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LiteLLM Minor Fixes & Improvements (10/24/2024) (#6421)
* fix(utils.py): support passing dynamic api base to validate_environment Returns True if just api base is required and api base is passed * fix(litellm_pre_call_utils.py): feature flag sending client headers to llm api Fixes https://github.com/BerriAI/litellm/issues/6410 * fix(anthropic/chat/transformation.py): return correct error message * fix(http_handler.py): add error response text in places where we expect it * fix(factory.py): handle base case of no non-system messages to bedrock Fixes https://github.com/BerriAI/litellm/issues/6411 * feat(cohere/embed): Support cohere image embeddings Closes https://github.com/BerriAI/litellm/issues/6413 * fix(__init__.py): fix linting error * docs(supported_embedding.md): add image embedding example to docs * feat(cohere/embed): use cohere embedding returned usage for cost calc * build(model_prices_and_context_window.json): add embed-english-v3.0 details (image cost + 'supports_image_input' flag) * fix(cohere_transformation.py): fix linting error * test(test_proxy_server.py): cleanup test * test: cleanup test * fix: fix linting errors
This commit is contained in:
Krish Dholakia
GitHub
parent
17082d5e7d
commit
197655bf2a
23 changed files with 417 additions and 150 deletions
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@ -1,224 +0,0 @@
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import json
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import os
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import time
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import traceback
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import types
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from enum import Enum
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from typing import Any, Callable, Optional, Union
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import httpx # type: ignore
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import requests # type: ignore
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import litellm
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.utils import Choices, Message, ModelResponse, Usage
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def validate_environment(api_key, headers: dict):
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headers.update(
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{
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"Request-Source": "unspecified:litellm",
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"accept": "application/json",
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"content-type": "application/json",
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}
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)
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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return headers
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class CohereError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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self.request = httpx.Request(
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method="POST", url="https://api.cohere.ai/v1/generate"
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)
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self.response = httpx.Response(status_code=status_code, request=self.request)
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super().__init__(
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self.message
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) # Call the base class constructor with the parameters it needs
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def _process_embedding_response(
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embeddings: list,
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model_response: litellm.EmbeddingResponse,
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model: str,
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encoding: Any,
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input: list,
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) -> litellm.EmbeddingResponse:
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output_data = []
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for idx, embedding in enumerate(embeddings):
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output_data.append(
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{"object": "embedding", "index": idx, "embedding": embedding}
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)
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model_response.object = "list"
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model_response.data = output_data
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model_response.model = model
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input_tokens = 0
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for text in input:
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input_tokens += len(encoding.encode(text))
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setattr(
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model_response,
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"usage",
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Usage(
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prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
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),
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)
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return model_response
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async def async_embedding(
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model: str,
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data: dict,
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input: list,
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model_response: litellm.utils.EmbeddingResponse,
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timeout: Optional[Union[float, httpx.Timeout]],
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logging_obj: LiteLLMLoggingObj,
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optional_params: dict,
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api_base: str,
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api_key: Optional[str],
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headers: dict,
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encoding: Callable,
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client: Optional[AsyncHTTPHandler] = None,
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):
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## LOGGING
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logging_obj.pre_call(
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input=input,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"headers": headers,
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"api_base": api_base,
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},
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)
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## COMPLETION CALL
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if client is None:
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client = AsyncHTTPHandler(concurrent_limit=1, timeout=timeout)
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try:
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response = await client.post(api_base, headers=headers, data=json.dumps(data))
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except httpx.HTTPStatusError as e:
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## LOGGING
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logging_obj.post_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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original_response=e.response.text,
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)
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raise e
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except Exception as e:
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## LOGGING
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logging_obj.post_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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original_response=str(e),
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)
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raise e
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## LOGGING
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logging_obj.post_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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original_response=response.text,
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)
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embeddings = response.json()["embeddings"]
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## PROCESS RESPONSE ##
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return _process_embedding_response(
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embeddings=embeddings,
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model_response=model_response,
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model=model,
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encoding=encoding,
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input=input,
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)
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def embedding(
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model: str,
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input: list,
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model_response: litellm.EmbeddingResponse,
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logging_obj: LiteLLMLoggingObj,
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optional_params: dict,
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headers: dict,
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encoding: Any,
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data: Optional[dict] = None,
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complete_api_base: Optional[str] = None,
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api_key: Optional[str] = None,
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aembedding: Optional[bool] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = httpx.Timeout(None),
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client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
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):
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headers = validate_environment(api_key, headers=headers)
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embed_url = complete_api_base or "https://api.cohere.ai/v1/embed"
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model = model
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data = data or {"model": model, "texts": input, **optional_params}
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if "3" in model and "input_type" not in data:
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# cohere v3 embedding models require input_type, if no input_type is provided, default to "search_document"
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data["input_type"] = "search_document"
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## ROUTING
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if aembedding is True:
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return async_embedding(
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model=model,
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data=data,
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input=input,
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model_response=model_response,
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timeout=timeout,
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logging_obj=logging_obj,
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optional_params=optional_params,
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api_base=embed_url,
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api_key=api_key,
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headers=headers,
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encoding=encoding,
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)
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## LOGGING
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logging_obj.pre_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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)
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## COMPLETION CALL
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if client is None or not isinstance(client, HTTPHandler):
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client = HTTPHandler(concurrent_limit=1)
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response = client.post(embed_url, headers=headers, data=json.dumps(data))
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## LOGGING
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logging_obj.post_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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original_response=response,
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)
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"""
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response
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{
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'object': "list",
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'data': [
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]
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'model',
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'usage'
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}
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"""
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if response.status_code != 200:
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raise CohereError(message=response.text, status_code=response.status_code)
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embeddings = response.json()["embeddings"]
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return _process_embedding_response(
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embeddings=embeddings,
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model_response=model_response,
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model=model,
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encoding=encoding,
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input=input,
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)
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