Spaces:
Runtime error
Runtime error
File size: 7,632 Bytes
129cd69 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
import logging
from typing import Any, Dict, List, Mapping, Optional, cast
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import (
ChatGeneration,
ChatResult,
)
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
logger = logging.getLogger(__name__)
# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" [call-arg]
class ChatParams(BaseModel, extra=Extra.allow): # type: ignore[call-arg]
"""Parameters for the `Javelin AI Gateway` LLM."""
temperature: float = 0.0
stop: Optional[List[str]] = None
max_tokens: Optional[int] = None
class ChatJavelinAIGateway(BaseChatModel):
"""`Javelin AI Gateway` chat models API.
To use, you should have the ``javelin_sdk`` python package installed.
For more information, see https://docs.getjavelin.io
Example:
.. code-block:: python
from langchain.chat_models import ChatJavelinAIGateway
chat = ChatJavelinAIGateway(
gateway_uri="<javelin-ai-gateway-uri>",
route="<javelin-ai-gateway-chat-route>",
params={
"temperature": 0.1
}
)
"""
route: str
"""The route to use for the Javelin AI Gateway API."""
gateway_uri: Optional[str] = None
"""The URI for the Javelin AI Gateway API."""
params: Optional[ChatParams] = None
"""Parameters for the Javelin AI Gateway LLM."""
client: Any
"""javelin client."""
javelin_api_key: Optional[SecretStr] = None
"""The API key for the Javelin AI Gateway."""
def __init__(self, **kwargs: Any):
try:
from javelin_sdk import (
JavelinClient,
UnauthorizedError,
)
except ImportError:
raise ImportError(
"Could not import javelin_sdk python package. "
"Please install it with `pip install javelin_sdk`."
)
super().__init__(**kwargs)
if self.gateway_uri:
try:
self.client = JavelinClient(
base_url=self.gateway_uri,
api_key=cast(SecretStr, self.javelin_api_key).get_secret_value(),
)
except UnauthorizedError as e:
raise ValueError("Javelin: Incorrect API Key.") from e
@property
def _default_params(self) -> Dict[str, Any]:
params: Dict[str, Any] = {
"gateway_uri": self.gateway_uri,
"javelin_api_key": cast(SecretStr, self.javelin_api_key).get_secret_value(),
"route": self.route,
**(self.params.dict() if self.params else {}),
}
return params
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts = [
ChatJavelinAIGateway._convert_message_to_dict(message)
for message in messages
]
data: Dict[str, Any] = {
"messages": message_dicts,
**(self.params.dict() if self.params else {}),
}
resp = self.client.query_route(self.route, query_body=data)
return ChatJavelinAIGateway._create_chat_result(resp.dict())
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts = [
ChatJavelinAIGateway._convert_message_to_dict(message)
for message in messages
]
data: Dict[str, Any] = {
"messages": message_dicts,
**(self.params.dict() if self.params else {}),
}
resp = await self.client.aquery_route(self.route, query_body=data)
return ChatJavelinAIGateway._create_chat_result(resp.dict())
@property
def _identifying_params(self) -> Dict[str, Any]:
return self._default_params
def _get_invocation_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> Dict[str, Any]:
"""Get the parameters used to invoke the model FOR THE CALLBACKS."""
return {
**self._default_params,
**super()._get_invocation_params(stop=stop, **kwargs),
}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "javelin-ai-gateway-chat"
@staticmethod
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
content = _dict["content"]
if role == "user":
return HumanMessage(content=content)
elif role == "assistant":
return AIMessage(content=content)
elif role == "system":
return SystemMessage(content=content)
else:
return ChatMessage(content=content, role=role)
@staticmethod
def _raise_functions_not_supported() -> None:
raise ValueError(
"Function messages are not supported by the Javelin AI Gateway. Please"
" create a feature request at https://docs.getjavelin.io"
)
@staticmethod
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):
raise ValueError(
"Function messages are not supported by the Javelin AI Gateway. Please"
" create a feature request at https://docs.getjavelin.io"
)
else:
raise ValueError(f"Got unknown message type: {message}")
if "function_call" in message.additional_kwargs:
ChatJavelinAIGateway._raise_functions_not_supported()
if message.additional_kwargs:
logger.warning(
"Additional message arguments are unsupported by Javelin AI Gateway "
" and will be ignored: %s",
message.additional_kwargs,
)
return message_dict
@staticmethod
def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
generations = []
for candidate in response["llm_response"]["choices"]:
message = ChatJavelinAIGateway._convert_dict_to_message(
candidate["message"]
)
message_metadata = candidate.get("metadata", {})
gen = ChatGeneration(
message=message,
generation_info=dict(message_metadata),
)
generations.append(gen)
response_metadata = response.get("metadata", {})
return ChatResult(generations=generations, llm_output=response_metadata)
|