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https://github.com/paperless-ngx/paperless-ngx.git
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69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
import logging
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from llama_index.core.llms import ChatMessage
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from llama_index.core.program.function_program import get_function_tool
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from llama_index.llms.ollama import Ollama
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from llama_index.llms.openai import OpenAI
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from paperless.config import AIConfig
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from paperless_ai.base_model import DocumentClassifierSchema
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logger = logging.getLogger("paperless_ai.client")
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class AIClient:
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"""
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A client for interacting with an LLM backend.
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"""
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def __init__(self):
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self.settings = AIConfig()
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self.llm = self.get_llm()
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def get_llm(self) -> Ollama | OpenAI:
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if self.settings.llm_backend == "ollama":
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return Ollama(
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model=self.settings.llm_model or "llama3",
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base_url=self.settings.llm_endpoint or "http://localhost:11434",
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request_timeout=120,
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)
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elif self.settings.llm_backend == "openai":
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return OpenAI(
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model=self.settings.llm_model or "gpt-3.5-turbo",
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api_key=self.settings.llm_api_key,
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)
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else:
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raise ValueError(f"Unsupported LLM backend: {self.settings.llm_backend}")
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def run_llm_query(self, prompt: str) -> str:
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logger.debug(
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"Running LLM query against %s with model %s",
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self.settings.llm_backend,
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self.settings.llm_model,
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)
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user_msg = ChatMessage(role="user", content=prompt)
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tool = get_function_tool(DocumentClassifierSchema)
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result = self.llm.chat_with_tools(
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tools=[tool],
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user_msg=user_msg,
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chat_history=[],
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)
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tool_calls = self.llm.get_tool_calls_from_response(
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result,
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error_on_no_tool_calls=True,
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)
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logger.debug("LLM query result: %s", tool_calls)
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parsed = DocumentClassifierSchema(**tool_calls[0].tool_kwargs)
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return parsed.model_dump()
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def run_chat(self, messages: list[ChatMessage]) -> str:
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logger.debug(
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"Running chat query against %s with model %s",
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self.settings.llm_backend,
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self.settings.llm_model,
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)
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result = self.llm.chat(messages)
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logger.debug("Chat result: %s", result)
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return result
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