admin-valentin/open-notebook
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

revised models page

Browse source
This commit is contained in:
LUIS NOVO 2025-06-08 19:39:17 -03:00
parent 6532411d33
commit e4e2384587
1 changed files with 211 additions and 200 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

235
pages/7_🤖_Models.py
View file

@ -1,10 +1,10 @@
import os import os
import streamlit as st import streamlit as st
from esperanto import AIFactory
from open_notebook.config import CONFIG from open_notebook.config import CONFIG
from open_notebook.domain.models import DefaultModels, Model, model_manager from open_notebook.domain.models import DefaultModels, Model, model_manager
from open_notebook.models import MODEL_CLASS_MAP
from pages.components.model_selector import model_selector from pages.components.model_selector import model_selector
from pages.stream_app.utils import setup_page from pages.stream_app.utils import setup_page
@ -13,8 +13,6 @@ setup_page("🤖 Models", only_check_mandatory_models=False, stop_on_model_error
st.title("🤖 Models") st.title("🤖 Models")
model_tab, model_defaults_tab = st.tabs(["Models", "Model Defaults"])
provider_status = {} provider_status = {}
model_types = [ model_types = [
@ -25,39 +23,44 @@ model_types = [
"speech_to_text", "speech_to_text",
] ]
provider_status["ollama"] = os.environ.get("OLLAMA_API_BASE") is not None
provider_status["openai"] = os.environ.get("OPENAI_API_KEY") is not None def check_available_providers():
provider_status["groq"] = os.environ.get("GROQ_API_KEY") is not None provider_status["ollama"] = os.environ.get("OLLAMA_API_BASE") is not None
provider_status["xai"] = os.environ.get("XAI_API_KEY") is not None provider_status["openai"] = os.environ.get("OPENAI_API_KEY") is not None
provider_status["vertexai"] = ( provider_status["groq"] = os.environ.get("GROQ_API_KEY") is not None
provider_status["xai"] = os.environ.get("XAI_API_KEY") is not None
provider_status["vertexai"] = (
os.environ.get("VERTEX_PROJECT") is not None os.environ.get("VERTEX_PROJECT") is not None
and os.environ.get("VERTEX_LOCATION") is not None and os.environ.get("VERTEX_LOCATION") is not None
and os.environ.get("GOOGLE_APPLICATION_CREDENTIALS") is not None and os.environ.get("GOOGLE_APPLICATION_CREDENTIALS") is not None
) )
provider_status["vertexai-anthropic"] = ( # provider_status["vertexai-anthropic"] = (
os.environ.get("VERTEX_PROJECT") is not None # os.environ.get("VERTEX_PROJECT") is not None
and os.environ.get("VERTEX_LOCATION") is not None # and os.environ.get("VERTEX_LOCATION") is not None
and os.environ.get("GOOGLE_APPLICATION_CREDENTIALS") is not None # and os.environ.get("GOOGLE_APPLICATION_CREDENTIALS") is not None
) # )
provider_status["gemini"] = os.environ.get("GOOGLE_API_KEY") is not None provider_status["gemini"] = os.environ.get("GOOGLE_API_KEY") is not None
provider_status["openrouter"] = ( provider_status["openrouter"] = (
os.environ.get("OPENROUTER_API_KEY") is not None os.environ.get("OPENROUTER_API_KEY") is not None
and os.environ.get("OPENAI_API_KEY") is not None and os.environ.get("OPENAI_API_KEY") is not None
and os.environ.get("OPENROUTER_BASE_URL") is not None and os.environ.get("OPENROUTER_BASE_URL") is not None
) )
provider_status["anthropic"] = os.environ.get("ANTHROPIC_API_KEY") is not None provider_status["anthropic"] = os.environ.get("ANTHROPIC_API_KEY") is not None
provider_status["elevenlabs"] = os.environ.get("ELEVENLABS_API_KEY") is not None provider_status["elevenlabs"] = os.environ.get("ELEVENLABS_API_KEY") is not None
provider_status["litellm"] = ( provider_status["voyage"] = os.environ.get("VORAGE_API_KEY") is not None
provider_status["ollama"] provider_status["azure"] = (
or provider_status["vertexai"] os.environ.get("AZURE_OPENAI_API_KEY") is not None
or provider_status["vertexai-anthropic"] and os.environ.get("AZURE_OPENAI_ENDPOINT") is not None
or provider_status["anthropic"] and os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME") is not None
or provider_status["openai"] and os.environ.get("AZURE_OPENAI_API_VERSION") is not None
or provider_status["gemini"] )
) provider_status["mistral"] = os.environ.get("MISTRAL_API_KEY") is not None
provider_status["deepseek"] = os.environ.get("DEEPSEEK_API_KEY") is not None
available_providers = [k for k, v in provider_status.items() if v] available_providers = [k for k, v in provider_status.items() if v]
unavailable_providers = [k for k, v in provider_status.items() if not v] unavailable_providers = [k for k, v in provider_status.items() if not v]
return available_providers, unavailable_providers
def generate_new_models(models, suggested_models): def generate_new_models(models, suggested_models):
@ -91,46 +94,49 @@ def generate_new_models(models, suggested_models):
default_models = DefaultModels() default_models = DefaultModels()
all_models = Model.get_all() all_models = Model.get_all()
esperanto_available_providers = AIFactory.get_available_providers()
with model_tab:
st.subheader("Add Model")
provider = st.selectbox("Provider", available_providers) st.subheader("Provider Availability")
if len(unavailable_providers) > 0: st.markdown(
st.caption( "Below, you'll find all AI providers supported and their current availability status. To enable more providers, you need to setup some of their ENV Variables. Please check [the documentation](https://github.com/lfnovo/open-notebook) for instructions on how to do so."
f"Unavailable Providers: {', '.join(unavailable_providers)}. Please check docs page if you wish to enable them." )
) available_providers, unavailable_providers = check_available_providers()
with st.expander("Available Providers"):
st.write(available_providers)
with st.expander("Unavailable Providers"):
st.write(unavailable_providers)
# Filter model types based on provider availability in MODEL_CLASS_MAP st.divider()
available_model_types = [] st.subheader("Add Model")
for model_type in model_types: st.markdown(
if model_type in MODEL_CLASS_MAP and provider in MODEL_CLASS_MAP[model_type]: "Even though a lot of models can be supported, not all will perform optimally. Some are more fit for use in this tool than others. To help you decide which models to use, please refer to [Which model to choose?](https://github.com/lfnovo/open-notebook/blob/main/docs/SETUP.md#which-model-to-choose) for more information. You can also play with some models in the [Transformations](https://try-it-out.open-notebook.com) page to see if they match your needs."
available_model_types.append(model_type) )
if not available_model_types: available_model_types = esperanto_available_providers.keys()
st.error(f"No compatible model types available for provider: {provider}") model_type = st.selectbox(
else:
model_type = st.selectbox(
"Model Type", "Model Type",
available_model_types, available_model_types,
help="Use language for text generation models, text_to_speech for TTS models for generating podcasts, etc.", help="Use language for text generation models, text_to_speech for TTS models for generating podcasts, etc.",
) )
if model_type == "text_to_speech" and provider == "gemini": provider = st.selectbox("Provider", esperanto_available_providers[model_type])
if model_type == "text_to_speech" and provider == "gemini":
model_name = "gemini-default" model_name = "gemini-default"
st.markdown("Gemini models are pre-configured. Using the default model.") st.markdown("Gemini models are pre-configured. Using the default model.")
else: else:
model_name = st.text_input( model_name = st.text_input(
"Model Name", "", help="gpt-4o-mini, claude, gemini, llama3, etc" "Model Name", "", help="gpt-4o-mini, claude, gemini, llama3, etc"
) )
if st.button("Save"): if st.button("Save"):
model = Model(name=model_name, provider=provider, type=model_type) model = Model(name=model_name, provider=provider, type=model_type)
model.save() model.save()
st.success("Saved") st.success("Saved")
st.divider() st.divider()
suggested_models = CONFIG.get("suggested_models", []) suggested_models = CONFIG.get("suggested_models", [])
recommendations = generate_new_models(all_models, suggested_models) recommendations = generate_new_models(all_models, suggested_models)
if len(recommendations) > 0: if len(recommendations) > 0:
with st.expander("💁‍♂️ Recommended models to get you started.."): with st.expander("💁‍♂️ Recommended models to get you started.."):
for recommendation in recommendations: for recommendation in recommendations:
st.markdown( st.markdown(
@ -140,15 +146,17 @@ with model_tab:
new_model = Model(**recommendation) new_model = Model(**recommendation)
new_model.save() new_model.save()
st.rerun() st.rerun()
st.subheader("Configured Models") st.divider()
model_types_available = {
st.subheader("Configured Models")
model_types_available = {
# "vision": False, # "vision": False,
"language": False, "language": False,
"embedding": False, "embedding": False,
"text_to_speech": False, "text_to_speech": False,
"speech_to_text": False, "speech_to_text": False,
} }
for model in all_models: for model in all_models:
model_types_available[model.type] = True model_types_available[model.type] = True
with st.container(border=True): with st.container(border=True):
st.markdown(f"{model.name} ({model.provider}, {model.type})") st.markdown(f"{model.name} ({model.provider}, {model.type})")
@ -156,121 +164,124 @@ with model_tab:
model.delete() model.delete()
st.rerun() st.rerun()
for model_type, available in model_types_available.items(): for model_type, available in model_types_available.items():
if not available: if not available:
st.warning(f"No models available for {model_type}") st.warning(f"No models available for {model_type}")
with model_defaults_tab:
text_generation_models = [model for model in all_models if model.type == "language"]
text_to_speech_models = [ st.divider()
st.subheader("Select Default Models")
text_generation_models = [model for model in all_models if model.type == "language"]
text_to_speech_models = [
model for model in all_models if model.type == "text_to_speech" model for model in all_models if model.type == "text_to_speech"
] ]
speech_to_text_models = [ speech_to_text_models = [
model for model in all_models if model.type == "speech_to_text" model for model in all_models if model.type == "speech_to_text"
] ]
vision_models = [model for model in all_models if model.type == "vision"] vision_models = [model for model in all_models if model.type == "vision"]
embedding_models = [model for model in all_models if model.type == "embedding"] embedding_models = [model for model in all_models if model.type == "embedding"]
st.write( st.write(
"In this section, you can select the default models to be used on the various content operations done by Open Notebook. Some of these can be overriden in the different modules." "In this section, you can select the default models to be used on the various content operations done by Open Notebook. Some of these can be overriden in the different modules."
) )
defs = {} defs = {}
# Handle chat model selection # Handle chat model selection
selected_model = model_selector( selected_model = model_selector(
"Default Chat Model", "Default Chat Model",
"default_chat_model", "default_chat_model",
selected_id=default_models.default_chat_model, selected_id=default_models.default_chat_model,
help="This model will be used for chat.", help="This model will be used for chat.",
model_type="language", model_type="language",
) )
if selected_model: if selected_model:
default_models.default_chat_model = selected_model.id default_models.default_chat_model = selected_model.id
st.divider() st.divider()
# Handle transformation model selection # Handle transformation model selection
selected_model = model_selector( selected_model = model_selector(
"Default Transformation Model", "Default Transformation Model",
"default_transformation_model", "default_transformation_model",
selected_id=default_models.default_transformation_model, selected_id=default_models.default_transformation_model,
help="This model will be used for text transformations such as summaries, insights, etc.", help="This model will be used for text transformations such as summaries, insights, etc.",
model_type="language", model_type="language",
) )
if selected_model: if selected_model:
default_models.default_transformation_model = selected_model.id default_models.default_transformation_model = selected_model.id
st.caption("You can use a cheap model here like gpt-4o-mini, llama3, etc.") st.caption("You can use a cheap model here like gpt-4o-mini, llama3, etc.")
st.divider() st.divider()
# Handle tools model selection # Handle tools model selection
selected_model = model_selector( selected_model = model_selector(
"Default Tools Model", "Default Tools Model",
"default_tools_model", "default_tools_model",
selected_id=default_models.default_tools_model, selected_id=default_models.default_tools_model,
help="This model will be used for calling tools. Currently, it's best to use Open AI and Anthropic for this.", help="This model will be used for calling tools. Currently, it's best to use Open AI and Anthropic for this.",
model_type="language", model_type="language",
) )
if selected_model: if selected_model:
default_models.default_tools_model = selected_model.id default_models.default_tools_model = selected_model.id
st.caption("Recommended to use a capable model here, like gpt-4o, claude, etc.") st.caption("Recommended to use a capable model here, like gpt-4o, claude, etc.")
st.divider() st.divider()
# Handle large context model selection # Handle large context model selection
selected_model = model_selector( selected_model = model_selector(
"Large Context Model", "Large Context Model",
"large_context_model", "large_context_model",
selected_id=default_models.large_context_model, selected_id=default_models.large_context_model,
help="This model will be used for larger context generation -- recommended: Gemini", help="This model will be used for larger context generation -- recommended: Gemini",
model_type="language", model_type="language",
) )
if selected_model: if selected_model:
default_models.large_context_model = selected_model.id default_models.large_context_model = selected_model.id
st.caption("Recommended to use Gemini models for larger context processing") st.caption("Recommended to use Gemini models for larger context processing")
st.divider() st.divider()
# Handle text-to-speech model selection # Handle text-to-speech model selection
selected_model = model_selector( selected_model = model_selector(
"Default Text to Speech Model", "Default Text to Speech Model",
"default_text_to_speech_model", "default_text_to_speech_model",
selected_id=default_models.default_text_to_speech_model, selected_id=default_models.default_text_to_speech_model,
help="This is the default model for converting text to speech (podcasts, etc)", help="This is the default model for converting text to speech (podcasts, etc)",
model_type="text_to_speech", model_type="text_to_speech",
) )
st.caption("You can override this model on different podcasts") st.caption("You can override this model on different podcasts")
if selected_model: if selected_model:
default_models.default_text_to_speech_model = selected_model.id default_models.default_text_to_speech_model = selected_model.id
st.divider() st.divider()
# Handle speech-to-text model selection # Handle speech-to-text model selection
selected_model = model_selector( selected_model = model_selector(
"Default Speech to Text Model", "Default Speech to Text Model",
selected_id=default_models.default_speech_to_text_model, selected_id=default_models.default_speech_to_text_model,
help="This is the default model for converting speech to text (audio transcriptions, etc)", help="This is the default model for converting speech to text (audio transcriptions, etc)",
model_type="speech_to_text", model_type="speech_to_text",
key="default_speech_to_text_model", key="default_speech_to_text_model",
) )
if selected_model: if selected_model:
default_models.default_speech_to_text_model = selected_model.id default_models.default_speech_to_text_model = selected_model.id
st.divider() st.divider()
# Handle embedding model selection # Handle embedding model selection
selected_model = model_selector( selected_model = model_selector(
"Default Speech to Text Model", "Default Embedding Model",
"default_embedding_model", "default_embedding_model",
selected_id=default_models.default_embedding_model, selected_id=default_models.default_embedding_model,
help="This is the default model for embeddings (semantic search, etc)", help="This is the default model for embeddings (semantic search, etc)",
model_type="embedding", model_type="embedding",
) )
if selected_model: if selected_model:
default_models.default_embedding_model = selected_model.id default_models.default_embedding_model = selected_model.id
st.warning( st.warning(
"Caution: you cannot change the embedding model once there is embeddings or they will need to be regenerated" "Caution: you cannot change the embedding model once there is embeddings or they will need to be regenerated"
) )
for k, v in defs.items(): for k, v in defs.items():
if v: if v:
defs[k] = v.id defs[k] = v.id
if st.button("Save Defaults"): if st.button("Save Defaults"):
default_models.patch(defs) default_models.patch(defs)
model_manager.refresh_defaults() model_manager.refresh_defaults()
st.success("Saved") st.success("Saved")