b4ff370823
feat(graphiti): Define entity and edge types in new graphiti_schema for structured data extraction feat(graphiti): Enhance ingest_episode_event_step with schema integration for improved episode processing
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
"""Graphiti Knowledge-Graph – zentraler Client-Singleton.
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Wird von ingest_episode_event_step und query_graph_step genutzt.
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Lazy-initialisiert beim ersten Aufruf.
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"""
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import asyncio
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import os
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from typing import Any, Optional
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from graphiti_core import Graphiti
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from graphiti_core.llm_client import LLMConfig
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from graphiti_core.llm_client.openai_generic_client import OpenAIGenericClient
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from graphiti_core.embedder import OpenAIEmbedder, OpenAIEmbedderConfig
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from services.graphiti_tracer import build_langfuse_tracer
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class GraphitiError(Exception):
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"""Fehler beim Zugriff auf den Graphiti-Client."""
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_graphiti_client: Graphiti | None = None
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_graphiti_init_lock = asyncio.Lock()
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async def get_graphiti(ctx: Optional[Any] = None) -> Graphiti:
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"""Gibt den gecachten Graphiti-Client zurück (Singleton).
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Args:
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ctx: Optionaler Motia-Context für Logging während der Initialisierung.
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Benötigte Umgebungsvariablen:
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NEO4J_URI – bolt://host:7687
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NEO4J_USER – neo4j
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NEO4J_PASSWORD – Pflicht
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XAI_API_KEY – xAI-Key für LLM (grok)
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XAI_BASE_URL – optional, Standard: https://api.x.ai/v1
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XAI_MODEL – optional, Standard: grok-4-1-fast-reasoning
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OPENAI_API_KEY – OpenAI-Key für Embeddings
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GRAPHITI_EMBED_BASE_URL – optional, Standard: https://api.openai.com/v1
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GRAPHITI_EMBED_MODEL – optional, Standard: text-embedding-3-small
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"""
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global _graphiti_client
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if _graphiti_client is None:
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async with _graphiti_init_lock:
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if _graphiti_client is None:
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_log(ctx, "Initialisiere Graphiti-Client...")
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try:
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_graphiti_client = await _build_graphiti()
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_log(ctx, "Graphiti-Client bereit.")
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except KeyError as e:
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raise GraphitiError(f"Konfigurationsfehler – Umgebungsvariable fehlt: {e}") from e
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return _graphiti_client
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def _log(ctx: Optional[Any], message: str, level: str = "info") -> None:
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"""Loggt via ctx.logger falls vorhanden, sonst print."""
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if ctx is not None and hasattr(ctx, "logger"):
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getattr(ctx.logger, level)(f"[GraphitiClient] {message}")
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else:
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print(f"[GraphitiClient] {message}")
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async def _build_graphiti() -> Graphiti:
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neo4j_uri = os.environ["NEO4J_URI"]
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neo4j_user = os.environ.get("NEO4J_USER", "neo4j")
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neo4j_password = os.environ["NEO4J_PASSWORD"]
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xai_base_url = os.environ.get("XAI_BASE_URL", "https://api.x.ai/v1")
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xai_api_key = os.environ["XAI_API_KEY"]
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xai_model = os.environ.get("XAI_MODEL", "grok-4-1-fast-reasoning")
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embed_api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("GRAPHITI_EMBED_API_KEY", xai_api_key)
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embed_base_url = os.environ.get("GRAPHITI_EMBED_BASE_URL", "https://api.openai.com/v1")
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embed_model = os.environ.get("GRAPHITI_EMBED_MODEL", "text-embedding-3-small")
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llm_client = OpenAIGenericClient(
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config=LLMConfig(
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api_key=xai_api_key,
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base_url=xai_base_url,
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model=xai_model,
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)
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)
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embedder = OpenAIEmbedder(
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config=OpenAIEmbedderConfig(
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api_key=embed_api_key,
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base_url=embed_base_url,
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embedding_model=embed_model,
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embedding_dim=1536,
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)
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)
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tracer = build_langfuse_tracer(span_prefix="graphiti", ctx=None)
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client = Graphiti(
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uri=neo4j_uri,
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user=neo4j_user,
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password=neo4j_password,
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llm_client=llm_client,
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embedder=embedder,
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tracer=tracer,
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)
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await client.build_indices_and_constraints()
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return client
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