Explore with Athena Copilot:

Turn questions into clear, data-backed insights using
your portfolio, research history, and live market signals.

app/services/rag_synthesis.py

def _format_chunks_for_prompt(chunks: List<Dict>, limit: int = 10) -> str:
    lines: List<str> = []
    for idx, item in enumerate(chunks[:limit], start=1):
        metadata = item.get("metadata") or {}
        source = metadata.get("source", "Unknown")
        date_val = metadata.get("date", "Unknown date")
        text = (item.get("text") or "").strip()
        if not text:
            continue
        lines.append(f"[{idx}] Source: {source} | Date: {date_val}\n{text}")
    return "\n\n".join(lines)

Source metadata, dates, and passage text are assembled into a structured prompt for grounded synthesis.

app/routes/portfolio.py

@router.get("/", response_model=List<HoldingOut>)
def get_holdings(
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db),
):
    holdings = (
        db.query(PortfolioHolding)
        .filter(PortfolioHolding.user_id == current_user.id)
        .order_by(PortfolioHolding.ticker)
        .all()
    )

Live positions, cost basis, and exposure sourced from your portfolio for consistent analysis context and personalization.

app/services/personalization.py

    cutoff = datetime.now(timezone.utc) - timedelta(days=RECOMMENDATION_MEMORY_RETENTION_DAYS)
    rows = (
        db.query(RecommendationMemory)
        .filter(
            RecommendationMemory.user_id == user_id,
            RecommendationMemory.created_at >= cutoff,
        )
        .order_by(RecommendationMemory.created_at.desc(), RecommendationMemory.id.desc())
        .limit(MAX_RECOMMENDATION_MEMORY_ROWS)
        .all()
    )

Recent recommendation memory, retention window, and recency ordering feed the copilot's context before output.

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Advanced for the Informed Investor

Structured Research Flow:

Built on Data Trusted by Institutions.

Explore with Athena Copilot:

How do we Stack up?

Frequently AskedQuestions

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