cbazza
4fea148d61
- extractor.py: Text-Extraktion für MD/TXT/PDF/DOCX/Bilder inkl. OCR (Tesseract) - normalizer.py: KI-Normalisierung via Claude Haiku (Typ-Erkennung) + Sonnet (Transformation) - ingest.py: Vollpipeline v2 mit watch, delete, dry-run, quality-min Flags - ingest_anythingllm.py: Einfaches Script v1 (nur MD/TXT/PDF, kein OCR) - README.md: Alle drei Tools dokumentiert
342 lines
11 KiB
Python
342 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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RAG Ingestion Script für Job-Matching Workflow
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Liest MD/TXT/PDF Dokumente, chunked sie und speichert Embeddings
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in der lokalen pgvector Instanz auf der NAS (Port 5433).
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Verwendung:
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python ingest_job_matching.py file <pfad> # Einzelne Datei
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python ingest_job_matching.py dir <pfad> # Verzeichnis
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python ingest_job_matching.py list # Alle Dokumente anzeigen
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python ingest_job_matching.py clear <filename> # Dokument löschen
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"""
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import os
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import uuid
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from datetime import datetime
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from pathlib import Path
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from dotenv import load_dotenv
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from openai import OpenAI
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import psycopg2
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from psycopg2.extras import execute_values
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import tiktoken
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# PDF Support (optional)
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try:
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from pypdf import PdfReader
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PDF_SUPPORT = True
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except ImportError:
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PDF_SUPPORT = False
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# Lade job_matching spezifische .env Datei
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env_path = Path(__file__).parent / ".env.job_matching"
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load_dotenv(dotenv_path=env_path)
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# --- Konfiguration ---
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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DB_HOST = os.getenv("JM_DB_HOST", "192.168.178.128")
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DB_PORT = int(os.getenv("JM_DB_PORT", 5433))
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DB_NAME = os.getenv("JM_DB_NAME", "job_matching")
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DB_USER = os.getenv("JM_DB_USER", "jobmatch")
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DB_PASSWORD = os.getenv("JM_DB_PASSWORD")
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CHUNK_SIZE = int(os.getenv("CHUNK_SIZE", 500))
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CHUNK_OVERLAP = int(os.getenv("CHUNK_OVERLAP", 50))
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EMBED_MODEL = "text-embedding-3-small"
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EMBED_DIM = 1536
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client = OpenAI(api_key=OPENAI_API_KEY)
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# --- Datenbank ---
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def get_db_connection():
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"""Direkte Verbindung zur pgvector Instanz auf der NAS."""
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return psycopg2.connect(
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host=DB_HOST,
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port=DB_PORT,
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dbname=DB_NAME,
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user=DB_USER,
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password=DB_PASSWORD
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)
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def ensure_table(conn):
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"""Stellt sicher dass die profile_documents Tabelle existiert."""
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with conn.cursor() as cur:
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cur.execute("CREATE EXTENSION IF NOT EXISTS vector;")
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cur.execute(f"""
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CREATE TABLE IF NOT EXISTS profile_documents (
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id SERIAL PRIMARY KEY,
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filename TEXT NOT NULL,
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chunk_index INTEGER NOT NULL,
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content TEXT NOT NULL,
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embedding vector({EMBED_DIM}),
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created_at TIMESTAMP DEFAULT NOW(),
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UNIQUE(filename, chunk_index)
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);
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""")
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cur.execute("""
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CREATE INDEX IF NOT EXISTS profile_documents_embedding_idx
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ON profile_documents
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USING ivfflat (embedding vector_cosine_ops)
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WITH (lists = 10);
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""")
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conn.commit()
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print(" ✅ Tabelle profile_documents bereit")
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# --- Text-Extraktion ---
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def read_file(path: Path) -> str:
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"""Liest MD, TXT oder PDF und gibt den Text zurück."""
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suffix = path.suffix.lower()
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if suffix in (".md", ".txt"):
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return path.read_text(encoding="utf-8")
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elif suffix == ".pdf":
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if not PDF_SUPPORT:
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raise ImportError("pypdf nicht installiert: pip install pypdf")
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reader = PdfReader(str(path))
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return "\n\n".join(page.extract_text() or "" for page in reader.pages)
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else:
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raise ValueError(f"Nicht unterstütztes Dateiformat: {suffix}")
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# --- Chunking ---
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def chunk_text(text: str, chunk_size: int = CHUNK_SIZE, overlap: int = CHUNK_OVERLAP) -> list[str]:
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"""Teilt Text in überlappende Chunks auf (token-basiert)."""
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enc = tiktoken.get_encoding("cl100k_base")
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tokens = enc.encode(text)
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chunks = []
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start = 0
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while start < len(tokens):
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end = min(start + chunk_size, len(tokens))
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chunk_tokens = tokens[start:end]
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chunks.append(enc.decode(chunk_tokens))
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start += chunk_size - overlap
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return chunks
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# --- Embeddings ---
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def get_embeddings(texts: list[str]) -> list[list[float]]:
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"""Erstellt Embeddings via OpenAI text-embedding-3-small."""
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response = client.embeddings.create(
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model=EMBED_MODEL,
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input=texts
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)
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return [item.embedding for item in response.data]
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# --- Ingestion ---
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def check_existing(conn, filename: str) -> int:
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"""Prüft ob Dokument bereits in der DB vorhanden ist."""
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with conn.cursor() as cur:
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cur.execute(
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"SELECT COUNT(*) FROM profile_documents WHERE filename = %s",
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(filename,)
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)
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return cur.fetchone()[0]
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def delete_existing(conn, filename: str):
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"""Löscht alle Chunks eines Dokuments."""
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with conn.cursor() as cur:
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cur.execute(
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"DELETE FROM profile_documents WHERE filename = %s",
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(filename,)
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)
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conn.commit()
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print(f" 🗑️ Bestehende Chunks für '{filename}' gelöscht.")
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def insert_chunks(conn, chunks: list[str], embeddings: list[list[float]], filename: str):
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"""Fügt Chunks mit Embeddings in pgvector ein."""
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records = [
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(filename, i, chunk, embedding)
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for i, (chunk, embedding) in enumerate(zip(chunks, embeddings))
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]
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with conn.cursor() as cur:
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execute_values(
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cur,
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"""
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INSERT INTO profile_documents (filename, chunk_index, content, embedding)
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VALUES %s
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ON CONFLICT (filename, chunk_index) DO UPDATE
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SET content = EXCLUDED.content,
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embedding = EXCLUDED.embedding,
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created_at = NOW()
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""",
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records,
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template="(%s, %s, %s, %s::vector)"
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)
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conn.commit()
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def ingest_file(file_path: str, force: bool = False) -> bool:
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"""Verarbeitet eine einzelne Datei."""
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path = Path(file_path).resolve()
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if not path.exists():
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print(f"❌ Datei nicht gefunden: {path}")
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return False
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print(f"\n📄 Verarbeite: {path.name}")
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# Text lesen
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try:
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text = read_file(path)
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print(f" ✅ Text gelesen ({len(text)} Zeichen)")
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except Exception as e:
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print(f" ❌ Fehler beim Lesen: {e}")
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return False
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# Chunken
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chunks = chunk_text(text)
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print(f" ✅ {len(chunks)} Chunks erstellt")
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# Embeddings
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print(f" ⏳ Erstelle Embeddings via OpenAI ({EMBED_MODEL})...")
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try:
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embeddings = []
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batch_size = 100
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for i in range(0, len(chunks), batch_size):
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batch = chunks[i:i + batch_size]
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embeddings.extend(get_embeddings(batch))
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print(f" ✅ {len(embeddings)} Embeddings erstellt")
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except Exception as e:
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print(f" ❌ Fehler bei Embeddings: {e}")
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return False
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# Datenbank
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print(f" ⏳ Verbinde mit pgvector auf {DB_HOST}:{DB_PORT}...")
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try:
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conn = get_db_connection()
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ensure_table(conn)
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existing = check_existing(conn, path.name)
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if existing > 0:
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if force:
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delete_existing(conn, path.name)
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else:
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print(f" ⚠️ '{path.name}' bereits in DB ({existing} Chunks). Nutze --force zum Überschreiben.")
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conn.close()
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return False
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insert_chunks(conn, chunks, embeddings, path.name)
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conn.close()
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print(f" ✅ {len(chunks)} Chunks in pgvector gespeichert")
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except Exception as e:
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print(f" ❌ Datenbankfehler: {e}")
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return False
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return True
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def ingest_directory(dir_path: str, force: bool = False):
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"""Verarbeitet alle MD, TXT und PDF Dateien in einem Verzeichnis."""
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path = Path(dir_path).resolve()
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if not path.is_dir():
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print(f"❌ Verzeichnis nicht gefunden: {path}")
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return
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files = (
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list(path.glob("*.md")) +
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list(path.glob("*.txt")) +
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list(path.glob("*.pdf"))
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)
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files = [f for f in files if not f.name.startswith(".")]
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if not files:
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print(f"❌ Keine unterstützten Dateien in: {path}")
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return
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print(f"\n📁 Verarbeite {len(files)} Dateien aus: {path}")
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success = 0
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for f in sorted(files):
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if ingest_file(str(f), force=force):
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success += 1
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print(f"\n✅ Fertig: {success}/{len(files)} Dateien erfolgreich importiert.")
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def list_documents():
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"""Zeigt alle Dokumente in der Datenbank an."""
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print(f"\n📋 Dokumente in profile_documents ({DB_HOST}:{DB_PORT}/{DB_NAME}):")
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try:
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conn = get_db_connection()
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with conn.cursor() as cur:
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cur.execute("""
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SELECT filename, COUNT(*) as chunks, MAX(created_at) as imported_at
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FROM profile_documents
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GROUP BY filename
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ORDER BY MAX(created_at) DESC
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""")
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rows = cur.fetchall()
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conn.close()
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if not rows:
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print(" Keine Dokumente gefunden.")
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else:
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print(f"\n {'Dateiname':<55} {'Chunks':>6} {'Importiert'}")
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print(" " + "-" * 80)
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for filename, chunks, imported_at in rows:
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ts = imported_at.strftime("%d.%m.%Y %H:%M") if imported_at else "-"
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print(f" {filename:<55} {chunks:>6} {ts}")
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print()
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except Exception as e:
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print(f"❌ Fehler: {e}")
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def clear_document(filename: str):
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"""Löscht ein Dokument aus der Datenbank."""
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try:
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conn = get_db_connection()
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existing = check_existing(conn, filename)
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if existing == 0:
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print(f"⚠️ '{filename}' nicht in der Datenbank gefunden.")
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else:
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delete_existing(conn, filename)
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print(f"✅ '{filename}' gelöscht ({existing} Chunks).")
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conn.close()
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except Exception as e:
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print(f"❌ Fehler: {e}")
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# --- CLI ---
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(
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description="Job-Matching RAG Ingestion – Profil-Dokumente in pgvector importieren"
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)
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subparsers = parser.add_subparsers(dest="command")
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p_file = subparsers.add_parser("file", help="Einzelne Datei importieren")
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p_file.add_argument("path", help="Pfad zur Datei (MD, TXT, PDF)")
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p_file.add_argument("--force", action="store_true", help="Bestehende Chunks überschreiben")
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p_dir = subparsers.add_parser("dir", help="Verzeichnis importieren")
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p_dir.add_argument("path", help="Pfad zum Verzeichnis")
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p_dir.add_argument("--force", action="store_true", help="Bestehende Chunks überschreiben")
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subparsers.add_parser("list", help="Alle Dokumente in der DB anzeigen")
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p_clear = subparsers.add_parser("clear", help="Dokument aus DB löschen")
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p_clear.add_argument("filename", help="Dateiname (z.B. 'Lebenslauf - 2026 - Sebastian Fröhlich.md')")
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args = parser.parse_args()
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if args.command == "file":
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ingest_file(args.path, force=args.force)
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elif args.command == "dir":
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ingest_directory(args.path, force=args.force)
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elif args.command == "list":
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list_documents()
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elif args.command == "clear":
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clear_document(args.filename)
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else:
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parser.print_help()
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