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* VoyageEmbeddingConfig * fix voyage logic to get params * add voyage embedding transformation * add get_provider_embedding_config * use BaseEmbeddingConfig * voyage clean up * use llm http handler for embedding transformations * test_voyage_ai_embedding_extra_params * add voyage async * test_voyage_ai_embedding_extra_params * add async for llm http handler * update BaseLLMEmbeddingTest * test_voyage_ai_embedding_extra_params * fix linting * fix get_provider_embedding_config * fix anthropic text test * update location of base/chat/transformation * fix import path * fix IBMWatsonXAIConfig
140 lines
3.8 KiB
Python
140 lines
3.8 KiB
Python
import json
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import os
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import time
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import types
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from enum import Enum
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from typing import Any, Callable, List, Optional, Union
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import httpx
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import litellm
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from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.types.llms.openai import AllMessageValues
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from litellm.utils import ModelResponse, Usage
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from ..common_utils import NLPCloudError
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from .transformation import NLPCloudConfig
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nlp_config = NLPCloudConfig()
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def completion(
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model: str,
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messages: list,
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api_base: str,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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optional_params: dict,
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litellm_params: dict,
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logger_fn=None,
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default_max_tokens_to_sample=None,
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client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
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headers={},
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):
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headers = nlp_config.validate_environment(
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api_key=api_key,
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headers=headers,
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model=model,
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messages=messages,
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optional_params=optional_params,
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)
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## Load Config
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config = litellm.NLPCloudConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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completion_url_fragment_1 = api_base
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completion_url_fragment_2 = "/generation"
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model = model
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completion_url = completion_url_fragment_1 + model + completion_url_fragment_2
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data = nlp_config.transform_request(
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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headers=headers,
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)
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## LOGGING
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logging_obj.pre_call(
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input=None,
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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": completion_url,
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},
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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 = _get_httpx_client()
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response = client.post(
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completion_url,
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headers=headers,
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data=json.dumps(data),
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stream=optional_params["stream"] if "stream" in optional_params else False,
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)
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if "stream" in optional_params and optional_params["stream"] is True:
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return clean_and_iterate_chunks(response)
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else:
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return nlp_config.transform_response(
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model=model,
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raw_response=response,
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model_response=model_response,
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logging_obj=logging_obj,
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api_key=api_key,
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request_data=data,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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encoding=encoding,
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)
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# def clean_and_iterate_chunks(response):
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# def process_chunk(chunk):
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# print(f"received chunk: {chunk}")
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# cleaned_chunk = chunk.decode("utf-8")
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# # Perform further processing based on your needs
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# return cleaned_chunk
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# for line in response.iter_lines():
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# if line:
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# yield process_chunk(line)
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def clean_and_iterate_chunks(response):
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buffer = b""
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for chunk in response.iter_content(chunk_size=1024):
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if not chunk:
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break
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buffer += chunk
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while b"\x00" in buffer:
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buffer = buffer.replace(b"\x00", b"")
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yield buffer.decode("utf-8")
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buffer = b""
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# No more data expected, yield any remaining data in the buffer
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if buffer:
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yield buffer.decode("utf-8")
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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