Delete obsolete agent.py

2026-06-05 13:07:09 +00:00
parent 624947c7bf
commit abfa2ae700
1 changed files with 0 additions and 69 deletions
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 """Agent implementation using LangChain 1.x.
 The agent uses a simple tool‑based architecture.  The LLM is a local
 `ChatOllama` model (llama3).  Two tools are available:
 * ``search_local_kb`` – semantic search in Qdrant.
 * ``web_search`` – web search via Tavily.
 The system prompt instructs the LLM to decide which tool to use based on the
 question.  The response always contains a marker indicating the source
 (`chromadb`/`tavily`).  The marker is added by the LLM itself.
 """
 from __future__ import annotations
 from typing import List
 from langchain_ollama import ChatOllama
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 # Import tools without relative import to allow top‑level import
 import tools
 # ---------------------------------------------------------------------------
 # Helper: create a tool list
 # ---------------------------------------------------------------------------
 def get_tools() -> List[BaseTool]:
    """Return the list of tools used by the agent."""
    return [tools.search_local_kb, tools.web_search]
 # ---------------------------------------------------------------------------
 # System prompt
 # ---------------------------------------------------------------------------
 SYSTEM_PROMPT = (
    "You are an AI assistant that can answer questions using two sources: "
    "1) a local knowledge base (Qdrant) and 2) the web via Tavily. "
    "If the answer can be found in the local KB, use the `search_local_kb` tool. "
    "If the answer requires up‑to‑date information, use the `web_search` tool. "
    "Return the answer followed by a source marker on a new line: "
    "`Source: chromadb` or `Source: tavily`."
 )
 # ---------------------------------------------------------------------------
 # Agent construction
 # ---------------------------------------------------------------------------
 def create_agent() -> Runnable:
    """Create a runnable agent.
    The agent is a simple chain: system prompt → user message → tool calls → LLM
    response.  It uses the default tool‑calling behaviour of LangChain 1.x.
    """
    llm = ChatOllama(model="llama3", temperature=0.0)
    # AgentExecutor can accept a system message via agent_kwargs
    from langchain.agents import AgentExecutor
    agent = AgentExecutor.from_llm_and_tools(
        llm=llm,
        tools=get_tools(),
        verbose=True,
        # Provide the system prompt so the LLM knows how to behave
        agent_kwargs={"system_message": SYSTEM_PROMPT},
    )
    return agent