50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import openai
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from embedchain.config import AppConfig, ChatConfig
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from embedchain.embedchain import EmbedChain
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class App(EmbedChain):
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"""
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The EmbedChain app.
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Has two functions: add and query.
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adds(data_type, url): adds the data from the given URL to the vector db.
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query(query): finds answer to the given query using vector database and LLM.
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dry_run(query): test your prompt without consuming tokens.
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"""
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def __init__(self, config: AppConfig = None):
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"""
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:param config: AppConfig instance to load as configuration. Optional.
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"""
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if config is None:
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config = AppConfig()
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super().__init__(config)
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def get_llm_model_answer(self, prompt, config: ChatConfig):
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messages = []
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messages.append({"role": "user", "content": prompt})
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response = openai.ChatCompletion.create(
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model=config.model or "gpt-3.5-turbo-0613",
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messages=messages,
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temperature=config.temperature,
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max_tokens=config.max_tokens,
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top_p=config.top_p,
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stream=config.stream,
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)
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if config.stream:
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return self._stream_llm_model_response(response)
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else:
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return response["choices"][0]["message"]["content"]
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def _stream_llm_model_response(self, response):
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"""
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This is a generator for streaming response from the OpenAI completions API
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"""
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for line in response:
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chunk = line["choices"][0].get("delta", {}).get("content", "")
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yield chunk
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