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maamalama 2024-07-24 17:42:07 -07:00
parent 98bed2a248
commit 41811218b2
1 changed files with 39 additions and 39 deletions
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78
litellm/integrations/helicone.py
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@ -17,50 +17,50 @@ class HeliconeLogger:
self.key = os.getenv("HELICONE_API_KEY")
def claude_mapping(self, model, messages, response_obj):
from anthropic import HUMAN_PROMPT, AI_PROMPT
from anthropic import HUMAN_PROMPT, AI_PROMPT
prompt = f"{HUMAN_PROMPT}"
for message in messages:
if "role" in message:
if message["role"] == "user":
prompt += f"{HUMAN_PROMPT}{message['content']}"
else:
prompt += f"{AI_PROMPT}{message['content']}"
else:
prompt += f"{HUMAN_PROMPT}{message['content']}"
prompt += f"{AI_PROMPT}"
claude_provider_request = {"model": model, "prompt": prompt}
prompt = f"{HUMAN_PROMPT}"
for message in messages:
if "role" in message:
if message["role"] == "user":
prompt += f"{HUMAN_PROMPT}{message['content']}"
else:
prompt += f"{AI_PROMPT}{message['content']}"
else:
prompt += f"{HUMAN_PROMPT}{message['content']}"
prompt += f"{AI_PROMPT}"
claude_provider_request = {"model": model, "prompt": prompt}
choice = response_obj["choices"][0]
message = choice["message"]
choice = response_obj["choices"][0]
message = choice["message"]
content = []
if "tool_calls" in message and message["tool_calls"]:
for tool_call in message["tool_calls"]:
content.append({
"type": "tool_use",
"id": tool_call["id"],
"name": tool_call["function"]["name"],
"input": tool_call["function"]["arguments"]
})
elif "content" in message and message["content"]:
content = [{"type": "text", "text": message["content"]}]
content = []
if "tool_calls" in message and message["tool_calls"]:
for tool_call in message["tool_calls"]:
content.append({
"type": "tool_use",
"id": tool_call["id"],
"name": tool_call["function"]["name"],
"input": tool_call["function"]["arguments"]
})
elif "content" in message and message["content"]:
content = [{"type": "text", "text": message["content"]}]
claude_response_obj = {
"id": response_obj["id"],
"type": "message",
"role": "assistant",
"model": model,
"content": content,
"stop_reason": choice["finish_reason"],
"stop_sequence": None,
"usage": {
"input_tokens": response_obj["usage"]["prompt_tokens"],
"output_tokens": response_obj["usage"]["completion_tokens"]
}
}
claude_response_obj = {
"id": response_obj["id"],
"type": "message",
"role": "assistant",
"model": model,
"content": content,
"stop_reason": choice["finish_reason"],
"stop_sequence": None,
"usage": {
"input_tokens": response_obj["usage"]["prompt_tokens"],
"output_tokens": response_obj["usage"]["completion_tokens"]
}
}
return claude_provider_request, claude_response_obj
return claude_provider_request, claude_response_obj
@staticmethod
def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict: