add observer thought token count and llm cost (#1595)
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@@ -146,7 +146,7 @@ class LLMAPIHandlerFactory:
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observer_thought=observer_thought,
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ai_suggestion=ai_suggestion,
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)
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if step:
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if step or observer_thought:
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try:
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llm_cost = litellm.completion_cost(completion_response=response)
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except Exception as e:
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@@ -154,14 +154,24 @@ class LLMAPIHandlerFactory:
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llm_cost = 0
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prompt_tokens = response.get("usage", {}).get("prompt_tokens", 0)
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completion_tokens = response.get("usage", {}).get("completion_tokens", 0)
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await app.DATABASE.update_step(
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task_id=step.task_id,
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step_id=step.step_id,
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organization_id=step.organization_id,
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incremental_cost=llm_cost,
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incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
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incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
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)
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if step:
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await app.DATABASE.update_step(
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task_id=step.task_id,
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step_id=step.step_id,
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organization_id=step.organization_id,
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incremental_cost=llm_cost,
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incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
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incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
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)
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if observer_thought:
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await app.DATABASE.update_observer_thought(
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observer_thought_id=observer_thought.observer_thought_id,
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organization_id=observer_thought.organization_id,
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input_token_count=prompt_tokens if prompt_tokens > 0 else None,
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output_token_count=completion_tokens if completion_tokens > 0 else None,
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thought_cost=llm_cost,
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)
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parsed_response = parse_api_response(response, llm_config.add_assistant_prefix)
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await app.ARTIFACT_MANAGER.create_llm_artifact(
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data=json.dumps(parsed_response, indent=2).encode("utf-8"),
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@@ -292,7 +302,7 @@ class LLMAPIHandlerFactory:
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ai_suggestion=ai_suggestion,
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)
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if step:
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if step or observer_thought:
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try:
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llm_cost = litellm.completion_cost(completion_response=response)
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except Exception as e:
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@@ -300,14 +310,23 @@ class LLMAPIHandlerFactory:
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llm_cost = 0
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prompt_tokens = response.get("usage", {}).get("prompt_tokens", 0)
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completion_tokens = response.get("usage", {}).get("completion_tokens", 0)
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await app.DATABASE.update_step(
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task_id=step.task_id,
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step_id=step.step_id,
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organization_id=step.organization_id,
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incremental_cost=llm_cost,
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incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
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incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
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)
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if step:
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await app.DATABASE.update_step(
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task_id=step.task_id,
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step_id=step.step_id,
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organization_id=step.organization_id,
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incremental_cost=llm_cost,
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incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
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incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
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)
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if observer_thought:
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await app.DATABASE.update_observer_thought(
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observer_thought_id=observer_thought.observer_thought_id,
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organization_id=observer_thought.organization_id,
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input_token_count=prompt_tokens if prompt_tokens > 0 else None,
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output_token_count=completion_tokens if completion_tokens > 0 else None,
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thought_cost=llm_cost,
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)
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parsed_response = parse_api_response(response, llm_config.add_assistant_prefix)
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await app.ARTIFACT_MANAGER.create_llm_artifact(
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data=json.dumps(parsed_response, indent=2).encode("utf-8"),
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