172 lines
6.5 KiB
Python
172 lines
6.5 KiB
Python
from src.models.embedding import Reranker
|
||
from src.utils.logging_config import setup_logger
|
||
logger = setup_logger("server-common")
|
||
|
||
|
||
class Retriever:
|
||
|
||
def __init__(self, config, dbm, model):
|
||
self.config = config
|
||
self.dbm = dbm
|
||
self.model = model
|
||
|
||
if self.config.enable_reranker:
|
||
self.reranker = Reranker(config)
|
||
|
||
def retrieval(self, query, history, meta):
|
||
|
||
refs = {"query": query, "history": history, "meta": meta}
|
||
refs["entities"] = self.reco_entities(query, history, refs)
|
||
refs["knowledge_base"] = self.query_knowledgebase(query, history, refs)
|
||
refs["graph_base"] = self.query_graph(query, history, refs)
|
||
|
||
return refs
|
||
|
||
def construct_query(self, query, refs, meta):
|
||
if len(refs) == 0:
|
||
return query
|
||
|
||
external = ""
|
||
|
||
# 解析知识库的结果
|
||
kb_res = refs.get("knowledge_base").get("results", [])
|
||
if len(kb_res) > 0:
|
||
kb_text = "\n".join([f"{r['id']}: {r['entity']['text']}" for r in kb_res])
|
||
external += f"知识库信息: \n\n{kb_text}"
|
||
|
||
# 解析图数据库的结果
|
||
db_res = refs.get("graph_base").get("results", [])
|
||
if len(db_res["nodes"]) > 0:
|
||
db_text = '\n'.join([f"{edge['source_name']}和{edge['target_name']}的关系是{edge['type']}" for edge in db_res['edges']])
|
||
external += f"图数据库信息: \n\n{db_text}"
|
||
|
||
# 构造查询
|
||
if len(external) > 0:
|
||
query = f"以下是参考资料：\n\n\n{external}\n\n\n请根据前面的知识回答：{query}"
|
||
|
||
return query
|
||
|
||
def query_classification(self, query):
|
||
"""判断是否需要查询
|
||
- 对于完全基于用户给定信息的任务，称之为“足够”“sufficient”，不需要检索；
|
||
- 否则，称之为“不足”“insufficient”，可能需要检索，
|
||
"""
|
||
raise NotImplementedError
|
||
|
||
def query_graph(self, query, history, refs):
|
||
# res = model.predict("qiansdgsa, dasdh ashdsakjdk ak ").content
|
||
|
||
results = []
|
||
if refs["meta"].get("use_graph") and self.config.enable_knowledge_base:
|
||
for entity in refs["entities"]:
|
||
result = self.dbm.graph_base.query_by_vector(entity)
|
||
if result != []:
|
||
results.extend(result)
|
||
return {"results": self.format_query_results(results)}
|
||
|
||
def query_knowledgebase(self, query, history, refs):
|
||
"""查询知识库"""
|
||
rw_query = self.rewrite_query(query, history, refs)
|
||
|
||
kb_res = []
|
||
final_res = []
|
||
if refs["meta"].get("db_name") and self.config.enable_knowledge_base:
|
||
|
||
db_name = refs["meta"]["db_name"]
|
||
kb = self.dbm.metaname2db[db_name]
|
||
limit = refs["meta"].get("queryCount", 10)
|
||
|
||
kb_res = self.dbm.knowledge_base.search(rw_query, db_name, limit=limit)
|
||
for r in kb_res:
|
||
r["file"] = kb.id2file(r["entity"]["file_id"])
|
||
|
||
if self.config.enable_reranker:
|
||
RERANK_THRESHOLD = 0.1
|
||
for r in kb_res:
|
||
r["rerank_score"] = self.reranker.compute_score([query, r["entity"]["text"]], normalize=True)
|
||
kb_res.sort(key=lambda x: x["rerank_score"], reverse=True)
|
||
final_res = [_res for _res in kb_res if _res["rerank_score"] > RERANK_THRESHOLD]
|
||
|
||
else:
|
||
final_res = kb_res[:5]
|
||
|
||
return {"results": final_res, "all_results": kb_res, "rw_query": rw_query}
|
||
|
||
def rewrite_query(self, query, history, refs):
|
||
"""重写查询"""
|
||
rewrite_query_span = refs["meta"].get("rewrite_query", None)
|
||
if rewrite_query_span is None or rewrite_query_span == "OFF":
|
||
rewritten_query = query
|
||
else:
|
||
from src.utils.prompts import rewritten_query_prompt_template
|
||
history_query = [entry['content'] for entry in history if entry['role'] == 'user'] if history else ""
|
||
rewritten_query_prompt = rewritten_query_prompt_template.format(history=history_query, query=query)
|
||
rewritten_query = self.model.predict(rewritten_query_prompt).content
|
||
|
||
if rewrite_query_span == "HyDE":
|
||
hy_doc = self.model.predict(rewritten_query).content
|
||
rewritten_query = f"{rewritten_query} {hy_doc}"
|
||
|
||
return rewritten_query
|
||
|
||
def reco_entities(self, query, history, refs):
|
||
"""识别句子中的实体"""
|
||
query = refs.get("rewritten_query", query)
|
||
|
||
entities = []
|
||
if refs["meta"].get("use_graph"):
|
||
from src.utils.prompts import entity_extraction_prompt_template
|
||
entity_extraction_prompt = entity_extraction_prompt_template.format(text=query)
|
||
entities = self.model.predict(entity_extraction_prompt).content.split(",")
|
||
entities = [entity for entity in entities if all(char.isalnum() or char in '汉字' for char in entity)]
|
||
|
||
return entities
|
||
|
||
def format_query_results(self, results):
|
||
formatted_results = {"nodes": [], "edges": []}
|
||
|
||
node_dict = {}
|
||
|
||
for item in results:
|
||
if not isinstance(item[1], list) or len(item[1]) == 0:
|
||
continue
|
||
|
||
relationship = item[1][0]
|
||
rel_id = relationship.element_id
|
||
nodes = relationship.nodes
|
||
if len(nodes) != 2:
|
||
continue
|
||
|
||
source, target = nodes
|
||
|
||
source_id = source.element_id
|
||
target_id = target.element_id
|
||
source_name = item[0]
|
||
target_name = item[2] if len(item) > 2 else 'unknown'
|
||
|
||
if source_id not in node_dict:
|
||
node_dict[source_id] = {"id": source_id, "name": source_name}
|
||
if target_id not in node_dict:
|
||
node_dict[target_id] = {"id": target_id, "name": target_name}
|
||
|
||
relationship_type = relationship._properties.get('type', 'unknown')
|
||
if relationship_type == 'unknown':
|
||
relationship_type = relationship.type
|
||
|
||
formatted_results["edges"].append({
|
||
"id": rel_id,
|
||
"type": relationship_type,
|
||
"source_id": source_id,
|
||
"target_id": target_id,
|
||
"source_name": source_name,
|
||
"target_name": target_name
|
||
})
|
||
|
||
formatted_results["nodes"] = list(node_dict.values())
|
||
|
||
return formatted_results
|
||
|
||
def __call__(self, query, history, meta):
|
||
refs = self.retrieval(query, history, meta)
|
||
query = self.construct_query(query, refs, meta)
|
||
return query, refs |