import base64
import json
import os
import random
import re
import time
import traceback
from typing import Callable, Optional
import numpy as np
def float_list_to_base64(float_array: np.ndarray) -> str:
# Convert the list to a float32 array that the OpenAPI client expects
# float_array = np.array(float_list, dtype="float32")
# Get raw bytes
bytes_array = float_array.tobytes()
# Encode bytes into base64
encoded_bytes = base64.b64encode(bytes_array)
# Turn raw base64 encoded bytes into ASCII
ascii_string = encoded_bytes.decode('ascii')
return ascii_string
def debug_msg(*args, **kwargs):
from extensions.openai.script import params
if os.environ.get("OPENEDAI_DEBUG", params.get('debug', 0)):
print(*args, **kwargs)
def _start_cloudflared(port: int, tunnel_id: str, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None):
try:
from flask_cloudflared import _run_cloudflared
except ImportError:
print('You should install flask_cloudflared manually')
raise Exception(
'flask_cloudflared not installed. Make sure you installed the requirements.txt for this extension.')
for _ in range(max_attempts):
try:
if tunnel_id is not None:
public_url = _run_cloudflared(port, port + 1, tunnel_id=tunnel_id)
else:
public_url = _run_cloudflared(port, port + 1)
if on_start:
on_start(public_url)
return
except Exception:
traceback.print_exc()
time.sleep(3)
raise Exception('Could not start cloudflared.')
def getToolCallId() -> str:
letter_bytes = "abcdefghijklmnopqrstuvwxyz0123456789"
b = [random.choice(letter_bytes) for _ in range(8)]
return "call_" + "".join(b).lower()
def checkAndSanitizeToolCallCandidate(candidate_dict: dict, tool_names: list[str]):
# check if property 'function' exists and is a dictionary, otherwise adapt dict
if 'function' not in candidate_dict and 'name' in candidate_dict and isinstance(candidate_dict['name'], str):
candidate_dict = {"type": "function", "function": candidate_dict}
if 'function' in candidate_dict and isinstance(candidate_dict['function'], str):
candidate_dict['name'] = candidate_dict['function']
del candidate_dict['function']
candidate_dict = {"type": "function", "function": candidate_dict}
if 'function' in candidate_dict and isinstance(candidate_dict['function'], dict):
# check if 'name' exists within 'function' and is part of known tools
if 'name' in candidate_dict['function'] and candidate_dict['function']['name'] in tool_names:
candidate_dict["type"] = "function" # ensure required property 'type' exists and has the right value
# map property 'parameters' used by some older models to 'arguments'
if "arguments" not in candidate_dict["function"] and "parameters" in candidate_dict["function"]:
candidate_dict["function"]["arguments"] = candidate_dict["function"]["parameters"]
del candidate_dict["function"]["parameters"]
return candidate_dict
return None
def _extractBalancedJson(text: str, start: int) -> str | None:
"""Extract a balanced JSON object from text starting at the given position.
Walks through the string tracking brace depth and string boundaries
to correctly handle arbitrary nesting levels.
"""
if start >= len(text) or text[start] != '{':
return None
depth = 0
in_string = False
escape_next = False
for i in range(start, len(text)):
c = text[i]
if escape_next:
escape_next = False
continue
if c == '\\' and in_string:
escape_next = True
continue
if c == '"':
in_string = not in_string
continue
if in_string:
continue
if c == '{':
depth += 1
elif c == '}':
depth -= 1
if depth == 0:
return text[start:i + 1]
return None
def _parseChannelToolCalls(answer: str, tool_names: list[str]):
"""Parse channel-based tool calls used by GPT-OSS and similar models.
Format:
<|channel|>commentary to=functions.func_name <|constrain|>json<|message|>{"arg": "value"}
"""
matches = []
for m in re.finditer(
r'<\|channel\|>commentary to=functions\.([^<\s]+)\s*(?:<\|constrain\|>json)?<\|message\|>',
answer
):
func_name = m.group(1).strip()
if func_name not in tool_names:
continue
json_str = _extractBalancedJson(answer, m.end())
if json_str is None:
continue
try:
arguments = json.loads(json_str)
matches.append({
"type": "function",
"function": {
"name": func_name,
"arguments": arguments
}
})
except json.JSONDecodeError:
pass
return matches
def _parseBareNameToolCalls(answer: str, tool_names: list[str]):
"""Parse bare function-name style tool calls used by Mistral and similar models.
Format:
functionName{"arg": "value"}
Multiple calls are concatenated directly or separated by whitespace.
"""
matches = []
# Match tool name followed by opening brace, then extract balanced JSON
escaped_names = [re.escape(name) for name in tool_names]
pattern = r'(?:' + '|'.join(escaped_names) + r')\s*\{'
for match in re.finditer(pattern, answer):
text = match.group(0)
name = None
for n in tool_names:
if text.startswith(n):
name = n
break
if not name:
continue
brace_start = match.end() - 1
json_str = _extractBalancedJson(answer, brace_start)
if json_str is None:
continue
try:
arguments = json.loads(json_str)
matches.append({
"type": "function",
"function": {
"name": name,
"arguments": arguments
}
})
except json.JSONDecodeError:
pass
return matches
def _parseXmlParamToolCalls(answer: str, tool_names: list[str]):
"""Parse XML-parameter style tool calls used by Qwen3.5 and similar models.
Format:
value
"""
matches = []
for tc_match in re.finditer(r'\s*(.*?)\s*', answer, re.DOTALL):
tc_content = tc_match.group(1)
func_match = re.search(r']+)>', tc_content)
if not func_match:
continue
func_name = func_match.group(1).strip()
if func_name not in tool_names:
continue
arguments = {}
for param_match in re.finditer(r']+)>\s*(.*?)\s*', tc_content, re.DOTALL):
param_name = param_match.group(1).strip()
param_value = param_match.group(2).strip()
try:
param_value = json.loads(param_value)
except (json.JSONDecodeError, ValueError):
pass # keep as string
arguments[param_name] = param_value
matches.append({
"type": "function",
"function": {
"name": func_name,
"arguments": arguments
}
})
return matches
def _parseKimiToolCalls(answer: str, tool_names: list[str]):
"""Parse Kimi-K2-style tool calls using pipe-delimited tokens.
Format:
<|tool_calls_section_begin|>
<|tool_call_begin|>functions.func_name:index<|tool_call_argument_begin|>{"arg": "value"}<|tool_call_end|>
<|tool_calls_section_end|>
"""
matches = []
for m in re.finditer(
r'<\|tool_call_begin\|>\s*(?:functions\.)?(\S+?)(?::\d+)?\s*<\|tool_call_argument_begin\|>\s*',
answer
):
func_name = m.group(1).strip()
if func_name not in tool_names:
continue
json_str = _extractBalancedJson(answer, m.end())
if json_str is None:
continue
try:
arguments = json.loads(json_str)
matches.append({
"type": "function",
"function": {
"name": func_name,
"arguments": arguments
}
})
except json.JSONDecodeError:
pass
return matches
def _parseMiniMaxToolCalls(answer: str, tool_names: list[str]):
"""Parse MiniMax-style tool calls using invoke/parameter XML tags.
Format:
value
"""
matches = []
for tc_match in re.finditer(r'\s*(.*?)\s*', answer, re.DOTALL):
tc_content = tc_match.group(1)
# Split on to handle multiple parallel calls in one block
for invoke_match in re.finditer(r'(.*?)', tc_content, re.DOTALL):
func_name = invoke_match.group(1).strip()
if func_name not in tool_names:
continue
invoke_body = invoke_match.group(2)
arguments = {}
for param_match in re.finditer(r'\s*(.*?)\s*', invoke_body, re.DOTALL):
param_name = param_match.group(1).strip()
param_value = param_match.group(2).strip()
try:
param_value = json.loads(param_value)
except (json.JSONDecodeError, ValueError):
pass # keep as string
arguments[param_name] = param_value
matches.append({
"type": "function",
"function": {
"name": func_name,
"arguments": arguments
}
})
return matches
def _parseDeepSeekToolCalls(answer: str, tool_names: list[str]):
"""Parse DeepSeek-style tool calls using fullwidth Unicode token delimiters.
Format:
<|tool▁calls▁begin|><|tool▁call▁begin|>func_name<|tool▁sep|>{"arg": "value"}<|tool▁call▁end|><|tool▁calls▁end|>
"""
matches = []
for m in re.finditer(
r'<|tool▁call▁begin|>\s*(\S+?)\s*<|tool▁sep|>\s*',
answer
):
func_name = m.group(1).strip()
if func_name not in tool_names:
continue
json_str = _extractBalancedJson(answer, m.end())
if json_str is None:
continue
try:
arguments = json.loads(json_str)
matches.append({
"type": "function",
"function": {
"name": func_name,
"arguments": arguments
}
})
except json.JSONDecodeError:
pass
return matches
def _parseGlmToolCalls(answer: str, tool_names: list[str]):
"""Parse GLM-style tool calls using arg_key/arg_value XML pairs.
Format:
function_name
key1
value1
"""
matches = []
for tc_match in re.finditer(r'\s*(.*?)\s*', answer, re.DOTALL):
tc_content = tc_match.group(1)
# First non-tag text is the function name
name_match = re.match(r'([^<\s]+)', tc_content.strip())
if not name_match:
continue
func_name = name_match.group(1).strip()
if func_name not in tool_names:
continue
# Extract arg_key/arg_value pairs
keys = [k.group(1).strip() for k in re.finditer(r'\s*(.*?)\s*', tc_content, re.DOTALL)]
vals = [v.group(1).strip() for v in re.finditer(r'\s*(.*?)\s*', tc_content, re.DOTALL)]
if len(keys) != len(vals):
continue
arguments = {}
for k, v in zip(keys, vals):
try:
v = json.loads(v)
except (json.JSONDecodeError, ValueError):
pass # keep as string
arguments[k] = v
matches.append({
"type": "function",
"function": {
"name": func_name,
"arguments": arguments
}
})
return matches
def _parsePythonicToolCalls(answer: str, tool_names: list[str]):
"""Parse pythonic-style tool calls used by Llama 4 and similar models.
Format:
[func_name(param1="value1", param2="value2"), func_name2(...)]
"""
matches = []
# Match a bracketed list of function calls
bracket_match = re.search(r'\[([^\[\]]+)\]', answer)
if not bracket_match:
return matches
inner = bracket_match.group(1)
# Build pattern for known tool names
escaped_names = [re.escape(name) for name in tool_names]
name_pattern = '|'.join(escaped_names)
for call_match in re.finditer(
r'(' + name_pattern + r')\(([^)]*)\)',
inner
):
func_name = call_match.group(1)
params_str = call_match.group(2).strip()
arguments = {}
if params_str:
# Parse key="value" pairs, handling commas inside quoted values
for param_match in re.finditer(
r'(\w+)\s*=\s*("(?:[^"\\]|\\.)*"|\'(?:[^\'\\]|\\.)*\'|[^,\)]+)',
params_str
):
param_name = param_match.group(1)
param_value = param_match.group(2).strip()
# Strip surrounding quotes
if (param_value.startswith('"') and param_value.endswith('"')) or \
(param_value.startswith("'") and param_value.endswith("'")):
param_value = param_value[1:-1]
# Try to parse as JSON for numeric/bool/null values
try:
param_value = json.loads(param_value)
except (json.JSONDecodeError, ValueError):
pass
arguments[param_name] = param_value
matches.append({
"type": "function",
"function": {
"name": func_name,
"arguments": arguments
}
})
return matches
def parseToolCall(answer: str, tool_names: list[str]):
matches = []
# abort on very short answers to save computation cycles
if len(answer) < 10:
return matches
# Check for DeepSeek-style tool calls (fullwidth Unicode token delimiters)
matches = _parseDeepSeekToolCalls(answer, tool_names)
if matches:
return matches
# Check for Kimi-K2-style tool calls (pipe-delimited tokens)
matches = _parseKimiToolCalls(answer, tool_names)
if matches:
return matches
# Check for channel-based tool calls (e.g. GPT-OSS format)
matches = _parseChannelToolCalls(answer, tool_names)
if matches:
return matches
# Check for MiniMax-style tool calls (invoke/parameter XML tags)
matches = _parseMiniMaxToolCalls(answer, tool_names)
if matches:
return matches
# Check for GLM-style tool calls (arg_key/arg_value XML pairs)
matches = _parseGlmToolCalls(answer, tool_names)
if matches:
return matches
# Check for XML-parameter style tool calls (e.g. Qwen3.5 format)
matches = _parseXmlParamToolCalls(answer, tool_names)
if matches:
return matches
# Check for bare function-name style tool calls (e.g. Mistral format)
matches = _parseBareNameToolCalls(answer, tool_names)
if matches:
return matches
# Check for pythonic-style tool calls (e.g. Llama 4 format)
matches = _parsePythonicToolCalls(answer, tool_names)
if matches:
return matches
# Define the regex pattern to find the JSON content wrapped in , , , and other tags observed from various models
patterns = [r"(```[^\n]*)\n(.*?)```", r"<([^>]+)>(.*?)\1>"]
for pattern in patterns:
for match in re.finditer(pattern, answer, re.DOTALL):
# print(match.group(2))
if match.group(2) is None:
continue
# remove backtick wraps if present
candidate = re.sub(r"^```(json|xml|python[^\n]*)\n", "", match.group(2).strip())
candidate = re.sub(r"```$", "", candidate.strip())
# unwrap inner tags
candidate = re.sub(pattern, r"\2", candidate.strip(), flags=re.DOTALL)
# llm might have generated multiple json objects separated by linebreaks, check for this pattern and try parsing each object individually
if re.search(r"\}\s*\n\s*\{", candidate) is not None:
candidate = re.sub(r"\}\s*\n\s*\{", "},\n{", candidate)
if not candidate.strip().startswith("["):
candidate = "[" + candidate + "]"
candidates = []
try:
# parse the candidate JSON into a dictionary
candidates = json.loads(candidate)
if not isinstance(candidates, list):
candidates = [candidates]
except json.JSONDecodeError:
# Ignore invalid JSON silently
continue
for candidate_dict in candidates:
checked_candidate = checkAndSanitizeToolCallCandidate(candidate_dict, tool_names)
if checked_candidate is not None:
matches.append(checked_candidate)
# last resort if nothing has been mapped: LLM might have produced plain json tool call without xml-like tags
if len(matches) == 0:
try:
candidate = answer
# llm might have generated multiple json objects separated by linebreaks, check for this pattern and try parsing each object individually
if re.search(r"\}\s*\n\s*\{", candidate) is not None:
candidate = re.sub(r"\}\s*\n\s*\{", "},\n{", candidate)
if not candidate.strip().startswith("["):
candidate = "[" + candidate + "]"
# parse the candidate JSON into a dictionary
candidates = json.loads(candidate)
if not isinstance(candidates, list):
candidates = [candidates]
for candidate_dict in candidates:
checked_candidate = checkAndSanitizeToolCallCandidate(candidate_dict, tool_names)
if checked_candidate is not None:
matches.append(checked_candidate)
except json.JSONDecodeError:
# Ignore invalid JSON silently
pass
return matches