Ragas using custom ollama -llamaindex

[Senthselvi]

what is the code for llamaindex def generate_text( self, prompt: PromptValue, n: int = 1, temperature: float = 1e-8, stop: t.Optional[t.List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: return self.get_llm_result(prompt)

async def agenerate_text(
        self,
        prompt: PromptValue,
        n: int = 1,
        temperature: float = 1e-8,
        stop: t.Optional[t.List[str]] = None,
        callbacks: Callbacks = None,
) -> LLMResult:
    generations = []
    llm_output = {}
    token_total = 0
    content = prompt.to_string()
    try:
        text, history = await asyncio.wait_for(
            asyncio.get_event_loop().run_in_executor(None, self.base_llm.chat, self.tokenizer, content, []),
            timeout=150
        )
    except asyncio.TimeoutError:
        print("Operation timed out, please check the code or increase the timeout")
    except asyncio.CancelledError:
        print("Task was cancelled, please check the code")
    except Exception as e:
        print(f"An unknown error occurred: {e}")

    generations.append([Generation(text=text)])
    token_total += len(text)
    llm_output['token_total'] = token_total
    return LLMResult(generations=generations, llm_output=llm_output)

[dosubot[bot]]

The generate_text and agenerate_text methods in the LlamaIndexLLMWrapper class are designed to handle synchronous and asynchronous text generation, respectively. Here is the code for these methods:

def generate_text(
    self,
    prompt: PromptValue,
    n: int = 1,
    temperature: float = 1e-8,
    stop: t.Optional[t.List[str]] = None,
    callbacks: Callbacks = None,
) -> LLMResult:
    kwargs = self.check_args(n, temperature, stop, callbacks)
    li_response = self.llm.complete(prompt.to_string(), **kwargs)

    return LLMResult(generations=[[Generation(text=li_response.text)]])

async def agenerate_text(
    self,
    prompt: PromptValue,
    n: int = 1,
    temperature: t.Optional[float] = None,
    stop: t.Optional[t.List[str]] = None,
    callbacks: Callbacks = None,
) -> LLMResult:
    if temperature is None:
        temperature = 1e-8

    kwargs = self.check_args(n, temperature, stop, callbacks)
    li_response = await self.llm.acomplete(prompt.to_string(), **kwargs)

    return LLMResult(generations=[[Generation(text=li_response.text)]])

In the provided code snippet, the generate_text method synchronously generates text using the complete method of the llm object, while the agenerate_text method asynchronously generates text using the acomplete method of the llm object. Both methods return an LLMResult object containing the generated text encapsulated in Generation objects ^[1].

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[Senthselvi]

this is my def complete(self, prompt: str, kwargs: Any) -> CompletionResponse: payload = { self.ollama.prompt_key: prompt, "model": self.ollama.model, "options": self.ollama._model_kwargs, "stream": False, kwargs, }

[Senthselvi]

LLMResult is from lanchain I am not uisng langchain--PromptValue, NameError: name 'PromptValue' is not defined