68f663f2f4
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
184 lines
7.0 KiB
Python
184 lines
7.0 KiB
Python
import os
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import pipmaster as pm # Pipmaster for dynamic library install
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# install specific modules
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if not pm.is_installed("aiohttp"):
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pm.install("aiohttp")
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if not pm.is_installed("tenacity"):
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pm.install("tenacity")
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import numpy as np
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import base64
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import aiohttp
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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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async def fetch_data(url, headers, data):
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async with aiohttp.ClientSession() as session:
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async with session.post(url, headers=headers, json=data) as response:
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if response.status != 200:
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error_text = await response.text()
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# Check if the error response is HTML (common for 502, 503, etc.)
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content_type = response.headers.get("content-type", "").lower()
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is_html_error = (
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error_text.strip().startswith("<!DOCTYPE html>")
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or "text/html" in content_type
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)
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if is_html_error:
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# Provide clean, user-friendly error messages for HTML error pages
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if response.status == 502:
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clean_error = "Bad Gateway (502) - Jina AI service temporarily unavailable. Please try again in a few minutes."
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elif response.status == 503:
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clean_error = "Service Unavailable (503) - Jina AI service is temporarily overloaded. Please try again later."
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elif response.status == 504:
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clean_error = "Gateway Timeout (504) - Jina AI service request timed out. Please try again."
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else:
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clean_error = f"HTTP {response.status} - Jina AI service error. Please try again later."
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else:
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# Use original error text if it's not HTML
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clean_error = error_text
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logger.error(f"Jina API error {response.status}: {clean_error}")
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raise aiohttp.ClientResponseError(
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request_info=response.request_info,
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history=response.history,
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status=response.status,
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message=f"Jina API error: {clean_error}",
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)
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response_json = await response.json()
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data_list = response_json.get("data", [])
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return data_list
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@wrap_embedding_func_with_attrs(
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embedding_dim=2048,
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max_token_size=8192,
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model_name="jina-embeddings-v4",
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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=(
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retry_if_exception_type(aiohttp.ClientError)
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| retry_if_exception_type(aiohttp.ClientResponseError)
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),
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)
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async def jina_embed(
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texts: list[str],
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model: str = "jina-embeddings-v4",
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embedding_dim: int = 2048,
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late_chunking: bool = False,
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base_url: str = None,
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api_key: str = None,
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context: str | None = None,
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task: str | None = None,
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) -> np.ndarray:
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"""Generate embeddings for a list of texts using Jina AI's API.
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Args:
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texts: List of texts to embed.
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model: The Jina embedding model to use (default: jina-embeddings-v4).
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Supported models: jina-embeddings-v3, jina-embeddings-v4, etc.
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embedding_dim: The embedding dimensions (default: 2048 for jina-embeddings-v4).
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**IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper.
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Do NOT manually pass this parameter when calling the function directly.
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The dimension is controlled by the @wrap_embedding_func_with_attrs decorator.
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Manually passing a different value will trigger a warning and be ignored.
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When provided (by EmbeddingFunc), it will be passed to the Jina API for dimension reduction.
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late_chunking: Whether to use late chunking.
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base_url: Optional base URL for the Jina API.
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api_key: Optional Jina API key. If None, uses the JINA_API_KEY environment variable.
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context: The embedding context - "query" for search queries, "document" for indexed content.
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**IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper
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when supports_asymmetric=True. When ``task`` is left at its default of None,
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``context`` drives the task selection.
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task: Embedding task mode. Default is None so that ``context`` (when present)
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picks the right Jina task:
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- "retrieval.query" for context="query"
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- "retrieval.passage" for context="document"
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- "text-matching" otherwise (true backward-compatible default)
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Any explicit non-None task value overrides context-based selection.
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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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aiohttp.ClientError: If there is a connection error with the Jina API.
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aiohttp.ClientResponseError: If the Jina API returns an error response.
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"""
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if api_key:
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os.environ["JINA_API_KEY"] = api_key
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if "JINA_API_KEY" not in os.environ:
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raise ValueError("JINA_API_KEY environment variable is required")
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url = base_url or "https://api.jina.ai/v1/embeddings"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {os.environ['JINA_API_KEY']}",
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}
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# Determine task based on context if not explicitly provided
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if task is None:
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if context == "query":
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task = "retrieval.query"
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elif context == "document":
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task = "retrieval.passage"
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else:
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task = "text-matching" # Default for backward compatibility
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data = {
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"model": model,
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"task": task,
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"dimensions": embedding_dim,
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"embedding_type": "base64",
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"input": texts,
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}
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# Only add optional parameters if they have non-default values
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if late_chunking:
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data["late_chunking"] = late_chunking
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logger.debug(
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f"Jina embedding request: {len(texts)} texts, dimensions: {embedding_dim}"
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)
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try:
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data_list = await fetch_data(url, headers, data)
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if not data_list:
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logger.error("Jina API returned empty data list")
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raise ValueError("Jina API returned empty data list")
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if len(data_list) != len(texts):
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logger.error(
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f"Jina API returned {len(data_list)} embeddings for {len(texts)} texts"
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)
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raise ValueError(
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f"Jina API returned {len(data_list)} embeddings for {len(texts)} texts"
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)
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embeddings = np.array(
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[
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np.frombuffer(base64.b64decode(dp["embedding"]), dtype=np.float32)
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for dp in data_list
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]
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)
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logger.debug(f"Jina 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"Jina embedding error: {e}")
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raise
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