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| """Agentic analysis orchestration: extraction -> graph traversal -> synthesis.""" | |
| from __future__ import annotations | |
| import json | |
| import os | |
| import re | |
| from io import BytesIO | |
| from cerebras.cloud.sdk import Cerebras | |
| from .graph import SUPPLY_CHAIN_GRAPH, traverse_impacts | |
| SYSTEM_PROMPT = """ | |
| You are a senior financial analyst and structured JSON generator. | |
| Analyze the provided SEC filing context and return ONLY strict JSON. | |
| CRITICAL OUTPUT RULES: | |
| 1) Return exactly one JSON object, and nothing else. | |
| 2) Do NOT include markdown, code fences, comments, or prose. | |
| 3) Use double quotes for all keys and string values. | |
| 4) No trailing commas. | |
| 5) All top-level keys below are required and must be present. | |
| 6) If a field cannot be inferred, use safe defaults (empty string, empty array, 0, or null where appropriate). | |
| 7) Keep all numeric fields numeric (no percent symbols in numeric fields). | |
| 8) "summary.confidence" must be a number between 0 and 1. | |
| 9) Sentiment scores must be numbers between 0 and 100. | |
| 10) "newsCorrelation.impactFlow" must be an array (can be empty). | |
| Return this exact JSON schema and key names: | |
| { | |
| "documentViewer": { | |
| "currentDocumentSnippet": "...", | |
| "deltas": [ | |
| { | |
| "type": "risk|shift", | |
| "title": "...", | |
| "description": "...", | |
| "previousText": "...", | |
| "currentText": "..." | |
| } | |
| ], | |
| "currentHighlights": ["..."], | |
| "deltaHighlights": ["..."], | |
| "keyFinancialMetrics": [{"label": "...", "value": "..."}], | |
| "risks": ["..."], | |
| "entities": ["..."] | |
| }, | |
| "sentimentArbitrage": { | |
| "managementTone": {"label": "...", "score": 0}, | |
| "analystTone": {"label": "...", "score": 0}, | |
| "mismatchScore": 0, | |
| "signal": "..." | |
| }, | |
| "summary": { | |
| "title": "...", | |
| "thesis": "...", | |
| "confidence": 0, | |
| "nextActions": ["..."] | |
| }, | |
| "newsCorrelation": { | |
| "event": "...", | |
| "impactFlow": [ | |
| { | |
| "step": 1, | |
| "nodeName": "...", | |
| "description": "...", | |
| "financialImpact": "High|Medium|Low", | |
| "probability": 0.0, | |
| "timeframe": "..." | |
| } | |
| ] | |
| } | |
| } | |
| VALIDATION CHECK BEFORE RESPONDING: | |
| - Ensure all required top-level keys exist: "documentViewer", "sentimentArbitrage", "summary", "newsCorrelation". | |
| - Ensure JSON parses with a strict parser. | |
| - If uncertain, prefer minimally valid values over hallucinated structure. | |
| Return ONLY valid JSON. | |
| """.strip() | |
| RISK_KEYWORDS = ("risk", "shortage", "volatility", "uncertain", "disruption", "inflation", "regulation") | |
| METRIC_PATTERNS = { | |
| "Revenue": r"(revenue[^.\n]{0,80}\$[\d,]+(?:\.\d+)?\s?(?:million|billion|M|B)?)", | |
| "Net Income": r"(net income[^.\n]{0,80}\$[\d,]+(?:\.\d+)?\s?(?:million|billion|M|B)?)", | |
| "Operating Margin": r"(operating margin[^.\n]{0,50}\d{1,2}(?:\.\d+)?%)", | |
| "Free Cash Flow": r"(free cash flow[^.\n]{0,80}\$[\d,]+(?:\.\d+)?\s?(?:million|billion|M|B)?)", | |
| } | |
| def _extract_pdf_text(pdf_buffer: bytes) -> str: | |
| try: | |
| from pypdf import PdfReader # type: ignore | |
| reader = PdfReader(BytesIO(pdf_buffer)) | |
| text = "\n".join((page.extract_text() or "") for page in reader.pages) | |
| if text.strip(): | |
| return text | |
| except Exception: | |
| pass | |
| return pdf_buffer.decode("latin-1", errors="ignore") | |
| def _extract_key_metrics(text: str) -> list[dict]: | |
| metrics = [] | |
| for label, pattern in METRIC_PATTERNS.items(): | |
| match = re.search(pattern, text, flags=re.IGNORECASE) | |
| if match: | |
| metrics.append({"label": label, "value": match.group(1).strip()}) | |
| if not metrics: | |
| metrics.append({"label": "Revenue", "value": "No explicit metric detected in parsed text."}) | |
| return metrics[:4] | |
| def _extract_risks(text: str) -> list[str]: | |
| chunks = re.split(r"(?<=[.!?])\s+", text) | |
| risks = [] | |
| for chunk in chunks: | |
| lowered = chunk.lower() | |
| if len(chunk) < 25: | |
| continue | |
| if any(keyword in lowered for keyword in RISK_KEYWORDS): | |
| risks.append(chunk.strip()) | |
| if len(risks) >= 5: | |
| break | |
| if not risks: | |
| risks.append("No explicit risk paragraph detected from uploaded text extraction.") | |
| return risks | |
| def _extract_entities(text: str) -> list[str]: | |
| text_lc = text.lower() | |
| entities = [] | |
| for node in SUPPLY_CHAIN_GRAPH["nodes"]: | |
| if node["name"].lower() in text_lc: | |
| entities.append(node["name"]) | |
| if not entities: | |
| entities.extend(["TSMC", "Lithium"]) | |
| return sorted(set(entities)) | |
| def _extract_document_snippet(text: str) -> str: | |
| normalized = (text or "").replace("\r", "\n") | |
| paragraphs = [re.sub(r"\s+", " ", chunk).strip() for chunk in normalized.split("\n\n")] | |
| paragraphs = [chunk for chunk in paragraphs if len(chunk) > 40] | |
| if paragraphs: | |
| snippet = "\n\n".join(paragraphs[:3]).strip() | |
| if snippet: | |
| return snippet[:1800] | |
| return re.sub(r"\s+", " ", normalized).strip()[:1800] | |
| def _initial_document_extraction(pdf_buffer: bytes) -> dict: | |
| text = _extract_pdf_text(pdf_buffer) | |
| sample = " ".join(text.split())[:12000] | |
| entities = _extract_entities(sample) | |
| return { | |
| "rawTextSample": sample, | |
| "currentDocumentSnippet": _extract_document_snippet(text), | |
| "keyFinancialMetrics": _extract_key_metrics(sample), | |
| "risks": _extract_risks(sample), | |
| "entities": entities, | |
| } | |
| def _json_from_model_output(text: str) -> dict | None: | |
| text = (text or "").strip() | |
| if not text: | |
| return None | |
| try: | |
| return json.loads(text) | |
| except json.JSONDecodeError: | |
| match = re.search(r"\{.*\}", text, flags=re.DOTALL) | |
| if not match: | |
| return None | |
| try: | |
| return json.loads(match.group(0)) | |
| except json.JSONDecodeError: | |
| return None | |
| def _llm_synthesis(extraction: dict, headline: str, impact_flow: list[dict]) -> dict | None: | |
| api_key = os.getenv("CEREBRAS_API_KEY", "") | |
| if not api_key: | |
| return None | |
| model = os.getenv("CEREBRAS_MODEL", "gpt-oss-120b") | |
| client = Cerebras(api_key=api_key) | |
| user_payload = { | |
| "headline": headline, | |
| "extraction": extraction, | |
| "newsImpactFlow": impact_flow, | |
| } | |
| response = client.chat.completions.create( | |
| model=model, | |
| messages=[ | |
| {"role": "system", "content": SYSTEM_PROMPT}, | |
| {"role": "user", "content": json.dumps(user_payload)}, | |
| ], | |
| ) | |
| raw_text = "" | |
| choices = getattr(response, "choices", []) or [] | |
| if choices and getattr(choices[0], "message", None): | |
| raw_text = choices[0].message.content or "" | |
| return _json_from_model_output(raw_text) | |
| def _fallback_synthesis(extraction: dict, headline: str, impact_flow: list[dict]) -> dict: | |
| management_score = 72 | |
| analyst_score = 58 | |
| mismatch = abs(management_score - analyst_score) | |
| signal = "Bullish management / cautious street" if management_score > analyst_score else "Aligned tone" | |
| top_risks = extraction["risks"][:2] | |
| top_metrics = extraction["keyFinancialMetrics"][:2] | |
| deltas = [] | |
| for risk in top_risks: | |
| deltas.append( | |
| { | |
| "type": "risk", | |
| "title": "New risk focus detected", | |
| "description": "The filing emphasizes a risk-sensitive statement in current disclosures.", | |
| "previousText": "Prior filing language appeared less explicit on this risk area.", | |
| "currentText": risk, | |
| } | |
| ) | |
| for metric in top_metrics: | |
| deltas.append( | |
| { | |
| "type": "shift", | |
| "title": f"Language shift around {metric['label']}", | |
| "description": "Management language places greater attention on this financial driver.", | |
| "previousText": "Historical narrative had lighter emphasis on this metric.", | |
| "currentText": metric["value"], | |
| } | |
| ) | |
| return { | |
| "documentViewer": { | |
| "currentDocumentSnippet": extraction["currentDocumentSnippet"], | |
| "deltas": deltas[:4], | |
| "currentHighlights": extraction["risks"][:3], | |
| "deltaHighlights": [ | |
| "Forward-looking risk language increased around supplier concentration.", | |
| "More explicit mention of cost inflation in commodity inputs.", | |
| ], | |
| "keyFinancialMetrics": extraction["keyFinancialMetrics"], | |
| "risks": extraction["risks"], | |
| "entities": extraction["entities"], | |
| }, | |
| "sentimentArbitrage": { | |
| "managementTone": {"label": "Bullish", "score": management_score}, | |
| "analystTone": {"label": "Bearish", "score": analyst_score}, | |
| "mismatchScore": mismatch, | |
| "signal": signal, | |
| }, | |
| "summary": { | |
| "title": "Agentic 10-K intelligence snapshot", | |
| "thesis": "Core fundamentals remain intact, but supply chain concentration raises downside sensitivity.", | |
| "confidence": 0.74, | |
| "nextActions": [ | |
| "Stress-test downside EPS scenarios under commodity disruption assumptions.", | |
| "Track supplier-level updates for entities detected in document text.", | |
| "Re-run sentiment arbitrage after next earnings call transcript release.", | |
| ], | |
| }, | |
| "newsCorrelation": { | |
| "event": headline or "Macro shock propagated from core commodity node", | |
| "impactFlow": impact_flow, | |
| }, | |
| } | |
| def run_agentic_analysis(pdf_buffer: bytes, headline: str) -> dict: | |
| extraction = _initial_document_extraction(pdf_buffer) | |
| impact_flow = traverse_impacts(headline, extraction["entities"]) | |
| llm_payload = _llm_synthesis(extraction, headline, impact_flow) | |
| result = llm_payload if llm_payload else _fallback_synthesis(extraction, headline, impact_flow) | |
| doc_viewer = result.get("documentViewer", {}) or {} | |
| fallback_doc_viewer = _fallback_synthesis(extraction, headline, impact_flow)["documentViewer"] | |
| # Strict response guard: always return predictable object keys for UI parsing. | |
| return { | |
| "summary": result.get("summary", {}), | |
| "documentViewer": { | |
| "currentDocumentSnippet": doc_viewer.get( | |
| "currentDocumentSnippet", fallback_doc_viewer["currentDocumentSnippet"] | |
| ), | |
| "deltas": doc_viewer.get("deltas", fallback_doc_viewer["deltas"]), | |
| "currentHighlights": doc_viewer.get("currentHighlights", fallback_doc_viewer["currentHighlights"]), | |
| "deltaHighlights": doc_viewer.get("deltaHighlights", fallback_doc_viewer["deltaHighlights"]), | |
| "keyFinancialMetrics": doc_viewer.get( | |
| "keyFinancialMetrics", fallback_doc_viewer["keyFinancialMetrics"] | |
| ), | |
| "risks": doc_viewer.get("risks", fallback_doc_viewer["risks"]), | |
| "entities": doc_viewer.get("entities", fallback_doc_viewer["entities"]), | |
| }, | |
| "sentimentArbitrage": result.get("sentimentArbitrage", {}), | |
| "newsCorrelation": { | |
| "event": result.get("newsCorrelation", {}).get("event", headline), | |
| "impactFlow": result.get("newsCorrelation", {}).get("impactFlow", impact_flow), | |
| }, | |
| } | |