diff --git a/.dockerignore b/.dockerignore index 164cf77b..dcf9035b 100644 --- a/.dockerignore +++ b/.dockerignore @@ -8,4 +8,5 @@ build .gitignore tmp debug -.fastembed_cache \ No newline at end of file +.fastembed_cache +generated_models \ No newline at end of file diff --git a/.gitignore b/.gitignore index 81923a11..33c06363 100644 --- a/.gitignore +++ b/.gitignore @@ -11,4 +11,5 @@ app_data tmp debug .fastembed_cache -my-doc.txt \ No newline at end of file +my-doc.txt +generated_models \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index cc303b52..a3fc8e44 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,12 +2,11 @@ FROM python:3.11-slim-bookworm # Install Node.js and npm RUN apt-get update && apt-get install -y \ - nginx \ curl \ redis-server - # Install Node.js 20 using NodeSource repository +# Install Node.js 20 using NodeSource repository RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \ apt-get install -y nodejs @@ -43,7 +42,7 @@ RUN npm run build WORKDIR /app -# Copy FastAPI and start script +# Copy FastAPI COPY servers/fastapi/ ./servers/fastapi/ COPY start.js LICENSE NOTICE ./ diff --git a/Dockerfile.dev b/Dockerfile.dev index 3c84ac4b..db61b4b7 100644 --- a/Dockerfile.dev +++ b/Dockerfile.dev @@ -7,9 +7,9 @@ RUN apt-get update && apt-get install -y \ redis-server - # Install Node.js 20 using NodeSource repository +# Install Node.js 20 using NodeSource repository RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \ - apt-get install -y nodejs + apt-get install -y nodejs # Change working directory diff --git a/docker-compose.yml b/docker-compose.yml index 64798520..81239bba 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -14,12 +14,15 @@ services: - LLM=${LLM} - OPENAI_API_KEY=${OPENAI_API_KEY} - GOOGLE_API_KEY=${GOOGLE_API_KEY} + - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} + - ANTHROPIC_MODEL=${ANTHROPIC_MODEL} - OLLAMA_URL=${OLLAMA_URL} - OLLAMA_MODEL=${OLLAMA_MODEL} - CUSTOM_LLM_URL=${CUSTOM_LLM_URL} - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY} - CUSTOM_MODEL=${CUSTOM_MODEL} - PEXELS_API_KEY=${PEXELS_API_KEY} + - EXTENDED_REASONING=${EXTENDED_REASONING} - DATABASE_URL=${DATABASE_URL} production-gpu: @@ -44,12 +47,15 @@ services: - LLM=${LLM} - OPENAI_API_KEY=${OPENAI_API_KEY} - GOOGLE_API_KEY=${GOOGLE_API_KEY} + - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} + - ANTHROPIC_MODEL=${ANTHROPIC_MODEL} - OLLAMA_URL=${OLLAMA_URL} - OLLAMA_MODEL=${OLLAMA_MODEL} - CUSTOM_LLM_URL=${CUSTOM_LLM_URL} - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY} - CUSTOM_MODEL=${CUSTOM_MODEL} - PEXELS_API_KEY=${PEXELS_API_KEY} + - EXTENDED_REASONING=${EXTENDED_REASONING} - DATABASE_URL=${DATABASE_URL} development: @@ -67,12 +73,15 @@ services: - LLM=${LLM} - OPENAI_API_KEY=${OPENAI_API_KEY} - GOOGLE_API_KEY=${GOOGLE_API_KEY} + - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} + - ANTHROPIC_MODEL=${ANTHROPIC_MODEL} - OLLAMA_URL=${OLLAMA_URL} - OLLAMA_MODEL=${OLLAMA_MODEL} - CUSTOM_LLM_URL=${CUSTOM_LLM_URL} - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY} - CUSTOM_MODEL=${CUSTOM_MODEL} - PEXELS_API_KEY=${PEXELS_API_KEY} + - EXTENDED_REASONING=${EXTENDED_REASONING} - DATABASE_URL=${DATABASE_URL} development-gpu: @@ -97,10 +106,13 @@ services: - LLM=${LLM} - OPENAI_API_KEY=${OPENAI_API_KEY} - GOOGLE_API_KEY=${GOOGLE_API_KEY} + - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} + - ANTHROPIC_MODEL=${ANTHROPIC_MODEL} - OLLAMA_URL=${OLLAMA_URL} - OLLAMA_MODEL=${OLLAMA_MODEL} - CUSTOM_LLM_URL=${CUSTOM_LLM_URL} - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY} - CUSTOM_MODEL=${CUSTOM_MODEL} - PEXELS_API_KEY=${PEXELS_API_KEY} + - EXTENDED_REASONING=${EXTENDED_REASONING} - DATABASE_URL=${DATABASE_URL} \ No newline at end of file diff --git a/servers/fastapi/api/main.py b/servers/fastapi/api/main.py index 93517b12..80eea709 100644 --- a/servers/fastapi/api/main.py +++ b/servers/fastapi/api/main.py @@ -1,10 +1,8 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from fastapi.staticfiles import StaticFiles from api.lifespan import app_lifespan from api.middlewares import UserConfigEnvUpdateMiddleware from api.v1.ppt.router import API_V1_PPT_ROUTER -from utils.asset_directory_utils import get_exports_directory, get_images_directory, get_uploads_directory app = FastAPI(lifespan=app_lifespan) @@ -13,25 +11,6 @@ app = FastAPI(lifespan=app_lifespan) # Routers app.include_router(API_V1_PPT_ROUTER) -# Static files -app.mount("/static", StaticFiles(directory="static"), name="static") -app.mount( - "/app_data/images", - StaticFiles(directory=get_images_directory()), - name="app_data/images", -) -app.mount( - "/app_data/exports", - StaticFiles(directory=get_exports_directory()), - name="app_data/exports", -) -app.mount( - "/app_data/uploads", - StaticFiles(directory=get_uploads_directory()), - name="app_data/uploads", -) - - # Middlewares origins = ["*"] app.add_middleware( diff --git a/servers/fastapi/api/v1/ppt/endpoints/anthropic.py b/servers/fastapi/api/v1/ppt/endpoints/anthropic.py new file mode 100644 index 00000000..e3e1d569 --- /dev/null +++ b/servers/fastapi/api/v1/ppt/endpoints/anthropic.py @@ -0,0 +1,16 @@ +from typing import Annotated, List +from fastapi import APIRouter, Body, HTTPException + +from utils.available_models import list_available_anthropic_models + +ANTHROPIC_ROUTER = APIRouter(prefix="/anthropic", tags=["Anthropic"]) + + +@ANTHROPIC_ROUTER.post("/models/available", response_model=List[str]) +async def get_available_models( + api_key: Annotated[str, Body(embed=True)], +): + try: + return await list_available_anthropic_models(api_key) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/servers/fastapi/api/v1/ppt/endpoints/custom_llm.py b/servers/fastapi/api/v1/ppt/endpoints/custom_llm.py deleted file mode 100644 index 8a44cb22..00000000 --- a/servers/fastapi/api/v1/ppt/endpoints/custom_llm.py +++ /dev/null @@ -1,14 +0,0 @@ -from typing import Annotated, List, Optional -from fastapi import APIRouter, Body - -from utils.custom_llm_provider import list_available_custom_models - -CUSTOM_LLM_ROUTER = APIRouter(prefix="/custom_llm", tags=["Custom LLM"]) - - -@CUSTOM_LLM_ROUTER.post("/models/available", response_model=List[str]) -async def get_available_models( - url: Annotated[Optional[str], Body()] = None, - api_key: Annotated[Optional[str], Body()] = None, -): - return await list_available_custom_models(url, api_key) diff --git a/servers/fastapi/api/v1/ppt/endpoints/google.py b/servers/fastapi/api/v1/ppt/endpoints/google.py new file mode 100644 index 00000000..4c83627f --- /dev/null +++ b/servers/fastapi/api/v1/ppt/endpoints/google.py @@ -0,0 +1,14 @@ +from typing import Annotated, List +from fastapi import APIRouter, Body, HTTPException + +from utils.available_models import list_available_google_models + +GOOGLE_ROUTER = APIRouter(prefix="/google", tags=["Google"]) + + +@GOOGLE_ROUTER.post("/models/available", response_model=List[str]) +async def get_available_models(api_key: Annotated[str, Body(embed=True)]): + try: + return await list_available_google_models(api_key) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/servers/fastapi/api/v1/ppt/endpoints/openai.py b/servers/fastapi/api/v1/ppt/endpoints/openai.py new file mode 100644 index 00000000..8e2f00e6 --- /dev/null +++ b/servers/fastapi/api/v1/ppt/endpoints/openai.py @@ -0,0 +1,17 @@ +from typing import Annotated, List +from fastapi import APIRouter, Body, HTTPException + +from utils.available_models import list_available_openai_compatible_models + +OPENAI_ROUTER = APIRouter(prefix="/openai", tags=["OpenAI"]) + + +@OPENAI_ROUTER.post("/models/available", response_model=List[str]) +async def get_available_models( + url: Annotated[str, Body()], + api_key: Annotated[str, Body()], +): + try: + return await list_available_openai_compatible_models(url, api_key) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/servers/fastapi/api/v1/ppt/endpoints/outlines.py b/servers/fastapi/api/v1/ppt/endpoints/outlines.py index fa1e45bd..b5ff9f5a 100644 --- a/servers/fastapi/api/v1/ppt/endpoints/outlines.py +++ b/servers/fastapi/api/v1/ppt/endpoints/outlines.py @@ -54,7 +54,6 @@ async def stream_outlines( presentation.outlines = [ each.model_dump() for each in presentation_content.slides ] - presentation.notes = presentation_content.notes sql_session.add(presentation) await sql_session.commit() diff --git a/servers/fastapi/api/v1/ppt/endpoints/presentation.py b/servers/fastapi/api/v1/ppt/endpoints/presentation.py index f7fa7865..b8eaf018 100644 --- a/servers/fastapi/api/v1/ppt/endpoints/presentation.py +++ b/servers/fastapi/api/v1/ppt/endpoints/presentation.py @@ -2,10 +2,11 @@ import asyncio import json import os import random +import importlib from typing import Annotated, List, Literal, Optional from fastapi import APIRouter, Body, Depends, File, HTTPException, UploadFile from fastapi.responses import StreamingResponse -from sqlalchemy import String, cast, delete +from sqlalchemy import delete from sqlalchemy.ext.asyncio import AsyncSession from sqlmodel import select from constants.documents import UPLOAD_ACCEPTED_FILE_TYPES @@ -19,7 +20,7 @@ from models.pptx_models import PptxPresentationModel from models.presentation_layout import PresentationLayoutModel from models.presentation_structure_model import PresentationStructureModel from models.presentation_with_slides import PresentationWithSlides -from services.get_layout_by_name import get_layout_by_name +from utils.get_layout_by_name import get_layout_by_name from services.icon_finder_service import IconFinderService from services.image_generation_service import ImageGenerationService from utils.dict_utils import deep_update @@ -217,9 +218,11 @@ async def stream_presentation( ).to_string() for i, slide_layout_index in enumerate(structure.slides): slide_layout = layout.slides[slide_layout_index] + slide_content = await get_slide_content_from_type_and_outline( slide_layout, outline.slides[i], presentation.language ) + slide = SlideModel( presentation=presentation_id, layout_group=layout.name, @@ -236,9 +239,6 @@ async def stream_presentation( ) ) - # Give control to the event loop - await asyncio.sleep(0) - yield SSEResponse( event="response", data=json.dumps({"type": "chunk", "chunk": slide.model_dump_json()}), @@ -475,7 +475,6 @@ async def from_template( new_slide_data = list(filter(lambda x: x.index == each_slide.index, data.data)) if new_slide_data: updated_content = deep_update(each_slide.content, new_slide_data[0].content) - print(f"Updated content for slide {each_slide.index}: {updated_content}") new_slides.append( each_slide.get_new_slide(new_presentation.id, updated_content) ) diff --git a/servers/fastapi/api/v1/ppt/endpoints/slide.py b/servers/fastapi/api/v1/ppt/endpoints/slide.py index 3a254453..a0c81107 100644 --- a/servers/fastapi/api/v1/ppt/endpoints/slide.py +++ b/servers/fastapi/api/v1/ppt/endpoints/slide.py @@ -1,3 +1,4 @@ +import importlib from typing import Annotated, Optional from fastapi import APIRouter, Body, Depends, HTTPException from sqlalchemy.ext.asyncio import AsyncSession @@ -13,6 +14,7 @@ from utils.llm_calls.edit_slide_html import get_edited_slide_html from utils.llm_calls.select_slide_type_on_edit import get_slide_layout_from_prompt from utils.process_slides import process_old_and_new_slides_and_fetch_assets from utils.randomizers import get_random_uuid +from utils.schema_utils import remove_fields_from_schema SLIDE_ROUTER = APIRouter(prefix="/slide", tags=["Slide"]) @@ -32,12 +34,12 @@ async def edit_slide( raise HTTPException(status_code=404, detail="Presentation not found") presentation_layout = presentation.get_layout() - slide_layout = await get_slide_layout_from_prompt( prompt, presentation_layout, slide ) + edited_slide_content = await get_edited_slide_content( - prompt, slide_layout, slide, presentation.language + prompt, slide, presentation.language, slide_layout ) image_generation_service = ImageGenerationService(get_images_directory()) diff --git a/servers/fastapi/api/v1/ppt/router.py b/servers/fastapi/api/v1/ppt/router.py index 865afc19..2f22e1b5 100644 --- a/servers/fastapi/api/v1/ppt/router.py +++ b/servers/fastapi/api/v1/ppt/router.py @@ -1,6 +1,8 @@ from fastapi import APIRouter -from api.v1.ppt.endpoints.custom_llm import CUSTOM_LLM_ROUTER +from api.v1.ppt.endpoints.anthropic import ANTHROPIC_ROUTER +from api.v1.ppt.endpoints.google import GOOGLE_ROUTER +from api.v1.ppt.endpoints.openai import OPENAI_ROUTER from api.v1.ppt.endpoints.files import FILES_ROUTER from api.v1.ppt.endpoints.icons import ICONS_ROUTER from api.v1.ppt.endpoints.images import IMAGES_ROUTER @@ -19,4 +21,6 @@ API_V1_PPT_ROUTER.include_router(SLIDE_ROUTER) API_V1_PPT_ROUTER.include_router(IMAGES_ROUTER) API_V1_PPT_ROUTER.include_router(ICONS_ROUTER) API_V1_PPT_ROUTER.include_router(OLLAMA_ROUTER) -API_V1_PPT_ROUTER.include_router(CUSTOM_LLM_ROUTER) +API_V1_PPT_ROUTER.include_router(OPENAI_ROUTER) +API_V1_PPT_ROUTER.include_router(ANTHROPIC_ROUTER) +API_V1_PPT_ROUTER.include_router(GOOGLE_ROUTER) diff --git a/servers/fastapi/chroma/chroma.sqlite3 b/servers/fastapi/chroma/chroma.sqlite3 index b39add05..30b90af8 100644 Binary files a/servers/fastapi/chroma/chroma.sqlite3 and b/servers/fastapi/chroma/chroma.sqlite3 differ diff --git a/servers/fastapi/constants/llm.py b/servers/fastapi/constants/llm.py new file mode 100644 index 00000000..ac4bd527 --- /dev/null +++ b/servers/fastapi/constants/llm.py @@ -0,0 +1,6 @@ +OPENAI_URL = "https://api.openai.com/v1" + +# Default models +DEFAULT_OPENAI_MODEL = "gpt-4.1" +DEFAULT_GOOGLE_MODEL = "models/gemini-2.0-flash" +DEFAULT_ANTHROPIC_MODEL = "claude-3-5-sonnet-20240620" diff --git a/servers/fastapi/constants/supported_ollama_models.py b/servers/fastapi/constants/supported_ollama_models.py index a46b5774..6ae3ac4e 100644 --- a/servers/fastapi/constants/supported_ollama_models.py +++ b/servers/fastapi/constants/supported_ollama_models.py @@ -7,7 +7,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3:8b", description="❌ Graphs not supported.", size="4.7GB", - supports_graph=False, icon="/static/icons/meta.png", ), "llama3:70b": OllamaModelMetadata( @@ -15,7 +14,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3:70b", description="✅ Graphs supported.", size="40GB", - supports_graph=True, icon="/static/icons/meta.png", ), "llama3.1:8b": OllamaModelMetadata( @@ -23,7 +21,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3.1:8b", description="❌ Graphs not supported.", size="4.9GB", - supports_graph=False, icon="/static/icons/meta.png", ), "llama3.1:70b": OllamaModelMetadata( @@ -31,7 +28,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3.1:70b", description="✅ Graphs supported.", size="43GB", - supports_graph=True, icon="/static/icons/meta.png", ), "llama3.1:405b": OllamaModelMetadata( @@ -39,7 +35,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3.1:405b", description="✅ Graphs supported.", size="243GB", - supports_graph=True, icon="/static/icons/meta.png", ), "llama3.2:1b": OllamaModelMetadata( @@ -47,7 +42,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3.2:1b", description="❌ Graphs not supported.", size="1.3GB", - supports_graph=False, icon="/static/icons/meta.png", ), "llama3.2:3b": OllamaModelMetadata( @@ -55,7 +49,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3.2:3b", description="❌ Graphs not supported.", size="2GB", - supports_graph=False, icon="/static/icons/meta.png", ), "llama3.3:70b": OllamaModelMetadata( @@ -63,7 +56,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama3.3:70b", description="✅ Graphs supported.", size="43GB", - supports_graph=True, icon="/static/icons/meta.png", ), "llama4:16x17b": OllamaModelMetadata( @@ -71,7 +63,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama4:16x17b", description="✅ Graphs supported.", size="67GB", - supports_graph=True, icon="/static/icons/meta.png", ), "llama4:128x17b": OllamaModelMetadata( @@ -79,7 +70,6 @@ SUPPORTED_OLLAMA_MODELS = { value="llama4:128x17b", description="✅ Graphs supported.", size="245GB", - supports_graph=True, icon="/static/icons/meta.png", ), } @@ -90,7 +80,6 @@ SUPPORTED_GEMMA_MODELS = { value="gemma3:1b", description="❌ Graphs not supported.", size="815MB", - supports_graph=False, icon="/static/icons/gemma.png", ), "gemma3:4b": OllamaModelMetadata( @@ -98,7 +87,6 @@ SUPPORTED_GEMMA_MODELS = { value="gemma3:4b", description="❌ Graphs not supported.", size="3.3GB", - supports_graph=False, icon="/static/icons/gemma.png", ), "gemma3:12b": OllamaModelMetadata( @@ -106,7 +94,6 @@ SUPPORTED_GEMMA_MODELS = { value="gemma3:12b", description="❌ Graphs not supported.", size="8.1GB", - supports_graph=False, icon="/static/icons/gemma.png", ), "gemma3:27b": OllamaModelMetadata( @@ -114,7 +101,6 @@ SUPPORTED_GEMMA_MODELS = { value="gemma3:27b", description="✅ Graphs supported.", size="17GB", - supports_graph=True, icon="/static/icons/gemma.png", ), } @@ -125,7 +111,6 @@ SUPPORTED_DEEPSEEK_MODELS = { value="deepseek-r1:1.5b", description="❌ Graphs not supported.", size="1.1GB", - supports_graph=False, icon="/static/icons/deepseek.png", ), "deepseek-r1:7b": OllamaModelMetadata( @@ -133,7 +118,6 @@ SUPPORTED_DEEPSEEK_MODELS = { value="deepseek-r1:7b", description="❌ Graphs not supported.", size="4.7GB", - supports_graph=False, icon="/static/icons/deepseek.png", ), "deepseek-r1:8b": OllamaModelMetadata( @@ -141,7 +125,6 @@ SUPPORTED_DEEPSEEK_MODELS = { value="deepseek-r1:8b", description="❌ Graphs not supported.", size="5.2GB", - supports_graph=False, icon="/static/icons/deepseek.png", ), "deepseek-r1:14b": OllamaModelMetadata( @@ -149,7 +132,6 @@ SUPPORTED_DEEPSEEK_MODELS = { value="deepseek-r1:14b", description="❌ Graphs not supported.", size="9GB", - supports_graph=False, icon="/static/icons/deepseek.png", ), "deepseek-r1:32b": OllamaModelMetadata( @@ -157,7 +139,6 @@ SUPPORTED_DEEPSEEK_MODELS = { value="deepseek-r1:32b", description="✅ Graphs supported.", size="20GB", - supports_graph=True, icon="/static/icons/deepseek.png", ), "deepseek-r1:70b": OllamaModelMetadata( @@ -165,7 +146,6 @@ SUPPORTED_DEEPSEEK_MODELS = { value="deepseek-r1:70b", description="✅ Graphs supported.", size="43GB", - supports_graph=True, icon="/static/icons/deepseek.png", ), "deepseek-r1:671b": OllamaModelMetadata( @@ -173,7 +153,6 @@ SUPPORTED_DEEPSEEK_MODELS = { value="deepseek-r1:671b", description="✅ Graphs supported.", size="404GB", - supports_graph=True, icon="/static/icons/deepseek.png", ), } @@ -184,7 +163,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:0.6b", description="❌ Graphs not supported.", size="523MB", - supports_graph=False, icon="/static/icons/qwen.png", ), "qwen3:1.7b": OllamaModelMetadata( @@ -192,7 +170,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:1.7b", description="❌ Graphs not supported.", size="1.4GB", - supports_graph=False, icon="/static/icons/qwen.png", ), "qwen3:4b": OllamaModelMetadata( @@ -200,7 +177,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:4b", description="❌ Graphs not supported.", size="2.6GB", - supports_graph=False, icon="/static/icons/qwen.png", ), "qwen3:8b": OllamaModelMetadata( @@ -208,7 +184,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:8b", description="❌ Graphs not supported.", size="5.2GB", - supports_graph=False, icon="/static/icons/qwen.png", ), "qwen3:14b": OllamaModelMetadata( @@ -216,7 +191,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:14b", description="❌ Graphs not supported.", size="9.3GB", - supports_graph=False, icon="/static/icons/qwen.png", ), "qwen3:30b": OllamaModelMetadata( @@ -224,7 +198,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:30b", description="✅ Graphs supported.", size="19GB", - supports_graph=True, icon="/static/icons/qwen.png", ), "qwen3:32b": OllamaModelMetadata( @@ -232,7 +205,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:32b", description="✅ Graphs supported.", size="20GB", - supports_graph=True, icon="/static/icons/qwen.png", ), "qwen3:235b": OllamaModelMetadata( @@ -240,7 +212,6 @@ SUPPORTED_QWEN_MODELS = { value="qwen3:235b", description="✅ Graphs supported.", size="142GB", - supports_graph=True, icon="/static/icons/qwen.png", ), } diff --git a/servers/fastapi/enums/llm_provider.py b/servers/fastapi/enums/llm_provider.py index a935425c..049d365f 100644 --- a/servers/fastapi/enums/llm_provider.py +++ b/servers/fastapi/enums/llm_provider.py @@ -5,4 +5,5 @@ class LLMProvider(Enum): OLLAMA = "ollama" OPENAI = "openai" GOOGLE = "google" + ANTHROPIC = "anthropic" CUSTOM = "custom" diff --git a/servers/fastapi/models/llm_message.py b/servers/fastapi/models/llm_message.py new file mode 100644 index 00000000..51284173 --- /dev/null +++ b/servers/fastapi/models/llm_message.py @@ -0,0 +1,7 @@ +from typing import Literal +from pydantic import BaseModel + + +class LLMMessage(BaseModel): + role: Literal["user", "system"] + content: str diff --git a/servers/fastapi/models/ollama_model_metadata.py b/servers/fastapi/models/ollama_model_metadata.py index ce2abae3..766924db 100644 --- a/servers/fastapi/models/ollama_model_metadata.py +++ b/servers/fastapi/models/ollama_model_metadata.py @@ -7,4 +7,3 @@ class OllamaModelMetadata(BaseModel): description: str icon: str size: str - supports_graph: bool diff --git a/servers/fastapi/models/presentation_layout.py b/servers/fastapi/models/presentation_layout.py index 5e90dc85..784e41fc 100644 --- a/servers/fastapi/models/presentation_layout.py +++ b/servers/fastapi/models/presentation_layout.py @@ -1,4 +1,5 @@ from typing import List, Optional +from fastapi import HTTPException from pydantic import BaseModel, Field from models.presentation_structure_model import PresentationStructureModel @@ -12,10 +13,18 @@ class SlideLayoutModel(BaseModel): class PresentationLayoutModel(BaseModel): - name: Optional[str] = None + name: str ordered: bool = Field(default=False) slides: List[SlideLayoutModel] + def get_slide_layout_index(self, slide_layout_id: str) -> int: + for index, slide in enumerate(self.slides): + if slide.id == slide_layout_id: + return index + raise HTTPException( + status_code=404, detail=f"Slide layout {slide_layout_id} not found" + ) + def to_presentation_structure(self): return PresentationStructureModel( slides=[index for index in range(len(self.slides))] diff --git a/servers/fastapi/models/presentation_outline_model.py b/servers/fastapi/models/presentation_outline_model.py index ed06485b..39383390 100644 --- a/servers/fastapi/models/presentation_outline_model.py +++ b/servers/fastapi/models/presentation_outline_model.py @@ -15,7 +15,6 @@ class PresentationOutlineModel(BaseModel): title: str = Field( description="Title of the presentation in about 3 to 8 words", ) - notes: Optional[List[str]] = Field(default=None, description="Notes for the presentation") slides: List[SlideOutlineModel] = Field(description="List of slides") def to_string(self): @@ -25,8 +24,8 @@ class PresentationOutlineModel(BaseModel): message += f" - Title: {slide.title} \n" message += f" - Body: {slide.body} \n" - if self.notes: - message += f"# Notes: \n" - for note in self.notes: - message += f" - {note} \n" + # if self.notes: + # message += f"# Notes: \n" + # for note in self.notes: + # message += f" - {note} \n" return message diff --git a/servers/fastapi/models/sql/presentation.py b/servers/fastapi/models/sql/presentation.py index 3fa5f779..a6f26604 100644 --- a/servers/fastapi/models/sql/presentation.py +++ b/servers/fastapi/models/sql/presentation.py @@ -46,7 +46,7 @@ class PresentationModel(SQLModel, table=True): return PresentationOutlineModel( title=self.title, slides=[SlideOutlineModel(**each) for each in self.outlines], - notes=self.notes, + # notes=self.notes, ) def get_layout(self): diff --git a/servers/fastapi/models/user_config.py b/servers/fastapi/models/user_config.py index 930aa1e5..4f8f5e53 100644 --- a/servers/fastapi/models/user_config.py +++ b/servers/fastapi/models/user_config.py @@ -4,13 +4,32 @@ from pydantic import BaseModel class UserConfig(BaseModel): LLM: Optional[str] = None + + # OpenAI OPENAI_API_KEY: Optional[str] = None + OPENAI_MODEL: Optional[str] = None + + # Google GOOGLE_API_KEY: Optional[str] = None + GOOGLE_MODEL: Optional[str] = None + + # Anthropic + ANTHROPIC_API_KEY: Optional[str] = None + ANTHROPIC_MODEL: Optional[str] = None + + # Ollama OLLAMA_URL: Optional[str] = None OLLAMA_MODEL: Optional[str] = None + + # Custom LLM CUSTOM_LLM_URL: Optional[str] = None CUSTOM_LLM_API_KEY: Optional[str] = None CUSTOM_MODEL: Optional[str] = None - PEXELS_API_KEY: Optional[str] = None + + # Image Provider IMAGE_PROVIDER: Optional[str] = None + PEXELS_API_KEY: Optional[str] = None PIXABAY_API_KEY: Optional[str] = None + + # Reasoning + EXTENDED_REASONING: Optional[bool] = None diff --git a/servers/fastapi/requirements.txt b/servers/fastapi/requirements.txt index 9693177a..7ccb1000 100644 --- a/servers/fastapi/requirements.txt +++ b/servers/fastapi/requirements.txt @@ -4,12 +4,15 @@ aiomysql==0.2.0 aiosignal==1.4.0 aiosqlite==0.21.0 annotated-types==0.7.0 +anthropic==0.60.0 anyio==4.9.0 +argcomplete==3.6.2 async-timeout==5.0.1 asyncpg==0.30.0 attrs==25.3.0 backoff==2.2.1 bcrypt==4.3.0 +black==25.1.0 build==1.2.2.post1 cachetools==5.5.2 certifi==2025.7.14 @@ -31,6 +34,7 @@ filelock==3.18.0 flatbuffers==25.2.10 frozenlist==1.7.0 fsspec==2025.7.0 +genson==1.3.0 google-auth==2.40.3 google-genai==1.25.0 googleapis-common-protos==1.70.0 @@ -49,7 +53,9 @@ hyperframe==6.1.0 idna==3.10 importlib_metadata==8.7.0 importlib_resources==6.5.2 +inflect==7.5.0 iniconfig==2.1.0 +isort==6.0.1 Jinja2==3.1.6 jiter==0.10.0 jsonschema==4.25.0 @@ -61,8 +67,10 @@ markdown-it-py==3.0.0 MarkupSafe==3.0.2 mdurl==0.1.2 mmh3==5.1.0 +more-itertools==10.7.0 mpmath==1.3.0 multidict==6.6.3 +mypy_extensions==1.1.0 numpy==2.3.2 oauthlib==3.3.1 onnxruntime==1.22.1 @@ -76,10 +84,12 @@ opentelemetry-semantic-conventions==0.56b0 orjson==3.11.1 overrides==7.7.0 packaging==25.0 +pathspec==0.12.1 pathvalidate==3.3.1 pdfminer.six==20250506 pdfplumber==0.11.7 pillow==11.3.0 +platformdirs==4.3.8 pluggy==1.6.0 portalocker==3.2.0 posthog==5.4.0 @@ -122,7 +132,9 @@ starlette==0.47.1 sympy==1.14.0 tenacity==8.5.0 tokenizers==0.21.2 +tomli==2.2.1 tqdm==4.67.1 +typeguard==4.4.4 typer==0.16.0 typing-inspection==0.4.1 typing_extensions==4.14.1 diff --git a/servers/fastapi/server.py b/servers/fastapi/server.py index b03ba2ac..60712ed7 100644 --- a/servers/fastapi/server.py +++ b/servers/fastapi/server.py @@ -8,14 +8,15 @@ if __name__ == "__main__": "--port", type=int, required=True, help="Port number to run the server on" ) parser.add_argument( - "--reload", type=bool, default=False, help="Reload the server on code changes" + "--reload", type=str, default="false", help="Reload the server on code changes" ) args = parser.parse_args() + reload = args.reload == "true" uvicorn.run( "api.main:app", host="0.0.0.0", port=args.port, log_level="info", - reload=args.reload, + reload=reload, ) diff --git a/servers/fastapi/services/image_generation_service.py b/servers/fastapi/services/image_generation_service.py index 667425ac..a0159b68 100644 --- a/servers/fastapi/services/image_generation_service.py +++ b/servers/fastapi/services/image_generation_service.py @@ -3,12 +3,12 @@ import os import aiohttp from google import genai from google.genai.types import GenerateContentConfig +from openai import AsyncOpenAI from models.image_prompt import ImagePrompt from models.sql.image_asset import ImageAsset from utils.download_helpers import download_file from utils.get_env import get_pexels_api_key_env from utils.get_env import get_pixabay_api_key_env -from utils.llm_provider import get_llm_client from utils.image_provider import ( is_pixels_selected, is_pixabay_selected, @@ -80,7 +80,7 @@ class ImageGenerationService: return "/static/images/placeholder.jpg" async def generate_image_openai(self, prompt: str, output_directory: str) -> str: - client = get_llm_client() + client = AsyncOpenAI() result = await client.images.generate( model="dall-e-3", prompt=prompt, diff --git a/servers/fastapi/services/llm_client.py b/servers/fastapi/services/llm_client.py new file mode 100644 index 00000000..e75545a4 --- /dev/null +++ b/servers/fastapi/services/llm_client.py @@ -0,0 +1,509 @@ +import asyncio +import json +from typing import List +from fastapi import HTTPException +from openai import AsyncOpenAI +from google import genai +from google.genai.types import GenerateContentConfig +from anthropic import AsyncAnthropic +from anthropic.types import Message as AnthropicMessage +from anthropic import MessageStreamEvent as AnthropicMessageStreamEvent +from enums.llm_provider import LLMProvider +from models.llm_message import LLMMessage +from utils.async_iterator import iterator_to_async +from utils.get_env import ( + get_anthropic_api_key_env, + get_custom_llm_api_key_env, + get_custom_llm_url_env, + get_google_api_key_env, + get_ollama_url_env, + get_openai_api_key_env, +) +from utils.llm_provider import get_llm_provider +from utils.schema_utils import ensure_strict_json_schema + + +class LLMClient: + def __init__(self, max_tokens: int = 4000): + self.llm_provider = get_llm_provider() + self._client = self._get_client() + self.max_tokens = max_tokens + + # ? Clients + def _get_client(self): + match self.llm_provider: + case LLMProvider.OPENAI: + return self._get_openai_client() + case LLMProvider.GOOGLE: + return self._get_google_client() + case LLMProvider.ANTHROPIC: + return self._get_anthropic_client() + case LLMProvider.OLLAMA: + return self._get_ollama_client() + case LLMProvider.CUSTOM: + return self._get_custom_client() + case _: + raise HTTPException( + status_code=400, + detail="LLM Provider must be either openai, google, anthropic, ollama, or custom", + ) + + def _get_openai_client(self): + if not get_openai_api_key_env(): + raise HTTPException( + status_code=400, + detail="OpenAI API Key is not set", + ) + return AsyncOpenAI() + + def _get_google_client(self): + if not get_google_api_key_env(): + raise HTTPException( + status_code=400, + detail="Google API Key is not set", + ) + return genai.Client() + + def _get_anthropic_client(self): + if not get_anthropic_api_key_env(): + raise HTTPException( + status_code=400, + detail="Anthropic API Key is not set", + ) + return AsyncAnthropic() + + def _get_ollama_client(self): + return AsyncOpenAI( + base_url=(get_ollama_url_env() or "http://localhost:11434") + "/v1", + api_key="ollama", + ) + + def _get_custom_client(self): + if not (get_custom_llm_api_key_env() and get_custom_llm_url_env()): + raise HTTPException( + status_code=400, + detail="Custom LLM API Key is not set", + ) + return AsyncOpenAI( + base_url=get_custom_llm_url_env(), + api_key=get_custom_llm_api_key_env(), + ) + + # ? Prompts + def _get_system_prompt(self, messages: List[LLMMessage]) -> str: + for message in messages: + if message.role == "system": + return message.content + return "" + + def _get_user_prompts(self, messages: List[LLMMessage]) -> List[str]: + return [message.content for message in messages if message.role == "user"] + + def _get_user_llm_messages(self, messages: List[LLMMessage]) -> List[LLMMessage]: + return [message for message in messages if message.role == "user"] + + # ? Generate Unstructured Content + async def _generate_openai(self, model: str, messages: List[LLMMessage]): + client: AsyncOpenAI = self._client + response = await client.chat.completions.create( + model=model, + messages=[message.model_dump() for message in messages], + max_completion_tokens=self.max_tokens, + ) + return response.choices[0].message.content + + async def _generate_google(self, model: str, messages: List[LLMMessage]): + client: genai.Client = self._client + response = await asyncio.to_thread( + client.models.generate_content, + model=model, + contents=self._get_user_prompts(messages), + config=GenerateContentConfig( + system_instruction=self._get_system_prompt(messages), + response_mime_type="text/plain", + max_output_tokens=self.max_tokens, + ), + ) + return response.text + + async def _generate_anthropic(self, model: str, messages: List[LLMMessage]): + client: AsyncAnthropic = self._client + response: AnthropicMessage = await client.messages.create( + model=model, + system=self._get_system_prompt(messages), + messages=[ + message.model_dump() + for message in self._get_user_llm_messages(messages) + ], + max_tokens=self.max_tokens, + ) + text = "" + for content in response.content: + if content.type == "text" and isinstance(content.text, str): + text += content.text + if text == "": + return None + return text + + async def _generate_ollama(self, model: str, messages: List[LLMMessage]): + return await self._generate_openai(model, messages) + + async def _generate_custom(self, model: str, messages: List[LLMMessage]): + return await self._generate_openai(model, messages) + + async def generate(self, model: str, messages: List[LLMMessage]): + content = None + match self.llm_provider: + case LLMProvider.OPENAI: + content = await self._generate_openai(model, messages) + case LLMProvider.GOOGLE: + content = await self._generate_google(model, messages) + case LLMProvider.ANTHROPIC: + content = await self._generate_anthropic(model, messages) + case LLMProvider.OLLAMA: + content = await self._generate_ollama(model, messages) + case LLMProvider.CUSTOM: + content = await self._generate_custom(model, messages) + if content is None: + raise HTTPException( + status_code=400, + detail="LLM did not return any content", + ) + return content + + # ? Generate Structured Content + async def _generate_openai_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ): + client: AsyncOpenAI = self._client + response_schema = response_format + if strict: + response_schema = ensure_strict_json_schema( + response_schema, + path=(), + root=response_schema, + ) + response = await client.chat.completions.create( + model=model, + messages=[message.model_dump() for message in messages], + response_format={ + "type": "json_schema", + "json_schema": ( + { + "name": "ResponseSchema", + "strict": strict, + "schema": response_schema, + } + ), + }, + max_completion_tokens=self.max_tokens, + ) + content = response.choices[0].message.content + if content: + return json.loads(content) + return None + + async def _generate_google_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + ): + client: genai.Client = self._client + response = await asyncio.to_thread( + client.models.generate_content, + model=model, + contents=self._get_user_prompts(messages), + config=GenerateContentConfig( + system_instruction=self._get_system_prompt(messages), + response_mime_type="application/json", + response_json_schema=response_format, + max_output_tokens=self.max_tokens, + ), + ) + content = None + if response.text: + content = json.loads(response.text) + + return content + + async def _generate_anthropic_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + ): + client: AsyncAnthropic = self._client + response: AnthropicMessage = await client.messages.create( + model=model, + system=self._get_system_prompt(messages), + messages=[ + message.model_dump() + for message in self._get_user_llm_messages(messages) + ], + max_tokens=self.max_tokens, + tools=[ + { + "name": "ResponseSchema", + "description": "A response to the user's message", + "input_schema": response_format, + } + ], + ) + content: dict | None = None + for content_block in response.content: + if content_block.type == "tool_use": + content = content_block.input + + return content + + async def _generate_ollama_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ): + return await self._generate_openai_structured( + model, messages, response_format, strict + ) + + async def _generate_custom_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ): + return await self._generate_openai_structured( + model, messages, response_format, strict + ) + + async def generate_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ) -> dict: + content = None + match self.llm_provider: + case LLMProvider.OPENAI: + content = await self._generate_openai_structured( + model, messages, response_format, strict + ) + case LLMProvider.GOOGLE: + content = await self._generate_google_structured( + model, messages, response_format + ) + case LLMProvider.ANTHROPIC: + content = await self._generate_anthropic_structured( + model, messages, response_format + ) + case LLMProvider.OLLAMA: + content = await self._generate_ollama_structured( + model, messages, response_format, strict + ) + case LLMProvider.CUSTOM: + content = await self._generate_custom_structured( + model, messages, response_format, strict + ) + if content is None: + raise HTTPException( + status_code=400, + detail="LLM did not return any content", + ) + return content + + # ? Stream Unstructured Content + async def _stream_openai(self, model: str, messages: List[LLMMessage]): + client: AsyncOpenAI = self._client + async with client.chat.completions.stream( + model=model, + messages=[message.model_dump() for message in messages], + max_completion_tokens=self.max_tokens, + ) as stream: + async for event in stream: + if event.type == "content.delta": + yield event.delta + + async def _stream_google(self, model: str, messages: List[LLMMessage]): + client: genai.Client = self._client + async for event in iterator_to_async(client.models.generate_content_stream)( + model=model, + contents=self._get_user_prompts(messages), + config=GenerateContentConfig( + system_instruction=self._get_system_prompt(messages), + response_mime_type="text/plain", + max_output_tokens=self.max_tokens, + ), + ): + if event.text: + yield event.text + + async def _stream_anthropic(self, model: str, messages: List[LLMMessage]): + client: AsyncAnthropic = self._client + async with client.messages.stream( + model=model, + system=self._get_system_prompt(messages), + messages=[ + message.model_dump() + for message in self._get_user_llm_messages(messages) + ], + max_tokens=self.max_tokens, + ) as stream: + async for event in stream: + event: AnthropicMessageStreamEvent = event + if event.type == "text" and isinstance(event.text, str): + yield event.text + + def _stream_ollama(self, model: str, messages: List[LLMMessage]): + return self._stream_openai(model, messages) + + def _stream_custom(self, model: str, messages: List[LLMMessage]): + return self._stream_openai(model, messages) + + def stream(self, model: str, messages: List[LLMMessage]): + match self.llm_provider: + case LLMProvider.OPENAI: + return self._stream_openai(model, messages) + case LLMProvider.GOOGLE: + return self._stream_google(model, messages) + case LLMProvider.ANTHROPIC: + return self._stream_anthropic(model, messages) + case LLMProvider.OLLAMA: + return self._stream_ollama(model, messages) + case LLMProvider.CUSTOM: + return self._stream_custom(model, messages) + + # ? Stream Structured Content + async def _stream_openai_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ): + client: AsyncOpenAI = self._client + response_schema = response_format + if strict: + response_schema = ensure_strict_json_schema( + response_schema, + path=(), + root=response_schema, + ) + async with client.chat.completions.stream( + model=model, + messages=[message.model_dump() for message in messages], + max_completion_tokens=self.max_tokens, + response_format=( + { + "type": "json_schema", + "json_schema": { + "name": "ResponseSchema", + "strict": strict, + "schema": response_schema, + }, + } + ), + ) as stream: + async for event in stream: + if event.type == "content.delta": + yield event.delta + + async def _stream_google_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + ): + client: genai.Client = self._client + async for event in iterator_to_async(client.models.generate_content_stream)( + model=model, + contents=self._get_user_prompts(messages), + config=GenerateContentConfig( + system_instruction=self._get_system_prompt(messages), + response_mime_type="application/json", + response_json_schema=response_format, + max_output_tokens=self.max_tokens, + ), + ): + if event.text: + yield event.text + + async def _stream_anthropic_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + ): + client: AsyncAnthropic = self._client + async with client.messages.stream( + model=model, + system=self._get_system_prompt(messages), + messages=[ + message.model_dump() + for message in self._get_user_llm_messages(messages) + ], + max_tokens=self.max_tokens, + tools=[ + { + "name": "ResponseSchema", + "description": "A response to the user's message", + "input_schema": response_format, + } + ], + ) as stream: + async for event in stream: + event: AnthropicMessageStreamEvent = event + if event.type == "input_json" and isinstance(event.partial_json, str): + yield event.partial_json + + def _stream_ollama_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ): + return self._stream_openai_structured(model, messages, response_format, strict) + + def _stream_custom_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ): + return self._stream_openai_structured(model, messages, response_format, strict) + + def stream_structured( + self, + model: str, + messages: List[LLMMessage], + response_format: dict, + strict: bool = False, + ): + match self.llm_provider: + case LLMProvider.OPENAI: + return self._stream_openai_structured( + model, messages, response_format, strict + ) + case LLMProvider.GOOGLE: + return self._stream_google_structured(model, messages, response_format) + case LLMProvider.ANTHROPIC: + return self._stream_anthropic_structured( + model, messages, response_format + ) + case LLMProvider.OLLAMA: + return self._stream_ollama_structured( + model, messages, response_format, strict + ) + case LLMProvider.CUSTOM: + return self._stream_custom_structured( + model, messages, response_format, strict + ) diff --git a/servers/fastapi/services/temp_file_service.py b/servers/fastapi/services/temp_file_service.py index 544f9246..eaa09e97 100644 --- a/servers/fastapi/services/temp_file_service.py +++ b/servers/fastapi/services/temp_file_service.py @@ -9,8 +9,7 @@ class TempFileService: def __init__(self): self.base_dir = get_temp_directory_env() or "/tmp/presenton" - # TODO: Uncomment this when we want to cleanup the base dir on startup - # self.cleanup_base_dir() + self.cleanup_base_dir() os.makedirs(self.base_dir, exist_ok=True) def create_dir_in_dir(self, base_dir: str, dir_name: Optional[str] = None) -> str: diff --git a/servers/fastapi/utils/available_models.py b/servers/fastapi/utils/available_models.py new file mode 100644 index 00000000..539533ad --- /dev/null +++ b/servers/fastapi/utils/available_models.py @@ -0,0 +1,21 @@ +from anthropic import AsyncAnthropic +from openai import AsyncOpenAI +from google import genai + + +async def list_available_openai_compatible_models(url: str, api_key: str) -> list[str]: + client = AsyncOpenAI(api_key=api_key, base_url=url) + models = (await client.models.list()).data + if models: + return list(map(lambda x: x.id, models)) + return [] + + +async def list_available_anthropic_models(api_key: str) -> list[str]: + client = AsyncAnthropic(api_key=api_key) + return list(map(lambda x: x.id, (await client.models.list(limit=50)).data)) + + +async def list_available_google_models(api_key: str) -> list[str]: + client = genai.Client(api_key=api_key) + return list(map(lambda x: x.name, client.models.list(config={"page_size": 50}))) diff --git a/servers/fastapi/utils/custom_llm_provider.py b/servers/fastapi/utils/custom_llm_provider.py deleted file mode 100644 index c2e2af79..00000000 --- a/servers/fastapi/utils/custom_llm_provider.py +++ /dev/null @@ -1,18 +0,0 @@ -from typing import Optional -from openai import AsyncOpenAI - -from utils.llm_provider import get_llm_client - - -async def list_available_custom_models( - url: Optional[str] = None, api_key: Optional[str] = None -) -> list[str]: - if not url: - client = get_llm_client() - else: - client = AsyncOpenAI(api_key=api_key or "null", base_url=url) - models = [] - async for model in client.models.list(): - print(model) - models.append(model.id) - return models diff --git a/servers/fastapi/utils/dict_utils.py b/servers/fastapi/utils/dict_utils.py index e7e860b5..72c69c53 100644 --- a/servers/fastapi/utils/dict_utils.py +++ b/servers/fastapi/utils/dict_utils.py @@ -78,3 +78,12 @@ def deep_update(original: dict, updates: dict) -> dict: if not isinstance(value, (dict, list)): original[key] = value return original + + +def has_more_than_n_keys(obj: dict[str, object], n: int) -> bool: + i = 0 + for _ in obj.keys(): + i += 1 + if i > n: + return True + return False diff --git a/servers/fastapi/utils/get_dynamic_models.py b/servers/fastapi/utils/get_dynamic_models.py index 9d49c497..4dfded99 100644 --- a/servers/fastapi/utils/get_dynamic_models.py +++ b/servers/fastapi/utils/get_dynamic_models.py @@ -22,12 +22,6 @@ def get_presentation_outline_model_with_n_slides(n_slides: int): min_length=10, max_length=50, ) - notes: Optional[List[str]] = Field( - default=None, - description="Important notes for the presentation styling and formatting", - min_length=0, - max_length=10, - ) slides: List[SlideOutlineModelWithValidation] = Field( description="List of slides", min_items=n_slides, max_items=n_slides ) diff --git a/servers/fastapi/utils/get_env.py b/servers/fastapi/utils/get_env.py index 85af41c9..f2301b69 100644 --- a/servers/fastapi/utils/get_env.py +++ b/servers/fastapi/utils/get_env.py @@ -25,6 +25,14 @@ def get_llm_provider_env(): return os.getenv("LLM") +def get_anthropic_api_key_env(): + return os.getenv("ANTHROPIC_API_KEY") + + +def get_anthropic_model_env(): + return os.getenv("ANTHROPIC_MODEL") + + def get_ollama_url_env(): return os.getenv("OLLAMA_URL") @@ -37,10 +45,18 @@ def get_openai_api_key_env(): return os.getenv("OPENAI_API_KEY") +def get_openai_model_env(): + return os.getenv("OPENAI_MODEL") + + def get_google_api_key_env(): return os.getenv("GOOGLE_API_KEY") +def get_google_model_env(): + return os.getenv("GOOGLE_MODEL") + + def get_custom_llm_api_key_env(): return os.getenv("CUSTOM_LLM_API_KEY") @@ -79,3 +95,7 @@ def get_redis_db_env(): def get_redis_password_env(): return os.getenv("REDIS_PASSWORD") + + +def get_extended_reasoning_env(): + return os.getenv("EXTENDED_REASONING") diff --git a/servers/fastapi/services/get_layout_by_name.py b/servers/fastapi/utils/get_layout_by_name.py similarity index 100% rename from servers/fastapi/services/get_layout_by_name.py rename to servers/fastapi/utils/get_layout_by_name.py diff --git a/servers/fastapi/utils/llm_calls/edit_slide.py b/servers/fastapi/utils/llm_calls/edit_slide.py index 9069a459..a8df598a 100644 --- a/servers/fastapi/utils/llm_calls/edit_slide.py +++ b/servers/fastapi/utils/llm_calls/edit_slide.py @@ -1,15 +1,8 @@ -import asyncio -import json - +from models.llm_message import LLMMessage from models.presentation_layout import SlideLayoutModel from models.sql.slide import SlideModel -from google.genai.types import GenerateContentConfig -from utils.llm_provider import ( - get_google_llm_client, - get_large_model, - get_llm_client, - is_google_selected, -) +from services.llm_client import LLMClient +from utils.llm_provider import get_model from utils.schema_utils import remove_fields_from_schema system_prompt = """ @@ -42,64 +35,40 @@ def get_user_prompt(prompt: str, slide_data: dict, language: str): """ -def get_prompt_to_edit_slide_content( +def get_messages( prompt: str, slide_data: dict, language: str, ): return [ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": get_user_prompt(prompt, slide_data, language), - }, + LLMMessage( + role="system", + content=system_prompt, + ), + LLMMessage( + role="user", + content=get_user_prompt(prompt, slide_data, language), + ), ] async def get_edited_slide_content( prompt: str, - slide_layout: SlideLayoutModel, slide: SlideModel, language: str, + slide_layout: SlideLayoutModel, ): - model = get_large_model() + model = get_model() + response_schema = remove_fields_from_schema( slide_layout.json_schema, ["__image_url__", "__icon_url__"] ) - if is_google_selected(): - client = get_google_llm_client() - response = await asyncio.to_thread( - client.models.generate_content, - model=model, - contents=[get_user_prompt(prompt, slide.content, language)], - config=GenerateContentConfig( - system_instruction=system_prompt, - response_mime_type="application/json", - response_json_schema=response_schema, - ), - ) - slide_content_json = json.loads(response.text) - else: - client = get_llm_client() - response = await client.beta.chat.completions.parse( - model=model, - messages=get_prompt_to_edit_slide_content( - prompt, - slide.content, - language, - ), - response_format={ - "type": "json_schema", - "json_schema": { - "name": "slide_content", - "schema": response_schema, - }, - }, - ) - slide_content_json = json.loads(response.choices[0].message.content) - - return slide_content_json + client = LLMClient() + response = await client.generate_structured( + model=model, + messages=get_messages(prompt, slide.content, language), + response_format=response_schema, + strict=False, + ) + return response diff --git a/servers/fastapi/utils/llm_calls/edit_slide_html.py b/servers/fastapi/utils/llm_calls/edit_slide_html.py index b20f3cf7..a5e2dfad 100644 --- a/servers/fastapi/utils/llm_calls/edit_slide_html.py +++ b/servers/fastapi/utils/llm_calls/edit_slide_html.py @@ -1,12 +1,7 @@ -import asyncio from typing import Optional -from google.genai.types import GenerateContentConfig -from utils.llm_provider import ( - get_google_llm_client, - get_large_model, - is_google_selected, - get_llm_client, -) +from models.llm_message import LLMMessage +from services.llm_client import LLMClient +from utils.llm_provider import get_model system_prompt = """ You are an expert HTML slide editor. Your task is to modify slide HTML content based on user prompts while maintaining proper structure, styling, and functionality. @@ -52,35 +47,17 @@ def get_user_prompt(prompt: str, html: str): async def get_edited_slide_html(prompt: str, html: str): - model = get_large_model() - llm_response = None - if is_google_selected(): - client = get_google_llm_client() - response = await asyncio.to_thread( - client.models.generate_content, - model=model, - contents=[get_user_prompt(prompt, html)], - config=GenerateContentConfig( - system_instruction=system_prompt, - response_mime_type="text/plain", - ), - ) - llm_response = response.text - else: - client = get_llm_client() - response = await client.chat.completions.create( - model=model, - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": get_user_prompt(prompt, html)}, - ], - ) - llm_response = response.choices[0].message.content + model = get_model() - if not llm_response: - return html - - return extract_html_from_response(llm_response) or html + client = LLMClient() + response = await client.generate( + model=model, + messages=[ + LLMMessage(role="system", content=system_prompt), + LLMMessage(role="user", content=get_user_prompt(prompt, html)), + ], + ) + return extract_html_from_response(response) or html def extract_html_from_response(response_text: str) -> Optional[str]: diff --git a/servers/fastapi/utils/llm_calls/generate_document_summary.py b/servers/fastapi/utils/llm_calls/generate_document_summary.py index 612359a9..044d28e8 100644 --- a/servers/fastapi/utils/llm_calls/generate_document_summary.py +++ b/servers/fastapi/utils/llm_calls/generate_document_summary.py @@ -1,8 +1,9 @@ import asyncio from typing import List -from openai.types.chat.chat_completion import ChatCompletion -from utils.llm_provider import get_llm_client, get_nano_model +from models.llm_message import LLMMessage +from services.llm_client import LLMClient +from utils.llm_provider import get_model sysmte_prompt = """ @@ -23,23 +24,21 @@ Maintain as much information as possible. async def generate_document_summary(documents: List[str]): - client = get_llm_client() - model = get_nano_model() + client = LLMClient() + model = get_model() coroutines = [] for document in documents: truncated_text = document[:200000] - coroutine = client.chat.completions.create( + coroutine = client.generate( model=model, messages=[ - {"role": "system", "content": sysmte_prompt}, - {"role": "user", "content": truncated_text}, + LLMMessage(role="system", content=sysmte_prompt), + LLMMessage(role="user", content=truncated_text), ], ) coroutines.append(coroutine) - completions: List[ChatCompletion] = await asyncio.gather(*coroutines) - combined = "\n\n\n\n".join( - [completion.choices[0].message.content for completion in completions] - ) + completions: List[str] = await asyncio.gather(*coroutines) + combined = "\n\n\n\n".join(completions) return combined diff --git a/servers/fastapi/utils/llm_calls/generate_presentation_outlines.py b/servers/fastapi/utils/llm_calls/generate_presentation_outlines.py index fb18d56f..54e41d61 100644 --- a/servers/fastapi/utils/llm_calls/generate_presentation_outlines.py +++ b/servers/fastapi/utils/llm_calls/generate_presentation_outlines.py @@ -1,16 +1,10 @@ +import asyncio from typing import Optional -from google.genai.types import GenerateContentConfig -from openai.types.chat.chat_completion_chunk import ChoiceDelta -from utils.async_iterator import iterator_to_async +from models.llm_message import LLMMessage +from services.llm_client import LLMClient from utils.get_dynamic_models import get_presentation_outline_model_with_n_slides -from utils.llm_provider import ( - get_google_llm_client, - get_large_model, - get_llm_client, - is_google_selected, -) -from pydantic import BaseModel +from utils.llm_provider import get_model system_prompt = """ You are an expert presentation creator. Generate structured presentations based on user requirements and format them according to the specified JSON schema with markdown content. @@ -62,61 +56,34 @@ def get_user_prompt(prompt: str, n_slides: int, language: str, content: str): """ -def get_prompt_template(prompt: str, n_slides: int, language: str, content: str): +def get_messages(prompt: str, n_slides: int, language: str, content: str): return [ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": get_user_prompt(prompt, n_slides, language, content), - }, + LLMMessage( + role="system", + content=system_prompt, + ), + LLMMessage( + role="user", + content=get_user_prompt(prompt, n_slides, language, content), + ), ] -def get_response_format(response_model: BaseModel): - return { - "type": "json_schema", - "json_schema": { - "name": "PresentationOutlineModel", - "schema": response_model.model_json_schema(), - }, - } - - async def generate_ppt_outline( prompt: Optional[str], n_slides: int, language: Optional[str] = None, content: Optional[str] = None, ): - model = get_large_model() + model = get_model() response_model = get_presentation_outline_model_with_n_slides(n_slides) - if not is_google_selected(): - client = get_llm_client() - async for response in await client.chat.completions.create( - model=model, - messages=get_prompt_template(prompt, n_slides, language, content), - stream=True, - response_format=get_response_format(response_model), - ): - delta: ChoiceDelta = response.choices[0].delta - if delta.content: - yield delta.content + client = LLMClient() - else: - client = get_google_llm_client() - generate_stream = iterator_to_async(client.models.generate_content_stream) - async for event in generate_stream( - model=model, - contents=[get_user_prompt(prompt, n_slides, language, content)], - config=GenerateContentConfig( - system_instruction=system_prompt, - response_mime_type="application/json", - response_json_schema=response_model.model_json_schema(), - ), - ): - if event.text: - yield event.text + async for chunk in client.stream_structured( + model, + get_messages(prompt, n_slides, language, content), + response_model.model_json_schema(), + strict=True, + ): + yield chunk diff --git a/servers/fastapi/utils/llm_calls/generate_presentation_structure.py b/servers/fastapi/utils/llm_calls/generate_presentation_structure.py index f4d90fb2..47f47dba 100644 --- a/servers/fastapi/utils/llm_calls/generate_presentation_structure.py +++ b/servers/fastapi/utils/llm_calls/generate_presentation_structure.py @@ -1,24 +1,19 @@ +from models.llm_message import LLMMessage from models.presentation_layout import PresentationLayoutModel from models.presentation_outline_model import PresentationOutlineModel -from utils.llm_provider import ( - get_large_model, - get_llm_client, - get_nano_model, - get_small_model, -) -from utils.get_dynamic_models import ( - get_presentation_structure_model_with_n_slides, -) -from models.presentation_structure_model import ( - PresentationStructureModel, -) +from services.llm_client import LLMClient +from utils.llm_provider import get_model +from utils.get_dynamic_models import get_presentation_structure_model_with_n_slides +from models.presentation_structure_model import PresentationStructureModel -def get_prompt(presentation_layout: PresentationLayoutModel, n_slides: int, data: str): +def get_messages( + presentation_layout: PresentationLayoutModel, n_slides: int, data: str +): return [ - { - "role": "system", - "content": f""" + LLMMessage( + role="system", + content=f""" You're a professional presentation designer with creative freedom to design engaging presentations. {presentation_layout.to_string()} @@ -51,13 +46,13 @@ def get_prompt(presentation_layout: PresentationLayoutModel, n_slides: int, data Select layout index for each of the {n_slides} slides based on what will best serve the presentation's goals. """, - }, - { - "role": "user", - "content": f""" + ), + LLMMessage( + role="user", + content=f""" {data} """, - }, + ), ] @@ -66,20 +61,20 @@ async def generate_presentation_structure( presentation_layout: PresentationLayoutModel, ) -> PresentationStructureModel: - client = get_llm_client() - model = get_large_model() + client = LLMClient() + model = get_model() response_model = get_presentation_structure_model_with_n_slides( len(presentation_outline.slides) ) - response = await client.beta.chat.completions.parse( + response = await client.generate_structured( model=model, - messages=get_prompt( + messages=get_messages( presentation_layout, len(presentation_outline.slides), presentation_outline.to_string(), ), - response_format=response_model, + response_format=response_model.model_json_schema(), + strict=True, ) - print(response.choices[0].message.parsed) - return response.choices[0].message.parsed + return PresentationStructureModel(**response) diff --git a/servers/fastapi/utils/llm_calls/generate_slide_content.py b/servers/fastapi/utils/llm_calls/generate_slide_content.py index e4fe86b0..f75ed979 100644 --- a/servers/fastapi/utils/llm_calls/generate_slide_content.py +++ b/servers/fastapi/utils/llm_calls/generate_slide_content.py @@ -1,14 +1,8 @@ -import asyncio -import json -from google.genai.types import GenerateContentConfig +from models.llm_message import LLMMessage from models.presentation_layout import SlideLayoutModel from models.presentation_outline_model import SlideOutlineModel -from utils.llm_provider import ( - get_google_llm_client, - get_large_model, - get_llm_client, - is_google_selected, -) +from services.llm_client import LLMClient +from utils.llm_provider import get_model from utils.schema_utils import remove_fields_from_schema system_prompt = """ @@ -45,57 +39,38 @@ def get_user_prompt(title: str, outline: str, language: str): """ -def get_prompt_to_generate_slide_content(title: str, outline: str, language: str): +def get_messages(title: str, outline: str, language: str): return [ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": get_user_prompt(title, outline, language), - }, + LLMMessage( + role="system", + content=system_prompt, + ), + LLMMessage( + role="user", + content=get_user_prompt(title, outline, language), + ), ] async def get_slide_content_from_type_and_outline( slide_layout: SlideLayoutModel, outline: SlideOutlineModel, language: str ): - model = get_large_model() + client = LLMClient() + model = get_model() response_schema = remove_fields_from_schema( slide_layout.json_schema, ["__image_url__", "__icon_url__"] ) - if not is_google_selected(): - client = get_llm_client() - response = await client.beta.chat.completions.parse( - model=model, - messages=get_prompt_to_generate_slide_content( - outline.title, - outline.body, - language, - ), - response_format={ - "type": "json_schema", - "json_schema": { - "name": "SlideContent", - "schema": response_schema, - }, - }, - ) - return json.loads(response.choices[0].message.content) - else: - client = get_google_llm_client() - response = await asyncio.to_thread( - client.models.generate_content, - model=model, - contents=[get_user_prompt(outline.title, outline.body, language)], - config=GenerateContentConfig( - system_instruction=system_prompt, - response_mime_type="application/json", - response_json_schema=response_schema, - ), - ) - return json.loads(response.text) + response = await client.generate_structured( + model=model, + messages=get_messages( + outline.title, + outline.body, + language, + ), + response_format=response_schema, + strict=False, + ) + return response diff --git a/servers/fastapi/utils/llm_calls/select_slide_type_on_edit.py b/servers/fastapi/utils/llm_calls/select_slide_type_on_edit.py index 861621d1..f3532b48 100644 --- a/servers/fastapi/utils/llm_calls/select_slide_type_on_edit.py +++ b/servers/fastapi/utils/llm_calls/select_slide_type_on_edit.py @@ -1,19 +1,21 @@ +from models.llm_message import LLMMessage from models.presentation_layout import PresentationLayoutModel, SlideLayoutModel from models.slide_layout_index import SlideLayoutIndex from models.sql.slide import SlideModel -from utils.llm_provider import get_large_model, get_llm_client +from services.llm_client import LLMClient +from utils.llm_provider import get_model -def get_prompt_to_select_slide_layout( +def get_messages( prompt: str, slide_data: dict, layout: PresentationLayoutModel, current_slide_layout: int, ): return [ - { - "role": "system", - "content": f""" + LLMMessage( + role="system", + content=f""" Select a Slide Layout index based on provided user prompt and current slide data. {layout.to_string()} @@ -23,15 +25,15 @@ def get_prompt_to_select_slide_layout( - If user prompt is not clear, select the layout that is most relevant to the slide data. **Go through all notes and steps and make sure they are followed, including mentioned constraints** """, - }, - { - "role": "user", - "content": f""" + ), + LLMMessage( + role="user", + content=f""" - User Prompt: {prompt} - Current Slide Data: {slide_data} - Current Slide Layout: {current_slide_layout} """, - }, + ), ] @@ -41,21 +43,21 @@ async def get_slide_layout_from_prompt( slide: SlideModel, ) -> SlideLayoutModel: - client = get_llm_client() - model = get_large_model() + client = LLMClient() + model = get_model() - slide_layout_ids = list(map(lambda x: x.id, layout.slides)) + slide_layout_index = layout.get_slide_layout_index(slide.layout) - response = await client.beta.chat.completions.parse( + response = await client.generate_structured( model=model, - temperature=0.2, - messages=get_prompt_to_select_slide_layout( + messages=get_messages( prompt, slide.content, layout, - slide_layout_ids.index(slide.layout), + slide_layout_index, ), - response_format=SlideLayoutIndex, + response_format=SlideLayoutIndex.model_json_schema(), + strict=True, ) - index = response.choices[0].message.parsed.index + index = SlideLayoutIndex(**response).index return layout.slides[index] diff --git a/servers/fastapi/utils/llm_provider.py b/servers/fastapi/utils/llm_provider.py index f4e801ea..aabc8f61 100644 --- a/servers/fastapi/utils/llm_provider.py +++ b/servers/fastapi/utils/llm_provider.py @@ -1,36 +1,31 @@ -import os from fastapi import HTTPException -from openai import AsyncOpenAI -from google import genai +from constants.llm import ( + DEFAULT_ANTHROPIC_MODEL, + DEFAULT_GOOGLE_MODEL, + DEFAULT_OPENAI_MODEL, +) from enums.llm_provider import LLMProvider from utils.get_env import ( - get_custom_llm_api_key_env, - get_custom_llm_url_env, + get_anthropic_model_env, get_custom_model_env, - get_google_api_key_env, + get_google_model_env, get_llm_provider_env, get_ollama_model_env, - get_ollama_url_env, - get_openai_api_key_env, + get_openai_model_env, ) - def get_llm_provider(): try: return LLMProvider(get_llm_provider_env()) except: raise HTTPException( status_code=500, - detail=f"Invalid LLM provider. Please select one of: openai, google, ollama, custom", + detail=f"Invalid LLM provider. Please select one of: openai, google, anthropic, ollama, custom", ) -def get_ollama_url(): - return get_ollama_url_env() or "http://localhost:11434" - - def is_openai_selected(): return get_llm_provider() == LLMProvider.OPENAI @@ -39,6 +34,10 @@ def is_google_selected(): return get_llm_provider() == LLMProvider.GOOGLE +def is_anthropic_selected(): + return get_llm_provider() == LLMProvider.ANTHROPIC + + def is_ollama_selected(): return get_llm_provider() == LLMProvider.OLLAMA @@ -47,85 +46,20 @@ def is_custom_llm_selected(): return get_llm_provider() == LLMProvider.CUSTOM -def get_model_base_url(): - selected_llm = get_llm_provider() - - if selected_llm == LLMProvider.OPENAI: - return "https://api.openai.com/v1" - elif selected_llm == LLMProvider.GOOGLE: - return "https://generativelanguage.googleapis.com/v1beta/openai" - elif selected_llm == LLMProvider.OLLAMA: - return os.path.join(get_ollama_url(), "v1") - elif selected_llm == LLMProvider.CUSTOM: - return get_custom_llm_url_env() - else: - raise HTTPException(f"LLM provider {selected_llm} is not supported") - - -def get_llm_api_key(): +def get_model(): selected_llm = get_llm_provider() if selected_llm == LLMProvider.OPENAI: - return get_openai_api_key_env() + return get_openai_model_env() or DEFAULT_OPENAI_MODEL elif selected_llm == LLMProvider.GOOGLE: - return get_google_api_key_env() + return get_google_model_env() or DEFAULT_GOOGLE_MODEL + elif selected_llm == LLMProvider.ANTHROPIC: + return get_anthropic_model_env() or DEFAULT_ANTHROPIC_MODEL elif selected_llm == LLMProvider.OLLAMA: - return "ollama" + return get_ollama_model_env() elif selected_llm == LLMProvider.CUSTOM: - return get_custom_llm_api_key_env() or "none" + return get_custom_model_env() else: - raise HTTPException(f"LLM provider {selected_llm} is not supported") - - -def get_llm_client(): - client = AsyncOpenAI( - base_url=get_model_base_url(), - api_key=get_llm_api_key(), + raise HTTPException( + status_code=500, + detail=f"Invalid LLM provider. Please select one of: openai, google, anthropic, ollama, custom", ) - return client - - -def get_google_llm_client(): - client = genai.Client(api_key=get_llm_api_key()) - return client - - -def get_large_model(): - selected_llm = get_llm_provider() - if selected_llm == LLMProvider.OPENAI: - return "gpt-4.1" - elif selected_llm == LLMProvider.GOOGLE: - return "gemini-2.0-flash" - elif selected_llm == LLMProvider.OLLAMA: - return get_ollama_model_env() - elif selected_llm == LLMProvider.CUSTOM: - return get_custom_model_env() - else: - raise ValueError(f"Invalid LLM model") - - -def get_small_model(): - selected_llm = get_llm_provider() - if selected_llm == LLMProvider.OPENAI: - return "gpt-4.1-mini" - elif selected_llm == LLMProvider.GOOGLE: - return "gemini-2.0-flash" - elif selected_llm == LLMProvider.OLLAMA: - return get_ollama_model_env() - elif selected_llm == LLMProvider.CUSTOM: - return get_custom_model_env() - else: - raise ValueError(f"Invalid LLM model") - - -def get_nano_model(): - selected_llm = get_llm_provider() - if selected_llm == LLMProvider.OPENAI: - return "gpt-4.1-nano" - elif selected_llm == LLMProvider.GOOGLE: - return "gemini-2.0-flash" - elif selected_llm == LLMProvider.OLLAMA: - return get_ollama_model_env() - elif selected_llm == LLMProvider.CUSTOM: - return get_custom_model_env() - else: - raise ValueError(f"Invalid LLM model") diff --git a/servers/fastapi/utils/model_availability.py b/servers/fastapi/utils/model_availability.py index 210fcdd2..a546640e 100644 --- a/servers/fastapi/utils/model_availability.py +++ b/servers/fastapi/utils/model_availability.py @@ -1,11 +1,19 @@ -import os from constants.supported_ollama_models import SUPPORTED_OLLAMA_MODELS +from constants.llm import OPENAI_URL from enums.image_provider import ImageProvider from enums.llm_provider import LLMProvider -from utils.custom_llm_provider import list_available_custom_models +from utils.available_models import ( + list_available_anthropic_models, + list_available_google_models, + list_available_openai_compatible_models, +) from utils.get_env import ( + get_anthropic_api_key_env, + get_anthropic_model_env, get_can_change_keys_env, + get_google_model_env, get_openai_api_key_env, + get_openai_model_env, get_pixabay_api_key_env, get_pexels_api_key_env, ) @@ -20,13 +28,7 @@ from utils.llm_provider import ( is_ollama_selected, ) from utils.ollama import pull_ollama_model -from utils.image_provider import ( - get_selected_image_provider, - is_pixels_selected, - is_pixabay_selected, - is_gemini_flash_selected, - is_dalle3_selected, -) +from utils.image_provider import get_selected_image_provider async def check_llm_and_image_provider_api_or_model_availability(): @@ -36,11 +38,41 @@ async def check_llm_and_image_provider_api_or_model_availability(): openai_api_key = get_openai_api_key_env() if not openai_api_key: raise Exception("OPENAI_API_KEY must be provided") + openai_model = get_openai_model_env() + if openai_model: + available_models = await list_available_openai_compatible_models( + OPENAI_URL, openai_api_key + ) + if openai_model not in available_models: + print("-" * 50) + print("Available models: ", available_models) + raise Exception(f"Model {openai_model} is not available") elif get_llm_provider() == LLMProvider.GOOGLE: google_api_key = get_google_api_key_env() if not google_api_key: raise Exception("GOOGLE_API_KEY must be provided") + google_model = get_google_model_env() + if google_model: + available_models = await list_available_google_models(google_api_key) + if google_model not in available_models: + print("-" * 50) + print("Available models: ", available_models) + raise Exception(f"Model {google_model} is not available") + + elif get_llm_provider() == LLMProvider.ANTHROPIC: + anthropic_api_key = get_anthropic_api_key_env() + if not anthropic_api_key: + raise Exception("ANTHROPIC_API_KEY must be provided") + anthropic_model = get_anthropic_model_env() + if anthropic_model: + available_models = await list_available_anthropic_models( + anthropic_api_key + ) + if anthropic_model not in available_models: + print("-" * 50) + print("Available models: ", available_models) + raise Exception(f"Model {anthropic_model} is not available") elif is_ollama_selected(): ollama_model = get_ollama_model_env() @@ -67,14 +99,12 @@ async def check_llm_and_image_provider_api_or_model_availability(): raise Exception("CUSTOM_LLM_URL must be provided") if not custom_llm_api_key: raise Exception("CUSTOM_LLM_API_KEY must be provided") - print("-" * 50) - print("Selecting model: ", custom_model) - models = await list_available_custom_models( + available_models = await list_available_openai_compatible_models( custom_llm_url, custom_llm_api_key ) - print("Available models: ", models) print("-" * 50) - if custom_model not in models: + print("Available models: ", available_models) + if custom_model not in available_models: raise Exception(f"Model {custom_model} is not available") # Check for Image Provider and API keys diff --git a/servers/fastapi/utils/ollama.py b/servers/fastapi/utils/ollama.py index 10c8500f..928ee895 100644 --- a/servers/fastapi/utils/ollama.py +++ b/servers/fastapi/utils/ollama.py @@ -1,7 +1,7 @@ -from http.client import HTTPException import json from typing import AsyncGenerator import aiohttp +from fastapi import HTTPException from models.ollama_model_status import OllamaModelStatus from utils.get_env import get_ollama_url_env diff --git a/servers/fastapi/utils/process_slides.py b/servers/fastapi/utils/process_slides.py index 6ffca66f..81a0a6b5 100644 --- a/servers/fastapi/utils/process_slides.py +++ b/servers/fastapi/utils/process_slides.py @@ -21,19 +21,19 @@ async def process_slide_and_fetch_assets( icon_paths = get_dict_paths_with_key(slide.content, "__icon_query__") for image_path in image_paths: - image_prompt_parent = get_dict_at_path(slide.content, image_path) + __image_prompt__parent = get_dict_at_path(slide.content, image_path) async_tasks.append( image_generation_service.generate_image( ImagePrompt( - prompt=image_prompt_parent["__image_prompt__"], + prompt=__image_prompt__parent["__image_prompt__"], ) ) ) for icon_path in icon_paths: - icon_query_parent = get_dict_at_path(slide.content, icon_path) + __icon_query__parent = get_dict_at_path(slide.content, icon_path) async_tasks.append( - icon_finder_service.search_icons(icon_query_parent["__icon_query__"]) + icon_finder_service.search_icons(__icon_query__parent["__icon_query__"]) ) results = await asyncio.gather(*async_tasks) diff --git a/servers/fastapi/utils/schema_utils.py b/servers/fastapi/utils/schema_utils.py index 0f2ad6d7..ae65f002 100644 --- a/servers/fastapi/utils/schema_utils.py +++ b/servers/fastapi/utils/schema_utils.py @@ -1,30 +1,25 @@ from copy import deepcopy -from typing import List +from typing import Any, List -from utils.dict_utils import get_dict_paths_with_key, get_dict_at_path +from openai import NOT_GIVEN +from utils.dict_utils import ( + get_dict_paths_with_key, + get_dict_at_path, + has_more_than_n_keys, +) -def resolve_refs(schema, defs): - if isinstance(schema, dict): - if "$ref" in schema: - ref_path = schema["$ref"] - if ref_path.startswith("#/$defs/"): - def_key = ref_path.replace("#/$defs/", "") - return resolve_refs(defs[def_key], defs) - else: - raise ValueError(f"Unsupported $ref path: {ref_path}") - else: - return {k: resolve_refs(v, defs) for k, v in schema.items()} - elif isinstance(schema, list): - return [resolve_refs(item, defs) for item in schema] - else: - return schema - - -def flatten_schema(schema): - schema = deepcopy(schema) - defs = schema.pop("$defs", {}) - return resolve_refs(schema, defs) +supported_string_formats = [ + "date-time", + "time", + "date", + "duration", + "email", + "hostname", + "ipv4", + "ipv6", + "uuid", +] def remove_fields_from_schema(schema: dict, fields_to_remove: List[str]): @@ -50,6 +45,138 @@ def remove_fields_from_schema(schema: dict, fields_to_remove: List[str]): return schema +# From OpenAI +def ensure_strict_json_schema( + json_schema: object, + *, + path: tuple[str, ...], + root: dict[str, object], +) -> dict[str, Any]: + """Mutates the given JSON schema to ensure it conforms to the `strict` standard + that the API expects. + """ + if not isinstance(json_schema, dict): + raise TypeError(f"Expected {json_schema} to be a dictionary; path={path}") + + defs = json_schema.get("$defs") + if isinstance(defs, dict): + for def_name, def_schema in defs.items(): + ensure_strict_json_schema( + def_schema, path=(*path, "$defs", def_name), root=root + ) + + definitions = json_schema.get("definitions") + if isinstance(definitions, dict): + for definition_name, definition_schema in definitions.items(): + ensure_strict_json_schema( + definition_schema, + path=(*path, "definitions", definition_name), + root=root, + ) + + typ = json_schema.get("type") + if typ == "object" and "additionalProperties" not in json_schema: + json_schema["additionalProperties"] = False + + # object types + # { 'type': 'object', 'properties': { 'a': {...} } } + properties = json_schema.get("properties") + if isinstance(properties, dict): + json_schema["required"] = [prop for prop in properties.keys()] + json_schema["properties"] = { + key: ensure_strict_json_schema( + prop_schema, path=(*path, "properties", key), root=root + ) + for key, prop_schema in properties.items() + } + + # arrays + # { 'type': 'array', 'items': {...} } + items = json_schema.get("items") + if isinstance(items, dict): + json_schema["items"] = ensure_strict_json_schema( + items, path=(*path, "items"), root=root + ) + + # unions + any_of = json_schema.get("anyOf") + if isinstance(any_of, list): + json_schema["anyOf"] = [ + ensure_strict_json_schema(variant, path=(*path, "anyOf", str(i)), root=root) + for i, variant in enumerate(any_of) + ] + + # intersections + all_of = json_schema.get("allOf") + if isinstance(all_of, list): + if len(all_of) == 1: + json_schema.update( + ensure_strict_json_schema( + all_of[0], path=(*path, "allOf", "0"), root=root + ) + ) + json_schema.pop("allOf") + else: + json_schema["allOf"] = [ + ensure_strict_json_schema( + entry, path=(*path, "allOf", str(i)), root=root + ) + for i, entry in enumerate(all_of) + ] + + # string + if typ == "string": + if "format" in json_schema: + if json_schema["format"] not in supported_string_formats: + del json_schema["format"] + + # strip `None` defaults as there's no meaningful distinction here + # the schema will still be `nullable` and the model will default + # to using `None` anyway + if json_schema.get("default", NOT_GIVEN) is None: + json_schema.pop("default") + + # we can't use `$ref`s if there are also other properties defined, e.g. + # `{"$ref": "...", "description": "my description"}` + # + # so we unravel the ref + # `{"type": "string", "description": "my description"}` + ref = json_schema.get("$ref") + if ref and has_more_than_n_keys(json_schema, 1): + assert isinstance(ref, str), f"Received non-string $ref - {ref}" + + resolved = resolve_ref(root=root, ref=ref) + if not isinstance(resolved, dict): + raise ValueError( + f"Expected `$ref: {ref}` to resolved to a dictionary but got {resolved}" + ) + + # properties from the json schema take priority over the ones on the `$ref` + json_schema.update({**resolved, **json_schema}) + json_schema.pop("$ref") + # Since the schema expanded from `$ref` might not have `additionalProperties: false` applied, + # we call `_ensure_strict_json_schema` again to fix the inlined schema and ensure it's valid. + return ensure_strict_json_schema(json_schema, path=path, root=root) + + return json_schema + + +def resolve_ref(*, root: dict[str, object], ref: str) -> object: + if not ref.startswith("#/"): + raise ValueError(f"Unexpected $ref format {ref!r}; Does not start with #/") + + path = ref[2:].split("/") + resolved = root + for key in path: + value = resolved[key] + assert isinstance( + value, dict + ), f"encountered non-dictionary entry while resolving {ref} - {resolved}" + resolved = value + + return resolved + + # ? Not used def generate_constraint_sentences(schema: dict) -> str: """ diff --git a/servers/fastapi/utils/set_env.py b/servers/fastapi/utils/set_env.py index 836c8caa..5566d800 100644 --- a/servers/fastapi/utils/set_env.py +++ b/servers/fastapi/utils/set_env.py @@ -25,10 +25,26 @@ def set_openai_api_key_env(value): os.environ["OPENAI_API_KEY"] = value +def set_openai_model_env(value): + os.environ["OPENAI_MODEL"] = value + + def set_google_api_key_env(value): os.environ["GOOGLE_API_KEY"] = value +def set_google_model_env(value): + os.environ["GOOGLE_MODEL"] = value + + +def set_anthropic_api_key_env(value): + os.environ["ANTHROPIC_API_KEY"] = value + + +def set_anthropic_model_env(value): + os.environ["ANTHROPIC_MODEL"] = value + + def set_custom_llm_api_key_env(value): os.environ["CUSTOM_LLM_API_KEY"] = value @@ -51,3 +67,7 @@ def set_image_provider_env(value): def set_pixabay_api_key_env(value): os.environ["PIXABAY_API_KEY"] = value + + +def set_extended_reasoning_env(value): + os.environ["EXTENDED_REASONING"] = value diff --git a/servers/fastapi/utils/user_config.py b/servers/fastapi/utils/user_config.py index b1065a2b..d1ddd20a 100644 --- a/servers/fastapi/utils/user_config.py +++ b/servers/fastapi/utils/user_config.py @@ -3,31 +3,41 @@ import json from models.user_config import UserConfig from utils.get_env import ( + get_anthropic_api_key_env, + get_anthropic_model_env, get_custom_llm_api_key_env, get_custom_llm_url_env, get_custom_model_env, get_google_api_key_env, + get_google_model_env, get_llm_provider_env, get_ollama_model_env, get_ollama_url_env, get_openai_api_key_env, + get_openai_model_env, get_pexels_api_key_env, get_user_config_path_env, get_image_provider_env, - get_pixabay_api_key_env + get_pixabay_api_key_env, + get_extended_reasoning_env, ) from utils.set_env import ( + set_anthropic_api_key_env, + set_anthropic_model_env, set_custom_llm_api_key_env, set_custom_llm_url_env, set_custom_model_env, + set_extended_reasoning_env, set_google_api_key_env, + set_google_model_env, set_llm_provider_env, set_ollama_model_env, set_ollama_url_env, set_openai_api_key_env, + set_openai_model_env, set_pexels_api_key_env, set_image_provider_env, - set_pixabay_api_key_env + set_pixabay_api_key_env, ) @@ -43,10 +53,21 @@ def get_user_config(): print("Error while loading user config") pass + new_extended_reasoning = ( + existing_config.EXTENDED_REASONING or get_extended_reasoning_env() + ) + if new_extended_reasoning is not None: + new_extended_reasoning = bool(new_extended_reasoning) + return UserConfig( LLM=existing_config.LLM or get_llm_provider_env(), OPENAI_API_KEY=existing_config.OPENAI_API_KEY or get_openai_api_key_env(), + OPENAI_MODEL=existing_config.OPENAI_MODEL or get_openai_model_env(), GOOGLE_API_KEY=existing_config.GOOGLE_API_KEY or get_google_api_key_env(), + GOOGLE_MODEL=existing_config.GOOGLE_MODEL or get_google_model_env(), + ANTHROPIC_API_KEY=existing_config.ANTHROPIC_API_KEY + or get_anthropic_api_key_env(), + ANTHROPIC_MODEL=existing_config.ANTHROPIC_MODEL or get_anthropic_model_env(), OLLAMA_URL=existing_config.OLLAMA_URL or get_ollama_url_env(), OLLAMA_MODEL=existing_config.OLLAMA_MODEL or get_ollama_model_env(), CUSTOM_LLM_URL=existing_config.CUSTOM_LLM_URL or get_custom_llm_url_env(), @@ -56,6 +77,7 @@ def get_user_config(): IMAGE_PROVIDER=existing_config.IMAGE_PROVIDER or get_image_provider_env(), PIXABAY_API_KEY=existing_config.PIXABAY_API_KEY or get_pixabay_api_key_env(), PEXELS_API_KEY=existing_config.PEXELS_API_KEY or get_pexels_api_key_env(), + EXTENDED_REASONING=new_extended_reasoning, ) @@ -65,8 +87,16 @@ def update_env_with_user_config(): set_llm_provider_env(user_config.LLM) if user_config.OPENAI_API_KEY: set_openai_api_key_env(user_config.OPENAI_API_KEY) + if user_config.OPENAI_MODEL: + set_openai_model_env(user_config.OPENAI_MODEL) if user_config.GOOGLE_API_KEY: set_google_api_key_env(user_config.GOOGLE_API_KEY) + if user_config.GOOGLE_MODEL: + set_google_model_env(user_config.GOOGLE_MODEL) + if user_config.ANTHROPIC_API_KEY: + set_anthropic_api_key_env(user_config.ANTHROPIC_API_KEY) + if user_config.ANTHROPIC_MODEL: + set_anthropic_model_env(user_config.ANTHROPIC_MODEL) if user_config.OLLAMA_URL: set_ollama_url_env(user_config.OLLAMA_URL) if user_config.OLLAMA_MODEL: @@ -83,3 +113,6 @@ def update_env_with_user_config(): set_pixabay_api_key_env(user_config.PIXABAY_API_KEY) if user_config.PEXELS_API_KEY: set_pexels_api_key_env(user_config.PEXELS_API_KEY) + if user_config.EXTENDED_REASONING: + if user_config.EXTENDED_REASONING: + set_extended_reasoning_env(str(user_config.EXTENDED_REASONING)) diff --git a/servers/nextjs/app/api/user-config/route.ts b/servers/nextjs/app/api/user-config/route.ts index 39ba481e..71219c0c 100644 --- a/servers/nextjs/app/api/user-config/route.ts +++ b/servers/nextjs/app/api/user-config/route.ts @@ -35,7 +35,11 @@ export async function POST(request: Request) { const mergedConfig: LLMConfig = { LLM: userConfig.LLM || existingConfig.LLM, OPENAI_API_KEY: userConfig.OPENAI_API_KEY || existingConfig.OPENAI_API_KEY, + OPENAI_MODEL: userConfig.OPENAI_MODEL || existingConfig.OPENAI_MODEL, GOOGLE_API_KEY: userConfig.GOOGLE_API_KEY || existingConfig.GOOGLE_API_KEY, + GOOGLE_MODEL: userConfig.GOOGLE_MODEL || existingConfig.GOOGLE_MODEL, + ANTHROPIC_API_KEY: userConfig.ANTHROPIC_API_KEY || existingConfig.ANTHROPIC_API_KEY, + ANTHROPIC_MODEL: userConfig.ANTHROPIC_MODEL || existingConfig.ANTHROPIC_MODEL, OLLAMA_URL: userConfig.OLLAMA_URL || existingConfig.OLLAMA_URL, OLLAMA_MODEL: userConfig.OLLAMA_MODEL || existingConfig.OLLAMA_MODEL, CUSTOM_LLM_URL: userConfig.CUSTOM_LLM_URL || existingConfig.CUSTOM_LLM_URL, @@ -50,6 +54,10 @@ export async function POST(request: Request) { userConfig.USE_CUSTOM_URL === undefined ? existingConfig.USE_CUSTOM_URL : userConfig.USE_CUSTOM_URL, + EXTENDED_REASONING: + userConfig.EXTENDED_REASONING === undefined + ? existingConfig.EXTENDED_REASONING + : userConfig.EXTENDED_REASONING, }; fs.writeFileSync(userConfigPath, JSON.stringify(mergedConfig)); return NextResponse.json(mergedConfig); diff --git a/servers/nextjs/app/settings/SettingPage.tsx b/servers/nextjs/app/settings/SettingPage.tsx index ece5f16b..b6e447ca 100644 --- a/servers/nextjs/app/settings/SettingPage.tsx +++ b/servers/nextjs/app/settings/SettingPage.tsx @@ -29,7 +29,6 @@ const SettingsPage = () => { const userConfigState = useSelector((state: RootState) => state.userConfig); const [llmConfig, setLlmConfig] = useState(userConfigState.llm_config); const canChangeKeys = userConfigState.can_change_keys; - const [isLoading, setIsLoading] = useState(false); const [buttonState, setButtonState] = useState({ isLoading: false, isDisabled: false, @@ -55,16 +54,13 @@ const SettingsPage = () => { const handleSaveConfig = async () => { try { - setIsLoading(true); setButtonState(prev => ({ ...prev, isLoading: true, isDisabled: true, text: "Saving Configuration..." })); - await handleSaveLLMConfig(llmConfig); - if (llmConfig.LLM === "ollama" && llmConfig.OLLAMA_MODEL) { const isPulled = await checkIfSelectedOllamaModelIsPulled(llmConfig.OLLAMA_MODEL); if (!isPulled) { @@ -72,24 +68,16 @@ const SettingsPage = () => { await handleModelDownload(); } } - toast.info("Configuration saved successfully"); - setIsLoading(false); setButtonState(prev => ({ ...prev, isLoading: false, isDisabled: false, text: "Save Configuration" })); - router.back(); + router.push("/upload"); } catch (error) { - console.error("Error:", error); - toast.info( - error instanceof Error - ? error.message - : "Failed to save configuration" - ); - setIsLoading(false); + toast.info(error instanceof Error ? error.message : "Failed to save configuration"); setButtonState(prev => ({ ...prev, isLoading: false, @@ -102,8 +90,8 @@ const SettingsPage = () => { const handleModelDownload = async () => { try { await pullOllamaModel(llmConfig.OLLAMA_MODEL!, setDownloadingModel); - } catch (error) { - console.error("Error downloading model:", error); + } + finally { setDownloadingModel(null); setShowDownloadModal(false); } diff --git a/servers/nextjs/app/storeInitializer.tsx b/servers/nextjs/app/storeInitializer.tsx index 44fd16f8..13ac3153 100644 --- a/servers/nextjs/app/storeInitializer.tsx +++ b/servers/nextjs/app/storeInitializer.tsx @@ -53,7 +53,7 @@ export function StoreInitializer({ children }: { children: React.ReactNode }) { } } if (llmConfig.LLM === 'custom') { - const isAvailable = await checkIfSelectedCustomModelIsAvailable(llmConfig.CUSTOM_MODEL); + const isAvailable = await checkIfSelectedCustomModelIsAvailable(llmConfig); if (!isAvailable) { router.push('/'); setLoadingToFalseAfterNavigatingTo('/'); @@ -83,16 +83,20 @@ export function StoreInitializer({ children }: { children: React.ReactNode }) { } - const checkIfSelectedCustomModelIsAvailable = async (customModel: string) => { + const checkIfSelectedCustomModelIsAvailable = async (llmConfig: LLMConfig) => { try { - const response = await fetch('/api/v1/ppt/custom_llm/models/available', { + const response = await fetch('/api/v1/ppt/openai/models/available', { method: 'POST', headers: { 'Content-Type': 'application/json', }, + body: JSON.stringify({ + url: llmConfig.CUSTOM_LLM_URL, + api_key: llmConfig.CUSTOM_LLM_API_KEY, + }), }); const data = await response.json(); - return data.includes(customModel); + return data.includes(llmConfig.CUSTOM_MODEL); } catch (error) { console.error('Error fetching custom models:', error); return false; diff --git a/servers/nextjs/components/AnthropicConfig.tsx b/servers/nextjs/components/AnthropicConfig.tsx new file mode 100644 index 00000000..47aa340c --- /dev/null +++ b/servers/nextjs/components/AnthropicConfig.tsx @@ -0,0 +1,230 @@ +"use client"; +import { useEffect, useState } from "react"; +import { Check, ChevronsUpDown, Loader2 } from "lucide-react"; +import { Button } from "./ui/button"; +import { + Command, + CommandEmpty, + CommandGroup, + CommandInput, + CommandItem, + CommandList, +} from "./ui/command"; +import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover"; +import { cn } from "@/lib/utils"; +import { toast } from "sonner"; +import { Switch } from "./ui/switch"; + +interface AnthropicConfigProps { + anthropicApiKey: string; + anthropicModel: string; + extendedReasoning: boolean; + onInputChange: (value: string | boolean, field: string) => void; +} + + +export default function AnthropicConfig({ + anthropicApiKey, + anthropicModel, + extendedReasoning, + onInputChange, +}: AnthropicConfigProps) { + const [openModelSelect, setOpenModelSelect] = useState(false); + const [availableModels, setAvailableModels] = useState([]); + const [modelsLoading, setModelsLoading] = useState(false); + const [modelsChecked, setModelsChecked] = useState(false); + const [apiKey, setApiKey] = useState(anthropicApiKey); + + useEffect(() => { + setAvailableModels([]); + setModelsChecked(false); + onInputChange("", "anthropic_model"); + }, [apiKey]); + + const onApiKeyChange = (value: string) => { + setApiKey(value); + onInputChange(value, "anthropic_api_key"); + }; + + const fetchAvailableModels = async () => { + if (!anthropicApiKey) return; + + setModelsLoading(true); + try { + const response = await fetch('/api/v1/ppt/anthropic/models/available', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + api_key: anthropicApiKey + }), + }); + + if (response.ok) { + const data = await response.json(); + setAvailableModels(data); + setModelsChecked(true); + } else { + console.error('Failed to fetch models'); + setAvailableModels([]); + setModelsChecked(true); + } + } catch (error) { + console.error('Error fetching models:', error); + toast.error('Error fetching models'); + setAvailableModels([]); + setModelsChecked(true); + } finally { + setModelsLoading(false); + } + }; + + return ( +
+ {/* API Key Input */} +
+ +
+ onApiKeyChange(e.target.value)} + className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors" + placeholder="Enter your Anthropic API key" + /> +
+

+ + Your API key will be stored locally and never shared +

+
+ + {/* Extended Reasoning Toggle */} + {/*
+
+ + onInputChange(checked, "extended_reasoning")} + /> +
+

+ + Enable extended reasoning for more detailed and thorough responses +

+
*/} + + {/* Check for available models button - show when no models checked or no models found */} + {(!modelsChecked || (modelsChecked && availableModels.length === 0)) && ( +
+ +
+ )} + + {/* Show message if no models found */} + {modelsChecked && availableModels.length === 0 && ( +
+

+ No models found. Please make sure your API key is valid and has access to Anthropic models. +

+
+ )} + + {/* Model Selection - only show if models are available */} + {modelsChecked && availableModels.length > 0 ? ( +
+ +
+ + + + + + + + + No model found. + + {availableModels.map((model, index) => ( + { + onInputChange(value, "anthropic_model"); + setOpenModelSelect(false); + }} + > + +
+
+
+ + {model} + +
+
+
+ + ))} + + + + + +
+
+ ) : null} +
+ ); +} \ No newline at end of file diff --git a/servers/nextjs/components/CustomConfig.tsx b/servers/nextjs/components/CustomConfig.tsx index f0a9cb4c..41e26fda 100644 --- a/servers/nextjs/components/CustomConfig.tsx +++ b/servers/nextjs/components/CustomConfig.tsx @@ -1,4 +1,5 @@ "use client"; +import { useState, useEffect } from "react"; import { Check, ChevronsUpDown, Loader2 } from "lucide-react"; import { Button } from "./ui/button"; import { @@ -11,34 +12,83 @@ import { } from "./ui/command"; import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover"; import { cn } from "@/lib/utils"; +import { toast } from "sonner"; interface CustomConfigProps { customLlmUrl: string; customLlmApiKey: string; customModel: string; - customModels: string[]; - customModelsLoading: boolean; - customModelsChecked: boolean; - openModelSelect: boolean; onInputChange: (value: string, field: string) => void; - onOpenModelSelectChange: (open: boolean) => void; - onFetchCustomModels: () => void; } export default function CustomConfig({ customLlmUrl, customLlmApiKey, customModel, - customModels, - customModelsLoading, - customModelsChecked, - openModelSelect, onInputChange, - onOpenModelSelectChange, - onFetchCustomModels, }: CustomConfigProps) { + const [customModels, setCustomModels] = useState([]); + const [customModelsLoading, setCustomModelsLoading] = useState(false); + const [customModelsChecked, setCustomModelsChecked] = useState(false); + const [openModelSelect, setOpenModelSelect] = useState(false); + const [url, setUrl] = useState(customLlmUrl); + const [apiKey, setApiKey] = useState(customLlmApiKey); + + useEffect(() => { + setCustomModels([]); + setCustomModelsChecked(false); + onInputChange("", "custom_model"); + }, [url, apiKey]); + + const onUrlChange = (value: string) => { + setUrl(value); + onInputChange(value, "custom_llm_url"); + }; + + const onApiKeyChange = (value: string) => { + setApiKey(value); + onInputChange(value, "custom_llm_api_key"); + }; + + const fetchCustomModels = async () => { + if (!customLlmUrl) return; + + try { + setCustomModelsLoading(true); + const response = await fetch("/api/v1/ppt/openai/models/available", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ + url: customLlmUrl, + api_key: customLlmApiKey, + }), + }); + + if (response.ok) { + const data = await response.json(); + setCustomModels(data); + setCustomModelsChecked(true); + } else { + console.error('Failed to fetch custom models'); + setCustomModels([]); + setCustomModelsChecked(true); + toast.error('Failed to fetch custom models'); + } + } catch (error) { + console.error('Error fetching custom models:', error); + toast.error('Error fetching custom models'); + setCustomModels([]); + setCustomModelsChecked(true); + } finally { + setCustomModelsLoading(false); + } + }; + return ( - <> +
+ {/* URL Input */}
+ + {/* API Key Input */}
+ {/* Check for available models button - show when no models checked or no models found */} + {(!customModelsChecked || (customModelsChecked && customModels.length === 0)) && ( +
+ +
+ )} + + {/* Show message if no models found */} + {customModelsChecked && customModels.length === 0 && ( +
+

+ No models found. Please make sure your API key is valid and has access to models. +

+
+ )} + {/* Model selection dropdown - only show if models are available */} {customModelsChecked && customModels.length > 0 && (
@@ -90,7 +170,7 @@ export default function CustomConfig({
)} - - {/* Check for available models button - show when no models checked or no models found */} - {(!customModelsChecked || - (customModelsChecked && customModels.length === 0)) && ( -
- -
- )} - - {/* Show message if no models found */} - {customModelsChecked && customModels.length === 0 && ( -
-

- No models found. Please make sure models are available. -

-
- )} - +
); } \ No newline at end of file diff --git a/servers/nextjs/components/GoogleConfig.tsx b/servers/nextjs/components/GoogleConfig.tsx index d595fbd4..81ed6914 100644 --- a/servers/nextjs/components/GoogleConfig.tsx +++ b/servers/nextjs/components/GoogleConfig.tsx @@ -1,27 +1,209 @@ +"use client"; +import { useEffect, useState } from "react"; +import { Check, ChevronsUpDown, Loader2 } from "lucide-react"; +import { Button } from "./ui/button"; +import { + Command, + CommandEmpty, + CommandGroup, + CommandInput, + CommandItem, + CommandList, +} from "./ui/command"; +import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover"; +import { cn } from "@/lib/utils"; +import { toast } from "sonner"; + interface GoogleConfigProps { googleApiKey: string; + googleModel: string; onInputChange: (value: string, field: string) => void; } -export default function GoogleConfig({ googleApiKey, onInputChange }: GoogleConfigProps) { +export default function GoogleConfig({ + googleApiKey, + googleModel, + onInputChange +}: GoogleConfigProps) { + const [openModelSelect, setOpenModelSelect] = useState(false); + const [availableModels, setAvailableModels] = useState([]); + const [modelsLoading, setModelsLoading] = useState(false); + const [modelsChecked, setModelsChecked] = useState(false); + const [apiKey, setApiKey] = useState(googleApiKey); + + useEffect(() => { + setAvailableModels([]); + setModelsChecked(false); + onInputChange("", "google_model"); + }, [apiKey]); + + const onApiKeyChange = (value: string) => { + setApiKey(value); + onInputChange(value, "google_api_key"); + }; + + const fetchAvailableModels = async () => { + if (!googleApiKey) return; + + setModelsLoading(true); + try { + const response = await fetch('/api/v1/ppt/google/models/available', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + api_key: googleApiKey + }), + }); + + if (response.ok) { + const data = await response.json(); + setAvailableModels(data); + setModelsChecked(true); + } else { + console.error('Failed to fetch models'); + setAvailableModels([]); + setModelsChecked(true); + } + } catch (error) { + console.error('Error fetching models:', error); + toast.error('Error fetching models'); + setAvailableModels([]); + setModelsChecked(true); + } finally { + setModelsLoading(false); + } + }; + return ( -
- -
- onInputChange(e.target.value, "google_api_key")} - className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors" - placeholder="Enter your API key" - /> +
+ {/* API Key Input */} +
+ +
+ onApiKeyChange(e.target.value)} + className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors" + placeholder="Enter your API key" + /> +
+

+ + Your API key will be stored locally and never shared +

-

- - Your API key will be stored locally and never shared -

+ + {/* Check for available models button - show when no models checked or no models found */} + {(!modelsChecked || (modelsChecked && availableModels.length === 0)) && ( +
+ +
+ )} + + {/* Show message if no models found */} + {modelsChecked && availableModels.length === 0 && ( +
+

+ No models found. Please make sure your API key is valid and has access to Google models. +

+
+ )} + + {/* Model Selection - only show if models are available */} + {modelsChecked && availableModels.length > 0 ? ( +
+ +
+ + + + + + + + + No model found. + + {availableModels.map((model, index) => ( + { + onInputChange(value, "google_model"); + setOpenModelSelect(false); + }} + > + +
+
+
+ + {model} + +
+
+
+ + ))} + + + + + +
+
+ ) : null}
); } \ No newline at end of file diff --git a/servers/nextjs/components/Home.tsx b/servers/nextjs/components/Home.tsx index f3941d1d..059219b5 100644 --- a/servers/nextjs/components/Home.tsx +++ b/servers/nextjs/components/Home.tsx @@ -27,7 +27,6 @@ export default function Home() { const router = useRouter(); const config = useSelector((state: RootState) => state.userConfig); const [llmConfig, setLlmConfig] = useState(config.llm_config); - const [isLoading, setIsLoading] = useState(false); const [downloadingModel, setDownloadingModel] = useState<{ name: string; @@ -54,7 +53,6 @@ export default function Home() { const handleSaveConfig = async () => { try { - setIsLoading(true); setButtonState(prev => ({ ...prev, isLoading: true, @@ -70,7 +68,6 @@ export default function Home() { } } toast.info("Configuration saved successfully"); - setIsLoading(false); setButtonState(prev => ({ ...prev, isLoading: false, @@ -79,8 +76,7 @@ export default function Home() { })); router.push("/upload"); } catch (error) { - toast.info("Failed to save configuration"); - setIsLoading(false); + toast.info(error instanceof Error ? error.message : "Failed to save configuration"); setButtonState(prev => ({ ...prev, isLoading: false, @@ -93,8 +89,8 @@ export default function Home() { const handleModelDownload = async () => { try { await pullOllamaModel(llmConfig.OLLAMA_MODEL!, setDownloadingModel); - } catch (error) { - console.info("Error downloading model:", error); + } + finally { setDownloadingModel(null); setShowDownloadModal(false); } diff --git a/servers/nextjs/components/LLMSelection.tsx b/servers/nextjs/components/LLMSelection.tsx index 670d1c00..88351edf 100644 --- a/servers/nextjs/components/LLMSelection.tsx +++ b/servers/nextjs/components/LLMSelection.tsx @@ -15,16 +15,13 @@ import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover"; import { cn } from "@/lib/utils"; import OpenAIConfig from "./OpenAIConfig"; import GoogleConfig from "./GoogleConfig"; +import AnthropicConfig from "./AnthropicConfig"; import OllamaConfig from "./OllamaConfig"; import CustomConfig from "./CustomConfig"; import { - OllamaModel, LLMConfig, updateLLMConfig, changeProvider as changeProviderUtil, - fetchOllamaModelsWithConfig, - setOllamaConfig, - fetchCustomModels, } from "@/utils/providerUtils"; import { IMAGE_PROVIDERS, LLM_PROVIDERS } from "@/utils/providerConstants"; @@ -51,29 +48,34 @@ export default function LLMProviderSelection({ setButtonState, }: LLMProviderSelectionProps) { const [llmConfig, setLlmConfig] = useState(initialLLMConfig); - const [ollamaModels, setOllamaModels] = useState([]); - const [customModels, setCustomModels] = useState([]); - const [customModelsLoading, setCustomModelsLoading] = useState(false); - const [customModelsChecked, setCustomModelsChecked] = useState(false); - const [ollamaModelsLoading, setOllamaModelsLoading] = useState(false); - const [useCustomOllamaUrl, setUseCustomOllamaUrl] = useState( - initialLLMConfig.USE_CUSTOM_URL || false - ); - const [openModelSelect, setOpenModelSelect] = useState(false); const [openImageProviderSelect, setOpenImageProviderSelect] = useState(false); + useEffect(() => { + if (!llmConfig.USE_CUSTOM_URL) { + setLlmConfig({ ...llmConfig, OLLAMA_URL: "http://localhost:11434" }); + } else { + if (!llmConfig.OLLAMA_URL) { + setLlmConfig({ ...llmConfig, OLLAMA_URL: "http://localhost:11434" }); + } + } + }, [llmConfig.USE_CUSTOM_URL]); + useEffect(() => { onConfigChange(llmConfig); }, [llmConfig]); useEffect(() => { const needsModelSelection = + (llmConfig.LLM === "openai" && !llmConfig.OPENAI_MODEL) || + (llmConfig.LLM === "google" && !llmConfig.GOOGLE_MODEL) || (llmConfig.LLM === "ollama" && !llmConfig.OLLAMA_MODEL) || - (llmConfig.LLM === "custom" && !llmConfig.CUSTOM_MODEL); + (llmConfig.LLM === "custom" && !llmConfig.CUSTOM_MODEL) || + (llmConfig.LLM === "anthropic" && !llmConfig.ANTHROPIC_MODEL); const needsApiKey = ((llmConfig.IMAGE_PROVIDER === "dall-e-3" || llmConfig.LLM === "openai") && !llmConfig.OPENAI_API_KEY) || ((llmConfig.IMAGE_PROVIDER === "gemini_flash" || llmConfig.LLM === "google") && !llmConfig.GOOGLE_API_KEY) || + (llmConfig.LLM === "anthropic" && !llmConfig.ANTHROPIC_API_KEY) || (llmConfig.IMAGE_PROVIDER === "pexels" && !llmConfig.PEXELS_API_KEY) || (llmConfig.IMAGE_PROVIDER === "pixabay" && !llmConfig.PIXABAY_API_KEY); @@ -86,7 +88,7 @@ export default function LLMProviderSelection({ }, [llmConfig]); - const input_field_changed = (new_value: string, field: string) => { + const input_field_changed = (new_value: string | boolean, field: string) => { const updatedConfig = updateLLMConfig(llmConfig, field, new_value); setLlmConfig(updatedConfig); }; @@ -94,68 +96,8 @@ export default function LLMProviderSelection({ const handleProviderChange = (provider: string) => { const newConfig = changeProviderUtil(llmConfig, provider); setLlmConfig(newConfig); - if (provider === "ollama") { - fetchOllamaModels(); - } }; - const fetchOllamaModels = async () => { - try { - setOllamaModelsLoading(true); - const result = await fetchOllamaModelsWithConfig(llmConfig); - setOllamaModels(result.models); - if (result.updatedConfig) { - setLlmConfig(result.updatedConfig); - } - } catch (error) { - console.error("Error fetching Ollama models:", error); - setOllamaModels([]); - } finally { - setOllamaModelsLoading(false); - } - }; - - const fetchCustomModelsHandler = async () => { - try { - setCustomModelsLoading(true); - const models = await fetchCustomModels( - llmConfig.CUSTOM_LLM_URL || "", - llmConfig.CUSTOM_LLM_API_KEY || "" - ); - setCustomModels(models); - setCustomModelsChecked(true); - } catch (error) { - console.error("Error fetching custom models:", error); - setCustomModels([]); - } finally { - setCustomModelsLoading(false); - } - }; - - const setOllamaConfigHandler = () => { - const updatedConfig = setOllamaConfig(llmConfig, useCustomOllamaUrl); - setLlmConfig(updatedConfig); - }; - - useEffect(() => { - if (llmConfig.LLM === "ollama") { - setOllamaConfigHandler(); - fetchOllamaModels(); - } - }, [llmConfig.LLM]); - - useEffect(() => { - setOllamaConfigHandler(); - }, [useCustomOllamaUrl]); - - useEffect(() => { - if (llmConfig.LLM === "custom") { - setCustomModels([]); - setCustomModelsChecked(false); - setLlmConfig({ ...llmConfig, CUSTOM_MODEL: "" }); - } - }, [llmConfig.CUSTOM_LLM_URL, llmConfig.CUSTOM_LLM_API_KEY]); - useEffect(() => { if (!llmConfig.IMAGE_PROVIDER) { if (llmConfig.LLM === "openai") { @@ -177,9 +119,10 @@ export default function LLMProviderSelection({ onValueChange={handleProviderChange} className="w-full" > - + OpenAI Google + Anthropic Ollama Custom @@ -198,6 +141,7 @@ export default function LLMProviderSelection({ @@ -206,6 +150,17 @@ export default function LLMProviderSelection({ + + + {/* Anthropic Content */} + + @@ -215,16 +170,8 @@ export default function LLMProviderSelection({ { - input_field_changed(modelName, "ollama_model"); - }} /> @@ -234,19 +181,13 @@ export default function LLMProviderSelection({ customLlmUrl={llmConfig.CUSTOM_LLM_URL || ""} customLlmApiKey={llmConfig.CUSTOM_LLM_API_KEY || ""} customModel={llmConfig.CUSTOM_MODEL || ""} - customModels={customModels} - customModelsLoading={customModelsLoading} - customModelsChecked={customModelsChecked} - openModelSelect={openModelSelect} onInputChange={input_field_changed} - onOpenModelSelectChange={setOpenModelSelect} - onFetchCustomModels={fetchCustomModelsHandler} /> {/* Image Provider Selection */} -
+
@@ -388,7 +329,13 @@ export default function LLMProviderSelection({ ? llmConfig.OLLAMA_MODEL ?? "xxxxx" : llmConfig.LLM === "custom" ? llmConfig.CUSTOM_MODEL ?? "xxxxx" - : LLM_PROVIDERS[llmConfig.LLM!]?.model_label || "xxxxx"}{" "} + : llmConfig.LLM === "anthropic" + ? llmConfig.ANTHROPIC_MODEL ?? "xxxxx" + : llmConfig.LLM === "google" + ? llmConfig.GOOGLE_MODEL ?? "xxxxx" + : llmConfig.LLM === "openai" + ? llmConfig.OPENAI_MODEL ?? "xxxxx" + : "xxxxx"}{" "} for text generation and{" "} {llmConfig.IMAGE_PROVIDER && IMAGE_PROVIDERS[llmConfig.IMAGE_PROVIDER] diff --git a/servers/nextjs/components/OllamaConfig.tsx b/servers/nextjs/components/OllamaConfig.tsx index f0f09b76..aab9b880 100644 --- a/servers/nextjs/components/OllamaConfig.tsx +++ b/servers/nextjs/components/OllamaConfig.tsx @@ -1,5 +1,5 @@ "use client"; -import { useState } from "react"; +import { useState, useEffect } from "react"; import { Check, ChevronsUpDown, Loader2 } from "lucide-react"; import { Button } from "./ui/button"; import { @@ -13,6 +13,7 @@ import { import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover"; import { cn } from "@/lib/utils"; import { Switch } from "./ui/switch"; +import { toast } from "sonner"; interface OllamaModel { label: string; @@ -26,30 +27,84 @@ interface OllamaConfigProps { ollamaModel: string; ollamaUrl: string; useCustomUrl: boolean; - ollamaModels: OllamaModel[]; - ollamaModelsLoading?: boolean; - onInputChange: (value: string, field: string) => void; - onUseCustomUrlChange: (checked: boolean) => void; - openModelSelect: boolean; - onOpenModelSelectChange: (open: boolean) => void; - onModelSelect?: (modelName: string) => void; + onInputChange: (value: string | boolean, field: string) => void; } export default function OllamaConfig({ ollamaModel, ollamaUrl, useCustomUrl, - ollamaModels, - ollamaModelsLoading = false, onInputChange, - onUseCustomUrlChange, - openModelSelect, - onOpenModelSelectChange, - onModelSelect, }: OllamaConfigProps) { + const [ollamaModels, setOllamaModels] = useState([]); + const [ollamaModelsLoading, setOllamaModelsLoading] = useState(false); + const [openModelSelect, setOpenModelSelect] = useState(false); + + const fetchOllamaModels = async () => { + try { + setOllamaModelsLoading(true); + const response = await fetch('/api/v1/ppt/ollama/models/supported'); + + if (response.ok) { + const data = await response.json(); + console.log(data); + setOllamaModels(data); + } else { + console.error('Failed to fetch Ollama models'); + setOllamaModels([]); + toast.error('Failed to fetch Ollama models'); + } + } catch (error) { + console.error('Error fetching Ollama models:', error); + toast.error('Error fetching Ollama models'); + setOllamaModels([]); + } finally { + setOllamaModelsLoading(false); + } + }; + + useEffect(() => { + fetchOllamaModels(); + }, []); + return ( - <> -
+
+ {/* URL Configuration */} +
+
+ + onInputChange(checked, "use_custom_url")} + /> +
+ {useCustomUrl && ( +
+ +
+ onInputChange(e.target.value, "ollama_url")} + /> +
+

+ + Change this if you are using a custom Ollama instance +

+
+ )} +
+ + {/* Model Selection */} +
@@ -64,7 +119,7 @@ export default function OllamaConfig({ ) : ollamaModels && ollamaModels.length > 0 ? (
-
-
- - -
- {useCustomUrl && ( - <> -
- -
- - onInputChange(e.target.value, "ollama_url") - } - /> -
-

- - Change this if you are using a custom Ollama instance -

-
- - )} -
- +
); } \ No newline at end of file diff --git a/servers/nextjs/components/OpenAIConfig.tsx b/servers/nextjs/components/OpenAIConfig.tsx index 6696493a..ab642bd6 100644 --- a/servers/nextjs/components/OpenAIConfig.tsx +++ b/servers/nextjs/components/OpenAIConfig.tsx @@ -1,27 +1,214 @@ +"use client"; +import { useEffect, useState } from "react"; +import { Check, ChevronsUpDown, Loader2 } from "lucide-react"; +import { Button } from "./ui/button"; +import { + Command, + CommandEmpty, + CommandGroup, + CommandInput, + CommandItem, + CommandList, +} from "./ui/command"; +import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover"; +import { cn } from "@/lib/utils"; +import { toast } from "sonner"; + interface OpenAIConfigProps { openaiApiKey: string; + openaiModel: string; onInputChange: (value: string, field: string) => void; } -export default function OpenAIConfig({ openaiApiKey, onInputChange }: OpenAIConfigProps) { +export default function OpenAIConfig({ + openaiApiKey, + openaiModel, + onInputChange +}: OpenAIConfigProps) { + const [openModelSelect, setOpenModelSelect] = useState(false); + const [availableModels, setAvailableModels] = useState([]); + const [modelsLoading, setModelsLoading] = useState(false); + const [modelsChecked, setModelsChecked] = useState(false); + const [apiKey, setApiKey] = useState(openaiApiKey); + + const openaiUrl = "https://api.openai.com/v1"; + + useEffect(() => { + setAvailableModels([]); + setModelsChecked(false); + onInputChange("", "openai_model"); + }, [apiKey]); + + const onApiKeyChange = (value: string) => { + setApiKey(value); + onInputChange(value, "openai_api_key"); + }; + + const fetchAvailableModels = async () => { + if (!openaiApiKey) return; + + setModelsLoading(true); + try { + const response = await fetch('/api/v1/ppt/openai/models/available', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + url: openaiUrl, + api_key: openaiApiKey + }), + }); + + if (response.ok) { + const data = await response.json(); + setAvailableModels(data); + setModelsChecked(true); + } else { + console.error('Failed to fetch models'); + setAvailableModels([]); + setModelsChecked(true); + } + } catch (error) { + console.error('Error fetching models:', error); + toast.error('Error fetching models'); + setAvailableModels([]); + setModelsChecked(true); + } finally { + setModelsLoading(false); + } + }; + return ( -
- -
- onInputChange(e.target.value, "openai_api_key")} - className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors" - placeholder="Enter your API key" - /> +
+ {/* API Key Input */} +
+ +
+ onApiKeyChange(e.target.value)} + className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors" + placeholder="Enter your API key" + /> +
+

+ + Your API key will be stored locally and never shared +

-

- - Your API key will be stored locally and never shared -

+ + + + {/* Check for available models button - show when no models checked or no models found */} + {(!modelsChecked || (modelsChecked && availableModels.length === 0)) && ( +
+ +
+ )} + + {/* Show message if no models found */} + {modelsChecked && availableModels.length === 0 && ( +
+

+ No models found. Please make sure your API key is valid and has access to OpenAI models. +

+
+ )} + + {/* Model Selection - only show if models are available */} + {modelsChecked && availableModels.length > 0 ? ( +
+ +
+ + + + + + + + + No model found. + + {availableModels.map((model, index) => ( + { + onInputChange(value, "openai_model"); + setOpenModelSelect(false); + }} + > + +
+
+
+ + {model} + +
+
+
+ + ))} + + + + + +
+
+ ) : null}
); -} \ No newline at end of file +} \ No newline at end of file diff --git a/servers/nextjs/types/global.d.ts b/servers/nextjs/types/global.d.ts index 2dee9ebb..4b1caa32 100644 --- a/servers/nextjs/types/global.d.ts +++ b/servers/nextjs/types/global.d.ts @@ -15,17 +15,36 @@ interface TextFrameProps { interface LLMConfig { LLM?: string; + + // OpenAI OPENAI_API_KEY?: string; + OPENAI_MODEL?: string; + + // Google GOOGLE_API_KEY?: string; + GOOGLE_MODEL?: string; + + // Anthropic + ANTHROPIC_API_KEY?: string; + ANTHROPIC_MODEL?: string; + + // Ollama OLLAMA_URL?: string; OLLAMA_MODEL?: string; + + // Custom LLM CUSTOM_LLM_URL?: string; CUSTOM_LLM_API_KEY?: string; CUSTOM_MODEL?: string; + + // Image providers IMAGE_PROVIDER?: string; PIXABAY_API_KEY?: string; PEXELS_API_KEY?: string; + // Extended reasoning + EXTENDED_REASONING?: boolean; + // Only used in UI settings USE_CUSTOM_URL?: boolean; } \ No newline at end of file diff --git a/servers/nextjs/utils/providerConstants.ts b/servers/nextjs/utils/providerConstants.ts index 5fb28a7e..0dd53af9 100644 --- a/servers/nextjs/utils/providerConstants.ts +++ b/servers/nextjs/utils/providerConstants.ts @@ -67,16 +67,17 @@ export const LLM_PROVIDERS: Record = { openai: { value: "openai", label: "OpenAI", - description: "OpenAI's latest image generation model", - model_value: "gpt-4.1", - model_label: "GPT-4.1" + description: "OpenAI's latest text generation model", }, google: { value: "google", label: "Google", - description: "Google's primary image generation model", - model_value: "gemini-2.0-flash", - model_label: "Gemini 2.0 Flash" + description: "Google's primary text generation model", + }, + anthropic: { + value: "anthropic", + label: "Anthropic", + description: "Anthropic's Claude models", }, ollama: { value: "ollama", diff --git a/servers/nextjs/utils/providerUtils.ts b/servers/nextjs/utils/providerUtils.ts index 37e27b92..0d1ac62c 100644 --- a/servers/nextjs/utils/providerUtils.ts +++ b/servers/nextjs/utils/providerUtils.ts @@ -1,5 +1,3 @@ -import { toast } from "sonner"; - export interface OllamaModel { label: string; value: string; @@ -18,16 +16,37 @@ export interface DownloadingModel { export interface LLMConfig { LLM?: string; + + // OpenAI OPENAI_API_KEY?: string; + OPENAI_MODEL?: string; + + // Google GOOGLE_API_KEY?: string; + GOOGLE_MODEL?: string; + + // Anthropic + ANTHROPIC_API_KEY?: string; + ANTHROPIC_MODEL?: string; + + // Ollama OLLAMA_URL?: string; OLLAMA_MODEL?: string; + + // Custom LLM CUSTOM_LLM_URL?: string; CUSTOM_LLM_API_KEY?: string; CUSTOM_MODEL?: string; + + // Image providers + IMAGE_PROVIDER?: string; PEXELS_API_KEY?: string; PIXABAY_API_KEY?: string; - IMAGE_PROVIDER?: string; + + // Extended reasoning + EXTENDED_REASONING?: boolean; + + // Only used in UI settings USE_CUSTOM_URL?: boolean; } @@ -42,11 +61,15 @@ export interface OllamaModelsResult { export const updateLLMConfig = ( currentConfig: LLMConfig, field: string, - value: string + value: string | boolean ): LLMConfig => { const fieldMappings: Record = { openai_api_key: "OPENAI_API_KEY", + openai_model: "OPENAI_MODEL", google_api_key: "GOOGLE_API_KEY", + google_model: "GOOGLE_MODEL", + anthropic_api_key: "ANTHROPIC_API_KEY", + anthropic_model: "ANTHROPIC_MODEL", ollama_url: "OLLAMA_URL", ollama_model: "OLLAMA_MODEL", custom_llm_url: "CUSTOM_LLM_URL", @@ -55,6 +78,8 @@ export const updateLLMConfig = ( pexels_api_key: "PEXELS_API_KEY", pixabay_api_key: "PIXABAY_API_KEY", image_provider: "IMAGE_PROVIDER", + extended_reasoning: "EXTENDED_REASONING", + use_custom_url: "USE_CUSTOM_URL", }; const configKey = fieldMappings[field]; @@ -86,53 +111,6 @@ export const changeProvider = ( return newConfig; }; -/** - * Fetches supported Ollama models - */ -export const fetchOllamaModels = async (): Promise => { - try { - const response = await fetch("/api/v1/ppt/ollama/models/supported"); - const models = await response.json(); - return models || []; - } catch (error) { - console.error("Error fetching ollama models:", error); - return []; // Ensure we always return an empty array on error - } -}; - -/** - * Fetches Ollama models and validates current selection - * Returns models and updated config if needed - */ -export const fetchOllamaModelsWithConfig = async ( - config: LLMConfig -): Promise => { - try { - const models = await fetchOllamaModels(); - - // Check if currently selected model is still available - let updatedConfig: LLMConfig | undefined; - if (config.OLLAMA_MODEL && models && models.length > 0) { - const isModelAvailable = models.some( - (model: OllamaModel) => model.value === config.OLLAMA_MODEL - ); - if (!isModelAvailable) { - updatedConfig = { ...config, OLLAMA_MODEL: "" }; - } - } - - return { - models, - updatedConfig - }; - } catch (error) { - console.error("Error fetching ollama models:", error); - return { - models: [], - updatedConfig: { ...config, OLLAMA_MODEL: "" } - }; - } -}; export const checkIfSelectedOllamaModelIsPulled = async (ollamaModel: string) => { try { @@ -146,37 +124,6 @@ export const checkIfSelectedOllamaModelIsPulled = async (ollamaModel: string) => } } -/** - * Fetches available custom models - */ -export const fetchCustomModels = async ( - url: string, - apiKey: string -): Promise => { - try { - const response = await fetch("/api/v1/ppt/custom_llm/models/available", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - url: url || "", - api_key: apiKey || "", - }), - }); - - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - - const data = await response.json(); - return data; - } catch (error) { - toast.info("Could not fetch custom models"); - console.error("Error fetching custom models:", error); - throw error; - } -}; /** * Resets downloading model state @@ -234,51 +181,4 @@ export const pullOllamaModel = async ( } }, 1000); }); -}; - -/** - * Sets Ollama configuration based on custom URL preference - */ -export const setOllamaConfig = ( - currentConfig: LLMConfig, - useCustomUrl: boolean -): LLMConfig => { - let customUrl = "http://localhost:11434"; - if (!useCustomUrl) { - return { - ...currentConfig, - OLLAMA_URL: customUrl, - USE_CUSTOM_URL: false, - }; - } else { - return { ...currentConfig, USE_CUSTOM_URL: true, OLLAMA_URL: customUrl }; - } -}; - -/** - * Handles saving configuration with error handling - */ -export const handleSaveConfiguration = async ( - llmConfig: LLMConfig, - handleSaveLLMConfig: (config: LLMConfig) => Promise, - pullOllamaModels?: () => Promise -): Promise => { - try { - await handleSaveLLMConfig(llmConfig); - if (llmConfig.LLM === "ollama" && pullOllamaModels) { - await pullOllamaModels(); - } - toast.success("Configuration saved successfully"); - } catch (error) { - console.error("Error:", error); - toast.error( - error instanceof Error - ? error.message - : "Failed to save configuration", - { - description: "Failed to save configuration", - } - ); - throw error; - } -}; \ No newline at end of file +}; \ No newline at end of file diff --git a/servers/nextjs/utils/storeHelpers.ts b/servers/nextjs/utils/storeHelpers.ts index 3ed7d0f3..90c07dff 100644 --- a/servers/nextjs/utils/storeHelpers.ts +++ b/servers/nextjs/utils/storeHelpers.ts @@ -16,8 +16,30 @@ export const handleSaveLLMConfig = async (llmConfig: LLMConfig) => { export const hasValidLLMConfig = (llmConfig: LLMConfig) => { if (!llmConfig.LLM) return false; if (!llmConfig.IMAGE_PROVIDER) return false; - const OPENAI_API_KEY = llmConfig.OPENAI_API_KEY; - const GOOGLE_API_KEY = llmConfig.GOOGLE_API_KEY; + + const isOpenAIConfigValid = + llmConfig.OPENAI_MODEL !== "" && + llmConfig.OPENAI_MODEL !== null && + llmConfig.OPENAI_MODEL !== undefined && + llmConfig.OPENAI_API_KEY !== "" && + llmConfig.OPENAI_API_KEY !== null && + llmConfig.OPENAI_API_KEY !== undefined; + + const isGoogleConfigValid = + llmConfig.GOOGLE_MODEL !== "" && + llmConfig.GOOGLE_MODEL !== null && + llmConfig.GOOGLE_MODEL !== undefined && + llmConfig.GOOGLE_API_KEY !== "" && + llmConfig.GOOGLE_API_KEY !== null && + llmConfig.GOOGLE_API_KEY !== undefined; + + const isAnthropicConfigValid = + llmConfig.ANTHROPIC_MODEL !== "" && + llmConfig.ANTHROPIC_MODEL !== null && + llmConfig.ANTHROPIC_MODEL !== undefined && + llmConfig.ANTHROPIC_API_KEY !== "" && + llmConfig.ANTHROPIC_API_KEY !== null && + llmConfig.ANTHROPIC_API_KEY !== undefined; const isOllamaConfigValid = llmConfig.OLLAMA_MODEL !== "" && @@ -42,9 +64,9 @@ export const hasValidLLMConfig = (llmConfig: LLMConfig) => { case "pixabay": return llmConfig.PIXABAY_API_KEY && llmConfig.PIXABAY_API_KEY !== ""; case "dall-e-3": - return OPENAI_API_KEY && OPENAI_API_KEY !== ""; + return llmConfig.OPENAI_API_KEY && llmConfig.OPENAI_API_KEY !== ""; case "gemini_flash": - return GOOGLE_API_KEY && GOOGLE_API_KEY !== ""; + return llmConfig.GOOGLE_API_KEY && llmConfig.GOOGLE_API_KEY !== ""; default: return false; } @@ -52,18 +74,16 @@ export const hasValidLLMConfig = (llmConfig: LLMConfig) => { const isLLMConfigValid = llmConfig.LLM === "openai" - ? OPENAI_API_KEY !== "" && - OPENAI_API_KEY !== null && - OPENAI_API_KEY !== undefined + ? isOpenAIConfigValid : llmConfig.LLM === "google" - ? GOOGLE_API_KEY !== "" && - GOOGLE_API_KEY !== null && - GOOGLE_API_KEY !== undefined - : llmConfig.LLM === "ollama" - ? isOllamaConfigValid - : llmConfig.LLM === "custom" - ? isCustomConfigValid - : false; + ? isGoogleConfigValid + : llmConfig.LLM === "anthropic" + ? isAnthropicConfigValid + : llmConfig.LLM === "ollama" + ? isOllamaConfigValid + : llmConfig.LLM === "custom" + ? isCustomConfigValid + : false; return isLLMConfigValid && isImageConfigValid(); }; diff --git a/start.js b/start.js index 67056780..6eadf2ae 100644 --- a/start.js +++ b/start.js @@ -38,15 +38,20 @@ const setupUserConfigFromEnv = () => { const userConfig = { LLM: process.env.LLM || existingConfig.LLM, OPENAI_API_KEY: process.env.OPENAI_API_KEY || existingConfig.OPENAI_API_KEY, + OPENAI_MODEL: process.env.OPENAI_MODEL || existingConfig.OPENAI_MODEL, GOOGLE_API_KEY: process.env.GOOGLE_API_KEY || existingConfig.GOOGLE_API_KEY, + GOOGLE_MODEL: process.env.GOOGLE_MODEL || existingConfig.GOOGLE_MODEL, OLLAMA_URL: process.env.OLLAMA_URL || existingConfig.OLLAMA_URL, OLLAMA_MODEL: process.env.OLLAMA_MODEL || existingConfig.OLLAMA_MODEL, + ANTHROPIC_API_KEY: process.env.ANTHROPIC_API_KEY || existingConfig.ANTHROPIC_API_KEY, + ANTHROPIC_MODEL: process.env.ANTHROPIC_MODEL || existingConfig.ANTHROPIC_MODEL, CUSTOM_LLM_URL: process.env.CUSTOM_LLM_URL || existingConfig.CUSTOM_LLM_URL, CUSTOM_LLM_API_KEY: process.env.CUSTOM_LLM_API_KEY || existingConfig.CUSTOM_LLM_API_KEY, CUSTOM_MODEL: process.env.CUSTOM_MODEL || existingConfig.CUSTOM_MODEL, PEXELS_API_KEY: process.env.PEXELS_API_KEY || existingConfig.PEXELS_API_KEY, PIXABAY_API_KEY: process.env.PIXABAY_API_KEY || existingConfig.PIXABAY_API_KEY, IMAGE_PROVIDER: process.env.IMAGE_PROVIDER || existingConfig.IMAGE_PROVIDER, + EXTENDED_REASONING: process.env.EXTENDED_REASONING || existingConfig.EXTENDED_REASONING, USE_CUSTOM_URL: process.env.USE_CUSTOM_URL || existingConfig.USE_CUSTOM_URL, };