c81b0fc1
 tangwang
scripts/evaluatio...
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"""HTTP clients for search API, reranker, and DashScope chat (relevance labeling)."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple
import requests
from .constants import VALID_LABELS
from .prompts import (
classify_batch_complex_prompt,
classify_batch_simple_prompt,
extract_query_profile_prompt,
)
from .utils import build_label_doc_line, extract_json_blob, safe_json_dumps
class SearchServiceClient:
def __init__(self, base_url: str, tenant_id: str):
self.base_url = base_url.rstrip("/")
self.tenant_id = str(tenant_id)
self.session = requests.Session()
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tangwang
eval框架前端
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def search(self, query: str, size: int, from_: int = 0, language: str = "en", *, debug: bool = False) -> Dict[str, Any]:
payload: Dict[str, Any] = {
"query": query,
"size": size,
"from": from_,
"language": language,
}
if debug:
payload["debug"] = True
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tangwang
scripts/evaluatio...
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timeout=120,
)
response.raise_for_status()
return response.json()
class RerankServiceClient:
def __init__(self, service_url: str):
self.service_url = service_url.rstrip("/")
self.session = requests.Session()
def rerank(self, query: str, docs: Sequence[str], normalize: bool = False, top_n: Optional[int] = None) -> Tuple[List[float], Dict[str, Any]]:
payload: Dict[str, Any] = {
"query": query,
"docs": list(docs),
"normalize": normalize,
}
if top_n is not None:
payload["top_n"] = int(top_n)
response = self.session.post(self.service_url, json=payload, timeout=180)
response.raise_for_status()
data = response.json()
return list(data.get("scores") or []), dict(data.get("meta") or {})
class DashScopeLabelClient:
def __init__(self, model: str, base_url: str, api_key: str, batch_size: int = 40):
self.model = model
self.base_url = base_url.rstrip("/")
self.api_key = api_key
self.batch_size = int(batch_size)
self.session = requests.Session()
def _chat(self, prompt: str) -> Tuple[str, str]:
response = self.session.post(
f"{self.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
json={
"model": self.model,
"messages": [{"role": "user", "content": prompt}],
"temperature": 0,
"top_p": 0.1,
},
timeout=180,
)
response.raise_for_status()
data = response.json()
content = str(((data.get("choices") or [{}])[0].get("message") or {}).get("content") or "").strip()
return content, safe_json_dumps(data)
def classify_batch_simple(
self,
query: str,
docs: Sequence[Dict[str, Any]],
) -> Tuple[List[str], str]:
numbered_docs = [build_label_doc_line(idx + 1, doc) for idx, doc in enumerate(docs)]
prompt = classify_batch_simple_prompt(query, numbered_docs)
content, raw_response = self._chat(prompt)
labels = []
for line in str(content or "").splitlines():
label = line.strip()
if label in VALID_LABELS:
labels.append(label)
if len(labels) != len(docs):
payload = extract_json_blob(content)
if isinstance(payload, dict) and isinstance(payload.get("labels"), list):
labels = []
for item in payload["labels"][: len(docs)]:
if isinstance(item, dict):
label = str(item.get("label") or "").strip()
else:
label = str(item).strip()
if label in VALID_LABELS:
labels.append(label)
if len(labels) != len(docs) or any(label not in VALID_LABELS for label in labels):
raise ValueError(f"unexpected simple label output: {content!r}")
return labels, raw_response
def extract_query_profile(
self,
query: str,
parser_hints: Dict[str, Any],
) -> Tuple[Dict[str, Any], str]:
prompt = extract_query_profile_prompt(query, parser_hints)
content, raw_response = self._chat(prompt)
payload = extract_json_blob(content)
if not isinstance(payload, dict):
raise ValueError(f"unexpected query profile payload: {content!r}")
payload.setdefault("normalized_query_en", query)
payload.setdefault("primary_category", "")
payload.setdefault("allowed_categories", [])
payload.setdefault("required_attributes", [])
payload.setdefault("notes", [])
return payload, raw_response
def classify_batch_complex(
self,
query: str,
query_profile: Dict[str, Any],
docs: Sequence[Dict[str, Any]],
) -> Tuple[List[str], str]:
numbered_docs = [build_label_doc_line(idx + 1, doc) for idx, doc in enumerate(docs)]
prompt = classify_batch_complex_prompt(query, query_profile, numbered_docs)
content, raw_response = self._chat(prompt)
payload = extract_json_blob(content)
if not isinstance(payload, dict) or not isinstance(payload.get("labels"), list):
raise ValueError(f"unexpected label payload: {content!r}")
labels_payload = payload["labels"]
labels: List[str] = []
for item in labels_payload[: len(docs)]:
if not isinstance(item, dict):
continue
label = str(item.get("label") or "").strip()
if label in VALID_LABELS:
labels.append(label)
if len(labels) != len(docs) or any(label not in VALID_LABELS for label in labels):
raise ValueError(f"unexpected label output: {content!r}")
return labels, raw_response
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