| from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks, Request, UploadFile, File, Form, Query |
| from typing import List, Dict, Any, Optional |
|
|
| from ...core.neo4j_store import Neo4jStore |
| from ...retrieval.agent import AgentRetrievalSystem |
| from ...ingestion.pipeline import IngestionPipeline |
| from ...config import settings |
| from ...api.models import * |
| from ...api.auth import get_current_user, User |
| import redis |
| from ..dependencies import get_graph_store, get_retrieval_agent, get_ingestion_pipeline, get_redis_client |
|
|
| router = APIRouter() |
|
|
| from ...core.storage import get_storage |
| storage = get_storage() |
|
|
| @router.post("/api/report", response_model=ReportResponse, tags=["Report"]) |
| async def generate_report( |
| request: ReportRequest, |
| current_user: User = Depends(get_current_user), |
| ): |
| """ |
| Generate an analytical report using the full ReACT ReportAgent. |
| |
| The agent: |
| 1. Decomposes the topic into sub-questions |
| 2. Iteratively calls InsightForge / PanoramaSearch / QuickSearch tools |
| 3. Writes each section from retrieved knowledge graph data |
| 4. Compiles a structured markdown report |
| |
| Inspired by MiroFish's report_agent.py + InsightForge/PanoramaSearch/QuickSearch. |
| """ |
| llm = UnifiedLLMProvider(provider=settings.default_llm_provider) |
| agent = ReportAgent(store=request.app.state.graph_store, llm=llm) |
| result = await agent.generate_report( |
| topic=request.topic, |
| report_type=request.report_type or "detailed", |
| target_entity=request.target_entity, |
| ) |
| return ReportResponse( |
| topic=result.topic, |
| executive_summary=result.executive_summary, |
| sections=result.sections, |
| key_entities=result.key_entities, |
| confidence=result.confidence, |
| tool_calls_made=result.tool_calls_made, |
| generated_at=result.generated_at, |
| markdown=result.markdown, |
| ) |
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