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Merge branch 'main' into refactor-image-validation

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This commit is contained in:
Beehive Innovations
2025-08-07 23:12:00 -07:00
committed by GitHub
parent 48cff76c99 19ae3c5e9c
commit f7a079bc35
55 changed files with 2491 additions and 623 deletions
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120
providers/openai_compatible.py
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@@ -1,5 +1,7 @@
"""Base class for OpenAI-compatible API providers."""
import base64
import copy
import ipaddress
import logging
import os
@@ -219,10 +221,20 @@ class OpenAICompatibleProvider(ModelProvider):
# Create httpx client with minimal config to avoid proxy conflicts
# Note: proxies parameter was removed in httpx 0.28.0
http_client = httpx.Client(
timeout=timeout_config,
follow_redirects=True,
)
# Check for test transport injection
if hasattr(self, "_test_transport"):
# Use custom transport for testing (HTTP recording/replay)
http_client = httpx.Client(
transport=self._test_transport,
timeout=timeout_config,
follow_redirects=True,
)
else:
# Normal production client
http_client = httpx.Client(
timeout=timeout_config,
follow_redirects=True,
)
# Keep client initialization minimal to avoid proxy parameter conflicts
client_kwargs = {
@@ -263,6 +275,63 @@ class OpenAICompatibleProvider(ModelProvider):
return self._client
def _sanitize_for_logging(self, params: dict) -> dict:
"""Sanitize sensitive data from parameters before logging.
Args:
params: Dictionary of API parameters
Returns:
dict: Sanitized copy of parameters safe for logging
"""
sanitized = copy.deepcopy(params)
# Sanitize messages content
if "input" in sanitized:
for msg in sanitized.get("input", []):
if isinstance(msg, dict) and "content" in msg:
for content_item in msg.get("content", []):
if isinstance(content_item, dict) and "text" in content_item:
# Truncate long text and add ellipsis
text = content_item["text"]
if len(text) > 100:
content_item["text"] = text[:100] + "... [truncated]"
# Remove any API keys that might be in headers/auth
sanitized.pop("api_key", None)
sanitized.pop("authorization", None)
return sanitized
def _safe_extract_output_text(self, response) -> str:
"""Safely extract output_text from o3-pro response with validation.
Args:
response: Response object from OpenAI SDK
Returns:
str: The output text content
Raises:
ValueError: If output_text is missing, None, or not a string
"""
logging.debug(f"Response object type: {type(response)}")
logging.debug(f"Response attributes: {dir(response)}")
if not hasattr(response, "output_text"):
raise ValueError(f"o3-pro response missing output_text field. Response type: {type(response).__name__}")
content = response.output_text
logging.debug(f"Extracted output_text: '{content}' (type: {type(content)})")
if content is None:
raise ValueError("o3-pro returned None for output_text")
if not isinstance(content, str):
raise ValueError(f"o3-pro output_text is not a string. Got type: {type(content).__name__}")
return content
def _generate_with_responses_endpoint(
self,
model_name: str,
@@ -308,30 +377,23 @@ class OpenAICompatibleProvider(ModelProvider):
max_retries = 4
retry_delays = [1, 3, 5, 8]
last_exception = None
actual_attempts = 0
for attempt in range(max_retries):
try: # Log the exact payload being sent for debugging
try: # Log sanitized payload for debugging
import json
sanitized_params = self._sanitize_for_logging(completion_params)
logging.info(
f"o3-pro API request payload: {json.dumps(completion_params, indent=2, ensure_ascii=False)}"
f"o3-pro API request (sanitized): {json.dumps(sanitized_params, indent=2, ensure_ascii=False)}"
)
# Use OpenAI client's responses endpoint
response = self.client.responses.create(**completion_params)
# Extract content and usage from responses endpoint format
# The response format is different for responses endpoint
content = ""
if hasattr(response, "output") and response.output:
if hasattr(response.output, "content") and response.output.content:
# Look for output_text in content
for content_item in response.output.content:
if hasattr(content_item, "type") and content_item.type == "output_text":
content = content_item.text
break
elif hasattr(response.output, "text"):
content = response.output.text
# Extract content from responses endpoint format
# Use validation helper to safely extract output_text
content = self._safe_extract_output_text(response)
# Try to extract usage information
usage = None
@@ -370,14 +432,13 @@ class OpenAICompatibleProvider(ModelProvider):
if is_retryable and attempt < max_retries - 1:
delay = retry_delays[attempt]
logging.warning(
f"Retryable error for o3-pro responses endpoint, attempt {attempt + 1}/{max_retries}: {str(e)}. Retrying in {delay}s..."
f"Retryable error for o3-pro responses endpoint, attempt {actual_attempts}/{max_retries}: {str(e)}. Retrying in {delay}s..."
)
time.sleep(delay)
else:
break
# If we get here, all retries failed
actual_attempts = attempt + 1 # Convert from 0-based index to human-readable count
error_msg = f"o3-pro responses endpoint error after {actual_attempts} attempt{'s' if actual_attempts > 1 else ''}: {str(last_exception)}"
logging.error(error_msg)
raise RuntimeError(error_msg) from last_exception
@@ -387,7 +448,7 @@ class OpenAICompatibleProvider(ModelProvider):
prompt: str,
model_name: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
temperature: float = 0.3,
max_output_tokens: Optional[int] = None,
images: Optional[list[str]] = None,
**kwargs,
@@ -480,7 +541,7 @@ class OpenAICompatibleProvider(ModelProvider):
completion_params[key] = value
# Check if this is o3-pro and needs the responses endpoint
if resolved_model == "o3-pro-2025-06-10":
if resolved_model == "o3-pro":
# This model requires the /v1/responses endpoint
# If it fails, we should not fall back to chat/completions
return self._generate_with_responses_endpoint(
@@ -496,8 +557,10 @@ class OpenAICompatibleProvider(ModelProvider):
retry_delays = [1, 3, 5, 8] # Progressive delays: 1s, 3s, 5s, 8s
last_exception = None
actual_attempts = 0
for attempt in range(max_retries):
actual_attempts = attempt + 1 # Convert from 0-based index to human-readable count
try:
# Generate completion
response = self.client.chat.completions.create(**completion_params)
@@ -535,12 +598,11 @@ class OpenAICompatibleProvider(ModelProvider):
# Log retry attempt
logging.warning(
f"{self.FRIENDLY_NAME} error for model {model_name}, attempt {attempt + 1}/{max_retries}: {str(e)}. Retrying in {delay}s..."
f"{self.FRIENDLY_NAME} error for model {model_name}, attempt {actual_attempts}/{max_retries}: {str(e)}. Retrying in {delay}s..."
)
time.sleep(delay)
# If we get here, all retries failed
actual_attempts = attempt + 1 # Convert from 0-based index to human-readable count
error_msg = f"{self.FRIENDLY_NAME} API error for model {model_name} after {actual_attempts} attempt{'s' if actual_attempts > 1 else ''}: {str(last_exception)}"
logging.error(error_msg)
raise RuntimeError(error_msg) from last_exception
@@ -575,11 +637,7 @@ class OpenAICompatibleProvider(ModelProvider):
try:
encoding = tiktoken.encoding_for_model(model_name)
except KeyError:
# Try common encodings based on model patterns
if "gpt-4" in model_name or "gpt-3.5" in model_name:
encoding = tiktoken.get_encoding("cl100k_base")
else:
encoding = tiktoken.get_encoding("cl100k_base") # Default
encoding = tiktoken.get_encoding("cl100k_base")
return len(encoding.encode(text))
@@ -678,11 +736,13 @@ class OpenAICompatibleProvider(ModelProvider):
"""
# Common vision-capable models - only include models that actually support images
vision_models = {
"gpt-5",
"gpt-5-mini",
"gpt-4o",
"gpt-4o-mini",
"gpt-4-turbo",
"gpt-4-vision-preview",
"gpt-4.1-2025-04-14", # GPT-4.1 supports vision
"gpt-4.1-2025-04-14",
"o3",
"o3-mini",
"o3-pro",