mirror of
https://github.com/deepset-ai/haystack.git
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73a4f9825a
* we create an env var `CONCURRENT_REQUEST_PER_WORKER` following your naming convention, (I came a few commit backwards to find the original name) * default to 4
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
import json
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import logging
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import time
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from pathlib import Path
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from typing import Dict, List, Optional, Union, Any
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from fastapi import APIRouter
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from pydantic import BaseModel
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from haystack import Pipeline
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from rest_api.config import PIPELINE_YAML_PATH, LOG_LEVEL, QUERY_PIPELINE_NAME, CONCURRENT_REQUEST_PER_WORKER
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from rest_api.controller.utils import RequestLimiter
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logging.getLogger("haystack").setLevel(LOG_LEVEL)
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logger = logging.getLogger("haystack")
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router = APIRouter()
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class Request(BaseModel):
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query: str
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filters: Optional[Dict[str, Optional[Union[str, List[str]]]]] = None
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top_k_retriever: Optional[int] = None
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top_k_reader: Optional[int] = None
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class Answer(BaseModel):
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answer: Optional[str]
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question: Optional[str]
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score: Optional[float] = None
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probability: Optional[float] = None
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context: Optional[str]
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offset_start: int
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offset_end: int
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offset_start_in_doc: Optional[int]
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offset_end_in_doc: Optional[int]
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document_id: Optional[str] = None
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meta: Optional[Dict[str, Any]]
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class Response(BaseModel):
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query: str
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answers: List[Answer]
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PIPELINE = Pipeline.load_from_yaml(Path(PIPELINE_YAML_PATH), pipeline_name=QUERY_PIPELINE_NAME)
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logger.info(f"Loaded pipeline nodes: {PIPELINE.graph.nodes.keys()}")
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concurrency_limiter = RequestLimiter(CONCURRENT_REQUEST_PER_WORKER)
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@router.post("/query", response_model=Response)
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def query(request: Request):
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with concurrency_limiter.run():
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result = _process_request(PIPELINE, request)
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return result
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def _process_request(pipeline, request) -> Response:
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start_time = time.time()
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filters = {}
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if request.filters:
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# put filter values into a list and remove filters with null value
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for key, values in request.filters.items():
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if values is None:
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continue
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if not isinstance(values, list):
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values = [values]
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filters[key] = values
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result = pipeline.run(query=request.query,
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filters=filters,
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top_k_retriever=request.top_k_retriever,
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top_k_reader=request.top_k_reader)
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end_time = time.time()
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logger.info(json.dumps({"request": request.dict(), "response": result, "time": f"{(end_time - start_time):.2f}"}))
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return result
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