Spaces:
Runtime error
Runtime error
File size: 7,968 Bytes
58d33f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from pydantic import BaseModel
from langchain.llms import OpenAI, OpenAIChat
from langchain.schema import LLMResult
class PromptLayerOpenAI(OpenAI, BaseModel):
"""Wrapper around OpenAI large language models.
To use, you should have the ``openai`` and ``promptlayer`` python
package installed, and the environment variable ``OPENAI_API_KEY``
and ``PROMPTLAYER_API_KEY`` set with your openAI API key and
promptlayer key respectively.
All parameters that can be passed to the OpenAI LLM can also
be passed here. The PromptLayerOpenAI LLM adds two optional
parameters:
``pl_tags``: List of strings to tag the request with.
``return_pl_id``: If True, the PromptLayer request ID will be
returned in the ``generation_info`` field of the
``Generation`` object.
Example:
.. code-block:: python
from langchain.llms import PromptLayerOpenAI
openai = PromptLayerOpenAI(model_name="text-davinci-003")
"""
pl_tags: Optional[List[str]]
return_pl_id: Optional[bool] = False
def _generate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Call OpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(prompts, stop)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = promptlayer_api_request(
"langchain.PromptLayerOpenAI",
"langchain",
[prompt],
self._identifying_params,
self.pl_tags,
resp,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info is None or not isinstance(
generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses
async def _agenerate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = await super()._agenerate(prompts, stop)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = promptlayer_api_request(
"langchain.PromptLayerOpenAI.async",
"langchain",
[prompt],
self._identifying_params,
self.pl_tags,
resp,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info is None or not isinstance(
generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses
class PromptLayerOpenAIChat(OpenAIChat, BaseModel):
"""Wrapper around OpenAI large language models.
To use, you should have the ``openai`` and ``promptlayer`` python
package installed, and the environment variable ``OPENAI_API_KEY``
and ``PROMPTLAYER_API_KEY`` set with your openAI API key and
promptlayer key respectively.
All parameters that can be passed to the OpenAIChat LLM can also
be passed here. The PromptLayerOpenAIChat adds two optional
parameters:
``pl_tags``: List of strings to tag the request with.
``return_pl_id``: If True, the PromptLayer request ID will be
returned in the ``generation_info`` field of the
``Generation`` object.
Example:
.. code-block:: python
from langchain.llms import PromptLayerOpenAIChat
openaichat = PromptLayerOpenAIChat(model_name="gpt-3.5-turbo")
"""
pl_tags: Optional[List[str]]
return_pl_id: Optional[bool] = False
def _generate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Call OpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(prompts, stop)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = promptlayer_api_request(
"langchain.PromptLayerOpenAIChat",
"langchain",
[prompt],
self._identifying_params,
self.pl_tags,
resp,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info is None or not isinstance(
generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses
async def _agenerate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = await super()._agenerate(prompts, stop)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = promptlayer_api_request(
"langchain.PromptLayerOpenAIChat.async",
"langchain",
[prompt],
self._identifying_params,
self.pl_tags,
resp,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info is None or not isinstance(
generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses
|