import asyncio import os from tqdm.asyncio import tqdm as tqdm_async from dataclasses import asdict, dataclass, field from datetime import datetime from functools import partial from typing import Type, cast, List, Dict, Any, Optional from .llm import ( gpt_4o_mini_complete, gpt_oss_120b_complete, local_sentence_embedding, openai_cloud_embedding, is_local_model, is_cloud_model, ) # Try to import the new functions, but don't fail if they don't exist try: from .llm import get_embedding_func_for_model, EMBEDDING_CONFIGS HAS_EMBEDDING_CONFIGS = True except ImportError: HAS_EMBEDDING_CONFIGS = False print("Warning: get_embedding_func_for_model not found in llm.py") print("Please update your llm.py file with the new version") from .operate import ( chunking_by_token_size, extract_entities, kg_query, ) from .indexing import DatabaseSchemaBuilder from .utils import ( EmbeddingFunc, compute_mdhash_id, limit_async_func_call, convert_response_to_json, logger, set_logger, ) from .base import ( BaseGraphStorage, BaseKVStorage, BaseVectorStorage, StorageNameSpace, QueryParam, ) from .storage import ( JsonKVStorage, NanoVectorDBStorage, NetworkXStorage, ) async def abuild_from_excel_files(self, excel_paths: List[str]) -> Dict[str, Any]: """Build KG from Excel files""" from .indexing import ExcelSchemaBuilder builder = ExcelSchemaBuilder( graph_storage=self.chunk_entity_relation_graph, entities_vdb=self.entities_vdb, relationships_vdb=self.relationships_vdb ) result = await builder.build_from_excel_files(excel_paths) await self._insert_done() logger.info(f"Excel KG build completed: {result}") return result def build_from_excel_files(self, excel_paths: List[str]): """Sync wrapper""" loop = always_get_an_event_loop() return loop.run_until_complete(self.abuild_from_excel_files(excel_paths)) def lazy_external_import(module_name: str, class_name: str): """Lazily import a class from an external module based on the package of the caller.""" import inspect caller_frame = inspect.currentframe().f_back module = inspect.getmodule(caller_frame) package = module.__package__ if module else None def import_class(*args, **kwargs): import importlib module = importlib.import_module(module_name, package=package) cls = getattr(module, class_name) return cls(*args, **kwargs) return import_class Neo4JStorage = lazy_external_import(".kg.neo4j_impl", "Neo4JStorage") OracleKVStorage = lazy_external_import(".kg.oracle_impl", "OracleKVStorage") OracleGraphStorage = lazy_external_import(".kg.oracle_impl", "OracleGraphStorage") OracleVectorDBStorage = lazy_external_import(".kg.oracle_impl", "OracleVectorDBStorage") MilvusVectorDBStorge = lazy_external_import(".kg.milvus_impl", "MilvusVectorDBStorge") MongoKVStorage = lazy_external_import(".kg.mongo_impl", "MongoKVStorage") ChromaVectorDBStorage = lazy_external_import(".kg.chroma_impl", "ChromaVectorDBStorage") TiDBKVStorage = lazy_external_import(".kg.tidb_impl", "TiDBKVStorage") TiDBVectorDBStorage = lazy_external_import(".kg.tidb_impl", "TiDBVectorDBStorage") AGEStorage = lazy_external_import(".kg.age_impl", "AGEStorage") def always_get_an_event_loop() -> asyncio.AbstractEventLoop: """ Ensure that there is always an event loop available. This function tries to get the current event loop. If the current event loop is closed or does not exist, it creates a new event loop and sets it as the current event loop. Returns: asyncio.AbstractEventLoop: The current or newly created event loop. """ try: current_loop = asyncio.get_event_loop() if current_loop.is_closed(): raise RuntimeError("Event loop is closed.") return current_loop except RuntimeError: logger.info("Creating a new event loop in main thread.") new_loop = asyncio.new_event_loop() asyncio.set_event_loop(new_loop) return new_loop @dataclass class QAFD_RAG: working_dir: str = field( default_factory=lambda: f"./QAFD_RAG_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}" ) embedding_cache_config: dict = field( default_factory=lambda: { "enabled": False, "similarity_threshold": 0.95, "use_llm_check": False, } ) kv_storage: str = field(default="JsonKVStorage") vector_storage: str = field(default="NanoVectorDBStorage") graph_storage: str = field(default="NetworkXStorage") current_log_level = logger.level log_level: str = field(default=current_log_level) # Chunking parameters chunk_token_size: int = 1200 chunk_overlap_token_size: int = 100 tiktoken_model_name: str = "gpt-4o-mini" # Entity extraction parameters entity_extract_max_gleaning: int = 1 entity_summary_to_max_tokens: int = 5000 # Node embedding algorithm node_embedding_algorithm: str = "node2vec" node2vec_params: dict = field( default_factory=lambda: { "dimensions": 1536, "num_walks": 10, "walk_length": 40, "window_size": 2, "iterations": 3, "random_seed": 3, } ) # ============================================================================ # EMBEDDING CONFIGURATION (NEW: Configurable embedding models) # ============================================================================ # Embedding model key (from EMBEDDING_CONFIGS in llm.py) embedding_model_key: Optional[str] = None # Embedding function and dimensions (auto-configured from embedding_model_key) embedding_func: Optional[EmbeddingFunc] = None embedding_dim: Optional[int] = None # Embedding parameters embedding_batch_num: int = 32 embedding_func_max_async: int = 16 max_embed_tokens: int = 8192 # Maximum tokens per embedding request # ============================================================================ # LLM CONFIGURATION # ============================================================================ llm_model_func: callable = gpt_4o_mini_complete llm_model_name: str = "gpt-4o-mini" llm_model_max_token_size: int = 32768 llm_model_max_async: int = 16 llm_model_kwargs: dict = field(default_factory=dict) # Vector DB storage parameters vector_db_storage_cls_kwargs: dict = field(default_factory=dict) enable_llm_cache: bool = True # Additional parameters addon_params: dict = field(default_factory=dict) convert_response_to_json_func: callable = convert_response_to_json def __post_init__(self): log_file = os.path.join("QAFD_RAG.log") set_logger(log_file) logger.setLevel(self.log_level) logger.info(f"Logger initialized for working directory: {self.working_dir}") # ============================================================================ # EMBEDDING MODEL CONFIGURATION LOGIC (NEW) # ============================================================================ if HAS_EMBEDDING_CONFIGS: # Priority 1: Use explicitly passed embedding_model_key if self.embedding_model_key: logger.info(f"[Embedding Config] Using explicit embedding_model_key: {self.embedding_model_key}") embedding_func, embedding_dim, emb_config = get_embedding_func_for_model(self.embedding_model_key) self.embedding_func = embedding_func self.embedding_dim = embedding_dim logger.info(f"[Embedding Config] {emb_config['description']}") logger.info(f"[Embedding Config] Dimensions: {embedding_dim}, Max tokens: {emb_config['max_tokens']}") # Priority 2: Check environment variable EMBEDDING_MODEL_KEY elif os.environ.get("EMBEDDING_MODEL_KEY"): embedding_key = os.environ.get("EMBEDDING_MODEL_KEY") logger.info(f"[Embedding Config] Using env EMBEDDING_MODEL_KEY: {embedding_key}") embedding_func, embedding_dim, emb_config = get_embedding_func_for_model(embedding_key) self.embedding_func = embedding_func self.embedding_dim = embedding_dim self.embedding_model_key = embedding_key logger.info(f"[Embedding Config] {emb_config['description']}") # Priority 3: Check environment variable USE_OPENAI_EMBEDDINGS (legacy) elif os.environ.get("USE_OPENAI_EMBEDDINGS") == "1": logger.info(f"[Embedding Config] Using legacy USE_OPENAI_EMBEDDINGS=1") self.embedding_func = openai_cloud_embedding self.embedding_dim = 1024 self.embedding_model_key = "openai-large" logger.info(f"[Embedding Config] OpenAI cloud embeddings (1024-dim)") elif os.environ.get("USE_OPENAI_EMBEDDINGS") == "0": logger.info(f"[Embedding Config] Using legacy USE_OPENAI_EMBEDDINGS=0") self.embedding_func = local_sentence_embedding self.embedding_dim = 1024 self.embedding_model_key = "jina-v3" logger.info(f"[Embedding Config] Local Jina v3 embeddings (1024-dim)") # Priority 4: Auto-detect based on LLM model name elif self.llm_model_name: model_name = self.llm_model_name.lower() if is_local_model(model_name): logger.info(f"[Embedding Config] Local LLM detected ({model_name}) → using local embeddings") self.embedding_func = local_sentence_embedding self.embedding_dim = 1024 self.embedding_model_key = "jina-v3" else: logger.info(f"[Embedding Config] Cloud LLM detected ({model_name}) → using OpenAI embeddings") self.embedding_func = openai_cloud_embedding self.embedding_dim = 1024 self.embedding_model_key = "openai-large" # Priority 5: Default to local Jina v3 else: logger.info(f"[Embedding Config] No configuration found → defaulting to Jina v3 (local)") self.embedding_func = local_sentence_embedding self.embedding_dim = 1024 self.embedding_model_key = "jina-v3" else: # Fallback to old behavior if new functions not available logger.warning("[Embedding Config] Using legacy embedding configuration") env_embedding_setting = os.environ.get("USE_OPENAI_EMBEDDINGS") if env_embedding_setting == "0": self.embedding_func = local_sentence_embedding self.embedding_dim = 1024 logger.info(f"[Embedding Override] Using local embeddings (1024-dim) - forced by USE_OPENAI_EMBEDDINGS=0") elif env_embedding_setting == "1": self.embedding_func = openai_cloud_embedding self.embedding_dim = 1024 logger.info(f"[Embedding Override] Using OpenAI embeddings (1024-dim) - forced by USE_OPENAI_EMBEDDINGS=1") elif is_local_model(self.llm_model_name.lower() if self.llm_model_name else ""): self.embedding_func = local_sentence_embedding self.embedding_dim = 1024 logger.info(f"[Embedding] Local model detected → using local embeddings (1024-dim)") else: self.embedding_func = openai_cloud_embedding self.embedding_dim = 1024 logger.info(f"[Embedding] Cloud model detected → using OpenAI embeddings (1024-dim)") # Validate embedding function was set if self.embedding_func is None: logger.error("[Embedding Config] Failed to configure embedding function!") raise ValueError("Embedding function not configured") if self.embedding_dim is None: self.embedding_dim = 1024 # Default logger.warning(f"[Embedding Config] embedding_dim not set, defaulting to 1024") logger.info(f"[Embedding Config] ✅ Final: {self.embedding_model_key if self.embedding_model_key else 'auto'} ({self.embedding_dim}-dim)") # ============================================================================ # STORAGE INITIALIZATION # ============================================================================ self.key_string_value_json_storage_cls: Type[BaseKVStorage] = ( self._get_storage_class()[self.kv_storage] ) self.vector_db_storage_cls: Type[BaseVectorStorage] = self._get_storage_class()[ self.vector_storage ] self.graph_storage_cls: Type[BaseGraphStorage] = self._get_storage_class()[ self.graph_storage ] if not os.path.exists(self.working_dir): logger.info(f"Creating working directory {self.working_dir}") os.makedirs(self.working_dir) self.llm_response_cache = ( self.key_string_value_json_storage_cls( namespace="llm_response_cache", global_config=asdict(self), embedding_func=None, ) if self.enable_llm_cache else None ) # Limit async calls for embedding function self.embedding_func = limit_async_func_call(self.embedding_func_max_async)( self.embedding_func ) # Initialize storage components with embedding function self.full_docs = self.key_string_value_json_storage_cls( namespace="full_docs", global_config=asdict(self), embedding_func=self.embedding_func, ) self.text_chunks = self.key_string_value_json_storage_cls( namespace="text_chunks", global_config=asdict(self), embedding_func=self.embedding_func, ) self.chunk_entity_relation_graph = self.graph_storage_cls( namespace="chunk_entity_relation", global_config=asdict(self), embedding_func=self.embedding_func, ) # Vector databases for entities, relationships, and chunks self.entities_vdb = self.vector_db_storage_cls( namespace="entities", global_config=asdict(self), embedding_func=self.embedding_func, meta_fields={"entity_name"}, ) self.relationships_vdb = self.vector_db_storage_cls( namespace="relationships", global_config=asdict(self), embedding_func=self.embedding_func, meta_fields={"src_id", "tgt_id"}, ) self.chunks_vdb = self.vector_db_storage_cls( namespace="chunks", global_config=asdict(self), embedding_func=self.embedding_func, ) # Configure LLM function self.llm_model_func = limit_async_func_call(self.llm_model_max_async)( partial( self.llm_model_func, hashing_kv=self.llm_response_cache if self.llm_response_cache and hasattr(self.llm_response_cache, "global_config") else self.key_string_value_json_storage_cls( global_config=asdict(self), ), **self.llm_model_kwargs, ) ) # Initialize database schema builder self.schema_builder = DatabaseSchemaBuilder( graph_storage=self.chunk_entity_relation_graph, entities_vdb=self.entities_vdb, relationships_vdb=self.relationships_vdb, llm_model_func=self.llm_model_func ) def _get_storage_class(self) -> dict[str, Type]: return { # Key-Value Storage "JsonKVStorage": JsonKVStorage, "OracleKVStorage": OracleKVStorage, "MongoKVStorage": MongoKVStorage, "TiDBKVStorage": TiDBKVStorage, # Vector Storage "NanoVectorDBStorage": NanoVectorDBStorage, "OracleVectorDBStorage": OracleVectorDBStorage, "MilvusVectorDBStorge": MilvusVectorDBStorge, "ChromaVectorDBStorage": ChromaVectorDBStorage, "TiDBVectorDBStorage": TiDBVectorDBStorage, # Graph Storage "NetworkXStorage": NetworkXStorage, "Neo4JStorage": Neo4JStorage, "OracleGraphStorage": OracleGraphStorage, "AGEStorage": AGEStorage, } def insert(self, string_or_strings, addon_params=None): loop = always_get_an_event_loop() return loop.run_until_complete(self.ainsert(string_or_strings, addon_params)) async def ainsert(self, string_or_strings, addon_params=None): update_storage = False try: if isinstance(string_or_strings, str): string_or_strings = [string_or_strings] new_docs = { compute_mdhash_id(c.strip(), prefix="doc-"): {"content": c.strip()} for c in string_or_strings } _add_doc_keys = await self.full_docs.filter_keys(list(new_docs.keys())) new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys} if not len(new_docs): logger.warning("All docs are already in the storage") return update_storage = True logger.info(f"[New Docs] inserting {len(new_docs)} docs") inserting_chunks = {} for doc_key, doc in tqdm_async( new_docs.items(), desc="Chunking documents", unit="doc" ): chunks = { compute_mdhash_id(dp["content"], prefix="chunk-"): { **dp, "full_doc_id": doc_key, } for dp in chunking_by_token_size( doc["content"], overlap_token_size=self.chunk_overlap_token_size, max_token_size=self.chunk_token_size, tiktoken_model=self.tiktoken_model_name, ) } inserting_chunks.update(chunks) _add_chunk_keys = await self.text_chunks.filter_keys( list(inserting_chunks.keys()) ) inserting_chunks = { k: v for k, v in inserting_chunks.items() if k in _add_chunk_keys } if not len(inserting_chunks): logger.warning("All chunks are already in the storage") return logger.info(f"[New Chunks] inserting {len(inserting_chunks)} chunks") await self.chunks_vdb.upsert(inserting_chunks) logger.info("[Entity Extraction]...") # Create a temporary config with custom addon_params if provided temp_config = asdict(self) if addon_params is not None: temp_config["addon_params"] = addon_params maybe_new_kg = await extract_entities( inserting_chunks, knowledge_graph_inst=self.chunk_entity_relation_graph, entity_vdb=self.entities_vdb, relationships_vdb=self.relationships_vdb, global_config=temp_config, ) if maybe_new_kg is None: logger.warning("No new entities and relationships found") return self.chunk_entity_relation_graph = maybe_new_kg await self.full_docs.upsert(new_docs) await self.text_chunks.upsert(inserting_chunks) finally: if update_storage: await self._insert_done() async def _insert_done(self): tasks = [] for storage_inst in [ self.full_docs, self.text_chunks, self.llm_response_cache, self.entities_vdb, self.relationships_vdb, self.chunks_vdb, self.chunk_entity_relation_graph, ]: if storage_inst is None: continue tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback()) await asyncio.gather(*tasks) def insert_custom_kg(self, custom_kg: dict): loop = always_get_an_event_loop() return loop.run_until_complete(self.ainsert_custom_kg(custom_kg)) async def ainsert_custom_kg(self, custom_kg: dict): update_storage = False try: all_chunks_data = {} chunk_to_source_map = {} for chunk_data in custom_kg.get("chunks", []): chunk_content = chunk_data["content"] source_id = chunk_data["source_id"] chunk_id = compute_mdhash_id(chunk_content.strip(), prefix="chunk-") chunk_entry = {"content": chunk_content.strip(), "source_id": source_id} all_chunks_data[chunk_id] = chunk_entry chunk_to_source_map[source_id] = chunk_id update_storage = True if self.chunks_vdb is not None and all_chunks_data: await self.chunks_vdb.upsert(all_chunks_data) if self.text_chunks is not None and all_chunks_data: await self.text_chunks.upsert(all_chunks_data) all_entities_data = [] for entity_data in custom_kg.get("entities", []): entity_name = f'"{entity_data["entity_name"].lower()}"' entity_type = entity_data.get("entity_type", "UNKNOWN") description = entity_data.get("description", "No description provided") source_chunk_id = entity_data.get("source_id", "UNKNOWN") source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN") if source_id == "UNKNOWN": logger.warning( f"Entity '{entity_name}' has an UNKNOWN source_id. Please check the source mapping." ) node_data = { "entity_type": entity_type, "description": description, "source_id": source_id, } await self.chunk_entity_relation_graph.upsert_node( entity_name, node_data=node_data ) node_data["entity_name"] = entity_name all_entities_data.append(node_data) update_storage = True all_relationships_data = [] for relationship_data in custom_kg.get("relationships", []): src_id = f'"{relationship_data["src_id"].lower()}"' tgt_id = f'"{relationship_data["tgt_id"].lower()}"' description = relationship_data["description"] keywords = relationship_data["keywords"] weight = relationship_data.get("weight", 1.0) source_chunk_id = relationship_data.get("source_id", "UNKNOWN") source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN") if source_id == "UNKNOWN": logger.warning( f"Relationship from '{src_id}' to '{tgt_id}' has an UNKNOWN source_id. Please check the source mapping." ) for need_insert_id in [src_id, tgt_id]: if not ( await self.chunk_entity_relation_graph.has_node(need_insert_id) ): await self.chunk_entity_relation_graph.upsert_node( need_insert_id, node_data={ "source_id": source_id, "description": "UNKNOWN", "entity_type": "UNKNOWN", }, ) await self.chunk_entity_relation_graph.upsert_edge( src_id, tgt_id, edge_data={ "weight": weight, "description": description, "keywords": keywords, "source_id": source_id, }, ) edge_data = { "src_id": src_id, "tgt_id": tgt_id, "description": description, "keywords": keywords, } all_relationships_data.append(edge_data) update_storage = True if self.entities_vdb is not None: data_for_vdb = { compute_mdhash_id(dp["entity_name"], prefix="ent-"): { "content": dp["entity_name"] + dp["description"], "entity_name": dp["entity_name"], } for dp in all_entities_data } await self.entities_vdb.upsert(data_for_vdb) if self.relationships_vdb is not None: data_for_vdb = { compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): { "src_id": dp["src_id"], "tgt_id": dp["tgt_id"], "content": dp["keywords"] + dp["src_id"] + dp["tgt_id"] + dp["description"], } for dp in all_relationships_data } await self.relationships_vdb.upsert(data_for_vdb) finally: if update_storage: await self._insert_done() def query(self, query: str, param: QueryParam = QueryParam()): loop = always_get_an_event_loop() return loop.run_until_complete(self.aquery(query, param)) async def aquery(self, query: str, param: QueryParam = QueryParam()): if param.mode in ["local", "global", "hybrid"]: response = await kg_query( query, self.chunk_entity_relation_graph, self.entities_vdb, self.relationships_vdb, self.text_chunks, param, asdict(self), hashing_kv=self.llm_response_cache if self.llm_response_cache and hasattr(self.llm_response_cache, "global_config") else self.key_string_value_json_storage_cls( global_config=asdict(self), ), ) else: raise ValueError(f"Unknown mode {param.mode}") await self._query_done() return response async def _query_done(self): tasks = [] for storage_inst in [self.llm_response_cache]: if storage_inst is None: continue tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback()) await asyncio.gather(*tasks) def delete_by_entity(self, entity_name: str): loop = always_get_an_event_loop() return loop.run_until_complete(self.adelete_by_entity(entity_name)) async def adelete_by_entity(self, entity_name: str): entity_name = f'"{entity_name.lower()}"' try: await self.entities_vdb.delete_entity(entity_name) await self.relationships_vdb.delete_relation(entity_name) await self.chunk_entity_relation_graph.delete_node(entity_name) logger.info( f"Entity '{entity_name}' and its relationships have been deleted." ) await self._delete_by_entity_done() except Exception as e: logger.error(f"Error while deleting entity '{entity_name}': {e}") async def _delete_by_entity_done(self): tasks = [] for storage_inst in [ self.entities_vdb, self.relationships_vdb, self.chunk_entity_relation_graph, ]: if storage_inst is None: continue tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback()) await asyncio.gather(*tasks) def build_from_database_schema(self, schema_file_path: str, metadata_file_path: str = None, language: str = "English"): """ Build knowledge graph from database schema JSON file This method manually constructs the knowledge graph from a JSON schema file, avoiding the chunking issues that can cause LLM errors. It follows the approach used in CoFD for database schema processing. Args: schema_file_path: Path to the JSON schema file metadata_file_path: Optional path to metadata file language: Output language for descriptions Returns: Dictionary containing build statistics """ loop = always_get_an_event_loop() return loop.run_until_complete(self.abuild_from_database_schema( schema_file_path, metadata_file_path, language )) async def abuild_from_database_schema(self, schema_file_path: str, metadata_file_path: str = None, language: str = "English"): """ Async version of build_from_database_schema """ try: # Use the schema builder to construct the knowledge graph result = await self.schema_builder.build_from_json_schema( schema_file_path, metadata_file_path, language ) # Update storage after building await self._insert_done() logger.info(f"Database schema build completed: {result}") return result except Exception as e: logger.error(f"Error building from database schema: {e}") raise