64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
from ollama import AsyncClient
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from haiku.rag.client import HaikuRAG
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from haiku.rag.config import Config
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from haiku.rag.qa.base import QuestionAnswerAgentBase
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OLLAMA_OPTIONS = {"temperature": 0.0, "seed": 42, "num_ctx": 16384}
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class QuestionAnswerOllamaAgent(QuestionAnswerAgentBase):
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def __init__(self, client: HaikuRAG, model: str = Config.QA_MODEL):
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super().__init__(client, model or self._model)
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async def answer(self, question: str) -> str:
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ollama_client = AsyncClient(host=Config.OLLAMA_BASE_URL)
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messages = [
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{"role": "system", "content": self._system_prompt},
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{"role": "user", "content": question},
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]
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max_rounds = 5 # Prevent infinite loops
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for _ in range(max_rounds):
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response = await ollama_client.chat(
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model=self._model,
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messages=messages,
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tools=self.tools,
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options=OLLAMA_OPTIONS,
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think=False,
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)
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if response.get("message", {}).get("tool_calls"):
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messages.append(response["message"])
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for tool_call in response["message"]["tool_calls"]:
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if tool_call["function"]["name"] == "search_documents":
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args = tool_call["function"]["arguments"]
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query = args.get("query", question)
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limit = int(args.get("limit", 3))
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search_results = await self._client.search(query, limit=limit)
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context_chunks = []
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for chunk, score in search_results:
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context_chunks.append(
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f"Content: {chunk.content}\nScore: {score:.4f}"
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)
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context = "\n\n".join(context_chunks)
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messages.append(
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{
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"role": "tool",
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"content": context,
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"tool_call_id": tool_call.get("id", "search_tool"),
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}
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)
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else:
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# No tool calls, return the response
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return response["message"]["content"]
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# If we've exhausted max rounds, return empty string
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return ""
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