mirror of https://github.com/HazyResearch/manifest
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
205 lines
6.4 KiB
Python
205 lines
6.4 KiB
Python
"""OpenAI client."""
|
|
import copy
|
|
import logging
|
|
import os
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
import numpy as np
|
|
import tiktoken
|
|
|
|
from manifest.clients.openai import OpenAIClient
|
|
from manifest.request import EmbeddingRequest
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
OPENAI_EMBEDDING_ENGINES = {
|
|
"text-embedding-ada-002",
|
|
}
|
|
|
|
|
|
class OpenAIEmbeddingClient(OpenAIClient):
|
|
"""OpenAI client."""
|
|
|
|
# User param -> (client param, default value)
|
|
PARAMS = {
|
|
"engine": ("model", "text-embedding-ada-002"),
|
|
}
|
|
REQUEST_CLS = EmbeddingRequest
|
|
NAME = "openaiembedding"
|
|
|
|
def connect(
|
|
self,
|
|
connection_str: Optional[str] = None,
|
|
client_args: Dict[str, Any] = {},
|
|
) -> None:
|
|
"""
|
|
Connect to the OpenAI server.
|
|
|
|
connection_str is passed as default OPENAI_API_KEY if variable not set.
|
|
|
|
Args:
|
|
connection_str: connection string.
|
|
client_args: client arguments.
|
|
"""
|
|
self.api_key = os.environ.get("OPENAI_API_KEY", connection_str)
|
|
if self.api_key is None:
|
|
raise ValueError(
|
|
"OpenAI API key not set. Set OPENAI_API_KEY environment "
|
|
"variable or pass through `client_connection`."
|
|
)
|
|
self.host = "https://api.openai.com/v1"
|
|
for key in self.PARAMS:
|
|
setattr(self, key, client_args.pop(key, self.PARAMS[key][1]))
|
|
if getattr(self, "engine") not in OPENAI_EMBEDDING_ENGINES:
|
|
raise ValueError(
|
|
f"Invalid engine {getattr(self, 'engine')}. "
|
|
f"Must be {OPENAI_EMBEDDING_ENGINES}."
|
|
)
|
|
|
|
def get_generation_url(self) -> str:
|
|
"""Get generation URL."""
|
|
return self.host + "/embeddings"
|
|
|
|
def supports_batch_inference(self) -> bool:
|
|
"""Return whether the client supports batch inference."""
|
|
return True
|
|
|
|
def get_model_params(self) -> Dict:
|
|
"""
|
|
Get model params.
|
|
|
|
By getting model params from the server, we can add to request
|
|
and make sure cache keys are unique to model.
|
|
|
|
Returns:
|
|
model params.
|
|
"""
|
|
return {"model_name": self.NAME, "engine": getattr(self, "engine")}
|
|
|
|
def validate_response(self, response: Dict, request: Dict) -> Dict[str, Any]:
|
|
"""
|
|
Format response to dict.
|
|
|
|
Args:
|
|
response: response
|
|
request: request
|
|
|
|
Return:
|
|
response as dict
|
|
"""
|
|
if "data" not in response:
|
|
raise ValueError(f"Invalid response: {response}")
|
|
if "usage" in response:
|
|
# Handle splitting the usages for batch requests
|
|
if len(response["data"]) == 1:
|
|
if isinstance(response["usage"], list):
|
|
response["usage"] = response["usage"][0]
|
|
response["usage"] = [response["usage"]]
|
|
else:
|
|
# Try to split usage
|
|
split_usage = self.split_usage(request, response["data"])
|
|
if split_usage:
|
|
response["usage"] = split_usage
|
|
return response
|
|
|
|
def _format_request_for_embedding(self, request_params: Dict[str, Any]) -> Dict:
|
|
"""Format request params for embedding.
|
|
|
|
Args:
|
|
request_params: request params.
|
|
|
|
Returns:
|
|
formatted request params.
|
|
"""
|
|
# Format for embedding model
|
|
request_params = copy.deepcopy(request_params)
|
|
prompt = request_params.pop("prompt")
|
|
if isinstance(prompt, str):
|
|
prompt_list = [prompt]
|
|
else:
|
|
prompt_list = prompt
|
|
request_params["input"] = prompt_list
|
|
return request_params
|
|
|
|
def _format_request_from_embedding(self, response_dict: Dict[str, Any]) -> Dict:
|
|
"""Format response from embedding for standard response.
|
|
|
|
Args:
|
|
response_dict: response.
|
|
|
|
Return:
|
|
formatted response.
|
|
"""
|
|
new_choices = []
|
|
response_dict = copy.deepcopy(response_dict)
|
|
for res in response_dict.pop("data"):
|
|
new_choices.append({"array": np.array(res["embedding"])})
|
|
response_dict["choices"] = new_choices
|
|
return response_dict
|
|
|
|
def _run_completion(
|
|
self, request_params: Dict[str, Any], retry_timeout: int
|
|
) -> Dict:
|
|
"""Execute completion request.
|
|
|
|
Args:
|
|
request_params: request params.
|
|
retry_timeout: retry timeout.
|
|
|
|
Returns:
|
|
response as dict.
|
|
"""
|
|
# Format for embedding model
|
|
request_params = self._format_request_for_embedding(request_params)
|
|
response_dict = super()._run_completion(request_params, retry_timeout)
|
|
# Reformat for text model
|
|
response_dict = self._format_request_from_embedding(response_dict)
|
|
return response_dict
|
|
|
|
async def _arun_completion(
|
|
self, request_params: Dict[str, Any], retry_timeout: int, batch_size: int
|
|
) -> Dict:
|
|
"""Async execute completion request.
|
|
|
|
Args:
|
|
request_params: request params.
|
|
retry_timeout: retry timeout.
|
|
batch_size: batch size for requests.
|
|
|
|
Returns:
|
|
response as dict.
|
|
"""
|
|
# Format for embedding model
|
|
request_params = self._format_request_for_embedding(request_params)
|
|
response_dict = await super()._arun_completion(
|
|
request_params, retry_timeout, batch_size
|
|
)
|
|
# Reformat for text model
|
|
response_dict = self._format_request_from_embedding(response_dict)
|
|
return response_dict
|
|
|
|
def split_usage(self, request: Dict, choices: List[str]) -> List[Dict[str, int]]:
|
|
"""Split usage into list of usages for each prompt."""
|
|
try:
|
|
encoding = tiktoken.encoding_for_model(getattr(self, "engine"))
|
|
except Exception:
|
|
return []
|
|
prompt = request["input"]
|
|
if isinstance(prompt, str):
|
|
prompts = [prompt]
|
|
else:
|
|
prompts = prompt
|
|
assert len(prompts) == len(choices)
|
|
usages = []
|
|
for pmt in prompts:
|
|
pmt_tokens = len(encoding.encode(pmt))
|
|
# No completion tokens for embedding models
|
|
chc_tokens = 0
|
|
usage = {
|
|
"prompt_tokens": pmt_tokens,
|
|
"completion_tokens": chc_tokens,
|
|
"total_tokens": pmt_tokens + chc_tokens,
|
|
}
|
|
usages.append(usage)
|
|
return usages
|