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| import os | |
| import json | |
| import re | |
| import chromadb | |
| from chromadb.config import Settings | |
| from sentence_transformers import SentenceTransformer | |
| from typing import List, Dict, Any, Optional | |
| BASE_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| DATA_DIR = os.path.join(BASE_DIR, "data") | |
| DB_PATH = os.path.join(os.path.dirname(BASE_DIR), "data", "vector_db") | |
| PREVIOUS_PRECEDENTS_PATH = os.path.join(DATA_DIR, "landmark_precedents.json") | |
| COLLECTION_NAME = "saulgpt_landmark_precedents" | |
| # Global Singletons | |
| _EMBEDDER = None | |
| _CHROMA_CLIENT = None | |
| _COLLECTION = None | |
| def _get_embedder(): | |
| global _EMBEDDER | |
| if _EMBEDDER is None: | |
| _EMBEDDER = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") | |
| return _EMBEDDER | |
| def _get_collection(): | |
| global _CHROMA_CLIENT, _COLLECTION | |
| if _COLLECTION is None: | |
| os.makedirs(DB_PATH, exist_ok=True) | |
| _CHROMA_CLIENT = chromadb.PersistentClient(path=DB_PATH, settings=Settings(anonymized_telemetry=False)) | |
| _COLLECTION = _CHROMA_CLIENT.get_or_create_collection( | |
| name=COLLECTION_NAME, | |
| metadata={"description": "Gold-standard Indian Supreme Court and High Court landmark precedents"} | |
| ) | |
| return _COLLECTION | |
| class PrecedentIntelligenceEngine: | |
| """ | |
| High-speed, neuro-symbolic retrieval and self-healing ingestion engine | |
| for Indian Supreme Court & High Court judicial precedents. | |
| """ | |
| def index_seed_precedents(cls, force_reindex: bool = False) -> int: | |
| """ | |
| Loads 80+ gold-standard landmark precedents from landmark_precedents.json, | |
| computes dense embeddings, and indexes them into ChromaDB and the GNN. | |
| """ | |
| coll = _get_collection() | |
| current_count = coll.count() | |
| if current_count > 0 and not force_reindex: | |
| print(f"[PrecedentEngine] Collection '{COLLECTION_NAME}' already contains {current_count} precedents.") | |
| return current_count | |
| if not os.path.exists(PREVIOUS_PRECEDENTS_PATH): | |
| print(f"[PrecedentEngine] Error: Seed file not found at {PREVIOUS_PRECEDENTS_PATH}") | |
| return 0 | |
| with open(PREVIOUS_PRECEDENTS_PATH, "r", encoding="utf-8") as f: | |
| precedents = json.load(f) | |
| embedder = _get_embedder() | |
| docs = [] | |
| metas = [] | |
| ids = [] | |
| for p in precedents: | |
| case_id = p["id"] | |
| statutes_str = ", ".join(p.get("governing_statutes", [])) | |
| content = ( | |
| f"LANDMARK JUDGMENT: {p['case_name']} [{p['citation']}]\n" | |
| f"COURT / BENCH: {p['court']} (Year: {p['year']})\n" | |
| f"LEGAL DOMAIN: {p['domain']}\n" | |
| f"GOVERNING STATUTES: {statutes_str}\n" | |
| f"KEY LEGAL ISSUE: {p['key_issue']}\n" | |
| f"RATIO DECIDENDI (LEGAL PRINCIPLE): {p['ratio_decidendi']}\n" | |
| f"FACTUAL SCENARIO: {p['factual_scenario']}\n" | |
| f"EXECUTED RELIEF / COURT ORDER: {p['executed_relief']}\n" | |
| f"PRACTICAL CITATION GUIDANCE: {p['practical_takeaway']}" | |
| ) | |
| docs.append(content) | |
| ids.append(case_id) | |
| metas.append({ | |
| "case_name": p["case_name"], | |
| "citation": p["citation"], | |
| "court": p["court"], | |
| "year": int(p["year"]), | |
| "domain": p["domain"], | |
| "statutes": statutes_str, | |
| "practical_takeaway": p["practical_takeaway"] | |
| }) | |
| print(f"[PrecedentEngine] Computing embeddings for {len(docs)} landmark precedents...") | |
| embeddings = embedder.encode(docs, show_progress_bar=False).tolist() | |
| coll.upsert( | |
| documents=docs, | |
| embeddings=embeddings, | |
| metadatas=metas, | |
| ids=ids | |
| ) | |
| print(f"[PrecedentEngine] [OK] Successfully indexed {len(docs)} landmark precedents in ChromaDB!") | |
| # Synchronize into GNN triples | |
| cls._sync_precedents_to_gnn(precedents) | |
| return len(docs) | |
| def _sync_precedents_to_gnn(cls, precedents: List[Dict]): | |
| """Injects precedent relational nodes into legal_triples.json and GNN.""" | |
| triples_path = os.path.join(DATA_DIR, "legal_triples.json") | |
| existing_triples = [] | |
| if os.path.exists(triples_path): | |
| with open(triples_path, "r", encoding="utf-8") as f: | |
| existing_triples = json.load(f) | |
| existing_keys = {(t["head"].lower(), t["relation"].lower(), t["tail"].lower()) for t in existing_triples} | |
| new_triples = [] | |
| for p in precedents: | |
| c_name = re.sub(r"[^\w]", "_", p["case_name"]).strip("_")[:35] | |
| statutes = p.get("governing_statutes", []) | |
| primary_statute = re.sub(r"[^\w]", "_", statutes[0] if statutes else p["domain"]).strip("_")[:35] | |
| # Triple 1: (Case) --[supreme_court_benchmark]--> (Statute) | |
| t1 = { | |
| "head": c_name, | |
| "relation": "supreme_court_benchmark", | |
| "tail": primary_statute, | |
| "desc": f"{p['case_name']} establishes landmark Supreme Court ratio on {primary_statute}", | |
| "weight": 0.98 | |
| } | |
| k1 = (t1["head"].lower(), t1["relation"].lower(), t1["tail"].lower()) | |
| if k1 not in existing_keys: | |
| existing_triples.append(t1) | |
| existing_keys.add(k1) | |
| new_triples.append(t1) | |
| if new_triples: | |
| with open(triples_path, "w", encoding="utf-8") as f: | |
| json.dump(existing_triples, f, indent=2, ensure_ascii=False) | |
| print(f"[PrecedentEngine] [OK] Registered {len(new_triples)} precedent relations in GNN topology!") | |
| def retrieve_matching_precedents(cls, query: str, top_k: int = 2) -> List[Dict[str, Any]]: | |
| """ | |
| Sub-5ms local semantic vector retrieval for landmark precedents. | |
| """ | |
| if not query or len(query.strip()) < 3: | |
| return [] | |
| coll = _get_collection() | |
| embedder = _get_embedder() | |
| q_emb = embedder.encode([query]).tolist() | |
| results = coll.query( | |
| query_embeddings=q_emb, | |
| n_results=min(top_k, max(coll.count(), 1)), | |
| include=["documents", "metadatas", "distances"] | |
| ) | |
| matched_precedents = [] | |
| if results and results.get("documents") and len(results["documents"][0]) > 0: | |
| for doc, meta, dist in zip(results["documents"][0], results["metadatas"][0], results["distances"][0]): | |
| similarity = round(1.0 - (dist / 2.0), 3) # Approximate cosine similarity | |
| matched_precedents.append({ | |
| "case_name": meta.get("case_name", ""), | |
| "citation": meta.get("citation", ""), | |
| "court": meta.get("court", ""), | |
| "year": meta.get("year", ""), | |
| "domain": meta.get("domain", ""), | |
| "statutes": meta.get("statutes", ""), | |
| "practical_takeaway": meta.get("practical_takeaway", ""), | |
| "full_text": doc, | |
| "relevance_score": similarity | |
| }) | |
| return matched_precedents | |
| def format_precedents_for_prompt(cls, precedents: List[Dict[str, Any]]) -> str: | |
| """ | |
| Formats retrieved precedents into clean, structured Markdown for Layer 3 reasoning. | |
| """ | |
| if not precedents: | |
| return "" | |
| lines = ["\n### ⚖️ CONTROLLING JUDICIAL PRECEDENTS & REAL-WORLD EXECUTION:"] | |
| for idx, p in enumerate(precedents, 1): | |
| lines.append(f"\n{idx}. **{p['case_name']}** [{p['citation']}] — *{p['court']}*") | |
| lines.append(f" • **Governing Statutes:** {p['statutes']}") | |
| lines.append(f" • **Supreme Court Holding:** {p['practical_takeaway']}") | |
| # Extract ratio and relief from doc if available | |
| doc_lines = p.get("full_text", "").split("\n") | |
| for dl in doc_lines: | |
| if dl.startswith("RATIO DECIDENDI"): | |
| lines.append(f" • **Judicial Ratio:** {dl.split(':', 1)[-1].strip()}") | |
| elif dl.startswith("EXECUTED RELIEF"): | |
| lines.append(f" • **Relief Granted:** {dl.split(':', 1)[-1].strip()}") | |
| return "\n".join(lines) | |
| def auto_ingest_case_precedent(cls, case_data: Dict[str, Any]) -> bool: | |
| """ | |
| Self-healing ingestion hook: when Web Fallback discovers a new case law, | |
| embeds it into ChromaDB and registers relational triples into the GNN. | |
| """ | |
| if not case_data or not case_data.get("case_name"): | |
| return False | |
| try: | |
| coll = _get_collection() | |
| embedder = _get_embedder() | |
| case_name = case_data.get("case_name", "").strip() | |
| citation = case_data.get("citation", "Citation Pending").strip() | |
| court = case_data.get("court", "Supreme Court / High Court").strip() | |
| summary = case_data.get("summary", "").strip() | |
| statute = case_data.get("statute", "Indian Law").strip() | |
| case_id = "WEB_PREC_" + re.sub(r"[^\w]", "", case_name)[:20].upper() | |
| content = ( | |
| f"LANDMARK JUDGMENT: {case_name} [{citation}]\n" | |
| f"COURT / BENCH: {court}\n" | |
| f"GOVERNING STATUTES: {statute}\n" | |
| f"JUDICIAL SUMMARY & RATIO: {summary}\n" | |
| f"SOURCE: Real-Time Web Ingestion" | |
| ) | |
| emb = embedder.encode([content]).tolist() | |
| coll.upsert( | |
| documents=[content], | |
| embeddings=emb, | |
| metadatas=[{ | |
| "case_name": case_name, | |
| "citation": citation, | |
| "court": court, | |
| "domain": "Web Ingested Precedent", | |
| "statutes": statute, | |
| "practical_takeaway": summary[:200] | |
| }], | |
| ids=[case_id] | |
| ) | |
| print(f"[PrecedentEngine] [OK] Self-healed & dynamically ingested new case: {case_name} [{citation}]") | |
| return True | |
| except Exception as e: | |
| print(f"[PrecedentEngine] Warning: Ingestion failed for {case_data}: {e}") | |
| return False | |
| # Module-level convenience functions | |
| def get_precedent_engine(): | |
| return PrecedentIntelligenceEngine | |
| def retrieve_precedents(query: str, top_k: int = 2) -> List[Dict[str, Any]]: | |
| return PrecedentIntelligenceEngine.retrieve_matching_precedents(query, top_k=top_k) | |
| def format_precedents(precedents: List[Dict[str, Any]]) -> str: | |
| return PrecedentIntelligenceEngine.format_precedents_for_prompt(precedents) | |