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https://github.com/BerriAI/litellm.git
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b82add11ba
* fix(generic_api_callback.py): remove requests lib usage * fix(budget_manager.py): remove requests lib usgae * fix(main.py): cleanup requests lib usage * fix(utils.py): remove requests lib usage * fix(argilla.py): fix argilla test * fix(athina.py): replace 'requests' lib usage with litellm module * fix(greenscale.py): replace 'requests' lib usage with httpx * fix: remove unused 'requests' lib import + replace usage in some places * fix(prompt_layer.py): remove 'requests' lib usage from prompt layer * fix(ollama_chat.py): remove 'requests' lib usage * fix(baseten.py): replace 'requests' lib usage * fix(codestral/): replace 'requests' lib usage * fix(predibase/): replace 'requests' lib usage * refactor: cleanup unused 'requests' lib imports * fix(oobabooga.py): cleanup 'requests' lib usage * fix(invoke_handler.py): remove unused 'requests' lib usage * refactor: cleanup unused 'requests' lib import * fix: fix linting errors * refactor(ollama/): move ollama to using base llm http handler removes 'requests' lib dep for ollama integration * fix(ollama_chat.py): fix linting errors * fix(ollama/completion/transformation.py): convert non-jpeg/png image to jpeg/png before passing to ollama
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
#### What this does ####
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# On success, logs events to Promptlayer
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import os
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import traceback
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from pydantic import BaseModel
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import litellm
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class PromptLayerLogger:
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# Class variables or attributes
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def __init__(self):
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# Instance variables
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self.key = os.getenv("PROMPTLAYER_API_KEY")
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def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
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# Method definition
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try:
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new_kwargs = {}
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new_kwargs["model"] = kwargs["model"]
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new_kwargs["messages"] = kwargs["messages"]
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# add kwargs["optional_params"] to new_kwargs
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for optional_param in kwargs["optional_params"]:
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new_kwargs[optional_param] = kwargs["optional_params"][optional_param]
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# Extract PromptLayer tags from metadata, if such exists
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tags = []
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metadata = {}
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if "metadata" in kwargs["litellm_params"]:
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if "pl_tags" in kwargs["litellm_params"]["metadata"]:
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tags = kwargs["litellm_params"]["metadata"]["pl_tags"]
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# Remove "pl_tags" from metadata
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metadata = {
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k: v
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for k, v in kwargs["litellm_params"]["metadata"].items()
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if k != "pl_tags"
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}
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print_verbose(
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f"Prompt Layer Logging - Enters logging function for model kwargs: {new_kwargs}\n, response: {response_obj}"
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)
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# python-openai >= 1.0.0 returns Pydantic objects instead of jsons
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if isinstance(response_obj, BaseModel):
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response_obj = response_obj.model_dump()
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request_response = litellm.module_level_client.post(
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"https://api.promptlayer.com/rest/track-request",
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json={
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"function_name": "openai.ChatCompletion.create",
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"kwargs": new_kwargs,
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"tags": tags,
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"request_response": dict(response_obj),
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"request_start_time": int(start_time.timestamp()),
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"request_end_time": int(end_time.timestamp()),
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"api_key": self.key,
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# Optional params for PromptLayer
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# "prompt_id": "<PROMPT ID>",
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# "prompt_input_variables": "<Dictionary of variables for prompt>",
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# "prompt_version":1,
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},
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)
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response_json = request_response.json()
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if not request_response.json().get("success", False):
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raise Exception("Promptlayer did not successfully log the response!")
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print_verbose(
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f"Prompt Layer Logging: success - final response object: {request_response.text}"
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)
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if "request_id" in response_json:
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if metadata:
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response = litellm.module_level_client.post(
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"https://api.promptlayer.com/rest/track-metadata",
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json={
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"request_id": response_json["request_id"],
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"api_key": self.key,
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"metadata": metadata,
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},
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)
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print_verbose(
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f"Prompt Layer Logging: success - metadata post response object: {response.text}"
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)
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except Exception:
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print_verbose(f"error: Prompt Layer Error - {traceback.format_exc()}")
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pass
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