remove old model management code
This commit is contained in:
LUIS NOVO
parent
bea43f3ce7
commit
6532411d33
5 changed files with 0 additions and 567 deletions
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@ -1,77 +0,0 @@
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from typing import Dict, Type, Union
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from open_notebook.models.embedding_models import (
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EmbeddingModel,
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GeminiEmbeddingModel,
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OllamaEmbeddingModel,
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OpenAIEmbeddingModel,
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VertexEmbeddingModel,
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)
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from open_notebook.models.llms import (
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AnthropicLanguageModel,
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GeminiLanguageModel,
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GroqLanguageModel,
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LanguageModel,
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LiteLLMLanguageModel,
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OllamaLanguageModel,
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OpenAILanguageModel,
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OpenRouterLanguageModel,
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VertexAILanguageModel,
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VertexAnthropicLanguageModel,
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XAILanguageModel,
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)
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from open_notebook.models.speech_to_text_models import (
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GroqSpeechToTextModel,
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OpenAISpeechToTextModel,
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SpeechToTextModel,
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)
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from open_notebook.models.text_to_speech_models import (
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ElevenLabsTextToSpeechModel,
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GeminiTextToSpeechModel,
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OpenAITextToSpeechModel,
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TextToSpeechModel,
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)
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ModelType = Union[LanguageModel, EmbeddingModel, SpeechToTextModel, TextToSpeechModel]
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ProviderMap = Dict[str, Type[ModelType]]
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MODEL_CLASS_MAP: Dict[str, ProviderMap] = {
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"language": {
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"ollama": OllamaLanguageModel,
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"openrouter": OpenRouterLanguageModel,
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"vertexai-anthropic": VertexAnthropicLanguageModel,
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"litellm": LiteLLMLanguageModel,
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"vertexai": VertexAILanguageModel,
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"anthropic": AnthropicLanguageModel,
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"openai": OpenAILanguageModel,
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"gemini": GeminiLanguageModel,
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"xai": XAILanguageModel,
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"groq": GroqLanguageModel,
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},
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"embedding": {
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"openai": OpenAIEmbeddingModel,
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"gemini": GeminiEmbeddingModel,
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"vertexai": VertexEmbeddingModel,
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"ollama": OllamaEmbeddingModel,
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},
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"speech_to_text": {
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"openai": OpenAISpeechToTextModel,
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"groq": GroqSpeechToTextModel,
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},
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"text_to_speech": {
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"openai": OpenAITextToSpeechModel,
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"elevenlabs": ElevenLabsTextToSpeechModel,
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"gemini": GeminiTextToSpeechModel,
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},
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}
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__all__ = [
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"MODEL_CLASS_MAP",
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"EmbeddingModel",
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"LanguageModel",
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"SpeechToTextModel",
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"TextToSpeechModel",
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"ModelType",
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]
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@ -1,104 +0,0 @@
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"""
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Classes for supporting different embedding models
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"""
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from __future__ import annotations
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import os
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import List, Optional
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import requests
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# todo: add support for multiple embeddings (array)
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@dataclass
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class EmbeddingModel(ABC):
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"""
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Abstract base class for language models.
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"""
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model_name: Optional[str] = None
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@abstractmethod
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def embed(self, text: str) -> List[float]:
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"""
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Generates an embedding
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"""
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raise NotImplementedError
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@dataclass
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class OllamaEmbeddingModel(EmbeddingModel):
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model_name: str
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base_url: str = os.environ.get("OLLAMA_API_BASE", "http://localhost:11434")
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def embed(self, text: str) -> List[float]:
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"""
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Embeds the content using Open AI embedding
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"""
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text = text.replace("\n", " ")
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response = requests.post(
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f"{self.base_url}/api/embed",
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json={"model": self.model_name, "input": [text]},
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)
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return response.json()["embeddings"][0]
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@dataclass
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class GeminiEmbeddingModel(EmbeddingModel):
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model_name: str
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def embed(self, text: str) -> List[float]:
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import google.generativeai as genai
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"""
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Embeds the content using Open AI embedding
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"""
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model_name = (
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self.model_name
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if self.model_name.startswith("models/")
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else f"models/{self.model_name}"
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)
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result = genai.embed_content(model=model_name, content=text)
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return result["embedding"]
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@dataclass
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class VertexEmbeddingModel(EmbeddingModel):
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model_name: str
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def embed(self, text: str) -> List[float]:
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from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
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texts = [text]
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# The dimensionality of the output embeddings.
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# dimensionality = 256
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# The task type for embedding. Check the available tasks in the model's documentation.
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model = TextEmbeddingModel.from_pretrained(self.model_name)
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inputs = [TextEmbeddingInput(text) for text in texts]
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embeddings = model.get_embeddings(inputs)
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return embeddings[0].values
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@dataclass
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class OpenAIEmbeddingModel(EmbeddingModel):
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model_name: str
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def embed(self, text: str) -> List[float]:
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from openai import OpenAI
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"""
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Embeds the content using Open AI embedding
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"""
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# todo: make this Singleton
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client = OpenAI()
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text = text.replace("\n", " ")
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return (
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client.embeddings.create(input=[text], model=self.model_name)
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.data[0]
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.embedding
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)
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@ -1,294 +0,0 @@
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"""
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Classes for supporting different language models
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"""
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import os
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any, Dict, Optional
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from langchain_anthropic import ChatAnthropic
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from langchain_community.chat_models import ChatLiteLLM
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_google_vertexai import ChatVertexAI
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from langchain_google_vertexai.model_garden import ChatAnthropicVertex
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from langchain_groq.chat_models import ChatGroq
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from langchain_ollama.chat_models import ChatOllama
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from langchain_openai.chat_models import ChatOpenAI
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from pydantic import SecretStr
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# future: is there a value on returning langchain specific models?
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@dataclass
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class LanguageModel(ABC):
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"""
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Abstract base class for language models.
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"""
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model_name: Optional[str] = None
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max_tokens: Optional[int] = 850
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temperature: Optional[float] = 1.0
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streaming: bool = True
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top_p: Optional[float] = 0.9
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kwargs: Dict[str, Any] = field(default_factory=dict)
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json: bool = False
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@abstractmethod
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def to_langchain(self) -> BaseChatModel:
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"""
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Convert the language model to a LangChain chat model.
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"""
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raise NotImplementedError
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@dataclass
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class OllamaLanguageModel(LanguageModel):
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"""
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Language model that uses the Ollama chat model.
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"""
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model_name: str
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base_url: str = os.environ.get("OLLAMA_API_BASE", "http://localhost:11434")
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max_tokens: Optional[int] = 650
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json: bool = False
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def to_langchain(self) -> ChatOllama:
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"""
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Convert the language model to a LangChain chat model.
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"""
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return ChatOllama(
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# api_key="ollama",
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model=self.model_name,
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base_url=self.base_url,
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# keep_alive="10m",
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num_predict=self.max_tokens,
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temperature=self.temperature or 0.5,
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verbose=True,
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top_p=self.top_p,
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)
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@dataclass
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class VertexAnthropicLanguageModel(LanguageModel):
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"""
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Language model that uses the Vertex Anthropic chat model.
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"""
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model_name: str
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project: Optional[str] = os.environ.get("VERTEX_PROJECT", "no-project")
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location: Optional[str] = os.environ.get("VERTEX_LOCATION", "us-central1")
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def to_langchain(self) -> ChatAnthropicVertex:
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"""
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Convert the language model to a LangChain chat model.
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"""
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return ChatAnthropicVertex(
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model=self.model_name,
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project=self.project,
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location=self.location,
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max_tokens=self.max_tokens,
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streaming=False,
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kwargs=self.kwargs,
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top_p=self.top_p,
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temperature=self.temperature or 0.5,
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)
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@dataclass
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class LiteLLMLanguageModel(LanguageModel):
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"""
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Language model that uses the LiteLLM chat model.
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"""
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model_name: str
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def to_langchain(self) -> ChatLiteLLM:
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"""
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Convert the language model to a LangChain chat model.
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"""
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return ChatLiteLLM(
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model=self.model_name,
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temperature=self.temperature or 0.5,
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max_tokens=self.max_tokens,
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streaming=self.streaming,
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top_p=self.top_p,
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)
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@dataclass
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class VertexAILanguageModel(LanguageModel):
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"""
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Language model that uses the Vertex AI chat model.
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"""
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model_name: str
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project: Optional[str] = os.environ.get("VERTEX_PROJECT", "no-project")
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location: Optional[str] = os.environ.get("VERTEX_LOCATION", "us-central1")
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def to_langchain(self) -> ChatVertexAI:
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"""
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Convert the language model to a LangChain chat model.
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"""
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return ChatVertexAI(
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model=self.model_name,
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streaming=self.streaming,
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max_tokens=self.max_tokens,
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top_p=self.top_p,
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location=self.location,
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project=self.project,
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safety_settings=None,
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temperature=self.temperature or 0.5,
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)
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@dataclass
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class GeminiLanguageModel(LanguageModel):
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"""
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Language model that uses the Gemini Family of chat models.
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"""
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model_name: str
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def to_langchain(self) -> ChatGoogleGenerativeAI:
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"""
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Convert the language model to a LangChain chat model.
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"""
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return ChatGoogleGenerativeAI(
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model=self.model_name,
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max_tokens=self.max_tokens,
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temperature=self.temperature or 0.5,
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)
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@dataclass
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class OpenRouterLanguageModel(LanguageModel):
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"""
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Language model that uses the OpenAI chat model.
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"""
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model_name: str
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def to_langchain(self) -> ChatOpenAI:
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"""
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Convert the language model to a LangChain chat model for Open Router.
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"""
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kwargs = self.kwargs
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if self.json:
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kwargs["response_format"] = {"type": "json_object"}
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return ChatOpenAI(
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model=self.model_name,
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temperature=self.temperature or 0.5,
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base_url=os.environ.get(
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"OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"
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),
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max_tokens=self.max_tokens,
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model_kwargs=kwargs,
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streaming=self.streaming,
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api_key=SecretStr(os.environ.get("OPENROUTER_API_KEY", "openrouter")),
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top_p=self.top_p,
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)
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@dataclass
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class GroqLanguageModel(LanguageModel):
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"""
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Language model that uses the Groq chat model.
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"""
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model_name: str
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def to_langchain(self) -> ChatGroq:
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"""
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Convert the language model to a LangChain chat model for Groq.
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"""
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kwargs = self.kwargs
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kwargs["top_p"] = self.top_p
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return ChatGroq(
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model=self.model_name,
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temperature=self.temperature or 0.5,
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max_tokens=self.max_tokens,
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model_kwargs=kwargs,
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stop_sequences=None,
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)
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@dataclass
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class XAILanguageModel(LanguageModel):
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"""
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Language model that uses the OpenAI chat model for X.AI.
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"""
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model_name: str
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def to_langchain(self) -> ChatOpenAI:
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"""
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Convert the language model to a LangChain chat model.
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"""
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kwargs = self.kwargs
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if self.json:
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kwargs["response_format"] = {"type": "json_object"}
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return ChatOpenAI(
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model=self.model_name,
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temperature=self.temperature or 0.5,
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base_url=os.environ.get("XAI_BASE_URL", "https://api.x.ai/v1"),
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max_tokens=self.max_tokens,
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model_kwargs=kwargs,
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streaming=self.streaming,
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api_key=SecretStr(os.environ.get("XAI_API_KEY", "xai")),
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top_p=self.top_p,
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)
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@dataclass
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class AnthropicLanguageModel(LanguageModel):
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"""
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Language model that uses the Anthropic chat model.
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"""
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model_name: str
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def to_langchain(self) -> ChatAnthropic:
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"""
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Convert the language model to a LangChain chat model.
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"""
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return ChatAnthropic( # type: ignore[call-arg]
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model_name=self.model_name,
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max_tokens_to_sample=self.max_tokens or 850,
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model_kwargs=self.kwargs,
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streaming=False,
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timeout=30,
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top_p=self.top_p,
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temperature=self.temperature or 0.5,
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)
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@dataclass
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class OpenAILanguageModel(LanguageModel):
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"""
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Language model that uses the OpenAI chat model.
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"""
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model_name: str
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def to_langchain(self) -> ChatOpenAI:
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"""
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Convert the language model to a LangChain chat model.
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"""
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kwargs = self.kwargs.copy() # Make a copy to avoid modifying the original
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if self.json:
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kwargs["response_format"] = {"type": "json_object"}
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return ChatOpenAI(
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model=self.model_name,
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temperature=self.temperature or 0.5,
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streaming=self.streaming,
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max_tokens=self.max_tokens,
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top_p=self.top_p,
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model_kwargs=kwargs,
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)
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@ -1,61 +0,0 @@
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"""
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Classes for supporting different transcription models
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"""
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Optional
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@dataclass
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class SpeechToTextModel(ABC):
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"""
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Abstract base class for speech to text models.
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"""
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model_name: Optional[str] = None
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@abstractmethod
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def transcribe(self, audio_file_path: str) -> str:
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"""
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Generates a text transcription from audio
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"""
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raise NotImplementedError
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@dataclass
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class OpenAISpeechToTextModel(SpeechToTextModel):
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model_name: str
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def transcribe(self, audio_file_path: str) -> str:
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"""
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Transcribes an audio file into text
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"""
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from openai import OpenAI
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# todo: make this Singleton
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client = OpenAI()
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with open(audio_file_path, "rb") as audio:
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transcription = client.audio.transcriptions.create(
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model=self.model_name, file=audio
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)
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return transcription.text
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@dataclass
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class GroqSpeechToTextModel(SpeechToTextModel):
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model_name: str
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def transcribe(self, audio_file_path: str) -> str:
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"""
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Transcribes an audio file into text
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"""
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from groq import Groq
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# todo: make this Singleton
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client = Groq()
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with open(audio_file_path, "rb") as audio:
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transcription = client.audio.transcriptions.create(
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model=self.model_name, file=audio
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)
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return transcription.text
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@ -1,31 +0,0 @@
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"""
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Classes for supporting different text to speech models
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||||
"""
|
||||
|
||||
from abc import ABC
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
|
||||
@dataclass
|
||||
class TextToSpeechModel(ABC):
|
||||
"""
|
||||
Abstract base class for text to speech models.
|
||||
"""
|
||||
|
||||
model_name: Optional[str] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class OpenAITextToSpeechModel(TextToSpeechModel):
|
||||
model_name: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class ElevenLabsTextToSpeechModel(TextToSpeechModel):
|
||||
model_name: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class GeminiTextToSpeechModel(TextToSpeechModel):
|
||||
model_name: str
|
||||
Loading…
Reference in a new issue