feat(app-instance): 集成Beaver后端并更新配置管理
集成新的Beaver后端服务到应用实例中，替换原有的nanobot实现。 主要变更包括： - 在Dockerfile和环境配置中添加Beaver相关路径和配置变量 - 更新工作目录结构从.nanobot到.beaver - 实现Beaver引擎加载器，支持配置文件加载和工具组装 - 添加内置工具如ListDirectoryTool、ReadFileTool、SearchFilesTool - 更新消息处理流程，支持通道适配器和网关模式 - 重构技能系统，支持显式工具提示和嵌入式检索 - 改进错误处理和生命周期管理 此变更使应用实例能够使用统一的Beaver后端进行AI代理运行时管理。
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app-instance/backend/beaver/foundation/embedding.py
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205
app-instance/backend/beaver/foundation/embedding.py
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"""Shared embedding-based semantic retrieval utilities."""
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from __future__ import annotations
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import asyncio
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import json
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import math
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import os
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from typing import Any
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from urllib import request
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class EmbeddingRetriever:
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"""Use an OpenAI-compatible embeddings API to rank lightweight candidates."""
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def __init__(
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self,
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*,
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api_key_env: str = "OPENAI_API_KEY",
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api_base_env: str = "OPENAI_API_BASE",
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model: str = "text-embedding-v4",
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timeout_seconds: float = 20.0,
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) -> None:
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self.api_key_env = api_key_env
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self.api_base_env = api_base_env
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self.model = model
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self.timeout_seconds = timeout_seconds
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async def retrieve(
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self,
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*,
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query: str,
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candidates: list[dict[str, str]],
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top_k: int,
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api_key: str | None = None,
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api_base: str | None = None,
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model: str | None = None,
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extra_headers: dict[str, str] | None = None,
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timeout_seconds: float | None = None,
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fallback_top_k: int | None = None,
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) -> list[dict[str, str]]:
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"""Return candidates ordered by embedding similarity.
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If embedding config is missing or the request fails, return the original
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candidate order. This keeps retrieval non-blocking for the main run.
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"""
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if not candidates or top_k <= 0:
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return []
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fallback = self._fallback_candidates(candidates, fallback_top_k=fallback_top_k)
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resolved_api_key = api_key or os.getenv(self.api_key_env)
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resolved_api_base = api_base or os.getenv(self.api_base_env)
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if not resolved_api_key or not resolved_api_base:
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return fallback
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try:
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query_embedding = await self._embed_texts(
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api_key=resolved_api_key,
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api_base=resolved_api_base,
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texts=[query],
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model=model or self.model,
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extra_headers=extra_headers,
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timeout_seconds=timeout_seconds,
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)
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candidate_embeddings = await self._embed_texts(
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api_key=resolved_api_key,
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api_base=resolved_api_base,
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texts=[self._candidate_text(item) for item in candidates],
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model=model or self.model,
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extra_headers=extra_headers,
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timeout_seconds=timeout_seconds,
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)
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except Exception:
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return fallback
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if not query_embedding or not query_embedding[0] or len(candidate_embeddings) != len(candidates):
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return fallback
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query_vector = query_embedding[0]
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scored: list[tuple[float, dict[str, str]]] = []
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for candidate, vector in zip(candidates, candidate_embeddings, strict=False):
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if vector:
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scored.append((self._cosine_similarity(query_vector, vector), candidate))
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scored.sort(key=lambda item: item[0], reverse=True)
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return [item[1] for item in scored[:top_k]]
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async def _embed_texts(
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self,
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*,
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api_key: str,
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api_base: str,
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texts: list[str],
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model: str,
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extra_headers: dict[str, str] | None = None,
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timeout_seconds: float | None = None,
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) -> list[list[float]]:
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all_vectors: list[list[float]] = []
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endpoint = self._normalize_embeddings_endpoint(api_base)
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for start in range(0, len(texts), 10):
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batch = texts[start:start + 10]
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payload = await self._post_embeddings(
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endpoint=endpoint,
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api_key=api_key,
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model=model,
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texts=batch,
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extra_headers=extra_headers,
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timeout_seconds=timeout_seconds,
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)
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embeddings = payload.get("data") or []
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embeddings = sorted(embeddings, key=lambda item: item.get("index", 0))
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all_vectors.extend([list(item.get("embedding") or []) for item in embeddings])
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return all_vectors
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async def _post_embeddings(
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self,
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*,
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endpoint: str,
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api_key: str,
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model: str,
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texts: list[str],
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extra_headers: dict[str, str] | None = None,
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timeout_seconds: float | None = None,
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) -> dict[str, Any]:
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return await asyncio.to_thread(
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self._post_embeddings_sync,
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endpoint=endpoint,
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api_key=api_key,
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model=model,
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texts=texts,
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extra_headers=extra_headers,
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timeout_seconds=timeout_seconds,
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)
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def _post_embeddings_sync(
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self,
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*,
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endpoint: str,
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api_key: str,
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model: str,
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texts: list[str],
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extra_headers: dict[str, str] | None = None,
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timeout_seconds: float | None = None,
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) -> dict[str, Any]:
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body = json.dumps(
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{
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"model": model,
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"input": texts if len(texts) > 1 else texts[0],
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"encoding_format": "float",
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}
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).encode("utf-8")
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req = request.Request(
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endpoint,
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data=body,
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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**(extra_headers or {}),
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},
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method="POST",
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)
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with request.urlopen(req, timeout=timeout_seconds or self.timeout_seconds) as response:
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return json.loads(response.read().decode("utf-8"))
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@staticmethod
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def _fallback_candidates(
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candidates: list[dict[str, str]],
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*,
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fallback_top_k: int | None,
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) -> list[dict[str, str]]:
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if fallback_top_k is None:
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return list(candidates)
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if fallback_top_k <= 0:
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return []
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return candidates[:fallback_top_k]
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@staticmethod
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def _candidate_text(candidate: dict[str, str]) -> str:
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parts = [
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(candidate.get("name") or "").strip(),
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(candidate.get("description") or "").strip(),
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(candidate.get("input_schema") or "").strip(),
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]
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return "\n".join(part for part in parts if part)
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@staticmethod
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def _normalize_embeddings_endpoint(api_base: str) -> str:
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base = api_base.rstrip("/")
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if base.endswith("/embeddings"):
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return base
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if base.endswith("/v1"):
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return f"{base}/embeddings"
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return f"{base}/v1/embeddings"
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@staticmethod
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def _cosine_similarity(left: list[float], right: list[float]) -> float:
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if not left or not right or len(left) != len(right):
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return -1.0
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dot = sum(a * b for a, b in zip(left, right, strict=False))
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left_norm = math.sqrt(sum(a * a for a in left))
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right_norm = math.sqrt(sum(b * b for b in right))
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if left_norm == 0 or right_norm == 0:
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return -1.0
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return dot / (left_norm * right_norm)
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