Merge pull request #52 from presenton/removes_langchain

feat(ollama): adds support for custom ollama url, refactor: removes langchain

.dockerignore

@@ -7,4 +7,5 @@ build
 .git
 .gitignore
 tmp
-debug
+debug
+.fastembed_cache

.gitignore

@@ -8,4 +8,5 @@ node_modules
 out
 user_data
 tmp
-debug
+debug
+.fastembed_cache

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")

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"

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("<br>", "\n")
+            parsed_doc = parsed_doc.replace("<br>", "\n")
             with open(file_path, "w") as text_file:
                 text_file.write(parsed_doc)
             document_paths.append(file_path)

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

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:

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

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"]):

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(

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"]
         ]

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"

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"

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")

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):

servers/fastapi/document_processor/loader.py

@@ -1,11 +1,10 @@
+import asyncio
 import mimetypes
 import os
 from typing import List, Tuple
 from fastapi import HTTPException
-from langchain_community.document_loaders import TextLoader, PDFPlumberLoader
-from langchain_core.documents import Document
-from langchain_text_splitters import CharacterTextSplitter, MarkdownTextSplitter
 from pptx import Presentation
+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

servers/fastapi/graph_processor/models.py

@@ -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,
-}

servers/fastapi/graph_processor/utils.py

@@ -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

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]

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

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
 

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

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

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

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()

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

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,

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]

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]

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]

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)

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

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

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"

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"

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"

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
     )

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");
       }

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))

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<string, ProviderConfig> = {
     openai: {
@@ -55,14 +71,22 @@ const SettingsPage = () => {
         done: false,
     });
     const [isLoading, setIsLoading] = useState<boolean>(false);
+    const [openModelSelect, setOpenModelSelect] = useState(false);
+    const [useCustomOllamaUrl, setUseCustomOllamaUrl] = useState<boolean>(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 = () => {
                                     </label>
                                     <div className="w-full">
                                         {ollamaModels.length > 0 ? (
-                                            <Select value={llmConfig.OLLAMA_MODEL} onValueChange={(value) => setLlmConfig({ ...llmConfig, OLLAMA_MODEL: value })}>
-                                                <SelectTrigger className="w-full h-12 px-4 py-4 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors hover:border-gray-400">
-                                                    <div className="flex items-center justify-between w-full">
+                                            <Popover open={openModelSelect} onOpenChange={setOpenModelSelect}>
+                                                <PopoverTrigger asChild>
+                                                    <Button
+                                                        variant="outline"
+                                                        role="combobox"
+                                                        aria-expanded={openModelSelect}
+                                                        className="w-full h-12 px-4 py-4 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors hover:border-gray-400 justify-between"
+                                                    >
                                                         <div className="flex gap-3 items-center">
-                                                            <div className="w-6 h-6 rounded-lg flex items-center justify-center flex-shrink-0">
-                                                                <img
-                                                                    src={ollamaModels.find(m => m.value === llmConfig.OLLAMA_MODEL)?.icon}
-                                                                    alt={`${llmConfig.OLLAMA_MODEL} icon`}
-                                                                    className="rounded-sm"
-                                                                />
-                                                            </div>
+                                                            {llmConfig.MODEL && (
+                                                                <div className="w-6 h-6 rounded-lg flex items-center justify-center flex-shrink-0">
+                                                                    <img
+                                                                        src={ollamaModels.find(m => m.value === llmConfig.MODEL)?.icon}
+                                                                        alt={`${llmConfig.MODEL} icon`}
+                                                                        className="rounded-sm"
+                                                                    />
+                                                                </div>
+                                                            )}
                                                             <span className="text-sm font-medium text-gray-900">
-                                                                {llmConfig.OLLAMA_MODEL ? (
-                                                                    ollamaModels.find(m => m.value === llmConfig.OLLAMA_MODEL)?.label || llmConfig.OLLAMA_MODEL
+                                                                {llmConfig.MODEL ? (
+                                                                    ollamaModels.find(m => m.value === llmConfig.MODEL)?.label || llmConfig.MODEL
                                                                 ) : (
                                                                     'Select a model'
                                                                 )}
                                                             </span>
-                                                            {llmConfig.OLLAMA_MODEL && (
+                                                            {llmConfig.MODEL && (
                                                                 <span className="text-xs text-gray-500 bg-gray-100 rounded-full px-2 py-1">
-                                                                    {ollamaModels.find(m => m.value === llmConfig.OLLAMA_MODEL)?.size}
+                                                                    {ollamaModels.find(m => m.value === llmConfig.MODEL)?.size}
                                                                 </span>
                                                             )}
                                                         </div>
-                                                    </div>
-                                                </SelectTrigger>
-                                                <SelectContent className="max-h-80">
-                                                    <div className="p-2">
-                                                        <div className="text-xs font-semibold text-gray-500 uppercase tracking-wide mb-3 pt-3 px-2">
-                                                            Available Models
-                                                        </div>
-                                                        {ollamaModels.map((model, index) => (
-                                                            <SelectItem
-                                                                key={index}
-                                                                value={model.value}
-                                                                className="relative cursor-pointer rounded-md py-3 hover:bg-gray-50 focus:bg-gray-50 focus:outline-none transition-colors"
-                                                            >
-                                                                <div className="flex gap-3 items-center">
-                                                                    <div className="w-8 h-8 rounded-lg flex items-center justify-center flex-shrink-0">
-                                                                        <img
-                                                                            src={model.icon}
-                                                                            alt={`${model.label} icon`}
-                                                                            className=" rounded-sm"
+                                                        <ChevronsUpDown className="w-4 h-4 text-gray-500" />
+                                                    </Button>
+                                                </PopoverTrigger>
+                                                <PopoverContent className="p-0" align="start" style={{ width: 'var(--radix-popover-trigger-width)' }}>
+                                                    <Command>
+                                                        <CommandInput placeholder="Search model..." />
+                                                        <CommandList>
+                                                            <CommandEmpty>No model found.</CommandEmpty>
+                                                            <CommandGroup>
+                                                                {ollamaModels.map((model, index) => (
+                                                                    <CommandItem
+                                                                        key={index}
+                                                                        value={model.value}
+                                                                        onSelect={(value) => {
+                                                                            setLlmConfig({ ...llmConfig, MODEL: value });
+                                                                            setOpenModelSelect(false);
+                                                                        }}
+                                                                    >
+                                                                        <Check
+                                                                            className={cn(
+                                                                                "mr-2 h-4 w-4",
+                                                                                llmConfig.MODEL === model.value ? "opacity-100" : "opacity-0"
+                                                                            )}
                                                                         />
-                                                                    </div>
-                                                                    <div className="flex flex-col space-y-1 flex-1">
-                                                                        <div className="flex items-center justify-between gap-2">
-                                                                            <span className="text-sm font-medium text-gray-900 capitalize">
-                                                                                {model.label}
-                                                                            </span>
-                                                                            <span className="text-xs text-gray-500 bg-gray-100 px-2 py-1 rounded-full">
-                                                                                {model.size}
-                                                                            </span>
+                                                                        <div className="flex gap-3 items-center">
+                                                                            <div className="w-8 h-8 rounded-lg flex items-center justify-center flex-shrink-0">
+                                                                                <img
+                                                                                    src={model.icon}
+                                                                                    alt={`${model.label} icon`}
+                                                                                    className="rounded-sm"
+                                                                                />
+                                                                            </div>
+                                                                            <div className="flex flex-col space-y-1 flex-1">
+                                                                                <div className="flex items-center justify-between gap-2">
+                                                                                    <span className="text-sm font-medium text-gray-900 capitalize">
+                                                                                        {model.label}
+                                                                                    </span>
+                                                                                    <span className="text-xs text-gray-500 bg-gray-100 px-2 py-1 rounded-full">
+                                                                                        {model.size}
+                                                                                    </span>
+                                                                                </div>
+                                                                                <span className="text-xs text-gray-600 leading-relaxed">
+                                                                                    {model.description}
+                                                                                </span>
+                                                                            </div>
                                                                         </div>
-                                                                        <span className="text-xs text-gray-600 leading-relaxed">
-                                                                            {model.description}
-                                                                        </span>
-                                                                    </div>
-                                                                </div>
-                                                            </SelectItem>
-                                                        ))}
-                                                    </div>
-                                                </SelectContent>
-                                            </Select>
+                                                                    </CommandItem>
+                                                                ))}
+                                                            </CommandGroup>
+                                                        </CommandList>
+                                                    </Command>
+                                                </PopoverContent>
+                                            </Popover>
                                         ) : (
                                             <div className="w-full border border-gray-300 rounded-lg p-4">
                                                 <div className="flex items-center space-x-3">
@@ -344,6 +396,62 @@ const SettingsPage = () => {
                                         </p>
                                     )}
                                 </div>
+
+                                {/* Custom Ollama URL Configuration */}
+                                <div>
+                                    <div className="flex items-center justify-between mb-4 bg-green-50 p-2 rounded-sm">
+                                        <label className="text-sm font-medium text-gray-700">
+                                            Use custom Ollama URL
+                                        </label>
+                                        <Switch
+                                            checked={useCustomOllamaUrl}
+                                            onCheckedChange={setUseCustomOllamaUrl}
+                                        />
+                                    </div>
+                                    {useCustomOllamaUrl && (
+                                        <>
+                                            <div className="mb-4">
+                                                <label className="block text-sm font-medium text-gray-700 mb-2">
+                                                    Ollama URL
+                                                </label>
+                                                <div className="relative">
+                                                    <input
+                                                        type="text"
+                                                        required
+                                                        placeholder="Enter your Ollama URL"
+                                                        className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+                                                        value={llmConfig.LLM_PROVIDER_URL || ''}
+                                                        onChange={(e) => api_key_changed(e.target.value, 'ollama_url')}
+                                                    />
+                                                </div>
+                                                <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+                                                    <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+                                                    Change this if you are using a custom Ollama instance
+                                                </p>
+                                            </div>
+                                            <div className="mb-4">
+                                                <label className="block text-sm font-medium text-gray-700 mb-2">
+                                                    Ollama API Key
+                                                </label>
+                                                <div className="relative">
+                                                    <input
+                                                        type="text"
+                                                        required
+                                                        placeholder="Enter your Ollama API key"
+                                                        className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+                                                        value={llmConfig.LLM_API_KEY || ''}
+                                                        onChange={(e) => api_key_changed(e.target.value, 'ollama_api_key')}
+                                                    />
+                                                </div>
+                                                <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+                                                    <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+                                                    Provide this if you are using a custom Ollama instance
+                                                </p>
+                                            </div>
+                                        </>
+                                    )}
+                                </div>
+
                                 <div>
                                     <label className="block text-sm font-medium text-gray-700 mb-2">
                                         Pexels API Key (required for images)
@@ -355,12 +463,12 @@ const SettingsPage = () => {
                                             placeholder="Enter your Pexels API key"
                                             className="flex-1 px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
                                             value={llmConfig.PEXELS_API_KEY || ''}
-                                            onChange={(e) => api_key_changed(e.target.value)}
+                                            onChange={(e) => api_key_changed(e.target.value, 'pexels')}
                                         />
                                         <button
                                             onClick={handleSaveConfig}
-                                            disabled={isLoading || !llmConfig.OLLAMA_MODEL}
-                                            className={`px-4 py-2 rounded-lg transition-colors ${isLoading || !llmConfig.OLLAMA_MODEL
+                                            disabled={isLoading || !llmConfig.MODEL}
+                                            className={`px-4 py-2 rounded-lg transition-colors ${isLoading || !llmConfig.MODEL
                                                 ? 'bg-gray-400 cursor-not-allowed'
                                                 : 'bg-blue-600 hover:bg-blue-700'
                                                 } text-white`}
@@ -374,7 +482,7 @@ const SettingsPage = () => {
                                                     }
                                                 </div>
                                             ) : (
-                                                !llmConfig.OLLAMA_MODEL ? 'Select Model' : 'Save'
+                                                !llmConfig.MODEL ? 'Select Model' : 'Save'
                                             )}
                                         </button>
                                     </div>

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('/');

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<string, ProviderConfig> = {
         },
     },
     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<boolean>(false);
+    const [openModelSelect, setOpenModelSelect] = useState(false);
+    const [useCustomOllamaUrl, setUseCustomOllamaUrl] = useState<boolean>(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() {
                                 </label>
                                 <div className="w-full">
                                     {ollamaModels.length > 0 ? (
-                                        <Select value={llmConfig.OLLAMA_MODEL} onValueChange={(value) => setLlmConfig({ ...llmConfig, OLLAMA_MODEL: value })}>
-                                            <SelectTrigger className="w-full h-12 px-4 py-4 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors hover:border-gray-400">
-                                                <div className="flex items-center justify-between w-full">
+                                        <Popover open={openModelSelect} onOpenChange={setOpenModelSelect}>
+                                            <PopoverTrigger asChild>
+                                                <Button
+                                                    variant="outline"
+                                                    role="combobox"
+                                                    aria-expanded={openModelSelect}
+                                                    className="w-full h-12 px-4 py-4 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors hover:border-gray-400 justify-between"
+                                                >
                                                     <div className="flex gap-3 items-center">
-                                                        <div className="w-6 h-6 rounded-lg flex items-center justify-center flex-shrink-0">
-                                                            <img
-                                                                src={ollamaModels.find(m => m.value === llmConfig.OLLAMA_MODEL)?.icon}
-                                                                alt={`${llmConfig.OLLAMA_MODEL} icon`}
-                                                                className="rounded-sm"
-                                                            />
-                                                        </div>
+                                                        {llmConfig.MODEL && (
+                                                            <div className="w-6 h-6 rounded-lg flex items-center justify-center flex-shrink-0">
+                                                                <img
+                                                                    src={ollamaModels.find(m => m.value === llmConfig.MODEL)?.icon}
+                                                                    alt={`${llmConfig.MODEL} icon`}
+                                                                    className="rounded-sm"
+                                                                />
+                                                            </div>
+                                                        )}
                                                         <span className="text-sm font-medium text-gray-900">
-                                                            {llmConfig.OLLAMA_MODEL ? (
-                                                                ollamaModels.find(m => m.value === llmConfig.OLLAMA_MODEL)?.label || llmConfig.OLLAMA_MODEL
+                                                            {llmConfig.MODEL ? (
+                                                                ollamaModels.find(m => m.value === llmConfig.MODEL)?.label || llmConfig.MODEL
                                                             ) : (
                                                                 'Select a model'
                                                             )}
                                                         </span>
-                                                        {llmConfig.OLLAMA_MODEL && (
+                                                        {llmConfig.MODEL && (
                                                             <span className="text-xs text-gray-500 bg-gray-100 rounded-full px-2 py-1">
-                                                                {ollamaModels.find(m => m.value === llmConfig.OLLAMA_MODEL)?.size}
+                                                                {ollamaModels.find(m => m.value === llmConfig.MODEL)?.size}
                                                             </span>
                                                         )}
                                                     </div>
-                                                </div>
-                                            </SelectTrigger>
-                                            <SelectContent className="max-h-80">
-                                                <div className="p-2">
-                                                    <div className="text-xs font-semibold text-gray-500 uppercase tracking-wide mb-3 pt-3 px-2">
-                                                        Available Models
-                                                    </div>
-                                                    {ollamaModels.map((model, index) => (
-                                                        <SelectItem
-                                                            key={index}
-                                                            value={model.value}
-                                                            className="relative cursor-pointer rounded-md py-3 hover:bg-gray-50 focus:bg-gray-50 focus:outline-none transition-colors"
-                                                        >
-                                                            <div className="flex gap-3 items-center">
-                                                                <div className="w-8 h-8 rounded-lg flex items-center justify-center flex-shrink-0">
-                                                                    <img
-                                                                        src={model.icon}
-                                                                        alt={`${model.label} icon`}
-                                                                        className=" rounded-sm"
+                                                    <ChevronsUpDown className="w-4 h-4 text-gray-500" />
+                                                </Button>
+                                            </PopoverTrigger>
+                                            <PopoverContent className="p-0" align="start" style={{ width: 'var(--radix-popover-trigger-width)' }}>
+                                                <Command>
+                                                    <CommandInput placeholder="Search model..." />
+                                                    <CommandList>
+                                                        <CommandEmpty>No model found.</CommandEmpty>
+                                                        <CommandGroup>
+                                                            {ollamaModels.map((model, index) => (
+                                                                <CommandItem
+                                                                    key={index}
+                                                                    value={model.value}
+                                                                    onSelect={(value) => {
+                                                                        setLlmConfig({ ...llmConfig, MODEL: value });
+                                                                        setOpenModelSelect(false);
+                                                                    }}
+                                                                >
+                                                                    <Check
+                                                                        className={cn(
+                                                                            "mr-2 h-4 w-4",
+                                                                            llmConfig.MODEL === model.value ? "opacity-100" : "opacity-0"
+                                                                        )}
                                                                     />
-                                                                </div>
-                                                                <div className="flex flex-col space-y-1 flex-1">
-                                                                    <div className="flex items-center justify-between gap-2">
-                                                                        <span className="text-sm font-medium text-gray-900 capitalize">
-                                                                            {model.label}
-                                                                        </span>
-                                                                        <span className="text-xs text-gray-500 bg-gray-100 px-2 py-1 rounded-full">
-                                                                            {model.size}
-                                                                        </span>
+                                                                    <div className="flex gap-3 items-center">
+                                                                        <div className="w-8 h-8 rounded-lg flex items-center justify-center flex-shrink-0">
+                                                                            <img
+                                                                                src={model.icon}
+                                                                                alt={`${model.label} icon`}
+                                                                                className="rounded-sm"
+                                                                            />
+                                                                        </div>
+                                                                        <div className="flex flex-col space-y-1 flex-1">
+                                                                            <div className="flex items-center justify-between gap-2">
+                                                                                <span className="text-sm font-medium text-gray-900 capitalize">
+                                                                                    {model.label}
+                                                                                </span>
+                                                                                <span className="text-xs text-gray-500 bg-gray-100 px-2 py-1 rounded-full">
+                                                                                    {model.size}
+                                                                                </span>
+                                                                            </div>
+                                                                            <span className="text-xs text-gray-600 leading-relaxed">
+                                                                                {model.description}
+                                                                            </span>
+                                                                        </div>
                                                                     </div>
-                                                                    <span className="text-xs text-gray-600 leading-relaxed">
-                                                                        {model.description}
-                                                                    </span>
-                                                                </div>
-                                                            </div>
-                                                        </SelectItem>
-                                                    ))}
-                                                </div>
-                                            </SelectContent>
-                                        </Select>
+                                                                </CommandItem>
+                                                            ))}
+                                                        </CommandGroup>
+                                                    </CommandList>
+                                                </Command>
+                                            </PopoverContent>
+                                        </Popover>
                                     ) : (
                                         <div className="w-full border border-gray-300 rounded-lg p-4">
                                             <div className="flex items-center space-x-3">
@@ -437,6 +461,59 @@ export default function Home() {
                                     </p>
                                 )}
                             </div>
+                            <div className="mb-8">
+                                <div className="flex items-center justify-between mb-4 bg-green-50 p-2 rounded-sm">
+                                    <label className="text-sm font-medium text-gray-700">
+                                        Use custom Ollama URL
+                                    </label>
+                                    <Switch
+                                        checked={useCustomOllamaUrl}
+                                        onCheckedChange={setUseCustomOllamaUrl}
+                                    />
+                                </div>
+                                {useCustomOllamaUrl && (
+                                    <>
+                                        <div className="mb-4">
+                                            <label className="block text-sm font-medium text-gray-700 mb-2">
+                                                Ollama URL
+                                            </label>
+                                            <div className="relative">
+                                                <input
+                                                    type="text"
+                                                    required
+                                                    placeholder="Enter your Ollama URL"
+                                                    className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+                                                    value={llmConfig.LLM_PROVIDER_URL || ''}
+                                                    onChange={(e) => api_key_changed(e.target.value, 'ollama_url')}
+                                                />
+                                            </div>
+                                            <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+                                                <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+                                                Change this if you are using a custom Ollama instance
+                                            </p>
+                                        </div>
+                                        <div className="mb-4">
+                                            <label className="block text-sm font-medium text-gray-700 mb-2">
+                                                Ollama API Key
+                                            </label>
+                                            <div className="relative">
+                                                <input
+                                                    type="text"
+                                                    required
+                                                    placeholder="Enter your Ollama API key"
+                                                    className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+                                                    value={llmConfig.LLM_API_KEY || ''}
+                                                    onChange={(e) => api_key_changed(e.target.value, 'ollama_api_key')}
+                                                />
+                                            </div>
+                                            <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+                                                <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+                                                Provide this if you are using a custom Ollama instance
+                                            </p>
+                                        </div>
+                                    </>
+                                )}
+                            </div>
                             <div className="mb-8">
                                 <label className="block text-sm font-medium text-gray-700 mb-2">
                                     Pexels API Key (required for images)
@@ -448,7 +525,7 @@ export default function Home() {
                                         placeholder="Enter your Pexels API key"
                                         className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
                                         value={llmConfig.PEXELS_API_KEY || ''}
-                                        onChange={(e) => api_key_changed(e.target.value)}
+                                        onChange={(e) => api_key_changed(e.target.value, 'pexels')}
                                     />
                                 </div>
                                 <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
@@ -468,7 +545,7 @@ export default function Home() {
                                     Selected Models
                                 </h3>
                                 <p className="text-sm text-blue-700">
-                                    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
                                 </p>
@@ -544,7 +621,7 @@ export default function Home() {
                                 }
                             </div>
                         ) : (
-                            llmConfig.LLM === 'ollama' && !llmConfig.OLLAMA_MODEL
+                            llmConfig.LLM === 'ollama' && !llmConfig.MODEL
                                 ? 'Please Select a Model'
                                 : 'Save Configuration'
                         )}

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;
 }

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;
 }