langchain/libs/community/langchain_community/llms/javelin_ai_gateway.py
Bagatur ed58eeb9c5
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 13:53:30 -08:00

152 lines
4.6 KiB
Python

from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import BaseModel, Extra
# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class Params(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 JavelinAIGateway(LLM):
"""Javelin AI Gateway LLMs.
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_community.llms import JavelinAIGateway
completions = JavelinAIGateway(
gateway_uri="<your-javelin-ai-gateway-uri>",
route="<your-javelin-ai-gateway-completions-route>",
params={
"temperature": 0.1
}
)
"""
route: str
"""The route to use for the Javelin AI Gateway API."""
client: Optional[Any] = None
"""The Javelin AI Gateway client."""
gateway_uri: Optional[str] = None
"""The URI of the Javelin AI Gateway API."""
params: Optional[Params] = None
"""Parameters for the Javelin AI Gateway API."""
javelin_api_key: Optional[str] = None
"""The API key for the Javelin AI Gateway API."""
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=self.javelin_api_key
)
except UnauthorizedError as e:
raise ValueError("Javelin: Incorrect API Key.") from e
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Javelin AI Gateway API."""
params: Dict[str, Any] = {
"gateway_uri": self.gateway_uri,
"route": self.route,
"javelin_api_key": self.javelin_api_key,
**(self.params.dict() if self.params else {}),
}
return params
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return self._default_params
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call the Javelin AI Gateway API."""
data: Dict[str, Any] = {
"prompt": prompt,
**(self.params.dict() if self.params else {}),
}
if s := (stop or (self.params.stop if self.params else None)):
data["stop"] = s
if self.client is not None:
resp = self.client.query_route(self.route, query_body=data)
else:
raise ValueError("Javelin client is not initialized.")
resp_dict = resp.dict()
try:
return resp_dict["llm_response"]["choices"][0]["text"]
except KeyError:
return ""
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call async the Javelin AI Gateway API."""
data: Dict[str, Any] = {
"prompt": prompt,
**(self.params.dict() if self.params else {}),
}
if s := (stop or (self.params.stop if self.params else None)):
data["stop"] = s
if self.client is not None:
resp = await self.client.aquery_route(self.route, query_body=data)
else:
raise ValueError("Javelin client is not initialized.")
resp_dict = resp.dict()
try:
return resp_dict["llm_response"]["choices"][0]["text"]
except KeyError:
return ""
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "javelin-ai-gateway"