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Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/integrations/helicone.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/litellm/integrations/helicone.py | 188 |
1 files changed, 188 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/integrations/helicone.py b/.venv/lib/python3.12/site-packages/litellm/integrations/helicone.py new file mode 100644 index 00000000..a526a74f --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/integrations/helicone.py @@ -0,0 +1,188 @@ +#### What this does #### +# On success, logs events to Helicone +import os +import traceback + +import litellm + + +class HeliconeLogger: + # Class variables or attributes + helicone_model_list = [ + "gpt", + "claude", + "command-r", + "command-r-plus", + "command-light", + "command-medium", + "command-medium-beta", + "command-xlarge-nightly", + "command-nightly", + ] + + def __init__(self): + # Instance variables + self.provider_url = "https://api.openai.com/v1" + self.key = os.getenv("HELICONE_API_KEY") + + def claude_mapping(self, model, messages, response_obj): + from anthropic import AI_PROMPT, HUMAN_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}" + + 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"]}] + + 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_response_obj + + @staticmethod + def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict: + """ + Adds metadata from proxy request headers to Helicone logging if keys start with "helicone_" + and overwrites litellm_params.metadata if already included. + + For example if you want to add custom property to your request, send + `headers: { ..., helicone-property-something: 1234 }` via proxy request. + """ + if litellm_params is None: + return metadata + + if litellm_params.get("proxy_server_request") is None: + return metadata + + if metadata is None: + metadata = {} + + proxy_headers = ( + litellm_params.get("proxy_server_request", {}).get("headers", {}) or {} + ) + + for header_key in proxy_headers: + if header_key.startswith("helicone_"): + metadata[header_key] = proxy_headers.get(header_key) + + return metadata + + def log_success( + self, model, messages, response_obj, start_time, end_time, print_verbose, kwargs + ): + # Method definition + try: + print_verbose( + f"Helicone Logging - Enters logging function for model {model}" + ) + litellm_params = kwargs.get("litellm_params", {}) + kwargs.get("litellm_call_id", None) + metadata = litellm_params.get("metadata", {}) or {} + metadata = self.add_metadata_from_header(litellm_params, metadata) + model = ( + model + if any( + accepted_model in model + for accepted_model in self.helicone_model_list + ) + else "gpt-3.5-turbo" + ) + provider_request = {"model": model, "messages": messages} + if isinstance(response_obj, litellm.EmbeddingResponse) or isinstance( + response_obj, litellm.ModelResponse + ): + response_obj = response_obj.json() + + if "claude" in model: + response_obj = self.claude_mapping( + model=model, messages=messages, response_obj=response_obj + ) + + providerResponse = { + "json": response_obj, + "headers": {"openai-version": "2020-10-01"}, + "status": 200, + } + + # Code to be executed + provider_url = self.provider_url + url = "https://api.hconeai.com/oai/v1/log" + if "claude" in model: + url = "https://api.hconeai.com/anthropic/v1/log" + provider_url = "https://api.anthropic.com/v1/messages" + headers = { + "Authorization": f"Bearer {self.key}", + "Content-Type": "application/json", + } + start_time_seconds = int(start_time.timestamp()) + start_time_milliseconds = int( + (start_time.timestamp() - start_time_seconds) * 1000 + ) + end_time_seconds = int(end_time.timestamp()) + end_time_milliseconds = int( + (end_time.timestamp() - end_time_seconds) * 1000 + ) + meta = {"Helicone-Auth": f"Bearer {self.key}"} + meta.update(metadata) + data = { + "providerRequest": { + "url": provider_url, + "json": provider_request, + "meta": meta, + }, + "providerResponse": providerResponse, + "timing": { + "startTime": { + "seconds": start_time_seconds, + "milliseconds": start_time_milliseconds, + }, + "endTime": { + "seconds": end_time_seconds, + "milliseconds": end_time_milliseconds, + }, + }, # {"seconds": .., "milliseconds": ..} + } + response = litellm.module_level_client.post(url, headers=headers, json=data) + if response.status_code == 200: + print_verbose("Helicone Logging - Success!") + else: + print_verbose( + f"Helicone Logging - Error Request was not successful. Status Code: {response.status_code}" + ) + print_verbose(f"Helicone Logging - Error {response.text}") + except Exception: + print_verbose(f"Helicone Logging Error - {traceback.format_exc()}") + pass |