mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
|
from typing import List, Optional
|
||
|
|
||
|
import aiohttp
|
||
|
import requests
|
||
|
from langchain_core.callbacks import (
|
||
|
AsyncCallbackManagerForRetrieverRun,
|
||
|
CallbackManagerForRetrieverRun,
|
||
|
)
|
||
|
from langchain_core.documents import Document
|
||
|
from langchain_core.retrievers import BaseRetriever
|
||
|
|
||
|
|
||
|
class DataberryRetriever(BaseRetriever):
|
||
|
"""`Databerry API` retriever."""
|
||
|
|
||
|
datastore_url: str
|
||
|
top_k: Optional[int]
|
||
|
api_key: Optional[str]
|
||
|
|
||
|
def _get_relevant_documents(
|
||
|
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
||
|
) -> List[Document]:
|
||
|
response = requests.post(
|
||
|
self.datastore_url,
|
||
|
json={
|
||
|
"query": query,
|
||
|
**({"topK": self.top_k} if self.top_k is not None else {}),
|
||
|
},
|
||
|
headers={
|
||
|
"Content-Type": "application/json",
|
||
|
**(
|
||
|
{"Authorization": f"Bearer {self.api_key}"}
|
||
|
if self.api_key is not None
|
||
|
else {}
|
||
|
),
|
||
|
},
|
||
|
)
|
||
|
data = response.json()
|
||
|
return [
|
||
|
Document(
|
||
|
page_content=r["text"],
|
||
|
metadata={"source": r["source"], "score": r["score"]},
|
||
|
)
|
||
|
for r in data["results"]
|
||
|
]
|
||
|
|
||
|
async def _aget_relevant_documents(
|
||
|
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
|
||
|
) -> List[Document]:
|
||
|
async with aiohttp.ClientSession() as session:
|
||
|
async with session.request(
|
||
|
"POST",
|
||
|
self.datastore_url,
|
||
|
json={
|
||
|
"query": query,
|
||
|
**({"topK": self.top_k} if self.top_k is not None else {}),
|
||
|
},
|
||
|
headers={
|
||
|
"Content-Type": "application/json",
|
||
|
**(
|
||
|
{"Authorization": f"Bearer {self.api_key}"}
|
||
|
if self.api_key is not None
|
||
|
else {}
|
||
|
),
|
||
|
},
|
||
|
) as response:
|
||
|
data = await response.json()
|
||
|
return [
|
||
|
Document(
|
||
|
page_content=r["text"],
|
||
|
metadata={"source": r["source"], "score": r["score"]},
|
||
|
)
|
||
|
for r in data["results"]
|
||
|
]
|