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- from agent.core.es import es
- from agent.core.vector import get_embeddings
- query = '职业:电动机加工制造,投向:电动机加工制造,用途:电动机加工制造'
- resp_bm25 = es.search(
- index='ai-tagging',
- size=10,
- query={
- "multi_match": {
- "query": query,
- "fields": ["tag_path", "tag_remark", "tag_prompt"]
- }
- }
- )
- for rank, hit in enumerate(resp_bm25['hits']['hits'], start=1):
- hit["_source"]["tag_vector"] = None
- doc_id = hit['_id']
- tag_nm = hit['_source']['tag_name']
- print(doc_id,tag_nm)
- print('-----------------')
- query_vector = get_embeddings([query])[0]
- resp_vector = es.search(
- index='ai-tagging',
- size=10,
- knn={
- "field": "tag_vector",
- "query_vector": query_vector,
- "k": 10,
- "num_candidates": 100
- }
- )
- for rank, hit in enumerate(resp_vector['hits']['hits'], start=1):
- hit["_source"]["tag_vector"] = None
- doc_id = hit['_id']
- tag_nm = hit['_source']['tag_name']
- print(doc_id,tag_nm)
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