diff --git a/.dockerignore b/.dockerignore index 616ca25a..164cf77b 100644 --- a/.dockerignore +++ b/.dockerignore @@ -7,4 +7,5 @@ build .git .gitignore tmp -debug \ No newline at end of file +debug +.fastembed_cache \ No newline at end of file diff --git a/.gitignore b/.gitignore index 6d9d48ad..14712bdd 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,5 @@ node_modules out user_data tmp -debug \ No newline at end of file +debug +.fastembed_cache \ No newline at end of file diff --git a/servers/fastapi/api/main.py b/servers/fastapi/api/main.py index 56fba941..4908a063 100644 --- a/servers/fastapi/api/main.py +++ b/servers/fastapi/api/main.py @@ -8,16 +8,17 @@ from contextlib import asynccontextmanager from api.routers.presentation.router import presentation_router from api.services.database import sql_engine from api.utils.supported_ollama_models import SUPPORTED_OLLAMA_MODELS -from api.utils.utils import is_ollama_selected, update_env_with_user_config +from api.utils.utils import update_env_with_user_config +from api.utils.model_utils import is_ollama_selected can_change_keys = os.getenv("CAN_CHANGE_KEYS") != "false" # Ollama model download if not can_change_keys and is_ollama_selected(): - ollama_model = os.getenv("OLLAMA_MODEL") + ollama_model = os.getenv("MODEL") pexels_api_key = os.getenv("PEXELS_API_KEY") if not (ollama_model or pexels_api_key): - raise Exception("OLLAMA_MODEL and PEXELS_API_KEY must be provided") + raise Exception("MODEL and PEXELS_API_KEY must be provided") if ollama_model not in SUPPORTED_OLLAMA_MODELS: raise Exception(f"Model {ollama_model} is not supported") diff --git a/servers/fastapi/api/models.py b/servers/fastapi/api/models.py index 15d38ae4..96d7b8d0 100644 --- a/servers/fastapi/api/models.py +++ b/servers/fastapi/api/models.py @@ -1,3 +1,4 @@ +from enum import Enum import json from typing import Optional from pydantic import BaseModel @@ -62,7 +63,9 @@ class UserConfig(BaseModel): LLM: Optional[str] = None OPENAI_API_KEY: Optional[str] = None GOOGLE_API_KEY: Optional[str] = None - OLLAMA_MODEL: Optional[str] = None + MODEL: Optional[str] = None + LLM_PROVIDER_URL: Optional[str] = None + LLM_API_KEY: Optional[str] = None PEXELS_API_KEY: Optional[str] = None @@ -73,3 +76,10 @@ class OllamaModelMetadata(BaseModel): icon: str size: str supports_graph: bool + + +class SelectedLLMProvider(Enum): + OLLAMA = "ollama" + OPENAI = "openai" + GOOGLE = "google" + CUSTOM = "custom" diff --git a/servers/fastapi/api/routers/presentation/handlers/decompose_documents.py b/servers/fastapi/api/routers/presentation/handlers/decompose_documents.py index 24e5519b..937bca28 100644 --- a/servers/fastapi/api/routers/presentation/handlers/decompose_documents.py +++ b/servers/fastapi/api/routers/presentation/handlers/decompose_documents.py @@ -1,5 +1,3 @@ -import asyncio -from typing import List import uuid from api.models import LogMetadata from api.routers.presentation.models import ( @@ -37,7 +35,7 @@ class DecomposeDocumentsHandler: file_path = TEMP_FILE_SERVICE.create_temp_file_path( f"{str(uuid.uuid4())}.txt", self.temp_dir ) - parsed_doc = parsed_doc.page_content.replace("
", "\n") + parsed_doc = parsed_doc.replace("
", "\n") with open(file_path, "w") as text_file: text_file.write(parsed_doc) document_paths.append(file_path) diff --git a/servers/fastapi/api/routers/presentation/handlers/edit.py b/servers/fastapi/api/routers/presentation/handlers/edit.py index 5e5aa1d5..6cc1d514 100644 --- a/servers/fastapi/api/routers/presentation/handlers/edit.py +++ b/servers/fastapi/api/routers/presentation/handlers/edit.py @@ -14,8 +14,8 @@ from api.utils.supported_ollama_models import SUPPORTED_OLLAMA_MODELS from api.utils.utils import ( get_presentation_dir, get_presentation_images_dir, - is_ollama_selected, ) +from api.utils.model_utils import is_ollama_selected from image_processor.icons_vectorstore_utils import get_icons_vectorstore from image_processor.images_finder import generate_image from image_processor.icons_finder import get_icon @@ -69,7 +69,7 @@ class PresentationEditHandler: new_slide_type = new_slide_type.slide_type if is_ollama_selected(): - model = SUPPORTED_OLLAMA_MODELS[os.getenv("OLLAMA_MODEL")] + model = SUPPORTED_OLLAMA_MODELS[os.getenv("MODEL")] if not model.supports_graph: if new_slide_type == 5: new_slide_type = 1 diff --git a/servers/fastapi/api/routers/presentation/handlers/generate_data.py b/servers/fastapi/api/routers/presentation/handlers/generate_data.py index a6408592..8375d7eb 100644 --- a/servers/fastapi/api/routers/presentation/handlers/generate_data.py +++ b/servers/fastapi/api/routers/presentation/handlers/generate_data.py @@ -11,7 +11,7 @@ from api.routers.presentation.models import PresentationGenerateRequest from api.services.logging import LoggingService from api.sql_models import KeyValueSqlModel, PresentationSqlModel from api.services.database import get_sql_session -from api.utils.utils import is_ollama_selected +from api.utils.model_utils import is_ollama_selected from ppt_config_generator.models import PresentationMarkdownModel, SlideStructureModel from ppt_config_generator.structure_generator import generate_presentation_structure @@ -54,7 +54,7 @@ class PresentationGenerateDataHandler: ) ) supports_graph = True - model = SUPPORTED_OLLAMA_MODELS[os.getenv("OLLAMA_MODEL")] + model = SUPPORTED_OLLAMA_MODELS[os.getenv("MODEL")] supports_graph = model.supports_graph for each in presentation_structure.slides: diff --git a/servers/fastapi/api/routers/presentation/handlers/generate_outlines.py b/servers/fastapi/api/routers/presentation/handlers/generate_outlines.py index e7a30bf7..326fe9c3 100644 --- a/servers/fastapi/api/routers/presentation/handlers/generate_outlines.py +++ b/servers/fastapi/api/routers/presentation/handlers/generate_outlines.py @@ -1,5 +1,4 @@ import uuid -import re from api.models import LogMetadata from api.routers.presentation.models import GenerateOutlinesRequest diff --git a/servers/fastapi/api/routers/presentation/handlers/generate_presentation.py b/servers/fastapi/api/routers/presentation/handlers/generate_presentation.py index 692b09ab..3123452e 100644 --- a/servers/fastapi/api/routers/presentation/handlers/generate_presentation.py +++ b/servers/fastapi/api/routers/presentation/handlers/generate_presentation.py @@ -1,3 +1,4 @@ +import json from typing import List import uuid, aiohttp from fastapi import HTTPException @@ -17,7 +18,8 @@ from api.services.database import get_sql_session from api.services.instances import TEMP_FILE_SERVICE from api.services.logging import LoggingService from api.sql_models import PresentationSqlModel, SlideSqlModel -from api.utils.utils import get_presentation_dir, is_ollama_selected +from api.utils.utils import get_presentation_dir +from api.utils.model_utils import is_ollama_selected from document_processor.loader import DocumentsLoader from ppt_config_generator.document_summary_generator import generate_document_summary from ppt_config_generator.models import PresentationMarkdownModel @@ -25,14 +27,9 @@ from ppt_config_generator.ppt_outlines_generator import generate_ppt_content from ppt_generator.generator import generate_presentation from ppt_generator.models.llm_models import ( LLM_CONTENT_TYPE_MAPPING, - LLMPresentationModel, ) -from langchain_core.output_parsers import JsonOutputParser - from ppt_generator.models.slide_model import SlideModel -output_parser = JsonOutputParser(pydantic_object=LLMPresentationModel) - class GeneratePresentationHandler(FetchAssetsOnPresentationGenerationMixin): @@ -79,19 +76,17 @@ class GeneratePresentationHandler(FetchAssetsOnPresentationGenerationMixin): print("-" * 40) print("Generating Presentation") - presentation_text = ( - await generate_presentation( - PresentationMarkdownModel( - title=presentation_content.title, - slides=presentation_content.slides, - notes=presentation_content.notes, - ) + presentation_text = await generate_presentation( + PresentationMarkdownModel( + title=presentation_content.title, + slides=presentation_content.slides, + notes=presentation_content.notes, ) - ).content + ) print("-" * 40) print("Parsing Presentation") - presentation_json = output_parser.parse(presentation_text) + presentation_json = json.loads(presentation_text) slide_models: List[SlideModel] = [] for i, slide in enumerate(presentation_json["slides"]): diff --git a/servers/fastapi/api/routers/presentation/handlers/generate_stream.py b/servers/fastapi/api/routers/presentation/handlers/generate_stream.py index 60dee4a5..6a4bb663 100644 --- a/servers/fastapi/api/routers/presentation/handlers/generate_stream.py +++ b/servers/fastapi/api/routers/presentation/handlers/generate_stream.py @@ -17,13 +17,13 @@ from api.routers.presentation.models import ( from api.services.database import get_sql_session from api.services.logging import LoggingService from api.sql_models import KeyValueSqlModel, PresentationSqlModel, SlideSqlModel -from api.utils.utils import get_presentation_dir, is_ollama_selected +from api.utils.utils import get_presentation_dir +from api.utils.model_utils import is_ollama_selected from ppt_config_generator.models import ( PresentationMarkdownModel, PresentationStructureModel, ) from ppt_generator.generator import generate_presentation_stream -from ppt_generator.models.content_type_models import CONTENT_TYPE_MAPPING from ppt_generator.models.llm_models import ( LLM_CONTENT_TYPE_MAPPING, LLMPresentationModel, @@ -31,12 +31,9 @@ from ppt_generator.models.llm_models import ( ) from ppt_generator.models.slide_model import SlideModel from api.services.instances import TEMP_FILE_SERVICE -from langchain_core.output_parsers import JsonOutputParser from ppt_generator.slide_generator import get_slide_content_from_type_and_outline -output_parser = JsonOutputParser(pydantic_object=LLMPresentationModel) - class PresentationGenerateStreamHandler(FetchAssetsOnPresentationGenerationMixin): @@ -144,20 +141,21 @@ class PresentationGenerateStreamHandler(FetchAssetsOnPresentationGenerationMixin async def generate_presentation_openai_google(self): presentation_text = "" - async for chunk in generate_presentation_stream( + async for event in await generate_presentation_stream( PresentationMarkdownModel( title=self.title, slides=self.outlines, notes=self.presentation.notes, ) ): - presentation_text += chunk.content + chunk = event.choices[0].delta.content + presentation_text += chunk yield SSEResponse( event="response", - data=json.dumps({"type": "chunk", "chunk": chunk.content}), + data=json.dumps({"type": "chunk", "chunk": chunk}), ).to_string() - self.presentation_json = output_parser.parse(presentation_text) + self.presentation_json = json.loads(presentation_text) async def generate_presentation_ollama(self): presentation_structure = PresentationStructureModel( diff --git a/servers/fastapi/api/routers/presentation/handlers/list_ollama_pulled_models.py b/servers/fastapi/api/routers/presentation/handlers/list_ollama_pulled_models.py index 65fab26a..c96db431 100644 --- a/servers/fastapi/api/routers/presentation/handlers/list_ollama_pulled_models.py +++ b/servers/fastapi/api/routers/presentation/handlers/list_ollama_pulled_models.py @@ -1,7 +1,9 @@ -import ollama +import os +import aiohttp from api.models import LogMetadata from api.routers.presentation.models import OllamaModelStatusResponse from api.services.logging import LoggingService +from api.utils.model_utils import get_llm_api_key_or, get_llm_provider_url_or class ListPulledOllamaModelsHandler: @@ -12,20 +14,25 @@ class ListPulledOllamaModelsHandler: extra=log_metadata.model_dump(), ) - response = ollama.list() + async with aiohttp.ClientSession() as session: + async with session.get( + f"{get_llm_provider_url_or()}/api/tags", + headers={"Authorization": f"Bearer {get_llm_api_key_or()}"}, + ) as response: + response_data = await response.json() logging_service.logger.info( - logging_service.message(response.model_dump(mode="json")), + logging_service.message(response_data), extra=log_metadata.model_dump(), ) return [ OllamaModelStatusResponse( - name=model.model, - size=model.size, + name=model["model"], + size=model["size"], status="pulled", - downloaded=model.size, + downloaded=model["size"], done=True, ) - for model in response.models + for model in response_data["models"] ] diff --git a/servers/fastapi/api/routers/presentation/handlers/pull_ollama_model.py b/servers/fastapi/api/routers/presentation/handlers/pull_ollama_model.py index b942ddb8..f14dd239 100644 --- a/servers/fastapi/api/routers/presentation/handlers/pull_ollama_model.py +++ b/servers/fastapi/api/routers/presentation/handlers/pull_ollama_model.py @@ -1,5 +1,5 @@ -import asyncio import json +import aiohttp from fastapi import BackgroundTasks, HTTPException from api.models import LogMetadata from api.routers.presentation.handlers.list_supported_ollama_models import ( @@ -8,7 +8,7 @@ from api.routers.presentation.handlers.list_supported_ollama_models import ( from api.routers.presentation.models import OllamaModelStatusResponse from api.services.instances import REDIS_SERVICE from api.services.logging import LoggingService -import ollama +from api.utils.model_utils import get_llm_api_key_or, get_llm_provider_url_or class PullOllamaModelHandler: @@ -33,19 +33,34 @@ class PullOllamaModelHandler: detail=f"Model {self.name} is not supported", ) - pulled_models = ollama.list().models - filtered_models = list( - filter(lambda model: model.model == self.name, pulled_models) - ) + # Check if model is already pulled using LLM_PROVIDER_URL/api/tags + try: + async with aiohttp.ClientSession() as session: + async with session.get( + f"{get_llm_provider_url_or()}/api/tags", + headers={"Authorization": f"Bearer {get_llm_api_key_or()}"}, + ) as response: + if response.status == 200: + pulled_models = await response.json() + filtered_models = [ + model + for model in pulled_models["models"] + if model["model"] == self.name + ] - # If the model is already pulled, return the model - if filtered_models: - return OllamaModelStatusResponse( - name=self.name, - size=filtered_models[0].size, - status="pulled", - downloaded=filtered_models[0].size, - done=True, + # If the model is already pulled, return the model + if filtered_models: + return OllamaModelStatusResponse( + name=self.name, + size=filtered_models[0]["size"], + status="pulled", + downloaded=filtered_models[0]["size"], + done=True, + ) + except Exception as e: + logging_service.logger.warning( + f"Failed to check pulled models: {e}", + extra=log_metadata.model_dump(), ) saved_model_status = REDIS_SERVICE.get(f"ollama_models/{self.name}") @@ -64,33 +79,64 @@ class PullOllamaModelHandler: ) async def pull_model_in_background(self): - await asyncio.to_thread(self.pull_model) + await self.pull_model() - def pull_model(self): + async def pull_model(self): saved_model_status = OllamaModelStatusResponse( name=self.name, status="pulling", done=False, ) log_event_count = 0 - for event in ollama.pull(self.name, stream=True): - log_event_count += 1 - if log_event_count != 1 and log_event_count % 20 != 0: - continue - if event.completed: - saved_model_status.downloaded = event.completed + try: + async with aiohttp.ClientSession() as session: + async with session.post( + f"{get_llm_provider_url_or()}/api/pull", + json={"model": self.name}, + headers={"Authorization": f"Bearer {get_llm_api_key_or()}"}, + ) as response: + if response.status != 200: + raise HTTPException( + status_code=response.status, + detail=f"Failed to pull model: {await response.text()}", + ) - if not saved_model_status.size and event.total: - saved_model_status.size = event.total + async for line in response.content: + if not line.strip(): + continue - if event.status: - saved_model_status.status = event.status + try: + event = json.loads(line.decode("utf-8")) + except json.JSONDecodeError: + continue + log_event_count += 1 + if log_event_count != 1 and log_event_count % 20 != 0: + continue + + if "completed" in event: + saved_model_status.downloaded = event["completed"] + + if not saved_model_status.size and "total" in event: + saved_model_status.size = event["total"] + + if "status" in event: + saved_model_status.status = event["status"] + + REDIS_SERVICE.set( + f"ollama_models/{self.name}", + json.dumps(saved_model_status.model_dump(mode="json")), + ) + + except Exception as e: + saved_model_status.status = "error" + saved_model_status.done = True REDIS_SERVICE.set( f"ollama_models/{self.name}", json.dumps(saved_model_status.model_dump(mode="json")), ) + raise e saved_model_status.done = True saved_model_status.status = "pulled" diff --git a/servers/fastapi/api/routers/presentation/router.py b/servers/fastapi/api/routers/presentation/router.py index eb7d4806..be48b529 100644 --- a/servers/fastapi/api/routers/presentation/router.py +++ b/servers/fastapi/api/routers/presentation/router.py @@ -82,6 +82,9 @@ from api.routers.presentation.models import ( ) from api.sql_models import PresentationSqlModel from api.utils.utils import handle_errors +from image_processor.images_finder import ( + generate_image_google, +) from ppt_generator.models.slide_model import SlideModel route_prefix = "/api/v1/ppt" diff --git a/servers/fastapi/api/utils/model_utils.py b/servers/fastapi/api/utils/model_utils.py new file mode 100644 index 00000000..b2ee037a --- /dev/null +++ b/servers/fastapi/api/utils/model_utils.py @@ -0,0 +1,84 @@ +import os + +from openai import AsyncOpenAI + +from api.models import SelectedLLMProvider + + +def is_ollama_selected() -> bool: + return get_selected_llm_provider() == SelectedLLMProvider.OLLAMA + + +def get_llm_provider_url_or(): + return os.getenv("LLM_PROVIDER_URL") or "http://localhost:11434" + + +def get_llm_api_key_or(): + return os.getenv("LLM_API_KEY") or "ollama" + + +def get_selected_llm_provider() -> SelectedLLMProvider: + return SelectedLLMProvider(os.getenv("LLM")) + + +def get_model_base_url(): + selected_llm = get_selected_llm_provider() + + if selected_llm == SelectedLLMProvider.OPENAI: + return "https://api.openai.com/v1" + elif selected_llm == SelectedLLMProvider.GOOGLE: + return "https://generativelanguage.googleapis.com/v1beta/openai" + elif selected_llm == SelectedLLMProvider.OLLAMA: + return os.path.join(get_llm_provider_url_or(), "v1") + else: + raise ValueError(f"Invalid LLM provider") + + +def get_llm_api_key(): + selected_llm = get_selected_llm_provider() + if selected_llm == SelectedLLMProvider.OPENAI: + return os.getenv("OPENAI_API_KEY") + elif selected_llm == SelectedLLMProvider.GOOGLE: + return os.getenv("GOOGLE_API_KEY") + elif selected_llm == SelectedLLMProvider.OLLAMA: + return get_llm_api_key_or() + else: + raise ValueError(f"Invalid LLM API key") + + +def get_llm_client(): + client = AsyncOpenAI( + base_url=get_model_base_url(), + api_key=get_llm_api_key(), + ) + return client + + +def get_large_model(): + selected_llm = get_selected_llm_provider() + if selected_llm == SelectedLLMProvider.OPENAI: + return "gpt-4.1" + elif selected_llm == SelectedLLMProvider.GOOGLE: + return "gemini-2.0-flash" + else: + return os.getenv("MODEL") + + +def get_small_model(): + selected_llm = get_selected_llm_provider() + if selected_llm == SelectedLLMProvider.OPENAI: + return "gpt-4.1-mini" + elif selected_llm == SelectedLLMProvider.GOOGLE: + return "gemini-2.0-flash" + else: + return os.getenv("MODEL") + + +def get_nano_model(): + selected_llm = get_selected_llm_provider() + if selected_llm == SelectedLLMProvider.OPENAI: + return "gpt-4.1-nano" + elif selected_llm == SelectedLLMProvider.GOOGLE: + return "gemini-2.0-flash" + else: + return os.getenv("MODEL") diff --git a/servers/fastapi/api/utils/utils.py b/servers/fastapi/api/utils/utils.py index 90882961..e297cc96 100644 --- a/servers/fastapi/api/utils/utils.py +++ b/servers/fastapi/api/utils/utils.py @@ -9,48 +9,11 @@ from typing import List, Optional import aiohttp from fastapi import HTTPException, UploadFile from fastapi.responses import StreamingResponse -from langchain_google_genai import ChatGoogleGenerativeAI -from langchain_ollama import ChatOllama -from langchain_openai import ChatOpenAI from api.models import LogMetadata, UserConfig from api.services.logging import LoggingService -def is_ollama_selected() -> bool: - return os.getenv("LLM") == "ollama" - - -def get_large_model(): - selected_llm = os.getenv("LLM") - if selected_llm == "openai": - return ChatOpenAI(model="gpt-4.1") - elif selected_llm == "google": - return ChatGoogleGenerativeAI(model="gemini-2.0-flash") - else: - return ChatOllama(model=os.getenv("OLLAMA_MODEL"), temperature=0.8) - - -def get_small_model(): - selected_llm = os.getenv("LLM") - if selected_llm == "openai": - return ChatOpenAI(model="gpt-4.1-mini") - elif selected_llm == "google": - return ChatGoogleGenerativeAI(model="gemini-2.0-flash") - else: - return ChatOllama(model=os.getenv("OLLAMA_MODEL"), temperature=0.8) - - -def get_nano_model(): - selected_llm = os.getenv("LLM") - if selected_llm == "openai": - return ChatOpenAI(model="gpt-4.1-nano") - elif selected_llm == "google": - return ChatGoogleGenerativeAI(model="gemini-2.0-flash") - else: - return ChatOllama(model=os.getenv("OLLAMA_MODEL"), temperature=0.8) - - def get_presentation_dir(presentation_id: str) -> str: presentation_dir = os.path.join(os.getenv("APP_DATA_DIRECTORY"), presentation_id) os.makedirs(presentation_dir, exist_ok=True) @@ -81,8 +44,11 @@ def get_user_config(): LLM=existing_config.LLM or os.getenv("LLM"), OPENAI_API_KEY=existing_config.OPENAI_API_KEY or os.getenv("OPENAI_API_KEY"), GOOGLE_API_KEY=existing_config.GOOGLE_API_KEY or os.getenv("GOOGLE_API_KEY"), - OLLAMA_MODEL=existing_config.OLLAMA_MODEL or os.getenv("OLLAMA_MODEL"), + MODEL=existing_config.MODEL or os.getenv("MODEL"), PEXELS_API_KEY=existing_config.PEXELS_API_KEY or os.getenv("PEXELS_API_KEY"), + LLM_PROVIDER_URL=existing_config.LLM_PROVIDER_URL + or os.getenv("LLM_PROVIDER_URL"), + LLM_API_KEY=existing_config.LLM_API_KEY or os.getenv("LLM_API_KEY"), ) @@ -94,10 +60,14 @@ def update_env_with_user_config(): os.environ["OPENAI_API_KEY"] = user_config.OPENAI_API_KEY if user_config.GOOGLE_API_KEY: os.environ["GOOGLE_API_KEY"] = user_config.GOOGLE_API_KEY - if user_config.OLLAMA_MODEL: - os.environ["OLLAMA_MODEL"] = user_config.OLLAMA_MODEL + if user_config.MODEL: + os.environ["MODEL"] = user_config.MODEL if user_config.PEXELS_API_KEY: os.environ["PEXELS_API_KEY"] = user_config.PEXELS_API_KEY + if user_config.LLM_PROVIDER_URL: + os.environ["LLM_PROVIDER_URL"] = user_config.LLM_PROVIDER_URL + if user_config.LLM_API_KEY: + os.environ["LLM_API_KEY"] = user_config.LLM_API_KEY def get_resource(relative_path): diff --git a/servers/fastapi/assets/icons_vectorstore.json b/servers/fastapi/assets/icons_vectorstore.json index e15d6aa6..5330e92d 100644 --- a/servers/fastapi/assets/icons_vectorstore.json +++ b/servers/fastapi/assets/icons_vectorstore.json @@ -1,591194 +1,583634 @@ { - "acorn-bold": { - "id": "acorn-bold", - "vector": [ - -0.015130310319364071, - -0.007156228180974722, - -0.08790618181228638, - -0.007908126339316368, - 0.011318053118884563, - -0.05215802043676376, - -0.03764769434928894, - -0.026659421622753143, - 0.02491818368434906, - -0.10479091852903366, - -0.015064354985952377, - -0.01371885184198618, - 0.008857892826199532, - -0.019694466143846512, - 0.010467220097780228, - 0.05561411380767822, - -0.0037825775798410177, - 0.024904992431402206, - -0.002334842924028635, - 0.06358160078525543, - 0.016396665945649147, - -0.03384862840175629, - -0.012195267714560032, - -0.08173269778490067, - 0.030550828203558922, - -0.016686871647834778, - -0.014681809581816196, - -0.0017198032001033425, - -0.11048952490091324, - -0.10869551450014114, - 0.011581876315176487, - -0.0024865418672561646, - 0.04382117837667465, - -0.01480053085833788, - 0.051076341420412064, - -0.03448180481791496, - -0.048411715775728226, - -0.0006410100613720715, - -0.05062783882021904, - 0.001930862432345748, - 0.07550644874572754, - -0.005609559360891581, - -0.013824380934238434, - 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+import pdfplumber from docx import Document as DocxDocument from image_processor.utils import get_page_images_from_pdf_async @@ -30,23 +29,13 @@ class DocumentsLoader: def __init__(self, documents: List[str]): self._document_paths = documents - self._documents: List[Document] = [] - self._splitted_documents: List[Document] = [] + self._documents: List[str] = [] self._images: List[List[str]] = [] - self._markdown_splitter = MarkdownTextSplitter(chunk_size=500, chunk_overlap=50) - self._text_splitter = CharacterTextSplitter( - separator="/n", chunk_size=500, chunk_overlap=50 - ) - @property def documents(self): return self._documents - @property - def splitted_documents(self): - return self._splitted_documents - @property def images(self): return self._images @@ -54,90 +43,69 @@ class DocumentsLoader: async def load_documents( self, temp_dir: str, - split_documents: bool = False, - load_markdown: bool = True, + load_text: bool = True, load_images: bool = False, ): - documents: List[Document] = [] + documents: List[str] = [] images: List[str] = [] - splitted_documents: List[Document] = [] for file_path in self._document_paths: if not os.path.exists(file_path): raise HTTPException( status_code=404, detail=f"File {file_path} not found" ) - docs = [] + document = "" imgs = [] mime_type = mimetypes.guess_type(file_path)[0] if mime_type in PDF_MIME_TYPES: - docs, imgs = await self.load_pdf( - file_path, load_markdown, load_images, temp_dir + document, imgs = await self.load_pdf( + file_path, load_text, load_images, temp_dir ) elif mime_type in TEXT_MIME_TYPES: - docs = self.load_text(file_path) + document = await self.load_text(file_path) elif mime_type in POWERPOINT_TYPES: - docs = self.load_powerpoint(file_path) + document = self.load_powerpoint(file_path) elif mime_type in WORD_TYPES: - docs = self.load_msword(file_path) + document = self.load_msword(file_path) - documents.extend(docs) + documents.append(document) images.append(imgs) - if split_documents: - splitted_documents.extend(self.split_documents(docs, mime_type)) - self._documents = documents - self._splitted_documents = splitted_documents self._images = images - def split_documents(self, documents: List[Document], mime_type): - return self._text_splitter.split_documents(documents) - - def clip_longer_documents(self, documents: List[Document], clip_after: int = 1200): - for document in documents: - document.page_content = document.page_content[:clip_after] - return documents - async def load_pdf( self, file_path: str, - load_markdown: bool, + load_text: bool, load_images: bool, temp_dir: str, - ) -> Tuple[List[Document], List[str]]: + ) -> Tuple[str, List[str]]: image_paths = [] - documents: List[Document] = [] + document: str = "" - if load_markdown: - loader = PDFPlumberLoader(file_path) - docs = loader.load() - pdf_document = Document(page_content="") - pdf_document.metadata = docs[0].metadata - for doc in docs: - pdf_document.page_content += doc.page_content - documents.append(pdf_document) + if load_text: + with pdfplumber.open(file_path) as pdf: + for page in pdf.pages: + document += await asyncio.to_thread(page.extract_text) if load_images: image_paths = await get_page_images_from_pdf_async(file_path, temp_dir) - return documents, image_paths + return document, image_paths - async def decompose_pdf_to_markdown(self, document_path: str) -> str: - raise Exception("Not Implemented") + async def load_text(self, file_path: str) -> str: + with open(file_path, "r") as file: + return await asyncio.to_thread(file.read) - def load_text(self, file_path: str) -> List[Document]: - loader = TextLoader(file_path) - return loader.load() - - def load_msword(self, file_path: str) -> List[Document]: + def load_msword(self, file_path: str) -> str: document = DocxDocument(file_path) text = "\n".join([paragraph.text for paragraph in document.paragraphs]) - return [Document(page_content=text)] + return text - def load_powerpoint(self, file_path: str) -> List[Document]: + def load_powerpoint(self, file_path: str) -> str: presentation = Presentation(file_path) extracted_text = "" @@ -149,4 +117,4 @@ class DocumentsLoader: extracted_text += f"{paragraph.text}\n" extracted_text += "\n" extracted_text += "\n\n" - return [Document(page_content=extracted_text)] + return extracted_text diff --git a/servers/fastapi/graph_processor/models.py b/servers/fastapi/graph_processor/models.py deleted file mode 100644 index 6ced32b1..00000000 --- a/servers/fastapi/graph_processor/models.py +++ /dev/null @@ -1,132 +0,0 @@ -from enum import Enum -from typing import List, Optional -from pydantic import BaseModel, Field, model_validator - -from graph_processor.utils import clip_text - - -class PointModel(BaseModel): - x: float - y: float - - def to_list(self) -> List[float]: - return [self.x, self.y] - - -class PointWithRadius(PointModel): - radius: Optional[float] = None - - -class BarSeriesModel(BaseModel): - name: str - data: List[float] = Field( - description="Only numbers should be given out in data. Don't include text/string in data." - ) - - def get_name(self) -> str: - return clip_text(self.name) - - -class ScatterSeriesModel(BaseModel): - name: str - points: List[PointModel] - - def get_name(self) -> str: - return clip_text(self.name) - - -class BubbleSeriesModel(BaseModel): - name: str - points: List[PointWithRadius] - - def get_name(self) -> str: - return clip_text(self.name) - - -class LineSeriesModel(BaseModel): - name: str - data: List[float] = Field( - description="Only numbers should be given out in data. Don't include text/string in data." - ) - - def get_name(self) -> str: - return clip_text(self.name) - - -class PieChartSeriesModel(BaseModel): - data: List[float] - - -class BarGraphDataModel(BaseModel): - categories: List[str] - series: List[BarSeriesModel] = Field( - description="There should be no more than 3 series" - ) - - def get_categories(self) -> List[str]: - return [clip_text(category) for category in self.categories] - - -class ScatterChartDataModel(BaseModel): - series: List[ScatterSeriesModel] - - -class BubbleChartDataModel(BaseModel): - series: List[BubbleSeriesModel] - - -class LineChartDataModel(BaseModel): - categories: List[str] - series: List[LineSeriesModel] = Field( - description="There should be no more than 3 series" - ) - - def get_categories(self) -> List[str]: - return [clip_text(category) for category in self.categories] - - -class PieChartDataModel(BaseModel): - categories: List[str] - series: List[PieChartSeriesModel] = Field( - description="One series model with list of data", - min_length=1, - ) - - @model_validator(mode="after") - def limit_series(self): - self.series = self.series[:1] - return self - - def get_categories(self) -> List[str]: - return [clip_text(category) for category in self.categories] - - -# class TableDataModel(BaseModel): -# categories: List[str] -# series: List[BarSeriesModel] - -# def get_categories(self) -> List[str]: -# return [clip_text(category) for category in self.categories] - - -class GraphTypeEnum(Enum): - pie = "pie" - bar = "bar" - line = "line" - - -class GraphModel(BaseModel): - style: Optional[dict] = {} - name: str - type: GraphTypeEnum - unit: Optional[str] = Field( - description="Unit of the data in the graph. Example: %, kg, million USD, tonnes, etc." - ) - data: PieChartDataModel | LineChartDataModel | BarGraphDataModel - - -GRAPH_TYPE_MAPPING = { - GraphTypeEnum.pie: PieChartDataModel, - GraphTypeEnum.bar: BarGraphDataModel, - GraphTypeEnum.line: LineChartDataModel, -} diff --git a/servers/fastapi/graph_processor/utils.py b/servers/fastapi/graph_processor/utils.py deleted file mode 100644 index 502145f0..00000000 --- a/servers/fastapi/graph_processor/utils.py +++ /dev/null @@ -1,3 +0,0 @@ -def clip_text(text: str, max_length: int = 6) -> str: - # return text[:max_length] + ".." if len(text) > max_length else text - return text diff --git a/servers/fastapi/image_processor/icons_finder.py b/servers/fastapi/image_processor/icons_finder.py index c08d8fdb..55b1df23 100644 --- a/servers/fastapi/image_processor/icons_finder.py +++ b/servers/fastapi/image_processor/icons_finder.py @@ -5,17 +5,17 @@ from ppt_generator.models.query_and_prompt_models import ( IconCategoryEnum, IconQueryCollectionWithData, ) -from langchain_core.vectorstores import InMemoryVectorStore +from fastembed_vectorstore import FastembedVectorstore async def get_icon( - vector_store: InMemoryVectorStore, + vector_store: FastembedVectorstore, input: IconQueryCollectionWithData, ) -> str: try: query = input.icon_query - results = vector_store.similarity_search(query=query, k=1) - icon_name = results[0].page_content + results = vector_store.search(query, 1) + icon_name = results[0][0].split("||")[0] return get_resource(f"assets/icons/bold/{icon_name}.png") except Exception as e: print("Error finding icon: ", e) @@ -23,7 +23,7 @@ async def get_icon( async def get_icons( - vector_store: InMemoryVectorStore, + vector_store: FastembedVectorstore, query: str, page: int, limit: int, @@ -31,7 +31,7 @@ async def get_icons( temp_dir: str, ) -> List[str]: - results = await vector_store.asimilarity_search(query=query, k=limit) - icon_names = [result.page_content for result in results] + results = vector_store.search(query, limit) + icon_names = [result[0].split("||")[0] for result in results] return [get_resource(f"assets/icons/bold/{each}.png") for each in icon_names] diff --git a/servers/fastapi/image_processor/icons_vectorstore_utils.py b/servers/fastapi/image_processor/icons_vectorstore_utils.py index 6315d752..ee7e0786 100644 --- a/servers/fastapi/image_processor/icons_vectorstore_utils.py +++ b/servers/fastapi/image_processor/icons_vectorstore_utils.py @@ -1,37 +1,26 @@ import json import os -from langchain_core.vectorstores import InMemoryVectorStore -from langchain_core.documents import Document from api.utils.utils import get_resource -from langchain_community.embeddings.fastembed import FastEmbedEmbeddings - -# Pyinstaller -import fastembed +from fastembed_vectorstore import FastembedVectorstore, FastembedEmbeddingModel def get_icons_vectorstore(): vector_store_path = get_resource("assets/icons_vectorstore.json") - - embeddings = FastEmbedEmbeddings() + embedding_model = FastembedEmbeddingModel.BGESmallENV15 if os.path.exists(vector_store_path): - vector_store = InMemoryVectorStore.load(vector_store_path, embeddings) - return vector_store - - vector_store = InMemoryVectorStore(embeddings) - - vector_store.dump(vector_store_path) + return FastembedVectorstore.load(embedding_model, vector_store_path) + vector_store = FastembedVectorstore(embedding_model) with open(get_resource("assets/icons.json"), "r") as f: icons = json.load(f) - - icon_names = [icon["name"] for icon in icons["icons"]] documents = [] - for each in icon_names: - if each.split("-")[-1] == "bold": - documents.append(Document(id=each, page_content=each)) + for each in icons["icons"]: + if each["name"].split("-")[-1] == "bold": + documents.append(f"{each['name']}||{each['tags']}") + + vector_store.embed_documents(documents) + vector_store.save(vector_store_path) - vector_store.add_documents(documents) - vector_store.dump(vector_store_path) return vector_store diff --git a/servers/fastapi/image_processor/images_finder.py b/servers/fastapi/image_processor/images_finder.py index 9c7dfd12..d915f914 100644 --- a/servers/fastapi/image_processor/images_finder.py +++ b/servers/fastapi/image_processor/images_finder.py @@ -3,13 +3,14 @@ import base64 import os import uuid import aiohttp -from langchain_google_genai import ChatGoogleGenerativeAI -from openai import OpenAI +from google import genai +from google.genai.types import GenerateContentConfig from ppt_generator.models.query_and_prompt_models import ( ImagePromptWithThemeAndAspectRatio, ) -from api.utils.utils import download_file, get_resource, is_ollama_selected +from api.utils.utils import download_file, get_resource +from api.utils.model_utils import get_llm_client, is_ollama_selected async def generate_image( @@ -46,7 +47,7 @@ async def generate_image( async def generate_image_openai(prompt: str, output_directory: str) -> str: - client = OpenAI() + client = get_llm_client() result = await asyncio.to_thread( client.images.generate, model="dall-e-3", @@ -66,20 +67,20 @@ async def generate_image_openai(prompt: str, output_directory: str) -> str: async def generate_image_google(prompt: str, output_directory: str) -> str: - response = await ChatGoogleGenerativeAI( - model="gemini-2.0-flash-preview-image-generation" - ).ainvoke([prompt], generation_config={"response_modalities": ["TEXT", "IMAGE"]}) - - image_block = next( - block - for block in response.content - if isinstance(block, dict) and block.get("image_url") + client = genai.Client() + response = client.models.generate_content( + model="gemini-2.0-flash-preview-image-generation", + contents=[prompt], + config=GenerateContentConfig(response_modalities=["TEXT", "IMAGE"]), ) - base64_image = image_block["image_url"].get("url").split(",")[-1] - image_path = os.path.join(output_directory, f"{str(uuid.uuid4())}.jpg") - with open(image_path, "wb") as f: - f.write(base64.b64decode(base64_image)) + for part in response.candidates[0].content.parts: + if part.text is not None: + print(part.text) + elif part.inline_data is not None: + image_path = os.path.join(output_directory, f"{str(uuid.uuid4())}.jpg") + with open(image_path, "wb") as f: + f.write(part.inline_data.data) return image_path diff --git a/servers/fastapi/ppt_config_generator/document_summary_generator.py b/servers/fastapi/ppt_config_generator/document_summary_generator.py index 8e0c7943..6d43f791 100644 --- a/servers/fastapi/ppt_config_generator/document_summary_generator.py +++ b/servers/fastapi/ppt_config_generator/document_summary_generator.py @@ -1,14 +1,8 @@ import asyncio -import os from typing import List -from langchain_core.documents import Document -from langchain_google_genai import ChatGoogleGenerativeAI -from langchain_openai import ChatOpenAI -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.messages import BaseMessage -from langchain_text_splitters import CharacterTextSplitter +from openai.types.chat.chat_completion import ChatCompletion -from api.utils.utils import get_nano_model +from api.utils.model_utils import get_llm_client, get_nano_model sysmte_prompt = """ Generate a blog-style summary of the provided document in **more than 2000 words**. @@ -26,26 +20,25 @@ Maintain as much information as possible. - If **slides structure is mentioned** in document, structure the summary in the same way. """ -prompt_template = ChatPromptTemplate.from_messages( - [ - ("system", sysmte_prompt), - ("user", "{text}"), - ] -) - -async def generate_document_summary(documents: List[Document]): +async def generate_document_summary(documents: List[str]): + client = get_llm_client() model = get_nano_model() - text_splitter = CharacterTextSplitter(chunk_size=200000, chunk_overlap=0) - chain = prompt_template | model coroutines = [] for document in documents: - text = document.page_content - truncated_text = text_splitter.split_text(text)[0] - coroutine = chain.ainvoke({"text": truncated_text}) + truncated_text = document[:200000] + coroutine = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": sysmte_prompt}, + {"role": "user", "content": truncated_text}, + ], + ) coroutines.append(coroutine) - completions: List[BaseMessage] = await asyncio.gather(*coroutines) - combined = "\n\n\n\n".join([completion.content for completion in completions]) + completions: List[ChatCompletion] = await asyncio.gather(*coroutines) + combined = "\n\n\n\n".join( + [completion.choices[0].message.content for completion in completions] + ) return combined diff --git a/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py b/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py index 246768af..06b5c1ce 100644 --- a/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py +++ b/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py @@ -1,32 +1,17 @@ from typing import Optional -from langchain_core.prompts import ChatPromptTemplate -from api.utils.utils import get_large_model +from api.utils.model_utils import get_large_model, get_llm_client from api.utils.variable_length_models import ( get_presentation_markdown_model_with_n_slides, ) from ppt_config_generator.models import PresentationMarkdownModel -from ppt_generator.fix_validation_errors import get_validated_response -user_prompt_text = { - "type": "text", - "text": """ - **Input:** - - Prompt: {prompt} - - Output Language: {language} - - Number of Slides: {n_slides} - - Additional Information: {content} - """, -} - - -def get_prompt_template(): - return ChatPromptTemplate.from_messages( - [ - ( - "system", - """ +def get_prompt_template(prompt: str, n_slides: int, language: str, content: str): + return [ + { + "role": "system", + "content": """ Create a presentation based on the provided prompt, number of slides, output language, and additional informational details. Format the output in the specified JSON schema with structured markdown content. @@ -49,13 +34,18 @@ def get_prompt_template(): - Slide **title** should not be in markdown format. - There must be exact **Number of Slides** as specified. """, - ), - ( - "user", - [user_prompt_text], - ), - ], - ) + }, + { + "role": "user", + "content": f""" + **Input:** + - Prompt: {prompt} + - Output Language: {language} + - Number of Slides: {n_slides} + - Additional Information: {content} + """, + }, + ] async def generate_ppt_content( @@ -64,21 +54,14 @@ async def generate_ppt_content( language: Optional[str] = None, content: Optional[str] = None, ) -> PresentationMarkdownModel: + client = get_llm_client() model = get_large_model() response_model = get_presentation_markdown_model_with_n_slides(n_slides) - chain = get_prompt_template() | model.with_structured_output( - response_model.model_json_schema() - ) - - return await get_validated_response( - chain, - { - "prompt": prompt, - "n_slides": n_slides, - "language": language or "English", - "content": content, - }, - response_model, - PresentationMarkdownModel, + response = await client.beta.chat.completions.parse( + model=model, + temperature=0.2, + messages=get_prompt_template(prompt, n_slides, language, content), + response_format=response_model, ) + return response.choices[0].message.parsed diff --git a/servers/fastapi/ppt_config_generator/structure_generator.py b/servers/fastapi/ppt_config_generator/structure_generator.py index 28f3c8f6..9e0f4972 100644 --- a/servers/fastapi/ppt_config_generator/structure_generator.py +++ b/servers/fastapi/ppt_config_generator/structure_generator.py @@ -1,6 +1,4 @@ -from langchain_core.prompts import ChatPromptTemplate - -from api.utils.utils import get_small_model +from api.utils.model_utils import get_llm_client, get_small_model from api.utils.variable_length_models import ( get_presentation_structure_model_with_n_slides, ) @@ -8,13 +6,13 @@ from ppt_config_generator.models import ( PresentationStructureModel, PresentationMarkdownModel, ) -from ppt_generator.fix_validation_errors import get_validated_response -prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - """ + +def get_prompt(n_slides: int, data: str): + return [ + { + "role": "system", + "content": f""" You're a professional presentation designer with years of experience in designing clear and engaging presentations. # Slide Types @@ -44,33 +42,32 @@ prompt = ChatPromptTemplate.from_messages( **Go through notes and steps and make sure they are all followed. Rule breaks are strictly not allowed.** """, - ), - ( - "human", - """ - {data} + }, + { + "role": "user", + "content": f""" + {data} """, - ), + }, ] -) async def generate_presentation_structure( presentation_outline: PresentationMarkdownModel, ) -> PresentationStructureModel: + client = get_llm_client() model = get_small_model() response_model = get_presentation_structure_model_with_n_slides( len(presentation_outline.slides) ) - chain = prompt | model.with_structured_output(response_model.model_json_schema()) - return await get_validated_response( - chain, - { - "n_slides": len(presentation_outline.slides), - "data": presentation_outline.to_string(), - }, - response_model, - PresentationStructureModel, + response = await client.beta.chat.completions.parse( + model=model, + temperature=0.2, + messages=get_prompt( + len(presentation_outline.slides), presentation_outline.to_string() + ), + response_format=response_model, ) + return response.choices[0].message.parsed diff --git a/servers/fastapi/ppt_generator/fix_validation_errors.py b/servers/fastapi/ppt_generator/fix_validation_errors.py index 73a4c15e..02a60ff0 100644 --- a/servers/fastapi/ppt_generator/fix_validation_errors.py +++ b/servers/fastapi/ppt_generator/fix_validation_errors.py @@ -1,12 +1,11 @@ import os from typing import Optional from fastapi import HTTPException -from langchain_google_genai import ChatGoogleGenerativeAI -from langchain_openai import ChatOpenAI +from langchain_ollama import ChatOllama from langchain_core.prompts import ChatPromptTemplate from pydantic import BaseModel, ValidationError -from api.utils.utils import get_large_model +from api.utils.model_utils import get_large_model def get_prompt_template(): @@ -41,7 +40,7 @@ def get_prompt_template(): async def fix_validation_errors(response_model: BaseModel, response, errors): - model = get_large_model() + model = ChatOllama(model=get_large_model(), temperature=0.8) chain = get_prompt_template() | model.with_structured_output( response_model.model_json_schema() diff --git a/servers/fastapi/ppt_generator/generator.py b/servers/fastapi/ppt_generator/generator.py index 76cd22f1..88d7b677 100644 --- a/servers/fastapi/ppt_generator/generator.py +++ b/servers/fastapi/ppt_generator/generator.py @@ -1,11 +1,11 @@ -from typing import AsyncIterator - -from langchain_core.messages import ( - HumanMessage, - AIMessageChunk, - AIMessage, +from openai import AsyncStream +from openai.types.chat.chat_completion_chunk import ChatCompletionChunk +from api.models import SelectedLLMProvider +from api.utils.model_utils import ( + get_large_model, + get_llm_client, + get_selected_llm_provider, ) -from api.utils.utils import get_large_model from ppt_config_generator.models import PresentationMarkdownModel from ppt_generator.models.llm_models_with_validations import ( LLMPresentationModelWithValidation, @@ -70,42 +70,87 @@ CREATE_PRESENTATION_PROMPT = """ **Go through notes and steps and make sure they are all followed. Rule breaks are strictly not allowed.** """ -schema = LLMPresentationModelWithValidation.model_json_schema() - -system_prompt = f""" +system_prompt_with_schema = f""" {CREATE_PRESENTATION_PROMPT} -Follow this schema while giving out response: {schema}. - -Make description short and obey the character limits. Output should be in JSON format. Give out only JSON, nothing else. -""" - -ollama_system_prompt = f""" -{CREATE_PRESENTATION_PROMPT} +Follow this schema while giving out response: {LLMPresentationModelWithValidation.model_json_schema()}. Make description short and obey the character limits. Output should be in JSON format. Give out only JSON, nothing else. """ -def get_model_and_messages( +def get_system_prompt(): + is_google_selected = get_selected_llm_provider() == SelectedLLMProvider.GOOGLE + return ( + system_prompt_with_schema if is_google_selected else CREATE_PRESENTATION_PROMPT + ) + + +def get_response_format(): + is_google_selected = get_selected_llm_provider() == SelectedLLMProvider.GOOGLE + return ( + { + "type": "json_object", + } + if is_google_selected + else { + "type": "json_schema", + "json_schema": { + "name": "LLMPresentationModel", + "schema": LLMPresentationModelWithValidation.model_json_schema(), + }, + } + ) + + +async def generate_presentation_stream( presentation_outline: PresentationMarkdownModel, -): - user_message = HumanMessage(presentation_outline.to_string()) +) -> AsyncStream[ChatCompletionChunk]: + client = get_llm_client() model = get_large_model() - return model, system_prompt, user_message + response_format = get_response_format() + response = await client.chat.completions.create( + model=model, + messages=[ + { + "role": "system", + "content": get_system_prompt(), + }, + { + "role": "user", + "content": presentation_outline.to_string(), + }, + ], + response_format=response_format, + stream=True, + ) -def generate_presentation_stream( - presentation_outline: PresentationMarkdownModel, -) -> AsyncIterator[AIMessageChunk]: - model, system_prompt, user_message = get_model_and_messages(presentation_outline) - - return model.astream([system_prompt, user_message]) + return response async def generate_presentation( presentation_outline: PresentationMarkdownModel, -) -> AIMessage: - model, system_prompt, user_message = get_model_and_messages(presentation_outline) - return await model.ainvoke([system_prompt, user_message]) +) -> str: + client = get_llm_client() + model = get_large_model() + + response_format = get_response_format() + + response = await client.chat.completions.create( + model=model, + messages=[ + { + "role": "system", + "content": get_system_prompt(), + }, + { + "role": "user", + "content": presentation_outline.to_string(), + }, + ], + response_format=response_format, + ) + + return response.choices[0].message.content diff --git a/servers/fastapi/ppt_generator/models/content_type_models.py b/servers/fastapi/ppt_generator/models/content_type_models.py index f13cb76c..0bebd343 100644 --- a/servers/fastapi/ppt_generator/models/content_type_models.py +++ b/servers/fastapi/ppt_generator/models/content_type_models.py @@ -1,4 +1,4 @@ -from typing import List, Mapping +from typing import List, Mapping, Union from pydantic import BaseModel from ppt_generator.models.other_models import ( @@ -12,7 +12,17 @@ from ppt_generator.models.other_models import ( TYPE8, TYPE9, ) -from graph_processor.models import GraphModel + + +class TableDataModel(BaseModel): + x_labels: List[str] + y_labels: List[str] + data: List[List[float]] + + +class TableModel(BaseModel): + name: str + data: TableDataModel class HeadingModel(BaseModel): @@ -47,9 +57,6 @@ class HeadingModel(BaseModel): class SlideContentModel(BaseModel): title: str - def to_llm_content(self): - raise NotImplementedError("to_llm_content method not implemented") - class Type1Content(SlideContentModel): body: str @@ -110,7 +117,7 @@ class Type4Content(SlideContentModel): class Type5Content(SlideContentModel): body: str - graph: GraphModel + table: TableModel def to_llm_content(self): from ppt_generator.models.llm_models import LLMType5Content @@ -118,7 +125,7 @@ class Type5Content(SlideContentModel): return LLMType5Content( title=self.title, body=self.body, - graph=self.graph, + table=self.table, ) @@ -174,7 +181,7 @@ class Type8Content(SlideContentModel): class Type9Content(SlideContentModel): body: List[HeadingModel] - graph: GraphModel + table: TableModel def to_llm_content(self): from ppt_generator.models.llm_models import LLMType9Content @@ -182,11 +189,23 @@ class Type9Content(SlideContentModel): return LLMType9Content( title=self.title, body=[item.to_llm_content() for item in self.body], - graph=self.graph, + table=self.table, ) -CONTENT_TYPE_MAPPING: Mapping[int, SlideContentModel] = { +ContentUnion = Union[ + Type1Content, + Type2Content, + Type3Content, + Type4Content, + Type5Content, + Type6Content, + Type7Content, + Type8Content, + Type9Content, +] + +CONTENT_TYPE_MAPPING: Mapping[int, ContentUnion] = { TYPE1: Type1Content, TYPE2: Type2Content, TYPE3: Type3Content, diff --git a/servers/fastapi/ppt_generator/models/llm_models.py b/servers/fastapi/ppt_generator/models/llm_models.py index 1ff43feb..430af6c3 100644 --- a/servers/fastapi/ppt_generator/models/llm_models.py +++ b/servers/fastapi/ppt_generator/models/llm_models.py @@ -1,10 +1,10 @@ -from typing import List, Mapping +from typing import List, Mapping, Union from pydantic import BaseModel -from graph_processor.models import GraphModel from ppt_generator.models.content_type_models import ( HeadingModel, - SlideContentModel, + TableDataModel, + TableModel, Type1Content, Type2Content, Type3Content, @@ -28,6 +28,17 @@ from ppt_generator.models.other_models import ( ) +class LLMTableDataModel(TableDataModel): + x_labels: List[str] + y_labels: List[str] + data: List[List[float]] + + +class LLMTableModel(TableModel): + name: str + data: LLMTableDataModel + + class LLMHeadingModel(BaseModel): heading: str description: str @@ -42,17 +53,26 @@ class LLMHeadingModel(BaseModel): class LLMHeadingModelWithImagePrompt(LLMHeadingModel): image_prompt: str + def to_content(self) -> HeadingModel: + return HeadingModel( + heading=self.heading, + description=self.description, + ) + class LLMHeadingModelWithIconQuery(LLMHeadingModel): icon_query: str + def to_content(self) -> HeadingModel: + return HeadingModel( + heading=self.heading, + description=self.description, + ) + class LLMSlideContentModel(BaseModel): title: str - def to_content(self) -> SlideContentModel: - raise NotImplementedError("to_content method not implemented") - class LLMType1Content(LLMSlideContentModel): body: str @@ -101,13 +121,13 @@ class LLMType4Content(LLMSlideContentModel): class LLMType5Content(LLMSlideContentModel): body: str - graph: GraphModel + table: LLMTableModel def to_content(self) -> Type5Content: return Type5Content( title=self.title, body=self.body, - graph=self.graph, + table=self.table, ) @@ -149,17 +169,29 @@ class LLMType8Content(LLMSlideContentModel): class LLMType9Content(LLMSlideContentModel): body: List[LLMHeadingModel] - graph: GraphModel + table: LLMTableModel def to_content(self) -> Type9Content: return Type9Content( title=self.title, body=[each.to_content() for each in self.body], - graph=self.graph, + table=self.table, ) -LLM_CONTENT_TYPE_MAPPING: Mapping[int, LLMSlideContentModel] = { +LLMContentUnion = Union[ + LLMType1Content, + LLMType2Content, + LLMType3Content, + LLMType4Content, + LLMType5Content, + LLMType6Content, + LLMType7Content, + LLMType8Content, + LLMType9Content, +] + +LLM_CONTENT_TYPE_MAPPING: Mapping[int, LLMContentUnion] = { TYPE1: LLMType1Content, TYPE2: LLMType2Content, TYPE3: LLMType3Content, @@ -174,17 +206,8 @@ LLM_CONTENT_TYPE_MAPPING: Mapping[int, LLMSlideContentModel] = { class LLMSlideModel(BaseModel): type: int - content: ( - LLMType1Content - | LLMType2Content - | LLMType4Content - | LLMType5Content - | LLMType6Content - | LLMType7Content - | LLMType8Content - | LLMType9Content - ) + content: LLMContentUnion class LLMPresentationModel(BaseModel): - slides: list[LLMSlideModel] + slides: List[LLMSlideModel] diff --git a/servers/fastapi/ppt_generator/models/llm_models_with_validations.py b/servers/fastapi/ppt_generator/models/llm_models_with_validations.py index 685d2c21..00074152 100644 --- a/servers/fastapi/ppt_generator/models/llm_models_with_validations.py +++ b/servers/fastapi/ppt_generator/models/llm_models_with_validations.py @@ -1,7 +1,6 @@ -from typing import List, Mapping +from typing import List, Mapping, Union from pydantic import Field -from graph_processor.models import GraphModel from ppt_generator.models.other_models import ( TYPE1, TYPE2, @@ -14,6 +13,8 @@ from ppt_generator.models.other_models import ( TYPE9, ) from ppt_generator.models.llm_models import ( + LLMTableDataModel, + LLMTableModel, LLMHeadingModel, LLMHeadingModelWithImagePrompt, LLMHeadingModelWithIconQuery, @@ -32,239 +33,179 @@ from ppt_generator.models.llm_models import ( ) +class LLMTableDataModelWithValidation(LLMTableDataModel): + x_labels: List[str] = Field( + description="X labels of the table", + min_length=1, + max_length=5, + ) + y_labels: List[str] = Field( + description="Y labels of the table", + min_length=1, + max_length=3, + ) + data: List[List[float]] = Field( + description="Data of the table", + min_length=1, + max_length=5, + ) + + +class LLMTableModelWithValidation(LLMTableModel): + name: str = Field( + description="Name of the table in about 8 words", + min_length=10, + max_length=50, + ) + data: LLMTableDataModelWithValidation + + class LLMHeadingModelWithValidation(LLMHeadingModel): heading: str = Field( - description="List item heading to show in slide body", + description="Item heading in about 6 words", min_length=10, - max_length=30, + max_length=40, ) description: str = Field( - description="Description of list item in less than 20 words.", - min_length=80, - max_length=150, + description="Item description in about 12 words.", + min_length=50, + max_length=120, ) class LLMHeadingModelWithImagePromptWithValidation(LLMHeadingModelWithImagePrompt): image_prompt: str = Field( - description="Prompt used to generate image for this item", + description="Item image prompt in about 10 words", min_length=10, - max_length=50, + max_length=100, ) class LLMHeadingModelWithIconQueryWithValidation(LLMHeadingModelWithIconQuery): icon_query: str = Field( - description="Icon query to generate icon for this item", + description="Item icon query in about 4 words", min_length=10, - max_length=50, + max_length=40, + ) + + +class LLMSlideContentModelWithValidation(LLMSlideContentModel): + title: str = Field( + description="Slide title in about 8 words", + min_length=10, + max_length=80, ) class LLMType1ContentWithValidation(LLMType1Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) body: str = Field( - description="Slide content summary in less than 30 words.", - min_length=100, - max_length=200, + description="Slide content summary in about 30 words.", + min_length=50, + max_length=300, ) image_prompt: str = Field( - description="Prompt used to generate image for this slide.", + description="Slide image prompt in about 5 words", min_length=10, - max_length=50, + max_length=30, ) - @classmethod - def get_notes(cls): - return "" - class LLMType2ContentWithValidation(LLMType2Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) body: List[LLMHeadingModelWithValidation] = Field( - description="List items to show in slide's body", + description="Items to show in slide", min_length=1, max_length=4, ) - @classmethod - def get_notes(cls): - return """ - - The **Body** should include **1 to 4 HeadingModels**. - - Each **Heading** must consist of **1 to 3 words**. - - Each item **Description** can be upto 10 words. - """ - class LLMType3ContentWithValidation(LLMType3Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) body: List[LLMHeadingModelWithValidation] = Field( - description="List items to show in slide's body", + description="Items to show in slide", min_length=3, max_length=3, ) image_prompt: str = Field( - description="Prompt used to generate image for this slide", + description="Slide image prompt in about 5 words", min_length=10, - max_length=50, + max_length=30, ) - @classmethod - def get_notes(cls): - return """ - - The **Body** should include **3 HeadingModels**. - - Each **Heading** must consist of **1 to 3 words**. - - Each item **Description** can be upto 10 words. - """ - class LLMType4ContentWithValidation(LLMType4Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) body: List[LLMHeadingModelWithImagePromptWithValidation] = Field( - description="List items to show in slide's body", + description="Items to show in slide", min_length=1, max_length=3, ) - @classmethod - def get_notes(cls): - return """ - - The **Body** should include **1 to 3 HeadingModels**. - - Each **Heading** must consist of **1 to 3 words**. - - Each item **Description** can be upto 10 words. - """ - class LLMType5ContentWithValidation(LLMType5Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) body: str = Field( - description="Slide content summary in less than 30 words.", - min_length=100, - max_length=250, + description="Slide content summary in about 30 words.", + min_length=50, + max_length=300, ) - graph: GraphModel = Field(description="Graph to show in slide") - - @classmethod - def get_notes(self): - return "" + table: LLMTableModelWithValidation = Field(description="Table to show in slide") class LLMType6ContentWithValidation(LLMType6Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) description: str = Field( - description="Slide content summary in less than 20 words.", - min_length=80, - max_length=150, + description="Slide content summary in about 20 words.", + min_length=50, + max_length=300, ) body: List[LLMHeadingModelWithValidation] = Field( - description="List items to show in slide's body", + description="Items to show in slide", min_length=1, max_length=3, ) - @classmethod - def get_notes(cls): - return """ - - The **Body** should include **1 to 3 HeadingModels**. - - Each **Heading** must consist of **1 to 3 words**. - - Each item **Description** can be upto 10 words. - """ - class LLMType7ContentWithValidation(LLMType7Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) body: List[LLMHeadingModelWithIconQueryWithValidation] = Field( - description="List items to show in slide's body", + description="Items to show in slide", min_length=1, max_length=4, ) - @classmethod - def get_notes(cls): - return """ - - The **Body** should include **1 to 4 HeadingModels**. - - Each **Heading** must consist of **1 to 3 words**. - - Each item **Description** can be upto 10 words. - """ - class LLMType8ContentWithValidation(LLMType8Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) description: str = Field( - description="Slide content summary in less than 20 words.", - min_length=80, - max_length=150, + description="Slide content summary in about 20 words.", + min_length=50, + max_length=300, ) body: List[LLMHeadingModelWithImagePromptWithValidation] = Field( - description="List items to show in slide's body", + description="Items to show in slide", min_length=1, max_length=3, ) - @classmethod - def get_notes(cls): - return """ - - The **Body** should include **1 to 3 HeadingModels**. - - Each **Heading** must consist of **1 to 3 words**. - - Each item **Description** can be upto 10 words. - """ - class LLMType9ContentWithValidation(LLMType9Content): - title: str = Field( - description="Title of the slide", - min_length=10, - max_length=50, - ) body: List[LLMHeadingModelWithValidation] = Field( - description="List items to show in slide's body", + description="Items to show in slide", min_length=1, max_length=3, ) - graph: GraphModel = Field(description="Graph to show in slide") - - @classmethod - def get_notes(cls): - return """ - - The **Body** should include **1 to 3 HeadingModels**. - - Each **Heading** must consist of **1 to 3 words**. - - Each item **Description** can be upto 10 words. - """ + table: LLMTableModelWithValidation = Field(description="Table to show in slide") -LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING: Mapping[int, LLMSlideContentModel] = { +LLMContentUnionWithValidation = Union[ + LLMType1ContentWithValidation, + LLMType2ContentWithValidation, + LLMType3ContentWithValidation, + LLMType4ContentWithValidation, + LLMType5ContentWithValidation, + LLMType6ContentWithValidation, + LLMType7ContentWithValidation, + LLMType8ContentWithValidation, + LLMType9ContentWithValidation, +] + +LLM_CONTENT_TYPE_MAPPING_WITH_VALIDATION: Mapping[ + int, LLMContentUnionWithValidation +] = { TYPE1: LLMType1ContentWithValidation, TYPE2: LLMType2ContentWithValidation, TYPE3: LLMType3ContentWithValidation, @@ -279,17 +220,8 @@ LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING: Mapping[int, LLMSlideContentModel] = { class LLMSlideModelWithValidation(LLMSlideModel): type: int - content: ( - LLMType1ContentWithValidation - | LLMType2ContentWithValidation - | LLMType4ContentWithValidation - | LLMType5ContentWithValidation - | LLMType6ContentWithValidation - | LLMType7ContentWithValidation - | LLMType8ContentWithValidation - | LLMType9ContentWithValidation - ) + content: LLMContentUnionWithValidation class LLMPresentationModelWithValidation(LLMPresentationModel): - slides: list[LLMSlideModelWithValidation] + slides: List[LLMSlideModelWithValidation] diff --git a/servers/fastapi/ppt_generator/models/pptx_models.py b/servers/fastapi/ppt_generator/models/pptx_models.py index 327f21dc..6808e6de 100644 --- a/servers/fastapi/ppt_generator/models/pptx_models.py +++ b/servers/fastapi/ppt_generator/models/pptx_models.py @@ -6,8 +6,6 @@ from pptx.util import Pt from pptx.enum.text import PP_ALIGN from pptx.enum.shapes import MSO_AUTO_SHAPE_TYPE, MSO_CONNECTOR_TYPE -from graph_processor.models import GraphModel - class PptxBoxShapeEnum(Enum): RECTANGLE = "rectangle" @@ -138,14 +136,6 @@ class PptxPictureBoxModel(PptxShapeModel): picture: PptxPictureModel -class PptxGraphBoxModel(PptxShapeModel): - position: PptxPositionModel - category_font: Optional[PptxFontModel] = None - value_font: Optional[PptxFontModel] = None - legend_font: Optional[PptxFontModel] = None - graph: GraphModel - - class PptxConnectorModel(PptxShapeModel): type: MSO_CONNECTOR_TYPE = MSO_CONNECTOR_TYPE.STRAIGHT position: PptxPositionModel @@ -159,13 +149,10 @@ class PptxSlideModel(BaseModel): | PptxAutoShapeBoxModel | PptxConnectorModel | PptxPictureBoxModel - | PptxGraphBoxModel ] class PptxPresentationModel(BaseModel): - # theme: PresentationTheme - # watermark: bool background_color: str shapes: Optional[List[PptxShapeModel]] = None slides: List[PptxSlideModel] diff --git a/servers/fastapi/ppt_generator/pptx_presentation_creator.py b/servers/fastapi/ppt_generator/pptx_presentation_creator.py index 9bac33ec..aa00250e 100644 --- a/servers/fastapi/ppt_generator/pptx_presentation_creator.py +++ b/servers/fastapi/ppt_generator/pptx_presentation_creator.py @@ -6,26 +6,12 @@ from lxml import etree from pptx import Presentation from pptx.shapes.autoshape import Shape from pptx.slide import Slide -from pptx.chart.data import ChartData, BubbleChartData -from pptx.chart.chart import Chart from pptx.text.text import _Paragraph, TextFrame, Font, _Run -from pptx.enum.chart import ( - XL_CHART_TYPE, - XL_LEGEND_POSITION, - XL_LABEL_POSITION, -) from pptx.opc.constants import RELATIONSHIP_TYPE as RT from lxml.etree import fromstring, tostring from PIL import Image from pptx.util import Pt -from graph_processor.models import ( - BarGraphDataModel, - BubbleChartDataModel, - GraphTypeEnum, - LineChartDataModel, - PieChartDataModel, -) from pptx.dml.color import RGBColor from ppt_generator.models.pptx_models import ( PptxAutoShapeBoxModel, @@ -33,7 +19,6 @@ from ppt_generator.models.pptx_models import ( PptxConnectorModel, PptxFillModel, PptxFontModel, - PptxGraphBoxModel, PptxParagraphModel, PptxPictureBoxModel, PptxPositionModel, @@ -63,8 +48,6 @@ class PptxPresentationCreator: self._ppt_model = ppt_model self._slide_models = ppt_model.slides - # self._theme = ppt_model.theme - # self._watermark = ppt_model.watermark self._ppt = Presentation() self._ppt.slide_width = Pt(1280) @@ -73,7 +56,6 @@ class PptxPresentationCreator: self._slide_fill = PptxFillModel(color=ppt_model.background_color) def create_ppt(self): - # self.set_presentation_theme() for slide_model in self._slide_models: # Adding global shapes to slide @@ -120,16 +102,9 @@ class PptxPresentationCreator: elif model_type is PptxTextBoxModel: self.add_textbox(slide, shape_model) - elif model_type is PptxGraphBoxModel: - self.add_graph(slide, shape_model) - elif model_type is PptxConnectorModel: self.add_connector(slide, shape_model) - # if self._watermark: - # Adding watermark - # self.add_picture(slide, self.get_watermark_box_model()) - def add_connector(self, slide: Slide, connector_model: PptxConnectorModel): if connector_model.thickness == 0: return @@ -139,126 +114,6 @@ class PptxPresentationCreator: connector_shape.line.width = Pt(connector_model.thickness) connector_shape.line.color.rgb = RGBColor.from_string(connector_model.color) - def add_graph(self, slide: Slide, graph_box_model: PptxGraphBoxModel): - chart_data = None - chart_type = None - graph = graph_box_model.graph - match (graph.type): - case GraphTypeEnum.bar: - chart_data = self.get_bar_graph(graph.data) - chart_type = XL_CHART_TYPE.COLUMN_CLUSTERED - - case GraphTypeEnum.scatter: - chart_data = self.get_scatter_graph(graph.data) - chart_type = XL_CHART_TYPE.XY_SCATTER - - case GraphTypeEnum.bubble: - chart_data = self.get_bubble_graph(graph.data) - chart_type = XL_CHART_TYPE.BUBBLE - - case GraphTypeEnum.line: - chart_data = self.get_line_graph(graph.data) - chart_type = XL_CHART_TYPE.LINE - - case GraphTypeEnum.pie: - chart_data = self.get_pie_graph(graph.data) - chart_type = XL_CHART_TYPE.PIE - - if chart_data: - chart: Chart = slide.shapes.add_chart( - chart_type, *graph_box_model.position.to_pt_list(), chart_data - ).chart - self.apply_graph_styles(chart, graph_box_model) - - def apply_graph_styles(self, chart, graph_box_model: PptxGraphBoxModel): - graph = graph_box_model.graph - - if graph.type in [GraphTypeEnum.pie, GraphTypeEnum.scatter]: - chart.has_legend = True - chart.legend.position = XL_LEGEND_POSITION.RIGHT - else: - chart.has_legend = False - - if graph_box_model.legend_font: - self.apply_font(chart.font, graph_box_model.legend_font) - - try: - category_axis = chart.category_axis - if graph_box_model.category_font: - font = category_axis.tick_labels.font - self.apply_font(font, graph_box_model.category_font) - except: - print("-" * 20) - print("Could not apply category labels style") - - try: - value_axis = chart.value_axis - tick_labels = value_axis.tick_labels - if graph.postfix: - tick_labels.number_format = f'0"{graph.postfix}"' - if graph_box_model.value_font: - self.apply_font(tick_labels.font, graph_box_model.value_font) - except: - print("-" * 20) - print("Could not apply tick labels style") - - if graph_box_model.graph.type is GraphTypeEnum.pie: - for plot in chart.plots: - try: - plot.has_data_labels = True - plot.data_labels.position = ( - XL_LABEL_POSITION.OUTSIDE_END - if graph_box_model.graph.type is GraphTypeEnum.bar - else XL_LABEL_POSITION.CENTER - ) - if graph.postfix: - plot.data_labels.number_format = f'0"{graph.postfix}"' - if graph_box_model.value_font: - self.apply_font( - plot.data_labels.font, - ( - graph_box_model.value_font - if graph_box_model.graph.type is GraphTypeEnum.bar - else PptxFontModel( - # size=self._theme.fonts.p2, - size=16, - bold=True, - color="ffffff", - ) - ), - ) - except: - print("-" * 20) - print("Could not apply data labels style") - - def get_bar_graph(self, graph: BarGraphDataModel): - chart_data = ChartData() - chart_data.categories = graph.get_categories() - for series in graph.series: - chart_data.add_series(series.get_name(), series.data) - return chart_data - - def get_bubble_graph(self, graph: BubbleChartDataModel): - chart_data = BubbleChartData() - for each in graph.series: - series = chart_data.add_series(each.get_name()) - for point in each.points: - series.add_data_point(*point.to_list()) - return chart_data - - def get_line_graph(self, graph: LineChartDataModel): - chart_data = ChartData() - chart_data.categories = graph.get_categories() - for series in graph.series: - chart_data.add_series(series.get_name(), series.data) - return chart_data - - def get_pie_graph(self, graph: PieChartDataModel): - chart_data = ChartData() - chart_data.categories = graph.get_categories() - chart_data.add_series("", graph.series[0].data) - return chart_data - def add_picture(self, slide: Slide, picture_model: PptxPictureBoxModel): image_path = picture_model.picture.path if ( @@ -562,17 +417,5 @@ class PptxPresentationCreator: font.italic = font_model.italic font.size = Pt(font_model.size) - # def get_watermark_box_model(self): - # watermark_asset_path = f"assets/images/{'watermark_dark.png' if self._theme == PresentationTheme.dark else 'watermark.png'}" - - # return PptxPictureBoxModel( - # position=PptxPositionModel(left=1120, top=685, width=140), - # clip=False, - # picture=PptxPictureModel( - # is_network=False, - # path=watermark_asset_path, - # ), - # ) - def save(self, path: str): self._ppt.save(path) diff --git a/servers/fastapi/ppt_generator/slide_generator.py b/servers/fastapi/ppt_generator/slide_generator.py index 19e52af5..c37e6b44 100644 --- a/servers/fastapi/ppt_generator/slide_generator.py +++ b/servers/fastapi/ppt_generator/slide_generator.py @@ -1,149 +1,142 @@ from typing import Optional -from api.utils.utils import get_large_model, get_small_model -from ppt_config_generator.models import SlideMarkdownModel -from ppt_generator.fix_validation_errors import get_validated_response -from langchain_core.prompts import ChatPromptTemplate +from pydantic import BaseModel + +from api.utils.model_utils import get_large_model, get_llm_client, get_small_model +from ppt_config_generator.models import SlideMarkdownModel from ppt_generator.models.llm_models import ( LLM_CONTENT_TYPE_MAPPING, - LLMSlideContentModel, + LLMContentUnion, ) from ppt_generator.models.llm_models_with_validations import ( - LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING, + LLM_CONTENT_TYPE_MAPPING_WITH_VALIDATION, ) from ppt_generator.models.other_models import SlideTypeModel from ppt_generator.models.slide_model import SlideModel -prompt_template_to_generate_slide_content = ChatPromptTemplate.from_messages( - [ - ( - "system", - """ - Generate structured slide based on provided title and outline, follow mentioned steps and notes and provide structured output. +def get_prompt_to_generate_slide_content(title: str, outline: str): + return [ + { + "role": "system", + "content": f""" + Generate structured slide based on provided title and outline, follow mentioned steps and notes and provide structured output. + # Steps + 1. Analyze the outline and title. + 2. Generate structured slide based on the outline and title. + 3. Generate image prompts and icon queries if mentioned in schema. + 4. Generate graph if mentioned in schema. - # Steps - 1. Analyze the outline and title. - 2. Generate structured slide based on the outline and title. - 3. Generate image prompts and icon queries if mentioned in schema. - 4. Generate graph if mentioned in schema. - - # Notes - - Slide body should not use words like "This slide", "This presentation". - - Rephrase the slide body to make it flow naturally. - - Do not use markdown formatting in slide body. - - **Icon query** must be a generic single word noun. - - **Image prompt** should be a 2-3 words phrase. - - Try to make paragraphs as short as possible. - {notes} + # Notes + - Slide body should not use words like "This slide", "This presentation". + - Rephrase the slide body to make it flow naturally. + - Do not use markdown formatting in slide body. """, - ), - ( - "user", - """ - ## Slide Title - {title} + }, + { + "role": "user", + "content": f""" + ## Slide Title + {title} - ## Slide Outline - {outline} - """, - ), + ## Slide Outline + {outline} + """, + }, ] -) -prompt_template_to_edit_slide_content = ChatPromptTemplate.from_messages( - [ - ( - "system", - """ +def get_prompt_to_edit_slide_content( + prompt: str, + slide_data: dict, + theme: Optional[dict] = None, + language: Optional[str] = None, +): + return [ + { + "role": "system", + "content": """ Edit Slide data based on provided prompt, follow mentioned steps and notes and provide structured output. # Notes - Provide output in language mentioned in **Input**. - The goal is to change Slide data based on the provided prompt. - Do not change **Image prompts** and **Icon queries** if not asked for in prompt. - - Generate **Image prompts** and **Icon queries** if asked to generate or change image or icons in prompt. - - Ensure there are no line breaks in the JSON. - - Do not use special characters for highlighting. - {notes} + - Generate **Image prompts** and **Icon queries** if asked to generate or change in prompt. **Go through all notes and steps and make sure they are followed, including mentioned constraints** """, - ), - ( - "user", - """ - - Prompt: {prompt} - - Output Language: {language} - - Image Prompts and Icon Queries Language: English - - Theme: {theme} - - Slide data: {slide_data} - """, - ), + }, + { + "role": "user", + "content": f""" + - Prompt: {prompt} + - Output Language: {language} + - Image Prompts and Icon Queries Language: English + - Theme: {theme} + - Slide data: {slide_data} + """, + }, ] -) -prompt_template_to_select_slide_type = ChatPromptTemplate.from_messages( - [ - ( - "system", - """ - Select a Slide Type based on provided user prompt and current slide data. +def get_prompt_to_select_slide_type(prompt: str, slide_data: dict, slide_type: int): + return [ + { + "role": "system", + "content": """ + Select a Slide Type based on provided user prompt and current slide data. - Select slide based on following slide description and make sure it matches user requirement: - # Slide Types (Slide Type : Slide Description) - - **1**: contains title, description and image. - - **2**: contains title and list of items. - - **4**: contains title and list of items with images. - - **5**: contains title, description and a graph. - - **6**: contains title, description and list of items. - - **7**: contains title and list of items with icons. - - **8**: contains title, description and list of items with icons. - - **9**: contains title, list of items and a graph. + Select slide based on following slide description and make sure it matches user requirement: + # Slide Types (Slide Type : Slide Description) + - **1**: contains title, description and image. + - **2**: contains title and list of items. + - **4**: contains title and list of items with images. + - **5**: contains title, description and a graph. + - **6**: contains title, description and list of items. + - **7**: contains title and list of items with icons. + - **8**: contains title, description and list of items with icons. + - **9**: contains title, list of items and a graph. - # Notes - - Do not select different slide type than current unless absolutely necessary as per user prompt. + # Notes + - Do not select different slide type than current unless absolutely necessary as per user prompt. - **Go through all notes and steps and make sure they are followed, including mentioned constraints** - """, - ), - ( - "user", - """ - - User Prompt: {prompt} - - Current Slide Data: {slide_data} - - Current Slide Type: {slide_type} - """, - ), + **Go through all notes and steps and make sure they are followed, including mentioned constraints** + """, + }, + { + "role": "user", + "content": f""" + - User Prompt: {prompt} + - Current Slide Data: {slide_data} + - Current Slide Type: {slide_type} + """, + }, ] -) async def get_slide_content_from_type_and_outline( slide_type: int, outline: SlideMarkdownModel -) -> LLMSlideContentModel: - content_type_model_type = LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING[slide_type] - validation_model = LLM_CONTENT_TYPE_MAPPING[slide_type] - model = get_small_model().with_structured_output( - content_type_model_type.model_json_schema() - ) - chain = prompt_template_to_generate_slide_content | model +) -> LLMContentUnion: + response_model = LLM_CONTENT_TYPE_MAPPING_WITH_VALIDATION[slide_type] - return await get_validated_response( - chain, - { - "title": outline.title, - "outline": outline.body, - "notes": content_type_model_type.get_notes(), - }, - content_type_model_type, - validation_model, + client = get_llm_client() + model = get_small_model() + + response = await client.beta.chat.completions.parse( + model=model, + temperature=0.5, + messages=get_prompt_to_generate_slide_content( + outline.title, + outline.body, + ), + response_format=response_model, ) + return response.choices[0].message.parsed + async def get_edited_slide_content_model( prompt: str, @@ -151,29 +144,24 @@ async def get_edited_slide_content_model( slide: SlideModel, theme: Optional[dict] = None, language: Optional[str] = None, -): +) -> LLMContentUnion: + client = get_llm_client() model = get_large_model() - content_type_model_type = LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING[slide_type] - validation_model = LLM_CONTENT_TYPE_MAPPING[slide_type] - chain = prompt_template_to_edit_slide_content | model.with_structured_output( - content_type_model_type.model_json_schema() - ) + content_type_model_type = LLM_CONTENT_TYPE_MAPPING_WITH_VALIDATION[slide_type] slide_data = slide.content.to_llm_content().model_dump_json() - edited_content = await get_validated_response( - chain, - { - "prompt": prompt, - "language": language or "English", - "theme": theme, - "slide_data": slide_data, - "notes": "", - }, - content_type_model_type, - validation_model, + response = await client.beta.chat.completions.parse( + model=model, + temperature=0.2, + messages=get_prompt_to_edit_slide_content( + prompt, + slide_data, + theme, + language, + ), + response_format=content_type_model_type, ) - - return edited_content.to_content() + return response.choices[0].message.parsed async def get_slide_type_from_prompt( @@ -181,18 +169,15 @@ async def get_slide_type_from_prompt( slide: SlideModel, ) -> SlideTypeModel: + client = get_llm_client() model = get_small_model() - chain = prompt_template_to_select_slide_type | model.with_structured_output( - SlideTypeModel.model_json_schema() - ) - slide_data = slide.content.to_llm_content().model_dump_json() - return await get_validated_response( - chain, - { - "prompt": prompt, - "slide_data": slide_data, - "slide_type": slide.type, - }, - SlideTypeModel, + response = await client.beta.chat.completions.parse( + model=model, + temperature=0.2, + messages=get_prompt_to_select_slide_type( + prompt, slide.content.to_llm_content().model_dump_json(), slide.type + ), + response_format=SlideTypeModel, ) + return response.choices[0].message.parsed diff --git a/servers/fastapi/requirements.txt b/servers/fastapi/requirements.txt index 113c5fbf..81c4d998 100644 --- a/servers/fastapi/requirements.txt +++ b/servers/fastapi/requirements.txt @@ -19,7 +19,7 @@ dnspython==2.7.0 email_validator==2.2.0 fastapi==0.115.12 fastapi-cli==0.0.7 -fastembed==0.7.0 +fastembed_vectorstore==0.1.5 filelock==3.18.0 filetype==1.2.0 flatbuffers==25.2.10 @@ -28,6 +28,7 @@ fsspec==2025.3.2 google-ai-generativelanguage==0.6.18 google-api-core==2.24.2 google-auth==2.40.1 +google-genai==1.23.0 googleapis-common-protos==1.70.0 greenlet==3.2.2 grpcio==1.72.0rc1 @@ -44,14 +45,6 @@ Jinja2==3.1.6 jiter==0.9.0 jsonpatch==1.33 jsonpointer==3.0.0 -langchain==0.3.25 -langchain-community==0.3.24 -langchain-core==0.3.65 -langchain-google-genai==2.1.4 -langchain-ollama==0.3.3 -langchain-openai==0.3.16 -langchain-text-splitters==0.3.8 -langsmith==0.3.45 loguru==0.7.3 lxml==5.4.0 markdown-it-py==3.0.0 @@ -65,7 +58,7 @@ mypy_extensions==1.1.0 numpy==2.2.5 ollama==0.5.1 onnxruntime==1.22.0 -openai==1.78.1 +openai==1.91.0 orjson==3.10.18 packaging==24.2 pdfminer.six==20250327 @@ -102,7 +95,7 @@ SQLAlchemy==2.0.41 sqlmodel==0.0.24 starlette==0.46.2 sympy==1.14.0 -tenacity==9.1.2 +tenacity==8.5.0 tiktoken==0.9.0 tokenizers==0.21.1 tqdm==4.67.1 diff --git a/servers/fastapi/research_report/generator.py b/servers/fastapi/research_report/generator.py index ffb8e520..f7e7462e 100644 --- a/servers/fastapi/research_report/generator.py +++ b/servers/fastapi/research_report/generator.py @@ -1,58 +1,35 @@ import os from typing import Optional -from langchain_core.prompts import ChatPromptTemplate -from langchain_google_genai import ChatGoogleGenerativeAI -from langchain_openai import ChatOpenAI -# search_tool = DuckDuckGoSearchRun( -# api_wrapper=DuckDuckGoSearchAPIWrapper(max_results=50) -# ) -prompt_template = ChatPromptTemplate.from_messages( - [ - ( - "system", - """ - Use provided prompt and search results to create an elaborate and up-to-date research report in mentioned language. +def get_prompt_template(): + return [ + { + "role": "system", + "content": """ + Use provided prompt and search results to create an elaborate and up-to-date research report in mentioned language. - # Steps - 1. Analyze the prompt and search results. - 2. Extract topic of the report. - 3. Generate a report in markdown format. + # Steps + 1. Analyze the prompt and search results. + 2. Extract topic of the report. + 3. Generate a report in markdown format. - # Notes - - If language is not mentioned, use language from prompt. - - Format of report should be like *Research Report*. - - Ignore formatting if mentioned in prompt. + # Notes + - If language is not mentioned, use language from prompt. + - Format of report should be like *Research Report*. + - Ignore formatting if mentioned in prompt. """, - ), - ( - "human", - """ - - Prompt: {prompt} - - Language: {language} - - Search Results: {search_results} + }, + { + "role": "human", + "content": """ + - Prompt: {prompt} + - Language: {language} + - Search Results: {search_results} """, - ), + }, ] -) async def get_report(query: str, language: Optional[str]): - model = ( - ChatOpenAI(model="gpt-4.1-nano") - if os.getenv("LLM") == "openai" - else ChatGoogleGenerativeAI(model="gemini-2.0-flash") - ) - chain = prompt_template | model - - # search_results = await search_tool.ainvoke(query) - # response = await chain.ainvoke( - # { - # "prompt": query, - # "language": language, - # "search_results": search_results, - # } - # ) - # return response.content return "Research Report coming soon" diff --git a/servers/fastapi/server.py b/servers/fastapi/server.py index 40db9df9..aba13788 100644 --- a/servers/fastapi/server.py +++ b/servers/fastapi/server.py @@ -1,3 +1,4 @@ +import os import uvicorn import argparse @@ -8,6 +9,8 @@ from api.main import app app if __name__ == "__main__": + os.makedirs("debug", exist_ok=True) + parser = argparse.ArgumentParser(description="Run the FastAPI server") parser.add_argument( "--port", type=int, required=True, help="Port number to run the server on" diff --git a/servers/fastapi/server_autoreload.py b/servers/fastapi/server_autoreload.py index 4d5c00de..b6266161 100644 --- a/servers/fastapi/server_autoreload.py +++ b/servers/fastapi/server_autoreload.py @@ -1,8 +1,11 @@ +import os import uvicorn import argparse if __name__ == "__main__": + os.makedirs("debug", exist_ok=True) + parser = argparse.ArgumentParser(description="Run the FastAPI server") parser.add_argument( "--port", type=int, required=True, help="Port number to run the server on" diff --git a/servers/fastapi/server_test.py b/servers/fastapi/server_test.py index 4b547a5c..ed7fd67e 100644 --- a/servers/fastapi/server_test.py +++ b/servers/fastapi/server_test.py @@ -1,9 +1,12 @@ +import os import uvicorn from dotenv import load_dotenv load_dotenv() if __name__ == "__main__": + os.makedirs("debug", exist_ok=True) + uvicorn.run( "api.main:app", host="0.0.0.0", port=8000, log_level="info", reload=True ) diff --git a/servers/nextjs/app/(presentation-generator)/presentation/components/Header.tsx b/servers/nextjs/app/(presentation-generator)/presentation/components/Header.tsx index 3aac3035..dd33bf83 100644 --- a/servers/nextjs/app/(presentation-generator)/presentation/components/Header.tsx +++ b/servers/nextjs/app/(presentation-generator)/presentation/components/Header.tsx @@ -215,7 +215,7 @@ const Header = ({ if (response.ok) { const { path: pdfPath } = await response.json(); const staticFileUrl = getStaticFileUrl(pdfPath); - window.open(staticFileUrl, '_self'); + window.open(staticFileUrl, '_blank'); } else { throw new Error("Failed to export PDF"); } diff --git a/servers/nextjs/app/api/user-config/route.ts b/servers/nextjs/app/api/user-config/route.ts index f8fc32f5..1fc257b4 100644 --- a/servers/nextjs/app/api/user-config/route.ts +++ b/servers/nextjs/app/api/user-config/route.ts @@ -36,7 +36,9 @@ export async function POST(request: Request) { LLM: userConfig.LLM || existingConfig.LLM, OPENAI_API_KEY: userConfig.OPENAI_API_KEY || existingConfig.OPENAI_API_KEY, GOOGLE_API_KEY: userConfig.GOOGLE_API_KEY || existingConfig.GOOGLE_API_KEY, - OLLAMA_MODEL: userConfig.OLLAMA_MODEL || existingConfig.OLLAMA_MODEL, + MODEL: userConfig.MODEL || existingConfig.MODEL, + LLM_PROVIDER_URL: userConfig.LLM_PROVIDER_URL || existingConfig.LLM_PROVIDER_URL, + LLM_API_KEY: userConfig.LLM_API_KEY || existingConfig.LLM_API_KEY, PEXELS_API_KEY: userConfig.PEXELS_API_KEY || existingConfig.PEXELS_API_KEY, } fs.writeFileSync(userConfigPath, JSON.stringify(mergedConfig)) diff --git a/servers/nextjs/app/settings/SettingPage.tsx b/servers/nextjs/app/settings/SettingPage.tsx index af8ad73d..703d4ac4 100644 --- a/servers/nextjs/app/settings/SettingPage.tsx +++ b/servers/nextjs/app/settings/SettingPage.tsx @@ -2,13 +2,29 @@ import React, { useState, useEffect } from "react"; import Header from "../dashboard/components/Header"; import Wrapper from "@/components/Wrapper"; -import { Settings, Key, Loader2 } from 'lucide-react'; +import { Settings, Key, Loader2, Check, ChevronsUpDown } from 'lucide-react'; import { toast } from '@/hooks/use-toast'; import { RootState } from "@/store/store"; import { useSelector } from "react-redux"; import { handleSaveLLMConfig } from "@/utils/storeHelpers"; import { useRouter } from "next/navigation"; import { Select, SelectContent, SelectItem, SelectTrigger } from "@/components/ui/select"; +import { Button } from "@/components/ui/button"; +import { + Command, + CommandEmpty, + CommandGroup, + CommandInput, + CommandItem, + CommandList, +} from "@/components/ui/command"; +import { + Popover, + PopoverContent, + PopoverTrigger, +} from "@/components/ui/popover"; +import { cn } from "@/lib/utils"; +import { Switch } from "@/components/ui/switch"; const PROVIDER_CONFIGS: Record = { openai: { @@ -55,14 +71,22 @@ const SettingsPage = () => { done: false, }); const [isLoading, setIsLoading] = useState(false); + const [openModelSelect, setOpenModelSelect] = useState(false); + const [useCustomOllamaUrl, setUseCustomOllamaUrl] = useState(false); - const api_key_changed = (apiKey: string) => { + const api_key_changed = (apiKey: string, field?: string) => { if (llmConfig.LLM === 'openai') { setLlmConfig({ ...llmConfig, OPENAI_API_KEY: apiKey }); } else if (llmConfig.LLM === 'google') { setLlmConfig({ ...llmConfig, GOOGLE_API_KEY: apiKey }); } else if (llmConfig.LLM === 'ollama') { - setLlmConfig({ ...llmConfig, PEXELS_API_KEY: apiKey }); + if (field === 'pexels') { + setLlmConfig({ ...llmConfig, PEXELS_API_KEY: apiKey }); + } else if (field === 'ollama_url') { + setLlmConfig({ ...llmConfig, LLM_PROVIDER_URL: apiKey }); + } else if (field === 'ollama_api_key') { + setLlmConfig({ ...llmConfig, LLM_API_KEY: apiKey }); + } } } @@ -87,7 +111,7 @@ const SettingsPage = () => { } } try { - await handleSaveLLMConfig(llmConfig); + await handleSaveLLMConfig(llmConfig, useCustomOllamaUrl); toast({ title: 'Success', description: 'Configuration saved successfully', @@ -116,7 +140,7 @@ const SettingsPage = () => { return new Promise((resolve, reject) => { const interval = setInterval(async () => { try { - const response = await fetch(`/api/v1/ppt/ollama/pull-model?name=${llmConfig.OLLAMA_MODEL}`); + const response = await fetch(`/api/v1/ppt/ollama/pull-model?name=${llmConfig.MODEL}`); if (response.status === 200) { const data = await response.json(); @@ -163,6 +187,14 @@ const SettingsPage = () => { } }, [userConfigState.llm_config.LLM]); + useEffect(() => { + if (!useCustomOllamaUrl) { + setLlmConfig({ ...llmConfig, LLM_PROVIDER_URL: undefined, LLM_API_KEY: undefined }); + } else { + setLlmConfig({ ...llmConfig, LLM_PROVIDER_URL: 'http://localhost:11434', LLM_API_KEY: '' }); + } + }, [useCustomOllamaUrl]); + if (!canChangeKeys) { return null; } @@ -262,70 +294,90 @@ const SettingsPage = () => {
{ollamaModels.length > 0 ? ( - + + ))} + + + + + ) : (
@@ -344,6 +396,62 @@ const SettingsPage = () => {

)}
+ + {/* Custom Ollama URL Configuration */} +
+
+ + +
+ {useCustomOllamaUrl && ( + <> +
+ +
+ api_key_changed(e.target.value, 'ollama_url')} + /> +
+

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

+
+
+ +
+ api_key_changed(e.target.value, 'ollama_api_key')} + /> +
+

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

+
+ + )} +
+
) : ( - !llmConfig.OLLAMA_MODEL ? 'Select Model' : 'Save' + !llmConfig.MODEL ? 'Select Model' : 'Save' )}
diff --git a/servers/nextjs/app/storeInitializer.tsx b/servers/nextjs/app/storeInitializer.tsx index 279be8c8..edbb856d 100644 --- a/servers/nextjs/app/storeInitializer.tsx +++ b/servers/nextjs/app/storeInitializer.tsx @@ -41,11 +41,11 @@ export function StoreInitializer({ children }: { children: React.ReactNode }) { llmConfig.LLM = 'openai'; } dispatch(setLLMConfig(llmConfig)); - const isValid = hasValidLLMConfig(llmConfig); + const isValid = hasValidLLMConfig(llmConfig, false); if (isValid) { // Check if the selected Ollama model is pulled if (llmConfig.LLM === 'ollama') { - const isPulled = await checkIfSelectedOllamaModelIsPulled(llmConfig.OLLAMA_MODEL); + const isPulled = await checkIfSelectedOllamaModelIsPulled(llmConfig.MODEL); if (!isPulled) { router.push('/'); setLoadingToFalseAfterNavigatingTo('/'); diff --git a/servers/nextjs/components/Home.tsx b/servers/nextjs/components/Home.tsx index e246c772..b76ecbbb 100644 --- a/servers/nextjs/components/Home.tsx +++ b/servers/nextjs/components/Home.tsx @@ -2,7 +2,7 @@ import { useState, useEffect } from "react"; import { useRouter } from "next/navigation"; import { toast } from "@/hooks/use-toast"; -import { Info, ExternalLink, PlayCircle, Loader2 } from "lucide-react"; +import { Info, ExternalLink, PlayCircle, Loader2, Check, ChevronsUpDown } from "lucide-react"; import Link from "next/link"; import { Accordion, @@ -14,6 +14,22 @@ import { useSelector } from "react-redux"; import { RootState } from "@/store/store"; import { handleSaveLLMConfig } from "@/utils/storeHelpers"; import { Select, SelectContent, SelectItem, SelectTrigger } from "./ui/select"; +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 { Switch } from "./ui/switch"; interface ModelOption { value: string; @@ -100,36 +116,7 @@ const PROVIDER_CONFIGS: Record = { }, }, ollama: { - textModels: [ - { - value: "llama3.1:8b", - label: "Llama3.1:8b", - description: "Balanced model for most tasks", - icon: "/icons/ollama.png", - size: "8GB", - }, - { - value: "llama3.1:70b", - label: "Llama3.1:70b", - description: "Large model for complex tasks", - icon: "/icons/ollama.png", - size: "70GB", - }, - { - value: "llama3.1:14b", - label: "Llama3.1:14b", - description: "Large model for complex tasks", - icon: "/icons/ollama.png", - size: "14GB", - }, - { - value: "llama3.1:11b", - label: "Llama3.1:11b", - description: "Large model for complex tasks", - icon: "/icons/ollama.png", - size: "11GB", - }, - ], + textModels: [], imageModels: [ { value: "pexels", @@ -171,16 +158,24 @@ export default function Home() { done: false, }); const [isLoading, setIsLoading] = useState(false); + const [openModelSelect, setOpenModelSelect] = useState(false); + const [useCustomOllamaUrl, setUseCustomOllamaUrl] = useState(false); const canChangeKeys = config.can_change_keys; - const api_key_changed = (newApiKey: string) => { + const api_key_changed = (newApiKey: string, field?: string) => { if (llmConfig.LLM === 'openai') { setLlmConfig({ ...llmConfig, OPENAI_API_KEY: newApiKey }); } else if (llmConfig.LLM === 'google') { setLlmConfig({ ...llmConfig, GOOGLE_API_KEY: newApiKey }); } else if (llmConfig.LLM === 'ollama') { - setLlmConfig({ ...llmConfig, PEXELS_API_KEY: newApiKey }); + if (field === 'pexels') { + setLlmConfig({ ...llmConfig, PEXELS_API_KEY: newApiKey }); + } else if (field === 'ollama_url') { + setLlmConfig({ ...llmConfig, LLM_PROVIDER_URL: newApiKey }); + } else if (field === 'ollama_api_key') { + setLlmConfig({ ...llmConfig, LLM_API_KEY: newApiKey }); + } } } @@ -205,7 +200,7 @@ export default function Home() { } } try { - await handleSaveLLMConfig(llmConfig); + await handleSaveLLMConfig(llmConfig, useCustomOllamaUrl); toast({ title: 'Success', description: 'Configuration saved successfully', @@ -234,7 +229,7 @@ export default function Home() { return new Promise((resolve, reject) => { const interval = setInterval(async () => { try { - const response = await fetch(`/api/v1/ppt/ollama/pull-model?name=${llmConfig.OLLAMA_MODEL}`); + const response = await fetch(`/api/v1/ppt/ollama/pull-model?name=${llmConfig.MODEL}`); if (response.status === 200) { const data = await response.json(); @@ -277,6 +272,15 @@ export default function Home() { } }, []); + useEffect(() => { + if (!useCustomOllamaUrl) { + setLlmConfig({ ...llmConfig, LLM_PROVIDER_URL: undefined, LLM_API_KEY: undefined }); + } else { + setLlmConfig({ ...llmConfig, LLM_PROVIDER_URL: 'http://localhost:11434', LLM_API_KEY: '' }); + } + }, [useCustomOllamaUrl]); + + if (!canChangeKeys) { return null; } @@ -355,70 +359,90 @@ export default function Home() {
{ollamaModels.length > 0 ? ( - + + ))} + + + + + ) : (
@@ -437,6 +461,59 @@ export default function Home() {

)}
+
+
+ + +
+ {useCustomOllamaUrl && ( + <> +
+ +
+ api_key_changed(e.target.value, 'ollama_url')} + /> +
+

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

+
+
+ +
+ api_key_changed(e.target.value, 'ollama_api_key')} + /> +
+

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

+
+ + )} +

@@ -468,7 +545,7 @@ export default function Home() { Selected Models

- Using {llmConfig.LLM === 'ollama' ? llmConfig.OLLAMA_MODEL ?? '_____' : PROVIDER_CONFIGS[llmConfig.LLM!].textModels[0].label} for text + Using {llmConfig.LLM === 'ollama' ? llmConfig.MODEL ?? '_____' : PROVIDER_CONFIGS[llmConfig.LLM!].textModels[0].label} for text generation and {PROVIDER_CONFIGS[llmConfig.LLM!].imageModels[0].label} for images

@@ -544,7 +621,7 @@ export default function Home() { }
) : ( - llmConfig.LLM === 'ollama' && !llmConfig.OLLAMA_MODEL + llmConfig.LLM === 'ollama' && !llmConfig.MODEL ? 'Please Select a Model' : 'Save Configuration' )} diff --git a/servers/nextjs/types/global.d.ts b/servers/nextjs/types/global.d.ts index 1d52650a..d3269c53 100644 --- a/servers/nextjs/types/global.d.ts +++ b/servers/nextjs/types/global.d.ts @@ -18,5 +18,7 @@ interface LLMConfig { OPENAI_API_KEY?: string; GOOGLE_API_KEY?: string; PEXELS_API_KEY?: string; - OLLAMA_MODEL?: string; + LLM_PROVIDER_URL?: string; + LLM_API_KEY?: string; + MODEL?: string; } \ No newline at end of file diff --git a/servers/nextjs/utils/storeHelpers.ts b/servers/nextjs/utils/storeHelpers.ts index ccd37a51..dbb13274 100644 --- a/servers/nextjs/utils/storeHelpers.ts +++ b/servers/nextjs/utils/storeHelpers.ts @@ -1,8 +1,8 @@ import { setLLMConfig } from "@/store/slices/userConfig"; import { store } from "@/store/store"; -export const handleSaveLLMConfig = async (llmConfig: LLMConfig) => { - if (!hasValidLLMConfig(llmConfig)) { +export const handleSaveLLMConfig = async (llmConfig: LLMConfig, useCustomOllamaUrl: boolean) => { + if (!hasValidLLMConfig(llmConfig, useCustomOllamaUrl)) { throw new Error('API key cannot be empty'); } @@ -14,17 +14,21 @@ export const handleSaveLLMConfig = async (llmConfig: LLMConfig) => { store.dispatch(setLLMConfig(llmConfig)); } -export const hasValidLLMConfig = (llmConfig: LLMConfig) => { +export const hasValidLLMConfig = (llmConfig: LLMConfig, useCustomOllamaUrl: boolean) => { if (!llmConfig.LLM) return false; const OPENAI_API_KEY = llmConfig.OPENAI_API_KEY; const GOOGLE_API_KEY = llmConfig.GOOGLE_API_KEY; - const OLLAMA_MODEL = llmConfig.OLLAMA_MODEL; + const MODEL = llmConfig.MODEL; const PEXELS_API_KEY = llmConfig.PEXELS_API_KEY; + + const isOllamaBaseConfigValid = PEXELS_API_KEY !== '' && PEXELS_API_KEY !== null && PEXELS_API_KEY !== undefined && MODEL !== '' && MODEL !== null && MODEL !== undefined; + return llmConfig.LLM === 'openai' ? OPENAI_API_KEY !== '' && OPENAI_API_KEY !== null && OPENAI_API_KEY !== undefined : llmConfig.LLM === 'google' ? GOOGLE_API_KEY !== '' && GOOGLE_API_KEY !== null && GOOGLE_API_KEY !== undefined : llmConfig.LLM === 'ollama' ? - PEXELS_API_KEY !== '' && PEXELS_API_KEY !== null && PEXELS_API_KEY !== undefined && OLLAMA_MODEL !== '' && OLLAMA_MODEL !== null && OLLAMA_MODEL !== undefined : - false; + useCustomOllamaUrl ? + isOllamaBaseConfigValid && llmConfig.LLM_PROVIDER_URL !== '' && llmConfig.LLM_PROVIDER_URL !== null && llmConfig.LLM_PROVIDER_URL !== undefined && llmConfig.LLM_API_KEY !== '' && llmConfig.LLM_API_KEY !== null && llmConfig.LLM_API_KEY !== undefined : + isOllamaBaseConfigValid : false; } \ No newline at end of file