698 lines
26 KiB
Python
698 lines
26 KiB
Python
import asyncio
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import logging
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import threading
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import time
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from collections import defaultdict
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from collections.abc import Awaitable, Callable
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from typing import Any, Literal, NamedTuple, TypeVar
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import tiktoken
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from google import genai
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from google.genai import types as genai_types
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from nanoid import generate as generate_nanoid
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from openai import AsyncOpenAI
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from .config import EmbeddingModelConfig, resolve_embedding_model_config, settings
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logger = logging.getLogger(__name__)
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_T = TypeVar("_T")
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async def _emit_embedding_call(
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*,
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provider: str,
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model: str,
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texts: list[str],
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input_tokens_estimate: int,
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fn: Callable[[], Awaitable[_T]],
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is_final_attempt: bool = True,
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) -> _T:
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"""time a single embedding-provider call, emit
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`embedding.call.completed` on both success and exception, and return the
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call's result. Errors propagate unchanged — telemetry never bleeds into
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the caller's control flow.
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Caller-supplied `texts` is used only for `input_count`; we don't keep the
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list around for the event to avoid leaking content into telemetry.
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`is_final_attempt` defaults to True so one-shot callers (`embed`,
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`simple_batch_embed`) get correct semantics without changes. Retry-loop
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callers (`_process_batch`) pass the real attempt index so dashboards
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can distinguish exhausted retries from mid-retry failures.
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"""
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start = time.perf_counter()
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error: BaseException | None = None
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try:
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return await fn()
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except BaseException as exc:
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error = exc
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raise
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finally:
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if error is None:
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outcome: Literal["success", "error", "cancelled"] = "success"
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elif isinstance(error, asyncio.CancelledError):
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outcome = "cancelled"
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else:
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outcome = "error"
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_publish_embedding_event(
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provider=provider,
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model=model,
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input_count=len(texts),
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input_tokens_estimate=input_tokens_estimate,
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duration_ms=(time.perf_counter() - start) * 1000,
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outcome=outcome,
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error=error,
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is_final_attempt=is_final_attempt,
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)
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def _publish_embedding_event(
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*,
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provider: str,
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model: str,
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input_count: int,
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input_tokens_estimate: int,
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duration_ms: float,
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outcome: Literal["success", "error", "cancelled"],
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error: BaseException | None,
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is_final_attempt: bool,
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) -> None:
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"""Build and emit the EmbeddingCallCompletedEvent. Best-effort."""
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try:
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from src.telemetry.events import (
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EmbeddingCallCompletedEvent,
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EmbeddingCallPurpose,
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emit,
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)
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from src.utils.types import (
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get_embedding_call_purpose,
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get_embedding_parent_category,
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get_embedding_run_id,
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get_embedding_session_id,
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get_embedding_workspace_name,
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)
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# call_purpose travels via ContextVar so embedding callers don't have
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# to thread it through every call site. Unknown values drop to None
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# rather than raising — keeps telemetry resilient to drift.
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purpose_slug = get_embedding_call_purpose()
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call_purpose: EmbeddingCallPurpose | None = None
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if purpose_slug:
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try:
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call_purpose = EmbeddingCallPurpose(purpose_slug)
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except ValueError:
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logger.debug(
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"Unknown embedding_call_purpose=%r; emitting without",
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purpose_slug,
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)
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emit(
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EmbeddingCallCompletedEvent(
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workspace_name=get_embedding_workspace_name(),
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call_purpose=call_purpose,
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parent_category=get_embedding_parent_category(),
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provider=provider,
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model=model,
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input_count=input_count,
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input_tokens_estimate=input_tokens_estimate,
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duration_ms=duration_ms,
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outcome=outcome,
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is_final_attempt=is_final_attempt,
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error_class=type(error).__name__ if error is not None else None,
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run_id=get_embedding_run_id(),
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)
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)
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# Trace stream (ground-truth) — gated on payload tracing. Each embedding
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# gets its own span nested under the driving agent run (parent_span_id =
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# run_id), so multiple embeddings in one run don't share a span id.
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if settings.TELEMETRY.TRACE_PAYLOADS_ENABLED:
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from src.telemetry.events import EmbeddingCallTracedEvent, emit_trace
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run_id = get_embedding_run_id()
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span_id = generate_nanoid()
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emit_trace(
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EmbeddingCallTracedEvent(
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trace_id=run_id or span_id,
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span_id=span_id,
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parent_span_id=run_id,
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session_id=get_embedding_session_id(),
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call_purpose=purpose_slug,
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parent_category=get_embedding_parent_category(),
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provider=provider,
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model=model,
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provider_input_tokens=input_tokens_estimate,
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provider_output_tokens=0,
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input_count=input_count,
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)
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)
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except Exception: # pragma: no cover - telemetry must not raise
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logger.debug("Failed to emit EmbeddingCallCompletedEvent", exc_info=True)
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class BatchItem(NamedTuple):
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"""A single item in a batch with its metadata."""
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text: str
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text_id: str
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chunk_index: int
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token_count: int
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class _EmbeddingClient:
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"""
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Embedding client supporting OpenAI and Gemini with chunking and batching support.
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"""
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def __init__(
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self,
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config: EmbeddingModelConfig,
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*,
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vector_dimensions: int,
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max_input_tokens: int,
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max_tokens_per_request: int,
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send_dimensions: bool,
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):
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self.transport: str = config.transport
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self.model: str = config.model
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self.vector_dimensions: int = vector_dimensions
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self.send_dimensions: bool = send_dimensions
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if self.transport == "gemini":
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if not config.api_key:
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raise ValueError("Gemini API key is required")
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# 10-minute HTTP timeout, in lockstep with the LLM registry's Gemini
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# client (`src/llm/registry.py:_build_gemini_http_options`). Without
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# this, a stalled Gemini embedding socket wedges the deriver worker
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# exactly the way #785 describes for the LLM client.
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http_options = genai_types.HttpOptions(
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base_url=config.base_url,
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timeout=600_000,
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)
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self.client: genai.Client | AsyncOpenAI = genai.Client(
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api_key=config.api_key,
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http_options=http_options,
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)
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# Gemini has a 2048 token limit
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self.max_embedding_tokens: int = min(max_input_tokens, 2048)
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# Gemini batch size is not documented, using conservative estimate
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self.max_batch_size: int = config.max_batch_size or 100
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else: # openai
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if not config.api_key:
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raise ValueError("OpenAI API key is required")
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self.client = AsyncOpenAI(
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api_key=config.api_key,
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base_url=config.base_url,
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)
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self.max_embedding_tokens = max_input_tokens
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self.max_batch_size = config.max_batch_size or 2048
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try:
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self.encoding: tiktoken.Encoding = tiktoken.encoding_for_model(self.model)
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except KeyError:
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self.encoding = tiktoken.get_encoding("cl100k_base")
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self.max_embedding_tokens_per_request: int = max_tokens_per_request
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@property
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def provider(self) -> str:
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return self.transport
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def _validate_embedding_dimensions(self, embedding: list[float]) -> list[float]:
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if len(embedding) != self.vector_dimensions:
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raise ValueError(
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f"Embedding dimension mismatch for {self.transport}:{self.model}. "
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+ f"Expected {self.vector_dimensions}, got {len(embedding)}."
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)
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return embedding
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async def embed(self, query: str) -> list[float]:
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token_count = len(self.encoding.encode(query))
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if token_count > self.max_embedding_tokens:
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raise ValueError(
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f"Query exceeds maximum token limit of {self.max_embedding_tokens} tokens (got {token_count} tokens)"
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)
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# Bind the typed client at the dispatch site so pyright can narrow it
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# for the closures without needing `assert isinstance(...)` (bandit
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# B101). The closures close over the narrowed local, not `self.client`.
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if isinstance(self.client, genai.Client):
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gemini_client = self.client
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async def _call_gemini() -> list[float]:
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response = await gemini_client.aio.models.embed_content(
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model=self.model,
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contents=query,
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config={"output_dimensionality": self.vector_dimensions},
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)
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if not response.embeddings or not response.embeddings[0].values:
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raise ValueError("No embedding returned from Gemini API")
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return self._validate_embedding_dimensions(
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response.embeddings[0].values
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)
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return await _emit_embedding_call(
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provider=self.transport,
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model=self.model,
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texts=[query],
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input_tokens_estimate=token_count,
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fn=_call_gemini,
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)
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openai_client = self.client
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async def _call_openai() -> list[float]:
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openai_kwargs: dict[str, Any] = {"model": self.model, "input": [query]}
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if self.send_dimensions:
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openai_kwargs["dimensions"] = self.vector_dimensions
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response = await openai_client.embeddings.create(**openai_kwargs)
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return self._validate_embedding_dimensions(response.data[0].embedding)
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return await _emit_embedding_call(
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provider=self.transport,
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model=self.model,
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texts=[query],
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input_tokens_estimate=token_count,
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fn=_call_openai,
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)
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async def simple_batch_embed(self, texts: list[str]) -> list[list[float]]:
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"""
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Batch-embed a list of text strings. Each input must already fit within
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`max_embedding_tokens`; this method does not sub-chunk oversized inputs.
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Internally goes through the same token-aware batching pipeline as
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`batch_embed()` so the per-request token cap is respected.
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Args:
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texts: List of text strings to embed
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Returns:
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List of embedding vectors, one per input text (in order)
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Raises:
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ValueError: If any text exceeds token limits
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"""
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if not texts:
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return []
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# Validate per-input token limit and collect token counts for batching
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token_counts: list[int] = []
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for idx, text in enumerate(texts):
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tokens = len(self.encoding.encode(text))
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if tokens > self.max_embedding_tokens:
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raise ValueError(
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f"Text at index {idx} exceeds maximum token limit of {self.max_embedding_tokens} tokens (got {tokens} tokens)"
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)
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token_counts.append(tokens)
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# Use positional indices as text_ids so we can reassemble in input order.
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text_chunks: dict[str, list[tuple[str, int]]] = {
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str(i): [(text, token_counts[i])] for i, text in enumerate(texts)
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}
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batches = self._create_batches(text_chunks)
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batch_results = await asyncio.gather(
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*[self._process_batch(batch) for batch in batches],
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)
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combined: dict[str, list[list[float]]] = self._accumulate_embeddings(
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batch_results
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)
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return [combined[str(i)][0] for i in range(len(texts))]
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def prepare_chunks(self, id_resource_dict: dict[str, str]) -> dict[str, list[str]]:
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"""
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Public helper: tokenize and chunk texts using the same rules as
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`batch_embed()`. Returns ordered chunk texts per input id.
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Intended for callers that want to persist embeddable chunks
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before later embedding them off the request path.
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"""
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return {
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text_id: [chunk_text for chunk_text, _ in chunks]
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for text_id, chunks in self._prepare_chunks(id_resource_dict).items()
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}
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async def batch_embed(
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self, id_resource_dict: dict[str, str]
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) -> dict[str, list[list[float]]]:
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"""
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Embed multiple texts, chunking long ones and batching API calls.
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Args:
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id_resource_dict: Maps text IDs to text content
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Returns:
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Maps text IDs to lists of embedding vectors (one per chunk)
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"""
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if not id_resource_dict:
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return {}
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# 1. Prepare chunks for all texts if needed
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text_chunks = self._prepare_chunks(id_resource_dict)
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# 2. Create batches that fit API limits (max 2048 embeddings per request, max 300,000 tokens per request)
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batches = self._create_batches(text_chunks)
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# 3. Process all batches concurrently
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batch_results = await asyncio.gather(
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*[self._process_batch(batch) for batch in batches],
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)
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# 4. Accumulate results preserving chunk order
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return self._accumulate_embeddings(batch_results)
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def _prepare_chunks(
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self, id_resource_dict: dict[str, str]
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) -> dict[str, list[tuple[str, int]]]:
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"""
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Chunk texts that exceed token limits.
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Args:
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id_resource_dict: Maps text IDs to text content. We tokenize with
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the embedding client's own encoding so token IDs match the
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decoder vocabulary used by the target embedding API.
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Returns:
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Maps text IDs to lists of (chunk_text, token_count) tuples
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"""
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out: dict[str, list[tuple[str, int]]] = {}
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for text_id, text in id_resource_dict.items():
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tokens = self.encoding.encode(text)
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if len(tokens) > self.max_embedding_tokens:
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out[text_id] = _chunk_text_with_tokens(
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text, tokens, self.max_embedding_tokens, self.encoding
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)
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else:
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out[text_id] = [(text, len(tokens))]
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return out
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def _create_batches(
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self, text_chunks: dict[str, list[tuple[str, int]]]
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) -> list[list[BatchItem]]:
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"""
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Group chunks into batches that fit API limits.
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Args:
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text_chunks: Maps text IDs to lists of (chunk_text, token_count) tuples
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Returns:
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List of batches, each containing BatchItem objects
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"""
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batches: list[list[BatchItem]] = []
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current_batch: list[BatchItem] = []
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current_tokens = 0
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for text_id, chunks in text_chunks.items():
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for chunk_idx, (chunk_text, chunk_tokens) in enumerate(chunks):
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# Check if adding this chunk would exceed limits
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would_exceed_tokens = (
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current_tokens + chunk_tokens
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> self.max_embedding_tokens_per_request
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)
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would_exceed_count = len(current_batch) >= self.max_batch_size
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if current_batch and (would_exceed_tokens or would_exceed_count):
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batches.append(current_batch)
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current_batch = []
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current_tokens = 0
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current_batch.append(
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BatchItem(chunk_text, text_id, chunk_idx, chunk_tokens)
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)
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current_tokens += chunk_tokens
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if current_batch:
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batches.append(current_batch)
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return batches
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async def _process_batch(
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self, batch: list[BatchItem], max_retries: int = 3
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) -> dict[str, dict[int, list[float]]]:
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"""
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Process a single batch through the embeddings API with retry logic.
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Args:
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batch: List of BatchItem objects to embed
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max_retries: Maximum number of retry attempts (default: 3)
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Returns:
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Maps text IDs to {chunk_index: embedding_vector} dictionaries
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"""
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last_exception: Exception | None = None
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async def _call_provider() -> dict[str, dict[int, list[float]]]:
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"""One provider call. Lifted out of the retry loop so
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_emit_embedding_call emits a separate event per attempt — each
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attempt is a distinct provider hit and shows up as its own line
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item in analytics."""
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result: dict[str, dict[int, list[float]]] = defaultdict(dict)
|
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if isinstance(self.client, genai.Client):
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response = await self.client.aio.models.embed_content(
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model=self.model,
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contents=[item.text for item in batch],
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config={"output_dimensionality": self.vector_dimensions},
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)
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if response.embeddings:
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for item, embedding in zip(batch, response.embeddings, strict=True):
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if embedding.values:
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result[item.text_id][item.chunk_index] = (
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self._validate_embedding_dimensions(embedding.values)
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)
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else: # openai
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openai_kwargs: dict[str, Any] = {
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"model": self.model,
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"input": [item.text for item in batch],
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}
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if self.send_dimensions:
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openai_kwargs["dimensions"] = self.vector_dimensions
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response = await self.client.embeddings.create(**openai_kwargs)
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for item, embedding_data in zip(batch, response.data, strict=True):
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result[item.text_id][item.chunk_index] = (
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self._validate_embedding_dimensions(embedding_data.embedding)
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)
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return result
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# Token counts were computed during chunk prep; reuse them here so the
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# provider call doesn't re-encode every chunk just for the size proxy.
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batch_tokens_estimate = sum(item.token_count for item in batch)
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batch_texts = [item.text for item in batch]
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|
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for attempt in range(max_retries):
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try:
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result = await _emit_embedding_call(
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provider=self.transport,
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model=self.model,
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texts=batch_texts,
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input_tokens_estimate=batch_tokens_estimate,
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fn=_call_provider,
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is_final_attempt=(attempt >= max_retries - 1),
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)
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return dict(result)
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except Exception as e:
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last_exception = e
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if attempt < max_retries - 1:
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# Exponential backoff: 1s, 2s, 4s
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wait_time = 2**attempt
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logger.warning(
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f"Embedding batch failed (attempt {attempt + 1}/{max_retries}), "
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+ f"retrying in {wait_time}s: {e}"
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)
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await asyncio.sleep(wait_time)
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else:
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logger.exception("Error processing batch after all retries")
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|
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raise last_exception or RuntimeError("Batch processing failed")
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|
|
def _accumulate_embeddings(
|
|
self, batch_results: list[dict[str, dict[int, list[float]]]]
|
|
) -> dict[str, list[list[float]]]:
|
|
"""
|
|
Combine batch results into final output, preserving chunk order.
|
|
|
|
Args:
|
|
batch_results: List of batch results from _process_batch
|
|
|
|
Returns:
|
|
Maps text IDs to ordered lists of embedding vectors
|
|
"""
|
|
all_embeddings: dict[str, dict[int, list[float]]] = defaultdict(dict)
|
|
|
|
# Collect all embeddings by text_id and chunk_index
|
|
for batch_result in batch_results:
|
|
for text_id, chunk_dict in batch_result.items():
|
|
all_embeddings[text_id].update(chunk_dict)
|
|
|
|
# Convert to ordered lists
|
|
return {
|
|
text_id: [chunk_dict[i] for i in sorted(chunk_dict.keys())]
|
|
for text_id, chunk_dict in all_embeddings.items()
|
|
}
|
|
|
|
|
|
def _chunk_text_with_tokens(
|
|
text: str,
|
|
encoded_tokens: list[int],
|
|
max_tokens: int,
|
|
encoding: tiktoken.Encoding,
|
|
) -> list[tuple[str, int]]:
|
|
"""
|
|
Split text into chunks that fit within token limits, with 20% overlap.
|
|
|
|
Args:
|
|
text: Original text to chunk
|
|
encoded_tokens: Pre-encoded tokens for the text
|
|
max_tokens: Maximum tokens per chunk
|
|
encoding: Tiktoken encoding model
|
|
|
|
Returns:
|
|
List of (chunk_text, token_count) tuples
|
|
"""
|
|
if len(encoded_tokens) <= max_tokens:
|
|
return [(text, len(encoded_tokens))]
|
|
|
|
# Use 20% overlap for better semantic continuity
|
|
overlap_tokens = int(max_tokens * 0.2)
|
|
step_size = max_tokens - overlap_tokens
|
|
|
|
return [
|
|
(
|
|
encoding.decode(encoded_tokens[i : i + max_tokens]),
|
|
min(max_tokens, len(encoded_tokens) - i),
|
|
)
|
|
for i in range(0, len(encoded_tokens), step_size)
|
|
if i < len(encoded_tokens) # Ensure we don't create empty chunks
|
|
]
|
|
|
|
|
|
class EmbeddingClient:
|
|
"""
|
|
Singleton wrapper for the embedding client with deferred loading.
|
|
|
|
The actual client is only initialized on first use, improving startup time
|
|
and allowing the application to start even if API keys are not yet configured.
|
|
"""
|
|
|
|
_instance: "_EmbeddingClient | None" = None
|
|
_instance_signature: tuple[object, ...] | None = None
|
|
_lock: threading.Lock = threading.Lock()
|
|
_wrapper_instance: "EmbeddingClient | None" = None
|
|
|
|
def __new__(cls):
|
|
"""Ensure only one instance of EmbeddingClient exists."""
|
|
# We always return the same wrapper instance
|
|
if cls._wrapper_instance is None:
|
|
cls._wrapper_instance = super().__new__(cls)
|
|
return cls._wrapper_instance
|
|
|
|
def _get_client(self) -> _EmbeddingClient:
|
|
"""
|
|
Get or create the underlying embedding client instance.
|
|
|
|
Uses double-checked locking for thread-safe lazy initialization.
|
|
"""
|
|
signature = self._get_settings_signature()
|
|
if self._instance is None or self._instance_signature != signature:
|
|
with self._lock:
|
|
if self._instance is None or self._instance_signature != signature:
|
|
runtime_config = self._resolve_runtime_config()
|
|
self._instance = _EmbeddingClient(
|
|
runtime_config,
|
|
vector_dimensions=settings.EMBEDDING.VECTOR_DIMENSIONS,
|
|
max_input_tokens=settings.EMBEDDING.MAX_INPUT_TOKENS,
|
|
max_tokens_per_request=settings.EMBEDDING.MAX_TOKENS_PER_REQUEST,
|
|
send_dimensions=settings.EMBEDDING.resolve_send_dimensions(),
|
|
)
|
|
self._instance_signature = signature
|
|
logger.debug(
|
|
"Initialized embedding client with transport: %s model: %s",
|
|
runtime_config.transport,
|
|
runtime_config.model,
|
|
)
|
|
|
|
return self._instance
|
|
|
|
def _resolve_runtime_config(self) -> EmbeddingModelConfig:
|
|
return resolve_embedding_model_config(settings.EMBEDDING.MODEL_CONFIG)
|
|
|
|
def _get_settings_signature(self) -> tuple[object, ...]:
|
|
runtime_config = self._resolve_runtime_config()
|
|
return (
|
|
runtime_config.transport,
|
|
runtime_config.model,
|
|
runtime_config.api_key,
|
|
runtime_config.base_url,
|
|
runtime_config.max_batch_size,
|
|
settings.EMBEDDING.VECTOR_DIMENSIONS,
|
|
settings.EMBEDDING.MAX_INPUT_TOKENS,
|
|
settings.EMBEDDING.MAX_TOKENS_PER_REQUEST,
|
|
settings.EMBEDDING.resolve_send_dimensions(),
|
|
)
|
|
|
|
async def embed(self, query: str) -> list[float]:
|
|
"""Embed a single query string."""
|
|
return await self._get_client().embed(query)
|
|
|
|
async def simple_batch_embed(self, texts: list[str]) -> list[list[float]]:
|
|
"""Batch embed a list of text strings (each must fit token limit)."""
|
|
return await self._get_client().simple_batch_embed(texts)
|
|
|
|
def prepare_chunks(self, id_resource_dict: dict[str, str]) -> dict[str, list[str]]:
|
|
"""Chunk texts using the same rules as `batch_embed` (no network)."""
|
|
return self._get_client().prepare_chunks(id_resource_dict)
|
|
|
|
async def batch_embed(
|
|
self, id_resource_dict: dict[str, str]
|
|
) -> dict[str, list[list[float]]]:
|
|
"""Embed multiple texts, chunking long ones and batching API calls."""
|
|
return await self._get_client().batch_embed(id_resource_dict)
|
|
|
|
@property
|
|
def provider(self) -> str:
|
|
"""Get the provider name."""
|
|
return self._get_client().provider
|
|
|
|
@property
|
|
def model(self) -> str:
|
|
"""Get the model name."""
|
|
return self._get_client().model
|
|
|
|
@property
|
|
def transport(self) -> str:
|
|
"""Get the transport name."""
|
|
return self._get_client().transport
|
|
|
|
@property
|
|
def max_embedding_tokens(self) -> int:
|
|
"""Get the maximum embedding tokens."""
|
|
return self._get_client().max_embedding_tokens
|
|
|
|
@property
|
|
def vector_dimensions(self) -> int:
|
|
"""Get the configured embedding dimensions."""
|
|
return self._get_client().vector_dimensions
|
|
|
|
@property
|
|
def encoding(self) -> tiktoken.Encoding:
|
|
"""Get the tiktoken encoding.
|
|
|
|
Resolved without constructing the underlying client: tiktoken needs no
|
|
API key, and token-counting callers (e.g. the document dedup tie-break)
|
|
must work in environments with no embedding credentials, such as CI for
|
|
pull requests from forks.
|
|
"""
|
|
if self._instance is not None:
|
|
return self._instance.encoding
|
|
try:
|
|
return tiktoken.encoding_for_model(self._resolve_runtime_config().model)
|
|
except KeyError:
|
|
return tiktoken.get_encoding("cl100k_base")
|
|
|
|
|
|
# Shared singleton embedding client instance
|
|
embedding_client = EmbeddingClient()
|