563 lines
21 KiB
Python
563 lines
21 KiB
Python
|
"""OntoCast API server implementation.
|
||
|
|
|
||
|
|
This module provides a web server implementation for the OntoCast framework
|
||
|
|
using Robyn. It exposes REST API endpoints for processing documents and
|
||
|
|
extracting semantic triples with ontology assistance.
|
||
|
|
|
||
|
|
The server supports:
|
||
|
|
- Health check endpoint (/health)
|
||
|
|
- Service information endpoint (/info)
|
||
|
|
- Document processing endpoint (/process)
|
||
|
|
- Triple store flush endpoint (/flush)
|
||
|
|
- Multiple input formats (JSON, multipart/form-data)
|
||
|
|
- Streaming workflow execution
|
||
|
|
- Comprehensive error handling and logging
|
||
|
|
|
||
|
|
The server integrates with the OntoCast workflow graph to process documents
|
||
|
|
through the complete pipeline: chunking, ontology selection, fact extraction,
|
||
|
|
and aggregation.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
# With Fuseki backend (auto-detected from FUSEKI_URI and FUSEKI_AUTH)
|
||
|
|
ontocast --env-path .env
|
||
|
|
|
||
|
|
# Process specific file
|
||
|
|
ontocast --env-path .env --input-path ./document.pdf
|
||
|
|
|
||
|
|
# Process with chunk limit
|
||
|
|
ontocast --env-path .env --head-chunks 5
|
||
|
|
"""
|
||
|
|
|
||
|
|
import asyncio
|
||
|
|
import logging
|
||
|
|
import logging.config
|
||
|
|
import pathlib
|
||
|
|
|
||
|
|
import click
|
||
|
|
from dotenv import load_dotenv
|
||
|
|
from langchain_core.runnables import RunnableConfig
|
||
|
|
from langgraph.graph.state import CompiledStateGraph
|
||
|
|
|
||
|
|
from ontocast.cli.util import crawl_directories
|
||
|
|
from ontocast.config import Config, ServerConfig
|
||
|
|
from ontocast.onto.enum import RenderMode
|
||
|
|
from ontocast.onto.state import AgentState
|
||
|
|
from ontocast.stategraph import create_agent_graph
|
||
|
|
from ontocast.toolbox import ToolBox
|
||
|
|
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
def calculate_recursion_limit(
|
||
|
|
head_chunks: int | None,
|
||
|
|
server_config: ServerConfig,
|
||
|
|
) -> int:
|
||
|
|
"""Calculate the recursion limit based on max visits and head chunks.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
head_chunks: Optional maximum number of chunks to process
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
int: Calculated recursion limit
|
||
|
|
"""
|
||
|
|
if head_chunks is not None:
|
||
|
|
# If we know the number of chunks, calculate exact limit
|
||
|
|
return max(
|
||
|
|
server_config.base_recursion_limit,
|
||
|
|
server_config.max_visits_per_node * head_chunks * 10,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# If we don't know chunks, use a conservative estimate
|
||
|
|
return max(
|
||
|
|
server_config.base_recursion_limit,
|
||
|
|
server_config.max_visits_per_node * server_config.estimated_chunks * 10,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def create_app(
|
||
|
|
tools: ToolBox,
|
||
|
|
server_config: ServerConfig,
|
||
|
|
head_chunks: int | None = None,
|
||
|
|
):
|
||
|
|
from robyn import Headers, Request, Response, Robyn, jsonify
|
||
|
|
|
||
|
|
app = Robyn(__file__)
|
||
|
|
workflow: CompiledStateGraph = create_agent_graph(tools)
|
||
|
|
recursion_limit = calculate_recursion_limit(
|
||
|
|
head_chunks,
|
||
|
|
server_config,
|
||
|
|
)
|
||
|
|
|
||
|
|
@app.get("/health")
|
||
|
|
async def health_check():
|
||
|
|
"""MCP health check endpoint."""
|
||
|
|
try:
|
||
|
|
# Check if LLM is available
|
||
|
|
if tools.llm is None:
|
||
|
|
return Response(
|
||
|
|
status_code=503,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{"status": "unhealthy", "error": "LLM not initialized"}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
return Response(
|
||
|
|
status_code=200,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"status": "healthy",
|
||
|
|
"version": "0.1.1",
|
||
|
|
"llm_provider": tools.llm_provider,
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"Health check failed: {str(e)}")
|
||
|
|
return Response(
|
||
|
|
status_code=503,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify({"status": "unhealthy", "error": str(e)}),
|
||
|
|
)
|
||
|
|
|
||
|
|
@app.get("/info")
|
||
|
|
async def info():
|
||
|
|
"""MCP info endpoint."""
|
||
|
|
return Response(
|
||
|
|
status_code=200,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"name": "ontocast",
|
||
|
|
"version": "0.1.1",
|
||
|
|
"description": "Agentic ontology assisted framework "
|
||
|
|
"for semantic triple extraction",
|
||
|
|
"capabilities": ["text-to-triples", "ontology-extraction"],
|
||
|
|
"input_types": ["text", "json", "pdf", "markdown"],
|
||
|
|
"output_types": ["turtle", "json"],
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
@app.post("/flush")
|
||
|
|
async def flush(request: Request):
|
||
|
|
"""Flush/clean data from the triple store.
|
||
|
|
|
||
|
|
This endpoint deletes data from the configured triple store.
|
||
|
|
For Fuseki, you can specify a dataset query parameter to clean a specific dataset,
|
||
|
|
or omit it to clean all datasets. For Neo4j, this deletes all nodes (dataset parameter is ignored).
|
||
|
|
|
||
|
|
Query Parameters:
|
||
|
|
dataset (optional): For Fuseki only - name of the dataset to clean.
|
||
|
|
If omitted, cleans all datasets (main and ontologies).
|
||
|
|
|
||
|
|
Warning: This operation is irreversible and will delete all data.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
JSON response with status and message.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
# Clean all datasets (Fuseki) or entire database (Neo4j)
|
||
|
|
POST /flush
|
||
|
|
|
||
|
|
# Clean specific Fuseki dataset
|
||
|
|
POST /flush?dataset=my_dataset
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
if tools.triple_store_manager is None:
|
||
|
|
return Response(
|
||
|
|
status_code=400,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"status": "error",
|
||
|
|
"error": "No triple store manager configured",
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
# Extract dataset parameter (used by Fuseki, ignored by others)
|
||
|
|
dataset = request.query_params.get("dataset", None)
|
||
|
|
|
||
|
|
# All implementations accept the dataset parameter
|
||
|
|
# Fuseki uses it, Neo4j and Filesystem ignore it with a warning
|
||
|
|
await tools.triple_store_manager.clean(dataset=dataset)
|
||
|
|
|
||
|
|
# Generate appropriate success message
|
||
|
|
from ontocast.tool.triple_manager.fuseki import FusekiTripleStoreManager
|
||
|
|
|
||
|
|
if isinstance(tools.triple_store_manager, FusekiTripleStoreManager):
|
||
|
|
if dataset:
|
||
|
|
message = f"Fuseki dataset '{dataset}' flushed successfully"
|
||
|
|
else:
|
||
|
|
message = "Fuseki triple store flushed successfully (all datasets)"
|
||
|
|
else:
|
||
|
|
message = "Triple store flushed successfully"
|
||
|
|
|
||
|
|
return Response(
|
||
|
|
status_code=200,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"status": "success",
|
||
|
|
"message": message,
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"Error flushing triple store: {str(e)}")
|
||
|
|
return Response(
|
||
|
|
status_code=500,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"status": "error",
|
||
|
|
"error": str(e),
|
||
|
|
"error_type": type(e).__name__,
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
@app.post("/process")
|
||
|
|
async def process(request: Request):
|
||
|
|
"""MCP process endpoint."""
|
||
|
|
workflow_state: dict | None = None
|
||
|
|
try:
|
||
|
|
content_type = request.headers.get("content-type")
|
||
|
|
logger.debug(f"Content-Type: {content_type}")
|
||
|
|
logger.debug(f"Request headers: {request.headers}")
|
||
|
|
logger.debug(f"Request body: {request.body}")
|
||
|
|
|
||
|
|
# Extract parameters from query parameters
|
||
|
|
dataset = request.query_params.get("dataset", None)
|
||
|
|
if dataset:
|
||
|
|
logger.debug(f"Using dataset: {dataset}")
|
||
|
|
|
||
|
|
# Preferred rendering mode parameter
|
||
|
|
render_mode = request.query_params.get("render_mode", None)
|
||
|
|
if render_mode:
|
||
|
|
logger.debug(f"Using render_mode: {render_mode}")
|
||
|
|
|
||
|
|
# Extract user instructions from query parameters (available for both JSON and multipart)
|
||
|
|
ontology_user_instruction = request.query_params.get(
|
||
|
|
"ontology_user_instruction", ""
|
||
|
|
)
|
||
|
|
facts_user_instruction = request.query_params.get(
|
||
|
|
"facts_user_instruction", ""
|
||
|
|
)
|
||
|
|
if ontology_user_instruction:
|
||
|
|
logger.debug(
|
||
|
|
f"Query param - ontology_user_instruction: {ontology_user_instruction}"
|
||
|
|
)
|
||
|
|
if facts_user_instruction:
|
||
|
|
logger.debug(
|
||
|
|
f"Query param - facts_user_instruction: {facts_user_instruction}"
|
||
|
|
)
|
||
|
|
|
||
|
|
if content_type and content_type.startswith("application/json"):
|
||
|
|
data = request.body
|
||
|
|
# Convert string to bytes if needed
|
||
|
|
if isinstance(data, str):
|
||
|
|
bytes_data = data.encode("utf-8")
|
||
|
|
else:
|
||
|
|
bytes_data = data
|
||
|
|
logger.debug(
|
||
|
|
f"Parsed JSON data: {data}, bytes length: {len(bytes_data)}"
|
||
|
|
)
|
||
|
|
files = {"input.json": bytes_data}
|
||
|
|
# User instructions already extracted from query params above
|
||
|
|
# They can also be overridden by convert_document.py for JSON files
|
||
|
|
elif content_type and content_type.startswith("multipart/form-data"):
|
||
|
|
files = request.files
|
||
|
|
logger.debug(f"Files: {files.keys()}")
|
||
|
|
logger.debug(f"Files-types: {[(k, type(v)) for k, v in files.items()]}")
|
||
|
|
|
||
|
|
# Check if form data contains user instructions (overrides query params)
|
||
|
|
if hasattr(request, "form_data") and request.form_data:
|
||
|
|
form_ontology_instruction = request.form_data.get(
|
||
|
|
"ontology_user_instruction", ""
|
||
|
|
)
|
||
|
|
form_facts_instruction = request.form_data.get(
|
||
|
|
"facts_user_instruction", ""
|
||
|
|
)
|
||
|
|
if form_ontology_instruction:
|
||
|
|
ontology_user_instruction = form_ontology_instruction
|
||
|
|
logger.debug(
|
||
|
|
f"Form data - ontology_user_instruction: "
|
||
|
|
f"{ontology_user_instruction}"
|
||
|
|
)
|
||
|
|
if form_facts_instruction:
|
||
|
|
facts_user_instruction = form_facts_instruction
|
||
|
|
logger.debug(
|
||
|
|
f"Form data - facts_user_instruction: {facts_user_instruction}"
|
||
|
|
)
|
||
|
|
if not files:
|
||
|
|
return Response(
|
||
|
|
status_code=400,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"status": "error",
|
||
|
|
"error": "No file provided",
|
||
|
|
"error_type": "ValidationError",
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
logger.debug(f"Unsupported content type: {content_type}")
|
||
|
|
return Response(
|
||
|
|
status_code=400,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"status": "error",
|
||
|
|
"error": f"Unsupported content type: {content_type}",
|
||
|
|
"error_type": "ValidationError",
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
# Update dataset if provided (efficient - no model reloading)
|
||
|
|
if dataset:
|
||
|
|
await tools.update_dataset(dataset)
|
||
|
|
|
||
|
|
def parse_render_mode_param(value, default: RenderMode) -> RenderMode:
|
||
|
|
"""Parse render mode from query string or use default."""
|
||
|
|
if value is None:
|
||
|
|
return default
|
||
|
|
if isinstance(value, RenderMode):
|
||
|
|
return value
|
||
|
|
if isinstance(value, str):
|
||
|
|
normalized = value.lower().strip()
|
||
|
|
try:
|
||
|
|
return RenderMode(normalized)
|
||
|
|
except ValueError:
|
||
|
|
logger.warning(
|
||
|
|
f"Invalid render_mode '{value}', using default '{default.value}'"
|
||
|
|
)
|
||
|
|
return default
|
||
|
|
|
||
|
|
render_mode_value: RenderMode = parse_render_mode_param(
|
||
|
|
render_mode,
|
||
|
|
server_config.render_mode,
|
||
|
|
)
|
||
|
|
|
||
|
|
initial_state = AgentState(
|
||
|
|
files=files,
|
||
|
|
max_visits=server_config.max_visits_per_node,
|
||
|
|
max_chunks=head_chunks,
|
||
|
|
render_mode=render_mode_value,
|
||
|
|
ontology_max_triples=server_config.ontology_max_triples,
|
||
|
|
dataset=dataset,
|
||
|
|
ontology_user_instruction=ontology_user_instruction,
|
||
|
|
facts_user_instruction=facts_user_instruction,
|
||
|
|
)
|
||
|
|
|
||
|
|
async for chunk in workflow.astream(
|
||
|
|
initial_state,
|
||
|
|
stream_mode="values",
|
||
|
|
config=RunnableConfig(recursion_limit=recursion_limit),
|
||
|
|
):
|
||
|
|
workflow_state = chunk
|
||
|
|
|
||
|
|
if workflow_state is None:
|
||
|
|
raise ValueError("Workflow did not return a valid state")
|
||
|
|
|
||
|
|
# Extract budget tracker data if available
|
||
|
|
budget_tracker_data = {}
|
||
|
|
if workflow_state.get("budget_tracker"):
|
||
|
|
budget_tracker = workflow_state["budget_tracker"]
|
||
|
|
# Convert Pydantic model to dict using model_dump()
|
||
|
|
budget_tracker_data = budget_tracker.model_dump()
|
||
|
|
|
||
|
|
total_content_units = len(
|
||
|
|
workflow_state.get("content_units", workflow_state.get("chunks", []))
|
||
|
|
)
|
||
|
|
render_mode = workflow_state.get("render_mode")
|
||
|
|
render_facts_enabled = render_mode in (
|
||
|
|
RenderMode.FACTS,
|
||
|
|
RenderMode.ONTOLOGY_AND_FACTS,
|
||
|
|
RenderMode.FACTS.value,
|
||
|
|
RenderMode.ONTOLOGY_AND_FACTS.value,
|
||
|
|
)
|
||
|
|
if render_facts_enabled:
|
||
|
|
processed_content_units = len(
|
||
|
|
workflow_state.get("parallel_facts_units", [])
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
processed_content_units = total_content_units
|
||
|
|
chunks_remaining = max(total_content_units - processed_content_units, 0)
|
||
|
|
|
||
|
|
result = {
|
||
|
|
"status": "success",
|
||
|
|
"data": {
|
||
|
|
"facts": workflow_state["aggregated_facts"].serialize(
|
||
|
|
format="turtle"
|
||
|
|
)
|
||
|
|
if workflow_state.get("aggregated_facts")
|
||
|
|
else "",
|
||
|
|
"ontology": workflow_state["current_ontology"].graph.serialize(
|
||
|
|
format="turtle"
|
||
|
|
)
|
||
|
|
if workflow_state.get("current_ontology")
|
||
|
|
else "",
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"status": workflow_state["status"],
|
||
|
|
"chunks_processed": processed_content_units,
|
||
|
|
"chunks_remaining": chunks_remaining,
|
||
|
|
"budget": budget_tracker_data,
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
return Response(
|
||
|
|
status_code=200,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(result),
|
||
|
|
)
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"Error processing document: {str(e)}")
|
||
|
|
logger.error(f"Error type: {type(e)}")
|
||
|
|
logger.error("Error traceback:", exc_info=True)
|
||
|
|
|
||
|
|
# Try to get error details from workflow_state if available
|
||
|
|
error_details = None
|
||
|
|
if workflow_state:
|
||
|
|
error_details = {
|
||
|
|
"stage": workflow_state.get("failure_stage", "unknown"),
|
||
|
|
"reason": workflow_state.get("failure_reason", "unknown"),
|
||
|
|
}
|
||
|
|
|
||
|
|
return Response(
|
||
|
|
status_code=500,
|
||
|
|
headers=Headers({"Content-Type": "application/json"}),
|
||
|
|
description=jsonify(
|
||
|
|
{
|
||
|
|
"status": "error",
|
||
|
|
"error": str(e),
|
||
|
|
"error_type": type(e).__name__,
|
||
|
|
"error_details": error_details,
|
||
|
|
}
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
return app
|
||
|
|
|
||
|
|
|
||
|
|
@click.command()
|
||
|
|
@click.option(
|
||
|
|
"--env-file",
|
||
|
|
type=click.Path(path_type=pathlib.Path),
|
||
|
|
required=True,
|
||
|
|
default=".env",
|
||
|
|
help="Path to .env file containing backend and configuration settings",
|
||
|
|
)
|
||
|
|
@click.option("--input-path", type=click.Path(path_type=pathlib.Path), default=None)
|
||
|
|
@click.option("--head-chunks", type=int, default=None)
|
||
|
|
def run(
|
||
|
|
env_file: pathlib.Path,
|
||
|
|
input_path: pathlib.Path | None,
|
||
|
|
head_chunks: int | None,
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Main entry point for the OntoCast server/CLI.
|
||
|
|
|
||
|
|
Backend selection is automatically inferred from available configuration:
|
||
|
|
- Fuseki: If FUSEKI_URI and FUSEKI_AUTH are provided (preferred)
|
||
|
|
- Neo4j: If NEO4J_URI and NEO4J_AUTH are provided (fallback)
|
||
|
|
- Filesystem Triple Store: If ONTOCAST_WORKING_DIRECTORY and ONTOCAST_ONTOLOGY_DIRECTORY are provided
|
||
|
|
- Filesystem Manager: If ONTOCAST_WORKING_DIRECTORY is provided (can be combined with other backends)
|
||
|
|
|
||
|
|
No explicit backend configuration flags are needed - backends are automatically detected.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
_ = load_dotenv(dotenv_path=env_file.expanduser())
|
||
|
|
# Global configuration instance
|
||
|
|
config = Config()
|
||
|
|
|
||
|
|
# Validate LLM configuration
|
||
|
|
config.validate_llm_config()
|
||
|
|
|
||
|
|
if config.logging_level is not None:
|
||
|
|
try:
|
||
|
|
logger_conf = f"logging.{config.logging_level}.conf"
|
||
|
|
logging.config.fileConfig(logger_conf, disable_existing_loggers=False)
|
||
|
|
logger.debug("debug is on")
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"could set logging level correctly {e}")
|
||
|
|
|
||
|
|
if config.tool_config.path_config.working_directory is not None:
|
||
|
|
config.tool_config.path_config.working_directory = pathlib.Path(
|
||
|
|
config.tool_config.path_config.working_directory
|
||
|
|
).expanduser()
|
||
|
|
config.tool_config.path_config.working_directory.mkdir(
|
||
|
|
parents=True, exist_ok=True
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Working directory must be provided via CLI argument or WORKING_DIRECTORY config"
|
||
|
|
)
|
||
|
|
|
||
|
|
if config.tool_config.path_config.ontology_directory is not None:
|
||
|
|
config.tool_config.path_config.ontology_directory = pathlib.Path(
|
||
|
|
config.tool_config.path_config.ontology_directory
|
||
|
|
).expanduser()
|
||
|
|
|
||
|
|
# Create ToolBox with config
|
||
|
|
tools: ToolBox = ToolBox(config)
|
||
|
|
asyncio.run(tools.initialize())
|
||
|
|
|
||
|
|
workflow: CompiledStateGraph = create_agent_graph(tools)
|
||
|
|
|
||
|
|
if input_path:
|
||
|
|
input_path = input_path.expanduser()
|
||
|
|
|
||
|
|
files = sorted(
|
||
|
|
crawl_directories(
|
||
|
|
input_path,
|
||
|
|
suffixes=tuple([".json"] + list(tools.converter.supported_extensions)),
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
recursion_limit = calculate_recursion_limit(
|
||
|
|
head_chunks,
|
||
|
|
config.server,
|
||
|
|
)
|
||
|
|
|
||
|
|
async def process_files():
|
||
|
|
for file_path in files:
|
||
|
|
try:
|
||
|
|
state = AgentState(
|
||
|
|
files={file_path.as_posix(): file_path.read_bytes()},
|
||
|
|
max_visits=config.server.max_visits_per_node,
|
||
|
|
max_chunks=head_chunks,
|
||
|
|
render_mode=config.server.render_mode,
|
||
|
|
dataset=config.tool_config.fuseki.dataset,
|
||
|
|
)
|
||
|
|
async for _ in workflow.astream(
|
||
|
|
state,
|
||
|
|
stream_mode="values",
|
||
|
|
config=RunnableConfig(recursion_limit=recursion_limit),
|
||
|
|
):
|
||
|
|
pass
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"Error processing {file_path}: {str(e)}")
|
||
|
|
|
||
|
|
asyncio.run(process_files())
|
||
|
|
else:
|
||
|
|
app = create_app(
|
||
|
|
tools=tools,
|
||
|
|
server_config=config.server,
|
||
|
|
head_chunks=head_chunks,
|
||
|
|
)
|
||
|
|
logger.info(f"Starting Ontocast server on port {config.server.port}")
|
||
|
|
app.start(port=config.server.port)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
run()
|