add hybrid_search for MilvusClient (#2259)

Signed-off-by: zhenshan.cao <[email protected]>

examples/hybrid_search.py

@@ -16,6 +16,8 @@
 
 has = utility.has_collection("hello_milvus")
 print(f"Does collection hello_milvus exist in Milvus: {has}")
+if has:
+    utility.drop_collection("hello_milvus")
 
 fields = [
     FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100),

examples/milvus_client/hybrid_search.py

@@ -0,0 +1,75 @@
+import numpy as np
+from pymilvus import (
+    MilvusClient,
+    DataType,
+    AnnSearchRequest, RRFRanker, WeightedRanker,
+)
+
+fmt = "\n=== {:30} ===\n"
+search_latency_fmt = "search latency = {:.4f}s"
+num_entities, dim = 3000, 8
+
+collection_name = "hello_milvus"
+milvus_client = MilvusClient("http://localhost:19530")
+
+has_collection = milvus_client.has_collection(collection_name, timeout=5)
+if has_collection:
+    milvus_client.drop_collection(collection_name)
+
+schema = milvus_client.create_schema(auto_id=False, description="hello_milvus is the simplest demo to introduce the APIs")
+schema.add_field("pk", DataType.VARCHAR, is_primary=True, max_length=100)
+schema.add_field("random", DataType.DOUBLE)
+schema.add_field("embeddings", DataType.FLOAT_VECTOR, dim=dim)
+schema.add_field("embeddings2", DataType.FLOAT_VECTOR, dim=dim)
+
+index_params = milvus_client.prepare_index_params()
+index_params.add_index(field_name = "embeddings", index_type = "IVF_FLAT", metric_type="L2", nlist=128)
+index_params.add_index(field_name = "embeddings2",index_type = "IVF_FLAT", metric_type="L2", nlist=128)
+
+print(fmt.format("Create collection `hello_milvus`"))
+
+milvus_client.create_collection(collection_name, schema=schema, index_params=index_params, consistency_level="Strong")
+
+print(fmt.format("Start inserting entities"))
+rng = np.random.default_rng(seed=19530)
+entities = [
+    # provide the pk field because `auto_id` is set to False
+    [str(i) for i in range(num_entities)],
+    rng.random(num_entities).tolist(),  # field random, only supports list
+    rng.random((num_entities, dim)),    # field embeddings, supports numpy.ndarray and list
+    rng.random((num_entities, dim)),    # field embeddings2, supports numpy.ndarray and list
+]
+
+rows = [ {"pk": entities[0][i], "random": entities[1][i], "embeddings": entities[2][i], "embeddings2": entities[3][i]} for i in range (num_entities)]
+
+insert_result = milvus_client.insert(collection_name, rows)
+
+
+print(fmt.format("Start loading"))
+milvus_client.load_collection(collection_name)
+
+field_names = ["embeddings", "embeddings2"]
+field_names = ["embeddings"]
+
+req_list = []
+nq = 1
+default_limit = 5
+vectors_to_search = []
+
+for i in range(len(field_names)):
+    # 4. generate search data
+    vectors_to_search = rng.random((nq, dim))
+    search_param = {
+        "data": vectors_to_search,
+        "anns_field": field_names[i],
+        "param": {"metric_type": "L2"},
+        "limit": default_limit,
+        "expr": "random > 0.5"}
+    req = AnnSearchRequest(**search_param)
+    req_list.append(req)
+
+print("rank by RRFRanker")
+hybrid_res = milvus_client.hybrid_search(collection_name, req_list, RRFRanker(), default_limit, output_fields=["random"])
+for hits in hybrid_res:
+    for hit in hits:
+        print(f" hybrid search hit: {hit}")

pymilvus/milvus_client/milvus_client.py

@@ -4,6 +4,7 @@
 from typing import Dict, List, Optional, Union
 from uuid import uuid4
 
+from pymilvus.client.abstract import AnnSearchRequest, BaseRanker
 from pymilvus.client.constants import DEFAULT_CONSISTENCY_LEVEL
 from pymilvus.client.types import (
     ExceptionsMessage,
@@ -282,6 +283,77 @@ def upsert(
             }
         )
 
+    def hybrid_search(
+        self,
+        collection_name: str,
+        reqs: List[AnnSearchRequest],
+        ranker: BaseRanker,
+        limit: int = 10,
+        output_fields: Optional[List[str]] = None,
+        timeout: Optional[float] = None,
+        partition_names: Optional[List[str]] = None,
+        **kwargs,
+    ) -> List[List[dict]]:
+        """Conducts multi vector similarity search with a rerank for rearrangement.
+
+        Args:
+            collection_name(``string``): The name of collection.
+            reqs (``List[AnnSearchRequest]``): The vector search requests.
+            ranker (``BaseRanker``): The ranker for rearrange nummer of limit results.
+            limit (``int``): The max number of returned record, also known as `topk`.
+
+            partition_names (``List[str]``, optional): The names of partitions to search on.
+            output_fields (``List[str]``, optional):
+                The name of fields to return in the search result.  Can only get scalar fields.
+            round_decimal (``int``, optional):
+                The specified number of decimal places of returned distance.
+                Defaults to -1 means no round to returned distance.
+            timeout (``float``, optional): A duration of time in seconds to allow for the RPC.
+                If timeout is set to None, the client keeps waiting until the server
+                responds or an error occurs.
+            **kwargs (``dict``): Optional search params
+
+                * *offset* (``int``, optinal)
+                    offset for pagination.
+
+                * *consistency_level* (``str/int``, optional)
+                    Which consistency level to use when searching in the collection.
+
+                    Options of consistency level: Strong, Bounded, Eventually, Session, Customized.
+
+                    Note: this parameter overwrites the same one specified when creating collection,
+                    if no consistency level was specified, search will use the
+                    consistency level when you create the collection.
+
+        Returns:
+            List[List[dict]]: A nested list of dicts containing the result data.
+
+        Raises:
+            MilvusException: If anything goes wrong
+        """
+
+        conn = self._get_connection()
+        try:
+            res = conn.hybrid_search(
+                collection_name,
+                reqs,
+                ranker,
+                limit=limit,
+                partition_names=partition_names,
+                output_fields=output_fields,
+                timeout=timeout,
+                **kwargs,
+            )
+        except Exception as ex:
+            logger.error("Failed to hybrid search collection: %s", collection_name)
+            raise ex from ex
+
+        ret = []
+        for hits in res:
+            ret.append([hit.to_dict() for hit in hits])
+
+        return ExtraList(ret, extra=construct_cost_extra(res.cost))
+
     def search(
         self,
         collection_name: str,