mirror of
https://github.com/tiennm99/DocsGPT.git
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Alex
6b9b193337
A one-shot graph ranking diffuses over the whole neighbourhood; a question whose answer sits two hops away is better served by following the edges. The graph_search tool gives an agent search_entities, get_relationships and read_entity_pages over its graph sources. On a multi-hop corpus where the bridging entity is never named, answers went from 0/10 with classic vector retrieval to 10/10 with the tool, end to end through /stream. The tool has no setting of its own. It is offered exactly where the agent can already search: agentic and research agents always, a classic agent only for sources exposed as a search tool. A graph source left at prefetch is used for ranking only. Tests now pin GRAPHRAG_ENABLED to its shipped default, as CI has: with a dev .env enabling it, every agent test's graph check read the developer's real database and left a pool to it behind.
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
import logging
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from typing import Dict, Generator, Optional
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from docsgpt.agents.base import BaseAgent
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from docsgpt.agents.tools.graph_search import add_graph_search_tool
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from docsgpt.agents.tools.internal_search import add_internal_search_tool
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from docsgpt.agents.tools.wiki import add_wiki_tool
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from docsgpt.logging import LogContext
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logger = logging.getLogger(__name__)
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class AgenticAgent(BaseAgent):
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"""Agent where the LLM controls retrieval via tools.
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Unlike ClassicAgent which pre-fetches docs into the prompt,
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AgenticAgent gives the LLM an internal_search tool so it can
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decide when, what, and whether to search.
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"""
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def __init__(
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self,
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retriever_config: Optional[Dict] = None,
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wiki_config: Optional[Dict] = None,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.retriever_config = retriever_config or {}
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self.wiki_config = wiki_config or {}
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def _gen_inner(
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self, query: str, log_context: LogContext
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) -> Generator[Dict, None, None]:
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tools_dict = self.tool_executor.get_tools()
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add_internal_search_tool(tools_dict, self.retriever_config)
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add_graph_search_tool(tools_dict, self.retriever_config)
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if self.wiki_config:
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add_wiki_tool(tools_dict, self.wiki_config)
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self._prepare_tools(tools_dict)
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# 4. Build messages (prompt has NO pre-fetched docs)
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messages = self._build_messages(self.prompt, query)
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# 5. Call LLM — the handler manages the tool loop
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llm_response = self._llm_gen(messages, log_context)
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yield from self._handle_response(
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llm_response, tools_dict, messages, log_context
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)
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# 6. Collect sources from internal search tool results
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self._collect_internal_sources()
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yield {"sources": self.retrieved_docs}
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yield {"tool_calls": self._get_truncated_tool_calls()}
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log_context.stacks.append(
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{"component": "agent", "data": {"tool_calls": self.tool_calls.copy()}}
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)
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