langchain/libs/community/langchain_community/embeddings/javelin_ai_gateway.py
Leonid Ganeline 7cf2d2759d
community[patch]: docstrings update (#20301)
Added missed docstrings. Format docstings to the consistent form.
2024-04-11 16:23:27 -04:00

110 lines
3.6 KiB
Python

from __future__ import annotations
from typing import Any, Iterator, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
yield texts[i : i + size]
class JavelinAIGatewayEmbeddings(Embeddings, BaseModel):
"""Javelin AI Gateway embeddings.
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.embeddings import JavelinAIGatewayEmbeddings
embeddings = JavelinAIGatewayEmbeddings(
gateway_uri="<javelin-ai-gateway-uri>",
route="<your-javelin-gateway-embeddings-route>"
)
"""
client: Any
"""javelin client."""
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."""
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
def _query(self, texts: List[str]) -> List[List[float]]:
embeddings = []
for txt in _chunk(texts, 20):
try:
resp = self.client.query_route(self.route, query_body={"input": txt})
resp_dict = resp.dict()
embeddings_chunk = resp_dict.get("llm_response", {}).get("data", [])
for item in embeddings_chunk:
if "embedding" in item:
embeddings.append(item["embedding"])
except ValueError as e:
print("Failed to query route: " + str(e)) # noqa: T201
return embeddings
async def _aquery(self, texts: List[str]) -> List[List[float]]:
embeddings = []
for txt in _chunk(texts, 20):
try:
resp = await self.client.aquery_route(
self.route, query_body={"input": txt}
)
resp_dict = resp.dict()
embeddings_chunk = resp_dict.get("llm_response", {}).get("data", [])
for item in embeddings_chunk:
if "embedding" in item:
embeddings.append(item["embedding"])
except ValueError as e:
print("Failed to query route: " + str(e)) # noqa: T201
return embeddings
def embed_documents(self, texts: List[str]) -> List[List[float]]:
return self._query(texts)
def embed_query(self, text: str) -> List[float]:
return self._query([text])[0]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
return await self._aquery(texts)
async def aembed_query(self, text: str) -> List[float]:
result = await self._aquery([text])
return result[0]