As title.

First put the data into Redis as hash
df=embeddings.sql_query()
for index, row in df.iterrows():

        chunkid = row['chunkId']
        
        tags_dict = json.loads(row['Filetags']) 

        r.hset(f"chunkid:{chunkid}", "kId", row['kId'])
        r.hset(f"chunkid:{chunkid}", "Content", row['Content'])
        r.hset(f"chunkid:{chunkid}", "Filename", row['Filename'])
        r.hset(f"chunkid:{chunkid}", "Vector", np.array(row['Vector'], dtype=np.float32).tobytes())
        for tag, value in tags_dict.items():
            r.hset(f"chunkid:{chunkid}", tag, value)

Then, index using the hase

    INDEX_NAME = "name"                              
    DOC_PREFIX = "hash:" 
    try:
        r.ft(INDEX_NAME).info()
        print("Index already exists!")
    except:
        # schema
        schema = (

            TagField("Filename"),                   
            TextField("Content"),
            VectorField("vector",                  
                "FLAT", {                          
                    "TYPE": "FLOAT32",             
                    "DIM":1536,  
                    "DISTANCE_METRIC": "COSINE"   
                }
            ),
        )

        definition = IndexDefinition(prefix=[DOC_PREFIX], index_type=IndexType.HASH)

        r.ft(INDEX_NAME).create_index(fields=schema, definition=definition)

Finally, perform a vector search

        vec = embeddings.text_embedding(input)
        query_embedding = np.array(vec, dtype=np.float32)

        base_query = f'*=>[KNN {k} @vector $vec AS vector_score]'
        query = (
            Query(base_query)
            .sort_by('vector_score')
            .return_fields("id", "Filename", "Content", "vector_score")
            .paging(0, k)
            .dialect(2)
        )

        query_params = {
            "vec": query_embedding.tobytes()
        }

        results = r.ft(index_name).search(query, query_params)

But the execution results all return 0, like the following

Found 0 results　[]

Do you have any suggestions for adjustments?