mirror of
https://github.com/deepset-ai/haystack.git
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2c56305ed3
* correct serialization of numpy arrays and pandas dataframes * Update Documentation & Code Style * set additional json_encoders globally * Update Documentation & Code Style * add tests for non primitive return types Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
121 lines
3.7 KiB
Python
121 lines
3.7 KiB
Python
from typing import Dict, Any
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import collections
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import logging
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import time
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import json
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from pydantic import BaseConfig
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from fastapi import FastAPI, APIRouter
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import haystack
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from haystack import Pipeline
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from haystack.telemetry import send_event_if_public_demo
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from rest_api.utils import get_app, get_pipelines
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from rest_api.config import LOG_LEVEL
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from rest_api.schema import QueryRequest, QueryResponse
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logging.getLogger("haystack").setLevel(LOG_LEVEL)
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logger = logging.getLogger("haystack")
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BaseConfig.arbitrary_types_allowed = True
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router = APIRouter()
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app: FastAPI = get_app()
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query_pipeline: Pipeline = get_pipelines().get("query_pipeline", None)
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concurrency_limiter = get_pipelines().get("concurrency_limiter", None)
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@router.get("/initialized")
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def check_status():
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"""
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This endpoint can be used during startup to understand if the
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server is ready to take any requests, or is still loading.
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The recommended approach is to call this endpoint with a short timeout,
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like 500ms, and in case of no reply, consider the server busy.
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"""
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return True
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@router.get("/hs_version")
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def haystack_version():
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"""
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Get the running Haystack version.
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"""
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return {"hs_version": haystack.__version__}
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@router.post("/query", response_model=QueryResponse, response_model_exclude_none=True)
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def query(request: QueryRequest):
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"""
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This endpoint receives the question as a string and allows the requester to set
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additional parameters that will be passed on to the Haystack pipeline.
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"""
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with concurrency_limiter.run():
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result = _process_request(query_pipeline, request)
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return result
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@send_event_if_public_demo
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def _process_request(pipeline, request) -> Dict[str, Any]:
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start_time = time.time()
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params = request.params or {}
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# format global, top-level filters (e.g. "params": {"filters": {"name": ["some"]}})
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if "filters" in params.keys():
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params["filters"] = _format_filters(params["filters"])
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# format targeted node filters (e.g. "params": {"Retriever": {"filters": {"value"}}})
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for key in params.keys():
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if isinstance(params[key], collections.Mapping) and "filters" in params[key].keys():
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params[key]["filters"] = _format_filters(params[key]["filters"])
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result = pipeline.run(query=request.query, params=params, debug=request.debug)
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# Ensure answers and documents exist, even if they're empty lists
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if not "documents" in result:
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result["documents"] = []
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if not "answers" in result:
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result["answers"] = []
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logger.info(
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json.dumps({"request": request, "response": result, "time": f"{(time.time() - start_time):.2f}"}, default=str)
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)
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return result
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def _format_filters(filters):
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"""
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Adjust filters to compliant format:
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Put filter values into a list and remove filters with null value.
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"""
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new_filters = {}
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if filters is None:
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logger.warning(
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f"Request with deprecated filter format ('\"filters\": null'). "
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f"Remove empty filters from params to be compliant with future versions"
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)
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else:
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for key, values in filters.items():
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if values is None:
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logger.warning(
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f"Request with deprecated filter format ('{key}: null'). "
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f"Remove null values from filters to be compliant with future versions"
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)
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continue
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if not isinstance(values, list):
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logger.warning(
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f"Request with deprecated filter format ('{key}': {values}). "
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f"Change to '{key}':[{values}]' to be compliant with future versions"
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)
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values = [values]
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new_filters[key] = values
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return new_filters
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