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import json |
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import os.path |
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from pathlib import Path |
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from typing import List, Optional, Union |
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from langchain.schema import AIMessage, HumanMessage, SystemMessage |
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from gpt_engineer.core.ai import AI |
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from gpt_engineer.core.token_usage import TokenUsageLog |
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Message = Union[AIMessage, HumanMessage, SystemMessage] |
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class CachingAI(AI): |
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def __init__(self, *args, **kwargs): |
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self.temperature = 0.1 |
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self.azure_endpoint = "" |
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self.streaming = False |
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try: |
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self.model_name = "gpt-4-1106-preview" |
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self.llm = self._create_chat_model() |
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except: |
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self.model_name = "cached_response_model" |
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self.llm = None |
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self.streaming = False |
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self.token_usage_log = TokenUsageLog("gpt-4-1106-preview") |
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self.cache_file = Path(__file__).parent / "ai_cache.json" |
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def next( |
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self, |
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messages: List[Message], |
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prompt: Optional[str] = None, |
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*, |
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step_name: str, |
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) -> List[Message]: |
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""" |
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Advances the conversation by sending message history |
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to LLM and updating with the response. |
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Parameters |
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---------- |
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messages : List[Message] |
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The list of messages in the conversation. |
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prompt : Optional[str], optional |
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The prompt to use, by default None. |
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step_name : str |
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The name of the step. |
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Returns |
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------- |
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List[Message] |
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The updated list of messages in the conversation. |
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""" |
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""" |
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Advances the conversation by sending message history |
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to LLM and updating with the response. |
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""" |
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if prompt: |
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messages.append(HumanMessage(content=prompt)) |
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if os.path.isfile(self.cache_file): |
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with open(self.cache_file, "r") as cache_file: |
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cache = json.load(cache_file) |
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else: |
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cache = dict() |
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messages_key = self.serialize_messages(messages) |
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if messages_key not in cache: |
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callbacks = [] |
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print("calling backoff inference") |
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response = self.backoff_inference(messages, callbacks) |
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self.token_usage_log.update_log( |
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messages=messages, answer=response.content, step_name=step_name |
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) |
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print("called backoff inference") |
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print("cost in usd:", self.token_usage_log.usage_cost()) |
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messages.append(response) |
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cache[messages_key] = self.serialize_messages(messages) |
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with open(self.cache_file, "w") as cache_file: |
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json.dump(cache, cache_file) |
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cache_file.write("\n") |
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return self.deserialize_messages(cache[messages_key]) |
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