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temp reg anthropic

This commit is contained in:
marko-kraemer 2025-10-01 17:05:03 +02:00
parent 6f467c35c0
commit e0250092a6
5 changed files with 89 additions and 38 deletions
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28
backend/core/ai_models/ai_models.py
View File

@ -53,18 +53,6 @@ class ModelConfig:
extra_headers: Optional[Dict[str, str]] = None
def __post_init__(self):
"""Set intelligent defaults and validate configuration."""
# Merge headers if both are provided
if self.headers and self.extra_headers:
merged_headers = self.headers.copy()
merged_headers.update(self.extra_headers)
self.extra_headers = merged_headers
self.headers = None # Use extra_headers as the single source
elif self.headers and not self.extra_headers:
self.extra_headers = self.headers
self.headers = None
@dataclass
class Model:
@ -118,9 +106,9 @@ class Model:
params = {
"model": self.id,
"num_retries": 3,
"stream_options": {"include_usage": True}, # Default for all models
}
# Apply model-specific configuration if available
if self.config:
# Provider & API configuration parameters
@ -135,18 +123,22 @@ class Model:
if param_value is not None:
params[param_name] = param_value
# Handle headers specially
if self.config.headers:
params["headers"] = self.config.headers.copy()
if self.config.extra_headers:
params["extra_headers"] = self.config.extra_headers.copy()
elif self.config.headers:
params["extra_headers"] = self.config.headers.copy()
# Apply any runtime overrides
for key, value in override_params.items():
if value is not None:
# Handle extra_headers merging
if key == "extra_headers" and "extra_headers" in params:
# Handle headers and extra_headers merging separately
if key == "headers" and "headers" in params:
if isinstance(params["headers"], dict) and isinstance(value, dict):
params["headers"].update(value)
else:
params[key] = value
elif key == "extra_headers" and "extra_headers" in params:
if isinstance(params["extra_headers"], dict) and isinstance(value, dict):
params["extra_headers"].update(value)
else:

6
backend/core/ai_models/registry.py
View File

@ -21,7 +21,7 @@ class ModelRegistry:
def _initialize_models(self):
self.register(Model(
id="anthropic/claude-sonnet-4-5-20250929" if is_local else "bedrock/converse/arn:aws:bedrock:us-west-2:935064898258:inference-profile/global.anthropic.claude-sonnet-4-5-20250929-v1:0",
id="anthropic/claude-sonnet-4-5-20250929", # if is_local else "bedrock/converse/arn:aws:bedrock:us-west-2:935064898258:inference-profile/global.anthropic.claude-sonnet-4-5-20250929-v1:0"
name="Sonnet 4.5",
provider=ModelProvider.ANTHROPIC,
aliases=["claude-sonnet-4.5", "anthropic/claude-sonnet-4.5", "Claude Sonnet 4.5", "claude-sonnet-4-5-20250929", "global.anthropic.claude-sonnet-4-5-20250929-v1:0", "arn:aws:bedrock:us-west-2:935064898258:inference-profile/global.anthropic.claude-sonnet-4-5-20250929-v1:0"],
@ -42,7 +42,7 @@ class ModelRegistry:
enabled=True,
config=ModelConfig(
extra_headers={
"anthropic-beta" if is_local else "anthropic_beta": "context-1m-2025-08-07"
"anthropic-beta": "context-1m-2025-08-07"
},
)
))
@ -69,7 +69,7 @@ class ModelRegistry:
enabled=True,
config=ModelConfig(
extra_headers={
"anthropic-beta" if is_local else "anthropic_beta": "context-1m-2025-08-07"
"anthropic-beta": "context-1m-2025-08-07"
},
)
))

85
backend/core/services/llm.py
View File

@ -16,10 +16,16 @@ from core.utils.config import config
from core.agentpress.error_processor import ErrorProcessor
# Configure LiteLLM
os.environ['LITELLM_LOG'] = 'DEBUG'
# os.environ['LITELLM_LOG'] = 'DEBUG'
# litellm.set_verbose = True # Enable verbose logging
litellm.modify_params = True
litellm.drop_params = True
# Enable additional debug logging
# import logging
# litellm_logger = logging.getLogger("LiteLLM")
# litellm_logger.setLevel(logging.DEBUG)
# Constants
MAX_RETRIES = 3
provider_router = None
@ -110,6 +116,7 @@ def _add_tools_config(params: Dict[str, Any], tools: Optional[List[Dict[str, Any
})
# logger.debug(f"Added {len(tools)} tools to API parameters")
async def make_llm_api_call(
messages: List[Dict[str, Any]],
model_name: str,
@ -123,6 +130,8 @@ async def make_llm_api_call(
stream: bool = True, # Always stream for better UX
top_p: Optional[float] = None,
model_id: Optional[str] = None,
headers: Optional[Dict[str, str]] = None,
extra_headers: Optional[Dict[str, str]] = None,
) -> Union[Dict[str, Any], AsyncGenerator, ModelResponse]:
"""Make an API call to a language model using LiteLLM."""
logger.info(f"Making LLM API call to model: {model_name} with {len(messages)} messages")
@ -132,28 +141,61 @@ async def make_llm_api_call(
resolved_model_name = model_manager.resolve_model_id(model_name)
# logger.debug(f"Model resolution: '{model_name}' -> '{resolved_model_name}'")
# Get centralized model configuration from registry
params = model_manager.get_litellm_params(
resolved_model_name,
messages=messages,
temperature=temperature,
response_format=response_format,
top_p=top_p,
stream=stream,
api_key=api_key,
api_base=api_base
)
# Only pass headers/extra_headers if they are not None to avoid overriding model config
override_params = {
"messages": messages,
"temperature": temperature,
"response_format": response_format,
"top_p": top_p,
"stream": stream,
"api_key": api_key,
"api_base": api_base
}
# Only add headers if they are provided (not None)
if headers is not None:
override_params["headers"] = headers
if extra_headers is not None:
override_params["extra_headers"] = extra_headers
params = model_manager.get_litellm_params(resolved_model_name, **override_params)
# logger.debug(f"Parameters from model_manager.get_litellm_params: {params}")
# Add model_id separately if provided (to avoid duplicate argument error)
if model_id:
params["model_id"] = model_id
if stream:
params["stream_options"] = {"include_usage": True}
# Apply additional configurations that aren't in the model config yet
_configure_openai_compatible(params, model_name, api_key, api_base)
_add_tools_config(params, tools, tool_choice)
try:
# Log the complete parameters being sent to LiteLLM
# logger.debug(f"Calling LiteLLM acompletion for {resolved_model_name}")
# logger.debug(f"Complete LiteLLM parameters: {params}")
# # Save parameters to txt file for debugging
# import json
# import os
# from datetime import datetime
# debug_dir = "debug_logs"
# os.makedirs(debug_dir, exist_ok=True)
# timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
# filename = f"{debug_dir}/llm_params_{timestamp}.txt"
# with open(filename, 'w') as f:
# f.write(f"Timestamp: {datetime.now().isoformat()}\n")
# f.write(f"Model Name: {model_name}\n")
# f.write(f"Resolved Model Name: {resolved_model_name}\n")
# f.write(f"Parameters:\n{json.dumps(params, indent=2, default=str)}\n")
# logger.debug(f"LiteLLM parameters saved to: {filename}")
response = await provider_router.acompletion(**params)
# For streaming responses, we need to handle errors that occur during iteration
@ -181,3 +223,20 @@ async def _wrap_streaming_response(response) -> AsyncGenerator:
setup_api_keys()
setup_provider_router()
if __name__ == "__main__":
from litellm import completion
import os
setup_api_keys()
response = completion(
model="bedrock/anthropic.claude-sonnet-4-20250115-v1:0",
messages=[{"role": "user", "content": "Hello! Testing 1M context window."}],
max_tokens=100,
extra_headers={
"anthropic-beta": "context-1m-2025-08-07" # 👈 Enable 1M context
}
)

4
backend/core/tools/utils/custom_mcp_handler.py
View File

@ -235,9 +235,9 @@ class CustomMCPHandler:
'custom_config': server_config
}
tools_registered += 1
logger.debug(f"Registered custom tool: {tool_name}")
# logger.debug(f"Registered custom tool: {tool_name}")
logger.debug(f"Successfully initialized custom MCP {server_name} with {tools_registered} tools")
# logger.debug(f"Successfully initialized custom MCP {server_name} with {tools_registered} tools")
def _register_custom_tools_from_info(self, tools_info: List[Dict[str, Any]], server_name: str, enabled_tools: List[str], custom_type: str, server_config: Dict[str, Any]):
tools_registered = 0

4
backend/core/tools/utils/dynamic_tool_builder.py
View File

@ -34,7 +34,7 @@ class DynamicToolBuilder:
def _create_dynamic_method(self, tool_name: str, tool_info: Dict[str, Any], execute_callback: Callable[[str, Dict[str, Any]], Awaitable[ToolResult]]) -> Dict[str, Any]:
method_name, clean_tool_name, server_name = self._parse_tool_name(tool_name)
logger.debug(f"Creating dynamic method for tool '{tool_name}': clean_tool_name='{clean_tool_name}', method_name='{method_name}', server='{server_name}'")
# logger.debug(f"Creating dynamic method for tool '{tool_name}': clean_tool_name='{clean_tool_name}', method_name='{method_name}', server='{server_name}'")
async def dynamic_tool_method(**kwargs) -> ToolResult:
return await execute_callback(tool_name, kwargs)
@ -60,7 +60,7 @@ class DynamicToolBuilder:
self.dynamic_tools[tool_name] = tool_data
self.schemas[method_name] = [schema]
logger.debug(f"Created dynamic method '{method_name}' for MCP tool '{tool_name}' from server '{server_name}'")
# logger.debug(f"Created dynamic method '{method_name}' for MCP tool '{tool_name}' from server '{server_name}'")
return tool_data