feat: 重构知识库系统，移除Hermes集成，增强RAG和同步功能
主要变更: - 移除Hermes智能体及相关回调服务 - 新增知识库RAG、同步、调度、规范化和索引任务服务 - 重构orchestrator服务，增强运行时聊天功能 - 更新前端聊天、政策制度、设置等页面样式和逻辑 - 更新expense_claims和document_intelligence服务 - 删除llm_wiki相关服务和测试文件 - 更新docker-compose配置和启动脚本
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caoxiaozhu
197
.tmp/lightrag_inspect/lightrag_pkg/lightrag/llm/voyageai.py
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197
.tmp/lightrag_inspect/lightrag_pkg/lightrag/llm/voyageai.py
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import os
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import numpy as np
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import pipmaster as pm # Pipmaster for dynamic library install
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# Add Voyage AI import
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if not pm.is_installed("voyageai"):
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pm.install("voyageai")
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import voyageai
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from voyageai.error import (
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APIConnectionError,
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RateLimitError,
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ServerError,
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ServiceUnavailableError,
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Timeout,
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TryAgain,
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)
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_exponential,
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retry_if_exception_type,
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)
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from lightrag.utils import wrap_embedding_func_with_attrs, logger
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# Custom exceptions for VoyageAI errors
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class VoyageAIError(Exception):
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"""Generic VoyageAI API error"""
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pass
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@wrap_embedding_func_with_attrs(
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embedding_dim=1024, max_token_size=32000, supports_asymmetric=True
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)
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=4, max=60),
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retry=retry_if_exception_type(
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(
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APIConnectionError,
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RateLimitError,
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ServerError,
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ServiceUnavailableError,
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Timeout,
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TryAgain,
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)
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),
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)
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async def voyageai_embed(
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texts: list[str],
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model: str = "voyage-3",
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api_key: str | None = None,
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embedding_dim: int | None = None,
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input_type: str | None = None,
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truncation: bool | None = True,
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context: str | None = None,
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) -> np.ndarray:
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"""Generate embeddings for a list of texts using VoyageAI's API.
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Args:
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texts: List of texts to embed.
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model: The VoyageAI embedding model to use. Options include:
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- "voyage-3": General purpose (1024 dims, 32K context)
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- "voyage-3-lite": Lightweight (512 dims, 32K context)
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- "voyage-3-large": Highest accuracy (1024 dims, 32K context)
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- "voyage-code-3": Code optimized (1024 dims, 32K context)
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- "voyage-law-2": Legal documents (1024 dims, 16K context)
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- "voyage-finance-2": Finance (1024 dims, 32K context)
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api_key: Optional VoyageAI API key. If None, falls back to the
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``VOYAGE_API_KEY`` environment variable (the name VoyageAI's own
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SDK uses), then to ``VOYAGEAI_API_KEY`` for backward compatibility.
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embedding_dim: Optional Matryoshka output dimension. Only honored by
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models that support dimension reduction (e.g. voyage-3-large);
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ignored otherwise. The decorator default is 1024 to match
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``voyage-3``; if you select ``voyage-3-lite`` (512 dims) override
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``EMBEDDING_DIM`` accordingly so the vector store size matches.
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input_type: Optional input type hint for the model. Options:
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- "query": For search queries
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- "document": For documents to be indexed
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- None: Let the model decide (default)
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truncation: Whether the API should truncate texts that exceed the model's
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token limit. Defaults to True (matches the VoyageAI SDK default).
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context: Optional LightRAG embedding context. When ``input_type`` is not
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set, "query" maps to ``input_type="query"`` and "document" maps to
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``input_type="document"``.
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Returns:
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A numpy array of embeddings, one per input text.
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Raises:
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VoyageAIError: If the API call fails or returns invalid data.
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"""
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if not api_key:
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api_key = os.environ.get("VOYAGE_API_KEY") or os.environ.get("VOYAGEAI_API_KEY")
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if not api_key:
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logger.error(
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"VoyageAI API key not provided and neither VOYAGE_API_KEY nor "
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"VOYAGEAI_API_KEY environment variable is set"
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)
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raise ValueError(
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"VoyageAI API key is required: pass api_key, or set the "
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"VOYAGE_API_KEY (preferred) or VOYAGEAI_API_KEY environment variable"
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)
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if input_type is None and context in {"query", "document"}:
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input_type = context
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try:
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client = voyageai.AsyncClient(api_key=api_key)
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total_chars = sum(len(t) for t in texts)
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avg_chars = total_chars / len(texts) if texts else 0
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logger.debug(
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f"VoyageAI embedding request: {len(texts)} texts, "
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f"total_chars={total_chars}, avg_chars={avg_chars:.0f}, model={model}, "
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f"input_type={input_type}"
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)
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# Prepare API call parameters
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embed_params = dict(
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texts=texts,
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model=model,
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# Optional parameters -- if None, voyageai client uses defaults
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output_dimension=embedding_dim,
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truncation=truncation,
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input_type=input_type,
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)
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# Make API call with timing
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result = await client.embed(**embed_params)
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if not result.embeddings:
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err_msg = "VoyageAI API returned empty embeddings"
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logger.error(err_msg)
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raise VoyageAIError(err_msg)
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if len(result.embeddings) != len(texts):
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err_msg = f"VoyageAI API returned {len(result.embeddings)} embeddings for {len(texts)} texts"
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logger.error(err_msg)
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raise VoyageAIError(err_msg)
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# Convert to numpy array with timing
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embeddings = np.array(result.embeddings, dtype=np.float32)
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logger.debug(f"VoyageAI embeddings generated: shape {embeddings.shape}")
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return embeddings
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except Exception as e:
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logger.error(f"VoyageAI embedding error: {e}")
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raise
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# Optional: a helper function to get available embedding models
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def get_available_embedding_models() -> dict[str, dict]:
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"""
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Returns a dictionary of available Voyage AI embedding models and their properties.
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"""
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return {
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"voyage-3-large": {
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"context_length": 32000,
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"dimension": 1024,
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"description": "Best general-purpose and multilingual",
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},
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"voyage-3": {
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"context_length": 32000,
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"dimension": 1024,
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"description": "General-purpose and multilingual",
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},
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"voyage-3-lite": {
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"context_length": 32000,
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"dimension": 512,
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"description": "Optimized for latency and cost",
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},
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"voyage-code-3": {
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"context_length": 32000,
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"dimension": 1024,
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"description": "Optimized for code",
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},
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"voyage-finance-2": {
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"context_length": 32000,
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"dimension": 1024,
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"description": "Optimized for finance",
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},
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"voyage-law-2": {
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"context_length": 16000,
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"dimension": 1024,
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"description": "Optimized for legal",
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},
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"voyage-multimodal-3": {
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"context_length": 32000,
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"dimension": 1024,
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"description": "Multimodal text and images",
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},
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}
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