280 lines
11 KiB
Python
280 lines
11 KiB
Python
"""
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项目配置文件
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从.env文件读取配置
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"""
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import os
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from dotenv import load_dotenv
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from contextvars import ContextVar
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from typing import Optional, Dict, Any
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# 加载.env文件
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load_dotenv()
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# ==================== 大模型配置 ====================
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LLM_CONFIG = {
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# "model": "Qwen3.5-35B-A3B",
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"model": "46-qwen3.5-35B",
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# "model": "Qwen3-14B",
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"base_url": "http://192.168.0.46:59800/v1",
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"api_key": "gpustack_dee9ca823290886c_5edfc86aeeeceb1e9ee5162941cb2cb5",
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"temperature": float("0.1"),
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"max_tokens": int("9216"),
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"timeout": int("30")
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}
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# ==================== 嵌入模型配置 ====================
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EMBEDDING_CONFIG = {
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"model": "bge-m3",
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"base_url": "http://192.168.0.46:59700/v1/embeddings",
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"api_key": "gpustack_dee9ca823290886c_5edfc86aeeeceb1e9ee5162941cb2cb5",
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"base_url_v2" : "http://192.168.0.46:59700/v1",
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}
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# ==================== 线程安全的请求上下文配置 ====================
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# 使用 contextvars 存储每个请求的用户配置，确保多用户并发时不会相互干扰
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_request_user_config: ContextVar[Optional[Dict[str, str]]] = ContextVar('request_user_config', default=None)
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# RAG配置的默认值
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_RAG_CONFIG_DEFAULT = {
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"search_endpoint": "http://192.168.0.46:11000/api/v1/knowledge/search/",
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"x-user-id": "1",
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"x-user-name": "testuser",
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"x-role": "admin",
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"kb-id": None,
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"file-id": None
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}
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class DynamicRAGConfig(dict):
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"""
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动态RAG配置类，支持线程安全的请求级配置
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当访问用户相关配置时，自动从当前请求上下文读取
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其他配置项直接返回默认值
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兼容字典的所有操作方式（如 get(), [], in 等）
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"""
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def __getitem__(self, key: str):
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# 如果是用户相关配置或知识库参数，从当前请求上下文读取
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if key in ["x-user-id", "x-user-name", "x-role", "kb-id", "file-id"]:
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request_config = _request_user_config.get()
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if request_config and key in request_config:
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return request_config[key]
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# 如果没有设置，返回默认值
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return _RAG_CONFIG_DEFAULT[key]
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# 其他配置项直接返回默认值
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return _RAG_CONFIG_DEFAULT.get(key)
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def get(self, key: str, default=None):
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"""支持 get() 方法"""
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try:
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return self[key]
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except KeyError:
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return default
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def __contains__(self, key: str):
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"""支持 in 操作符"""
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return key in _RAG_CONFIG_DEFAULT
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def keys(self):
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"""返回所有键"""
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return _RAG_CONFIG_DEFAULT.keys()
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def values(self):
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"""返回所有值（动态读取用户配置）"""
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return [self[key] for key in _RAG_CONFIG_DEFAULT.keys()]
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def items(self):
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"""返回所有键值对（动态读取用户配置）"""
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return [(key, self[key]) for key in _RAG_CONFIG_DEFAULT.keys()]
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# ==================== RAG服务配置 ====================
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# 使用动态配置类，支持多用户并发时自动读取各自的配置
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RAG_CONFIG = DynamicRAGConfig()
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# ==================== Wiki pointer layer config ====================
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_HTKNOW_BASE_URL_DEFAULT = (
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_RAG_CONFIG_DEFAULT.get("search_endpoint", "").split("/api/v1/knowledge/search")[0].rstrip("/")
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or "http://htknow:8080"
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)
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WIKI_CONFIG = {
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"enabled": os.getenv("WIKI_ENABLED", "true").lower() == "true",
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"htknow_base_url": os.getenv("HTKNOW_BASE_URL", _HTKNOW_BASE_URL_DEFAULT).rstrip("/"),
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"default_user_id": os.getenv("WIKI_DEFAULT_USER_ID", "1"),
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"default_user_name": os.getenv("WIKI_DEFAULT_USER_NAME", "testuser"),
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"default_role": os.getenv("WIKI_DEFAULT_ROLE", "admin"),
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"default_sync_limit": int(os.getenv("WIKI_DEFAULT_SYNC_LIMIT", "20")),
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"max_slices_per_file": int(os.getenv("WIKI_MAX_SLICES_PER_FILE", "300")),
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"max_pages_per_file": int(os.getenv("WIKI_MAX_PAGES_PER_FILE", "25")),
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"llm_summary_enabled": os.getenv("WIKI_LLM_SUMMARY_ENABLED", "true").lower() == "true",
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"embedding_enabled": os.getenv("WIKI_EMBEDDING_ENABLED", "false").lower() == "true",
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"embedding_batch_size": int(os.getenv("WIKI_EMBEDDING_BATCH_SIZE", "16")),
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"embedding_vector_dim": int(os.getenv("WIKI_EMBEDDING_VECTOR_DIM", "1024")),
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"embedding_text_max_chars": int(os.getenv("WIKI_EMBEDDING_TEXT_MAX_CHARS", "2200")),
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"vector_search_top_k": int(os.getenv("WIKI_VECTOR_SEARCH_TOP_K", "32")),
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"slice_text_search_top_k": int(os.getenv("WIKI_SLICE_TEXT_SEARCH_TOP_K", "32")),
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"page_search_top_k": int(os.getenv("WIKI_PAGE_SEARCH_TOP_K", "12")),
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"vector_min_score": float(os.getenv("WIKI_VECTOR_MIN_SCORE", "0.2")),
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"max_vector_candidates": int(os.getenv("WIKI_MAX_VECTOR_CANDIDATES", "50000")),
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"rrf_k": int(os.getenv("WIKI_RRF_K", "60")),
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"evidence_slices_per_page": int(os.getenv("WIKI_EVIDENCE_SLICES_PER_PAGE", "6")),
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"language": os.getenv("WIKI_LANGUAGE", "Chinese"),
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"extraction_granularity": os.getenv("WIKI_EXTRACTION_GRANULARITY", "standard"),
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"full_content_max_chars": int(os.getenv("WIKI_FULL_CONTENT_MAX_CHARS", "120000")),
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"citation_concurrency": int(os.getenv("WIKI_CITATION_CONCURRENCY", "3")),
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"reduce_concurrency": int(os.getenv("WIKI_REDUCE_CONCURRENCY", "3")),
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"llm_concurrency": int(os.getenv("WIKI_LLM_CONCURRENCY", "4")),
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"llm_cache_enabled": os.getenv("WIKI_LLM_CACHE_ENABLED", "true").lower() == "true",
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"llm_cache_ttl_seconds": int(os.getenv("WIKI_LLM_CACHE_TTL_SECONDS", str(7 * 24 * 3600))),
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"llm_retry_count": int(os.getenv("WIKI_LLM_RETRY_COUNT", "2")),
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"structured_template_enabled": os.getenv("WIKI_STRUCTURED_TEMPLATE_ENABLED", "true").lower() == "true",
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"stage_progress_enabled": os.getenv("WIKI_STAGE_PROGRESS_ENABLED", "true").lower() == "true",
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"finalize_enabled": os.getenv("WIKI_FINALIZE_ENABLED", "true").lower() == "true",
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"finalize_debounce_seconds": int(os.getenv("WIKI_FINALIZE_DEBOUNCE_SECONDS", "20")),
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"slug_lock_ttl_seconds": int(os.getenv("WIKI_SLUG_LOCK_TTL_SECONDS", "600")),
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"queue_enabled": os.getenv("WIKI_QUEUE_ENABLED", "true").lower() == "true",
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"queue_worker_concurrency": int(os.getenv("WIKI_QUEUE_WORKER_CONCURRENCY", "2")),
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"queue_per_kb_concurrency": int(os.getenv("WIKI_QUEUE_PER_KB_CONCURRENCY", "2")),
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"queue_claim_stale_seconds": int(os.getenv("WIKI_QUEUE_CLAIM_STALE_SECONDS", "1800")),
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"queue_max_retries": int(os.getenv("WIKI_QUEUE_MAX_RETRIES", "3")),
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}
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# ==================== Rerank 服务配置 ====================
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RERANK_CONFIG = {
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"enabled": os.getenv("RERANK_ENABLED", "true").lower() == "true",
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"endpoint": os.getenv("RERANK_ENDPOINT", "http://192.168.0.46:59600/v1/rerank"),
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"model": os.getenv("RERANK_MODEL", "bge-rerank"),
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"timeout": float(os.getenv("RERANK_TIMEOUT", "30")),
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"max_candidates": int(os.getenv("RERANK_MAX_CANDIDATES", "24")),
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"top_k": int(os.getenv("RERANK_TOP_K", "10")),
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}
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def set_request_user_config(x_user_id: Optional[str] = None,
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x_user_name: Optional[str] = None,
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x_role: Optional[str] = None,
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text_kb_id: Optional[str] = None,
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text_file_id: Optional[str] = None):
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"""
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设置当前请求的用户配置和知识库参数（线程安全）
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Args:
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x_user_id: 用户ID
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x_user_name: 用户名称
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x_role: 角色
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text_kb_id: 文本检索知识库ID
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text_file_id: 文本检索文件ID
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"""
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config = {}
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if x_user_id is not None:
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config["x-user-id"] = x_user_id
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if x_user_name is not None:
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config["x-user-name"] = x_user_name
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if x_role is not None:
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config["x-role"] = x_role
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# 仅当非空字符串时才设置，空字符串时不传入 RAG 接口
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if text_kb_id is not None and str(text_kb_id).strip():
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config["kb-id"] = str(text_kb_id).strip()
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if text_file_id is not None and str(text_file_id).strip():
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config["file-id"] = str(text_file_id).strip()
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_request_user_config.set(config if config else None)
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# ==================== 图谱检索服务配置 ====================
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GRAPH_SEARCH_CONFIG = {
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"search_endpoint": "http://192.168.0.46:59085/search_graph",
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"graph_timeout": float("30.0"),
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"default_top_k": int("10"),
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"operation_search_endpoint": "http://192.168.0.46:59085/operation_graph_search"
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}
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# ==================== 设备到系统检索服务配置 ====================
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DEVICE_SYSTEM_CONFIG = {
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"endpoint": "http://192.168.0.46:59085/device_to_system",
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"timeout": float("30.0"),
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"default_min_hops": int("2"),
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"default_max_hops": int("8"),
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"default_top_k": int("10")
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}
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# ==================== 图册检索服务配置 ====================
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ATLAS_CONFIG = {
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"endpoint": "http://192.168.0.46:59085/atlas_retrieval",
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"timeout": float("30.0"),
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"default_top_k": int("10")
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}
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# ==================== 工作流配置 ====================
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WORKFLOW_CONFIG = {
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"max_retry_count": int("2"), # 最大重试次数
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"image_clarity_threshold": float("0.7"), # 图片清晰度阈值
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"default_timeout": int("30") # 默认超时时间（秒）
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}
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# ==================== 服务器配置 ====================
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SERVER_CONFIG = {
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"host": "192.168.0.46",
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"port": "59088",
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"base_url": "http://192.168.0.46:59088"
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}
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# ==================== ASR服务配置 ====================
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ASR_CONFIG = {
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"api_base": "http://192.168.0.46:59900/v1",
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"model": "base",
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"language": "zh",
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"timeout": int("30")
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}
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# ==================== VLM服务配置 ====================
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VLM_CONFIG = {
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"api_base" : "http://192.168.0.46:59801/v1",
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"model" : "Qwen3-VL-8B"
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}
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# ==================== Neo4J配置 ====================
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NEO4J_CONFIG = {
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"uri": "neo4j://192.168.0.46:57687",
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"user": "neo4j",
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"password": "zdht123@"
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}
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# ==================== 知识库匹配配置 ====================
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KB_TREE_CONFIG = {
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"url": "http://192.168.0.46:11000/api/v1/knowledge/knowledge_base/tree"
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}
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# ==================== 维修反馈统计配置 ====================
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REPAIR_FEEDBACK_CONFIG = {
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"url": "http://192.168.0.46:22000",
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"email": "15088888888@163.com",
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"password": "11223344"
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}
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# ==================== 数据库配置 ====================
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POSTGRES_CONNECTION_STRING = "postgresql://postgres:password@192.168.0.46:5432/an_webui"
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#POSTGRES_CONNECTION_STRING = "postgresql+psycopg://postgres:password@192.168.0.46:5432/an_webui"
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POSTGRES_SQLALCHEMY_URL = "postgresql+psycopg://postgres:password@192.168.0.46:5432/an_webui"
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# ==================== 索引配置 ====================
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INDEX_CONFIG = {
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"NAME_PROPERTY" : "名称",
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"FULLTEXT_PROPERTY" :"fulltext",
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"EMBEDDING_PROPERTY" : "embedding",
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"SEARCH_LABEL" : "Searchable",
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"VECTOR_INDEX_NAME" : "global_searchable_embedding",
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"FULLTEXT_INDEX_NAME" : "global_searchable_content_search"
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}
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# ==================== sql表格字段与neo4j节点映射 ====================
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MAP_INS={
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"故障模式" : "fault",
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"系统" : "system_name",
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"设备" : "device_name",
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"舷号" : "ship_number",
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}
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# ==================== neo4j配置 ====================
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NEO4J_CONFIG = {
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"uri": "neo4j://192.168.0.46:57687",
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"user": "neo4j",
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"password": "zdht123@"
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}
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