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Pricing

This commit is contained in:
luijait 2025-05-19 12:29:38 +02:00
parent 81e14210fe
commit 25bf8f8595
3 changed files with 82 additions and 18 deletions
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27
pricing.json Normal file
View File

@ -0,0 +1,27 @@
{
"alias0": {
"max_tokens": 128000,
"max_input_tokens": 200000,
"max_output_tokens": 128000,
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.00005,
"cache_creation_input_token_cost": 0.000005,
"cache_read_input_token_cost": 0.0000005,
"search_context_cost_per_query": {
"search_context_size_low": 1e-2,
"search_context_size_medium": 1e-2,
"search_context_size_high": 1e-2
},
"litellm_provider": "openai",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"tool_use_system_prompt_tokens": 159,
"supports_assistant_prefill": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_response_schema": true,
"deprecation_date": "2026-02-01",
"supports_tool_choice": true
}
}

52
src/cai/sdk/agents/models/openai_chatcompletions.py
View File

@ -625,9 +625,7 @@ class OpenAIChatCompletionsModel(Model):
# Get token count estimate before API call for consistent counting
estimated_input_tokens, _ = count_tokens_with_tiktoken(converted_messages)
response, stream = await self._fetch_response(
system_instructions,
input,
response, stream, messages_sent_to_api = await self._fetch_response(
model_settings,
tools,
output_schema,
@ -1237,8 +1235,8 @@ class OpenAIChatCompletionsModel(Model):
total_cost = calculate_model_cost(model_name, total_input, total_output)
# Explicit conversion to float with fallback to ensure they're never None or 0
interaction_cost = max(float(interaction_cost if interaction_cost is not None else 0.0), 0.00001)
total_cost = max(float(total_cost if total_cost is not None else 0.0), 0.00001)
interaction_cost = max(float(interaction_cost if interaction_cost is not None else 0.0), 0)
total_cost = max(float(total_cost if total_cost is not None else 0.0), 0)
# Store the total cost for future recording
self.total_cost = total_cost
@ -1311,7 +1309,7 @@ class OpenAIChatCompletionsModel(Model):
self.logger.rec_training_data(
{
"model": str(self.model),
"messages": final_converted_messages_for_log, # Use the messages actually sent
"messages": messages_sent_to_api, # Use the messages actually sent
"stream": True,
"tools": [t.params_json_schema for t in tools] if tools else [],
"tool_choice": model_settings.tool_choice
@ -1463,7 +1461,7 @@ class OpenAIChatCompletionsModel(Model):
model_str = str(kwargs["model"]).lower()
if "alias" in model_str:
kwargs["api_base"] = "http://api.aliasrobotics.com:666/"
kwargs["api_base"] = "http://11.0.0.4:4000/"
kwargs["custom_llm_provider"] = "openai"
kwargs["api_key"] = os.getenv("ALIAS_API_KEY", "REDACTED_ALIAS_KEY")
elif "/" in model_str:
@ -1554,7 +1552,13 @@ class OpenAIChatCompletionsModel(Model):
if is_qwen:
try:
# Use the specialized Qwen approach first
return await self._fetch_response_litellm_ollama(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
ollama_result = await self._fetch_response_litellm_ollama(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
if stream:
# ollama_result is (response, stream_obj)
return ollama_result[0], ollama_result[1], messages_for_api
else:
# ollama_result is completion
return ollama_result, messages_for_api
except Exception as qwen_e:
print(qwen_e)
# If that fails, try our direct OpenAI approach
@ -1584,11 +1588,11 @@ class OpenAIChatCompletionsModel(Model):
parallel_tool_calls=parallel_tool_calls or False,
)
stream_obj = await litellm.acompletion(**qwen_params)
return response, stream_obj
return response, stream_obj, messages_for_api
else:
# Non-streaming case
ret = litellm.completion(**qwen_params)
return ret
return ret, messages_for_api
except Exception as direct_e:
# All approaches failed, log and raise the original error
print(f"All Qwen approaches failed. Original error: {str(e)}, Direct error: {str(direct_e)}")
@ -1659,7 +1663,13 @@ class OpenAIChatCompletionsModel(Model):
except Exception as fix_error:
print(f"Failed to fix message sequence: {fix_error}")
return await self._fetch_response_litellm_openai(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
openai_res = await self._fetch_response_litellm_openai(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
if stream:
# openai_res is (response, stream_obj)
return openai_res[0], openai_res[1], messages_for_api
else:
# openai_res is completion
return openai_res, messages_for_api
# this captures an error related to the fact
# that the messages list contains an empty
@ -1671,7 +1681,11 @@ class OpenAIChatCompletionsModel(Model):
msg if msg.get("content") is not None else
{**msg, "content": ""} for msg in kwargs["messages"]
]
return await self._fetch_response_litellm_openai(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
openai_res = await self._fetch_response_litellm_openai(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
if stream:
return openai_res[0], openai_res[1], messages_for_api
else:
return openai_res, messages_for_api
# Handle Anthropic error for empty text content blocks
elif ("text content blocks must be non-empty" in str(e) or
@ -1689,7 +1703,11 @@ class OpenAIChatCompletionsModel(Model):
"content": "Empty content block"
} for msg in kwargs["messages"]
]
return await self._fetch_response_litellm_openai(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
openai_res = await self._fetch_response_litellm_openai(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
if stream:
return openai_res[0], openai_res[1], messages_for_api
else:
return openai_res, messages_for_api
else:
raise e
except litellm.exceptions.RateLimitError as e:
@ -1718,10 +1736,14 @@ class OpenAIChatCompletionsModel(Model):
except Exception as e: # pylint: disable=W0718
print(color("Error encountered: " + str(e), fg="yellow"))
try:
return await self._fetch_response_litellm_ollama(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
ollama_result = await self._fetch_response_litellm_ollama(kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
if stream:
return ollama_result[0], ollama_result[1], messages_for_api
else:
return ollama_result, messages_for_api
except Exception as execp: # pylint: disable=W0718
print("Error: " + str(execp))
return None
raise execp
async def _fetch_response_litellm_openai(
self,

21
src/cai/util.py
View File

@ -247,7 +247,6 @@ class CostTracker:
if os.environ.get("CAI_COST_DISPLAYED", "").lower() == "true":
return
print(f"\nTotal CAI Session Cost: ${self.session_total_cost:.6f}")
def get_model_pricing(self, model_name: str) -> tuple:
"""Get and cache pricing information for a model"""
# Use the centralized function to standardize model names
@ -256,8 +255,24 @@ class CostTracker:
# Check cache first
if model_name in self.model_pricing_cache:
return self.model_pricing_cache[model_name]
# Try to load pricing from local pricing.json first
try:
pricing_path = pathlib.Path("pricing.json")
if pricing_path.exists():
with open(pricing_path, "r", encoding="utf-8") as f:
local_pricing = json.load(f)
pricing_info = local_pricing.get("alias0", {})
input_cost = pricing_info.get("input_cost_per_token", 0)
output_cost = pricing_info.get("output_cost_per_token", 0)
# Cache and return local pricing
self.model_pricing_cache[model_name] = (input_cost, output_cost)
return input_cost, output_cost
except Exception as e:
print(f" WARNING: Error loading local pricing.json: {str(e)}")
# Fetch from LiteLLM API
# Fallback to LiteLLM API if local pricing not found
LITELLM_URL = (
"https://raw.githubusercontent.com/BerriAI/litellm/main/"
"model_prices_and_context_window.json"
@ -280,7 +295,7 @@ class CostTracker:
except Exception as e:
print(f" WARNING: Error fetching model pricing: {str(e)}")
# Default values if pricing not found
# Default values if no pricing found
default_pricing = (0, 0)
self.model_pricing_cache[model_name] = default_pricing
return default_pricing