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- from typing import Optional
- import openai
- from embedchain.config import AppConfig, ChatConfig
- from embedchain.embedchain import EmbedChain
- from embedchain.helper_classes.json_serializable import register_deserializable
- @register_deserializable
- class App(EmbedChain):
- """
- The EmbedChain app.
- Has two functions: add and query.
- adds(data_type, url): adds the data from the given URL to the vector db.
- query(query): finds answer to the given query using vector database and LLM.
- dry_run(query): test your prompt without consuming tokens.
- """
- def __init__(self, config: AppConfig = None, system_prompt: Optional[str] = None):
- """
- :param config: AppConfig instance to load as configuration. Optional.
- :param system_prompt: System prompt string. Optional.
- """
- if config is None:
- config = AppConfig()
- super().__init__(config, system_prompt)
- def get_llm_model_answer(self, prompt, config: ChatConfig):
- messages = []
- system_prompt = (
- self.system_prompt
- if self.system_prompt is not None
- else config.system_prompt
- if config.system_prompt is not None
- else None
- )
- if system_prompt:
- messages.append({"role": "system", "content": system_prompt})
- messages.append({"role": "user", "content": prompt})
- response = openai.ChatCompletion.create(
- model=config.model or "gpt-3.5-turbo-0613",
- messages=messages,
- temperature=config.temperature,
- max_tokens=config.max_tokens,
- top_p=config.top_p,
- stream=config.stream,
- )
- if config.stream:
- return self._stream_llm_model_response(response)
- else:
- return response["choices"][0]["message"]["content"]
- def _stream_llm_model_response(self, response):
- """
- This is a generator for streaming response from the OpenAI completions API
- """
- for line in response:
- chunk = line["choices"][0].get("delta", {}).get("content", "")
- yield chunk
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