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a392f04 8c79771 a392f04 8c79771 a392f04 8c79771 a392f04 8c79771 a392f04 8c79771 a392f04 626f6c1 8c79771 a392f04 626f6c1 a392f04 626f6c1 7598ba6 a392f04 626f6c1 a392f04 8c79771 a392f04 6efefd7 a392f04 8c79771 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from fastapi import FastAPI, BackgroundTasks, HTTPException
from fastapi.responses import FileResponse, HTMLResponse
from pydantic import BaseModel
from typing import Optional
import uvicorn
from config.settings import API_HOST, API_PORT, PROCESSED_DIR
from ingestion.sec_downloader import ingest_ticker
from ingestion.text_extractor import extract_all
from agents.analyst import analyze
app = FastAPI(title="SEC Analyst LLM")
class IngestRequest(BaseModel):
ticker: str
form_types: list = ["10-K", "10-Q"]
max_filings: int = 5
class AnalyzeRequest(BaseModel):
question: str
ticker: Optional[str] = None
n_results: int = 6
def run_pipeline(ticker, form_types, max_filings):
print(f"[PIPELINE] Starting {ticker}...")
ingest_ticker(ticker, form_types=form_types, max_filings=max_filings)
extract_all(ticker)
build_and_persist(ticker)
print(f"[PIPELINE] Done: {ticker}")
@app.get("/health")
def health():
return {"api": "ok", "ollama": "ok", "chromadb": "ok"}
@app.get("/tickers")
def list_tickers():
tickers = [d.name for d in PROCESSED_DIR.iterdir() if d.is_dir()] if PROCESSED_DIR.exists() else []
return {"tickers": sorted(tickers)}
@app.post("/ingest")
def ingest(req: IngestRequest, background_tasks: BackgroundTasks):
background_tasks.add_task(run_pipeline, req.ticker.upper(), req.form_types, req.max_filings)
return {"status": "started", "ticker": req.ticker.upper(), "message": f"Ingesting {req.ticker.upper()} — this takes 3-5 minutes. The ticker will appear on the left when done."}
@app.post("/analyze")
def analyze_endpoint(req: AnalyzeRequest):
try:
return analyze(question=req.question, ticker=req.ticker, n_results=req.n_results)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
ui_file = Path(__file__).parent.parent / "ui" / "index.html"
@app.get("/")
def serve_ui():
if ui_file.exists():
return FileResponse(str(ui_file))
return HTMLResponse("<h1>UI not found</h1>")
@app.get("/test-llm")
def test_llm():
import requests, os
from config.settings import HF_TOKEN
results = {}
models = [
"meta-llama/Llama-3.3-70B-Instruct",
"Qwen/QwQ-32B",
"meta-llama/Meta-Llama-3-8B-Instruct",
"HuggingFaceH4/zephyr-7b-beta",
"mistralai/Mistral-7B-Instruct-v0.2"
]
for model in models:
try:
url = f"https://api-inference.huggingface.co/models/{model}"
r = requests.post(url, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json={"inputs": "say hi", "parameters": {"max_new_tokens": 5}}, timeout=15)
results[model] = f"{r.status_code}: {r.text[:100]}"
except Exception as e:
results[model] = f"ERROR: {str(e)[:100]}"
return results
if __name__ == "__main__":
uvicorn.run("api.server:app", host=API_HOST, port=API_PORT, reload=False)
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