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Embedding utilities - Generate vector embeddings using DashScope.
"""
from __future__ import annotations
from http import HTTPStatus
import os
import time
from typing import List, Optional, Dict, Any
import dashscope
from dashscope import TextEmbedding
import numpy as np
import config
from utils.benchmark_logging import current_call_context, log_api_call, now_iso
class EmbeddingModel:
"""
Embedding model backed by DashScope's text-embedding-v4 API.
"""
_DIMENSION_CACHE: Dict[str, int] = {}
def __init__(self, model_name: str = None, api_key: Optional[str] = None):
self.model_name = model_name or config.EMBEDDING_MODEL
self.api_key = api_key or getattr(config, "DASHSCOPE_API_KEY", None) or os.getenv("DASHSCOPE_API_KEY") or os.getenv("OPENAI_API_KEY")
if not self.api_key:
raise ValueError("DASHSCOPE_API_KEY or OPENAI_API_KEY must be set for EmbeddingModel")
self.dimension = None
self.model_type = "dashscope_text_embedding"
self.batch_size = max(1, getattr(config, "EMBEDDING_BATCH_SIZE", 8))
dashscope.api_key = self.api_key
print(f"Initializing DashScope embedding model: {self.model_name}")
cached_dimension = self._DIMENSION_CACHE.get(self.model_name)
if cached_dimension is not None:
self.dimension = cached_dimension
print(f"Using cached DashScope embedding dimension: {self.dimension}")
else:
self._probe_dimension()
def encode(self, texts: List[str], is_query: bool = False) -> np.ndarray:
"""
Encode list of texts to vectors
"""
if isinstance(texts, str):
texts = [texts]
if not texts:
width = self.dimension or 0
return np.empty((0, width), dtype=np.float32)
batches = []
for start in range(0, len(texts), self.batch_size):
batches.append(texts[start:start + self.batch_size])
encoded_batches = []
for batch in batches:
encoded_batches.append(self._encode_batch(batch, is_query=is_query))
return np.vstack(encoded_batches)
def encode_single(self, text: str, is_query: bool = False) -> np.ndarray:
"""
Encode single text
"""
return self.encode([text], is_query=is_query)[0]
def encode_query(self, queries: List[str]) -> np.ndarray:
"""
Encode queries.
"""
return self.encode(queries, is_query=True)
def encode_documents(self, documents: List[str]) -> np.ndarray:
"""
Encode documents.
"""
return self.encode(documents, is_query=False)
def _probe_dimension(self) -> None:
vectors, usage, request_id = self._embed_batch(["dimension probe"], token_stage="setup_embedding_probe")
if not vectors or not vectors[0]:
raise RuntimeError("DashScope embedding dimension probe returned an empty vector")
self.dimension = len(vectors[0])
self._DIMENSION_CACHE[self.model_name] = self.dimension
print(f"Detected embedding dimension from DashScope: {self.dimension}")
self._log_embedding_call(
texts=["dimension probe"],
vectors=vectors,
usage=usage,
request_id=request_id,
success=True,
token_stage="setup_embedding_probe",
)
def _encode_batch(self, texts: List[str], is_query: bool = False) -> np.ndarray:
vectors, usage, request_id = self._embed_batch(
texts,
token_stage=None,
is_query=is_query,
)
matrix = np.array(vectors, dtype=np.float32)
matrix = self._normalize(matrix)
self._log_embedding_call(
texts=texts,
vectors=vectors,
usage=usage,
request_id=request_id,
success=True,
)
return matrix
def _embed_batch(
self,
texts: List[str],
token_stage: Optional[str],
is_query: bool = False,
) -> tuple[List[List[float]], Dict[str, Any], Optional[str]]:
last_exception = None
max_retries = getattr(config, "EMBEDDING_MAX_RETRIES", 5)
for attempt in range(max_retries):
try:
response = TextEmbedding.call(
model=self.model_name,
input=texts,
api_key=self.api_key,
)
status = getattr(response, "status_code", None)
if status not in (HTTPStatus.OK, 200):
raise RuntimeError(f"DashScope embedding call failed with status {status}: {response}")
output = getattr(response, "output", None) or response.get("output") or {}
embeddings = output.get("embeddings") or []
ordered = [None] * len(texts)
for item in embeddings:
index = item.get("text_index", 0)
ordered[index] = item.get("embedding")
if any(vector is None for vector in ordered):
raise RuntimeError(f"DashScope embedding response missing vectors for some inputs: {response}")
usage = self._usage_to_dict(getattr(response, "usage", None) or response.get("usage"))
request_id = getattr(response, "request_id", None) or response.get("request_id")
return ordered, usage, request_id
except Exception as exc:
last_exception = exc
self._log_embedding_call(
texts=texts,
vectors=[],
usage={},
request_id=None,
success=False,
error=exc,
token_stage=token_stage,
is_query=is_query,
attempt=attempt + 1,
)
if attempt < max_retries - 1:
wait_time = 2 ** attempt
print(f"Embedding API call failed (attempt {attempt + 1}/{max_retries}): {exc}")
print(f"Retrying in {wait_time} seconds...")
time.sleep(wait_time)
else:
print(f"Embedding API call failed after {max_retries} attempts: {exc}")
raise last_exception
def _usage_to_dict(self, usage: Any) -> Dict[str, Any]:
if usage is None:
return {}
if hasattr(usage, "items"):
return dict(usage)
return {"total_tokens": getattr(usage, "total_tokens", None)}
def _normalize(self, matrix: np.ndarray) -> np.ndarray:
norms = np.linalg.norm(matrix, axis=1, keepdims=True)
norms = np.where(norms == 0, 1.0, norms)
return matrix / norms
def _log_embedding_call(
self,
*,
texts: List[str],
vectors: List[List[float]],
usage: Dict[str, Any],
request_id: Optional[str],
success: bool,
error: Optional[Exception] = None,
token_stage: Optional[str] = None,
is_query: bool = False,
attempt: int = 1,
) -> None:
context = current_call_context()
log_api_call(
{
"timestamp": now_iso(),
"success": success,
"provider": "dashscope",
"api_kind": "embedding",
"stage": context.get("stage") if token_stage is None else "setup",
"token_stage": token_stage or context.get("embedding_stage") or context.get("stage"),
"patient_id": context.get("patient_id"),
"question_no": context.get("question_no"),
"text_id": context.get("text_id"),
"operation_id": context.get("operation_id"),
"attempt": attempt,
"model": self.model_name,
"request_id": request_id,
"input_count": len(texts),
"inputs": texts,
"is_query": is_query,
"vector_dimension": self.dimension or (len(vectors[0]) if vectors else None),
"usage": usage,
"error": repr(error) if error is not None else None,
}
)
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