milvus 2.4

api/core/rag/datasource/vdb/field.py

@@ -8,3 +8,4 @@ class Field(Enum):
     VECTOR = "vector"
     TEXT_KEY = "text"
     PRIMARY_KEY = "id"
+    BM25_KEY = "bm25_ef"

api/core/rag/datasource/vdb/milvus/milvus_vector.py

@@ -2,8 +2,12 @@ import logging
 from typing import Any, Optional
 from uuid import uuid4
 
+from langdetect import detect, LangDetectException
+from milvus_model.sparse import BM25EmbeddingFunction
+from milvus_model.sparse.bm25.tokenizers import build_default_analyzer
 from pydantic import BaseModel, root_validator
 from pymilvus import MilvusClient, MilvusException, connections
+from pymilvus.milvus_client import IndexParams
 
 from core.rag.datasource.vdb.field import Field
 from core.rag.datasource.vdb.vector_base import BaseVector
@@ -12,6 +16,19 @@ from extensions.ext_redis import redis_client
 
 logger = logging.getLogger(__name__)
 
+LANGUAGE_CODE_MAP = {
+    'en': 'en',  # English
+    'de': 'de',  # German
+    'fr': 'fr',  # French
+    'ru': 'ru',  # Russian
+    'es': 'sp',  # Spanish (langdetect returns 'es' for Spanish)
+    'it': 'it',  # Italian
+    'pt': 'pt',  # Portuguese
+    'zh-cn': 'zh',  # Chinese (Simplified)
+    'zh-tw': 'zh',  # Chinese (Traditional)
+    'ja': 'jp',  # Japanese
+    'ko': 'kr',  # Korean
+}
 
 class MilvusConfig(BaseModel):
     host: str
@@ -70,9 +87,11 @@ class MilvusVector(BaseVector):
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
         insert_dict_list = []
         for i in range(len(documents)):
+            bm25_ef = self._bm25_document_encode(documents[i].page_content)
             insert_dict = {
                 Field.CONTENT_KEY.value: documents[i].page_content,
                 Field.VECTOR.value: embeddings[i],
+                Field.BM25_KEY.value: bm25_ef,
                 Field.METADATA_KEY.value: documents[i].metadata
             }
             insert_dict_list.append(insert_dict)
@@ -171,7 +190,6 @@ class MilvusVector(BaseVector):
         result = self._client.query(collection_name=self._collection_name,
                                     filter=f'metadata["doc_id"] == "{id}"',
                                     output_fields=["id"])
-
         return len(result) > 0
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
@@ -180,6 +198,7 @@ class MilvusVector(BaseVector):
         results = self._client.search(collection_name=self._collection_name,
                                       data=[query_vector],
                                       limit=kwargs.get('top_k', 4),
+                                      anns_field=Field.VECTOR.value,
                                       output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
                                       )
         # Organize results.
@@ -195,7 +214,18 @@ class MilvusVector(BaseVector):
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        # milvus/zilliz doesn't support bm25 search
+        bm25_ef = self._bm25_query_encode(query)
+        search_params = {
+            "metric_type": "IP",
+            "params": {"drop_ratio_search": 0.2},  # the ratio of small vector values to be dropped during search.
+        }
+        results = self._client.search(collection_name=self._collection_name,
+                                      data=bm25_ef,
+                                      limit=kwargs.get('top_k', 4),
+                                      anns_field=Field.BM25_KEY.value,
+                                      output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+                                      search_params=search_params
+                                      )
         return []
 
     def create_collection(
@@ -229,6 +259,10 @@ class MilvusVector(BaseVector):
                 fields.append(
                     FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535)
                 )
+                # Create the bm25 field
+                fields.append(
+                    FieldSchema(Field.BM25_KEY.value, DataType.SPARSE_FLOAT_VECTOR)
+                )
                 # Create the primary key field
                 fields.append(
                     FieldSchema(
@@ -247,12 +281,23 @@ class MilvusVector(BaseVector):
                     self._fields.append(x.name)
                 # Since primary field is auto-id, no need to track it
                 self._fields.remove(Field.PRIMARY_KEY.value)
-
+                # Create Index params for the collection
+                index_params_obj = IndexParams()
+                index_params_obj.add_index(field_name=Field.VECTOR.value, **index_params)
+                index_params_obj.add_index(
+                    field_name=Field.BM25_KEY.value,
+                    index_name=f'{self._collection_name}_sparse_index',
+                    index_type="SPARSE_INVERTED_INDEX",
+                    # the type of index to be created. set to `SPARSE_INVERTED_INDEX` or `SPARSE_WAND`.
+                    metric_type="IP",
+                    # the metric type to be used for the index. Currently, only `IP` (Inner Product) is supported.
+                    params={"drop_ratio_build": 0.2},  # the ratio of small vector values to be dropped during indexing.
+                )
                 # Create the collection
                 collection_name = self._collection_name
-                self._client.create_collection_with_schema(collection_name=collection_name,
-                                                           schema=schema, index_param=index_params,
-                                                           consistency_level=self._consistency_level)
+                self._client.create_collection(collection_name=collection_name,
+                                               schema=schema, index_params=index_params_obj,
+                                               consistency_level=self._consistency_level)
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
     def _init_client(self, config) -> MilvusClient:
         if config.secure:
@@ -261,3 +306,25 @@ class MilvusVector(BaseVector):
             uri = "http://" + str(config.host) + ":" + str(config.port)
         client = MilvusClient(uri=uri, user=config.user, password=config.password,db_name=config.database)
         return client
+
+    def _bm25_document_encode(self, text: str):
+        language = self._get_text_language(text)
+        analyzer = build_default_analyzer(language=language)
+        bm25_ef = BM25EmbeddingFunction(analyzer)
+        bm25_ef.fit([text])
+        docs_embeddings = bm25_ef.encode_documents([text])
+        return docs_embeddings
+
+    def _bm25_query_encode(self, text: str):
+        language = self._get_text_language(text)
+        analyzer = build_default_analyzer(language=language)
+        bm25_ef = BM25EmbeddingFunction(analyzer)
+        docs_embeddings = bm25_ef.encode_queries([text])
+        return docs_embeddings
+
+    def _get_text_language(self, text: str) -> str:
+        try:
+            detected_language = detect(text)
+            return LANGUAGE_CODE_MAP.get(detected_language, "en")
+        except LangDetectException:
+            return "en"

api/requirements.txt

@@ -56,7 +56,7 @@ xinference-client==0.9.4
 safetensors~=0.4.3
 zhipuai==1.0.7
 werkzeug~=3.0.1
-pymilvus==2.3.1
+pymilvus==2.4.1
 qdrant-client==1.7.3
 cohere~=5.2.4
 pyyaml~=6.0.1

docker/docker-compose.milvus.yaml

@@ -38,7 +38,7 @@ services:
 
   milvus-standalone:
     container_name: milvus-standalone
-    image: milvusdb/milvus:v2.3.1
+    image: milvusdb/milvus:v2.4.1
     command: ["milvus", "run", "standalone"]
     environment:
       ETCD_ENDPOINTS: etcd:2379