refactor: classes and configs (#528)
This commit is contained in:
cachho
@@ -1,12 +1,11 @@
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import logging
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from typing import List, Optional
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from typing import Optional
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from langchain.schema import BaseMessage
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from embedchain.config import ChatConfig, CustomAppConfig
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from embedchain.config import CustomAppConfig
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from embedchain.embedchain import EmbedChain
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from embedchain.embedder.base_embedder import BaseEmbedder
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from embedchain.helper_classes.json_serializable import register_deserializable
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from embedchain.models import Providers
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from embedchain.llm.base_llm import BaseLlm
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from embedchain.vectordb.base_vector_db import BaseVectorDB
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@register_deserializable
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@@ -20,143 +19,49 @@ class CustomApp(EmbedChain):
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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: CustomAppConfig = None, system_prompt: Optional[str] = None):
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def __init__(
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self,
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config: CustomAppConfig = None,
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llm: BaseLlm = None,
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db: BaseVectorDB = None,
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embedder: BaseEmbedder = None,
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system_prompt: Optional[str] = None,
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):
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"""
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:param config: Optional. `CustomAppConfig` instance to load as configuration.
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:raises ValueError: Config must be provided for custom app
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:param system_prompt: Optional. System prompt string.
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"""
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# Config is not required, it has a default
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if config is None:
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raise ValueError("Config must be provided for custom app")
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config = CustomAppConfig()
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self.provider = config.provider
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if llm is None:
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raise ValueError("LLM must be provided for custom app. Please import from `embedchain.llm`.")
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if db is None:
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raise ValueError("Database must be provided for custom app. Please import from `embedchain.vectordb`.")
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if embedder is None:
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raise ValueError("Embedder must be provided for custom app. Please import from `embedchain.embedder`.")
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if config.provider == Providers.GPT4ALL:
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from embedchain import OpenSourceApp
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# Because these models run locally, they should have an instance running when the custom app is created
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self.open_source_app = OpenSourceApp(config=config.open_source_app_config)
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super().__init__(config, system_prompt)
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def set_llm_model(self, provider: Providers):
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self.provider = provider
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if provider == Providers.GPT4ALL:
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raise ValueError(
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"GPT4ALL needs to be instantiated with the model known, please create a new app instance instead"
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if not isinstance(config, CustomAppConfig):
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raise TypeError(
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"Config is not a `CustomAppConfig` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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if not isinstance(llm, BaseLlm):
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raise TypeError(
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"LLM is not a `BaseLlm` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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if not isinstance(db, BaseVectorDB):
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raise TypeError(
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"Database is not a `BaseVectorDB` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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if not isinstance(embedder, BaseEmbedder):
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raise TypeError(
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"Embedder is not a `BaseEmbedder` instance. "
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"Please make sure the type is right and that you are passing an instance."
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)
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def get_llm_model_answer(self, prompt, config: ChatConfig):
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# TODO: Quitting the streaming response here for now.
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# Idea: https://gist.github.com/jvelezmagic/03ddf4c452d011aae36b2a0f73d72f68
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if config.stream:
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raise NotImplementedError(
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"Streaming responses have not been implemented for this model yet. Please disable."
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)
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if config.system_prompt is None and self.system_prompt is not None:
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config.system_prompt = self.system_prompt
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try:
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if self.provider == Providers.OPENAI:
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return CustomApp._get_openai_answer(prompt, config)
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if self.provider == Providers.ANTHROPHIC:
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return CustomApp._get_athrophic_answer(prompt, config)
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if self.provider == Providers.VERTEX_AI:
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return CustomApp._get_vertex_answer(prompt, config)
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if self.provider == Providers.GPT4ALL:
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return self.open_source_app._get_gpt4all_answer(prompt, config)
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if self.provider == Providers.AZURE_OPENAI:
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return CustomApp._get_azure_openai_answer(prompt, config)
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except ImportError as e:
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raise ModuleNotFoundError(e.msg) from None
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@staticmethod
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def _get_openai_answer(prompt: str, config: ChatConfig) -> str:
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from langchain.chat_models import ChatOpenAI
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chat = ChatOpenAI(
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temperature=config.temperature,
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model=config.model or "gpt-3.5-turbo",
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max_tokens=config.max_tokens,
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streaming=config.stream,
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)
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if config.top_p and config.top_p != 1:
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logging.warning("Config option `top_p` is not supported by this model.")
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messages = CustomApp._get_messages(prompt, system_prompt=config.system_prompt)
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return chat(messages).content
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@staticmethod
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def _get_athrophic_answer(prompt: str, config: ChatConfig) -> str:
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from langchain.chat_models import ChatAnthropic
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chat = ChatAnthropic(temperature=config.temperature, model=config.model)
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if config.max_tokens and config.max_tokens != 1000:
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logging.warning("Config option `max_tokens` is not supported by this model.")
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messages = CustomApp._get_messages(prompt, system_prompt=config.system_prompt)
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return chat(messages).content
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@staticmethod
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def _get_vertex_answer(prompt: str, config: ChatConfig) -> str:
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from langchain.chat_models import ChatVertexAI
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chat = ChatVertexAI(temperature=config.temperature, model=config.model, max_output_tokens=config.max_tokens)
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if config.top_p and config.top_p != 1:
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logging.warning("Config option `top_p` is not supported by this model.")
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messages = CustomApp._get_messages(prompt, system_prompt=config.system_prompt)
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return chat(messages).content
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@staticmethod
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def _get_azure_openai_answer(prompt: str, config: ChatConfig) -> str:
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from langchain.chat_models import AzureChatOpenAI
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if not config.deployment_name:
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raise ValueError("Deployment name must be provided for Azure OpenAI")
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chat = AzureChatOpenAI(
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deployment_name=config.deployment_name,
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openai_api_version="2023-05-15",
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model_name=config.model or "gpt-3.5-turbo",
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temperature=config.temperature,
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max_tokens=config.max_tokens,
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streaming=config.stream,
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)
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if config.top_p and config.top_p != 1:
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logging.warning("Config option `top_p` is not supported by this model.")
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messages = CustomApp._get_messages(prompt, system_prompt=config.system_prompt)
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return chat(messages).content
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@staticmethod
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def _get_messages(prompt: str, system_prompt: Optional[str] = None) -> List[BaseMessage]:
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from langchain.schema import HumanMessage, SystemMessage
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messages = []
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if system_prompt:
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messages.append(SystemMessage(content=system_prompt))
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messages.append(HumanMessage(content=prompt))
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return messages
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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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super().__init__(config=config, llm=llm, db=db, embedder=embedder, system_prompt=system_prompt)
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