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Merge main into pdf-pptx-layout

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Suraj Jha 2025-08-09 15:36:43 +05:45
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1
README.md
View file

@ -91,6 +91,7 @@ You may want to directly provide your API KEYS as environment variables and keep
- **CUSTOM_MODEL=[Custom Model ID]**: Provide this if **LLM** is set to **custom**
- **TOOL_CALLS=[Enable/Disable Tool Calls on Custom LLM]**: If **true**, **LLM** will use Tool Call instead of Json Schema for Structured Output.
- **DISABLE_THINKING=[Enable/Disable Thinking on Custom LLM]**: If **true**, Thinking will be disabled.
- **WEB_GROUNDING=[Enable/Disable Web Search for OpenAI, Google And Anthropic]**: If **true**, LLM will be able to search web for better results.
You can also set the following environment variables to customize the image generation provider and API keys:

13
docker-compose.yml
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@ -25,6 +25,9 @@ services:
- CUSTOM_MODEL=${CUSTOM_MODEL}
- PEXELS_API_KEY=${PEXELS_API_KEY}
- EXTENDED_REASONING=${EXTENDED_REASONING}
- TOOL_CALLS=${TOOL_CALLS}
- DISABLE_THINKING=${DISABLE_THINKING}
- WEB_GROUNDING=${WEB_GROUNDING}
- DATABASE_URL=${DATABASE_URL}
production-gpu:
@ -60,6 +63,9 @@ services:
- CUSTOM_MODEL=${CUSTOM_MODEL}
- PEXELS_API_KEY=${PEXELS_API_KEY}
- EXTENDED_REASONING=${EXTENDED_REASONING}
- TOOL_CALLS=${TOOL_CALLS}
- DISABLE_THINKING=${DISABLE_THINKING}
- WEB_GROUNDING=${WEB_GROUNDING}
- DATABASE_URL=${DATABASE_URL}
development:
@ -87,6 +93,9 @@ services:
- CUSTOM_MODEL=${CUSTOM_MODEL}
- PEXELS_API_KEY=${PEXELS_API_KEY}
- EXTENDED_REASONING=${EXTENDED_REASONING}
- TOOL_CALLS=${TOOL_CALLS}
- DISABLE_THINKING=${DISABLE_THINKING}
- WEB_GROUNDING=${WEB_GROUNDING}
- DATABASE_URL=${DATABASE_URL}
development-gpu:
@ -122,3 +131,7 @@ services:
- PEXELS_API_KEY=${PEXELS_API_KEY}
- DATABASE_URL=${DATABASE_URL}
- EXTENDED_REASONING=${EXTENDED_REASONING}
- TOOL_CALLS=${TOOL_CALLS}
- DISABLE_THINKING=${DISABLE_THINKING}
- WEB_GROUNDING=${WEB_GROUNDING}
- DATABASE_URL=${DATABASE_URL}

2
servers/fastapi/api/v1/ppt/endpoints/ollama.py
View file

@ -64,7 +64,7 @@ async def pull_model(
# If the model is being pulled, return the model
if saved_model_status:
# If the model is being pulled, return the model
# ? If the model status is pulled in redis but was not found while listing pulled models,
# ? If the model status is pulled in database but was not found while listing pulled models,
# ? it means the model was deleted and we need to pull it again
if (
saved_model_status["status"] == "error"

6
servers/fastapi/api/v1/ppt/endpoints/outlines.py
View file

@ -72,6 +72,9 @@ async def stream_outlines(
presentation_outlines_json = json.loads(presentation_outlines_text)
except Exception as e:
print(e)
with open("./debug/outlines.txt", "w") as f:
f.write(presentation_outlines_text)
print(presentation_outlines_text)
raise HTTPException(
status_code=400,
detail="Failed to generate presentation outlines. Please try again.",
@ -87,7 +90,8 @@ async def stream_outlines(
presentation.outlines = presentation_outlines.model_dump()
presentation.title = (
presentation_outlines.slides[0][:50]
presentation_outlines.slides[0]
.content[:50]
.replace("#", "")
.replace("/", "")
.replace("\\", "")

11
servers/fastapi/api/v1/ppt/endpoints/presentation.py
View file

@ -11,7 +11,10 @@ from sqlmodel import select
from constants.documents import UPLOAD_ACCEPTED_FILE_TYPES
from models.presentation_and_path import PresentationPathAndEditPath
from models.presentation_from_template import GetPresentationUsingTemplateRequest
from models.presentation_outline_model import PresentationOutlineModel
from models.presentation_outline_model import (
PresentationOutlineModel,
SlideOutlineModel,
)
from models.pptx_models import PptxPresentationModel
from models.presentation_layout import PresentationLayoutModel
from models.presentation_structure_model import PresentationStructureModel
@ -126,7 +129,7 @@ async def create_presentation(
@PRESENTATION_ROUTER.post("/prepare", response_model=PresentationModel)
async def prepare_presentation(
presentation_id: Annotated[str, Body()],
outlines: Annotated[List[str], Body()],
outlines: Annotated[List[SlideOutlineModel], Body()],
layout: Annotated[PresentationLayoutModel, Body()],
title: Annotated[Optional[str], Body()] = None,
sql_session: AsyncSession = Depends(get_async_session),
@ -161,7 +164,9 @@ async def prepare_presentation(
presentation_structure.slides[index] = random_slide_index
sql_session.add(presentation)
presentation.outlines = PresentationOutlineModel(slides=outlines).model_dump()
presentation.outlines = PresentationOutlineModel(slides=outlines).model_dump(
mode="json"
)
presentation.title = title or presentation.title
presentation.set_layout(layout)
presentation.set_structure(presentation_structure)

4
servers/fastapi/constants/llm.py
View file

@ -2,5 +2,5 @@ OPENAI_URL = "https://api.openai.com/v1"
# Default models
DEFAULT_OPENAI_MODEL = "gpt-4.1"
DEFAULT_GOOGLE_MODEL = "models/gemini-2.0-flash"
DEFAULT_ANTHROPIC_MODEL = "claude-3-5-sonnet-20240620"
DEFAULT_GOOGLE_MODEL = "models/gemini-2.5-flash"
DEFAULT_ANTHROPIC_MODEL = "claude-sonnet-4-20250514"

43
servers/fastapi/constants/supported_ollama_models.py
View file

@ -6,61 +6,51 @@ SUPPORTED_OLLAMA_MODELS = {
label="Llama 3:8b",
value="llama3:8b",
size="4.7GB",
icon="/static/icons/meta.png",
),
"llama3:70b": OllamaModelMetadata(
label="Llama 3:70b",
value="llama3:70b",
size="40GB",
icon="/static/icons/meta.png",
),
"llama3.1:8b": OllamaModelMetadata(
label="Llama 3.1:8b",
value="llama3.1:8b",
size="4.9GB",
icon="/static/icons/meta.png",
),
"llama3.1:70b": OllamaModelMetadata(
label="Llama 3.1:70b",
value="llama3.1:70b",
size="43GB",
icon="/static/icons/meta.png",
),
"llama3.1:405b": OllamaModelMetadata(
label="Llama 3.1:405b",
value="llama3.1:405b",
size="243GB",
icon="/static/icons/meta.png",
),
"llama3.2:1b": OllamaModelMetadata(
label="Llama 3.2:1b",
value="llama3.2:1b",
size="1.3GB",
icon="/static/icons/meta.png",
),
"llama3.2:3b": OllamaModelMetadata(
label="Llama 3.2:3b",
value="llama3.2:3b",
size="2GB",
icon="/static/icons/meta.png",
),
"llama3.3:70b": OllamaModelMetadata(
label="Llama 3.3:70b",
value="llama3.3:70b",
size="43GB",
icon="/static/icons/meta.png",
),
"llama4:16x17b": OllamaModelMetadata(
label="Llama 4:16x17b",
value="llama4:16x17b",
size="67GB",
icon="/static/icons/meta.png",
),
"llama4:128x17b": OllamaModelMetadata(
label="Llama 4:128x17b",
value="llama4:128x17b",
size="245GB",
icon="/static/icons/meta.png",
),
}
@ -69,25 +59,21 @@ SUPPORTED_GEMMA_MODELS = {
label="Gemma 3:1b",
value="gemma3:1b",
size="815MB",
icon="/static/icons/gemma.png",
),
"gemma3:4b": OllamaModelMetadata(
label="Gemma 3:4b",
value="gemma3:4b",
size="3.3GB",
icon="/static/icons/gemma.png",
),
"gemma3:12b": OllamaModelMetadata(
label="Gemma 3:12b",
value="gemma3:12b",
size="8.1GB",
icon="/static/icons/gemma.png",
),
"gemma3:27b": OllamaModelMetadata(
label="Gemma 3:27b",
value="gemma3:27b",
size="17GB",
icon="/static/icons/gemma.png",
),
}
@ -96,43 +82,36 @@ SUPPORTED_DEEPSEEK_MODELS = {
label="DeepSeek R1:1.5b",
value="deepseek-r1:1.5b",
size="1.1GB",
icon="/static/icons/deepseek.png",
),
"deepseek-r1:7b": OllamaModelMetadata(
label="DeepSeek R1:7b",
value="deepseek-r1:7b",
size="4.7GB",
icon="/static/icons/deepseek.png",
),
"deepseek-r1:8b": OllamaModelMetadata(
label="DeepSeek R1:8b",
value="deepseek-r1:8b",
size="5.2GB",
icon="/static/icons/deepseek.png",
),
"deepseek-r1:14b": OllamaModelMetadata(
label="DeepSeek R1:14b",
value="deepseek-r1:14b",
size="9GB",
icon="/static/icons/deepseek.png",
),
"deepseek-r1:32b": OllamaModelMetadata(
label="DeepSeek R1:32b",
value="deepseek-r1:32b",
size="20GB",
icon="/static/icons/deepseek.png",
),
"deepseek-r1:70b": OllamaModelMetadata(
label="DeepSeek R1:70b",
value="deepseek-r1:70b",
size="43GB",
icon="/static/icons/deepseek.png",
),
"deepseek-r1:671b": OllamaModelMetadata(
label="DeepSeek R1:671b",
value="deepseek-r1:671b",
size="404GB",
icon="/static/icons/deepseek.png",
),
}
@ -141,49 +120,54 @@ SUPPORTED_QWEN_MODELS = {
label="Qwen 3:0.6b",
value="qwen3:0.6b",
size="523MB",
icon="/static/icons/qwen.png",
),
"qwen3:1.7b": OllamaModelMetadata(
label="Qwen 3:1.7b",
value="qwen3:1.7b",
size="1.4GB",
icon="/static/icons/qwen.png",
),
"qwen3:4b": OllamaModelMetadata(
label="Qwen 3:4b",
value="qwen3:4b",
size="2.6GB",
icon="/static/icons/qwen.png",
),
"qwen3:8b": OllamaModelMetadata(
label="Qwen 3:8b",
value="qwen3:8b",
size="5.2GB",
icon="/static/icons/qwen.png",
),
"qwen3:14b": OllamaModelMetadata(
label="Qwen 3:14b",
value="qwen3:14b",
size="9.3GB",
icon="/static/icons/qwen.png",
),
"qwen3:30b": OllamaModelMetadata(
label="Qwen 3:30b",
value="qwen3:30b",
size="19GB",
icon="/static/icons/qwen.png",
),
"qwen3:32b": OllamaModelMetadata(
label="Qwen 3:32b",
value="qwen3:32b",
size="20GB",
icon="/static/icons/qwen.png",
),
"qwen3:235b": OllamaModelMetadata(
label="Qwen 3:235b",
value="qwen3:235b",
size="142GB",
icon="/static/icons/qwen.png",
),
}
SUPPORTED_GPT_OSS_MODELS = {
"gpt-oss:20b": OllamaModelMetadata(
label="GPT-OSS 20b",
value="gpt-oss:20b",
size="14GB",
),
"gpt-oss:120b": OllamaModelMetadata(
label="GPT-OSS 120b",
value="gpt-oss:120b",
size="65GB",
),
}
@ -192,4 +176,5 @@ SUPPORTED_OLLAMA_MODELS = {
**SUPPORTED_GEMMA_MODELS,
**SUPPORTED_DEEPSEEK_MODELS,
**SUPPORTED_QWEN_MODELS,
**SUPPORTED_GPT_OSS_MODELS,
}

8
servers/fastapi/enums/llm_call_type.py Normal file
View file

@ -0,0 +1,8 @@
from enum import Enum
class LLMCallType(Enum):
UNSTRUCTURED = "unstructured"
UNSTRUCTURED_STREAM = "unstructured_stream"
STRUCTURED = "structured"
STRUCTURED_STREAM = "structured_stream"

6
servers/fastapi/models/document_chunk.py
View file

@ -1,5 +1,7 @@
from pydantic import BaseModel
from models.presentation_outline_model import SlideOutlineModel
class DocumentChunk(BaseModel):
heading: str
@ -7,5 +9,5 @@ class DocumentChunk(BaseModel):
heading_index: int
score: float
def to_slide_outline(self) -> str:
return f"{self.heading}\n{self.content}"
def to_slide_outline(self) -> SlideOutlineModel:
return SlideOutlineModel(content=f"{self.heading}\n{self.content}")

55
servers/fastapi/models/llm_message.py
View file

@ -1,7 +1,58 @@
from typing import Literal
from typing import Any, List, Literal, Optional
from pydantic import BaseModel
from google.genai.types import Content as GoogleContent
from models.llm_tool_call import AnthropicToolCall
class LLMMessage(BaseModel):
role: Literal["user", "system"]
pass
class LLMUserMessage(LLMMessage):
role: Literal["user"] = "user"
content: str
class LLMSystemMessage(LLMMessage):
role: Literal["system"] = "system"
content: str
class OpenAIAssistantMessage(LLMMessage):
role: Literal["assistant"] = "assistant"
content: str | None = None
tool_calls: Optional[List[dict]] = None
class GoogleAssistantMessage(LLMMessage):
role: Literal["assistant"] = "assistant"
content: GoogleContent
class AnthropicAssistantMessage(LLMMessage):
role: Literal["assistant"] = "assistant"
content: List[AnthropicToolCall]
class AnthropicToolCallMessage(LLMMessage):
type: Literal["tool_result"] = "tool_result"
tool_use_id: str
content: str
class AnthropicUserMessage(LLMMessage):
role: Literal["user"] = "user"
content: List[AnthropicToolCallMessage]
class OpenAIToolCallMessage(LLMMessage):
role: Literal["tool"] = "tool"
content: str
tool_call_id: str
class GoogleToolCallMessage(LLMMessage):
role: Literal["tool"] = "tool"
name: str
response: dict

29
servers/fastapi/models/llm_tool_call.py Normal file
View file

@ -0,0 +1,29 @@
from typing import Literal, Optional
from pydantic import BaseModel
class LLMToolCall(BaseModel):
pass
class OpenAIToolCallFunction(BaseModel):
name: str
arguments: str
class OpenAIToolCall(LLMToolCall):
id: str
type: Literal["function"] = "function"
function: OpenAIToolCallFunction
class GoogleToolCall(LLMToolCall):
name: str
arguments: Optional[dict] = None
class AnthropicToolCall(LLMToolCall):
type: Literal["tool_use"] = "tool_use"
id: str
name: str
input: object

29
servers/fastapi/models/llm_tools.py Normal file
View file

@ -0,0 +1,29 @@
from typing import Any, Callable, Coroutine, Optional
from pydantic import BaseModel, Field
class LLMTool(BaseModel):
pass
class LLMDynamicTool(LLMTool):
name: str
description: str
parameters: dict = {}
handler: Callable[..., Coroutine[Any, Any, str]]
class SearchWebTool(LLMTool):
"""
Search the web for information.
"""
query: str = Field(description="The query to search the web for")
class GetCurrentDatetimeTool(LLMTool):
"""
Get the current datetime.
"""
pass

1
servers/fastapi/models/ollama_model_metadata.py
View file

@ -4,5 +4,4 @@ from pydantic import BaseModel
class OllamaModelMetadata(BaseModel):
label: str
value: str
icon: str
size: str

6
servers/fastapi/models/presentation_outline_model.py
View file

@ -2,8 +2,12 @@ from typing import List
from pydantic import BaseModel
class SlideOutlineModel(BaseModel):
content: str
class PresentationOutlineModel(BaseModel):
slides: List[str]
slides: List[SlideOutlineModel]
def to_string(self):
message = ""

3
servers/fastapi/models/user_config.py
View file

@ -35,3 +35,6 @@ class UserConfig(BaseModel):
TOOL_CALLS: Optional[bool] = None
DISABLE_THINKING: Optional[bool] = None
EXTENDED_REASONING: Optional[bool] = None
# Web Search
WEB_GROUNDING: Optional[bool] = None

1
servers/fastapi/pyproject.toml
View file

@ -19,6 +19,7 @@ dependencies = [
"openai>=1.98.0",
"pathvalidate>=3.3.1",
"pdfplumber>=0.11.7",
"pytest>=8.4.1",
"python-pptx>=1.0.2",
"redis>=6.2.0",
"sqlmodel>=0.0.24",

1251
servers/fastapi/services/llm_client.py
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201
servers/fastapi/services/llm_tool_calls_handler.py Normal file
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@ -0,0 +1,201 @@
import asyncio
from datetime import datetime
import json
from typing import Any, Callable, Coroutine, List, Optional
from fastapi import HTTPException
from enums.llm_provider import LLMProvider
from models.llm_message import (
AnthropicToolCallMessage,
GoogleToolCallMessage,
OpenAIToolCallMessage,
)
from models.llm_tool_call import AnthropicToolCall, GoogleToolCall, OpenAIToolCall
from models.llm_tools import LLMDynamicTool, LLMTool, SearchWebTool
from utils.schema_utils import ensure_strict_json_schema, flatten_json_schema
class LLMToolCallsHandler:
def __init__(self, client):
from services.llm_client import LLMClient
self.client: LLMClient = client
self.tools_map: dict[str, Callable[..., Coroutine[Any, Any, str]]] = {
"SearchWebTool": self.search_web_tool_call_handler,
"GetCurrentDatetimeTool": self.get_current_datetime_tool_call_handler,
}
self.dynamic_tools: List[LLMDynamicTool] = []
def get_tool_handler(
self, tool_name: str
) -> Callable[..., Coroutine[Any, Any, str]]:
handler = self.tools_map.get(tool_name)
if handler:
return handler
else:
dynamic_tools = list(
filter(lambda tool: tool.name == tool_name, self.dynamic_tools)
)
if dynamic_tools:
return dynamic_tools[0].handler
raise HTTPException(status_code=500, detail=f"Tool {tool_name} not found")
def parse_tools(self, tools: Optional[List[type[LLMTool] | LLMDynamicTool]] = None):
if tools is None:
return None
parsed_tools = map(self.parse_tool, tools)
return list(parsed_tools)
def parse_tool(self, tool: type[LLMTool] | LLMDynamicTool, strict: bool = False):
if isinstance(tool, LLMDynamicTool):
self.dynamic_tools.append(tool)
match self.client.llm_provider:
case LLMProvider.OPENAI | LLMProvider.OLLAMA | LLMProvider.CUSTOM:
return self.parse_tool_openai(tool, strict)
case LLMProvider.ANTHROPIC:
return self.parse_tool_anthropic(tool)
case LLMProvider.GOOGLE:
return self.parse_tool_google(tool)
case _:
raise ValueError(
f"LLM provider must be either openai, anthropic, or google"
)
def parse_tool_openai(
self, tool: type[LLMTool] | LLMDynamicTool, strict: bool = False
):
if isinstance(tool, LLMDynamicTool):
name = tool.name
description = tool.description
parameters = tool.parameters
else:
name = tool.__name__
description = tool.__doc__ or ""
parameters = tool.model_json_schema()
if strict:
parameters = ensure_strict_json_schema(parameters, path=(), root=parameters)
return {
"type": "function",
"function": {
"name": name,
"description": description,
"strict": strict,
"parameters": parameters,
},
}
def parse_tool_google(self, tool: type[LLMTool] | LLMDynamicTool):
parsed = self.parse_tool_openai(tool)
# parsed["function"]["parameters"] = flatten_json_schema(
# parsed["function"]["parameters"]
# )
return {
"name": parsed["function"]["name"],
"description": parsed["function"]["description"],
"parameters": parsed["function"]["parameters"],
}
def parse_tool_anthropic(self, tool: type[LLMTool] | LLMDynamicTool):
parsed = self.parse_tool_openai(tool)
input_schema = parsed["function"]["parameters"]
return {
"name": parsed["function"]["name"],
"description": parsed["function"]["description"],
"input_schema": {"type": "object"} if input_schema == {} else input_schema,
}
async def handle_tool_calls_openai(
self,
tool_calls: List[OpenAIToolCall],
) -> List[OpenAIToolCallMessage]:
async_tool_calls_tasks = []
for tool_call in tool_calls:
tool_name = tool_call.function.name
tool_handler = self.get_tool_handler(tool_name)
async_tool_calls_tasks.append(tool_handler(tool_call.function.arguments))
tool_call_results: List[str] = await asyncio.gather(*async_tool_calls_tasks)
tool_call_messages = [
OpenAIToolCallMessage(
content=result,
tool_call_id=tool_call.id,
)
for tool_call, result in zip(tool_calls, tool_call_results)
]
return tool_call_messages
async def handle_tool_calls_google(
self,
tool_calls: List[GoogleToolCall],
) -> List[GoogleToolCallMessage]:
async_tool_calls_tasks = []
for tool_call in tool_calls:
tool_name = tool_call.name
tool_handler = self.get_tool_handler(tool_name)
async_tool_calls_tasks.append(tool_handler(json.dumps(tool_call.arguments)))
tool_call_results: List[str] = await asyncio.gather(*async_tool_calls_tasks)
tool_call_messages = [
GoogleToolCallMessage(
name=tool_call.name,
response={"result": result},
)
for tool_call, result in zip(tool_calls, tool_call_results)
]
return tool_call_messages
async def handle_tool_calls_anthropic(
self,
tool_calls: List[AnthropicToolCall],
) -> List[AnthropicToolCallMessage]:
async_tool_calls_tasks = []
for tool_call in tool_calls:
tool_name = tool_call.name
tool_handler = self.get_tool_handler(tool_name)
async_tool_calls_tasks.append(tool_handler(json.dumps(tool_call.input)))
tool_call_results: List[str] = await asyncio.gather(*async_tool_calls_tasks)
tool_call_messages = [
AnthropicToolCallMessage(
content=result,
tool_use_id=tool_call.id,
)
for tool_call, result in zip(tool_calls, tool_call_results)
]
return tool_call_messages
# ? Tool call handlers
# Search web tool call handler
async def search_web_tool_call_handler(self, arguments: str) -> str:
match self.client.llm_provider:
case LLMProvider.OPENAI:
return await self.search_web_tool_call_handler_openai(arguments)
case LLMProvider.ANTHROPIC:
return await self.search_web_tool_call_handler_anthropic(arguments)
case LLMProvider.GOOGLE:
return await self.search_web_tool_call_handler_google(arguments)
case _:
return (
"Web search tool call handler not implemented for this LLM provider: "
+ self.client.llm_provider.value
)
async def search_web_tool_call_handler_openai(self, arguments: str) -> str:
args = SearchWebTool.model_validate_json(arguments)
return await self.client._search_openai(args.query)
async def search_web_tool_call_handler_google(self, arguments: str) -> str:
args = SearchWebTool.model_validate_json(arguments)
return await self.client._search_google(args.query)
async def search_web_tool_call_handler_anthropic(self, arguments: str) -> str:
args = SearchWebTool.model_validate_json(arguments)
return await self.client._search_anthropic(args.query)
# Get current datetime tool call handler
async def get_current_datetime_tool_call_handler(self, _) -> str:
current_time = datetime.now()
return f"{current_time.strftime('%A, %B %d, %Y')} at {current_time.strftime('%I:%M:%S %p')}"

115
servers/fastapi/services/redis_service.py
View file

@ -1,115 +0,0 @@
from typing import Any, Optional
import redis
from redis.exceptions import RedisError
from utils.get_env import (
get_redis_db_env,
get_redis_host_env,
get_redis_password_env,
get_redis_port_env,
)
class RedisService:
def __init__(self):
self.redis_host = get_redis_host_env() or "localhost"
self.redis_port = int(get_redis_port_env() or "6379")
self.redis_db = int(get_redis_db_env() or "0")
self.redis_password = get_redis_password_env() or None
self.client = self._create_client()
def _create_client(self) -> redis.Redis:
return redis.Redis(
host=self.redis_host,
port=self.redis_port,
db=self.redis_db,
password=self.redis_password,
decode_responses=True,
)
def set(self, key: str, value: Any, expire: Optional[int] = None) -> bool:
try:
return self.client.set(key, value, ex=expire)
except RedisError:
return False
def get(self, key: str) -> Optional[str]:
try:
return self.client.get(key)
except RedisError:
return None
def delete(self, key: str) -> bool:
try:
return bool(self.client.delete(key))
except RedisError:
return False
def exists(self, key: str) -> bool:
try:
return bool(self.client.exists(key))
except RedisError:
return False
def set_hash(self, name: str, mapping: dict) -> bool:
try:
return self.client.hmset(name, mapping)
except RedisError:
return False
def get_hash(self, name: str) -> Optional[dict]:
try:
return self.client.hgetall(name)
except RedisError:
return None
def delete_hash(self, name: str, *fields: str) -> int:
try:
return self.client.hdel(name, *fields)
except RedisError:
return 0
def set_list(self, name: str, values: list) -> bool:
try:
self.client.delete(name)
if values:
self.client.rpush(name, *values)
return True
except RedisError:
return False
def get_list(self, name: str, start: int = 0, end: int = -1) -> Optional[list]:
try:
return self.client.lrange(name, start, end)
except RedisError:
return None
def add_to_set(self, name: str, *values: str) -> int:
try:
return self.client.sadd(name, *values)
except RedisError:
return 0
def get_set(self, name: str) -> Optional[set]:
try:
return self.client.smembers(name)
except RedisError:
return None
def remove_from_set(self, name: str, *values: str) -> int:
try:
return self.client.srem(name, *values)
except RedisError:
return 0
def clear(self) -> bool:
try:
return self.client.flushdb()
except RedisError:
return False
def close(self):
try:
self.client.close()
except RedisError:
pass

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2
servers/fastapi/utils/dummy_functions.py Normal file
View file

@ -0,0 +1,2 @@
async def do_nothing_async(_):
return None

16
servers/fastapi/utils/get_dynamic_models.py
View file

@ -1,13 +1,23 @@
from typing import List
from pydantic import Field
from models.presentation_outline_model import PresentationOutlineModel
from models.presentation_outline_model import (
PresentationOutlineModel,
SlideOutlineModel,
)
from models.presentation_structure_model import PresentationStructureModel
def get_presentation_outline_model_with_n_slides(n_slides: int):
class SlideOutlineModelWithNSlides(SlideOutlineModel):
content: str = Field(
description="Markdown content for each slide",
min_length=100,
max_length=300,
)
class PresentationOutlineModelWithNSlides(PresentationOutlineModel):
slides: List[str] = Field(
description="Markdown content for each slide in about 100 to 200 words",
slides: List[SlideOutlineModelWithNSlides] = Field(
description="List of slide outlines",
min_items=n_slides,
max_items=n_slides,
)

20
servers/fastapi/utils/get_env.py
View file

@ -81,22 +81,6 @@ def get_pixabay_api_key_env():
return os.getenv("PIXABAY_API_KEY")
def get_redis_host_env():
return os.getenv("REDIS_HOST")
def get_redis_port_env():
return os.getenv("REDIS_PORT")
def get_redis_db_env():
return os.getenv("REDIS_DB")
def get_redis_password_env():
return os.getenv("REDIS_PASSWORD")
def get_tool_calls_env():
return os.getenv("TOOL_CALLS")
@ -107,3 +91,7 @@ def get_disable_thinking_env():
def get_extended_reasoning_env():
return os.getenv("EXTENDED_REASONING")
def get_web_grounding_env():
return os.getenv("WEB_GROUNDING")

8
servers/fastapi/utils/llm_calls/edit_slide.py
View file

@ -1,4 +1,4 @@
from models.llm_message import LLMMessage
from models.llm_message import LLMSystemMessage, LLMUserMessage
from models.presentation_layout import SlideLayoutModel
from models.sql.slide import SlideModel
from services.llm_client import LLMClient
@ -41,12 +41,10 @@ def get_messages(
language: str,
):
return [
LLMMessage(
role="system",
LLMSystemMessage(
content=system_prompt,
),
LLMMessage(
role="user",
LLMUserMessage(
content=get_user_prompt(prompt, slide_data, language),
),
]

6
servers/fastapi/utils/llm_calls/edit_slide_html.py
View file

@ -1,5 +1,5 @@
from typing import Optional
from models.llm_message import LLMMessage
from models.llm_message import LLMSystemMessage, LLMUserMessage
from services.llm_client import LLMClient
from utils.llm_provider import get_model
@ -53,8 +53,8 @@ async def get_edited_slide_html(prompt: str, html: str):
response = await client.generate(
model=model,
messages=[
LLMMessage(role="system", content=system_prompt),
LLMMessage(role="user", content=get_user_prompt(prompt, html)),
LLMSystemMessage(content=system_prompt),
LLMUserMessage(content=get_user_prompt(prompt, html)),
],
)
return extract_html_from_response(response) or html

16
servers/fastapi/utils/llm_calls/generate_presentation_outlines.py
View file

@ -1,10 +1,13 @@
import asyncio
from typing import Optional
from models.llm_message import LLMMessage
from models.llm_message import LLMSystemMessage, LLMUserMessage
from models.llm_tools import GetCurrentDatetimeTool, SearchWebTool
from services.llm_client import LLMClient
from utils.get_dynamic_models import get_presentation_outline_model_with_n_slides
from utils.get_env import get_web_grounding_env
from utils.llm_provider import get_model
from utils.parsers import parse_bool_or_none
from utils.user_config import get_user_config
system_prompt = """
You are an expert presentation creator. Generate structured presentations based on user requirements and format them according to the specified JSON schema with markdown content.
@ -29,12 +32,10 @@ def get_user_prompt(prompt: str, n_slides: int, language: str, content: str):
def get_messages(prompt: str, n_slides: int, language: str, content: str):
return [
LLMMessage(
role="system",
LLMSystemMessage(
content=system_prompt,
),
LLMMessage(
role="user",
LLMUserMessage(
content=get_user_prompt(prompt, n_slides, language, content),
),
]
@ -51,10 +52,13 @@ async def generate_ppt_outline(
client = LLMClient()
tools = [SearchWebTool, GetCurrentDatetimeTool]
async for chunk in client.stream_structured(
model,
get_messages(prompt, n_slides, language, content),
response_model.model_json_schema(),
strict=True,
tools=tools if client.enable_web_grounding() else None,
):
yield chunk

8
servers/fastapi/utils/llm_calls/generate_presentation_structure.py
View file

@ -1,4 +1,4 @@
from models.llm_message import LLMMessage
from models.llm_message import LLMSystemMessage, LLMUserMessage
from models.presentation_layout import PresentationLayoutModel
from models.presentation_outline_model import PresentationOutlineModel
from services.llm_client import LLMClient
@ -11,8 +11,7 @@ def get_messages(
presentation_layout: PresentationLayoutModel, n_slides: int, data: str
):
return [
LLMMessage(
role="system",
LLMSystemMessage(
content=f"""
You're a professional presentation designer with creative freedom to design engaging presentations.
@ -47,8 +46,7 @@ def get_messages(
Select layout index for each of the {n_slides} slides based on what will best serve the presentation's goals.
""",
),
LLMMessage(
role="user",
LLMUserMessage(
content=f"""
{data}
""",

13
servers/fastapi/utils/llm_calls/generate_slide_content.py
View file

@ -1,5 +1,6 @@
from models.llm_message import LLMMessage
from models.llm_message import LLMSystemMessage, LLMUserMessage
from models.presentation_layout import SlideLayoutModel
from models.presentation_outline_model import SlideOutlineModel
from services.llm_client import LLMClient
from utils.llm_provider import get_model
from utils.schema_utils import remove_fields_from_schema
@ -38,19 +39,17 @@ def get_user_prompt(outline: str, language: str):
def get_messages(outline: str, language: str):
return [
LLMMessage(
role="system",
LLMSystemMessage(
content=system_prompt,
),
LLMMessage(
role="user",
LLMUserMessage(
content=get_user_prompt(outline, language),
),
]
async def get_slide_content_from_type_and_outline(
slide_layout: SlideLayoutModel, outline: str, language: str
slide_layout: SlideLayoutModel, outline: SlideOutlineModel, language: str
):
client = LLMClient()
model = get_model()
@ -62,7 +61,7 @@ async def get_slide_content_from_type_and_outline(
response = await client.generate_structured(
model=model,
messages=get_messages(
outline,
outline.content,
language,
),
response_format=response_schema,

8
servers/fastapi/utils/llm_calls/select_slide_type_on_edit.py
View file

@ -1,4 +1,4 @@
from models.llm_message import LLMMessage
from models.llm_message import LLMSystemMessage, LLMUserMessage
from models.presentation_layout import PresentationLayoutModel, SlideLayoutModel
from models.slide_layout_index import SlideLayoutIndex
from models.sql.slide import SlideModel
@ -13,8 +13,7 @@ def get_messages(
current_slide_layout: int,
):
return [
LLMMessage(
role="system",
LLMSystemMessage(
content=f"""
Select a Slide Layout index based on provided user prompt and current slide data.
{layout.to_string()}
@ -26,8 +25,7 @@ def get_messages(
**Go through all notes and steps and make sure they are followed, including mentioned constraints**
""",
),
LLMMessage(
role="user",
LLMUserMessage(
content=f"""
- User Prompt: {prompt}
- Current Slide Data: {slide_data}

53
servers/fastapi/utils/schema_utils.py
View file

@ -177,6 +177,59 @@ def resolve_ref(*, root: dict[str, object], ref: str) -> object:
return resolved
# Flattens a JSON schema by inlining all $ref references and removing $defs/definitions
def flatten_json_schema(schema: dict) -> dict:
root_schema = deepcopy(schema)
def _flatten(node: Any) -> Any:
if isinstance(node, dict):
# If node is a pure $ref (or combined with extra fields), inline it
if "$ref" in node:
ref_value = node["$ref"]
assert isinstance(ref_value, str), f"Received non-string $ref - {ref_value}"
resolved = resolve_ref(root=root_schema, ref=ref_value)
assert isinstance(resolved, dict), (
f"Expected `$ref: {ref_value}` to resolve to a dictionary but got {type(resolved)}"
)
# Merge: referenced first, then overlay current (excluding $ref)
merged: dict[str, Any] = deepcopy(resolved)
for key, value in node.items():
if key == "$ref":
continue
merged[key] = value
return _flatten(merged)
flattened: dict[str, Any] = {}
for key, value in node.items():
# Drop defs/definitions in output
if key in ("$defs", "definitions"):
continue
if key == "properties" and isinstance(value, dict):
flattened[key] = {prop_key: _flatten(prop_val) for prop_key, prop_val in value.items()}
elif key in ("items", "contains", "additionalProperties", "not"):
if isinstance(value, dict):
flattened[key] = _flatten(value)
elif isinstance(value, list):
flattened[key] = [_flatten(v) for v in value]
else:
flattened[key] = value
elif key in ("allOf", "anyOf", "oneOf", "prefixItems") and isinstance(value, list):
flattened[key] = [_flatten(v) for v in value]
else:
flattened[key] = _flatten(value) if isinstance(value, (dict, list)) else value
return flattened
if isinstance(node, list):
return [_flatten(v) for v in node]
return node
result = _flatten(schema)
# Ensure top-level cleanup just in case
if isinstance(result, dict):
result.pop("$defs", None)
result.pop("definitions", None)
return result
# ? Not used
def generate_constraint_sentences(schema: dict) -> str:
"""

4
servers/fastapi/utils/set_env.py
View file

@ -79,3 +79,7 @@ def set_disable_thinking_env(value):
def set_extended_reasoning_env(value):
os.environ["EXTENDED_REASONING"] = value
def set_web_grounding_env(value):
os.environ["WEB_GROUNDING"] = value

33
servers/fastapi/utils/user_config.py
View file

@ -22,6 +22,7 @@ from utils.get_env import (
get_image_provider_env,
get_pixabay_api_key_env,
get_extended_reasoning_env,
get_web_grounding_env,
)
from utils.parsers import parse_bool_or_none
from utils.set_env import (
@ -43,6 +44,7 @@ from utils.set_env import (
set_image_provider_env,
set_pixabay_api_key_env,
set_tool_calls_env,
set_web_grounding_env,
)
@ -76,12 +78,26 @@ def get_user_config():
IMAGE_PROVIDER=existing_config.IMAGE_PROVIDER or get_image_provider_env(),
PIXABAY_API_KEY=existing_config.PIXABAY_API_KEY or get_pixabay_api_key_env(),
PEXELS_API_KEY=existing_config.PEXELS_API_KEY or get_pexels_api_key_env(),
TOOL_CALLS=existing_config.TOOL_CALLS
or parse_bool_or_none(get_tool_calls_env()),
DISABLE_THINKING=existing_config.DISABLE_THINKING
or parse_bool_or_none(get_disable_thinking_env()),
EXTENDED_REASONING=existing_config.EXTENDED_REASONING
or parse_bool_or_none(get_extended_reasoning_env()),
TOOL_CALLS=(
existing_config.TOOL_CALLS
if existing_config.TOOL_CALLS is not None
else (parse_bool_or_none(get_tool_calls_env()) or False)
),
DISABLE_THINKING=(
existing_config.DISABLE_THINKING
if existing_config.DISABLE_THINKING is not None
else (parse_bool_or_none(get_disable_thinking_env()) or False)
),
EXTENDED_REASONING=(
existing_config.EXTENDED_REASONING
if existing_config.EXTENDED_REASONING is not None
else (parse_bool_or_none(get_extended_reasoning_env()) or False)
),
WEB_GROUNDING=(
existing_config.WEB_GROUNDING
if existing_config.WEB_GROUNDING is not None
else (parse_bool_or_none(get_web_grounding_env()) or False)
),
)
@ -122,5 +138,6 @@ def update_env_with_user_config():
if user_config.DISABLE_THINKING:
set_disable_thinking_env(str(user_config.DISABLE_THINKING))
if user_config.EXTENDED_REASONING:
if user_config.EXTENDED_REASONING:
set_extended_reasoning_env(str(user_config.EXTENDED_REASONING))
set_extended_reasoning_env(str(user_config.EXTENDED_REASONING))
if user_config.WEB_GROUNDING:
set_web_grounding_env(str(user_config.WEB_GROUNDING))

27
servers/fastapi/uv.lock generated
View file

@ -1061,6 +1061,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/a4/ed/1f1afb2e9e7f38a545d628f864d562a5ae64fe6f7a10e28ffb9b185b4e89/importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec", size = 37461, upload-time = "2025-01-03T18:51:54.306Z" },
]
[[package]]
name = "iniconfig"
version = "2.1.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/f2/97/ebf4da567aa6827c909642694d71c9fcf53e5b504f2d96afea02718862f3/iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7", size = 4793, upload-time = "2025-03-19T20:09:59.721Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050, upload-time = "2025-03-19T20:10:01.071Z" },
]
[[package]]
name = "isodate"
version = "0.7.2"
@ -1907,6 +1916,7 @@ dependencies = [
{ name = "openai" },
{ name = "pathvalidate" },
{ name = "pdfplumber" },
{ name = "pytest" },
{ name = "python-pptx" },
{ name = "redis" },
{ name = "sqlmodel" },
@ -1928,6 +1938,7 @@ requires-dist = [
{ name = "openai", specifier = ">=1.98.0" },
{ name = "pathvalidate", specifier = ">=3.3.1" },
{ name = "pdfplumber", specifier = ">=0.11.7" },
{ name = "pytest", specifier = ">=8.4.1" },
{ name = "python-pptx", specifier = ">=1.0.2" },
{ name = "redis", specifier = ">=6.2.0" },
{ name = "sqlmodel", specifier = ">=0.0.24" },
@ -2211,6 +2222,22 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/5a/dc/491b7661614ab97483abf2056be1deee4dc2490ecbf7bff9ab5cdbac86e1/pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6", size = 83178, upload-time = "2024-09-19T02:40:08.598Z" },
]
[[package]]
name = "pytest"
version = "8.4.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
{ name = "iniconfig" },
{ name = "packaging" },
{ name = "pluggy" },
{ name = "pygments" },
]
sdist = { url = "https://files.pythonhosted.org/packages/08/ba/45911d754e8eba3d5a841a5ce61a65a685ff1798421ac054f85aa8747dfb/pytest-8.4.1.tar.gz", hash = "sha256:7c67fd69174877359ed9371ec3af8a3d2b04741818c51e5e99cc1742251fa93c", size = 1517714, upload-time = "2025-06-18T05:48:06.109Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl", hash = "sha256:539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7", size = 365474, upload-time = "2025-06-18T05:48:03.955Z" },
]
[[package]]
name = "python-bidi"
version = "0.6.6"

4
servers/nextjs/app/(presentation-generator)/outline/components/GenerateButton.tsx
View file

@ -1,10 +1,10 @@
import React from "react";
import { Button } from "@/components/ui/button";
import { LoadingState, StreamState, LayoutGroup } from "../types/index";
import { LoadingState, LayoutGroup } from "../types/index";
interface GenerateButtonProps {
loadingState: LoadingState;
streamState: StreamState;
streamState: { isStreaming: boolean, isLoading: boolean };
selectedLayoutGroup: LayoutGroup | null;
onSubmit: () => void;
}

19
servers/nextjs/app/(presentation-generator)/outline/components/OutlineContent.tsx
View file

@ -18,7 +18,7 @@ import { Button } from "@/components/ui/button";
import { FileText } from "lucide-react";
interface OutlineContentProps {
outlines: string[] | null;
outlines: { content: string }[] | null;
isLoading: boolean;
isStreaming: boolean;
onDragEnd: (event: any) => void;
@ -32,7 +32,7 @@ const OutlineContent: React.FC<OutlineContentProps> = ({
onDragEnd,
onAddSlide
}) => {
console.log('isLoading', isLoading)
const sensors = useSensors(
useSensor(PointerSensor),
useSensor(KeyboardSensor, {
@ -83,7 +83,18 @@ const OutlineContent: React.FC<OutlineContentProps> = ({
collisionDetection={closestCenter}
onDragEnd={onDragEnd}
>
<SortableContext
{isStreaming ? (
outlines.map((item, index) => (
<OutlineItem
key={`slide-${index}`}
index={index + 1}
slideOutline={item}
isStreaming={isStreaming}
/>
))
) :
<SortableContext
items={outlines?.map((item, index) => ({ id: `slide-${index}` })) || []}
strategy={verticalListSortingStrategy}
>
@ -95,7 +106,7 @@ const OutlineContent: React.FC<OutlineContentProps> = ({
isStreaming={isStreaming}
/>
))}
</SortableContext>
</SortableContext>}
</DndContext>
<Button

10
servers/nextjs/app/(presentation-generator)/outline/components/OutlineItem.tsx
View file

@ -10,7 +10,9 @@ import { useEffect } from "react"
interface OutlineItemProps {
slideOutline: string,
slideOutline: {
content: string,
},
index: number
isStreaming: boolean
}
@ -38,7 +40,7 @@ export function OutlineItem({
}
}, [outlines.length]);
const handleSlideChange = (newOutline: string) => {
const handleSlideChange = (newOutline:any) => {
if (isStreaming) return;
const newData = outlines?.map((each, idx) => {
if (idx === index - 1) {
@ -100,10 +102,10 @@ export function OutlineItem({
{isStreaming ? <p
className="text-sm flex-1 font-normal"
>
{slideOutline || ''}
{slideOutline.content || ''}
</p> : <MarkdownEditor
key={index}
content={slideOutline || ''}
content={slideOutline.content || ''}
onChange={(content) => handleSlideChange(content)}
/>}

14
servers/nextjs/app/(presentation-generator)/outline/components/PageHeader.tsx
View file

@ -1,14 +0,0 @@
import React from "react";
const PageHeader: React.FC = () => (
<div className="mb-8">
{/* <h4 className="text-2xl font-bold mb-2 text-gray-900">
Customize Your Presentation
</h4> */}
{/* <p className="text-gray-600">
Review your outline and select a layout style for your presentation.
</p> */}
</div>
);
export default PageHeader;

8
servers/nextjs/app/(presentation-generator)/outline/hooks/useOutlineManagement.ts
View file

@ -3,7 +3,7 @@ import { useDispatch } from "react-redux";
import { arrayMove } from "@dnd-kit/sortable";
import { setOutlines } from "@/store/slices/presentationGeneration";
export const useOutlineManagement = (outlines: string[] | null) => {
export const useOutlineManagement = (outlines: { content: string }[] | null) => {
const dispatch = useDispatch();
const handleDragEnd = useCallback((event: any) => {
@ -12,8 +12,8 @@ export const useOutlineManagement = (outlines: string[] | null) => {
if (!active || !over || !outlines) return;
if (active.id !== over.id) {
const oldIndex = outlines.findIndex((item) => item === active.id);
const newIndex = outlines.findIndex((item) => item === over.id);
const oldIndex = outlines.findIndex((item) => item.content === active.id);
const newIndex = outlines.findIndex((item) => item.content === over.id);
const reorderedArray = arrayMove(outlines, oldIndex, newIndex);
dispatch(setOutlines(reorderedArray));
}
@ -22,7 +22,7 @@ export const useOutlineManagement = (outlines: string[] | null) => {
const handleAddSlide = useCallback(() => {
if (!outlines) return;
const updatedOutlines = [...outlines, "Outline title"];
const updatedOutlines = [...outlines, { content: "Outline title" }];
dispatch(setOutlines(updatedOutlines));
}, [outlines, dispatch]);

48
servers/nextjs/app/(presentation-generator)/outline/hooks/useOutlineStreaming.ts
View file

@ -3,18 +3,15 @@ import { useDispatch, useSelector } from "react-redux";
import { toast } from "sonner";
import { setOutlines } from "@/store/slices/presentationGeneration";
import { jsonrepair } from "jsonrepair";
import { StreamState } from "../types/index";
import { RootState } from "@/store/store";
const DEFAULT_STREAM_STATE: StreamState = {
isStreaming: false,
isLoading: true,
};
export const useOutlineStreaming = (presentationId: string | null) => {
const dispatch = useDispatch();
const { outlines } = useSelector((state: RootState) => state.presentationGeneration);
const [streamState, setStreamState] = useState<StreamState>(DEFAULT_STREAM_STATE);
const [isStreaming, setIsStreaming] = useState(true);
const [isLoading, setIsLoading] = useState(true);
useEffect(() => {
if (!presentationId || outlines.length > 0) return;
@ -23,8 +20,8 @@ export const useOutlineStreaming = (presentationId: string | null) => {
let accumulatedChunks = "";
const initializeStream = async () => {
setStreamState({ isStreaming: true, isLoading: true });
setIsStreaming(true)
setIsLoading(true)
try {
eventSource = new EventSource(
`/api/v1/ppt/outlines/stream?presentation_id=${presentationId}`
@ -34,13 +31,16 @@ export const useOutlineStreaming = (presentationId: string | null) => {
const data = JSON.parse(event.data);
switch (data.type) {
case "chunk":
// console.log('data', data)
accumulatedChunks += data.chunk;
// console.log('accumulatedChunks', accumulatedChunks)
try {
const repairedJson = jsonrepair(accumulatedChunks);
const partialData = JSON.parse(repairedJson);
console.log('partialData', partialData)
if (partialData.slides) {
dispatch(setOutlines(partialData.slides));
setStreamState(prev => ({ ...prev, isLoading: false }));
setIsLoading(false)
}
} catch (error) {
// JSON isn't complete yet, continue accumulating
@ -48,11 +48,13 @@ export const useOutlineStreaming = (presentationId: string | null) => {
break;
case "complete":
console.log('complete', data)
try {
const outlinesData: string[] = data.presentation.outlines.slides;
const outlinesData: { content: string }[] = data.presentation.outlines.slides;
dispatch(setOutlines(outlinesData));
setStreamState({ isStreaming: false, isLoading: false });
eventSource.close();
setIsStreaming(false)
setIsLoading(false)
eventSource.close();
} catch (error) {
console.error("Error parsing accumulated chunks:", error);
toast.error("Failed to parse presentation data");
@ -62,11 +64,15 @@ export const useOutlineStreaming = (presentationId: string | null) => {
break;
case "closing":
setStreamState({ isStreaming: false, isLoading: false });
console.log('closing', data)
setIsStreaming(false)
setIsLoading(false)
eventSource.close();
break;
case "error":
setStreamState({ isStreaming: false, isLoading: false });
console.log('error', data)
setIsStreaming(false)
setIsLoading(false)
eventSource.close();
toast.error('Error in outline streaming',
{
@ -78,18 +84,20 @@ export const useOutlineStreaming = (presentationId: string | null) => {
});
eventSource.onerror = () => {
setStreamState({ isStreaming: false, isLoading: false });
console.log('onerror')
setIsStreaming(false)
setIsLoading(false)
eventSource.close();
toast.error("Failed to connect to the server. Please try again.");
};
} catch (error) {
setStreamState({ isStreaming: false, isLoading: false });
console.log('error', error)
setIsStreaming(false)
setIsLoading(false)
toast.error("Failed to initialize connection");
}finally{
setStreamState({ isStreaming: false, isLoading: false });
}
};
initializeStream();
initializeStream();
return () => {
if (eventSource) {
eventSource.close();
@ -97,5 +105,5 @@ export const useOutlineStreaming = (presentationId: string | null) => {
};
}, [presentationId, dispatch]);
return streamState;
return { isStreaming, isLoading };
};

2
servers/nextjs/app/(presentation-generator)/outline/hooks/usePresentationGeneration.ts
View file

@ -15,7 +15,7 @@ const DEFAULT_LOADING_STATE: LoadingState = {
export const usePresentationGeneration = (
presentationId: string | null,
outlines: string[] | null,
outlines: { content: string }[] | null,
selectedLayoutGroup: LayoutGroup | null,
setActiveTab: (tab: string) => void
) => {

5
servers/nextjs/app/(presentation-generator)/outline/types/index.ts
View file

@ -14,10 +14,7 @@ export interface LoadingState {
duration: number;
}
export interface StreamState {
isStreaming: boolean;
isLoading: boolean;
}
export const TABS = {
OUTLINE: 'outline',

4
servers/nextjs/app/(presentation-generator)/upload/components/UploadPage.tsx
View file

@ -131,7 +131,7 @@ const UploadPage = () => {
config,
files: responses,
}));
dispatch(clearOutlines());
dispatch(clearOutlines())
router.push("/documents-preview");
};
@ -155,7 +155,7 @@ const UploadPage = () => {
});
dispatch(setPresentationId(createResponse.id));
dispatch(clearOutlines());
dispatch(clearOutlines())
router.push("/outline");
};

4
servers/nextjs/app/api/user-config/route.ts
View file

@ -57,6 +57,10 @@ export async function POST(request: Request) {
userConfig.EXTENDED_REASONING === undefined
? existingConfig.EXTENDED_REASONING
: userConfig.EXTENDED_REASONING,
WEB_GROUNDING:
userConfig.WEB_GROUNDING === undefined
? existingConfig.WEB_GROUNDING
: userConfig.WEB_GROUNDING,
USE_CUSTOM_URL:
userConfig.USE_CUSTOM_URL === undefined
? existingConfig.USE_CUSTOM_URL

21
servers/nextjs/components/AnthropicConfig.tsx
View file

@ -19,6 +19,7 @@ interface AnthropicConfigProps {
anthropicApiKey: string;
anthropicModel: string;
extendedReasoning: boolean;
webGrounding?: boolean;
onInputChange: (value: string | boolean, field: string) => void;
}
@ -27,6 +28,7 @@ export default function AnthropicConfig({
anthropicApiKey,
anthropicModel,
extendedReasoning,
webGrounding,
onInputChange,
}: AnthropicConfigProps) {
const [openModelSelect, setOpenModelSelect] = useState(false);
@ -65,7 +67,7 @@ export default function AnthropicConfig({
const data = await response.json();
setAvailableModels(data);
setModelsChecked(true);
onInputChange("claude-3-5-sonnet-20241022", "anthropic_model");
onInputChange("claude-sonnet-4-20250514", "anthropic_model");
} else {
console.error('Failed to fetch models');
setAvailableModels([]);
@ -226,6 +228,23 @@ export default function AnthropicConfig({
</div>
</div>
) : null}
{/* Web Grounding Toggle - at the end, below models dropdown */}
<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">
Enable Web Grounding
</label>
<Switch
checked={!!webGrounding}
onCheckedChange={(checked) => onInputChange(checked, "web_grounding")}
/>
</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>
If enabled, the model can use web search grounding when available.
</p>
</div>
</div>
);
}

24
servers/nextjs/components/GoogleConfig.tsx
View file

@ -13,16 +13,19 @@ import {
import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
import { cn } from "@/lib/utils";
import { toast } from "sonner";
import { Switch } from "./ui/switch";
interface GoogleConfigProps {
googleApiKey: string;
googleModel: string;
onInputChange: (value: string, field: string) => void;
webGrounding?: boolean;
onInputChange: (value: string | boolean, field: string) => void;
}
export default function GoogleConfig({
googleApiKey,
googleModel,
webGrounding,
onInputChange
}: GoogleConfigProps) {
const [openModelSelect, setOpenModelSelect] = useState(false);
@ -61,7 +64,7 @@ export default function GoogleConfig({
const data = await response.json();
setAvailableModels(data);
setModelsChecked(true);
onInputChange("models/gemini-2.0-flash", "google_model");
onInputChange("models/gemini-2.5-flash", "google_model");
} else {
console.error('Failed to fetch models');
setAvailableModels([]);
@ -205,6 +208,23 @@ export default function GoogleConfig({
</div>
</div>
) : null}
{/* Web Grounding Toggle - at the end, below models dropdown */}
<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">
Enable Web Grounding
</label>
<Switch
checked={!!webGrounding}
onCheckedChange={(checked) => onInputChange(checked, "web_grounding")}
/>
</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>
If enabled, the model can use web search grounding when available.
</p>
</div>
</div>
);
}

3
servers/nextjs/components/LLMSelection.tsx
View file

@ -149,6 +149,7 @@ export default function LLMProviderSelection({
<OpenAIConfig
openaiApiKey={llmConfig.OPENAI_API_KEY || ""}
openaiModel={llmConfig.OPENAI_MODEL || ""}
webGrounding={llmConfig.WEB_GROUNDING || false}
onInputChange={input_field_changed}
/>
</TabsContent>
@ -158,6 +159,7 @@ export default function LLMProviderSelection({
<GoogleConfig
googleApiKey={llmConfig.GOOGLE_API_KEY || ""}
googleModel={llmConfig.GOOGLE_MODEL || ""}
webGrounding={llmConfig.WEB_GROUNDING || false}
onInputChange={input_field_changed}
/>
</TabsContent>
@ -168,6 +170,7 @@ export default function LLMProviderSelection({
anthropicApiKey={llmConfig.ANTHROPIC_API_KEY || ""}
anthropicModel={llmConfig.ANTHROPIC_MODEL || ""}
extendedReasoning={llmConfig.EXTENDED_REASONING || false}
webGrounding={llmConfig.WEB_GROUNDING || false}
onInputChange={input_field_changed}
/>
</TabsContent>

21
servers/nextjs/components/OllamaConfig.tsx
View file

@ -19,7 +19,6 @@ interface OllamaModel {
label: string;
value: string;
size: string;
icon: string;
}
interface OllamaConfigProps {
@ -128,19 +127,6 @@ export default function OllamaConfig({
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">
{ollamaModel && (
<div className="w-6 h-6 rounded-lg flex items-center justify-center flex-shrink-0">
<img
src={
ollamaModels?.find(
(m) => m.value === ollamaModel
)?.icon
}
alt={`${ollamaModel} icon`}
className="rounded-sm"
/>
</div>
)}
<span className="text-sm font-medium text-gray-900">
{ollamaModel
? ollamaModels?.find(
@ -189,13 +175,6 @@ export default function OllamaConfig({
)}
/>
<div className="flex gap-3 items-center">
<div className="w-8 h-8 rounded-lg flex items-center justify-center flex-shrink-0">
<img
src={model.icon}
alt={`${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">

22
servers/nextjs/components/OpenAIConfig.tsx
View file

@ -13,16 +13,19 @@ import {
import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
import { cn } from "@/lib/utils";
import { toast } from "sonner";
import { Switch } from "./ui/switch";
interface OpenAIConfigProps {
openaiApiKey: string;
openaiModel: string;
onInputChange: (value: string, field: string) => void;
webGrounding?: boolean;
onInputChange: (value: string | boolean, field: string) => void;
}
export default function OpenAIConfig({
openaiApiKey,
openaiModel,
webGrounding,
onInputChange
}: OpenAIConfigProps) {
const [openModelSelect, setOpenModelSelect] = useState(false);
@ -210,6 +213,23 @@ export default function OpenAIConfig({
</div>
</div>
) : null}
{/* Web Grounding Toggle - show at the end, below models dropdown */}
<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">
Enable Web Grounding
</label>
<Switch
checked={!!webGrounding}
onCheckedChange={(checked) => onInputChange(checked, "web_grounding")}
/>
</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>
If enabled, the model can use web search grounding when available.
</p>
</div>
</div>
);
}

4
servers/nextjs/store/slices/presentationGeneration.ts
View file

@ -18,7 +18,7 @@ interface PresentationGenerationState {
presentation_id: string | null;
isLoading: boolean;
isStreaming: boolean | null;
outlines: string[];
outlines: { content: string }[];
error: string | null;
presentationData: PresentationData | null;
isSlidesRendered: boolean;
@ -72,7 +72,7 @@ const presentationGenerationSlice = createSlice({
state.outlines = [];
},
// Set outlines
setOutlines: (state, action: PayloadAction<string[]>) => {
setOutlines: (state, action: PayloadAction<{ content: string }[]>) => {
state.outlines = action.payload;
},
// Set presentation data

1
servers/nextjs/types/llm_config.ts
View file

@ -31,6 +31,7 @@ export interface LLMConfig {
TOOL_CALLS?: boolean;
DISABLE_THINKING?: boolean;
EXTENDED_REASONING?: boolean;
WEB_GROUNDING?: boolean;
// Only used in UI settings
USE_CUSTOM_URL?: boolean;

3
servers/nextjs/utils/providerUtils.ts
View file

@ -3,9 +3,7 @@ import { LLMConfig } from "@/types/llm_config";
export interface OllamaModel {
label: string;
value: string;
description: string;
size: string;
icon: string;
}
export interface DownloadingModel {
@ -48,6 +46,7 @@ export const updateLLMConfig = (
tool_calls: "TOOL_CALLS",
disable_thinking: "DISABLE_THINKING",
extended_reasoning: "EXTENDED_REASONING",
web_grounding: "WEB_GROUNDING",
};
const configKey = fieldMappings[field];

1
start.js
View file

@ -81,6 +81,7 @@ const setupUserConfigFromEnv = () => {
TOOL_CALLS: process.env.TOOL_CALLS || existingConfig.TOOL_CALLS,
DISABLE_THINKING: process.env.DISABLE_THINKING || existingConfig.DISABLE_THINKING,
EXTENDED_REASONING: process.env.EXTENDED_REASONING || existingConfig.EXTENDED_REASONING,
WEB_GROUNDING: process.env.WEB_GROUNDING || existingConfig.WEB_GROUNDING,
USE_CUSTOM_URL: process.env.USE_CUSTOM_URL || existingConfig.USE_CUSTOM_URL,
};