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闫旭隆
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atlas_rag/retriever/lkg_retriever/tog.py
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469
atlas_rag/retriever/lkg_retriever/tog.py
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from neo4j import GraphDatabase
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import faiss
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import numpy as np
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import random
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from collections import defaultdict
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from typing import List
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import time
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import logging
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from atlas_rag.llm_generator.llm_generator import LLMGenerator
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from atlas_rag.vectorstore.embedding_model import BaseEmbeddingModel
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from atlas_rag.retriever.lkg_retriever.base import BaseLargeKGEdgeRetriever
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import nltk
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from nltk.corpus import stopwords
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nltk.download('stopwords')
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class LargeKGToGRetriever(BaseLargeKGEdgeRetriever):
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def __init__(self, keyword: str, neo4j_driver: GraphDatabase,
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llm_generator: LLMGenerator, sentence_encoder: BaseEmbeddingModel, filter_encoder: BaseEmbeddingModel,
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node_index: faiss.Index,
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topN : int = 5,
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Dmax : int = 3,
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Wmax : int = 3,
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prune_size: int = 10,
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logger: logging.Logger = None):
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"""
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Initialize the LargeKGToGRetriever for billion-level KG retrieval using Neo4j.
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Args:
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keyword (str): Identifier for the KG dataset (e.g., 'cc_en').
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neo4j_driver (GraphDatabase): Neo4j driver for database access.
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llm_generator (LLMGenerator): LLM for NER, rating, and reasoning.
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sentence_encoder (BaseEmbeddingModel): Encoder for generating embeddings.
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node_index (faiss.Index): FAISS index for node embeddings.
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logger (Logger, optional): Logger for verbose output.
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"""
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self.keyword = keyword
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self.neo4j_driver = neo4j_driver
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self.llm_generator = llm_generator
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self.sentence_encoder = sentence_encoder
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self.filter_encoder = filter_encoder
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self.node_faiss_index = node_index
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self.verbose = logger is not None
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self.logger = logger
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self.topN = topN
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self.Dmax = Dmax
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self.Wmax = Wmax
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self.prune_size = prune_size
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def ner(self, text: str) -> List[str]:
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"""
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Extract named entities from the query text using the LLM.
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Args:
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text (str): The query text.
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Returns:
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List[str]: List of extracted entities.
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"""
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entities = self.llm_generator.large_kg_ner(text)
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if self.verbose:
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self.logger.info(f"Extracted entities: {entities}")
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return entities
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def retrieve_topk_nodes(self, query: str, top_k_nodes: int = 5) -> List[str]:
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"""
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Retrieve top-k nodes similar to entities in the query.
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Args:
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query (str): The user query.
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top_k_nodes (int): Number of nodes to retrieve per entity.
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Returns:
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List[str]: List of node numeric_ids.
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"""
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start_time = time.time()
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entities = self.ner(query)
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if self.verbose:
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ner_time = time.time() - start_time
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self.logger.info(f"NER took {ner_time:.2f} seconds, entities: {entities}")
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if not entities:
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entities = [query]
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initial_nodes = []
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for entity in entities:
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entity_embedding = self.sentence_encoder.encode([entity])
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D, I = self.node_faiss_index.search(entity_embedding, 3)
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if self.verbose:
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self.logger.info(f"Entity: {entity}, FAISS Distances: {D}, Indices: {I}")
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if len(I[0]) > 0: # Check if results exist
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initial_nodes.extend([str(i) for i in I[0]])
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# no need filtering as ToG pruning will handle it.
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topk_nodes_ids = list(set(initial_nodes))
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with self.neo4j_driver.session() as session:
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start_time = time.time()
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query = """
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MATCH (n:Node)
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WHERE n.numeric_id IN $topk_nodes_ids
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RETURN n.numeric_id AS id, n.name AS name
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"""
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result = session.run(query, topk_nodes_ids=topk_nodes_ids)
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topk_nodes_dict = {}
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for record in result:
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topk_nodes_dict[record["id"]] = record["name"]
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if self.verbose:
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neo4j_time = time.time() - start_time
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self.logger.info(f"Neo4j query took {neo4j_time:.2f} seconds, count: {len(topk_nodes_ids)}")
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if self.verbose:
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self.logger.info(f"Top-k nodes: {topk_nodes_dict}")
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return list(topk_nodes_dict.keys()), list(topk_nodes_dict.values()) # numeric_id of nodes returned
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def expand_paths(self, P: List[List[str]], PIDS: List[List[str]], PTYPES: List[List[str]], width: int, query: str) -> List[List[str]]:
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"""
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Expand each path by adding neighbors of the last node.
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Args:
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P (List[List[str]]): Current list of paths, where each path is a list of alternating node_ids and relation types.
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Returns:
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List[List[str]]: List of expanded paths.
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"""
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last_nodes = []
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last_node_ids = []
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last_node_types = []
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paths_end_with_text = []
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paths_end_with_text_id = []
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paths_end_with_text_type = []
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paths_end_with_node = []
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paths_end_with_node_id = []
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paths_end_with_node_type = []
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if self.verbose:
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self.logger.info(f"Expanding paths, current paths: {P}")
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for p, pid, ptype in zip(P, PIDS, PTYPES):
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if not p or not pid or not ptype: # Skip empty paths
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continue
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t = ptype[-1]
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if t == "Text":
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paths_end_with_text.append(p)
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paths_end_with_text_id.append(pid)
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paths_end_with_text_type.append(ptype)
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continue
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last_node = p[-1] # Last node in the path
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last_node_id = pid[-1] # Last node numeric_id
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last_nodes.append(last_node)
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last_node_ids.append(last_node_id)
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last_node_types.append(t)
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paths_end_with_node.append(p)
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paths_end_with_node_id.append(pid)
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paths_end_with_node_type.append(ptype)
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assert len(last_nodes) == len(last_node_ids) == len(last_node_types), "Mismatch in last nodes, ids, and types lengths"
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if not last_node_ids:
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return paths_end_with_text, paths_end_with_text_id, paths_end_with_text_type
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with self.neo4j_driver.session() as session:
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# Query Node relationships
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start_time = time.time()
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outgoing_query = """
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CALL apoc.cypher.runTimeboxed(
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"MATCH (n:Node)-[r:Relation]-(m:Node) WHERE n.numeric_id IN $last_node_ids
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WITH n, r, m ORDER BY rand() LIMIT 60000
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RETURN n.numeric_id AS source, n.name AS source_name, r.relation AS rel_type, m.numeric_id AS target, m.name AS target_name, 'Node' AS target_type",
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{last_node_ids: $last_node_ids},
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60000
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)
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YIELD value
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RETURN value.source AS source, value.source_name AS source_name, value.rel_type AS rel_type, value.target AS target, value.target_name AS target_name, value.target_type AS target_type
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"""
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outgoing_result = session.run(outgoing_query, last_node_ids=last_node_ids)
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outgoing = [(record["source"], record['source_name'], record["rel_type"], record["target"], record["target_name"], record["target_type"])
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for record in outgoing_result]
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if self.verbose:
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outgoing_time = time.time() - start_time
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self.logger.info(f"Outgoing relationships query took {outgoing_time:.2f} seconds, count: {len(outgoing)}")
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# # Query outgoing Node -> Text relationships
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# start_time = time.time()
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# outgoing_text_query = """
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# MATCH (n:Node)-[r:Source]->(t:Text)
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# WHERE n.numeric_id IN $last_node_ids
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# RETURN n.numeric_id AS source, n.name AS source_name, 'from Source' AS rel_type, t.numeric_id as target, t.original_text AS target_name, 'Text' AS target_type
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# """
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# outgoing_text_result = session.run(outgoing_text_query, last_node_ids=last_node_ids)
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# outgoing_text = [(record["source"], record["source_name"], record["rel_type"], record["target"], record["target_name"], record["target_type"])
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# for record in outgoing_text_result]
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# if self.verbose:
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# outgoing_text_time = time.time() - start_time
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# self.logger.info(f"Outgoing Node->Text relationships query took {outgoing_text_time:.2f} seconds, count: {len(outgoing_text)}")
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last_node_to_new_paths = defaultdict(list)
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last_node_to_new_paths_ids = defaultdict(list)
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last_node_to_new_paths_types = defaultdict(list)
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for p, pid, ptype in zip(P, PIDS, PTYPES):
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last_node = p[-1]
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last_node_id = pid[-1]
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# Outgoing Node -> Node
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for source, source_name, rel_type, target, target_name, target_type in outgoing:
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if source == last_node_id and target_name not in p:
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new_path = p + [rel_type, target_name]
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if target_name.lower() in stopwords.words('english'):
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continue
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last_node_to_new_paths[last_node].append(new_path)
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last_node_to_new_paths_ids[last_node].append(pid + [target])
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last_node_to_new_paths_types[last_node].append(ptype + [target_type])
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# # Outgoing Node -> Text
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# for source, source_name, rel_type, target, target_name, target_type in outgoing_text:
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# if source == last_node_id and target_name not in p:
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# new_path = p + [rel_type, target_name]
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# last_node_to_new_paths_text[last_node].append(new_path)
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# last_node_to_new_paths_text_ids[last_node].append(pid + [target])
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# last_node_to_new_paths_text_types[last_node].append(ptype + [target_type])
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# # Incoming Node -> Node
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# for source, rel_type, target, source_name, source_type in incoming:
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# if target == last_node_id and source not in p:
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# new_path = p + [rel_type, source_name]
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num_paths = 0
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for last_node, new_paths in last_node_to_new_paths.items():
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num_paths += len(new_paths)
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# for last_node, new_paths in last_node_to_new_paths_text.items():
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# num_paths += len(new_paths)
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new_paths = []
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new_pids = []
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new_ptypes = []
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if self.verbose:
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self.logger.info(f"Number of new paths before filtering: {num_paths}")
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self.logger.info(f"last nodes: {last_node_to_new_paths.keys()}")
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if num_paths > len(last_node_ids) * width:
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# Apply filtering when total paths exceed threshold
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for last_node, new_ps in last_node_to_new_paths.items():
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if len(new_ps) > width:
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path_embeddings = self.filter_encoder.encode(new_ps)
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query_embeddings = self.filter_encoder.encode([query])
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scores = np.dot(path_embeddings, query_embeddings.T).flatten()
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top_indices = np.argsort(scores)[-width:]
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new_paths.extend([new_ps[i] for i in top_indices])
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new_pids.extend([last_node_to_new_paths_ids[last_node][i] for i in top_indices])
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new_ptypes.extend([last_node_to_new_paths_types[last_node][i] for i in top_indices])
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else:
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new_paths.extend(new_ps)
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new_pids.extend(last_node_to_new_paths_ids[last_node])
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new_ptypes.extend(last_node_to_new_paths_types[last_node])
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else:
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# Collect all paths without filtering when total is at or below threshold
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for last_node, new_ps in last_node_to_new_paths.items():
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new_paths.extend(new_ps)
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new_pids.extend(last_node_to_new_paths_ids[last_node])
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new_ptypes.extend(last_node_to_new_paths_types[last_node])
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if self.verbose:
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self.logger.info(f"Expanded paths count: {len(new_paths)}")
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self.logger.info(f"Expanded paths: {new_paths}")
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return new_paths, new_pids, new_ptypes
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def path_to_string(self, path: List[str]) -> str:
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"""
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Convert a path to a human-readable string for LLM rating.
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Args:
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path (List[str]): Path as a list of node_ids and relation types.
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Returns:
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str: String representation of the path.
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"""
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if len(path) < 1:
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return ""
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path_str = []
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with self.neo4j_driver.session() as session:
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for i in range(0, len(path), 2):
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node_id = path[i]
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result = session.run("MATCH (n:Node {numeric_id: $node_id}) RETURN n.name", node_id=node_id)
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node_name = result.single()["n.name"] if result.single() else node_id
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if i + 1 < len(path):
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rel_type = path[i + 1]
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path_str.append(f"{node_name} ---> {rel_type} --->")
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else:
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path_str.append(node_name)
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return " ".join(path_str).strip()
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def prune(self, query: str, P: List[List[str]], PIDS: List[List[str]], PTYPES: List[List[str]], topN: int = 5) -> List[List[str]]:
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"""
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Prune paths to keep the top N based on LLM relevance ratings.
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Args:
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query (str): The user query.
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P (List[List[str]]): List of paths to prune.
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topN (int): Number of paths to retain.
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Returns:
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List[List[str]]: Top N paths.
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"""
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ratings = []
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path_strings = P
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# Process paths in chunks of 10
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for i in range(0, len(path_strings), self.prune_size):
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chunk = path_strings[i:i + self.prune_size]
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# Construct user prompt with the current chunk of paths listed
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user_prompt = f"Please rate the following paths based on how well they help answer the query (1-5, 0 if not relevant).\n\nQuery: {query}\n\nPaths:\n"
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for j, path_str in enumerate(chunk, 1):
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user_prompt += f"{j + i}. {path_str}\n"
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user_prompt += "\nProvide a list of integers, each corresponding to the rating of the path's ability to help answer the query."
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# Define system prompt to expect a list of integers
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system_prompt = "You are a rating machine that only provides a list of comma-separated integers (0-5) as a response, each rating how well the corresponding path helps answer the query."
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# Send the prompt to the language model
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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response = self.llm_generator.generate_response(messages, max_new_tokens=1024, temperature=0.0)
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if self.verbose:
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self.logger.info(f"LLM response for chunk {i // self.prune_size + 1}: {response}")
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# Parse the response into a list of ratings
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rating_str = response.strip()
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chunk_ratings = [int(r) for r in rating_str.split(',') if r.strip().isdigit()]
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if len(chunk_ratings) > len(chunk):
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chunk_ratings = chunk_ratings[:len(chunk)]
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if self.verbose:
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self.logger.warning(f"Received more ratings ({len(chunk_ratings)}) than paths in chunk ({len(chunk)}). Trimming ratings.")
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ratings.extend(chunk_ratings) # Concatenate ratings
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# Ensure ratings length matches number of paths, padding with 0s if necessary
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if len(ratings) < len(path_strings):
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# self.logger.warning(f"Number of ratings ({len(ratings)}) does not match number of paths ({len(path_strings)}). Padding with 0s.")
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# ratings += [0] * (len(path_strings) - len(ratings))
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# fall back to use filter encoder to get topN
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self.logger.warning(f"Number of ratings ({len(ratings)}) does not match number of paths ({len(path_strings)}). Using filter encoder to get topN paths.")
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path_embeddings = self.filter_encoder.encode(path_strings)
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query_embedding = self.filter_encoder.encode([query])
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scores = np.dot(path_embeddings, query_embedding.T).flatten()
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top_indices = np.argsort(scores)[-topN:]
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top_paths = [path_strings[i] for i in top_indices]
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return top_paths, [PIDS[i] for i in top_indices], [PTYPES[i] for i in top_indices]
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elif len(ratings) > len(path_strings):
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self.logger.warning(f"Number of ratings ({len(ratings)}) exceeds number of paths ({len(path_strings)}). Trimming ratings.")
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ratings = ratings[:len(path_strings)]
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# Sort indices based on ratings in descending order
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sorted_indices = sorted(range(len(ratings)), key=lambda i: ratings[i], reverse=True)
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# Filter out indices where the rating is 0
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filtered_indices = [i for i in sorted_indices if ratings[i] > 0]
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# Take the top N indices from the filtered list
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top_indices = filtered_indices[:topN]
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# Use the filtered indices to get the top paths, PIDS, and PTYPES
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if self.verbose:
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self.logger.info(f"Top indices after pruning: {top_indices}")
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self.logger.info(f"length of path_strings: {len(path_strings)}")
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top_paths = [path_strings[i] for i in top_indices]
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top_pids = [PIDS[i] for i in top_indices]
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top_ptypes = [PTYPES[i] for i in top_indices]
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# Log top paths if verbose mode is enabled
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if self.verbose:
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self.logger.info(f"Pruned to top {topN} paths: {top_paths}")
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return top_paths, top_pids, top_ptypes
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def reasoning(self, query: str, P: List[List[str]]) -> bool:
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"""
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Check if the current paths are sufficient to answer the query.
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Args:
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query (str): The user query.
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P (List[List[str]]): Current list of paths.
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Returns:
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bool: True if sufficient, False otherwise.
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"""
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triples = []
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with self.neo4j_driver.session() as session:
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for path in P:
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if len(path) < 3:
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continue
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for i in range(0, len(path) - 2, 2):
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node1_name = path[i]
|
||||
rel = path[i + 1]
|
||||
node2_name = path[i + 2]
|
||||
triples.append(f"({node1_name}, {rel}, {node2_name})")
|
||||
triples_str = ". ".join(triples)
|
||||
prompt = f"Are these triples, along with your knowledge, sufficient to answer the query?\nQuery: {query}\nTriples: {triples_str}"
|
||||
messages = [
|
||||
{"role": "system", "content": "Answer Yes or No only."},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
response = self.llm_generator.generate_response(messages,max_new_tokens=512)
|
||||
if self.verbose:
|
||||
self.logger.info(f"Reasoning result: {response}")
|
||||
return "yes" in response.lower()
|
||||
|
||||
def retrieve_passages(self, query: str) -> List[str]:
|
||||
"""
|
||||
Retrieve the top N paths to answer the query.
|
||||
|
||||
Args:
|
||||
query (str): The user query.
|
||||
topN (int): Number of paths to return.
|
||||
Dmax (int): Maximum depth of path expansion.
|
||||
Wmax (int): Maximum width of path expansion.
|
||||
|
||||
Returns:
|
||||
List[str]: List of triples as strings.
|
||||
"""
|
||||
topN = self.topN
|
||||
Dmax = self.Dmax
|
||||
Wmax = self.Wmax
|
||||
if self.verbose:
|
||||
self.logger.info(f"Retrieving paths for query: {query}")
|
||||
|
||||
initial_nodes_ids, initial_nodes = self.retrieve_topk_nodes(query, top_k_nodes=topN)
|
||||
if not initial_nodes:
|
||||
if self.verbose:
|
||||
self.logger.info("No initial nodes found.")
|
||||
return []
|
||||
|
||||
P = [[node] for node in initial_nodes]
|
||||
PIDS = [[node_id] for node_id in initial_nodes_ids]
|
||||
PTYPES = [["Node"] for _ in initial_nodes_ids] # Assuming all initial nodes are of type 'Node'
|
||||
for D in range(Dmax + 1):
|
||||
if self.verbose:
|
||||
self.logger.info(f"Depth {D}, Current paths: {len(P)}")
|
||||
P, PIDS, PTYPES = self.expand_paths(P, PIDS, PTYPES, Wmax, query)
|
||||
if not P:
|
||||
if self.verbose:
|
||||
self.logger.info("No paths to expand.")
|
||||
break
|
||||
P, PIDS, PTYPES = self.prune(query, P, PIDS, PTYPES, topN)
|
||||
if D == Dmax:
|
||||
if self.verbose:
|
||||
self.logger.info(f"Reached maximum depth {Dmax}, stopping expansion.")
|
||||
break
|
||||
if self.reasoning(query, P):
|
||||
if self.verbose:
|
||||
self.logger.info("Paths sufficient, stopping expansion.")
|
||||
break
|
||||
|
||||
# Extract final triples
|
||||
triples = []
|
||||
with self.neo4j_driver.session() as session:
|
||||
for path in P:
|
||||
for i in range(0, len(path) - 2, 2):
|
||||
node1_name = path[i]
|
||||
rel = path[i + 1]
|
||||
node2_name = path[i + 2]
|
||||
triples.append(f"({node1_name}, {rel}, {node2_name})")
|
||||
|
||||
if self.verbose:
|
||||
self.logger.info(f"Final triples: {triples}")
|
||||
return triples, 'N/A' # 'N/A' for passages_score as this retriever does not return passages
|
||||
Reference in New Issue
Block a user