mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
Derek TapleyandCursor
f0aa581318
* feat(integrations): add LangChain integration — retriever, vectorstore, tools Co-authored-by: Cursor <cursoragent@cursor.com> * fix(langchain): address Qodo review on HybridSearch hits and tools Read nested HybridSearch metadata so retriever/vectorstore Documents are not empty, make the agent tools real BaseTool subclasses, and stop slicing tool JSON into invalid payloads. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com>
134 lines
4.2 KiB
Python
134 lines
4.2 KiB
Python
"""
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SemanticaKGTool / SemanticaDecisionTool — LangChain ``BaseTool`` adapters
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for LangChain / LangGraph agents.
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"""
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from __future__ import annotations
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import json
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from typing import Any, Optional, Type
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from pydantic import BaseModel, ConfigDict, Field
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from semantica.utils.logging import get_logger
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logger = get_logger(__name__)
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# ---------------------------------------------------------------------------
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# Optional: LangChain core
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# ---------------------------------------------------------------------------
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LANGCHAIN_AVAILABLE = False
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LANGCHAIN_IMPORT_ERROR: Optional[str] = None
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_BaseTool: Any = object
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try:
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from langchain_core.tools import BaseTool as _BaseTool # type: ignore
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LANGCHAIN_AVAILABLE = True
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except ImportError: # pragma: no cover
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LANGCHAIN_IMPORT_ERROR = (
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"langchain-core is not installed. Install with: pip install langchain-core"
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)
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logger.debug(LANGCHAIN_IMPORT_ERROR)
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def _json(payload: Any) -> str:
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return json.dumps(payload, default=str, ensure_ascii=False)
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class QueryGraphInput(BaseModel):
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query: str = Field(..., description="Natural-language or keyword graph query")
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limit: int = Field(10, description="Maximum matching nodes to return")
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class QueryDecisionsInput(BaseModel):
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category: str = Field(
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"",
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description="Keyword to search recorded decisions; empty returns insights",
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)
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limit: int = Field(10, description="Maximum results when searching by keyword")
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class SemanticaKGTool(_BaseTool): # type: ignore[misc]
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"""LangChain tool for querying a Semantica ``ContextGraph``.
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Args:
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graph: A semantica.context.ContextGraph instance.
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Example:
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>>> tool = SemanticaKGTool(graph)
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>>> agent = create_react_agent(model, tools=[tool])
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"""
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model_config = ConfigDict(arbitrary_types_allowed=True)
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name: str = "semantica_query_graph"
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description: str = (
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"Query Semantica's shared context graph with a natural-language "
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"keyword query. Returns matching entities and relationships."
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)
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args_schema: Type[BaseModel] = QueryGraphInput
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graph: Any = None
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def __init__(self, graph: Any = None, **kwargs: Any) -> None:
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if LANGCHAIN_AVAILABLE:
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super().__init__(graph=graph, **kwargs)
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else:
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super().__init__()
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self.graph = graph
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def build(self) -> Any:
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"""Return this tool, or None if langchain-core is missing."""
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return self if LANGCHAIN_AVAILABLE else None
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def _run(self, query: str, limit: int = 10, **kwargs: Any) -> str:
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try:
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return _json(self.graph.query(query, limit=limit))
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except Exception as exc:
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return _json({"error": str(exc)})
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async def _arun(self, query: str, limit: int = 10, **kwargs: Any) -> str:
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return self._run(query, limit=limit)
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class SemanticaDecisionTool(_BaseTool): # type: ignore[misc]
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"""LangChain tool for searching Semantica's recorded decision log.
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Args:
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graph: A semantica.context.ContextGraph instance.
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"""
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model_config = ConfigDict(arbitrary_types_allowed=True)
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name: str = "semantica_query_decisions"
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description: str = (
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"Search Semantica's recorded decision log with a keyword query. "
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"Returns decisions, rationale, and context."
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)
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args_schema: Type[BaseModel] = QueryDecisionsInput
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graph: Any = None
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def __init__(self, graph: Any = None, **kwargs: Any) -> None:
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if LANGCHAIN_AVAILABLE:
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super().__init__(graph=graph, **kwargs)
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else:
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super().__init__()
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self.graph = graph
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def build(self) -> Any:
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"""Return this tool, or None if langchain-core is missing."""
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return self if LANGCHAIN_AVAILABLE else None
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def _run(self, category: str = "", limit: int = 10, **kwargs: Any) -> str:
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try:
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if category:
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return _json(self.graph.query(category, limit=limit))
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return _json(self.graph.get_decision_insights())
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except Exception as exc:
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return _json({"error": str(exc)})
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async def _arun(self, category: str = "", limit: int = 10, **kwargs: Any) -> str:
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return self._run(category=category, limit=limit)
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