model selector and model suggestions

open_notebook/domain/base.py

@@ -112,8 +112,6 @@ class ObjectModel(BaseModel):
         from open_notebook.domain.models import model_manager
         from open_notebook.models import EmbeddingModel
 
-        EMBEDDING_MODEL: EmbeddingModel = model_manager.embedding_model
-
         try:
             self.model_validate(self.model_dump(), strict=True)
             data = self._prepare_save_data()
@@ -122,6 +120,7 @@ class ObjectModel(BaseModel):
             if self.needs_embedding():
                 embedding_content = self.get_embedding_content()
                 if embedding_content:
+                    EMBEDDING_MODEL: EmbeddingModel = model_manager.embedding_model
                     data["embedding"] = EMBEDDING_MODEL.embed(embedding_content)
 
             if self.id is None:

open_notebook/domain/models.py

@@ -68,7 +68,9 @@ class ModelManager:
                 )
             return cached_model
 
-        assert model_id, "Model ID cannot be empty"
+        if not model_id:
+            return None
+
         model: Model = Model.get(model_id)
 
         if not model:
@@ -160,9 +162,6 @@ class ModelManager:
         elif model_type == "large_context":
             model_id = self.defaults.large_context_model
 
-        if not model_id:
-            raise ValueError(f"No default model configured for type: {model_type}")
-
         return self.get_model(model_id, **kwargs)
 
     def clear_cache(self):

open_notebook_config.yaml

@@ -11,3 +11,27 @@ youtube_transcripts:
     - de
     - hi
     - ja
+suggested_models:
+  openai:
+    language:
+      - gpt-4o-mini
+    embedding:
+      - text-embedding-3-small
+    text_to_speech:
+      - tts-1-hd
+    speech_to_text:
+      - whisper-1
+  gemini:
+    language:
+      - gemini-1.5-flash
+    text_to_speech:
+      - default
+  xai:
+    language:
+      - grok-beta
+  anthropic:
+    language:
+      - claude-3-5-sonnet-20241022
+  elevenlabs:
+    text_to_speech:
+      - eleven_turbo_v2_5

pages/3_🔍_Ask_and_Search.py

@@ -2,9 +2,10 @@ import asyncio
 
 import streamlit as st
 
-from open_notebook.domain.models import Model
+from open_notebook.domain.models import DefaultModels
 from open_notebook.domain.notebook import Note, Notebook, text_search, vector_search
 from open_notebook.graphs.ask import graph as ask_graph
+from pages.components.model_selector import model_selector
 from pages.stream_app.utils import convert_source_references, setup_page
 
 setup_page("🔍 Search")
@@ -52,23 +53,26 @@ with ask_tab:
         "The LLM will answer your query based on the documents in your knowledge base. "
     )
     question = st.text_input("Question", "")
-    models = Model.get_models_by_type("language")
+    default_model = DefaultModels().load().default_chat_model
-    strategy_model: Model = st.selectbox(
+    strategy_model = model_selector(
         "Query Strategy Model",
-        models,
+        "strategy_model",
-        format_func=lambda x: x.name,
+        selected_id=default_model,
+        model_type="language",
         help="This is the LLM that will be responsible for strategizing the search",
     )
-    answer_model: Model = st.selectbox(
+    answer_model = model_selector(
-        "Indivual Answer Model",
+        "Individual Answer Model",
-        models,
+        "answer_model",
-        format_func=lambda x: x.name,
+        model_type="language",
+        selected_id=default_model,
         help="This is the LLM that will be responsible for processing individual subqueries",
     )
-    final_answer_model: Model = st.selectbox(
+    final_answer_model = model_selector(
         "Final Answer Model",
-        models,
+        "final_answer_model",
-        format_func=lambda x: x.name,
+        model_type="language",
+        selected_id=default_model,
         help="This is the LLM that will be responsible for processing the final answer",
     )
     ask_bt = st.button("Ask")

pages/7_⚙️_Settings.py

@@ -2,9 +2,11 @@ import os
 
 import streamlit as st
 
+from open_notebook.config import CONFIG
 from open_notebook.domain.models import DefaultModels, Model, model_manager
 from open_notebook.domain.transformation import DefaultTransformations, Transformation
 from open_notebook.models import MODEL_CLASS_MAP
+from pages.components.model_selector import model_selector
 from pages.stream_app.utils import setup_page
 
 setup_page("⚙️ Settings")
@@ -59,8 +61,41 @@ provider_status["litellm"] = (
 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]
 
+
+def generate_new_models(models, suggested_models):
+    # Create a set of existing model keys for efficient lookup
+    existing_model_keys = {
+        f"{model.provider}-{model.name}-{model.type}" for model in models
+    }
+
+    new_models = []
+
+    # Iterate through suggested models by provider
+    for provider, types in suggested_models.items():
+        # Iterate through each type (language, embedding, etc.)
+        for type_, model_list in types.items():
+            for model_name in model_list:
+                model_key = f"{provider}-{model_name}-{type_}"
+
+                # Check if model already exists
+                if model_key not in existing_model_keys:
+                    new_models.append(
+                        {
+                            "name": model_name,
+                            "type": type_,
+                            "provider": provider,
+                        }
+                    )
+
+    return new_models
+
+
+default_models = DefaultModels().model_dump()
+all_models = Model.get_all()
+
 with model_tab:
     st.subheader("Add Model")
+
     provider = st.selectbox("Provider", available_providers)
     if len(unavailable_providers) > 0:
         st.caption(
@@ -92,8 +127,20 @@ with model_tab:
             model = Model(name=model_name, provider=provider, type=model_type)
             model.save()
             st.success("Saved")
+
     st.divider()
-    all_models = Model.get_all()
+    suggested_models = CONFIG.get("suggested_models", [])
+    recommendations = generate_new_models(all_models, suggested_models)
+    if len(recommendations) > 0:
+        with st.expander("💁‍♂️ Recommended models to get you started.."):
+            for recommendation in recommendations:
+                st.markdown(
+                    f"**{recommendation['name']}** ({recommendation['provider']}, {recommendation['type']})"
+                )
+                if st.button("Add", key=f"add_{recommendation['name']}"):
+                    new_model = Model(**recommendation)
+                    new_model.save()
+                    st.rerun()
     st.subheader("Configured Models")
     model_types_available = {
         # "vision": False,
@@ -114,20 +161,7 @@ with model_tab:
         if not available:
             st.warning(f"No models available for {model_type}")
 
-
-def get_selected_index(models, model_id, default=0):
-    """Returns the index of the selected model in the list of models"""
-    if not model_id or not models:
-        return default
-    for i, model in enumerate(models):
-        if model.id == model_id:
-            return i
-    return default
-
-
 with model_defaults_tab:
-    default_models = DefaultModels().model_dump()
-    all_models = Model.get_all()
     text_generation_models = [model for model in all_models if model.type == "language"]
 
     text_to_speech_models = [
@@ -143,93 +177,80 @@ with model_defaults_tab:
         "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["default_chat_model"] = st.selectbox(
+    defs["default_chat_model"] = model_selector(
         "Default Chat Model",
-        text_generation_models,
+        "default_chat_model",
-        format_func=lambda x: x.name,
+        selected_id=default_models.get("default_chat_model"),
         help="This model will be used for chat.",
-        index=get_selected_index(
+        model_type="language",
-            text_generation_models, default_models.get("default_chat_model")
-        ),
     )
     st.divider()
-    defs["default_transformation_model"] = st.selectbox(
+    defs["default_transformation_model"] = model_selector(
         "Default Transformation Model",
-        text_generation_models,
+        "default_transformation_model",
-        format_func=lambda x: x.name,
+        selected_id=default_models.get("default_transformation_model"),
         help="This model will be used for text transformations such as summaries, insights, etc.",
-        index=get_selected_index(
+        model_type="language",
-            text_generation_models, default_models.get("default_transformation_model")
-        ),
     )
+    st.caption("You can use a cheap model here like gpt-4o-mini, llama3, etc.")
     st.divider()
-    defs["default_tools_model"] = st.selectbox(
+    defs["default_tools_model"] = model_selector(
         "Default Tools Model",
-        text_generation_models,
+        "default_tools_model",
-        format_func=lambda x: x.name,
+        selected_id=default_models.get("default_tools_model"),
         help="This model will be used for calling tools. Currently, it's best to use Open AI and Anthropic for this.",
-        index=get_selected_index(
+        model_type="language",
-            text_generation_models, default_models.get("default_tools_model")
-        ),
     )
+    st.caption("Recommended to use a capable model here, like gpt-4o, claude, etc.")
     st.divider()
-    defs["large_context_model"] = st.selectbox(
+    defs["large_context_model"] = model_selector(
         "Large Context Model",
-        text_generation_models,
+        "large_context_model",
-        format_func=lambda x: x.name,
+        selected_id=default_models.get("large_context_model"),
         help="This model will be used for larger context generation -- recommended: Gemini",
-        index=get_selected_index(
+        model_type="language",
-            text_generation_models, default_models.get("large_context_model")
-        ),
     )
     st.caption("Recommended to use Gemini models for larger context processing")
     st.divider()
-    defs["default_text_to_speech_model"] = st.selectbox(
+    defs["default_text_to_speech_model"] = model_selector(
         "Default Text to Speech Model",
-        text_to_speech_models,
+        "default_text_to_speech_model",
-        format_func=lambda x: x.name,
+        selected_id=default_models.get("default_text_to_speech_model"),
         help="This is the default model for converting text to speech (podcasts, etc)",
-        index=get_selected_index(
+        model_type="text_to_speech",
-            text_to_speech_models, default_models.get("default_text_to_speech_model")
-        ),
     )
     st.caption("You can override this model on different podcasts")
     st.divider()
-    defs["default_speech_to_text_model"] = st.selectbox(
+    defs["default_speech_to_text_model"] = model_selector(
         "Default Speech to Text Model",
-        speech_to_text_models,
+        "default_speech_to_text_model",
-        format_func=lambda x: x.name,
+        selected_id=default_models.get("default_speech_to_text_model"),
         help="This is the default model for converting speech to text (audio transcriptions, etc)",
-        index=get_selected_index(
+        model_type="speech_to_text",
-            speech_to_text_models, default_models.get("default_speech_to_text_model")
-        ),
     )
+
     st.divider()
-    # defs["default_vision_model"] = st.selectbox(
+    # defs["default_vision_model"] = (
-    #     "Default Vision Model",
+    #     model_selector(
-    #     vision_models,
+    #         "Default Speech to Text Model",
-    #     format_func=lambda x: x.name,
+    #         "default_vision_model",
-    #     help="This is the default model for vision tasks (image recognition, PDF recognition, etc)",
+    #         selected_id=default_models.get("default_vision_model"),
-    #     index=get_selected_index(
+    #         help="This is the default model for vision tasks",
-    #         vision_models, default_models.get("default_vision_model")
+    #         model_type="vision",
     #     ),
     # )
-    # st.divider()
 
-    defs["default_embedding_model"] = st.selectbox(
+    defs["default_embedding_model"] = model_selector(
-        "Default Embedding Model",
+        "Default Speech to Text Model",
-        embedding_models,
+        "default_embedding_model",
-        format_func=lambda x: x.name,
+        selected_id=default_models.get("default_embedding_model"),
         help="This is the default model for embeddings (semantic search, etc)",
-        index=get_selected_index(
+        model_type="embedding",
-            embedding_models, default_models.get("default_embedding_model")
-        ),
     )
     st.caption(
         "Caution: you cannot change the embedding model once there is embeddings or they will need to be regenerated"
     )
 
-    # if st.button("Save Defaults", key="save_defaults"):
     for k, v in defs.items():
         if v:
             defs[k] = v.id

pages/8_🛝_Playground.py

@@ -1,8 +1,8 @@
 import streamlit as st
 import yaml
 
-from open_notebook.domain.models import Model
 from open_notebook.graphs.multipattern import graph as pattern_graph
+from pages.components.model_selector import model_selector
 from pages.stream_app.utils import setup_page
 
 setup_page("🛝 Playground")
@@ -22,12 +22,13 @@ transformation = st.selectbox(
 with st.expander("Details"):
     st.json(transformation)
 
-models = Model.get_models_by_type("language")
+model = model_selector(
-model = st.selectbox(
     "Pick a pattern model",
-    models,
+    key="model",
-    format_func=lambda x: x.name,
+    help="This is the model that will be used to run the transformation",
+    model_type="language",
 )
+
 input_text = st.text_area("Enter some text", height=200)
 
 if st.button("Run"):

pages/components/model_selector.py

@@ -0,0 +1,35 @@
+from typing import Literal
+
+import streamlit as st
+
+from open_notebook.domain.models import Model
+
+
+def model_selector(
+    label,
+    key,
+    selected_id=None,
+    help=None,
+    model_type: Literal[
+        "language", "embedding", "speech_to_text", "text_to_speech"
+    ] = "language",
+) -> Model:
+    models = Model.get_models_by_type(model_type)
+    models.sort(key=lambda x: (x.provider, x.name))
+    try:
+        index = (
+            next((i for i, m in enumerate(models) if m.id == selected_id), 0)
+            if selected_id
+            else 0
+        )
+    except Exception:
+        index = 0
+
+    return st.selectbox(
+        label,
+        models,
+        format_func=lambda x: f"{x.provider} - {x.name}",
+        help=help,
+        index=index,
+        key=key,
+    )

pages/stream_app/utils.py

@@ -116,34 +116,18 @@ def check_migration():
 
 def check_models():
     default_models = model_manager.defaults
-    if (
+    if not all(
-        not default_models.default_chat_model
+        [
-        or not default_models.default_transformation_model
+            default_models.default_chat_model,
+            default_models.default_transformation_model,
+            default_models.default_embedding_model,
+            default_models.default_speech_to_text_model,
+            default_models.large_context_model,
+        ]
     ):
         st.warning(
-            "You don't have default chat and transformation models selected. Please, select them on the settings page."
+            "You are missing some default models and the app might not work as expected. Please, select them on the settings page."
         )
-        st.stop()
-    elif not default_models.default_embedding_model:
-        st.warning(
-            "You don't have a default embedding model selected. Vector search will not be possible and your assistant will be less able to answer your queries. Please, select one on the settings page."
-        )
-        st.stop()
-    elif not default_models.default_speech_to_text_model:
-        st.warning(
-            "You don't have a default speech to text model selected. Your assistant will not be able to transcribe audio. Please, select one on the settings page."
-        )
-        st.stop()
-    elif not default_models.default_text_to_speech_model:
-        st.warning(
-            "You don't have a default text to speech model selected. Your assistant will not be able to generate audio and podcasts. Please, select one on the settings page."
-        )
-        st.stop()
-    elif not default_models.large_context_model:
-        st.warning(
-            "You don't have a large context model selected. Your assistant will not be able to process large documents. Please, select one on the settings page."
-        )
-        st.stop()
 
 
 def handle_error(func):