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LLM Client - Handles all LLM interactions
"""
import json
import time
from typing import List, Dict, Any, Optional
import httpx
from openai import OpenAI
import config
from utils.benchmark_logging import current_call_context, log_api_call, now_iso
class LLMClient:
"""
Unified LLM client interface
"""
def __init__(
self,
api_key: Optional[str] = None,
model: Optional[str] = None,
base_url: Optional[str] = None,
enable_thinking: Optional[bool] = None,
use_streaming: Optional[bool] = None,
):
self.api_key = api_key or config.OPENAI_API_KEY
self.model = model or config.LLM_MODEL
self.base_url = base_url or config.OPENAI_BASE_URL
self.enable_thinking = enable_thinking if enable_thinking is not None else config.ENABLE_THINKING
self.use_streaming = use_streaming if use_streaming is not None else config.USE_STREAMING
if not self.api_key:
raise ValueError("OPENAI_API_KEY or DASHSCOPE_API_KEY must be set for LLMClient")
# Initialize OpenAI client with optional base_url
client_kwargs = {"api_key": self.api_key}
if self.base_url:
client_kwargs["base_url"] = self.base_url
print(f"Using custom OpenAI base URL: {self.base_url}")
if self.enable_thinking:
print(f"Deep thinking mode enabled")
self.client = OpenAI(
base_url=self.base_url,
api_key=self.api_key,
http_client=httpx.Client(
trust_env=False,
timeout=httpx.Timeout(180.0, connect=30.0),
),
)
def chat_completion(
self,
messages: List[Dict[str, str]],
temperature: float = 0.2,
response_format: Optional[Dict[str, str]] = None,
max_retries: Optional[int] = None,
max_tokens: Optional[int] = None,
) -> str:
"""
Standard chat completion with optional thinking mode and retry mechanism
"""
result = self.chat_completion_full(
messages=messages,
temperature=temperature,
response_format=response_format,
max_retries=max_retries,
max_tokens=max_tokens,
)
return result["text"]
def chat_completion_full(
self,
messages: List[Dict[str, str]],
temperature: float = 0.2,
response_format: Optional[Dict[str, str]] = None,
max_retries: Optional[int] = None,
max_tokens: Optional[int] = None,
) -> Dict[str, Any]:
"""
Chat completion returning text plus usage metadata for benchmark logging.
"""
effective_retries = max_retries or getattr(config, "LLM_MAX_RETRIES", 3)
kwargs = {
"model": self.model,
"messages": messages,
"temperature": temperature,
}
if response_format:
kwargs["response_format"] = response_format
if max_tokens is not None:
kwargs["max_tokens"] = max_tokens
# Enable thinking mode if configured (for Qwen and compatible models only)
# Only add enable_thinking parameter for Qwen API (identified by base_url)
is_qwen_api = self.base_url and "dashscope.aliyuncs.com" in self.base_url
if is_qwen_api:
# Qwen API requires explicit enable_thinking parameter
# - Streaming + thinking: enable_thinking=True
# - Non-streaming: enable_thinking=False (required, not optional)
# - JSON format: enable_thinking=False (incompatible with thinking mode)
if self.use_streaming and self.enable_thinking and not response_format:
kwargs["extra_body"] = {"enable_thinking": True}
else:
# Explicitly set to False for non-streaming calls or JSON format
kwargs["extra_body"] = {"enable_thinking": False}
# For OpenAI and other APIs, don't add extra_body parameters
# Retry mechanism
last_exception = None
for attempt in range(effective_retries):
try:
# Use streaming if configured
if self.use_streaming:
kwargs["stream"] = True
text = self._handle_streaming_response(**kwargs)
usage = {}
request_id = None
model_name = self.model
else:
response = self.client.chat.completions.create(**kwargs)
text = response.choices[0].message.content or ""
usage = self._usage_to_dict(getattr(response, "usage", None))
request_id = getattr(response, "id", None)
model_name = getattr(response, "model", self.model)
self._log_success(
messages=messages,
response_text=text,
usage=usage,
request_id=request_id,
response_format=response_format,
temperature=temperature,
max_tokens=max_tokens,
model_name=model_name,
attempt=attempt + 1,
)
return {
"text": text,
"usage": usage,
"request_id": request_id,
"model": model_name,
}
except Exception as e:
last_exception = e
self._log_failure(
messages=messages,
error=e,
response_format=response_format,
temperature=temperature,
max_tokens=max_tokens,
attempt=attempt + 1,
)
if attempt < effective_retries - 1:
wait_time = (2 ** attempt) # Exponential backoff: 1s, 2s, 4s
print(f"LLM API call failed (attempt {attempt + 1}/{effective_retries}): {e}")
print(f"Retrying in {wait_time} seconds...")
time.sleep(wait_time)
else:
print(f"LLM API call failed after {effective_retries} attempts: {e}")
# If all retries failed, raise the last exception
raise last_exception
def _handle_streaming_response(self, **kwargs) -> str:
"""
Handle streaming response and collect full content
"""
full_content = []
stream = self.client.chat.completions.create(**kwargs)
# for chunk in stream:
# if chunk.choices is not None:
# print(chunk.choices[0].delta.content)
# print('---------')
for chunk in stream:
# print(chunk)
# fix list index out of range
if len(chunk.choices) > 0 and chunk.choices[0].delta.content is not None:
content = chunk.choices[0].delta.content
full_content.append(content)
# print(full_content)
# Optional: print streaming content in real-time
# print(content, end='', flush=True)
# print(full_content)
print()
return ''.join(full_content)
def _usage_to_dict(self, usage: Any) -> Dict[str, Any]:
if usage is None:
return {}
if hasattr(usage, "model_dump"):
return usage.model_dump()
if isinstance(usage, dict):
return dict(usage)
result = {}
for key in ("prompt_tokens", "completion_tokens", "total_tokens"):
value = getattr(usage, key, None)
if value is not None:
result[key] = value
return result
def _log_success(
self,
*,
messages: List[Dict[str, str]],
response_text: str,
usage: Dict[str, Any],
request_id: Optional[str],
response_format: Optional[Dict[str, str]],
temperature: float,
max_tokens: Optional[int],
model_name: str,
attempt: int,
) -> None:
context = current_call_context()
log_api_call(
{
"timestamp": now_iso(),
"success": True,
"provider": "dashscope" if self.base_url and "dashscope.aliyuncs.com" in self.base_url else "openai_compatible",
"api_kind": "chat_completion",
"stage": context.get("stage"),
"token_stage": context.get("llm_stage") or context.get("stage"),
"patient_id": context.get("patient_id"),
"question_no": context.get("question_no"),
"text_id": context.get("text_id"),
"operation_id": context.get("operation_id"),
"attempt": attempt,
"model": model_name,
"base_url": self.base_url,
"request_id": request_id,
"temperature": temperature,
"max_tokens": max_tokens,
"response_format": response_format,
"message_count": len(messages),
"messages": messages,
"response_text": response_text,
"usage": usage,
}
)
def _log_failure(
self,
*,
messages: List[Dict[str, str]],
error: Exception,
response_format: Optional[Dict[str, str]],
temperature: float,
max_tokens: Optional[int],
attempt: int,
) -> None:
context = current_call_context()
log_api_call(
{
"timestamp": now_iso(),
"success": False,
"provider": "dashscope" if self.base_url and "dashscope.aliyuncs.com" in self.base_url else "openai_compatible",
"api_kind": "chat_completion",
"stage": context.get("stage"),
"token_stage": context.get("llm_stage") or context.get("stage"),
"patient_id": context.get("patient_id"),
"question_no": context.get("question_no"),
"text_id": context.get("text_id"),
"operation_id": context.get("operation_id"),
"attempt": attempt,
"model": self.model,
"base_url": self.base_url,
"temperature": temperature,
"max_tokens": max_tokens,
"response_format": response_format,
"message_count": len(messages),
"messages": messages,
"error": repr(error),
}
)
def extract_json(self, text: str) -> Any:
"""
Extract JSON from LLM response with robust parsing
Supports multiple formats:
1. Pure JSON
2. ```json ... ```
3. ``` ... ``` (generic code block)
4. JSON embedded in text with common prefixes
5. Multiple JSON objects (returns first valid one)
"""
if not text or not text.strip():
raise ValueError("Empty response received")
text = text.strip()
# Remove common LLM prefixes/suffixes
common_prefixes = [
"Here's the JSON:",
"Here is the JSON:",
"The JSON is:",
"JSON:",
"Result:",
"Output:",
"Answer:",
]
for prefix in common_prefixes:
if text.lower().startswith(prefix.lower()):
text = text[len(prefix):].strip()
# Try direct parsing first
try:
return json.loads(text)
except json.JSONDecodeError:
pass
# Try extracting JSON from ```json ... ``` block
if "```json" in text.lower():
# Case insensitive search for ```json
start_marker = "```json"
start_idx = text.lower().find(start_marker)
if start_idx != -1:
start = start_idx + len(start_marker)
# Find the closing ```
end = text.find("```", start)
if end != -1:
json_str = text[start:end].strip()
try:
return json.loads(json_str)
except json.JSONDecodeError as e:
# Try to clean up common issues
json_str = self._clean_json_string(json_str)
try:
return json.loads(json_str)
except json.JSONDecodeError:
pass
# Try extracting from generic ``` ... ``` code block
if "```" in text:
start = text.find("```") + 3
# Skip language identifier if present
newline = text.find("\n", start)
if newline != -1 and newline - start < 20:
start = newline + 1
end = text.find("```", start)
if end != -1:
json_str = text[start:end].strip()
try:
return json.loads(json_str)
except json.JSONDecodeError:
# Try to clean up
json_str = self._clean_json_string(json_str)
try:
return json.loads(json_str)
except json.JSONDecodeError:
pass
# Try finding balanced JSON object/array by scanning for { or [
for start_char in ['{', '[']:
result = self._extract_balanced_json(text, start_char)
if result is not None:
return result
# Last resort: try to find any JSON-like structure and clean it
for start_char in ['{', '[']:
start_idx = text.find(start_char)
if start_idx != -1:
# Extract a large chunk and try to parse
chunk = text[start_idx:]
cleaned = self._clean_json_string(chunk)
try:
return json.loads(cleaned)
except json.JSONDecodeError:
pass
raise ValueError(f"Failed to extract valid JSON from response. First 300 chars: {text[:300]}...")
def _clean_json_string(self, json_str: str) -> str:
"""
Clean common issues in JSON strings from LLM output
"""
# Remove trailing commas before } or ]
import re
json_str = re.sub(r',(\s*[}\]])', r'\1', json_str)
# Remove comments (// and /* */)
json_str = re.sub(r'//.*?$', '', json_str, flags=re.MULTILINE)
json_str = re.sub(r'/\*.*?\*/', '', json_str, flags=re.DOTALL)
return json_str.strip()
def _extract_balanced_json(self, text: str, start_char: str) -> Any:
"""
Extract a balanced JSON object or array starting with start_char
"""
end_char = '}' if start_char == '{' else ']'
start_idx = text.find(start_char)
if start_idx == -1:
return None
# Track depth to find matching closing bracket
depth = 0
in_string = False
escape_next = False
for i in range(start_idx, len(text)):
char = text[i]
# Handle string escaping
if escape_next:
escape_next = False
continue
if char == '\\':
escape_next = True
continue
# Handle strings (don't count brackets inside strings)
if char == '"':
in_string = not in_string
continue
if in_string:
continue
# Count depth
if char == start_char:
depth += 1
elif char == end_char:
depth -= 1
if depth == 0:
json_str = text[start_idx:i+1]
try:
return json.loads(json_str)
except json.JSONDecodeError:
# Try cleaning and parsing again
cleaned = self._clean_json_string(json_str)
try:
return json.loads(cleaned)
except json.JSONDecodeError:
# Continue searching for next occurrence
break
return None
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