199 lines
7.0 KiB
Python
199 lines
7.0 KiB
Python
import os
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from typing import Any, Dict, Iterator, List, Optional, cast
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import openai
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from guardrails.classes.llm.llm_response import LLMResponse
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from guardrails.utils.safe_get import safe_get
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from guardrails.telemetry import trace_llm_call, trace_operation
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class OpenAIClientV1:
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def __init__(
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self,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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*args,
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**kwargs,
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):
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if api_key is None:
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api_key = os.environ.get("OPENAI_API_KEY")
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self.api_key = api_key
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self.api_base = api_base
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self.client = openai.Client(
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api_key=self.api_key,
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base_url=self.api_base,
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)
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def create_embedding(
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self,
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model: str,
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input: List[str],
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) -> List[List[float]]:
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embeddings = self.client.embeddings.create(
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model=model,
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input=input,
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)
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return [r.embedding for r in embeddings.data]
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def create_completion(
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self, engine: str, prompt: str, *args, **kwargs
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) -> LLMResponse:
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trace_operation(
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input_mime_type="application/json",
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input_value={
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**kwargs,
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"model": engine,
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"prompt": prompt,
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"args": args,
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},
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)
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trace_llm_call(
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invocation_parameters={
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**kwargs,
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"model": engine,
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"prompt": prompt,
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}
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)
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response = self.client.completions.create(
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model=engine, prompt=prompt, *args, **kwargs
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)
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trace_operation(output_mime_type="application/json", output_value=response)
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return self.construct_nonchat_response(
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stream=kwargs.get("stream", False),
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openai_response=response,
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)
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def construct_nonchat_response(
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self,
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stream: bool,
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openai_response: Any,
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) -> LLMResponse:
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"""Construct an LLMResponse from an OpenAI response.
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Splits execution based on whether the `stream` parameter is set
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in the kwargs.
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"""
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if stream:
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# If stream is defined and set to True,
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# openai returns a generator
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openai_response = cast(Iterator[Dict[str, Any]], openai_response)
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# Simply return the generator wrapped in an LLMResponse
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return LLMResponse(output="", streamOutput=openai_response)
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# If stream is not defined or is set to False,
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# return default behavior
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openai_response = cast(Dict[str, Any], openai_response)
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if not openai_response.choices:
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raise ValueError("No choices returned from OpenAI")
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if openai_response.usage is None:
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raise ValueError("No token counts returned from OpenAI")
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trace_llm_call(
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output_messages=[
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{"role": "assistant", "content": openai_response.choices[0].text}
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],
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token_count_completion=openai_response.usage.completion_tokens,
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token_count_prompt=openai_response.usage.prompt_tokens,
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token_count_total=openai_response.usage.total_tokens,
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)
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return LLMResponse(
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output=openai_response.choices[0].text, # type: ignore
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prompt_token_count=openai_response.usage.prompt_tokens, # type: ignore
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response_token_count=openai_response.usage.completion_tokens, # noqa: E501 # type: ignore
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)
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def create_chat_completion(
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self, model: str, messages: List[Any], *args, **kwargs
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) -> LLMResponse:
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trace_operation(
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input_mime_type="application/json",
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input_value={
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**kwargs,
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"model": model,
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"messages": messages,
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"args": args,
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},
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)
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function_calling_tools = [
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tool.get("function")
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for tool in kwargs.get("tools", [])
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if isinstance(tool, Dict) and tool.get("type") == "function"
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]
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trace_llm_call(
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input_messages=messages,
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model_name=model,
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invocation_parameters={**kwargs, "model": model, "messages": messages},
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function_call=kwargs.get(
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"function_call", safe_get(function_calling_tools, 0)
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),
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)
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response = self.client.chat.completions.create(
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model=model, messages=messages, *args, **kwargs
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)
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trace_operation(output_mime_type="application/json", output_value=response)
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return self.construct_chat_response(
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stream=kwargs.get("stream", False),
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openai_response=response,
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)
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def construct_chat_response(
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self,
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stream: bool,
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openai_response: Any,
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) -> LLMResponse:
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"""Construct an LLMResponse from an OpenAI response.
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Splits execution based on whether the `stream` parameter is set
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in the kwargs.
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"""
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if stream:
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# If stream is defined and set to True,
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# openai returns a generator object
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openai_response = cast(Iterator[Dict[str, Any]], openai_response)
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# Simply return the generator wrapped in an LLMResponse
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return LLMResponse(output="", streamOutput=openai_response)
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# If stream is not defined or is set to False,
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# extract string from response
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openai_response = cast(Dict[str, Any], openai_response)
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if not openai_response.choices:
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raise ValueError("No choices returned from OpenAI")
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if not openai_response.choices[0].message:
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raise ValueError("No message returned from OpenAI")
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if openai_response.usage is None:
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raise ValueError("No token counts returned from OpenAI")
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if openai_response.choices[0].message.content is not None:
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output = openai_response.choices[0].message.content
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else:
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try:
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output = openai_response.choices[0].message.function_call.arguments
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except AttributeError:
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try:
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choice = openai_response.choices[0]
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output = choice.message.tool_calls[-1].function.arguments
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except AttributeError as ae_tools:
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raise ValueError(
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"No message content or function"
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" call arguments returned from OpenAI"
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) from ae_tools
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trace_llm_call(
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output_messages=[choice.message for choice in openai_response.choices], # type: ignore
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token_count_completion=openai_response.usage.completion_tokens,
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token_count_prompt=openai_response.usage.prompt_tokens,
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token_count_total=openai_response.usage.total_tokens,
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)
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return LLMResponse(
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output=output,
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prompt_token_count=openai_response.usage.prompt_tokens, # type: ignore
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response_token_count=openai_response.usage.completion_tokens, # noqa: E501 # type: ignore
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)
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