64b8aac86f
Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com> Co-authored-by: hande-k <handekafkas7@gmail.com> Co-authored-by: Igor Ilic <igorilic03@gmail.com> Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Co-authored-by: Igor Ilic <30923996+dexters1@users.noreply.github.com>
134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
import os
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import base64
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from pathlib import Path
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from typing import Type
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import litellm
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import instructor
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from pydantic import BaseModel
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from cognee.infrastructure.llm.llm_interface import LLMInterface
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from cognee.infrastructure.llm.prompts import read_query_prompt
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class OpenAIAdapter(LLMInterface):
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name = "OpenAI"
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model: str
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api_key: str
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api_version: str
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"""Adapter for OpenAI's GPT-3, GPT=4 API"""
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def __init__(
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self,
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api_key: str,
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endpoint: str,
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api_version: str,
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model: str,
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transcription_model: str,
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streaming: bool = False,
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):
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self.aclient = instructor.from_litellm(litellm.acompletion)
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self.client = instructor.from_litellm(litellm.completion)
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self.transcription_model = transcription_model
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self.model = model
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self.api_key = api_key
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self.endpoint = endpoint
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self.api_version = api_version
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self.streaming = streaming
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async def acreate_structured_output(self, text_input: str, system_prompt: str, response_model: Type[BaseModel]) -> BaseModel:
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"""Generate a response from a user query."""
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return await self.aclient.chat.completions.create(
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model = self.model,
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messages = [{
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"role": "user",
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"content": f"""Use the given format to
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extract information from the following input: {text_input}. """,
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}, {
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"role": "system",
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"content": system_prompt,
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}],
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api_key = self.api_key,
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api_base = self.endpoint,
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api_version = self.api_version,
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response_model = response_model,
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max_retries = 5,
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)
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def create_structured_output(self, text_input: str, system_prompt: str, response_model: Type[BaseModel]) -> BaseModel:
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"""Generate a response from a user query."""
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return self.client.chat.completions.create(
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model = self.model,
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messages = [{
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"role": "user",
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"content": f"""Use the given format to
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extract information from the following input: {text_input}. """,
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}, {
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"role": "system",
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"content": system_prompt,
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}],
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api_key = self.api_key,
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api_base = self.endpoint,
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api_version = self.api_version,
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response_model = response_model,
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max_retries = 5,
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)
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def create_transcript(self, input):
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"""Generate a audio transcript from a user query."""
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if not os.path.isfile(input):
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raise FileNotFoundError(f"The file {input} does not exist.")
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# with open(input, 'rb') as audio_file:
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# audio_data = audio_file.read()
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transcription = litellm.transcription(
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model = self.transcription_model,
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file = Path(input),
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api_key=self.api_key,
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api_base=self.endpoint,
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api_version=self.api_version,
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max_retries = 5,
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)
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return transcription
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def transcribe_image(self, input) -> BaseModel:
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with open(input, "rb") as image_file:
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encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
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return litellm.completion(
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model = self.model,
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messages = [{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What’s in this image?",
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}, {
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{encoded_image}",
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},
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},
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],
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}],
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api_key=self.api_key,
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api_base=self.endpoint,
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api_version=self.api_version,
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max_tokens = 300,
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max_retries = 5,
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)
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def show_prompt(self, text_input: str, system_prompt: str) -> str:
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"""Format and display the prompt for a user query."""
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if not text_input:
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text_input = "No user input provided."
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if not system_prompt:
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raise ValueError("No system prompt path provided.")
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system_prompt = read_query_prompt(system_prompt)
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formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" if system_prompt else None
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return formatted_prompt
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