_df, background_tasks, storage_manager) + + +@app.post("/risk-analysis/upload", response_model=RiskAnalysisResponse) +def analyze_risk_upload( + background_tasks: BackgroundTasks, + file: Annotated[ + UploadFile, + File(description="Universe CSV with RP_ENTITY_ID + COMPANY_NAME columns."), + ], + request: Annotated[ + str, + Form( + description="JSON-encoded request body (same fields as POST /risk-analysis, minus companies)." + ), + ], + storage_manager: StorageManager = Depends(get_storage_manager), + _: str = Security(query_scheme), +) -> JSONResponse: + """Same as `POST /risk-analysis`, but the company universe comes from an uploaded CSV + (columns: `RP_ENTITY_ID` [alias `RP_COMPANY_ID`], `COMPANY_NAME`, and optionally + `TICKER`/`SECTOR`/`INDUSTRY`/`COUNTRY`) instead of a list of RP entity IDs. + """ + try: + parsed_request = RiskAnalysisRequestBase.model_validate_json(request) + except ValidationError as e: + raise HTTPException(status_code=422, detail=e.errors()) + + try: + universe_df = load_universe_csv(file.file, api_key=settings.BIGDATA_API_KEY) + except ValueError as e: + raise HTTPException(status_code=400, detail=str(e)) + + return _queue_analysis( + parsed_request, universe_df, background_tasks, storage_manager + ) + + @app.get( "/status/{request_id}", summary="Get the status of a risk analyzer report", diff --git a/bigdata_risk_analyzer/api/models.py b/bigdata_risk_analyzer/api/models.py index 92f31d0..a4ef0ad 100644 --- a/bigdata_risk_analyzer/api/models.py +++ b/bigdata_risk_analyzer/api/models.py @@ -1,21 +1,12 @@ from datetime import date, datetime, timedelta -from enum import Enum, StrEnum -from typing import List, Literal, Optional, Self +from enum import StrEnum +from typing import List, Optional, Self -from bigdata_client.models.search import DocumentType -from bigdata_research_tools.llm.base import LLMConfig from pydantic import BaseModel, Field, model_validator -from pydantic_core import ValidationError from bigdata_risk_analyzer.models import RiskAnalysisResponse - - -class FrequencyEnum(StrEnum): - daily = "D" - weekly = "W" - monthly = "M" - quarterly = "3M" - yearly = "Y" +from bigdata_risk_analyzer.taxonomy import DEFAULT_MAX_LEAF_LABELS +from bigdata_risk_analyzer.universe import WATCHLIST_REJECTED_MESSAGE class WorkflowStatus(StrEnum): @@ -25,45 +16,25 @@ class WorkflowStatus(StrEnum): FAILED = "failed" -class WatchlistExample(BaseModel): - id: str = Field(..., description="The unique identifier for the watchlist.") - name: str = Field(..., description="The name of the watchlist.") +# Example RP entity IDs for the demo UI's "quick fill" dropdown, sourced from +# Internal/mag7.csv. Watchlists are not supported (see WATCHLIST_REJECTED_MESSAGE). +EXAMPLE_COMPANY_LISTS: dict[str, list[str]] = { + "MAG_7": ["E09E2B", "D8442A", "228D42", "0157B1", "4A6F00", "12E454", "DD3BB1"], +} +DEFAULT_LLM_MODEL = "gpt-5.6-luna" +DEFAULT_CHUNK_PERCENTAGE = 0.05 -class ExampleWatchlists(Enum): - TOP_100_UK = WatchlistExample( - id="33d6f577-9256-4a53-944f-09127e42fdc2", name="UK Top 100" - ) - TOP_50_EU = WatchlistExample( - id="9baef470-8cf5-46fa-b30a-352bcb35cd94", name="Europe Top 50" - ) - US_LARGE_CAP_100 = WatchlistExample( - id="44118802-9104-4265-b97a-2e6d88d74893", name="US Top 100" - ) - TOP_40_DE = WatchlistExample( - id="8453c26f-47c5-4e78-b5c8-acf245caccad", name="Germany Top 40" - ) - TOP_40_FR = WatchlistExample( - id="9fb6ac2d-a552-4dbb-b62f-8657ef18bf29", name="France Top 40" - ) - DOW_30 = WatchlistExample(id="5b78837c-343d-4559-8f06-98668b09d1df", name="Dow 30") - NASDAQ_100 = WatchlistExample( - id="402acbcd-f1d8-4a55-997a-598819be0bbf", name="Nasdaq 100" - ) - MAG_7 = WatchlistExample( - id="814d0944-a2c1-44f6-8b42-a70c0795428e", name="Magnificent 7" - ) - def __iter__(self): - """Allows to create a dict from the enum - >>> dict(ExampleWatchlists) - {'POINT_72': {'id': '9ab396cf-a2bb-4c91-b9bf-ed737905803e', 'name': 'Point 72 Holdings'}, ...} - """ - yield self.name - yield self.value.model_dump() +class RiskAnalysisRequestBase(BaseModel): + """Shared risk-analysis request fields. + Used directly by the CSV-upload endpoint (whose company universe comes + from the uploaded file, not this model) and extended by + :class:`RiskAnalysisRequest` for the JSON endpoint (which additionally + takes a `companies` list of RP entity IDs). + """ -class RiskAnalysisRequest(BaseModel): main_theme: str = Field( ..., example="US Import Tariffs against China", @@ -78,152 +49,77 @@ class RiskAnalysisRequest(BaseModel): description="The analyst focus that provides an expert perspective on the scenario and helps break it down into risks.", ) - companies: list[str] | str = Field( - ..., - description="List of RavenPack entity IDs or a watchlist ID representing the companies to track in the generated brief.", - example=ExampleWatchlists.US_LARGE_CAP_100.value.id, - ) - - control_entities: Optional[dict[str, list[str]]] = Field( - default=None, - description="Dictionary specifying the countries, people, or organizations that characterize the risk scenario.", - example=None, - ) - start_date: str = Field( default="2024-01-01", - description="Start date of the analysis window (format: YYYY-MM-DD). Defaults to 6 months ago.", + description="Start date of the analysis window (format: YYYY-MM-DD). Defaults to 2024-01-01.", example=(date.today() - timedelta(days=30)).isoformat(), ) end_date: str = Field( default="2024-12-31", - description="End date of the analysis window (format: YYYY-MM-DD). Defaults to yesterday.", + description="End date of the analysis window (format: YYYY-MM-DD). Defaults to 2024-12-31.", example=date.today().isoformat(), ) keywords: List[str] | None = Field( default=None, - description="Key risk-related terms to drive content retrieval (e.g., 'tariffs').", + description="Key risk-related terms to emphasize when generating the risk taxonomy (e.g. 'tariffs').", example=None, ) - llm_model_config: str | LLMConfig = Field( - default="openai::gpt-4o-mini", - description="LLM model configuration with model name, temperature or reasoning effort, and custom chat kwargs. Used for taxonomy creation and semantic analysis.", - example="openai::gpt-4o-mini", - ) - document_type: Literal[DocumentType.NEWS] = Field( - default=DocumentType.NEWS, - description="Type of documents to analyze (only transcript supported for now).", - example=DocumentType.NEWS, - ) - fiscal_year: int | list[int] | None = Field( - default=None, - description="Fiscal year to filter documents (format: YYYY).", - example=None, + llm_model: str = Field( + default=DEFAULT_LLM_MODEL, + description="OpenAI model used for taxonomy generation, chunk labeling, and company summaries.", + example=DEFAULT_LLM_MODEL, ) rerank_threshold: Optional[float] = Field( default=None, - description="Optional threshold (0-1) to rerank and filter search results by relevance.", + description="Optional relevance threshold (0-1); chunks scoring below it are discarded.", example=None, ) - frequency: FrequencyEnum = Field( - default=FrequencyEnum.monthly, - description="Search frequency interval. Supported values: D (daily), W (weekly), M (monthly), Y (yearly).", - example=FrequencyEnum.monthly, + chunk_percentage: float = Field( + default=DEFAULT_CHUNK_PERCENTAGE, + ge=0.0, + le=1.0, + description="Fraction (0-1, not a percentage — e.g. 0.05 = 5%) of the estimated available chunks to retrieve per taxonomy leaf. Higher values cost more and take longer.", + example=DEFAULT_CHUNK_PERCENTAGE, ) - document_limit: int = Field( - default=100, - description="Maximum number of documents to retrieve per query to Bigdata API.", - example=100, + max_leaf_labels: Optional[int] = Field( + default=DEFAULT_MAX_LEAF_LABELS, + description="Maximum number of leaf sub-scenarios in the generated risk taxonomy. Use 0 or null for no cap.", + example=DEFAULT_MAX_LEAF_LABELS, ) - batch_size: int = Field( - default=10, - description="Number of entities to include in each batch for parallel querying.", - example=10, + max_taxonomy_depth: Optional[int] = Field( + default=None, + ge=2, + description=( + "Maximum number of levels in the generated risk taxonomy, counting the root " + "risk node as level 1 (e.g. 3 = root + risk channel + sub-scenario, dropping " + "the separate risk-factor layer). Defaults to the model's natural structure " + "(root + risk channel + risk factor + sub-scenario, 4 levels)." + ), + example=None, ) @model_validator(mode="after") - def fiscal_year_only_when_transcrips_or_filings(self) -> Self: - if self.fiscal_year is not None and self.document_type not in { - DocumentType.FILINGS, - DocumentType.TRANSCRIPTS, - DocumentType.ALL, - }: - raise ValueError( - "fiscal_year can only be set when document_type is FILINGS or TRANSCRIPTS" - ) + def check_date_range(self) -> Self: + # ISO-8601 calendar dates (YYYY-MM-DD) compare lexicographically. + if self.start_date > self.end_date: + raise ValueError("start_date must be earlier than end_date") return self - @model_validator(mode="before") - @classmethod - def check_date_range(cls, values): - try: - start_date = values["start_date"] - end_date = values["end_date"] - if ( - start_date > end_date - ): # We can compare directly as they are both ISO format strings - raise ValueError("start_date must be earlier than end_date") - except Exception as e: - raise ValidationError.from_exception_data( - title=cls.__name__, - line_errors=[ - { - "type": "value_error", - "loc": ("start_date", "end_date"), - "ctx": {"error": f"Invalid date format or range: {e}"}, - "input": { - "start_date": values["start_date"], - "end_date": values["end_date"], - }, - } - ], - ) - return values - - @model_validator(mode="before") - @classmethod - def check_frequency_vs_date_range(cls, values): - start_date = values["start_date"] - end_date = values["end_date"] - freq = values.get("frequency") - delta_days = ( - datetime.fromisoformat(end_date) - datetime.fromisoformat(start_date) - ).days + 1 # Adjust for inclusive range - freq_min_days = {"D": 1, "W": 7, "M": 30, "3M": 90, "Y": 365} - if isinstance(freq, str): - freq = FrequencyEnum(freq) - if not isinstance(freq, FrequencyEnum): - raise ValidationError.from_exception_data( - title=cls.__name__, - line_errors=[ - { - "type": "value_error", - "loc": ("frequency",), - "ctx": {"error": f"Invalid frequency: {freq}"}, - "input": {"frequency": freq}, - } - ], - ) - if delta_days < freq_min_days[freq.value]: - raise ValidationError.from_exception_data( - title=cls.__name__, - line_errors=[ - { - "type": "value_error", - "loc": ("start_date", "end_date"), - "ctx": { - "error": f"The number of days in the range between start_date={start_date} and end_date={end_date} ({delta_days} days) should be higher than the minimum required for the selected frequency '{freq.value}' ({freq_min_days[freq.value]} days)." - }, - "input": { - "start_date": values["start_date"], - "end_date": values["end_date"], - }, - } - ], - ) - return values + +class RiskAnalysisRequest(RiskAnalysisRequestBase): + companies: list[str] | str = Field( + ..., + description="List of RavenPack entity IDs representing the companies to track in the generated report. Watchlists are not supported.", + example=EXAMPLE_COMPANY_LISTS["MAG_7"], + ) + + @model_validator(mode="after") + def reject_watchlist(self) -> Self: + if isinstance(self.companies, str): + raise ValueError(WATCHLIST_REJECTED_MESSAGE) + return self class RiskAnalyzerAcceptedResponse(BaseModel): diff --git a/bigdata_risk_analyzer/api/sql_models.py b/bigdata_risk_analyzer/api/sql_models.py index 42e3270..89bb237 100644 --- a/bigdata_risk_analyzer/api/sql_models.py +++ b/bigdata_risk_analyzer/api/sql_models.py @@ -1,11 +1,10 @@ from datetime import datetime from uuid import UUID -from bigdata_research_tools.llm.base import LLMConfig from sqlalchemy.ext.mutable import MutableList from sqlmodel import JSON, Column, Field, SQLModel -from bigdata_risk_analyzer.api.models import RiskAnalysisRequest +from bigdata_risk_analyzer.api.models import RiskAnalysisRequestBase from bigdata_risk_analyzer.models import RiskAnalysisResponse @@ -21,41 +20,32 @@ class SQLWorkflowStatus(SQLModel, table=True): class SQLRiskAnalyzerReport(SQLModel, table=True): id: UUID = Field(primary_key=True) created_at: datetime = Field(default_factory=datetime.now) - companies: str | list[str] = Field(sa_column=Column(JSON)) - llm_model: str | LLMConfig = Field(sa_column=Column(JSON)) + companies: list[str] = Field(sa_column=Column(JSON)) + llm_model: str theme: str focus: str | None = None start_date: datetime end_date: datetime - document_type: str - fiscal_year: list[int] | None = Field(default=None, sa_column=Column(JSON)) rerank_threshold: float | None = None - frequency: str - document_limit: int - batch_size: int + chunk_percentage: float screener_report: dict = Field(sa_column=Column(JSON)) @staticmethod def from_risk_analyzer_response( request_id: UUID, - request: RiskAnalysisRequest, + request: RiskAnalysisRequestBase, + company_ids: list[str], response: RiskAnalysisResponse, ) -> "SQLRiskAnalyzerReport": return SQLRiskAnalyzerReport( id=request_id, - companies=request.companies, - llm_model=request.llm_model_config, + companies=company_ids, + llm_model=request.llm_model, theme=request.main_theme, focus=request.focus, start_date=datetime.fromisoformat(request.start_date), end_date=datetime.fromisoformat(request.end_date), - document_type=request.document_type.value, - fiscal_year=request.fiscal_year - if isinstance(request.fiscal_year, list | None) - else [request.fiscal_year], rerank_threshold=request.rerank_threshold, - frequency=request.frequency.value, - document_limit=request.document_limit, - batch_size=request.batch_size, + chunk_percentage=request.chunk_percentage, screener_report=response.model_dump(), ) diff --git a/bigdata_risk_analyzer/api/storage.py b/bigdata_risk_analyzer/api/storage.py index d9c923f..4da2053 100644 --- a/bigdata_risk_analyzer/api/storage.py +++ b/bigdata_risk_analyzer/api/storage.py @@ -5,7 +5,7 @@ from sqlmodel import Session, select from bigdata_risk_analyzer.api.models import ( - RiskAnalysisRequest, + RiskAnalysisRequestBase, RiskAnalyzerStatusResponse, WorkflowStatus, ) @@ -82,7 +82,8 @@ def get_logs(self, request_id: UUID) -> list[str] | None: def mark_workflow_as_completed( self, request_id: UUID, - request: RiskAnalysisRequest, + request: RiskAnalysisRequestBase, + company_ids: list[str], report: RiskAnalysisResponse, ): with self.lock: @@ -93,7 +94,7 @@ def mark_workflow_as_completed( ) workflow_status.status = WorkflowStatus.COMPLETED sql_report = SQLRiskAnalyzerReport.from_risk_analyzer_response( - request_id, request, report + request_id, request, company_ids, report ) self.db_session.add(workflow_status) diff --git a/bigdata_risk_analyzer/bigdata_kg.py b/bigdata_risk_analyzer/bigdata_kg.py new file mode 100644 index 0000000..fcaf303 --- /dev/null +++ b/bigdata_risk_analyzer/bigdata_kg.py @@ -0,0 +1,95 @@ +"""REST client for the Bigdata.com Knowledge Graph, used to resolve a bare +list of RP entity IDs into company metadata (name, sector, industry, country, +ticker) when the caller does not upload a universe CSV. + +Replaces ``bigdata_client.Bigdata().knowledge_graph.get_entities(ids)``. +""" + +from __future__ import annotations + +import os +from typing import Any + +import requests + +DEFAULT_API_BASE_URL = os.getenv("BIGDATA_API_BASE_URL", "https://api.bigdata.com") +ENTITIES_BY_ID_ENDPOINT = "/v1/knowledge-graph/entities/id" +MAX_IDS_PER_REQUEST = 100 +COMPANY_CATEGORY = "companies" + +# ``listing_values`` (e.g. "XNAS:AAPL") is not ordered by listing importance — a +# depositary-receipt or secondary listing on another exchange (e.g. "XBKK:AAPL80") +# can sort before the primary listing. Prefer major US exchanges, in order, before +# falling back to whatever is first in the list. +_PREFERRED_LISTING_EXCHANGES: tuple[str, ...] = ("XNAS", "XNYS", "XASE") + + +def _parse_ticker(entity: dict[str, Any]) -> str | None: + """Extract a ticker (e.g. ``AAPL``) from ``listing_values`` entries like ``XNAS:AAPL``. + + Prefers a listing on a major US exchange (Nasdaq/NYSE/NYSE American) over + other exchanges' listings for the same security, since those are usually + depositary receipts or secondary listings with a different ticker suffix. + """ + listings = [ + (exchange.strip(), ticker.strip()) + for entry in entity.get("listing_values") or [] + if isinstance(entry, str) and ":" in entry + for exchange, ticker in [entry.split(":", 1)] + if ticker.strip() + ] + + for preferred_exchange in _PREFERRED_LISTING_EXCHANGES: + for exchange, ticker in listings: + if exchange == preferred_exchange: + return ticker + + return listings[0][1] if listings else None + + +def resolve_companies( + ids: list[str], + api_key: str, + api_base_url: str | None = None, + timeout: float = 30.0, +) -> dict[str, dict[str, Any]]: + """Resolve RP entity IDs to company metadata via the knowledge-graph REST API. + + Returns a dict keyed by RP entity ID (only entities whose ``category`` is + ``"companies"`` are included) with keys: ``name``, ``ticker``, ``sector``, + ``industry``, ``country``. + + Raises: + ValueError: if none of the given IDs resolve to a company entity. + """ + base_url = api_base_url or DEFAULT_API_BASE_URL + url = f"{base_url}{ENTITIES_BY_ID_ENDPOINT}" + headers = {"X-API-KEY": api_key, "Content-Type": "application/json"} + + resolved: dict[str, dict[str, Any]] = {} + unique_ids = list(dict.fromkeys(ids)) + for start in range(0, len(unique_ids), MAX_IDS_PER_REQUEST): + batch = unique_ids[start : start + MAX_IDS_PER_REQUEST] + response = requests.post( + url, json={"values": batch}, headers=headers, timeout=timeout + ) + response.raise_for_status() + results = response.json().get("results", {}) + for entity_id, entity in results.items(): + if entity.get("category") != COMPANY_CATEGORY: + continue + resolved[entity_id] = { + "name": entity.get("name"), + "ticker": _parse_ticker(entity), + "sector": entity.get("sector"), + "industry": entity.get("industry"), + "country": entity.get("country"), + } + + if not resolved: + raise ValueError( + "No entities found in the provided universe. " + "Check that the RP entity IDs are valid company IDs." + ) + + return resolved diff --git a/bigdata_risk_analyzer/openai_utils.py b/bigdata_risk_analyzer/openai_utils.py new file mode 100644 index 0000000..3abe3bf --- /dev/null +++ b/bigdata_risk_analyzer/openai_utils.py @@ -0,0 +1,110 @@ +"""Small rate-limited parallel OpenAI chat helper. + +Trimmed port of ``Thematic_Screener_CLI/src/openai_parallel.py`` — drops the +CLI's progress-bar/metrics plumbing since this runs inside a FastAPI +background task rather than an interactive terminal. +""" + +from __future__ import annotations + +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass, field +from threading import Lock + +from openai import OpenAI + +from bigdata_risk_analyzer.settings import settings + +DEFAULT_MAX_CONCURRENT_REQUESTS = 20 +DEFAULT_MAX_RETRIES = 5 +DEFAULT_RETRY_BACKOFF_SECONDS = 1.0 + + +def build_openai_client(api_key: str | None = None) -> OpenAI: + """Build a client from configured settings. + + ``OpenAI()`` only reads ``os.environ``, which misses keys that reach the + service through the .env file. + """ + return OpenAI(api_key=api_key or settings.OPENAI_API_KEY) + + +@dataclass(slots=True, frozen=True) +class ChatRequest: + request_id: str + messages: list[dict[str, str]] + model: str + # Left unset (None) by default: some models (e.g. reasoning-tier models) only + # support the API default and reject an explicit temperature/seed value. + temperature: float | None = None + seed: int | None = None + response_format: dict[str, str] = field( + default_factory=lambda: {"type": "json_object"} + ) + + +@dataclass(slots=True) +class ChatResponse: + request_id: str + succeeded: bool + content: str | None = None + error: str | None = None + + +def _call_with_retries( + client: OpenAI, request: ChatRequest, max_retries: int +) -> ChatResponse: + last_error: Exception | None = None + for attempt in range(max_retries): + try: + kwargs: dict = { + "model": request.model, + "messages": request.messages, + "response_format": request.response_format, + } + if request.temperature is not None: + kwargs["temperature"] = request.temperature + if request.seed is not None: + kwargs["seed"] = request.seed + completion = client.chat.completions.create(**kwargs) + content = completion.choices[0].message.content + return ChatResponse( + request_id=request.request_id, succeeded=True, content=content + ) + except Exception as exc: # noqa: BLE001 - retry any transient API error + last_error = exc + if attempt + 1 < max_retries: + time.sleep(DEFAULT_RETRY_BACKOFF_SECONDS * (2**attempt)) + return ChatResponse( + request_id=request.request_id, succeeded=False, error=str(last_error) + ) + + +def run_chat_requests_parallel( + requests: list[ChatRequest], + client: OpenAI | None = None, + max_concurrent_requests: int = DEFAULT_MAX_CONCURRENT_REQUESTS, + max_retries: int = DEFAULT_MAX_RETRIES, +) -> list[ChatResponse]: + """Run many chat completion requests concurrently with bounded parallelism.""" + if not requests: + return [] + + openai_client = client if client is not None else build_openai_client() + responses: list[ChatResponse] = [] + responses_lock = Lock() + + with ThreadPoolExecutor(max_workers=max_concurrent_requests) as executor: + futures = { + executor.submit( + _call_with_retries, openai_client, request, max_retries + ): request + for request in requests + } + for future in as_completed(futures): + response = future.result() + with responses_lock: + responses.append(response) + + return responses diff --git a/bigdata_risk_analyzer/pipeline.py b/bigdata_risk_analyzer/pipeline.py new file mode 100644 index 0000000..e5bc026 --- /dev/null +++ b/bigdata_risk_analyzer/pipeline.py @@ -0,0 +1,448 @@ +"""Sentence labeling, company scoring, and report assembly. + +Ported from ``Thematic_Screener_CLI/src/screener.py`` (risk-analyzer mode: +``label_sentences``, ``build_labeled_dataframe``, ``summarize_companies``, +``build_screener_dataframe``, and the ``build_risk_*``/``build_content_chunks`` +report builders), plus the top-level orchestrator that used to be +``bigdata_research_tools.workflows.risk_analyzer.RiskAnalyzer.screen_companies``. +""" + +from __future__ import annotations + +import json +import logging +from datetime import datetime +from typing import Any, Callable + +import pandas as pd +from openai import OpenAI +from pydantic import BaseModel + +from bigdata_risk_analyzer import retrieval, taxonomy +from bigdata_risk_analyzer.openai_utils import ChatRequest, run_chat_requests_parallel +from bigdata_risk_analyzer.taxonomy import Node +from bigdata_risk_analyzer.universe import ( + COUNTRY_COLUMN, + ID_COLUMN, + INDUSTRY_COLUMN, + NAME_COLUMN, + SECTOR_COLUMN, + TICKER_COLUMN, + UNKNOWN_VALUE, +) + +logger = logging.getLogger(__name__) + +UNCLEAR_LABEL = "unclear" +MAX_MOTIVATIONS_CHARS = 120_000 + + +class CompanySummary(BaseModel): + summary: str + + +# --------------------------------------------------------------------------- +# Sentence labeling +# --------------------------------------------------------------------------- + + +def label_sentences( + sentences: list[dict[str, Any]], + main_theme: str, + root: Node, + model: str, + client: OpenAI | None = None, +) -> dict[str, dict[str, str]]: + """Label each sentence with a taxonomy leaf (or ``unclear``) via the LLM.""" + if not sentences: + return {} + + options = taxonomy.get_leaf_label_summary_options(root) + system_prompt = taxonomy.SYSTEM_PROMPT_RISK_LABELING.format( + main_theme=main_theme, labels=str(options) + ) + + requests = [ + ChatRequest( + request_id=str(sentence["sentence_id"]), + messages=[ + {"role": "system", "content": system_prompt}, + { + "role": "user", + "content": str( + { + "sentence_id": sentence["sentence_id"], + "text": sentence["text"], + "company_name": sentence["company_name"], + } + ), + }, + ], + model=model, + ) + for sentence in sentences + ] + + responses = run_chat_requests_parallel(requests, client=client) + + parsed: dict[str, dict[str, str]] = {} + for response in responses: + if not response.succeeded or not response.content: + logger.warning( + "Labeling request %s failed: %s", response.request_id, response.error + ) + continue + try: + payload = json.loads(response.content) + except json.JSONDecodeError: + logger.warning( + "Could not parse labeling response for %s", response.request_id + ) + continue + + if {"motivation", "label"}.issubset(payload): + parsed[response.request_id] = payload + continue + # The model sometimes wraps the fields under the sentence_id key. + for sentence_id, fields in payload.items(): + if isinstance(fields, dict) and {"motivation", "label"}.issubset(fields): + parsed[str(sentence_id)] = fields + + return parsed + + +def build_labeled_dataframe( + sentences: list[dict[str, Any]], + parsed_responses: dict[str, dict[str, str]], +) -> pd.DataFrame: + """Merge sentences with their labels and drop unclear/unlabeled rows.""" + if not sentences: + return pd.DataFrame( + columns=["sentence_id", "text", "company_name", "motivation", "label"] + ) + + sentences_df = pd.DataFrame(sentences) + sentences_df["sentence_id"] = sentences_df["sentence_id"].astype(str) + + responses_df = pd.DataFrame.from_dict(parsed_responses, orient="index").reset_index( + names="sentence_id" + ) + + merged_df = sentences_df.merge(responses_df, on="sentence_id", how="left") + if "label" not in merged_df.columns: + merged_df["label"] = pd.NA + if "motivation" not in merged_df.columns: + merged_df["motivation"] = pd.NA + + merged_df = merged_df[ + (~merged_df["company_name"].isna()) & (merged_df["label"] != UNCLEAR_LABEL) + ] + return merged_df.dropna(subset=["label"]).reset_index(drop=True) + + +# --------------------------------------------------------------------------- +# Company-level motivation summaries +# --------------------------------------------------------------------------- + + +def _company_evidence_block(rows: pd.DataFrame) -> str: + lines = [] + for row in rows.fillna("").itertuples(index=False): + motivation = getattr(row, "motivation", "") + label = getattr(row, "label", "") + if not str(motivation).strip(): + continue + lines.append(f"- sub_scenario={label}; motivation={motivation}") + block = "\n".join(lines) + if len(block) > MAX_MOTIVATIONS_CHARS: + block = ( + block[:MAX_MOTIVATIONS_CHARS] + + "\n\n[Truncated: additional motivations omitted.]" + ) + return block + + +def summarize_companies( + merged_df: pd.DataFrame, + main_theme: str, + model: str, + client: OpenAI | None = None, +) -> pd.DataFrame: + """Produce one cohesive risk-motivation summary per company.""" + if merged_df.empty or "motivation" not in merged_df.columns: + return pd.DataFrame(columns=["company_name", "summary"]) + + company_rows = [ + { + "company_name": company_name, + "motivations_text": _company_evidence_block(group), + } + for company_name, group in merged_df.groupby("company_name", sort=True) + ] + company_motivations = pd.DataFrame(company_rows) + company_motivations = company_motivations[ + company_motivations["motivations_text"].str.len() > 0 + ] + if company_motivations.empty: + return pd.DataFrame(columns=["company_name", "summary"]) + + system_prompt = taxonomy.RISK_SUMMARY_TEMPLATE.format(main_theme=main_theme) + requests = [ + ChatRequest( + request_id=row.company_name, + messages=[ + {"role": "system", "content": system_prompt}, + { + "role": "user", + "content": ( + f"Company: {row.company_name}\n\n" + f"Motivations ({main_theme}):\n{row.motivations_text}" + ), + }, + ], + model=model, + ) + for row in company_motivations.itertuples(index=False) + ] + + responses = run_chat_requests_parallel(requests, client=client) + + summaries: list[dict[str, str]] = [] + for response in responses: + if not response.succeeded or not response.content: + logger.warning( + "Summary request %s failed: %s", response.request_id, response.error + ) + continue + try: + summary = CompanySummary.model_validate( + json.loads(response.content) + ).summary + except (json.JSONDecodeError, ValueError) as exc: + logger.warning("Summary parse error for %s: %s", response.request_id, exc) + continue + summaries.append({"company_name": response.request_id, "summary": summary}) + + if not summaries: + return pd.DataFrame(columns=["company_name", "summary"]) + return pd.DataFrame(summaries).sort_values("company_name").reset_index(drop=True) + + +def build_screener_dataframe( + merged_df: pd.DataFrame, company_summaries_df: pd.DataFrame +) -> pd.DataFrame: + """Left-join company summaries onto the labeled sentences.""" + if company_summaries_df.empty: + result = merged_df.copy() + result["summary"] = pd.NA + return result + return merged_df.merge(company_summaries_df, on="company_name", how="left") + + +# --------------------------------------------------------------------------- +# Report assembly (risk_scoring / risk_taxonomy / content) +# --------------------------------------------------------------------------- + + +def _clean_scalar(value: Any) -> Any: + try: + if value is None or (pd.api.types.is_scalar(value) and pd.isna(value)): + return None + except (TypeError, ValueError): + return value + return value + + +def _split_timestamp(timestamp: Any) -> tuple[str, str]: + """Split an ISO timestamp into ``(date, time_period)`` strings, e.g. ``Jun 2026``.""" + if not isinstance(timestamp, str) or not timestamp: + return "", "" + date_part = timestamp[:10] + try: + parsed = datetime.fromisoformat(timestamp) + except ValueError: + return date_part, "" + return date_part, parsed.strftime("%b %Y") + + +def _company_metadata_lookup(universe_df: pd.DataFrame) -> dict[str, dict[str, Any]]: + columns = set(universe_df.columns) + lookup: dict[str, dict[str, Any]] = {} + for _, row in universe_df.iterrows(): + name = row[NAME_COLUMN] + lookup[name] = { + "ticker": _clean_scalar(row[TICKER_COLUMN]) + if TICKER_COLUMN in columns + else None, + "sector": (_clean_scalar(row[SECTOR_COLUMN]) or UNKNOWN_VALUE) + if SECTOR_COLUMN in columns + else UNKNOWN_VALUE, + "industry": (_clean_scalar(row[INDUSTRY_COLUMN]) or UNKNOWN_VALUE) + if INDUSTRY_COLUMN in columns + else UNKNOWN_VALUE, + "country": _clean_scalar(row[COUNTRY_COLUMN]) + if COUNTRY_COLUMN in columns + else None, + } + return lookup + + +def build_risk_taxonomy(root: Node) -> dict[str, Any]: + return root.model_dump() + + +def _risk_factor_channel(label: str, ancestry: dict[str, list[str]]) -> tuple[str, str]: + """Derive ``(risk_factor, risk_channel)`` for a leaf sub-scenario. + + ``risk_factor`` is the leaf's immediate parent and ``risk_channel`` its + grandparent. When the taxonomy has fewer levels (e.g. ``max_taxonomy_depth`` + capped the tree at root + one grouping level + leaf), there is no + grandparent distinct from the root theme itself — falling back to + ``chain[-2]`` in that case would make ``risk_channel`` a constant equal to + the theme name for every row, so instead ``risk_channel`` falls back to + ``risk_factor``. + """ + chain = ancestry.get(label, []) + risk_factor = chain[-1] if chain else label + risk_channel = chain[-2] if len(chain) >= 3 else risk_factor + return risk_factor, risk_channel + + +def build_content_chunks( + screener_df: pd.DataFrame, root: Node, universe_df: pd.DataFrame +) -> list[dict[str, Any]]: + ancestry = taxonomy.build_leaf_ancestry(root) + metadata = _company_metadata_lookup(universe_df) + + chunks: list[dict[str, Any]] = [] + for _, row in screener_df.iterrows(): + label = _clean_scalar(row.get("label")) or "" + company = _clean_scalar(row.get("company_name")) or "" + date, time_period = _split_timestamp(row.get("timestamp")) + risk_factor, risk_channel = _risk_factor_channel(label, ancestry) + company_meta = metadata.get(company, {}) + + chunks.append( + { + "time_period": time_period, + "date": date, + "company": company, + "sector": company_meta.get("sector", UNKNOWN_VALUE), + "industry": company_meta.get("industry", UNKNOWN_VALUE), + "country": company_meta.get("country"), + "ticker": company_meta.get("ticker"), + "document_id": _clean_scalar(row.get("document_id")) or "", + "headline": _clean_scalar(row.get("headline")) or "", + "quote": _clean_scalar(row.get("text")) or "", + "motivation": _clean_scalar(row.get("motivation")) or "", + "sub_scenario": label, + "risk_channel": risk_channel, + "risk_factor": risk_factor, + "highlights": [], + } + ) + return chunks + + +def build_risk_scoring( + screener_df: pd.DataFrame, universe_df: pd.DataFrame +) -> dict[str, Any]: + metadata = _company_metadata_lookup(universe_df) + scoring: dict[str, Any] = {} + + if screener_df.empty or "label" not in screener_df.columns: + return scoring + + summaries: dict[str, Any] = {} + if "summary" in screener_df.columns: + for company, group in screener_df.groupby("company_name", sort=True): + summaries[str(company)] = _clean_scalar(group["summary"].iloc[0]) + + for company_key, group in screener_df.groupby("company_name", sort=True): + company = str(company_key) + counts = group["label"].value_counts() + risks = {str(label): int(count) for label, count in counts.items()} + company_meta = metadata.get(company, {}) + scoring[company] = { + "ticker": company_meta.get("ticker"), + "sector": company_meta.get("sector", UNKNOWN_VALUE), + "industry": company_meta.get("industry", UNKNOWN_VALUE), + "composite_score": int(sum(risks.values())), + "motivation": summaries.get(company), + "risks": risks, + } + return scoring + + +def build_risk_analysis_json( + screener_df: pd.DataFrame, root: Node, universe_df: pd.DataFrame +) -> dict[str, Any]: + return { + "risk_scoring": build_risk_scoring(screener_df, universe_df), + "risk_taxonomy": build_risk_taxonomy(root), + "content": build_content_chunks(screener_df, root, universe_df), + } + + +# --------------------------------------------------------------------------- +# Top-level orchestrator (replaces RiskAnalyzer.screen_companies) +# --------------------------------------------------------------------------- + + +def run_risk_analysis( + main_theme: str, + focus: str, + keywords: list[str] | None, + start_date: str, + end_date: str, + model: str, + rerank_threshold: float | None, + chunk_percentage: float, + max_leaf_labels: int | None, + universe_df: pd.DataFrame, + on_progress: Callable[[str], None], + max_taxonomy_depth: int | None = None, +) -> dict[str, Any]: + """Run the full risk-analysis pipeline and return the report dict.""" + on_progress("Generating risk taxonomy") + focus_for_taxonomy = taxonomy.analyst_focus_with_keywords(focus, keywords) + root = taxonomy.generate_taxonomy( + main_theme=main_theme, + analyst_focus=focus_for_taxonomy, + model=model, + max_leaf_labels=max_leaf_labels, + max_depth=max_taxonomy_depth, + ) + leaf_search_queries = taxonomy.get_leaf_search_queries(root) + on_progress(f"Risk taxonomy generated with {len(leaf_search_queries)} leaves") + + company_ids = universe_df[ID_COLUMN].tolist() + on_progress("Searching companies for risk exposure") + documents = retrieval.search_universe( + company_ids=company_ids, + leaf_search_queries=leaf_search_queries, + start_date=start_date, + end_date=end_date, + chunk_percentage=chunk_percentage, + ) + sentences = retrieval.extract_sentences(documents, universe_df, rerank_threshold) + on_progress( + f"Search completed. {len(sentences)} chunks found for {len(company_ids)} companies." + ) + + on_progress( + f"Labelling {len(sentences)} chunks with {len(leaf_search_queries)} risks" + ) + parsed_responses = label_sentences(sentences, main_theme, root, model=model) + merged_df = build_labeled_dataframe(sentences, parsed_responses) + on_progress( + f"Labeling completed. {len(merged_df)} chunks labeled with risk factors." + ) + + on_progress("Post-processing results") + company_summaries_df = summarize_companies(merged_df, main_theme, model=model) + screener_df = build_screener_dataframe(merged_df, company_summaries_df) + report = build_risk_analysis_json(screener_df, root, universe_df) + on_progress("Results post-processed") + + return report diff --git a/bigdata_risk_analyzer/retrieval.py b/bigdata_risk_analyzer/retrieval.py new file mode 100644 index 0000000..8372ec6 --- /dev/null +++ b/bigdata_risk_analyzer/retrieval.py @@ -0,0 +1,138 @@ +"""Document/chunk retrieval via bigdata-smart-batching. + +Ported from ``Thematic_Screener_CLI/src/screener.py`` (``build_plans`` + +``run_search`` collapsed into in-memory calls, plus ``extract_sentences``). +Replaces ``bigdata_client``-based ``search.new()``/``run_search()``. +""" + +from __future__ import annotations + +import logging +from typing import Any + +import pandas as pd +from bigdata_smart_batching import deduplicate_documents, execute_search, plan_search + +from bigdata_risk_analyzer.settings import settings +from bigdata_risk_analyzer.universe import ID_COLUMN, NAME_COLUMN + +logger = logging.getLogger(__name__) + +DEFAULT_SEARCH_CATEGORY: dict[str, Any] = { + "mode": "INCLUDE", + "values": ["news_premium", "transcripts", "filings"], +} +DEFAULT_REQUESTS_PER_MINUTE = 350 + + +def search_universe( + company_ids: list[str], + leaf_search_queries: list[str], + start_date: str, + end_date: str, + chunk_percentage: float, + category: dict[str, Any] | None = None, + requests_per_minute: int = DEFAULT_REQUESTS_PER_MINUTE, + api_key: str | None = None, +) -> list[dict[str, Any]]: + """Plan and execute one smart-batching search per taxonomy leaf, deduplicated.""" + search_category = category if category is not None else DEFAULT_SEARCH_CATEGORY + # Smart batching otherwise falls back to os.environ, which misses keys that + # reach the service through the .env file. + search_api_key = api_key or settings.BIGDATA_API_KEY + + all_documents: list[dict[str, Any]] = [] + for search_query in leaf_search_queries: + plan = plan_search( + universe=company_ids, + start_date=start_date, + end_date=end_date, + volume_query_mode="iterative", + text=search_query, + category=search_category, + api_key=search_api_key, + ) + documents = execute_search( + search_plan=plan, + chunk_percentage=chunk_percentage, + requests_per_minute=requests_per_minute, + basket_filtered_entities=True, + api_key=search_api_key, + ) + all_documents.extend(documents) + + return deduplicate_documents(all_documents) + + +def _company_names_in_universe( + entity_ids: list[Any], id_to_name: dict[str, str] +) -> list[str]: + """Return universe company names for ``entity_ids``, preserving first-seen order. + + Smart-batching stores ``entity_ids`` as ``list(set(...))``, so the first + element is not a stable primary company. Attribute the chunk to every + universe company mentioned, not only ``entity_ids[0]``. + """ + names: list[str] = [] + seen: set[str] = set() + for entity_id in entity_ids: + company_name = id_to_name.get(entity_id) + if company_name is None or company_name in seen: + continue + seen.add(company_name) + names.append(company_name) + return names + + +def extract_sentences( + documents: list[dict[str, Any]], + universe_df: pd.DataFrame, + rerank_threshold: float | None = None, +) -> list[dict[str, Any]]: + """Flatten retrieved documents into per-chunk sentence records. + + Each matching universe company on a chunk gets its own sentence (looked up + from the universe DataFrame). Chunks whose ``relevance`` is below + ``rerank_threshold`` are dropped. + """ + id_to_name = dict(zip(universe_df[ID_COLUMN], universe_df[NAME_COLUMN])) + + sentences: list[dict[str, Any]] = [] + idx = 0 + for document in documents: + document_id = document.get("id") + headline = document.get("headline") + timestamp = document.get("timestamp") + for chunk in document.get("chunks", []): + relevance = chunk.get("relevance") + if ( + rerank_threshold is not None + and relevance is not None + and relevance < rerank_threshold + ): + continue + + company_names = _company_names_in_universe( + chunk.get("entity_ids") or [], id_to_name + ) + if not company_names: + continue + + for company_name in company_names: + sentences.append( + { + "sentence_id": idx, + "text": chunk.get("text"), + "document_id": document_id, + "headline": headline, + "timestamp": timestamp, + "relevance": relevance, + "company_name": company_name, + } + ) + idx += 1 + + logger.info( + "Extracted %d sentences from %d documents", len(sentences), len(documents) + ) + return sentences diff --git a/bigdata_risk_analyzer/service.py b/bigdata_risk_analyzer/service.py index 3fbc9c5..4008468 100644 --- a/bigdata_risk_analyzer/service.py +++ b/bigdata_risk_analyzer/service.py @@ -1,200 +1,66 @@ -import math from datetime import datetime -from importlib.metadata import version from uuid import UUID import pandas as pd -from bigdata_client import Bigdata -from bigdata_client.models.entities import Company -from bigdata_research_tools.mindmap.mindmap import MindMap -from bigdata_research_tools.utils.observer import OberserverNotification, Observer -from bigdata_research_tools.workflows.risk_analyzer import RiskAnalyzer -from bigdata_risk_analyzer.api.models import RiskAnalysisRequest, WorkflowStatus +from bigdata_risk_analyzer import pipeline +from bigdata_risk_analyzer.api.models import RiskAnalysisRequestBase, WorkflowStatus from bigdata_risk_analyzer.api.storage import StorageManager -from bigdata_risk_analyzer.models import ( - CompanyScoring, - LabeledChunk, - LabeledContent, - RiskAnalysisResponse, - RiskScore, - RiskScoring, - RiskTaxonomy, -) -from bigdata_risk_analyzer.traces import TraceEventName, send_trace - - -class WorkflowObserver(Observer): - def __init__(self, request_id: UUID, storage_manager: StorageManager): - self.request_id = request_id - self.storage_manager = storage_manager - - def update(self, message: OberserverNotification): - self.storage_manager.log_message( - request_id=self.request_id, - message=message.message, - ) - - -def prepare_companies( - companies: list[str] | str, - bigdata: Bigdata, -) -> list[Company]: - """Prepare the list of companies for analysis. Ensure at least one of the forms of providing - the companies is present and ensures all elements are companies.""" - if isinstance(companies, list): - entities = bigdata.knowledge_graph.get_entities(companies) - elif isinstance(companies, str): - entities = bigdata.knowledge_graph.get_entities( - bigdata.watchlists.get(companies).items - ) - else: - raise ValueError( - "Companies must be either a list of RP entity IDs or a string representing a watchlist ID." - ) - - # Ensure there is entities, there is no duplicates and all entities are companies - if len(entities) == 0: - raise ValueError("No entities found in the provided universe or watchlist.") - - companies_objects = [ - Company(**e.model_dump()) # ty: ignore[missing-argument] - for e in entities - if e is not None and e.entity_type == "COMP" - ] - - dedupped_companies = {c.id: c for c in companies_objects} - - return list(dedupped_companies.values()) +from bigdata_risk_analyzer.models import RiskAnalysisResponse +from bigdata_risk_analyzer.taxonomy import Node +from bigdata_risk_analyzer.universe import ID_COLUMN def build_response( - df_company: pd.DataFrame, - df_motivation: pd.DataFrame, - df_labeled: pd.DataFrame, - risk_tree: MindMap, + screener_df: pd.DataFrame, + root: Node, + universe_df: pd.DataFrame, ) -> RiskAnalysisResponse: - """ - Build the response for the output of the risk analysis workflow. - """ - risk_scoring = {} - for record in df_company.to_dict(orient="records"): - company = record.pop("Company") - ticker = record.pop("Ticker") - sector = record.pop("Sector") - industry = record.pop("Industry") - motivation = df_motivation.loc[df_motivation["Company"] == company][ - "Motivation" - ].values[0] - composite_score = record.pop("Composite Score") - risk_scoring[company] = CompanyScoring( - ticker=ticker, - sector=sector, - industry=industry, - motivation=motivation, - composite_score=composite_score, - risks=RiskScore( - root={k: v for k, v in record.items() if not math.isnan(v)} - ), - ) - - # Return results - return RiskAnalysisResponse( - risk_taxonomy=RiskTaxonomy(**risk_tree._to_dict()), # ty: ignore[missing-argument] - risk_scoring=RiskScoring(root=risk_scoring), - content=LabeledContent( - root=[ - LabeledChunk( - time_period=record["Time Period"], - date=record["Date"], - company=record["Company"], - sector=record["Sector"], - industry=record["Industry"], - country=record["Country"], - ticker=record["Ticker"], - document_id=record["Document ID"], - headline=record["Headline"], - quote=record["Quote"], - motivation=record["Motivation"], - sub_scenario=record["Sub-Scenario"], - risk_channel=record["Risk Channel"], - risk_factor=record["Risk Factor"], - highlights=record["Highlights"], - ) - for record in df_labeled.to_dict(orient="records") - ] - ), - ) + """Assemble the API response from the labeled sentences, taxonomy, and universe.""" + report = pipeline.build_risk_analysis_json(screener_df, root, universe_df) + return RiskAnalysisResponse(**report) def process_request( - request: RiskAnalysisRequest, - bigdata: Bigdata | None, + request: RiskAnalysisRequestBase, + universe_df: pd.DataFrame, request_id: UUID, storage_manager: StorageManager, ): try: storage_manager.update_status(request_id, WorkflowStatus.IN_PROGRESS) - if not bigdata: - raise ValueError("Bigdata client is not initialized.") - workflow_execution_start = datetime.now() + def on_progress(message: str) -> None: + storage_manager.log_message(request_id=request_id, message=message) - resolved_companies = prepare_companies(request.companies, bigdata) + workflow_execution_start = datetime.now() - analyzer = RiskAnalyzer( - llm_model_config=request.llm_model_config, + report = pipeline.run_risk_analysis( main_theme=request.main_theme, - companies=resolved_companies, + focus=request.focus, + keywords=request.keywords, start_date=request.start_date, end_date=request.end_date, - keywords=request.keywords, - document_type=request.document_type, - fiscal_year=request.fiscal_year, - control_entities=request.control_entities, + model=request.llm_model, rerank_threshold=request.rerank_threshold, - focus=request.focus, - ) - - analyzer.register_observer( - WorkflowObserver(request_id=request_id, storage_manager=storage_manager) - ) - - results = analyzer.screen_companies( - document_limit=request.document_limit, - batch_size=request.batch_size, - frequency=request.frequency, + chunk_percentage=request.chunk_percentage, + max_leaf_labels=request.max_leaf_labels, + max_taxonomy_depth=request.max_taxonomy_depth, + universe_df=universe_df, + on_progress=on_progress, ) - df_labeled = results["df_labeled"] - df_company = results["df_company"] - df_motivation = results["df_motivation"] - risk_tree = results["risk_tree"] - workflow_execution_end = datetime.now() - - # Send log - send_trace( - bigdata, - event_name=TraceEventName.RISK_ANALYZER_REPORT_GENERATED, - trace={ - "bigdataClientVersion": version("bigdata-client"), - "workflowStartDate": workflow_execution_start.isoformat( - timespec="seconds" - ), - "workflowEndDate": workflow_execution_end.isoformat(timespec="seconds"), - "watchlistLength": len(resolved_companies), - }, + on_progress( + "Workflow completed in " + f"{(workflow_execution_end - workflow_execution_start).total_seconds():.1f}s" ) - response = build_response( - df_company=df_company, - df_motivation=df_motivation, - df_labeled=df_labeled, - risk_tree=risk_tree, - ) + response = RiskAnalysisResponse(**report) - storage_manager.mark_workflow_as_completed(request_id, request, response) + storage_manager.mark_workflow_as_completed( + request_id, request, universe_df[ID_COLUMN].tolist(), response + ) return response except Exception as e: diff --git a/bigdata_risk_analyzer/settings.py b/bigdata_risk_analyzer/settings.py index 0724a6b..3a770e6 100644 --- a/bigdata_risk_analyzer/settings.py +++ b/bigdata_risk_analyzer/settings.py @@ -2,7 +2,7 @@ from typing import Literal from pydantic import field_validator, model_validator -from pydantic_settings import BaseSettings +from pydantic_settings import BaseSettings, SettingsConfigDict from bigdata_risk_analyzer import logger @@ -12,6 +12,13 @@ class Settings(BaseSettings): + # Real environment variables take precedence over the .env file. + model_config = SettingsConfigDict( + env_file=(PROJECT_DIRECTORY / ".env", ".env"), + env_file_encoding="utf-8", + extra="ignore", + ) + # Demo mode - disables "Run Analysis" functionality, only allows pre-computed demos # Only affects the frontend, to protect the backend, set ACCESS_TOKEN DEMO_MODE: bool = False diff --git a/bigdata_risk_analyzer/static/scripts/excel_export.js b/bigdata_risk_analyzer/static/scripts/excel_export.js new file mode 100644 index 0000000..e250887 --- /dev/null +++ b/bigdata_risk_analyzer/static/scripts/excel_export.js @@ -0,0 +1,328 @@ +// Excel export (Heatmap / Taxonomy / Evidence) — structure and color scheme +// modeled on Internal/Sample_Risk_Analyzer.xlsx. +// +// Works on whatever report is currently displayed (window.lastReport), same as +// the "Export JSON" button — so it covers live analyses, Quick Start demos, and +// uploaded JSON reports alike. Those arrive in two different shapes: +// - live analyses: raw API response (risk_scoring / risk_taxonomy / content) +// - demos/uploads: adapted "theme" shape (theme_scoring / theme_taxonomy / content) +// normalizeReport() below reconciles both into one shape to build the sheets from. + +const HEATMAP_SLATE_ARGB = 'FF2C3E50'; // "no evidence" cell color +const TITLE_BG_ARGB = 'FF0D1B2A'; +const HEADER_BG_ARGB = 'FF16213E'; +const COMPANY_BG_ARGB = 'FF1A1A2E'; +const TICKER_BG_ARGB = 'FFC0392B'; +const INDUSTRY_FONT_ARGB = 'FFD5D8DC'; +const WHITE_ARGB = 'FFFFFFFF'; +const FONT_SIZE = 11; +const ROW_HEIGHT_SCALE = 1.3; + +function normalizeReport(raw) { + const taxonomy = raw.risk_taxonomy || raw.theme_taxonomy || null; + const scoringSource = raw.risk_scoring || raw.theme_scoring || {}; + const scoring = {}; + for (const [company, entry] of Object.entries(scoringSource)) { + scoring[company] = { + ticker: entry.ticker || '', + sector: entry.sector || 'Unknown', + industry: entry.industry || 'Unknown', + composite_score: entry.composite_score || 0, + motivation: entry.motivation || '', + risks: entry.risks || entry.themes || {}, + }; + } + + let content = raw.content; + if (content && typeof content === 'object' && !Array.isArray(content) && Array.isArray(content.root)) { + content = content.root; + } + if (!Array.isArray(content)) content = []; + + return { taxonomy, scoring, content }; +} + +function rgbToArgb([r, g, b]) { + const hex = (n) => Math.max(0, Math.min(255, Math.round(n))).toString(16).padStart(2, '0').toUpperCase(); + return `FF${hex(r)}${hex(g)}${hex(b)}`; +} + +function lerpRgb(from, to, t) { + return from.map((v, i) => v + (to[i] - v) * t); +} + +// Red intensity scale relative to a column's own max value, matching the reference workbook: +// R ramps 80 -> 255, G=B ramps 20 -> 0, as value/colMax goes 0 -> 1. Empty/zero cells are slate. +function heatmapColor(value, colMax) { + if (!value || !colMax) return HEATMAP_SLATE_ARGB; + const ratio = Math.min(value / colMax, 1); + const r = Math.floor(80 + 175 * ratio); + const g = Math.floor(20 * (1 - ratio)); + return rgbToArgb([r, g, g]); +} + +function solidFill(argb) { + return { type: 'pattern', pattern: 'solid', fgColor: { argb } }; +} + +function buildHeatmapSheet(workbook, mainTheme, scoring) { + const sheet = workbook.addWorksheet('Heatmap', { + views: [{ state: 'frozen', xSplit: 4, ySplit: 2 }], + }); + + const riskLabels = Array.from( + new Set(Object.values(scoring).flatMap((c) => Object.keys(c.risks || {}))) + ).sort((a, b) => a.localeCompare(b)); + + const headers = ['Company', 'Ticker', 'Industry', 'Composite Score', ...riskLabels]; + sheet.columns = [ + { width: 30 }, + { width: 8 }, + { width: 22 }, + { width: 14 }, + ...riskLabels.map(() => ({ width: 6 })), + ]; + + sheet.mergeCells(1, 1, 1, headers.length); + const titleCell = sheet.getCell(1, 1); + titleCell.value = mainTheme; + titleCell.font = { bold: true, color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + titleCell.fill = solidFill(TITLE_BG_ARGB); + titleCell.alignment = { horizontal: 'left', vertical: 'center' }; + sheet.getRow(1).height = 22 * ROW_HEIGHT_SCALE; + + const headerRow = sheet.getRow(2); + headers.forEach((label, i) => { + const cell = headerRow.getCell(i + 1); + cell.value = label; + cell.font = { bold: true, color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + cell.fill = solidFill(HEADER_BG_ARGB); + cell.alignment = + i < 4 + ? { horizontal: 'center', vertical: 'bottom' } + : { horizontal: 'center', vertical: 'bottom', textRotation: 75 }; + }); + headerRow.height = 120 * ROW_HEIGHT_SCALE; + + const colMax = {}; + riskLabels.forEach((label) => { + colMax[label] = Math.max(0, ...Object.values(scoring).map((c) => c.risks[label] || 0)); + }); + const compositeMax = Math.max(0, ...Object.values(scoring).map((c) => c.composite_score || 0)); + + const rows = Object.entries(scoring).sort( + (a, b) => (b[1].composite_score || 0) - (a[1].composite_score || 0) + ); + + rows.forEach(([company, data], idx) => { + const row = sheet.getRow(idx + 3); + row.height = 15 * ROW_HEIGHT_SCALE; + + const companyCell = row.getCell(1); + companyCell.value = company; + companyCell.font = { color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + companyCell.fill = solidFill(COMPANY_BG_ARGB); + companyCell.alignment = { horizontal: 'left', vertical: 'center' }; + + const tickerCell = row.getCell(2); + tickerCell.value = data.ticker; + tickerCell.font = { bold: true, color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + tickerCell.fill = solidFill(TICKER_BG_ARGB); + tickerCell.alignment = { horizontal: 'center', vertical: 'center' }; + + const industryCell = row.getCell(3); + industryCell.value = data.industry; + industryCell.font = { color: { argb: INDUSTRY_FONT_ARGB }, size: FONT_SIZE }; + industryCell.fill = solidFill(COMPANY_BG_ARGB); + industryCell.alignment = { horizontal: 'left', vertical: 'center' }; + + const compositeCell = row.getCell(4); + compositeCell.value = data.composite_score; + compositeCell.font = { bold: true, color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + compositeCell.fill = solidFill(heatmapColor(data.composite_score, compositeMax)); + compositeCell.alignment = { horizontal: 'center', vertical: 'center' }; + + riskLabels.forEach((label, i) => { + const value = data.risks[label] || 0; + const cell = row.getCell(5 + i); + cell.value = value || null; + cell.font = { color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + cell.fill = solidFill(heatmapColor(value, colMax[label])); + cell.alignment = { horizontal: 'center', vertical: 'center' }; + }); + }); +} + +function taxonomyDepth(node, depth) { + if (!node.children || node.children.length === 0) return depth; + return Math.max(...node.children.map((child) => taxonomyDepth(child, depth + 1))); +} + +const TAXONOMY_HEADER_START = [26, 82, 118]; // #1A5276 +const TAXONOMY_HEADER_END = [46, 134, 193]; // #2E86C1 +const TAXONOMY_ROW_BASE = [13, 27, 42]; // #0D1B2A (depth 1) +const TAXONOMY_ROW_STEP = [8, 5, 13]; // lightening per depth level +const TAXONOMY_MAX_SHADE_DEPTH = 6; // cap how far the lightening ramp goes + +function buildTaxonomySheet(workbook, taxonomy) { + const sheet = workbook.addWorksheet('Taxonomy'); + if (!taxonomy || !taxonomy.label) return; + + const maxDepth = taxonomyDepth(taxonomy, 1); + const headers = Array.from({ length: maxDepth }, (_, i) => `Node ${i + 1}`).concat('Text'); + + sheet.columns = [...Array.from({ length: maxDepth }, () => ({ width: 28 })), { width: 60 }]; + + const headerRow = sheet.getRow(1); + headers.forEach((label, i) => { + const t = headers.length > 1 ? i / (headers.length - 1) : 0; + const cell = headerRow.getCell(i + 1); + cell.value = label; + cell.font = { bold: true, color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + cell.fill = solidFill(rgbToArgb(lerpRgb(TAXONOMY_HEADER_START, TAXONOMY_HEADER_END, t))); + cell.alignment = { horizontal: 'left', vertical: 'bottom', wrapText: true }; + }); + headerRow.height = 22 * ROW_HEIGHT_SCALE; + + let excelRowIndex = 2; + + function walk(node, ancestorLabels, depth) { + const row = sheet.getRow(excelRowIndex); + for (let i = 0; i < depth; i++) { + row.getCell(i + 1).value = ancestorLabels[i]; + } + row.getCell(maxDepth + 1).value = node.summary || ''; + + const shadeDepth = Math.min(depth - 1, TAXONOMY_MAX_SHADE_DEPTH); + const shade = rgbToArgb(TAXONOMY_ROW_BASE.map((v, i) => v + TAXONOMY_ROW_STEP[i] * shadeDepth)); + for (let c = 1; c <= maxDepth + 1; c++) { + const cell = row.getCell(c); + cell.font = { bold: c <= depth, color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + cell.fill = solidFill(shade); + cell.alignment = + c === maxDepth + 1 + ? { horizontal: 'left', vertical: 'center', wrapText: true } + : { horizontal: 'left', vertical: 'center', indent: depth - 1 }; + } + row.height = 16 * ROW_HEIGHT_SCALE; + excelRowIndex++; + + for (const child of node.children || []) { + walk(child, [...ancestorLabels, child.label], depth + 1); + } + } + + walk(taxonomy, [taxonomy.label], 1); +} + +const EVIDENCE_HEADERS = ['Time Period', 'Date', 'Company', 'Headline', 'Quote', 'Motivation', 'Risk Factor']; +const EVIDENCE_WRAP_COLUMNS = new Set([3, 4, 5]); // Headline, Quote, Motivation (0-indexed) + +function buildEvidenceSheet(workbook, content) { + const sheet = workbook.addWorksheet('Evidence', { views: [{ state: 'frozen', ySplit: 1 }] }); + sheet.columns = [ + { width: 12 }, + { width: 12 }, + { width: 22 }, + { width: 40 }, + { width: 60 }, + { width: 40 }, + { width: 22 }, + ]; + + const headerRow = sheet.getRow(1); + EVIDENCE_HEADERS.forEach((label, i) => { + const cell = headerRow.getCell(i + 1); + cell.value = label; + cell.font = { bold: true, color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + cell.fill = solidFill(HEADER_BG_ARGB); + cell.alignment = { horizontal: 'center', vertical: 'bottom', wrapText: true }; + }); + headerRow.height = 22; + + const sorted = [...content].sort((a, b) => { + const byCompany = (a.company || '').localeCompare(b.company || ''); + if (byCompany !== 0) return byCompany; + return (a.date || '').localeCompare(b.date || ''); + }); + + sorted.forEach((chunk, idx) => { + const row = sheet.getRow(idx + 2); + const values = [ + chunk.time_period, + chunk.date, + chunk.company, + chunk.headline, + chunk.quote, + chunk.motivation, + chunk.risk_factor || chunk.sub_scenario || '', + ]; + const shade = idx % 2 === 0 ? HEADER_BG_ARGB : COMPANY_BG_ARGB; + values.forEach((value, i) => { + const cell = row.getCell(i + 1); + cell.value = value || ''; + cell.font = { color: { argb: WHITE_ARGB }, size: FONT_SIZE }; + cell.fill = solidFill(shade); + cell.alignment = { horizontal: 'left', vertical: 'top', wrapText: EVIDENCE_WRAP_COLUMNS.has(i) }; + }); + }); +} + +function slugify(text) { + return (text || 'risk_analysis') + .toLowerCase() + .replace(/[^a-z0-9]+/g, '_') + .replace(/^_+|_+$/g, '') + .slice(0, 60) || 'risk_analysis'; +} + +async function exportToExcel() { + if (!window.lastReport) { + alert('No report loaded to export.'); + return; + } + if (typeof ExcelJS === 'undefined') { + alert('Excel export could not load its library (exceljs from a CDN). Check your network connection and try again.'); + return; + } + + const btn = document.getElementById('exportExcelBtn'); + const originalText = btn ? btn.innerHTML : null; + if (btn) { + btn.disabled = true; + btn.textContent = 'Exporting...'; + } + + try { + const { taxonomy, scoring, content } = normalizeReport(window.lastReport); + const mainTheme = (taxonomy && taxonomy.label) || document.getElementById('currentRiskScenario')?.textContent || 'Risk Analysis'; + + const workbook = new ExcelJS.Workbook(); + buildHeatmapSheet(workbook, mainTheme, scoring); + buildTaxonomySheet(workbook, taxonomy); + buildEvidenceSheet(workbook, content); + + const buffer = await workbook.xlsx.writeBuffer(); + const blob = new Blob([buffer], { + type: 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', + }); + const url = URL.createObjectURL(blob); + const link = document.createElement('a'); + link.href = url; + link.download = `${slugify(mainTheme)}_risk_analysis.xlsx`; + document.body.appendChild(link); + link.click(); + document.body.removeChild(link); + URL.revokeObjectURL(url); + } catch (err) { + console.error('Excel export failed:', err); + alert(`Excel export failed: ${err.message}`); + } finally { + if (btn) { + btn.disabled = false; + if (originalText !== null) btn.innerHTML = originalText; + } + } +} + +window.exportToExcel = exportToExcel; diff --git a/bigdata_risk_analyzer/static/scripts/form.js b/bigdata_risk_analyzer/static/scripts/form.js index 5906370..d6f2469 100644 --- a/bigdata_risk_analyzer/static/scripts/form.js +++ b/bigdata_risk_analyzer/static/scripts/form.js @@ -3,33 +3,33 @@ document.getElementById('riskForm').onsubmit = async function (e) { const spinner = document.getElementById('spinner'); const showJsonBtn = document.getElementById('showJsonBtn'); const submitBtn = document.querySelector('button[type="submit"]'); - + // Close config panel if (window.closeConfigPanel) { closeConfigPanel(); } - + if (showJsonBtn) showJsonBtn.style.display = 'none'; window.lastReport = null; if (window.updateDownloadButtonState) { window.updateDownloadButtonState(); } - + // Hide pre-computed demo indicator const precomputedDemo = document.getElementById('precomputedDemo'); if (precomputedDemo) { precomputedDemo.classList.add('hidden'); } - + // Reset frontend: hide results, show empty state, clear dashboard const emptyState = document.getElementById('emptyState'); const dashboardSection = document.getElementById('dashboardSection'); const dashboardCards = document.getElementById('dashboardCards'); - + if (emptyState) emptyState.style.display = 'none'; if (dashboardSection) dashboardSection.classList.add('hidden'); if (dashboardCards) dashboardCards.innerHTML = ''; - + // Open process logs const logViewerContainer = document.getElementById('logViewerContainer'); const logsIcon = document.getElementById('logsIcon'); @@ -37,11 +37,11 @@ document.getElementById('riskForm').onsubmit = async function (e) { logViewerContainer.classList.remove('hidden'); if (logsIcon) logsIcon.style.transform = 'rotate(180deg)'; } - + // Clear logs const logViewer = document.getElementById('logViewer'); if (logViewer) logViewer.innerHTML = '
Starting analysis...
'; - + // Reset all tabs if (window.tabController) { window.tabController.reset(); @@ -51,9 +51,8 @@ document.getElementById('riskForm').onsubmit = async function (e) { // Validate date range first const startDateInput = document.getElementById('start_date').value; const endDateInput = document.getElementById('end_date').value; - const frequencyInput = document.getElementById('frequency').value; - const dateValidation = validateDateRange(startDateInput, endDateInput, frequencyInput); + const dateValidation = validateDateRange(startDateInput, endDateInput); if (!dateValidation.isValid) { alert(`❌ ${dateValidation.message}`); return; @@ -66,80 +65,35 @@ document.getElementById('riskForm').onsubmit = async function (e) { // Gather form data const main_theme = document.getElementById('main_theme').value.trim(); const focus = document.getElementById('focus').value.trim(); - const control_entities = document.getElementById('control_entities')?.value?.trim() || ''; - const keywords = document.getElementById('keywords')?.value?.trim() || ''; - // Get companies and check if its available in the watchlists - let companies = document.getElementById('companies_text').value.trim(); - const foundWatchlist = watchlists.find(w => w.name === companies); - if (foundWatchlist) { - companies = foundWatchlist.id; - } - else if (!companies) { - alert(`❌ Error: Company Universe is required.`); + const companiesText = document.getElementById('companies_text').value.trim(); + const companiesFileInput = document.getElementById('companies_file'); + const companiesFile = companiesFileInput && companiesFileInput.files[0]; + + if (!companiesFile && !companiesText) { + alert('❌ Error: provide a comma-separated list of RP entity IDs, or upload a CSV.'); submitBtn.disabled = false; submitBtn.textContent = 'Run Analysis'; return; } + const start_date = document.getElementById('start_date').value; const end_date = document.getElementById('end_date').value; - - let fiscal_year = document.getElementById('fiscal_year').value.trim(); - if (fiscal_year.includes(',')) { - fiscal_year = fiscal_year.split(',').map(s => s.trim()).filter(Boolean); - // Check if all entries are numbers - if (!fiscal_year.every(yr => Number(yr))) { - throw new Error('Fiscal Year must be a number or a comma-separated list of numbers.'); - } - } else if (fiscal_year) { - fiscal_year = fiscal_year; - } else { - fiscal_year = undefined; - } - const llm_model = document.getElementById('llm_model').value.trim(); - const document_type = document.getElementById('document_type').value; const rerank_threshold = document.getElementById('rerank_threshold').value; - const frequency = document.getElementById('frequency').value; - const document_limit = document.getElementById('document_limit').value; - const batch_size = document.getElementById('batch_size').value; - - // Build request payload - let payload = { - main_theme, - focus, - llm_model, - document_type, - frequency, - document_limit: document_limit ? parseInt(document_limit) : undefined, - batch_size: batch_size ? parseInt(batch_size) : undefined, - fiscal_year: fiscal_year ? fiscal_year : undefined - }; - - // A list of companies - if (companies.includes(',')) { - payload.companies = companies.split(',').map(s => s.trim()).filter(Boolean); - // A single RP Entity ID - } else if (companies.length === 6) { - payload.companies = [companies]; - // A watchlist ID - } else if (companies.length > 6) { - payload.companies = companies; - } - if (control_entities) { - var clean_control_entities = control_entities.replaceAll(`'`, `"`); - try { - payload.control_entities = JSON.parse(clean_control_entities); - } catch (e) { - alert('❌ Invalid JSON for control entities.'); - submitBtn.disabled = false; - submitBtn.textContent = 'Run Analysis'; - return; - } - } + const chunk_percentage = document.getElementById('chunk_percentage').value; + const max_leaf_labels = document.getElementById('max_leaf_labels').value; + const max_taxonomy_depth = document.getElementById('max_taxonomy_depth').value; + // Build the shared request payload (everything except the company universe) + let payload = { main_theme, focus }; if (start_date) payload.start_date = start_date; if (end_date) payload.end_date = end_date; + if (llm_model) payload.llm_model = llm_model; if (rerank_threshold) payload.rerank_threshold = parseFloat(rerank_threshold); + // The field is entered as a percentage (0-100) in the UI; the API expects a 0-1 fraction. + if (chunk_percentage) payload.chunk_percentage = parseFloat(chunk_percentage) / 100; + if (max_leaf_labels) payload.max_leaf_labels = parseInt(max_leaf_labels); + if (max_taxonomy_depth) payload.max_taxonomy_depth = parseInt(max_taxonomy_depth); // Add token from URL param if present const params = new URLSearchParams(); @@ -148,11 +102,23 @@ document.getElementById('riskForm').onsubmit = async function (e) { params.append("token", token); } - const response = await fetch(`/risk-analysis?${params}`, { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify(payload) - }); + let response; + if (companiesFile) { + const formData = new FormData(); + formData.append('file', companiesFile); + formData.append('request', JSON.stringify(payload)); + response = await fetch(`/risk-analysis/upload?${params}`, { + method: 'POST', + body: formData + }); + } else { + payload.companies = companiesText.split(',').map(s => s.trim()).filter(Boolean); + response = await fetch(`/risk-analysis?${params}`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload) + }); + } if (!response.ok) { const errorData = await response.json(); // Iterate over errorData.detail if it's an array show the loc and msg fields @@ -205,25 +171,28 @@ document.getElementById('riskForm').onsubmit = async function (e) { if (statusData.status === 'completed') { // Update config badge BEFORE rendering so dashboard has access to it if (window.updateConfigBadge) { - // Use exactly what the user typed for display - const companiesText = document.getElementById('companies_text').value.trim(); updateConfigBadge({ main_theme: main_theme, - companies: companiesText || 'Custom Universe', + companies: companiesFile ? companiesFile.name : (companiesText || 'Custom Universe'), isDemo: false }); } - + // Render the report using the new interface if (window.renderRiskReport) { renderRiskReport(statusData.report); } + // Always land on the Overview tab for a freshly completed analysis, + // even if the user clicked another tab while it was running. + if (window.tabController) { + window.tabController.switchTab('overview'); + } if (showJsonBtn) showJsonBtn.style.display = 'inline-block'; - + // Show new analysis button const newAnalysisBtn = document.getElementById('newAnalysisBtn'); if (newAnalysisBtn) newAnalysisBtn.style.display = 'inline-flex'; - + window.lastReport = statusData.report; if (window.updateDownloadButtonState) { window.updateDownloadButtonState(); diff --git a/bigdata_risk_analyzer/static/scripts/mindmap.js b/bigdata_risk_analyzer/static/scripts/mindmap.js index 3d65767..17f518e 100644 --- a/bigdata_risk_analyzer/static/scripts/mindmap.js +++ b/bigdata_risk_analyzer/static/scripts/mindmap.js @@ -7,6 +7,13 @@ function renderMindmap(taxonomy) { if (!container || !taxonomy) return; mindmapData = taxonomy; + // Always start a freshly-rendered report in Tree View. Graph View's D3 layout + // needs the container's real on-screen width, which isn't available yet here + // (the Taxonomy tab is hidden until the user clicks it) — rendering the graph + // while hidden bakes in a wrong, clamped-fallback size that then sticks around + // (see switchMindmapView's re-render-on-switch below for why it doesn't self-heal + // on its own). + currentMindmapView = 'tree'; let html = `
@@ -78,9 +85,10 @@ function switchMindmapView() { } else { graphView.classList.remove('hidden'); treeView.classList.add('hidden'); - if (graphView.innerHTML === '') { - renderGraphView(mindmapData); - } + // Always recompute (not just when empty): the container is only guaranteed + // to have a real, visible width right now, at switch time. Re-rendering here + // both gets an accurate width and self-heals any earlier bad-width render. + renderGraphView(mindmapData); } } @@ -147,22 +155,43 @@ function toggleTreeNode(button) { } } +function countLeaves(node) { + if (!node || !node.children || node.children.length === 0) return 1; + return node.children.reduce((sum, child) => sum + countLeaves(child), 0); +} + function renderGraphView(taxonomy) { const container = document.getElementById('mindmapGraphView'); if (!container || !taxonomy) return; + if (typeof d3 === 'undefined') { + container.innerHTML = ` +
+ Graph view could not load its charting library (d3.js from a CDN). + This is usually caused by a blocked network request — check the + browser console, or use Tree View instead. +
+ `; + return; + } + + // Height scales with the number of leaves so large taxonomies don't cram/overlap. + const leafCount = countLeaves(taxonomy); + const height = Math.max(600, leafCount * 28 + 100); + // Clear and set up SVG container container.innerHTML = `
💡 Scroll to zoom • Hover for details • Tree grows left to right
-
+
`; - const width = container.offsetWidth - 40; - const height = 600; + // container.offsetWidth can be 0 if this runs while the tab is still hidden + // (display:none) — fall back to a sane default instead of a negative tree size. + const width = Math.max(container.offsetWidth - 40, 600); // Convert taxonomy to D3 hierarchy function taxonomyToHierarchy(node) { @@ -242,7 +271,9 @@ function renderGraphView(taxonomy) { .attr('fill', '#fff') .style('pointer-events', 'none'); - // Tooltip + // Tooltip — remove any leftover one from a previous render (renderGraphView can + // now run more than once per report, see switchMindmapView) before adding a fresh one. + d3.selectAll('.mindmap-tooltip').remove(); const tooltip = d3.select('body').append('div') .attr('class', 'mindmap-tooltip') .style('position', 'absolute') diff --git a/bigdata_risk_analyzer/static/scripts/validators.js b/bigdata_risk_analyzer/static/scripts/validators.js index f98cc05..61457e7 100644 --- a/bigdata_risk_analyzer/static/scripts/validators.js +++ b/bigdata_risk_analyzer/static/scripts/validators.js @@ -1,23 +1,9 @@ -// Calculate maximum number of days based on frequency (maximum 4 times the frequency) -function calculateMaxDays(frequency) { - const frequencyToDays = { - // Here we tweak this values to get reasonable max ranges - 'D': 14, // 2 weeks - 'W': 120, // 4 months - 'M': 180, // Half a year - '3M': 365, // Quarterly: 1 year - 'Y': 1460 // 4 years - }; +// Maximum date-range span accepted by the demo form (retrieval cost/time scales with it). +const MAX_DATE_RANGE_DAYS = 730; // 2 years - const baseDays = frequencyToDays[frequency] || 90; // Default to quarterly if unknown - return baseDays -} - - - -// Validate date range based on frequency -function validateDateRange(startDate, endDate, frequency) { - if (!startDate || !endDate || !frequency) { +// Validate date range against a flat maximum span +function validateDateRange(startDate, endDate) { + if (!startDate || !endDate) { return { isValid: true, message: '' }; // Skip validation if any value is missing } @@ -25,21 +11,11 @@ function validateDateRange(startDate, endDate, frequency) { const end = new Date(endDate); const diffTime = Math.abs(end - start); const diffDays = Math.ceil(diffTime / (1000 * 60 * 60 * 24)); - const maxDays = calculateMaxDays(frequency); - - if (diffDays > maxDays) { - const frequencyNames = { - 'D': 'Daily', - 'W': 'Weekly', - 'M': 'Monthly', - '3M': 'Quarterly', - 'Y': 'Yearly' - }; - const frequencyName = frequencyNames[frequency] || frequency; + if (diffDays > MAX_DATE_RANGE_DAYS) { return { isValid: false, - message: `Date range exceeds maximum allowed for ${frequencyName} frequency.
Maximum: ${maxDays} days.
Current range: ${diffDays} days.
Use the endpoint directly for full control` + message: `Date range exceeds the maximum allowed span.
Maximum: ${MAX_DATE_RANGE_DAYS} days.
Current range: ${diffDays} days.
Use the endpoint directly for full control` }; } @@ -51,9 +27,8 @@ function validateDateRange(startDate, endDate, frequency) { function validateAndShowError() { const startDateValue = document.getElementById('start_date').value; const endDateValue = document.getElementById('end_date').value; - const frequencyValue = document.getElementById('frequency').value; - const validation = validateDateRange(startDateValue, endDateValue, frequencyValue); + const validation = validateDateRange(startDateValue, endDateValue); // Remove any existing error message const existingError = document.getElementById('dateRangeError'); @@ -68,9 +43,9 @@ function validateAndShowError() { errorDiv.className = 'mb-5 p-3 bg-red-800 text-white rounded-lg border border-red-600'; errorDiv.innerHTML = `⚠️ Date Range Error:
${validation.message}`; - // Insert after the frequency field - const frequencyField = document.getElementById('frequency').closest('.mb-5'); - frequencyField.parentNode.insertBefore(errorDiv, frequencyField.nextSibling); + // Insert after the end_date field + const endDateField = document.getElementById('end_date').closest('.mb-5') || document.getElementById('end_date').parentElement; + endDateField.parentNode.insertBefore(errorDiv, endDateField.nextSibling); } } @@ -78,5 +53,4 @@ function validateAndShowError() { document.addEventListener('DOMContentLoaded', function () { document.getElementById('start_date').addEventListener('change', validateAndShowError); document.getElementById('end_date').addEventListener('change', validateAndShowError); - document.getElementById('frequency').addEventListener('change', validateAndShowError); -}); \ No newline at end of file +}); diff --git a/bigdata_risk_analyzer/static/scripts/visualization.js b/bigdata_risk_analyzer/static/scripts/visualization.js index a0611fe..b48ad38 100644 --- a/bigdata_risk_analyzer/static/scripts/visualization.js +++ b/bigdata_risk_analyzer/static/scripts/visualization.js @@ -2,17 +2,14 @@ const infoContents = { main_theme: `Main Theme:
The overarching risk scenario you want to analyze. It can be specified as a single word or as a short sentence. The risk analyzer will generate a list of sub-themes representing individual, self contained components of the main risk. It can contain multiple core concepts, but we recommend not adding too many core concepts in the same run.
Examples: "US Import Tariffs against China", "Energy Transition", "Regulatory Changes in AI"`, focus: `Focus:
Use this parameter to pass additional, custom instructions to the LLM when breaking down the theme into sub-risks. Guide the mindmap creation and customize it to your needs, inject your domain knowledge, and ensure the mindmap covers all required risk dimensions.`, - companies: `Company Universe:
The portfolio of companies you want to screen for exposure, you have several input options:
  • Select one of the public watchlists using the dropdown menu
  • Write list of RavenPack entity IDs (e.g., 4A6F00, D8442A)
  • Input a watchlist ID (e.g., 44118802-9104-4265-b97a-2e6d88d74893 )

Watchlists can be created programmatically using the Bigdata.com SDK or through the Bigdata app.`, - control_entities: `Control Entities:
A dict of entities to be co-mentioned with the sentence, companies, and keywords (e.g. {"place": ["China"]}). Specify companies, places, people, concepts, etc. Learn more.`, + companies: `Company Universe:
The portfolio of companies you want to screen for exposure, you have two input options:
  • Write a comma-separated list of RavenPack entity IDs (e.g., 4A6F00, D8442A)
  • Upload a CSV with RP_ENTITY_ID and COMPANY_NAME columns (optionally TICKER/SECTOR/INDUSTRY/COUNTRY)

Watchlists are not supported at this time.`, start_date: `Start/End Date:
The start and end of the time sample during which you want to screen your portfolio for thematic exposure. Format: YYYY-MM-DD.`, - keywords: `Keywords:
A list of keywords defining core concepts of the theme (or sub-theme) to be included in the query. Combined with OR and added to Similarity and Entity filter with AND.`, - document_type: `Document Type:
The type of documents to search over. Use this to analyze text data from news, corporate transcripts, or filings. Currently, only "TRANSCRIPTS" is supported.`, - fiscal_year: `Fiscal Year:
For Transcripts and Filings, filter documents by their reporting details. fiscal_year represents the annual reporting period and can be combined with start_date and end_date for time-sensitive queries. Not applicable to News.`, - rerank_threshold: `Rerank Threshold:
Optional, used with sentence search only. Ensures close cosine similarity between sentence embeddings and retrieved chunks. By default, not applied. For most use cases, one-step retrieval is sufficient. Learn more.`, - frequency: `Frequency:
Break down your sample range into higher frequency intervals (D, Y, M, 3M, Y). Useful for large samples to control document retrieval over time.`, - llm_model: `LLM Model:
The LLM model to be used for mindmap generation and text analysis. It has to be specified as a string containing both provider name and model name separated by two colons: .`, - document_limit: `Document Limit:
The maximum number of documents to be retrieved by each query. This is a single value that applies to any combination of query statement & date range.`, - batch_size: `Batch Size:
Set this parameter when screening a large portfolio of companies (i.e. 50 or more). It allows to break down the portfolio into smaller batches of fixed size, and instructs the search service to run parallel queries for each and every batch. This allows for improving the sampling across your portfolio, given the document limit constraint that has to be applied per query.`, + keywords: `Keywords:
Optional key terms to emphasize when generating the risk taxonomy.`, + rerank_threshold: `Rerank Threshold:
Optional relevance threshold (0-1); retrieved chunks scoring below it are discarded. By default, not applied.`, + chunk_percentage: `Retrieval %:
Percentage (0-100) of the estimated available chunks to retrieve per risk factor, e.g. 5 means 5%. Higher values cost more and take longer, but surface more evidence.`, + max_leaf_labels: `Max Risk Factors:
Maximum number of leaf sub-scenarios in the generated risk taxonomy. Leave empty for no cap.`, + max_taxonomy_depth: `Max Taxonomy Depth:
Maximum number of levels in the generated risk taxonomy, counting the root risk node as level 1. By default the taxonomy has 4 levels (root, risk channel, risk factor, sub-scenario); set this to 3 to drop the separate risk-factor layer so sub-scenarios sit directly under each risk channel. Leave empty for the default depth.`, + llm_model: `LLM Model:
The OpenAI model used for taxonomy generation, chunk labeling, and company summaries.`, headline_comment: `Headline:
Click on each headline to retrieve the DOCUMENT ID. The DOCUMENT ID identifies the document that contains that headline.`, }; @@ -31,6 +28,21 @@ function toggleAdvancedOptions() { } } +// Toggle the "Process Logs" panel visibility +function toggleProcessLogs() { + const container = document.getElementById('logViewerContainer'); + const icon = document.getElementById('logsIcon'); + if (!container) return; + + if (container.classList.contains('hidden')) { + container.classList.remove('hidden'); + if (icon) icon.style.transform = 'rotate(180deg)'; + } else { + container.classList.add('hidden'); + if (icon) icon.style.transform = 'rotate(0deg)'; + } +} + // Information modal for form fields function showInfoModal(label) { diff --git a/bigdata_risk_analyzer/taxonomy.py b/bigdata_risk_analyzer/taxonomy.py new file mode 100644 index 0000000..d3a8673 --- /dev/null +++ b/bigdata_risk_analyzer/taxonomy.py @@ -0,0 +1,339 @@ +"""Risk taxonomy generation and leaf-node helpers. + +Ported from ``Thematic_Screener_CLI/src/screener.py`` (risk-analyzer mode), +``src/prompts.py`` (risk-mode prompts only), ``src/helpers.py``, and +``src/search_query.py``. Replaces +``bigdata_research_tools.mindmap.mindmap.MindMap`` / ``MindMapGenerator``. +""" + +from __future__ import annotations + +import re + +from openai import OpenAI +from pydantic import BaseModel, Field + +from bigdata_risk_analyzer.openai_utils import build_openai_client + +DEFAULT_MAX_LEAF_LABELS = 15 + +# --------------------------------------------------------------------------- +# Prompts (risk-analyzer mode), verbatim from Thematic_Screener_CLI/src/prompts.py +# --------------------------------------------------------------------------- + +SYSTEM_MESSAGE_RISK = """ +Forget all previous prompts. +You are assisting a professional risk analyst tasked with creating a screener to measure the exposure of companies to the risk {main_theme}. +Your objective is to generate a comprehensive tree structure of distinct risk factors and sub-scenarios that will guide the analyst's research process. +Follow these steps strictly: +1. **Understand the Core Risk {main_theme}**: + - The risk {main_theme} is a central concept. All components are essential for a thorough understanding of how companies are exposed to it. +2. **Create a Taxonomy of Risk Factors for {main_theme}**: + - Decompose the main risk {main_theme} into concise, focused, and self-contained risk channels, risk factors, and specific sub-scenarios. + - Organize the tree so that the top-level children represent broad risk channels, their children represent specific risk factors, and the leaf nodes represent concrete, observable sub-scenarios. + - Each node should represent a singular, concise, informative, and clear aspect of the main risk. + - Expand each node to be relevant for the {main_theme}: a single word is not informative enough. + - Prioritize clarity and specificity. Leaf sub-scenarios should be specific enough to be detected in company news, filings, and transcripts. + - Avoid repetition and strive for diverse, non-overlapping angles of exposure. +3. **Iterate Based on the Analyst's Focus {analyst_focus}**: + - If no specific {analyst_focus} is provided, transition directly to formatting the JSON response. +3. **Format Your Response as a JSON Object**: + - Each node in the JSON object must include: + - `node`: an integer representing the unique identifier for the node. + - `label`: a string for the name of the risk channel, risk factor, or sub-scenario. + - `summary`: a string to explain briefly in maximum 15 words why the node is a risk related to {main_theme}. + - For the node referring to the first node {main_theme}, just define briefly in maximum 15 words the risk {main_theme}. + - `children`: an array of child nodes. + - Do not add the starting '''json and the ending '''. + +IMPORTANT: Your response MUST be a valid JSON object. Each node in the JSON object must include: + - `node`: an integer representing the unique identifier for the node. + - `label`: a string for the name of the risk factor or sub-scenario. + - `summary`: a string to explain briefly in maximum 15 words why the node is a risk related to the main risk. + - For the node referring to the main risk, just define briefly in maximum 15 words the risk. + - `children`: an array of child nodes. +Format the JSON object as a nested dictionary. Be careful when specifying keys and items. +Avoid overlapping labels. Break down joint concepts into unique parents so that each parent represents ONLY ONE concept. AVOID creating branch names such as 'Compliance and Regulatory Risk'. Keep risks separate and create a single branch for each risk, such as 'Compliance Risk' and 'Regulatory Risk', each with their own children. +Return ONLY the JSON object, with no extra text, explanation, or markdown. +You MUST use ONLY these field names: label, node, summary, children. Do NOT use underscores, spaces, or any other characters in field names. If you use any other field names, your answer will be rejected. +## Example Structure: +**Risk: US Government Shutdown** +{{ + "node": 1, + "label": "US Government Shutdown", + "summary": "A lapse in federal funding that halts government operations and spending", + "children": [ + {{"node": 2, "label": "Federal Spending Disruption", "summary": "A shutdown freezes or delays federal contracts and payments to companies", "children": [ + {{"node": 5, "label": "Delayed Government Contract Payments", "summary": "Companies relying on federal contracts face delayed or suspended payments"}}, + {{"node": 6, "label": "Reduced Federal Procurement", "summary": "New federal procurement and awards are paused during a shutdown"}} + ]}}, + {{"node": 3, "label": "Regulatory Slowdown", "summary": "Regulatory agencies reduce activity, delaying approvals and reviews", "children": [ + {{"node": 7, "label": "Delayed Drug and Product Approvals", "summary": "Agency review backlogs delay product approvals for companies"}}, + {{"node": 8, "label": "Stalled IPO and Filing Reviews", "summary": "Securities filing reviews are delayed, postponing capital raises"}} + ]}} + ] +}}""" + +USER_MESSAGE_RISK = "Your given Risk is: {main_theme}" + +SYSTEM_PROMPT_RISK_LABELING = """Forget all previous prompts. + You are assisting a professional risk analyst in evaluating the exposure of a company "Target Company" to the risk '{main_theme}'. + Your primary task is first, to ensure that each sentence is explicitly related to '{main_theme}', and second, to accurately associate each given sentence with + the relevant risk sub-scenario contained within the list '{labels}'. + + Please adhere strictly to the following guidelines: + + 1. **Analyze the Sentence**: + - Each input consists of a sentence ID, a company name ('Target Company'), and the sentence text. + - Analyze the sentence to understand if the content clearly establishes that "Target Company" is exposed to '{main_theme}'. + - Your primary goal is to label as 'unclear' the sentences that don't explicitly relate "Target Company" to '{main_theme}'. + - The list of labels '{labels}' is a Python list variable containing distinct sub-scenarios and their definition in format 'Label: Summary'. You must pick the label only from the 'Label' part, which means the left side of the colon for each Label:Summary pair. + - Your secondary goal is to select the most appropriate sub-scenario from '{labels}' that corresponds to the content of the sentence. + + 2. **First Label Assignment**: + - Assign the label 'unclear' to the sentence related to "Target Company" when it does not explicitly relate to '{main_theme}'. Otherwise, don't assign a label. + - Evaluate each sentence independently, focusing solely on the context provided within that specific sentence. + - Use only the information contained within the sentence for your label assignment. + - When evaluating the sentence, "Target Company" must clearly be exposed to or affected by '{main_theme}'. + - Many sentences are only tangentially connected to the risk '{main_theme}'. These sentences must be assigned the label 'unclear'. + + 3. **Second Label Assignment**: + - For the sentences not labeled as 'unclear' and only for them, assign a unique sub-scenario from the list '{labels}' to the sentence related to "Target Company". + - Evaluate each sentence independently, focusing solely on the context provided within that specific sentence. + - Use only the information contained within the sentence for your label assignment. + - Ensure that the sentence clearly establishes a connection to the sub-scenario you assigned and to the risk '{main_theme}'. + - You must not create a new label or choose a label that is not present in '{labels}'. + - If the sentence does not explicitly relate to the sub-scenario, assign the label 'unclear'. + - When evaluating the sentence, "Target Company" must clearly be exposed to the sub-scenario assigned and '{main_theme}'. + + 4. **Response Format**: + - Your output should be structured as a JSON object that includes: + 1. A brief motivation for your choice. + 2. The assigned label. + - Each entry must start with the sentence ID and contain a clear motivation that begins with "Target Company". + - The motivation should explain why the sub-scenario was selected from '{labels}' based on the information in the sentence and in the context of '{main_theme}'. + - Ensure that the exact context is understood and labels are based only on explicitly mentioned information in the sentence. Otherwise, assign the label 'unclear'. + - The assigned label should be only the string that precedes the character ':'. + - Format your JSON as follows: {{"": {{"motivation": "", "label": "
+ diff --git a/bigdata_risk_analyzer/traces.py b/bigdata_risk_analyzer/traces.py deleted file mode 100644 index 8468a70..0000000 --- a/bigdata_risk_analyzer/traces.py +++ /dev/null @@ -1,19 +0,0 @@ -from enum import StrEnum - -from bigdata_client.tracking_services import TraceEvent -from bigdata_client.tracking_services import send_trace as bigdata_send_trace - - -class TraceEventName(StrEnum): - SERVICE_START = "onPremRiskAnalyzerServiceStart" - RISK_ANALYZER_REPORT_GENERATED = "onPremRiskAnalyzerReportGenerated" - - -def send_trace(bigdata_client, event_name: TraceEventName, trace: dict): - try: - bigdata_send_trace( - bigdata_client=bigdata_client, - trace=TraceEvent(event_name=event_name, properties=trace), - ) - except Exception: - pass diff --git a/bigdata_risk_analyzer/universe.py b/bigdata_risk_analyzer/universe.py new file mode 100644 index 0000000..8e421d0 --- /dev/null +++ b/bigdata_risk_analyzer/universe.py @@ -0,0 +1,191 @@ +"""Company-universe loading: CSV upload or a bare list of RP entity IDs. + +Ported from ``Thematic_Screener_CLI/src/screener.py::load_universe`` and +``_company_metadata_lookup``. Watchlists are intentionally unsupported — see +:func:`reject_if_watchlist`. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import IO, Iterable + +import pandas as pd + +from bigdata_risk_analyzer import bigdata_kg + +ID_COLUMN = "RP_ENTITY_ID" +ID_ALIASES: tuple[str, ...] = ("RP_ENTITY_ID", "RP_COMPANY_ID") +NAME_COLUMN = "COMPANY_NAME" +NAME_ALIASES: tuple[str, ...] = ("COMPANY_NAME", "NAME", "COMPANY") +TICKER_COLUMN = "TICKER" +SECTOR_COLUMN = "SECTOR" +INDUSTRY_COLUMN = "INDUSTRY" +COUNTRY_COLUMN = "COUNTRY" + +UNKNOWN_VALUE = "Unknown" + +WATCHLIST_REJECTED_MESSAGE = ( + "Watchlist is not supported at this time. Provide a list of RP entity IDs " + '(`companies: ["D8442A", ...]`) or upload a CSV via /risk-analysis/upload.' +) + + +def reject_if_watchlist(companies: object) -> None: + """Raise a clear error if ``companies`` looks like a legacy watchlist ID.""" + if isinstance(companies, str): + raise ValueError(WATCHLIST_REJECTED_MESSAGE) + + +def _find_column(columns: Iterable[str], candidates: tuple[str, ...]) -> str | None: + normalized = {str(column).upper(): str(column) for column in columns} + for candidate in candidates: + if candidate.upper() in normalized: + return normalized[candidate.upper()] + return None + + +def _clean_optional_cell(value: object) -> str | None: + """Return a stripped string, or ``None`` for missing/blank cells (incl. NaN).""" + if pd.isna(value): + return None + text = str(value).strip() + return text or None + + +_METADATA_COLUMNS: tuple[str, ...] = ( + TICKER_COLUMN, + SECTOR_COLUMN, + INDUSTRY_COLUMN, + COUNTRY_COLUMN, +) +_METADATA_KG_KEYS: dict[str, str] = { + TICKER_COLUMN: "ticker", + SECTOR_COLUMN: "sector", + INDUSTRY_COLUMN: "industry", + COUNTRY_COLUMN: "country", +} + + +def _enrich_missing_metadata( + universe_df: pd.DataFrame, + api_key: str | None, + api_base_url: str | None, +) -> pd.DataFrame: + """Fill in missing ticker/sector/industry/country via the knowledge-graph API. + + Only cells that are missing (column absent, or blank/NaN for that row) are + filled in — values already present in the CSV are left untouched. No-op + when ``api_key`` is ``None`` or nothing is missing. + """ + for column in _METADATA_COLUMNS: + if column not in universe_df.columns: + universe_df[column] = None + + missing_mask = pd.Series(False, index=universe_df.index) + for column in _METADATA_COLUMNS: + missing_mask = missing_mask | universe_df[column].isna() + + if not missing_mask.any() or not api_key: + return universe_df + + ids = universe_df.loc[missing_mask, ID_COLUMN].tolist() + try: + resolved = bigdata_kg.resolve_companies( + ids, api_key=api_key, api_base_url=api_base_url + ) + except ValueError: + # None of the missing IDs resolved via the knowledge graph — keep the CSV as-is. + return universe_df + + for column, kg_key in _METADATA_KG_KEYS.items(): + cell_missing = universe_df[column].isna() + looked_up = universe_df.loc[cell_missing, ID_COLUMN].map( + lambda entity_id: (resolved.get(entity_id) or {}).get(kg_key) + ) + universe_df.loc[cell_missing, column] = looked_up + + return universe_df + + +def load_universe_csv( + file: str | Path | IO[str] | IO[bytes], + api_key: str | None = None, + api_base_url: str | None = None, +) -> pd.DataFrame: + """Load a company universe CSV. + + Requires ``RP_ENTITY_ID`` (alias ``RP_COMPANY_ID``) and ``COMPANY_NAME`` + (alias ``NAME``/``COMPANY``) columns. Optional ``TICKER``/``SECTOR``/ + ``INDUSTRY``/``COUNTRY`` columns enrich the response. Any of those that + are missing entirely, or blank for a given row, are backfilled via the + Bigdata.com knowledge-graph API (by RP entity ID) when ``api_key`` is + provided; anything still missing after that defaults to ``"Unknown"`` + (sector/industry) or ``None`` (ticker/country). + """ + raw_df = pd.read_csv(file) + + id_column = _find_column(raw_df.columns, ID_ALIASES) + if id_column is None: + raise ValueError( + f"universe CSV is missing a required ID column " + f"(expected one of: {', '.join(ID_ALIASES)})" + ) + name_column = _find_column(raw_df.columns, NAME_ALIASES) + if name_column is None: + raise ValueError( + f"universe CSV is missing a required name column " + f"(expected one of: {', '.join(NAME_ALIASES)})" + ) + + universe_df = pd.DataFrame( + { + ID_COLUMN: raw_df[id_column].astype(str).str.strip(), + NAME_COLUMN: raw_df[name_column].astype(str).str.strip(), + } + ) + + for column in _METADATA_COLUMNS: + found = _find_column(raw_df.columns, (column,)) + if found is not None: + universe_df[column] = raw_df[found].apply(_clean_optional_cell) + + if universe_df.empty: + raise ValueError("universe CSV contains no rows") + + universe_df = universe_df.drop_duplicates(subset=[ID_COLUMN]).reset_index(drop=True) + return _enrich_missing_metadata(universe_df, api_key, api_base_url) + + +def build_universe_from_ids( + ids: list[str], + api_key: str, + api_base_url: str | None = None, +) -> pd.DataFrame: + """Build a universe DataFrame from a plain list of RP entity IDs. + + Metadata (name, sector, industry, country, ticker) is filled in via the + Bigdata.com knowledge-graph REST API. IDs that don't resolve to a company + entity are dropped. + """ + if not ids: + raise ValueError("companies list must not be empty") + + resolved = bigdata_kg.resolve_companies( + ids, api_key=api_key, api_base_url=api_base_url + ) + + rows = [] + for entity_id, meta in resolved.items(): + rows.append( + { + ID_COLUMN: entity_id, + NAME_COLUMN: meta.get("name") or entity_id, + TICKER_COLUMN: meta.get("ticker"), + SECTOR_COLUMN: meta.get("sector") or UNKNOWN_VALUE, + INDUSTRY_COLUMN: meta.get("industry") or UNKNOWN_VALUE, + COUNTRY_COLUMN: meta.get("country"), + } + ) + + return pd.DataFrame(rows).reset_index(drop=True) diff --git a/pyproject.toml b/pyproject.toml index 43d3d91..cb33f39 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "bigdata_risk_analyzer" -version = "2.3.10" +version = "3.0.0" description = "Identify corportate exposure to uncertain scenarios and risk channels, grounded on thousands of sources provided by Bigdata.com" authors = [{name = "jaldana", email = "jaldana@ravenpack.com"}] readme = "README.md" @@ -14,9 +14,14 @@ dependencies = [ "structlog>=25.4.0", "fastapi>=0.116.1", "uvicorn>=0.35.0", - "bigdata-client==2.19.0", - "bigdata-research-tools[openai]==1.1.2", "sqlmodel>=0.0.24", + "bigdata-smart-batching>=1.4.1,<2.0.0", + "openai>=2.0.0,<3.0.0", + "requests>=2.31", + "python-dotenv>=1.0.0", + "python-multipart>=0.0.9", + "pandas>=2.0.0", + "openpyxl>=3.1.0", ] [dependency-groups] @@ -25,6 +30,10 @@ dev = [ "pytest-cov==5.0.0", ] +[tool.ruff.lint] +select = ["E", "F", "I"] +ignore = ["E501"] + [tool.hatch.build.targets.wheel] packages = ["bigdata_risk_analyzer"] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d5511c8 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,14 @@ +pydantic>=2.10.6 +pydantic-settings>=2.2.1 +jinja2==3.1.6 +structlog>=25.4.0 +fastapi>=0.116.1 +uvicorn>=0.35.0 +sqlmodel>=0.0.24 +bigdata-smart-batching>=1.4.1,<2.0.0 +openai>=2.0.0,<3.0.0 +requests>=2.31 +python-dotenv>=1.0.0 +python-multipart>=0.0.9 +pandas>=2.0.0 +openpyxl>=3.1.0 diff --git a/tests/test_api/test_models.py b/tests/test_api/test_models.py index b85fbe6..dd7615d 100644 --- a/tests/test_api/test_models.py +++ b/tests/test_api/test_models.py @@ -1,132 +1,97 @@ import pytest +from pydantic import ValidationError from bigdata_risk_analyzer.api.models import ( - DocumentType, - FrequencyEnum, - RiskAnalysisRequest, + EXAMPLE_COMPANY_LISTS, + RiskAnalysisRequestBase, ) -@pytest.mark.parametrize( - "main_theme,focus,companies,control_entities,start_date,end_date,keywords,llm_model_config,document_type,rerank_threshold,frequency,document_limit,batch_size,fiscal_year", - [ - # Minimal valid input with companies - ( - "US Import Tariffs against China", - "Taxonomy of risks for US companies", - ["4A6F00", "D8442A"], - {"place": ["China"]}, - "2025-06-01", - "2025-08-01", - ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.monthly, - 100, - 10, - None, - ), - # Minimal valid input with watchlist_id - ( - "US Import Tariffs against China", - "Taxonomy of risks for US companies", - "44118802-9104-4265-b97a-2e6d88d74893", - {"place": ["China"]}, - "2025-06-01", - "2025-08-01", - ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - 0.8, - FrequencyEnum.weekly, - 50, - 5, - None, - ), - # Different frequency and document type - ( - "Risk of supply chain disruption", - "Impact on global logistics", - ["A12345"], - {"place": ["USA"]}, - "2025-01-01", - "2025-12-31", - ["Disruption", "Logistics"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.yearly, - 200, - 20, - None, - ), - # Control entities with multiple places - ( - "Intellectual property risks", - "IP risk taxonomy", - ["B67890"], - {"place": ["China", "USA"]}, - "2025-07-01", - "2025-08-01", - ["IP", "Patent"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.daily, - 10, - 1, - None, - ), - ], -) -def test_risk_analysis_request_model( - main_theme, - focus, - companies, - control_entities, - start_date, - end_date, - keywords, - llm_model_config, - document_type, - rerank_threshold, - frequency, - document_limit, - batch_size, - fiscal_year, -): - req = RiskAnalysisRequest( - main_theme=main_theme, - focus=focus, - companies=companies, - control_entities=control_entities, - start_date=start_date, - end_date=end_date, - keywords=keywords, - llm_model_config=llm_model_config, - document_type=document_type, - rerank_threshold=rerank_threshold, - frequency=frequency, - document_limit=document_limit, - batch_size=batch_size, - fiscal_year=fiscal_year, +def test_example_company_lists_are_rp_entity_ids(): + assert "MAG_7" in EXAMPLE_COMPANY_LISTS + assert len(EXAMPLE_COMPANY_LISTS["MAG_7"]) == 7 + for entity_id in EXAMPLE_COMPANY_LISTS["MAG_7"]: + assert isinstance(entity_id, str) and entity_id + + +def test_risk_analysis_request_base_valid(): + req = RiskAnalysisRequestBase( + main_theme="US Import Tariffs against China", + focus="Taxonomy of risks for US companies", + start_date="2025-06-01", + end_date="2025-08-01", + chunk_percentage=0.05, + ) + assert req.main_theme == "US Import Tariffs against China" + assert req.chunk_percentage == 0.05 + assert req.llm_model == "gpt-5.6-luna" + + +@pytest.mark.parametrize("chunk_percentage", [-0.1, 1.5]) +def test_risk_analysis_request_base_chunk_percentage_out_of_range(chunk_percentage): + with pytest.raises(ValidationError): + RiskAnalysisRequestBase( + main_theme="Theme", + focus="Focus", + start_date="2025-06-01", + end_date="2025-08-01", + chunk_percentage=chunk_percentage, + ) + + +def test_risk_analysis_request_base_max_taxonomy_depth_defaults_to_none(): + req = RiskAnalysisRequestBase( + main_theme="Theme", + focus="Focus", + start_date="2025-06-01", + end_date="2025-08-01", + ) + assert req.max_taxonomy_depth is None + + +def test_risk_analysis_request_base_max_taxonomy_depth_accepts_three(): + req = RiskAnalysisRequestBase( + main_theme="Theme", + focus="Focus", + start_date="2025-06-01", + end_date="2025-08-01", + max_taxonomy_depth=3, + ) + assert req.max_taxonomy_depth == 3 + + +def test_risk_analysis_request_base_omitted_dates_use_defaults(): + req = RiskAnalysisRequestBase(main_theme="Theme", focus="Focus") + assert req.start_date == "2024-01-01" + assert req.end_date == "2024-12-31" + + +def test_risk_analysis_request_base_omitted_end_date_uses_default(): + req = RiskAnalysisRequestBase( + main_theme="Theme", focus="Focus", start_date="2024-06-01" ) - assert req.main_theme == main_theme - assert req.focus == focus - assert req.start_date == start_date - assert req.end_date == end_date - assert req.llm_model_config == llm_model_config - assert req.document_type == document_type - assert req.frequency == frequency - assert req.document_limit == document_limit - assert req.batch_size == batch_size - assert companies == companies - if control_entities: - assert req.control_entities == control_entities - if keywords: - assert req.keywords == keywords - if rerank_threshold is not None: - assert req.rerank_threshold == rerank_threshold - if fiscal_year: - assert req.fiscal_year == fiscal_year + assert req.start_date == "2024-06-01" + assert req.end_date == "2024-12-31" + + +def test_risk_analysis_request_base_start_after_end_is_rejected(): + with pytest.raises( + ValidationError, match="start_date must be earlier than end_date" + ): + RiskAnalysisRequestBase( + main_theme="Theme", + focus="Focus", + start_date="2025-08-01", + end_date="2025-06-01", + ) + + +def test_risk_analysis_request_base_max_taxonomy_depth_rejects_below_two(): + with pytest.raises(ValidationError): + RiskAnalysisRequestBase( + main_theme="Theme", + focus="Focus", + start_date="2025-06-01", + end_date="2025-08-01", + max_taxonomy_depth=1, + ) diff --git a/tests/test_app.py b/tests/test_app.py index 67088f1..5f8278f 100644 --- a/tests/test_app.py +++ b/tests/test_app.py @@ -1,3 +1,5 @@ +import io + import pytest from fastapi.testclient import TestClient @@ -17,3 +19,33 @@ def test_health_check(client): assert data["status"] == "ok" assert "version" in data assert isinstance(data["version"], str) + + +def test_analyze_risk_rejects_watchlist_string(client): + response = client.post( + "/risk-analysis", + json={ + "main_theme": "US Import Tariffs against China", + "focus": "Taxonomy of risks for US companies", + "companies": "44118802-9104-4265-b97a-2e6d88d74893", + "start_date": "2025-06-01", + "end_date": "2025-08-01", + }, + ) + assert response.status_code == 422 + assert "Watchlist is not supported" in response.text + + +def test_analyze_risk_upload_rejects_bad_csv(client): + bad_csv = io.BytesIO(b"NOT_AN_ID,NOT_A_NAME\nfoo,bar\n") + response = client.post( + "/risk-analysis/upload", + files={"file": ("universe.csv", bad_csv, "text/csv")}, + data={ + "request": ( + '{"main_theme": "Theme", "focus": "Focus", ' + '"start_date": "2025-06-01", "end_date": "2025-08-01"}' + ) + }, + ) + assert response.status_code == 400 diff --git a/tests/test_bigdata_kg.py b/tests/test_bigdata_kg.py new file mode 100644 index 0000000..234f084 --- /dev/null +++ b/tests/test_bigdata_kg.py @@ -0,0 +1,32 @@ +from bigdata_risk_analyzer.bigdata_kg import _parse_ticker + + +def test_parse_ticker_prefers_nasdaq_over_earlier_secondary_listing(): + # Apple: the Bangkok depositary-receipt listing sorts before the Nasdaq one. + entity = {"listing_values": ["XBKK:AAPL80", "XNAS:AAPL"]} + assert _parse_ticker(entity) == "AAPL" + + +def test_parse_ticker_prefers_nasdaq_among_many_secondary_listings(): + # NVIDIA: several European/Asian listings appear before the Nasdaq one. + entity = { + "listing_values": [ + "XBKK:NVDA80", + "XETR:NVD", + "XFRA:NVD", + "XFRA:NVDG", + "XLOM:0R1I", + "XNAS:NVDA", + ] + } + assert _parse_ticker(entity) == "NVDA" + + +def test_parse_ticker_falls_back_to_first_listing_when_no_preferred_exchange(): + entity = {"listing_values": ["NEOE:MSFT"]} + assert _parse_ticker(entity) == "MSFT" + + +def test_parse_ticker_returns_none_when_no_listings(): + assert _parse_ticker({"listing_values": []}) is None + assert _parse_ticker({}) is None diff --git a/tests/test_models.py b/tests/test_models.py index 11e730d..a32c3c3 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -1,32 +1,19 @@ import pytest from pydantic import ValidationError -from bigdata_risk_analyzer.api.models import ( - DocumentType, - FrequencyEnum, - RiskAnalysisRequest, -) +from bigdata_risk_analyzer.api.models import RiskAnalysisRequest @pytest.mark.parametrize( - "main_theme,focus,companies,control_entities,start_date,end_date,keywords,llm_model_config,document_type,rerank_threshold,frequency,document_limit,batch_size,fiscal_year,expected_error", + "main_theme,focus,companies,start_date,end_date,expected_error", [ - # Missing both companies + # Missing companies ( "US Import Tariffs against China", "Taxonomy of risks for US companies", None, - {"place": ["China"]}, "2025-06-01", "2025-08-01", - ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.monthly, - 100, - 10, - None, "Input should be a valid", ), # start_date after end_date @@ -34,212 +21,73 @@ "US Import Tariffs against China", "Taxonomy of risks for US companies", ["4A6F00"], - {"place": ["China"]}, "2025-08-01", "2025-06-01", - ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.monthly, - 100, - 10, - None, - "The number of days in the range between start_date", + "start_date must be earlier than end_date", ), - # Frequency interval too large for date range + # Bare-string companies (legacy watchlist ID) is rejected ( "US Import Tariffs against China", "Taxonomy of risks for US companies", - ["4A6F00"], - {"place": ["China"]}, - "2025-08-01", - "2025-08-10", - ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.monthly, - 100, - 10, - None, - "The number of days in the range between start_date", - ), - # Invalid frequency value - ( - "US Import Tariffs against China", - "Taxonomy of risks for US companies", - ["4A6F00"], - {"place": ["China"]}, + "44118802-9104-4265-b97a-2e6d88d74893", "2025-06-01", "2025-08-01", - ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - "invalid_freq", - 100, - 10, - None, - "invalid_freq", + "Watchlist is not supported", ), ], ) def test_risk_analysis_request_model_invalid( - main_theme, - focus, - companies, - control_entities, - start_date, - end_date, - keywords, - llm_model_config, - document_type, - rerank_threshold, - frequency, - document_limit, - batch_size, - fiscal_year, - expected_error, + main_theme, focus, companies, start_date, end_date, expected_error ): with pytest.raises((ValidationError, ValueError)) as exc_info: RiskAnalysisRequest( main_theme=main_theme, focus=focus, companies=companies, - control_entities=control_entities, start_date=start_date, end_date=end_date, - keywords=keywords, - llm_model_config=llm_model_config, - document_type=document_type, - rerank_threshold=rerank_threshold, - frequency=frequency, - document_limit=document_limit, - batch_size=batch_size, - fiscal_year=fiscal_year, ) assert expected_error in str(exc_info.value) @pytest.mark.parametrize( - "main_theme,focus,companies,control_entities,start_date,end_date,keywords,llm_model_config,document_type,rerank_threshold,frequency,document_limit,batch_size,fiscal_year", + "main_theme,focus,companies,start_date,end_date,keywords,llm_model,rerank_threshold,chunk_percentage,max_leaf_labels", [ - # Minimal valid input with companies ( "US Import Tariffs against China", "Taxonomy of risks for US companies", ["4A6F00", "D8442A"], - {"place": ["China"]}, "2025-06-01", "2025-08-01", ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.monthly, - 100, - 10, + "gpt-5.6-luna", None, + 0.02, + 15, ), - # Minimal valid input with watchlist_id - ( - "US Import Tariffs against China", - "Taxonomy of risks for US companies", - "44118802-9104-4265-b97a-2e6d88d74893", - {"place": ["China"]}, - "2025-06-01", - "2025-08-01", - ["Tariffs"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - 0.8, - FrequencyEnum.weekly, - 50, - 5, - None, - ), - # Different frequency and document type ( "Risk of supply chain disruption", "Impact on global logistics", ["A12345"], - {"place": ["USA"]}, "2025-01-01", "2025-12-31", ["Disruption", "Logistics"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.yearly, - 200, - 20, - None, - ), - ( - "Intellectual property risks", - "IP risk taxonomy", - ["B67890"], - {"place": ["China", "USA"]}, - "2025-07-01", - "2025-08-01", - ["IP", "Patent"], - "openai::gpt-4o-mini", - DocumentType.NEWS, - None, - FrequencyEnum.daily, - 10, - 1, + "gpt-5.6-luna", + 0.8, + 0.5, None, ), - ### Tests disabled, we only support NEWS for now so no need to test fiscal year - # ( # Multiple fiscal years - # "Intellectual property risks", - # "IP risk taxonomy", - # ["B67890"], - # {"place": ["China", "USA"]}, - # "2025-07-01", - # "2025-08-01", - # ["IP", "Patent"], - # "openai::gpt-4o-mini", - # DocumentType.TRANSCRIPTS, - # None, - # FrequencyEnum.daily, - # 10, - # 1, - # [2025, 2026, 2027], - # ), - # ( # Only one fiscal year but as a list - # "Intellectual property risks", - # "IP risk taxonomy", - # ["B67890"], - # {"place": ["China", "USA"]}, - # "2025-07-01", - # "2025-08-01", - # ["IP", "Patent"], - # "openai::gpt-4o-mini", - # DocumentType.TRANSCRIPTS, - # None, - # FrequencyEnum.daily, - # 10, - # 1, - # [2025], - # ), ( "Intellectual property risks", "IP risk taxonomy", ["B67890"], - {"place": ["China", "USA"]}, "2025-07-01", "2025-08-01", - ["IP", "Patent"], - "openai::gpt-5", - DocumentType.NEWS, None, - FrequencyEnum.daily, - 10, - 1, + "gpt-5", None, + 1.0, + 0, ), ], ) @@ -247,49 +95,35 @@ def test_risk_analysis_request_model( main_theme, focus, companies, - control_entities, start_date, end_date, keywords, - llm_model_config, - document_type, + llm_model, rerank_threshold, - frequency, - document_limit, - batch_size, - fiscal_year, + chunk_percentage, + max_leaf_labels, ): req = RiskAnalysisRequest( main_theme=main_theme, focus=focus, companies=companies, - control_entities=control_entities, start_date=start_date, end_date=end_date, keywords=keywords, - llm_model_config=llm_model_config, - document_type=document_type, + llm_model=llm_model, rerank_threshold=rerank_threshold, - frequency=frequency, - document_limit=document_limit, - batch_size=batch_size, - fiscal_year=fiscal_year, + chunk_percentage=chunk_percentage, + max_leaf_labels=max_leaf_labels, ) assert req.main_theme == main_theme assert req.focus == focus assert req.start_date == start_date assert req.end_date == end_date - assert req.llm_model_config == llm_model_config - assert req.document_type == document_type - assert req.frequency == frequency - assert req.document_limit == document_limit - assert req.batch_size == batch_size + assert req.llm_model == llm_model + assert req.chunk_percentage == chunk_percentage + assert req.max_leaf_labels == max_leaf_labels assert req.companies == companies - if control_entities: - assert req.control_entities == control_entities if keywords: assert req.keywords == keywords if rerank_threshold is not None: assert req.rerank_threshold == rerank_threshold - if fiscal_year: - assert req.fiscal_year == fiscal_year diff --git a/tests/test_openai_utils.py b/tests/test_openai_utils.py new file mode 100644 index 0000000..d4ac127 --- /dev/null +++ b/tests/test_openai_utils.py @@ -0,0 +1,10 @@ +from bigdata_risk_analyzer.openai_utils import build_openai_client + + +def test_build_openai_client_uses_the_configured_key(monkeypatch): + """The key can arrive via .env, which OpenAI() would not see on its own.""" + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + + client = build_openai_client(api_key="openai-from-settings") + + assert client.api_key == "openai-from-settings" diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 0000000..4b7c4db --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,19 @@ +from bigdata_risk_analyzer.pipeline import _risk_factor_channel + + +def test_risk_factor_channel_four_level_tree(): + # Leaf's ancestry chain: [root, channel, factor] + ancestry = {"Sub-Scenario": ["Main Risk", "Risk Channel", "Risk Factor"]} + risk_factor, risk_channel = _risk_factor_channel("Sub-Scenario", ancestry) + assert risk_factor == "Risk Factor" + assert risk_channel == "Risk Channel" + + +def test_risk_factor_channel_three_level_tree_falls_back_to_factor(): + # Leaf's ancestry chain (depth-capped tree): [root, channel] — no separate factor layer + ancestry = {"Sub-Scenario": ["Main Risk", "Risk Channel"]} + risk_factor, risk_channel = _risk_factor_channel("Sub-Scenario", ancestry) + assert risk_factor == "Risk Channel" + # Without the fallback this would incorrectly be the constant root theme name. + assert risk_channel == "Risk Channel" + assert risk_channel != "Main Risk" diff --git a/tests/test_retrieval.py b/tests/test_retrieval.py new file mode 100644 index 0000000..805abc2 --- /dev/null +++ b/tests/test_retrieval.py @@ -0,0 +1,68 @@ +import pandas as pd + +from bigdata_risk_analyzer.retrieval import extract_sentences + + +def _universe() -> pd.DataFrame: + return pd.DataFrame( + [ + {"RP_ENTITY_ID": "D8442A", "COMPANY_NAME": "Apple"}, + {"RP_ENTITY_ID": "228D42", "COMPANY_NAME": "Microsoft"}, + ] + ) + + +def test_extract_sentences_emits_one_row_per_universe_company_on_chunk(): + documents = [ + { + "id": "doc-1", + "headline": "Apple and Microsoft on tariffs", + "timestamp": "2025-01-01T00:00:00+00:00", + "chunks": [ + { + "text": "Both companies face tariff risk.", + "relevance": 0.9, + # Unstable set-like order: first id is not in the universe. + "entity_ids": ["DEAD00", "228D42", "D8442A"], + } + ], + } + ] + + sentences = extract_sentences(documents, _universe()) + + assert [row["company_name"] for row in sentences] == ["Microsoft", "Apple"] + assert [row["sentence_id"] for row in sentences] == [0, 1] + assert all(row["text"] == "Both companies face tariff risk." for row in sentences) + + +def test_extract_sentences_skips_chunks_with_no_universe_entity(): + documents = [ + { + "id": "doc-1", + "headline": "Unrelated", + "timestamp": "2025-01-01T00:00:00+00:00", + "chunks": [ + {"text": "Other firm", "relevance": 0.9, "entity_ids": ["ZZZZZZ"]} + ], + } + ] + + assert extract_sentences(documents, _universe()) == [] + + +def test_extract_sentences_drops_chunks_below_rerank_threshold(): + documents = [ + { + "id": "doc-1", + "headline": "Apple", + "timestamp": "2025-01-01T00:00:00+00:00", + "chunks": [ + {"text": "keep", "relevance": 0.9, "entity_ids": ["D8442A"]}, + {"text": "drop", "relevance": 0.2, "entity_ids": ["D8442A"]}, + ], + } + ] + + sentences = extract_sentences(documents, _universe(), rerank_threshold=0.5) + assert [row["text"] for row in sentences] == ["keep"] diff --git a/tests/test_service.py b/tests/test_service.py index 064c70e..b8fa0e3 100644 --- a/tests/test_service.py +++ b/tests/test_service.py @@ -1,6 +1,5 @@ import pandas as pd import pytest -from bigdata_research_tools.mindmap.mindmap import MindMap from bigdata_risk_analyzer.models import ( LabeledContent, @@ -9,107 +8,82 @@ RiskTaxonomy, ) from bigdata_risk_analyzer.service import build_response +from bigdata_risk_analyzer.taxonomy import Node @pytest.fixture -def df_company(): - return pd.DataFrame( - [ - { - "Company": "A", - "Ticker": "T1", - "Sector": "S1", - "Industry": "I1", - "Composite Score": 55, - "Risk1": 55, - }, - { - "Company": "B", - "Ticker": "T2", - "Sector": "S2", - "Industry": "I2", - "Composite Score": 50, - "Risk1": 45, - "Risk 2 with long name": 5, - }, - ] +def root(): + return Node( + node=1, + label="Root", + summary="Root node", + children=[ + Node(node=2, label="Risk1", summary="Risk1 for company"), + Node(node=3, label="Risk 2 with long name", summary="Risk 2 for company"), + ], ) @pytest.fixture -def df_motivation(): +def universe_df(): return pd.DataFrame( [ - {"Company": "A", "Motivation": "Growth"}, - {"Company": "B", "Motivation": "Decline"}, + { + "RP_ENTITY_ID": "AAAAAA", + "COMPANY_NAME": "A", + "TICKER": "T1", + "SECTOR": "S1", + "INDUSTRY": "I1", + "COUNTRY": "US", + }, + { + "RP_ENTITY_ID": "BBBBBB", + "COMPANY_NAME": "B", + "TICKER": "T2", + "SECTOR": "S2", + "INDUSTRY": "I2", + "COUNTRY": "UK", + }, ] ) @pytest.fixture -def df_labeled(): +def screener_df(): return pd.DataFrame( [ { - "Time Period": "2025Q1", - "Date": "2025-01-01", - "Company": "A", - "Sector": "S1", - "Industry": "I1", - "Country": "US", - "Ticker": "T1", - "Document ID": "D1", - "Headline": "Headline1", - "Quote": "Quote1", - "Motivation": "Growth", - "Sub-Scenario": "Sub1", - "Risk Channel": "Channel1", - "Risk Factor": "Factor1", - "Highlights": [], + "sentence_id": "0", + "text": "Quote1", + "document_id": "D1", + "headline": "Headline1", + "timestamp": "2025-01-01T00:00:00+00:00", + "company_name": "A", + "motivation": "Growth", + "label": "Risk1", + "summary": "Summary for A", }, { - "Time Period": "2025Q1", - "Date": "2025-01-02", - "Company": "B", - "Sector": "S2", - "Industry": "I2", - "Country": "UK", - "Ticker": "T2", - "Document ID": "D2", - "Headline": "Headline2", - "Quote": "Quote2", - "Motivation": "Decline", - "Sub-Scenario": "Sub2", - "Risk Channel": "Channel2", - "Risk Factor": "Factor2", - "Highlights": [ - "Highlight2.1", - "Highlight2.2", - ], + "sentence_id": "1", + "text": "Quote2", + "document_id": "D2", + "headline": "Headline2", + "timestamp": "2025-01-02T00:00:00+00:00", + "company_name": "B", + "motivation": "Decline", + "label": "Risk 2 with long name", + "summary": "Summary for B", }, ] ) -@pytest.fixture -def risk_tree(): - return MindMap( - label="Root", - node=1, - summary="Root node", - children=[ - MindMap(label="Risk1", node=2, summary="Risk1 for company"), - MindMap( - label="Risk 2 with long name", node=3, summary="Risk 2 for company" - ), - ], - ) - - -def test_build_response(df_company, df_motivation, df_labeled, risk_tree): - response = build_response(df_company, df_motivation, df_labeled, risk_tree) +def test_build_response(screener_df, root, universe_df): + response = build_response(screener_df, root, universe_df) assert isinstance(response, RiskAnalysisResponse) assert isinstance(response.risk_taxonomy, RiskTaxonomy) assert isinstance(response.risk_scoring, RiskScoring) assert isinstance(response.content, LabeledContent) assert len(response.content.root) == 2 + assert set(response.risk_scoring.root.keys()) == {"A", "B"} + assert response.risk_scoring.root["A"].composite_score == 1 diff --git a/tests/test_settings.py b/tests/test_settings.py new file mode 100644 index 0000000..1db4feb --- /dev/null +++ b/tests/test_settings.py @@ -0,0 +1,86 @@ +from pathlib import Path + +import pytest +from pydantic_settings import SettingsConfigDict + +from bigdata_risk_analyzer.settings import UNSET, Settings + + +@pytest.fixture +def no_api_keys_in_environ(monkeypatch): + """Drop the keys conftest exports, so only the .env file can supply them.""" + monkeypatch.delenv("BIGDATA_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + + +def _write_env_file(directory: Path, contents: str) -> Path: + env_file = directory / ".env" + env_file.write_text(contents) + return env_file + + +def _load_settings(env_file: Path) -> Settings: + """Load settings from one specific .env file, ignoring the project's own.""" + + class EnvFileSettings(Settings): + model_config = SettingsConfigDict( + env_file=env_file, env_file_encoding="utf-8", extra="ignore" + ) + + return EnvFileSettings() + + +def test_settings_read_api_keys_from_env_file(tmp_path, no_api_keys_in_environ): + env_file = _write_env_file( + tmp_path, "BIGDATA_API_KEY=bigdata-from-file\nOPENAI_API_KEY=openai-from-file\n" + ) + + settings = _load_settings(env_file) + + assert settings.BIGDATA_API_KEY == "bigdata-from-file" + assert settings.OPENAI_API_KEY == "openai-from-file" + + +def test_environment_variables_take_precedence_over_env_file( + tmp_path, monkeypatch, no_api_keys_in_environ +): + env_file = _write_env_file( + tmp_path, "BIGDATA_API_KEY=bigdata-from-file\nOPENAI_API_KEY=openai-from-file\n" + ) + monkeypatch.setenv("BIGDATA_API_KEY", "bigdata-from-environ") + + settings = _load_settings(env_file) + + assert settings.BIGDATA_API_KEY == "bigdata-from-environ" + assert settings.OPENAI_API_KEY == "openai-from-file" + + +def test_unrelated_env_file_keys_are_ignored(tmp_path, no_api_keys_in_environ): + env_file = _write_env_file( + tmp_path, + "BIGDATA_API_KEY=bigdata-from-file\n" + "OPENAI_API_KEY=openai-from-file\n" + "SOME_UNRELATED_TOOL_TOKEN=whatever\n", + ) + + settings = _load_settings(env_file) + + assert settings.BIGDATA_API_KEY == "bigdata-from-file" + + +def test_missing_api_keys_are_rejected_when_demo_mode_is_off( + tmp_path, no_api_keys_in_environ +): + env_file = _write_env_file(tmp_path, "BIGDATA_API_KEY=bigdata-from-file\n") + + with pytest.raises(ValueError, match="must be set when DEMO_MODE is disabled"): + _load_settings(env_file) + + +def test_demo_mode_does_not_require_api_keys(tmp_path, no_api_keys_in_environ): + env_file = _write_env_file(tmp_path, "DEMO_MODE=true\n") + + settings = _load_settings(env_file) + + assert settings.DEMO_MODE is True + assert settings.BIGDATA_API_KEY == UNSET diff --git a/tests/test_storage_schema.py b/tests/test_storage_schema.py new file mode 100644 index 0000000..fc4fd3e --- /dev/null +++ b/tests/test_storage_schema.py @@ -0,0 +1,40 @@ +import pytest +from sqlmodel import Session, SQLModel, create_engine, text + +from bigdata_risk_analyzer.api import app as app_module + + +@pytest.fixture +def sqlite_engine(tmp_path, monkeypatch): + engine = create_engine(f"sqlite:///{tmp_path / 'test.db'}") + monkeypatch.setattr(app_module, "engine", engine) + return engine + + +def test_check_storage_schema_accepts_a_freshly_created_database(sqlite_engine): + SQLModel.metadata.create_all(sqlite_engine) + + app_module.check_storage_schema() + + +def test_check_storage_schema_accepts_an_empty_database(sqlite_engine): + app_module.check_storage_schema() + + +def test_check_storage_schema_rejects_a_table_from_an_earlier_release(sqlite_engine): + # A 2.x report table: no chunk_percentage, plus columns that 3.0 dropped. + with Session(sqlite_engine) as session: + session.exec( + text( + "CREATE TABLE sqlriskanalyzerreport (" + "id CHAR(32) NOT NULL PRIMARY KEY, " + "created_at DATETIME NOT NULL, " + "llm_model VARCHAR NOT NULL, " + "theme VARCHAR NOT NULL, " + "document_limit INTEGER NOT NULL)" + ) + ) + session.commit() + + with pytest.raises(RuntimeError, match="chunk_percentage"): + app_module.check_storage_schema() diff --git a/tests/test_taxonomy.py b/tests/test_taxonomy.py new file mode 100644 index 0000000..fdaf5f0 --- /dev/null +++ b/tests/test_taxonomy.py @@ -0,0 +1,59 @@ +from bigdata_risk_analyzer.taxonomy import Node, get_leaf_labels, truncate_depth + + +def _four_level_tree() -> Node: + return Node( + node=1, + label="Main Risk", + summary="Root risk", + children=[ + Node( + node=2, + label="Risk Channel", + summary="Channel", + children=[ + Node( + node=3, + label="Risk Factor", + summary="Factor", + children=[ + Node(node=4, label="Sub-Scenario A", summary="A"), + Node(node=5, label="Sub-Scenario B", summary="B"), + ], + ) + ], + ) + ], + ) + + +def test_truncate_depth_none_is_noop(): + root = _four_level_tree() + assert truncate_depth(root, None) == root + + +def test_truncate_depth_to_three_drops_sub_scenario_layer(): + root = _four_level_tree() + truncated = truncate_depth(root, 3) + + # Root -> channel -> factor, with factor now a leaf (sub-scenarios cut off) + assert truncated.label == "Main Risk" + channel = truncated.children[0] + assert channel.label == "Risk Channel" + factor = channel.children[0] + assert factor.label == "Risk Factor" + assert factor.children == [] + assert get_leaf_labels(truncated) == ["Risk Factor"] + + +def test_truncate_depth_to_two_drops_two_layers(): + root = _four_level_tree() + truncated = truncate_depth(root, 2) + assert truncated.children[0].children == [] + assert get_leaf_labels(truncated) == ["Risk Channel"] + + +def test_truncate_depth_does_not_mutate_original(): + root = _four_level_tree() + truncate_depth(root, 2) + assert get_leaf_labels(root) == ["Sub-Scenario A", "Sub-Scenario B"] diff --git a/tests/test_traces.py b/tests/test_traces.py deleted file mode 100644 index 49e52a1..0000000 --- a/tests/test_traces.py +++ /dev/null @@ -1,18 +0,0 @@ -from unittest.mock import Mock - -import pytest - -from bigdata_risk_analyzer.traces import TraceEventName, send_trace - - -def test_send_trace_with_mock_client(): - mock_client = Mock() - for event_name in TraceEventName.__members__.values(): - trace_data = {"key": "value"} - - # The function should not raise when using a mock client - try: - send_trace(mock_client, event_name, trace_data) - except Exception as e: - pytest.fail(f"send_trace raised an exception: {e}") - assert mock_client._api.send_tracking_event.called diff --git a/tests/test_universe.py b/tests/test_universe.py new file mode 100644 index 0000000..4d0e8ff --- /dev/null +++ b/tests/test_universe.py @@ -0,0 +1,124 @@ +import io + +import pytest + +from bigdata_risk_analyzer import universe + + +def _csv(text: str) -> io.StringIO: + return io.StringIO(text) + + +def test_load_universe_csv_without_api_key_leaves_metadata_blank(): + df = universe.load_universe_csv( + _csv("RP_ENTITY_ID,COMPANY_NAME\nD8442A,Apple Inc.\n") + ) + assert df.loc[0, "TICKER"] is None + assert df.loc[0, "SECTOR"] is None + assert df.loc[0, "INDUSTRY"] is None + assert df.loc[0, "COUNTRY"] is None + + +def test_load_universe_csv_enriches_missing_metadata_via_kg(monkeypatch): + def fake_resolve_companies(ids, api_key, api_base_url=None, timeout=30.0): + assert api_key == "test-key" + assert set(ids) == {"D8442A"} + return { + "D8442A": { + "name": "Apple Inc.", + "ticker": "AAPL", + "sector": "Technology", + "industry": "Computer Hardware", + "country": "US", + } + } + + monkeypatch.setattr( + universe.bigdata_kg, "resolve_companies", fake_resolve_companies + ) + + df = universe.load_universe_csv( + _csv("RP_ENTITY_ID,COMPANY_NAME\nD8442A,Apple Inc.\n"), api_key="test-key" + ) + row = df.iloc[0] + assert row["TICKER"] == "AAPL" + assert row["SECTOR"] == "Technology" + assert row["INDUSTRY"] == "Computer Hardware" + assert row["COUNTRY"] == "US" + + +def test_load_universe_csv_does_not_overwrite_csv_provided_values(monkeypatch): + def fake_resolve_companies(ids, api_key, api_base_url=None, timeout=30.0): + # Both rows are missing SECTOR/INDUSTRY/COUNTRY (only TICKER was in the CSV). + assert set(ids) == {"D8442A", "E09E2B"} + return { + "D8442A": { + "name": "Apple Inc.", + "ticker": "AAPL-FROM-KG", + "sector": "Technology", + "industry": "Computer Hardware", + "country": "US", + }, + "E09E2B": { + "name": "NVIDIA Corp.", + "ticker": "NVDA-FROM-KG", + "sector": "Technology", + "industry": "Semiconductors", + "country": "US", + }, + } + + monkeypatch.setattr( + universe.bigdata_kg, "resolve_companies", fake_resolve_companies + ) + + csv_text = ( + "RP_ENTITY_ID,COMPANY_NAME,TICKER\n" + "D8442A,Apple Inc.,AAPL\n" + "E09E2B,NVIDIA Corp.,NVDA\n" + ) + df = universe.load_universe_csv(_csv(csv_text), api_key="test-key") + + apple = df[df["RP_ENTITY_ID"] == "D8442A"].iloc[0] + nvidia = df[df["RP_ENTITY_ID"] == "E09E2B"].iloc[0] + + # CSV-provided tickers are preserved, not overwritten by the KG lookup... + assert apple["TICKER"] == "AAPL" + assert nvidia["TICKER"] == "NVDA" + # ...but the missing sector/industry/country are backfilled for both rows. + assert apple["SECTOR"] == "Technology" + assert apple["INDUSTRY"] == "Computer Hardware" + assert nvidia["SECTOR"] == "Technology" + assert nvidia["INDUSTRY"] == "Semiconductors" + + +def test_load_universe_csv_kg_failure_is_non_fatal(monkeypatch): + def fake_resolve_companies(ids, api_key, api_base_url=None, timeout=30.0): + raise ValueError("No entities found") + + monkeypatch.setattr( + universe.bigdata_kg, "resolve_companies", fake_resolve_companies + ) + + df = universe.load_universe_csv( + _csv("RP_ENTITY_ID,COMPANY_NAME\nD8442A,Apple Inc.\n"), api_key="test-key" + ) + assert df.loc[0, "SECTOR"] is None + + +@pytest.mark.parametrize("blank_value", ["", " ", "nan"]) +def test_blank_and_nan_like_cells_are_treated_as_missing(blank_value, monkeypatch): + calls = {} + + def fake_resolve_companies(ids, api_key, api_base_url=None, timeout=30.0): + calls["ids"] = set(ids) + return {"D8442A": {"sector": "Technology"}} + + monkeypatch.setattr( + universe.bigdata_kg, "resolve_companies", 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