import time
import logging
import sys
import os
import base64
import textwrap
import asyncio
from aiocache import cached
from typing import Any, Optional
import random
import json
import html
import inspect
import re
import ast
from uuid import uuid4
from concurrent.futures import ThreadPoolExecutor
from fastapi import Request, HTTPException
from fastapi.responses import HTMLResponse
from starlette.responses import Response, StreamingResponse, JSONResponse
from open_webui.models.chats import Chats
from open_webui.models.folders import Folders
from open_webui.models.users import Users
from open_webui.socket.main import (
get_event_call,
get_event_emitter,
get_active_status_by_user_id,
)
from open_webui.routers.tasks import (
generate_queries,
generate_title,
generate_follow_ups,
generate_image_prompt,
generate_chat_tags,
)
from open_webui.routers.retrieval import process_web_search, SearchForm
from open_webui.routers.images import (
load_b64_image_data,
image_generations,
GenerateImageForm,
upload_image,
)
from open_webui.routers.pipelines import (
process_pipeline_inlet_filter,
process_pipeline_outlet_filter,
)
from open_webui.routers.memories import query_memory, QueryMemoryForm
from open_webui.utils.webhook import post_webhook
from open_webui.utils.files import get_image_url_from_base64
from open_webui.models.users import UserModel
from open_webui.models.functions import Functions
from open_webui.models.models import Models
from open_webui.retrieval.utils import get_sources_from_items
from open_webui.utils.chat import generate_chat_completion
from open_webui.utils.task import (
get_task_model_id,
rag_template,
tools_function_calling_generation_template,
)
from open_webui.utils.misc import (
deep_update,
get_message_list,
add_or_update_system_message,
add_or_update_user_message,
get_last_user_message,
get_last_assistant_message,
get_system_message,
prepend_to_first_user_message_content,
convert_logit_bias_input_to_json,
)
from open_webui.utils.tools import get_tools
from open_webui.utils.plugin import load_function_module_by_id
from open_webui.utils.filter import (
get_sorted_filter_ids,
process_filter_functions,
)
from open_webui.utils.code_interpreter import execute_code_jupyter
from open_webui.utils.payload import apply_system_prompt_to_body
from open_webui.utils.mcp.client import MCPClient
from open_webui.config import (
CACHE_DIR,
DEFAULT_TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
DEFAULT_CODE_INTERPRETER_PROMPT,
CODE_INTERPRETER_BLOCKED_MODULES,
)
from open_webui.env import (
SRC_LOG_LEVELS,
GLOBAL_LOG_LEVEL,
CHAT_RESPONSE_STREAM_DELTA_CHUNK_SIZE,
CHAT_RESPONSE_MAX_TOOL_CALL_RETRIES,
BYPASS_MODEL_ACCESS_CONTROL,
ENABLE_REALTIME_CHAT_SAVE,
ENABLE_QUERIES_CACHE,
)
from open_webui.constants import TASKS
logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["MAIN"])
DEFAULT_REASONING_TAGS = [
("", ""),
("", ""),
("", ""),
("", ""),
("", ""),
("", ""),
("<|begin_of_thought|>", "<|end_of_thought|>"),
("◁think▷", "◁/think▷"),
]
DEFAULT_SOLUTION_TAGS = [("<|begin_of_solution|>", "<|end_of_solution|>")]
DEFAULT_CODE_INTERPRETER_TAGS = [("", "")]
async def chat_completion_tools_handler(
request: Request, body: dict, extra_params: dict, user: UserModel, models, tools
) -> tuple[dict, dict]:
async def get_content_from_response(response) -> Optional[str]:
content = None
if hasattr(response, "body_iterator"):
async for chunk in response.body_iterator:
data = json.loads(chunk.decode("utf-8"))
content = data["choices"][0]["message"]["content"]
# Cleanup any remaining background tasks if necessary
if response.background is not None:
await response.background()
else:
content = response["choices"][0]["message"]["content"]
return content
def get_tools_function_calling_payload(messages, task_model_id, content):
user_message = get_last_user_message(messages)
history = "\n".join(
f"{message['role'].upper()}: \"\"\"{message['content']}\"\"\""
for message in messages[::-1][:4]
)
prompt = f"History:\n{history}\nQuery: {user_message}"
return {
"model": task_model_id,
"messages": [
{"role": "system", "content": content},
{"role": "user", "content": f"Query: {prompt}"},
],
"stream": False,
"metadata": {"task": str(TASKS.FUNCTION_CALLING)},
}
event_caller = extra_params["__event_call__"]
metadata = extra_params["__metadata__"]
task_model_id = get_task_model_id(
body["model"],
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
skip_files = False
sources = []
specs = [tool["spec"] for tool in tools.values()]
tools_specs = json.dumps(specs)
if request.app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE != "":
template = request.app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
else:
template = DEFAULT_TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
tools_function_calling_prompt = tools_function_calling_generation_template(
template, tools_specs
)
payload = get_tools_function_calling_payload(
body["messages"], task_model_id, tools_function_calling_prompt
)
try:
response = await generate_chat_completion(request, form_data=payload, user=user)
log.debug(f"{response=}")
content = await get_content_from_response(response)
log.debug(f"{content=}")
if not content:
return body, {}
try:
content = content[content.find("{") : content.rfind("}") + 1]
if not content:
raise Exception("No JSON object found in the response")
result = json.loads(content)
async def tool_call_handler(tool_call):
nonlocal skip_files
log.debug(f"{tool_call=}")
tool_function_name = tool_call.get("name", None)
if tool_function_name not in tools:
return body, {}
tool_function_params = tool_call.get("parameters", {})
try:
tool = tools[tool_function_name]
spec = tool.get("spec", {})
allowed_params = (
spec.get("parameters", {}).get("properties", {}).keys()
)
tool_function_params = {
k: v
for k, v in tool_function_params.items()
if k in allowed_params
}
if tool.get("direct", False):
tool_result = await event_caller(
{
"type": "execute:tool",
"data": {
"id": str(uuid4()),
"name": tool_function_name,
"params": tool_function_params,
"server": tool.get("server", {}),
"session_id": metadata.get("session_id", None),
},
}
)
else:
tool_function = tool["callable"]
tool_result = await tool_function(**tool_function_params)
except Exception as e:
tool_result = str(e)
tool_result_files = []
if isinstance(tool_result, list):
for item in tool_result:
# check if string
if isinstance(item, str) and item.startswith("data:"):
tool_result_files.append(item)
tool_result.remove(item)
if isinstance(tool_result, dict) or isinstance(tool_result, list):
tool_result = json.dumps(tool_result, indent=2)
if isinstance(tool_result, str):
tool = tools[tool_function_name]
tool_id = tool.get("tool_id", "")
tool_name = (
f"{tool_id}/{tool_function_name}"
if tool_id
else f"{tool_function_name}"
)
# Citation is enabled for this tool
sources.append(
{
"source": {
"name": (f"TOOL:{tool_name}"),
},
"document": [tool_result],
"metadata": [
{
"source": (f"TOOL:{tool_name}"),
"parameters": tool_function_params,
}
],
"tool_result": True,
}
)
# Citation is not enabled for this tool
body["messages"] = add_or_update_user_message(
f"\nTool `{tool_name}` Output: {tool_result}",
body["messages"],
)
if (
tools[tool_function_name]
.get("metadata", {})
.get("file_handler", False)
):
skip_files = True
# check if "tool_calls" in result
if result.get("tool_calls"):
for tool_call in result.get("tool_calls"):
await tool_call_handler(tool_call)
else:
await tool_call_handler(result)
except Exception as e:
log.debug(f"Error: {e}")
content = None
except Exception as e:
log.debug(f"Error: {e}")
content = None
log.debug(f"tool_contexts: {sources}")
if skip_files and "files" in body.get("metadata", {}):
del body["metadata"]["files"]
return body, {"sources": sources}
async def chat_memory_handler(
request: Request, form_data: dict, extra_params: dict, user
):
try:
results = await query_memory(
request,
QueryMemoryForm(
**{
"content": get_last_user_message(form_data["messages"]) or "",
"k": 3,
}
),
user,
)
except Exception as e:
log.debug(e)
results = None
user_context = ""
if results and hasattr(results, "documents"):
if results.documents and len(results.documents) > 0:
for doc_idx, doc in enumerate(results.documents[0]):
created_at_date = "Unknown Date"
if results.metadatas[0][doc_idx].get("created_at"):
created_at_timestamp = results.metadatas[0][doc_idx]["created_at"]
created_at_date = time.strftime(
"%Y-%m-%d", time.localtime(created_at_timestamp)
)
user_context += f"{doc_idx + 1}. [{created_at_date}] {doc}\n"
form_data["messages"] = add_or_update_system_message(
f"User Context:\n{user_context}\n", form_data["messages"], append=True
)
return form_data
async def chat_web_search_handler(
request: Request, form_data: dict, extra_params: dict, user
):
event_emitter = extra_params["__event_emitter__"]
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "Searching the web",
"done": False,
},
}
)
messages = form_data["messages"]
user_message = get_last_user_message(messages)
queries = []
try:
res = await generate_queries(
request,
{
"model": form_data["model"],
"messages": messages,
"prompt": user_message,
"type": "web_search",
},
user,
)
response = res["choices"][0]["message"]["content"]
try:
bracket_start = response.find("{")
bracket_end = response.rfind("}") + 1
if bracket_start == -1 or bracket_end == -1:
raise Exception("No JSON object found in the response")
response = response[bracket_start:bracket_end]
queries = json.loads(response)
queries = queries.get("queries", [])
except Exception as e:
queries = [response]
if ENABLE_QUERIES_CACHE:
request.state.cached_queries = queries
except Exception as e:
log.exception(e)
queries = [user_message]
# Check if generated queries are empty
if len(queries) == 1 and queries[0].strip() == "":
queries = [user_message]
# Check if queries are not found
if len(queries) == 0:
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "No search query generated",
"done": True,
},
}
)
return form_data
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search_queries_generated",
"queries": queries,
"done": False,
},
}
)
try:
results = await process_web_search(
request,
SearchForm(queries=queries),
user=user,
)
if results:
files = form_data.get("files", [])
if results.get("collection_names"):
for col_idx, collection_name in enumerate(
results.get("collection_names")
):
files.append(
{
"collection_name": collection_name,
"name": ", ".join(queries),
"type": "web_search",
"urls": results["filenames"],
"queries": queries,
}
)
elif results.get("docs"):
# Invoked when bypass embedding and retrieval is set to True
docs = results["docs"]
files.append(
{
"docs": docs,
"name": ", ".join(queries),
"type": "web_search",
"urls": results["filenames"],
"queries": queries,
}
)
form_data["files"] = files
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "Searched {{count}} sites",
"urls": results["filenames"],
"items": results.get("items", []),
"done": True,
},
}
)
else:
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "No search results found",
"done": True,
"error": True,
},
}
)
except Exception as e:
log.exception(e)
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "An error occurred while searching the web",
"queries": queries,
"done": True,
"error": True,
},
}
)
return form_data
async def chat_image_generation_handler(
request: Request, form_data: dict, extra_params: dict, user
):
__event_emitter__ = extra_params["__event_emitter__"]
await __event_emitter__(
{
"type": "status",
"data": {"description": "Creating image", "done": False},
}
)
messages = form_data["messages"]
user_message = get_last_user_message(messages)
prompt = user_message
negative_prompt = ""
if request.app.state.config.ENABLE_IMAGE_PROMPT_GENERATION:
try:
res = await generate_image_prompt(
request,
{
"model": form_data["model"],
"messages": messages,
},
user,
)
response = res["choices"][0]["message"]["content"]
try:
bracket_start = response.find("{")
bracket_end = response.rfind("}") + 1
if bracket_start == -1 or bracket_end == -1:
raise Exception("No JSON object found in the response")
response = response[bracket_start:bracket_end]
response = json.loads(response)
prompt = response.get("prompt", [])
except Exception as e:
prompt = user_message
except Exception as e:
log.exception(e)
prompt = user_message
system_message_content = ""
try:
images = await image_generations(
request=request,
form_data=GenerateImageForm(**{"prompt": prompt}),
user=user,
)
await __event_emitter__(
{
"type": "status",
"data": {"description": "Image created", "done": True},
}
)
await __event_emitter__(
{
"type": "files",
"data": {
"files": [
{
"type": "image",
"url": image["url"],
}
for image in images
]
},
}
)
system_message_content = "User is shown the generated image, tell the user that the image has been generated"
except Exception as e:
log.exception(e)
await __event_emitter__(
{
"type": "status",
"data": {
"description": f"An error occurred while generating an image",
"done": True,
},
}
)
system_message_content = "Unable to generate an image, tell the user that an error occurred"
if system_message_content:
form_data["messages"] = add_or_update_system_message(
system_message_content, form_data["messages"]
)
return form_data
async def chat_completion_files_handler(
request: Request, body: dict, extra_params: dict, user: UserModel
) -> tuple[dict, dict[str, list]]:
__event_emitter__ = extra_params["__event_emitter__"]
sources = []
if files := body.get("metadata", {}).get("files", None):
queries = []
try:
queries_response = await generate_queries(
request,
{
"model": body["model"],
"messages": body["messages"],
"type": "retrieval",
},
user,
)
queries_response = queries_response["choices"][0]["message"]["content"]
try:
bracket_start = queries_response.find("{")
bracket_end = queries_response.rfind("}") + 1
if bracket_start == -1 or bracket_end == -1:
raise Exception("No JSON object found in the response")
queries_response = queries_response[bracket_start:bracket_end]
queries_response = json.loads(queries_response)
except Exception as e:
queries_response = {"queries": [queries_response]}
queries = queries_response.get("queries", [])
except:
pass
if len(queries) == 0:
queries = [get_last_user_message(body["messages"])]
await __event_emitter__(
{
"type": "status",
"data": {
"action": "queries_generated",
"queries": queries,
"done": False,
},
}
)
try:
# Offload get_sources_from_items to a separate thread
loop = asyncio.get_running_loop()
with ThreadPoolExecutor() as executor:
sources = await loop.run_in_executor(
executor,
lambda: get_sources_from_items(
request=request,
items=files,
queries=queries,
embedding_function=lambda query, prefix: request.app.state.EMBEDDING_FUNCTION(
query, prefix=prefix, user=user
),
k=request.app.state.config.TOP_K,
reranking_function=(
(
lambda sentences: request.app.state.RERANKING_FUNCTION(
sentences, user=user
)
)
if request.app.state.RERANKING_FUNCTION
else None
),
k_reranker=request.app.state.config.TOP_K_RERANKER,
r=request.app.state.config.RELEVANCE_THRESHOLD,
hybrid_bm25_weight=request.app.state.config.HYBRID_BM25_WEIGHT,
hybrid_search=request.app.state.config.ENABLE_RAG_HYBRID_SEARCH,
full_context=request.app.state.config.RAG_FULL_CONTEXT,
user=user,
),
)
except Exception as e:
log.exception(e)
log.debug(f"rag_contexts:sources: {sources}")
unique_ids = set()
for source in sources or []:
if not source or len(source.keys()) == 0:
continue
documents = source.get("document") or []
metadatas = source.get("metadata") or []
src_info = source.get("source") or {}
for index, _ in enumerate(documents):
metadata = metadatas[index] if index < len(metadatas) else None
_id = (
(metadata or {}).get("source")
or (src_info or {}).get("id")
or "N/A"
)
unique_ids.add(_id)
sources_count = len(unique_ids)
await __event_emitter__(
{
"type": "status",
"data": {
"action": "sources_retrieved",
"count": sources_count,
"done": True,
},
}
)
return body, {"sources": sources}
def apply_params_to_form_data(form_data, model):
params = form_data.pop("params", {})
custom_params = params.pop("custom_params", {})
open_webui_params = {
"stream_response": bool,
"stream_delta_chunk_size": int,
"function_calling": str,
"reasoning_tags": list,
"system": str,
}
for key in list(params.keys()):
if key in open_webui_params:
del params[key]
if custom_params:
# Attempt to parse custom_params if they are strings
for key, value in custom_params.items():
if isinstance(value, str):
try:
# Attempt to parse the string as JSON
custom_params[key] = json.loads(value)
except json.JSONDecodeError:
# If it fails, keep the original string
pass
# If custom_params are provided, merge them into params
params = deep_update(params, custom_params)
if model.get("owned_by") == "ollama":
# Ollama specific parameters
form_data["options"] = params
else:
if isinstance(params, dict):
for key, value in params.items():
if value is not None:
form_data[key] = value
if "logit_bias" in params and params["logit_bias"] is not None:
try:
form_data["logit_bias"] = json.loads(
convert_logit_bias_input_to_json(params["logit_bias"])
)
except Exception as e:
log.exception(f"Error parsing logit_bias: {e}")
return form_data
async def process_chat_payload(request, form_data, user, metadata, model):
# Pipeline Inlet -> Filter Inlet -> Chat Memory -> Chat Web Search -> Chat Image Generation
# -> Chat Code Interpreter (Form Data Update) -> (Default) Chat Tools Function Calling
# -> Chat Files
form_data = apply_params_to_form_data(form_data, model)
log.debug(f"form_data: {form_data}")
system_message = get_system_message(form_data.get("messages", []))
if system_message:
try:
form_data = apply_system_prompt_to_body(
system_message.get("content"), form_data, metadata, user
)
except:
pass
event_emitter = get_event_emitter(metadata)
event_call = get_event_call(metadata)
oauth_token = None
try:
if request.cookies.get("oauth_session_id", None):
oauth_token = await request.app.state.oauth_manager.get_oauth_token(
user.id,
request.cookies.get("oauth_session_id", None),
)
except Exception as e:
log.error(f"Error getting OAuth token: {e}")
extra_params = {
"__event_emitter__": event_emitter,
"__event_call__": event_call,
"__user__": user.model_dump() if isinstance(user, UserModel) else {},
"__metadata__": metadata,
"__request__": request,
"__model__": model,
"__oauth_token__": oauth_token,
}
# Initialize events to store additional event to be sent to the client
# Initialize contexts and citation
if getattr(request.state, "direct", False) and hasattr(request.state, "model"):
models = {
request.state.model["id"]: request.state.model,
}
else:
models = request.app.state.MODELS
task_model_id = get_task_model_id(
form_data["model"],
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
events = []
sources = []
# Folder "Project" handling
# Check if the request has chat_id and is inside of a folder
chat_id = metadata.get("chat_id", None)
if chat_id and user:
chat = Chats.get_chat_by_id_and_user_id(chat_id, user.id)
if chat and chat.folder_id:
folder = Folders.get_folder_by_id_and_user_id(chat.folder_id, user.id)
if folder and folder.data:
if "system_prompt" in folder.data:
form_data = apply_system_prompt_to_body(
folder.data["system_prompt"], form_data, metadata, user
)
if "files" in folder.data:
form_data["files"] = [
*folder.data["files"],
*form_data.get("files", []),
]
# Model "Knowledge" handling
user_message = get_last_user_message(form_data["messages"])
model_knowledge = model.get("info", {}).get("meta", {}).get("knowledge", False)
if model_knowledge:
await event_emitter(
{
"type": "status",
"data": {
"action": "knowledge_search",
"query": user_message,
"done": False,
},
}
)
knowledge_files = []
for item in model_knowledge:
if item.get("collection_name"):
knowledge_files.append(
{
"id": item.get("collection_name"),
"name": item.get("name"),
"legacy": True,
}
)
elif item.get("collection_names"):
knowledge_files.append(
{
"name": item.get("name"),
"type": "collection",
"collection_names": item.get("collection_names"),
"legacy": True,
}
)
else:
knowledge_files.append(item)
files = form_data.get("files", [])
files.extend(knowledge_files)
form_data["files"] = files
variables = form_data.pop("variables", None)
# Process the form_data through the pipeline
try:
form_data = await process_pipeline_inlet_filter(
request, form_data, user, models
)
except Exception as e:
raise e
try:
filter_functions = [
Functions.get_function_by_id(filter_id)
for filter_id in get_sorted_filter_ids(
request, model, metadata.get("filter_ids", [])
)
]
form_data, flags = await process_filter_functions(
request=request,
filter_functions=filter_functions,
filter_type="inlet",
form_data=form_data,
extra_params=extra_params,
)
except Exception as e:
raise Exception(f"{e}")
features = form_data.pop("features", None)
if features:
if "memory" in features and features["memory"]:
form_data = await chat_memory_handler(
request, form_data, extra_params, user
)
if "web_search" in features and features["web_search"]:
form_data = await chat_web_search_handler(
request, form_data, extra_params, user
)
if "image_generation" in features and features["image_generation"]:
form_data = await chat_image_generation_handler(
request, form_data, extra_params, user
)
if "code_interpreter" in features and features["code_interpreter"]:
form_data["messages"] = add_or_update_user_message(
(
request.app.state.config.CODE_INTERPRETER_PROMPT_TEMPLATE
if request.app.state.config.CODE_INTERPRETER_PROMPT_TEMPLATE != ""
else DEFAULT_CODE_INTERPRETER_PROMPT
),
form_data["messages"],
)
tool_ids = form_data.pop("tool_ids", None)
files = form_data.pop("files", None)
# Remove files duplicates
if files:
files = list({json.dumps(f, sort_keys=True): f for f in files}.values())
metadata = {
**metadata,
"tool_ids": tool_ids,
"files": files,
}
form_data["metadata"] = metadata
# Server side tools
tool_ids = metadata.get("tool_ids", None)
# Client side tools
direct_tool_servers = metadata.get("tool_servers", None)
log.debug(f"{tool_ids=}")
log.debug(f"{direct_tool_servers=}")
tools_dict = {}
mcp_clients = []
mcp_tools_dict = {}
if tool_ids:
for tool_id in tool_ids:
if tool_id.startswith("server:mcp:"):
try:
server_id = tool_id[len("server:mcp:") :]
mcp_server_connection = None
for (
server_connection
) in request.app.state.config.TOOL_SERVER_CONNECTIONS:
if (
server_connection.get("type", "") == "mcp"
and server_connection.get("info", {}).get("id") == server_id
):
mcp_server_connection = server_connection
break
if not mcp_server_connection:
log.error(f"MCP server with id {server_id} not found")
continue
auth_type = mcp_server_connection.get("auth_type", "")
headers = {}
if auth_type == "bearer":
headers["Authorization"] = (
f"Bearer {mcp_server_connection.get('key', '')}"
)
elif auth_type == "none":
# No authentication
pass
elif auth_type == "session":
headers["Authorization"] = (
f"Bearer {request.state.token.credentials}"
)
elif auth_type == "system_oauth":
oauth_token = extra_params.get("__oauth_token__", None)
if oauth_token:
headers["Authorization"] = (
f"Bearer {oauth_token.get('access_token', '')}"
)
mcp_client = MCPClient()
await mcp_client.connect(
url=mcp_server_connection.get("url", ""),
headers=headers if headers else None,
)
tool_specs = await mcp_client.list_tool_specs()
for tool_spec in tool_specs:
def make_tool_function(function_name):
async def tool_function(**kwargs):
return await mcp_client.call_tool(
function_name,
function_args=kwargs,
)
return tool_function
tool_function = make_tool_function(tool_spec["name"])
mcp_tools_dict[tool_spec["name"]] = {
"spec": tool_spec,
"callable": tool_function,
"type": "mcp",
"client": mcp_client,
"direct": False,
}
mcp_clients.append(mcp_client)
except Exception as e:
log.debug(e)
continue
tools_dict = await get_tools(
request,
tool_ids,
user,
{
**extra_params,
"__model__": models[task_model_id],
"__messages__": form_data["messages"],
"__files__": metadata.get("files", []),
},
)
if mcp_tools_dict:
tools_dict = {**tools_dict, **mcp_tools_dict}
if direct_tool_servers:
for tool_server in direct_tool_servers:
tool_specs = tool_server.pop("specs", [])
for tool in tool_specs:
tools_dict[tool["name"]] = {
"spec": tool,
"direct": True,
"server": tool_server,
}
if mcp_clients:
metadata["mcp_clients"] = mcp_clients
if tools_dict:
if metadata.get("params", {}).get("function_calling") == "native":
# If the function calling is native, then call the tools function calling handler
metadata["tools"] = tools_dict
form_data["tools"] = [
{"type": "function", "function": tool.get("spec", {})}
for tool in tools_dict.values()
]
else:
# If the function calling is not native, then call the tools function calling handler
try:
form_data, flags = await chat_completion_tools_handler(
request, form_data, extra_params, user, models, tools_dict
)
sources.extend(flags.get("sources", []))
except Exception as e:
log.exception(e)
try:
form_data, flags = await chat_completion_files_handler(
request, form_data, extra_params, user
)
sources.extend(flags.get("sources", []))
except Exception as e:
log.exception(e)
# If context is not empty, insert it into the messages
if len(sources) > 0:
context_string = ""
citation_idx_map = {}
for source in sources:
is_tool_result = source.get("tool_result", False)
if "document" in source and not is_tool_result:
for document_text, document_metadata in zip(
source["document"], source["metadata"]
):
source_name = source.get("source", {}).get("name", None)
source_id = (
document_metadata.get("source", None)
or source.get("source", {}).get("id", None)
or "N/A"
)
if source_id not in citation_idx_map:
citation_idx_map[source_id] = len(citation_idx_map) + 1
context_string += (
f'{document_text}\n"
)
context_string = context_string.strip()
prompt = get_last_user_message(form_data["messages"])
if prompt is None:
raise Exception("No user message found")
if context_string != "":
# Workaround for Ollama 2.0+ system prompt issue
# TODO: replace with add_or_update_system_message
if model.get("owned_by") == "ollama":
form_data["messages"] = prepend_to_first_user_message_content(
rag_template(
request.app.state.config.RAG_TEMPLATE,
context_string,
prompt,
),
form_data["messages"],
)
else:
form_data["messages"] = add_or_update_system_message(
rag_template(
request.app.state.config.RAG_TEMPLATE,
context_string,
prompt,
),
form_data["messages"],
)
# If there are citations, add them to the data_items
sources = [
source
for source in sources
if source.get("source", {}).get("name", "")
or source.get("source", {}).get("id", "")
]
if len(sources) > 0:
events.append({"sources": sources})
if model_knowledge:
await event_emitter(
{
"type": "status",
"data": {
"action": "knowledge_search",
"query": user_message,
"done": True,
"hidden": True,
},
}
)
return form_data, metadata, events
async def process_chat_response(
request, response, form_data, user, metadata, model, events, tasks
):
async def background_tasks_handler():
messages_map = Chats.get_messages_map_by_chat_id(metadata["chat_id"])
message = messages_map.get(metadata["message_id"]) if messages_map else None
if message:
message_list = get_message_list(messages_map, metadata["message_id"])
# Remove details tags and files from the messages.
# as get_message_list creates a new list, it does not affect
# the original messages outside of this handler
messages = []
for message in message_list:
content = message.get("content", "")
if isinstance(content, list):
for item in content:
if item.get("type") == "text":
content = item["text"]
break
if isinstance(content, str):
content = re.sub(
r"]*>.*?<\/details>|!\[.*?\]\(.*?\)",
"",
content,
flags=re.S | re.I,
).strip()
messages.append(
{
**message,
"role": message.get(
"role", "assistant"
), # Safe fallback for missing role
"content": content,
}
)
if tasks and messages:
if (
TASKS.FOLLOW_UP_GENERATION in tasks
and tasks[TASKS.FOLLOW_UP_GENERATION]
):
res = await generate_follow_ups(
request,
{
"model": message["model"],
"messages": messages,
"message_id": metadata["message_id"],
"chat_id": metadata["chat_id"],
},
user,
)
if res and isinstance(res, dict):
if len(res.get("choices", [])) == 1:
follow_ups_string = (
res.get("choices", [])[0]
.get("message", {})
.get("content", "")
)
else:
follow_ups_string = ""
follow_ups_string = follow_ups_string[
follow_ups_string.find("{") : follow_ups_string.rfind("}")
+ 1
]
try:
follow_ups = json.loads(follow_ups_string).get(
"follow_ups", []
)
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"followUps": follow_ups,
},
)
await event_emitter(
{
"type": "chat:message:follow_ups",
"data": {
"follow_ups": follow_ups,
},
}
)
except Exception as e:
pass
if TASKS.TITLE_GENERATION in tasks:
user_message = get_last_user_message(messages)
if user_message and len(user_message) > 100:
user_message = user_message[:100] + "..."
if tasks[TASKS.TITLE_GENERATION]:
res = await generate_title(
request,
{
"model": message["model"],
"messages": messages,
"chat_id": metadata["chat_id"],
},
user,
)
if res and isinstance(res, dict):
if len(res.get("choices", [])) == 1:
title_string = (
res.get("choices", [])[0]
.get("message", {})
.get(
"content", message.get("content", user_message)
)
)
else:
title_string = ""
title_string = title_string[
title_string.find("{") : title_string.rfind("}") + 1
]
try:
title = json.loads(title_string).get(
"title", user_message
)
except Exception as e:
title = ""
if not title:
title = messages[0].get("content", user_message)
Chats.update_chat_title_by_id(metadata["chat_id"], title)
await event_emitter(
{
"type": "chat:title",
"data": title,
}
)
elif len(messages) == 2:
title = messages[0].get("content", user_message)
Chats.update_chat_title_by_id(metadata["chat_id"], title)
await event_emitter(
{
"type": "chat:title",
"data": message.get("content", user_message),
}
)
if TASKS.TAGS_GENERATION in tasks and tasks[TASKS.TAGS_GENERATION]:
res = await generate_chat_tags(
request,
{
"model": message["model"],
"messages": messages,
"chat_id": metadata["chat_id"],
},
user,
)
if res and isinstance(res, dict):
if len(res.get("choices", [])) == 1:
tags_string = (
res.get("choices", [])[0]
.get("message", {})
.get("content", "")
)
else:
tags_string = ""
tags_string = tags_string[
tags_string.find("{") : tags_string.rfind("}") + 1
]
try:
tags = json.loads(tags_string).get("tags", [])
Chats.update_chat_tags_by_id(
metadata["chat_id"], tags, user
)
await event_emitter(
{
"type": "chat:tags",
"data": tags,
}
)
except Exception as e:
pass
event_emitter = None
event_caller = None
if (
"session_id" in metadata
and metadata["session_id"]
and "chat_id" in metadata
and metadata["chat_id"]
and "message_id" in metadata
and metadata["message_id"]
):
event_emitter = get_event_emitter(metadata)
event_caller = get_event_call(metadata)
# Non-streaming response
if not isinstance(response, StreamingResponse):
if event_emitter:
try:
if isinstance(response, dict) or isinstance(response, JSONResponse):
if isinstance(response, list) and len(response) == 1:
# If the response is a single-item list, unwrap it #17213
response = response[0]
if isinstance(response, JSONResponse) and isinstance(
response.body, bytes
):
try:
response_data = json.loads(response.body.decode("utf-8"))
except json.JSONDecodeError:
response_data = {
"error": {"detail": "Invalid JSON response"}
}
else:
response_data = response
if "error" in response_data:
error = response_data.get("error")
if isinstance(error, dict):
error = error.get("detail", error)
else:
error = str(error)
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"error": {"content": error},
},
)
if isinstance(error, str) or isinstance(error, dict):
await event_emitter(
{
"type": "chat:message:error",
"data": {"error": {"content": error}},
}
)
if "selected_model_id" in response_data:
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"selectedModelId": response_data["selected_model_id"],
},
)
choices = response_data.get("choices", [])
if choices and choices[0].get("message", {}).get("content"):
content = response_data["choices"][0]["message"]["content"]
if content:
await event_emitter(
{
"type": "chat:completion",
"data": response_data,
}
)
title = Chats.get_chat_title_by_id(metadata["chat_id"])
await event_emitter(
{
"type": "chat:completion",
"data": {
"done": True,
"content": content,
"title": title,
},
}
)
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"role": "assistant",
"content": content,
},
)
# Send a webhook notification if the user is not active
if not get_active_status_by_user_id(user.id):
webhook_url = Users.get_user_webhook_url_by_id(user.id)
if webhook_url:
await post_webhook(
request.app.state.WEBUI_NAME,
webhook_url,
f"{title} - {request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}\n\n{content}",
{
"action": "chat",
"message": content,
"title": title,
"url": f"{request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}",
},
)
await background_tasks_handler()
if events and isinstance(events, list):
extra_response = {}
for event in events:
if isinstance(event, dict):
extra_response.update(event)
else:
extra_response[event] = True
response_data = {
**extra_response,
**response_data,
}
if isinstance(response, dict):
response = response_data
if isinstance(response, JSONResponse):
response = JSONResponse(
content=response_data,
headers=response.headers,
status_code=response.status_code,
)
except Exception as e:
log.debug(f"Error occurred while processing request: {e}")
pass
return response
else:
if events and isinstance(events, list) and isinstance(response, dict):
extra_response = {}
for event in events:
if isinstance(event, dict):
extra_response.update(event)
else:
extra_response[event] = True
response = {
**extra_response,
**response,
}
return response
# Non standard response
if not any(
content_type in response.headers["Content-Type"]
for content_type in ["text/event-stream", "application/x-ndjson"]
):
return response
oauth_token = None
try:
if request.cookies.get("oauth_session_id", None):
oauth_token = await request.app.state.oauth_manager.get_oauth_token(
user.id,
request.cookies.get("oauth_session_id", None),
)
except Exception as e:
log.error(f"Error getting OAuth token: {e}")
extra_params = {
"__event_emitter__": event_emitter,
"__event_call__": event_caller,
"__user__": user.model_dump() if isinstance(user, UserModel) else {},
"__metadata__": metadata,
"__oauth_token__": oauth_token,
"__request__": request,
"__model__": model,
}
filter_functions = [
Functions.get_function_by_id(filter_id)
for filter_id in get_sorted_filter_ids(
request, model, metadata.get("filter_ids", [])
)
]
# Streaming response
if event_emitter and event_caller:
task_id = str(uuid4()) # Create a unique task ID.
model_id = form_data.get("model", "")
def split_content_and_whitespace(content):
content_stripped = content.rstrip()
original_whitespace = (
content[len(content_stripped) :]
if len(content) > len(content_stripped)
else ""
)
return content_stripped, original_whitespace
def is_opening_code_block(content):
backtick_segments = content.split("```")
# Even number of segments means the last backticks are opening a new block
return len(backtick_segments) > 1 and len(backtick_segments) % 2 == 0
# Handle as a background task
async def response_handler(response, events):
def serialize_content_blocks(content_blocks, raw=False):
content = ""
for block in content_blocks:
if block["type"] == "text":
block_content = block["content"].strip()
if block_content:
content = f"{content}{block_content}\n"
elif block["type"] == "tool_calls":
attributes = block.get("attributes", {})
tool_calls = block.get("content", [])
results = block.get("results", [])
if content and not content.endswith("\n"):
content += "\n"
if results:
tool_calls_display_content = ""
for tool_call in tool_calls:
tool_call_id = tool_call.get("id", "")
tool_name = tool_call.get("function", {}).get(
"name", ""
)
tool_arguments = tool_call.get("function", {}).get(
"arguments", ""
)
tool_result = None
tool_result_files = None
for result in results:
if tool_call_id == result.get("tool_call_id", ""):
tool_result = result.get("content", None)
tool_result_files = result.get("files", None)
break
if tool_result is not None:
tool_result_embeds = result.get("embeds", "")
tool_calls_display_content = f'{tool_calls_display_content}\nTool Executed
\n \n'
else:
tool_calls_display_content = f'{tool_calls_display_content}\nExecuting...
\n \n'
if not raw:
content = f"{content}{tool_calls_display_content}"
else:
tool_calls_display_content = ""
for tool_call in tool_calls:
tool_call_id = tool_call.get("id", "")
tool_name = tool_call.get("function", {}).get(
"name", ""
)
tool_arguments = tool_call.get("function", {}).get(
"arguments", ""
)
tool_calls_display_content = f'{tool_calls_display_content}\n\nExecuting...
\n \n'
if not raw:
content = f"{content}{tool_calls_display_content}"
elif block["type"] == "reasoning":
reasoning_display_content = "\n".join(
(f"> {line}" if not line.startswith(">") else line)
for line in block["content"].splitlines()
)
reasoning_duration = block.get("duration", None)
start_tag = block.get("start_tag", "")
end_tag = block.get("end_tag", "")
if content and not content.endswith("\n"):
content += "\n"
if reasoning_duration is not None:
if raw:
content = (
f'{content}{start_tag}{block["content"]}{end_tag}\n'
)
else:
content = f'{content}\nThought for {reasoning_duration} seconds
\n{reasoning_display_content}\n \n'
else:
if raw:
content = (
f'{content}{start_tag}{block["content"]}{end_tag}\n'
)
else:
content = f'{content}\nThinking…
\n{reasoning_display_content}\n \n'
elif block["type"] == "code_interpreter":
attributes = block.get("attributes", {})
output = block.get("output", None)
lang = attributes.get("lang", "")
content_stripped, original_whitespace = (
split_content_and_whitespace(content)
)
if is_opening_code_block(content_stripped):
# Remove trailing backticks that would open a new block
content = (
content_stripped.rstrip("`").rstrip()
+ original_whitespace
)
else:
# Keep content as is - either closing backticks or no backticks
content = content_stripped + original_whitespace
if content and not content.endswith("\n"):
content += "\n"
if output:
output = html.escape(json.dumps(output))
if raw:
content = f'{content}\n{block["content"]}\n\n```output\n{output}\n```\n'
else:
content = f'{content}\nAnalyzed
\n```{lang}\n{block["content"]}\n```\n \n'
else:
if raw:
content = f'{content}\n{block["content"]}\n\n'
else:
content = f'{content}\nAnalyzing...
\n```{lang}\n{block["content"]}\n```\n \n'
else:
block_content = str(block["content"]).strip()
if block_content:
content = f"{content}{block['type']}: {block_content}\n"
return content.strip()
def convert_content_blocks_to_messages(content_blocks, raw=False):
messages = []
temp_blocks = []
for idx, block in enumerate(content_blocks):
if block["type"] == "tool_calls":
messages.append(
{
"role": "assistant",
"content": serialize_content_blocks(temp_blocks, raw),
"tool_calls": block.get("content"),
}
)
results = block.get("results", [])
for result in results:
messages.append(
{
"role": "tool",
"tool_call_id": result["tool_call_id"],
"content": result.get("content", "") or "",
}
)
temp_blocks = []
else:
temp_blocks.append(block)
if temp_blocks:
content = serialize_content_blocks(temp_blocks, raw)
if content:
messages.append(
{
"role": "assistant",
"content": content,
}
)
return messages
def tag_content_handler(content_type, tags, content, content_blocks):
end_flag = False
def extract_attributes(tag_content):
"""Extract attributes from a tag if they exist."""
attributes = {}
if not tag_content: # Ensure tag_content is not None
return attributes
# Match attributes in the format: key="value" (ignores single quotes for simplicity)
matches = re.findall(r'(\w+)\s*=\s*"([^"]+)"', tag_content)
for key, value in matches:
attributes[key] = value
return attributes
if content_blocks[-1]["type"] == "text":
for start_tag, end_tag in tags:
start_tag_pattern = rf"{re.escape(start_tag)}"
if start_tag.startswith("<") and start_tag.endswith(">"):
# Match start tag e.g., or
# remove both '<' and '>' from start_tag
# Match start tag with attributes
start_tag_pattern = (
rf"<{re.escape(start_tag[1:-1])}(\s.*?)?>"
)
match = re.search(start_tag_pattern, content)
if match:
try:
attr_content = (
match.group(1) if match.group(1) else ""
) # Ensure it's not None
except:
attr_content = ""
attributes = extract_attributes(
attr_content
) # Extract attributes safely
# Capture everything before and after the matched tag
before_tag = content[
: match.start()
] # Content before opening tag
after_tag = content[
match.end() :
] # Content after opening tag
# Remove the start tag and after from the currently handling text block
content_blocks[-1]["content"] = content_blocks[-1][
"content"
].replace(match.group(0) + after_tag, "")
if before_tag:
content_blocks[-1]["content"] = before_tag
if not content_blocks[-1]["content"]:
content_blocks.pop()
# Append the new block
content_blocks.append(
{
"type": content_type,
"start_tag": start_tag,
"end_tag": end_tag,
"attributes": attributes,
"content": "",
"started_at": time.time(),
}
)
if after_tag:
content_blocks[-1]["content"] = after_tag
tag_content_handler(
content_type, tags, after_tag, content_blocks
)
break
elif content_blocks[-1]["type"] == content_type:
start_tag = content_blocks[-1]["start_tag"]
end_tag = content_blocks[-1]["end_tag"]
if end_tag.startswith("<") and end_tag.endswith(">"):
# Match end tag e.g.,
end_tag_pattern = rf"{re.escape(end_tag)}"
else:
# Handle cases where end_tag is just a tag name
end_tag_pattern = rf"{re.escape(end_tag)}"
# Check if the content has the end tag
if re.search(end_tag_pattern, content):
end_flag = True
block_content = content_blocks[-1]["content"]
# Strip start and end tags from the content
start_tag_pattern = rf"<{re.escape(start_tag)}(.*?)>"
block_content = re.sub(
start_tag_pattern, "", block_content
).strip()
end_tag_regex = re.compile(end_tag_pattern, re.DOTALL)
split_content = end_tag_regex.split(block_content, maxsplit=1)
# Content inside the tag
block_content = (
split_content[0].strip() if split_content else ""
)
# Leftover content (everything after ``)
leftover_content = (
split_content[1].strip() if len(split_content) > 1 else ""
)
if block_content:
content_blocks[-1]["content"] = block_content
content_blocks[-1]["ended_at"] = time.time()
content_blocks[-1]["duration"] = int(
content_blocks[-1]["ended_at"]
- content_blocks[-1]["started_at"]
)
# Reset the content_blocks by appending a new text block
if content_type != "code_interpreter":
if leftover_content:
content_blocks.append(
{
"type": "text",
"content": leftover_content,
}
)
else:
content_blocks.append(
{
"type": "text",
"content": "",
}
)
else:
# Remove the block if content is empty
content_blocks.pop()
if leftover_content:
content_blocks.append(
{
"type": "text",
"content": leftover_content,
}
)
else:
content_blocks.append(
{
"type": "text",
"content": "",
}
)
# Clean processed content
start_tag_pattern = rf"{re.escape(start_tag)}"
if start_tag.startswith("<") and start_tag.endswith(">"):
# Match start tag e.g., or
# remove both '<' and '>' from start_tag
# Match start tag with attributes
start_tag_pattern = (
rf"<{re.escape(start_tag[1:-1])}(\s.*?)?>"
)
content = re.sub(
rf"{start_tag_pattern}(.|\n)*?{re.escape(end_tag)}",
"",
content,
flags=re.DOTALL,
)
return content, content_blocks, end_flag
message = Chats.get_message_by_id_and_message_id(
metadata["chat_id"], metadata["message_id"]
)
tool_calls = []
last_assistant_message = None
try:
if form_data["messages"][-1]["role"] == "assistant":
last_assistant_message = get_last_assistant_message(
form_data["messages"]
)
except Exception as e:
pass
content = (
message.get("content", "")
if message
else last_assistant_message if last_assistant_message else ""
)
content_blocks = [
{
"type": "text",
"content": content,
}
]
reasoning_tags_param = metadata.get("params", {}).get("reasoning_tags")
DETECT_REASONING_TAGS = reasoning_tags_param is not False
DETECT_CODE_INTERPRETER = metadata.get("features", {}).get(
"code_interpreter", False
)
reasoning_tags = []
if DETECT_REASONING_TAGS:
if (
isinstance(reasoning_tags_param, list)
and len(reasoning_tags_param) == 2
):
reasoning_tags = [
(reasoning_tags_param[0], reasoning_tags_param[1])
]
else:
reasoning_tags = DEFAULT_REASONING_TAGS
try:
for event in events:
await event_emitter(
{
"type": "chat:completion",
"data": event,
}
)
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
**event,
},
)
async def stream_body_handler(response, form_data):
nonlocal content
nonlocal content_blocks
response_tool_calls = []
delta_count = 0
delta_chunk_size = max(
CHAT_RESPONSE_STREAM_DELTA_CHUNK_SIZE,
int(
metadata.get("params", {}).get("stream_delta_chunk_size")
or 1
),
)
last_delta_data = None
async def flush_pending_delta_data(threshold: int = 0):
nonlocal delta_count
nonlocal last_delta_data
if delta_count >= threshold and last_delta_data:
await event_emitter(
{
"type": "chat:completion",
"data": last_delta_data,
}
)
delta_count = 0
last_delta_data = None
async for line in response.body_iterator:
line = line.decode("utf-8") if isinstance(line, bytes) else line
data = line
# Skip empty lines
if not data.strip():
continue
# "data:" is the prefix for each event
if not data.startswith("data:"):
continue
# Remove the prefix
data = data[len("data:") :].strip()
try:
data = json.loads(data)
data, _ = await process_filter_functions(
request=request,
filter_functions=filter_functions,
filter_type="stream",
form_data=data,
extra_params={"__body__": form_data, **extra_params},
)
if data:
if "event" in data:
await event_emitter(data.get("event", {}))
if "selected_model_id" in data:
model_id = data["selected_model_id"]
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"selectedModelId": model_id,
},
)
await event_emitter(
{
"type": "chat:completion",
"data": data,
}
)
else:
choices = data.get("choices", [])
# 17421
usage = data.get("usage", {}) or {}
usage.update(data.get("timings", {})) # llama.cpp
if usage:
await event_emitter(
{
"type": "chat:completion",
"data": {
"usage": usage,
},
}
)
if not choices:
error = data.get("error", {})
if error:
await event_emitter(
{
"type": "chat:completion",
"data": {
"error": error,
},
}
)
continue
delta = choices[0].get("delta", {})
delta_tool_calls = delta.get("tool_calls", None)
if delta_tool_calls:
for delta_tool_call in delta_tool_calls:
tool_call_index = delta_tool_call.get(
"index"
)
if tool_call_index is not None:
# Check if the tool call already exists
current_response_tool_call = None
for (
response_tool_call
) in response_tool_calls:
if (
response_tool_call.get("index")
== tool_call_index
):
current_response_tool_call = (
response_tool_call
)
break
if current_response_tool_call is None:
# Add the new tool call
delta_tool_call.setdefault(
"function", {}
)
delta_tool_call[
"function"
].setdefault("name", "")
delta_tool_call[
"function"
].setdefault("arguments", "")
response_tool_calls.append(
delta_tool_call
)
else:
# Update the existing tool call
delta_name = delta_tool_call.get(
"function", {}
).get("name")
delta_arguments = (
delta_tool_call.get(
"function", {}
).get("arguments")
)
if delta_name:
current_response_tool_call[
"function"
]["name"] += delta_name
if delta_arguments:
current_response_tool_call[
"function"
][
"arguments"
] += delta_arguments
value = delta.get("content")
reasoning_content = (
delta.get("reasoning_content")
or delta.get("reasoning")
or delta.get("thinking")
)
if reasoning_content:
if (
not content_blocks
or content_blocks[-1]["type"] != "reasoning"
):
reasoning_block = {
"type": "reasoning",
"start_tag": "",
"end_tag": "",
"attributes": {
"type": "reasoning_content"
},
"content": "",
"started_at": time.time(),
}
content_blocks.append(reasoning_block)
else:
reasoning_block = content_blocks[-1]
reasoning_block["content"] += reasoning_content
data = {
"content": serialize_content_blocks(
content_blocks
)
}
if value:
if (
content_blocks
and content_blocks[-1]["type"]
== "reasoning"
and content_blocks[-1]
.get("attributes", {})
.get("type")
== "reasoning_content"
):
reasoning_block = content_blocks[-1]
reasoning_block["ended_at"] = time.time()
reasoning_block["duration"] = int(
reasoning_block["ended_at"]
- reasoning_block["started_at"]
)
content_blocks.append(
{
"type": "text",
"content": "",
}
)
content = f"{content}{value}"
if not content_blocks:
content_blocks.append(
{
"type": "text",
"content": "",
}
)
content_blocks[-1]["content"] = (
content_blocks[-1]["content"] + value
)
if DETECT_REASONING_TAGS:
content, content_blocks, _ = (
tag_content_handler(
"reasoning",
reasoning_tags,
content,
content_blocks,
)
)
content, content_blocks, _ = (
tag_content_handler(
"solution",
DEFAULT_SOLUTION_TAGS,
content,
content_blocks,
)
)
if DETECT_CODE_INTERPRETER:
content, content_blocks, end = (
tag_content_handler(
"code_interpreter",
DEFAULT_CODE_INTERPRETER_TAGS,
content,
content_blocks,
)
)
if end:
break
if ENABLE_REALTIME_CHAT_SAVE:
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": serialize_content_blocks(
content_blocks
),
},
)
else:
data = {
"content": serialize_content_blocks(
content_blocks
),
}
if delta:
delta_count += 1
last_delta_data = data
if delta_count >= delta_chunk_size:
await flush_pending_delta_data(delta_chunk_size)
else:
await event_emitter(
{
"type": "chat:completion",
"data": data,
}
)
except Exception as e:
done = "data: [DONE]" in line
if done:
pass
else:
log.debug(f"Error: {e}")
continue
await flush_pending_delta_data()
if content_blocks:
# Clean up the last text block
if content_blocks[-1]["type"] == "text":
content_blocks[-1]["content"] = content_blocks[-1][
"content"
].strip()
if not content_blocks[-1]["content"]:
content_blocks.pop()
if not content_blocks:
content_blocks.append(
{
"type": "text",
"content": "",
}
)
if content_blocks[-1]["type"] == "reasoning":
reasoning_block = content_blocks[-1]
if reasoning_block.get("ended_at") is None:
reasoning_block["ended_at"] = time.time()
reasoning_block["duration"] = int(
reasoning_block["ended_at"]
- reasoning_block["started_at"]
)
if response_tool_calls:
tool_calls.append(response_tool_calls)
if response.background:
await response.background()
await stream_body_handler(response, form_data)
tool_call_retries = 0
while (
len(tool_calls) > 0
and tool_call_retries < CHAT_RESPONSE_MAX_TOOL_CALL_RETRIES
):
tool_call_retries += 1
response_tool_calls = tool_calls.pop(0)
content_blocks.append(
{
"type": "tool_calls",
"content": response_tool_calls,
}
)
await event_emitter(
{
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
},
}
)
tools = metadata.get("tools", {})
results = []
for tool_call in response_tool_calls:
print("tool_call", tool_call)
tool_call_id = tool_call.get("id", "")
tool_name = tool_call.get("function", {}).get("name", "")
tool_args = tool_call.get("function", {}).get("arguments", "{}")
tool_function_params = {}
try:
# json.loads cannot be used because some models do not produce valid JSON
tool_function_params = ast.literal_eval(tool_args)
except Exception as e:
log.debug(e)
# Fallback to JSON parsing
try:
tool_function_params = json.loads(tool_args)
except Exception as e:
log.error(
f"Error parsing tool call arguments: {tool_args}"
)
# Mutate the original tool call response params as they are passed back to the passed
# back to the LLM via the content blocks. If they are in a json block and are invalid json,
# this can cause downstream LLM integrations to fail (e.g. bedrock gateway) where response
# params are not valid json.
# Main case so far is no args = "" = invalid json.
log.debug(
f"Parsed args from {tool_args} to {tool_function_params}"
)
tool_call.setdefault("function", {})["arguments"] = json.dumps(
tool_function_params
)
tool_result = None
if tool_name in tools:
tool = tools[tool_name]
spec = tool.get("spec", {})
try:
allowed_params = (
spec.get("parameters", {})
.get("properties", {})
.keys()
)
tool_function_params = {
k: v
for k, v in tool_function_params.items()
if k in allowed_params
}
if tool.get("direct", False):
tool_result = await event_caller(
{
"type": "execute:tool",
"data": {
"id": str(uuid4()),
"name": tool_name,
"params": tool_function_params,
"server": tool.get("server", {}),
"session_id": metadata.get(
"session_id", None
),
},
}
)
else:
tool_function = tool["callable"]
tool_result = await tool_function(
**tool_function_params
)
except Exception as e:
tool_result = str(e)
tool_result_embeds = []
if isinstance(tool_result, HTMLResponse):
content_disposition = tool_result.headers.get(
"Content-Disposition", ""
)
if "inline" in content_disposition:
content = tool_result.body.decode("utf-8")
tool_result_embeds.append(content)
if 200 <= tool_result.status_code < 300:
tool_result = {
"status": "success",
"code": "ui_component",
"message": "Embedded UI result is active and visible to the user.",
}
elif 400 <= tool_result.status_code < 500:
tool_result = {
"status": "error",
"code": "ui_component",
"message": f"Client error {tool_result.status_code} from embedded UI result.",
}
elif 500 <= tool_result.status_code < 600:
tool_result = {
"status": "error",
"code": "ui_component",
"message": f"Server error {tool_result.status_code} from embedded UI result.",
}
else:
tool_result = {
"status": "error",
"code": "ui_component",
"message": f"Unexpected status code {tool_result.status_code} from embedded UI result.",
}
else:
tool_result = tool_result.body.decode("utf-8")
elif tool.get("type") == "external" and isinstance(
tool_result, tuple
):
tool_result, tool_response_headers = tool_result
if tool_response_headers:
content_disposition = tool_response_headers.get(
"Content-Disposition", ""
)
if "inline" in content_disposition:
content_type = tool_response_headers.get(
"Content-Type", ""
)
location = tool_response_headers.get("Location", "")
if "text/html" in content_type:
# Display as iframe embed
tool_result_embeds.append(tool_result)
tool_result = {
"status": "success",
"code": "ui_component",
"message": "Embedded UI result is active and visible to the user.",
}
elif location:
tool_result_embeds.append(location)
tool_result = {
"status": "success",
"code": "ui_component",
"message": "Embedded UI result is active and visible to the user.",
}
tool_result_files = []
if isinstance(tool_result, list):
for item in tool_result:
# check if string
if isinstance(item, str) and item.startswith("data:"):
tool_result_files.append(
{
"type": "data",
"content": item,
}
)
tool_result.remove(item)
if tool.get("type") == "mcp":
if (
isinstance(item, dict)
and item.get("type") == "image"
):
image_url = get_image_url_from_base64(
request,
f"data:{item.get('mimeType', 'image/png')};base64,{item.get('data', '')}",
{
"chat_id": metadata.get(
"chat_id", None
),
"message_id": metadata.get(
"message_id", None
),
"session_id": metadata.get(
"session_id", None
),
},
user,
)
tool_result_files.append(
{
"type": "image",
"url": image_url,
}
)
tool_result.remove(item)
if tool_result_files:
if not isinstance(tool_result, list):
tool_result = [
tool_result,
]
for file in tool_result_files:
tool_result.append(
{
"type": file.get("type", "data"),
"content": "Displayed",
}
)
if isinstance(tool_result, dict) or isinstance(
tool_result, list
):
tool_result = json.dumps(
tool_result, indent=2, ensure_ascii=False
)
results.append(
{
"tool_call_id": tool_call_id,
"content": tool_result or "",
**(
{"files": tool_result_files}
if tool_result_files
else {}
),
**(
{"embeds": tool_result_embeds}
if tool_result_embeds
else {}
),
}
)
content_blocks[-1]["results"] = results
content_blocks.append(
{
"type": "text",
"content": "",
}
)
await event_emitter(
{
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
},
}
)
try:
new_form_data = {
"model": model_id,
"stream": True,
"tools": form_data["tools"],
"messages": [
*form_data["messages"],
*convert_content_blocks_to_messages(
content_blocks, True
),
],
}
res = await generate_chat_completion(
request,
new_form_data,
user,
)
if isinstance(res, StreamingResponse):
await stream_body_handler(res, new_form_data)
else:
break
except Exception as e:
log.debug(e)
break
if DETECT_CODE_INTERPRETER:
MAX_RETRIES = 5
retries = 0
while (
content_blocks[-1]["type"] == "code_interpreter"
and retries < MAX_RETRIES
):
await event_emitter(
{
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
},
}
)
retries += 1
log.debug(f"Attempt count: {retries}")
output = ""
try:
if content_blocks[-1]["attributes"].get("type") == "code":
code = content_blocks[-1]["content"]
if CODE_INTERPRETER_BLOCKED_MODULES:
blocking_code = textwrap.dedent(
f"""
import builtins
BLOCKED_MODULES = {CODE_INTERPRETER_BLOCKED_MODULES}
_real_import = builtins.__import__
def restricted_import(name, globals=None, locals=None, fromlist=(), level=0):
if name.split('.')[0] in BLOCKED_MODULES:
importer_name = globals.get('__name__') if globals else None
if importer_name == '__main__':
raise ImportError(
f"Direct import of module {{name}} is restricted."
)
return _real_import(name, globals, locals, fromlist, level)
builtins.__import__ = restricted_import
"""
)
code = blocking_code + "\n" + code
if (
request.app.state.config.CODE_INTERPRETER_ENGINE
== "pyodide"
):
output = await event_caller(
{
"type": "execute:python",
"data": {
"id": str(uuid4()),
"code": code,
"session_id": metadata.get(
"session_id", None
),
},
}
)
elif (
request.app.state.config.CODE_INTERPRETER_ENGINE
== "jupyter"
):
output = await execute_code_jupyter(
request.app.state.config.CODE_INTERPRETER_JUPYTER_URL,
code,
(
request.app.state.config.CODE_INTERPRETER_JUPYTER_AUTH_TOKEN
if request.app.state.config.CODE_INTERPRETER_JUPYTER_AUTH
== "token"
else None
),
(
request.app.state.config.CODE_INTERPRETER_JUPYTER_AUTH_PASSWORD
if request.app.state.config.CODE_INTERPRETER_JUPYTER_AUTH
== "password"
else None
),
request.app.state.config.CODE_INTERPRETER_JUPYTER_TIMEOUT,
)
else:
output = {
"stdout": "Code interpreter engine not configured."
}
log.debug(f"Code interpreter output: {output}")
if isinstance(output, dict):
stdout = output.get("stdout", "")
if isinstance(stdout, str):
stdoutLines = stdout.split("\n")
for idx, line in enumerate(stdoutLines):
if "data:image/png;base64" in line:
image_url = get_image_url_from_base64(
request,
line,
metadata,
user,
)
if image_url:
stdoutLines[idx] = (
f""
)
output["stdout"] = "\n".join(stdoutLines)
result = output.get("result", "")
if isinstance(result, str):
resultLines = result.split("\n")
for idx, line in enumerate(resultLines):
if "data:image/png;base64" in line:
image_url = get_image_url_from_base64(
request,
line,
metadata,
user,
)
resultLines[idx] = (
f""
)
output["result"] = "\n".join(resultLines)
except Exception as e:
output = str(e)
content_blocks[-1]["output"] = output
content_blocks.append(
{
"type": "text",
"content": "",
}
)
await event_emitter(
{
"type": "chat:completion",
"data": {
"content": serialize_content_blocks(content_blocks),
},
}
)
try:
new_form_data = {
"model": model_id,
"stream": True,
"messages": [
*form_data["messages"],
{
"role": "assistant",
"content": serialize_content_blocks(
content_blocks, raw=True
),
},
],
}
res = await generate_chat_completion(
request,
new_form_data,
user,
)
if isinstance(res, StreamingResponse):
await stream_body_handler(res, new_form_data)
else:
break
except Exception as e:
log.debug(e)
break
title = Chats.get_chat_title_by_id(metadata["chat_id"])
data = {
"done": True,
"content": serialize_content_blocks(content_blocks),
"title": title,
}
if not ENABLE_REALTIME_CHAT_SAVE:
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": serialize_content_blocks(content_blocks),
},
)
# Send a webhook notification if the user is not active
if not get_active_status_by_user_id(user.id):
webhook_url = Users.get_user_webhook_url_by_id(user.id)
if webhook_url:
await post_webhook(
request.app.state.WEBUI_NAME,
webhook_url,
f"{title} - {request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}\n\n{content}",
{
"action": "chat",
"message": content,
"title": title,
"url": f"{request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}",
},
)
await event_emitter(
{
"type": "chat:completion",
"data": data,
}
)
await background_tasks_handler()
except asyncio.CancelledError:
log.warning("Task was cancelled!")
await event_emitter({"type": "chat:tasks:cancel"})
if not ENABLE_REALTIME_CHAT_SAVE:
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": serialize_content_blocks(content_blocks),
},
)
if response.background is not None:
await response.background()
return await response_handler(response, events)
else:
# Fallback to the original response
async def stream_wrapper(original_generator, events):
def wrap_item(item):
return f"data: {item}\n\n"
for event in events:
event, _ = await process_filter_functions(
request=request,
filter_functions=filter_functions,
filter_type="stream",
form_data=event,
extra_params=extra_params,
)
if event:
yield wrap_item(json.dumps(event))
async for data in original_generator:
data, _ = await process_filter_functions(
request=request,
filter_functions=filter_functions,
filter_type="stream",
form_data=data,
extra_params=extra_params,
)
if data:
yield data
return StreamingResponse(
stream_wrapper(response.body_iterator, events),
headers=dict(response.headers),
background=response.background,
)