FEAT: support milvus to full text search (#11430)

Signed-off-by: YoungLH <974840768@qq.com>

api/configs/middleware/vdb/milvus_config.py

@@ -33,3 +33,9 @@ class MilvusConfig(BaseSettings):
         description="Name of the Milvus database to connect to (default is 'default')",
         default="default",
     )
+
+    MILVUS_ENABLE_HYBRID_SEARCH: bool = Field(
+        description="Enable hybrid search features (requires Milvus >= 2.5.0). Set to false for compatibility with "
+        "older versions",
+        default=True,
+    )

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

@@ -6,6 +6,8 @@ class Field(Enum):
     METADATA_KEY = "metadata"
     GROUP_KEY = "group_id"
     VECTOR = "vector"
+    # Sparse Vector aims to support full text search
+    SPARSE_VECTOR = "sparse_vector"
     TEXT_KEY = "text"
     PRIMARY_KEY = "id"
     DOC_ID = "metadata.doc_id"

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

@@ -2,6 +2,7 @@ import json
 import logging
 from typing import Any, Optional
 
+from packaging import version
 from pydantic import BaseModel, model_validator
 from pymilvus import MilvusClient, MilvusException  # type: ignore
 from pymilvus.milvus_client import IndexParams  # type: ignore
@@ -20,16 +21,25 @@ logger = logging.getLogger(__name__)
 
 
 class MilvusConfig(BaseModel):
-    uri: str
-    token: Optional[str] = None
-    user: str
-    password: str
-    batch_size: int = 100
-    database: str = "default"
+    """
+    Configuration class for Milvus connection.
+    """
+
+    uri: str  # Milvus server URI
+    token: Optional[str] = None  # Optional token for authentication
+    user: str  # Username for authentication
+    password: str  # Password for authentication
+    batch_size: int = 100  # Batch size for operations
+    database: str = "default"  # Database name
+    enable_hybrid_search: bool = False  # Flag to enable hybrid search
 
     @model_validator(mode="before")
     @classmethod
     def validate_config(cls, values: dict) -> dict:
+        """
+        Validate the configuration values.
+        Raises ValueError if required fields are missing.
+        """
         if not values.get("uri"):
             raise ValueError("config MILVUS_URI is required")
         if not values.get("user"):
@@ -39,6 +49,9 @@ class MilvusConfig(BaseModel):
         return values
 
     def to_milvus_params(self):
+        """
+        Convert the configuration to a dictionary of Milvus connection parameters.
+        """
         return {
             "uri": self.uri,
             "token": self.token,
@@ -49,26 +62,57 @@ class MilvusConfig(BaseModel):
 
 
 class MilvusVector(BaseVector):
+    """
+    Milvus vector storage implementation.
+    """
+
     def __init__(self, collection_name: str, config: MilvusConfig):
         super().__init__(collection_name)
         self._client_config = config
         self._client = self._init_client(config)
-        self._consistency_level = "Session"
-        self._fields: list[str] = []
+        self._consistency_level = "Session"  # Consistency level for Milvus operations
+        self._fields: list[str] = []  # List of fields in the collection
+        self._hybrid_search_enabled = self._check_hybrid_search_support()  # Check if hybrid search is supported
+
+    def _check_hybrid_search_support(self) -> bool:
+        """
+        Check if the current Milvus version supports hybrid search.
+        Returns True if the version is >= 2.5.0, otherwise False.
+        """
+        if not self._client_config.enable_hybrid_search:
+            return False
+
+        try:
+            milvus_version = self._client.get_server_version()
+            return version.parse(milvus_version).base_version >= version.parse("2.5.0").base_version
+        except Exception as e:
+            logger.warning(f"Failed to check Milvus version: {str(e)}. Disabling hybrid search.")
+            return False
 
     def get_type(self) -> str:
+        """
+        Get the type of vector storage (Milvus).
+        """
         return VectorType.MILVUS
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        """
+        Create a collection and add texts with embeddings.
+        """
         index_params = {"metric_type": "IP", "index_type": "HNSW", "params": {"M": 8, "efConstruction": 64}}
         metadatas = [d.metadata if d.metadata is not None else {} for d in texts]
         self.create_collection(embeddings, metadatas, index_params)
         self.add_texts(texts, embeddings)
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        """
+        Add texts and their embeddings to the collection.
+        """
         insert_dict_list = []
         for i in range(len(documents)):
             insert_dict = {
+                # Do not need to insert the sparse_vector field separately, as the text_bm25_emb
+                # function will automatically convert the native text into a sparse vector for us.
                 Field.CONTENT_KEY.value: documents[i].page_content,
                 Field.VECTOR.value: embeddings[i],
                 Field.METADATA_KEY.value: documents[i].metadata,
@@ -76,12 +120,11 @@ class MilvusVector(BaseVector):
             insert_dict_list.append(insert_dict)
         # Total insert count
         total_count = len(insert_dict_list)
-
         pks: list[str] = []
 
         for i in range(0, total_count, 1000):
-            batch_insert_list = insert_dict_list[i : i + 1000]
             # Insert into the collection.
+            batch_insert_list = insert_dict_list[i : i + 1000]
             try:
                 ids = self._client.insert(collection_name=self._collection_name, data=batch_insert_list)
                 pks.extend(ids)
@@ -91,6 +134,9 @@ class MilvusVector(BaseVector):
         return pks
 
     def get_ids_by_metadata_field(self, key: str, value: str):
+        """
+        Get document IDs by metadata field key and value.
+        """
         result = self._client.query(
             collection_name=self._collection_name, filter=f'metadata["{key}"] == "{value}"', output_fields=["id"]
         )
@@ -100,12 +146,18 @@ class MilvusVector(BaseVector):
             return None
 
     def delete_by_metadata_field(self, key: str, value: str):
+        """
+        Delete documents by metadata field key and value.
+        """
         if self._client.has_collection(self._collection_name):
             ids = self.get_ids_by_metadata_field(key, value)
             if ids:
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        """
+        Delete documents by their IDs.
+        """
         if self._client.has_collection(self._collection_name):
             result = self._client.query(
                 collection_name=self._collection_name, filter=f'metadata["doc_id"] in {ids}', output_fields=["id"]
@@ -115,10 +167,16 @@ class MilvusVector(BaseVector):
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete(self) -> None:
+        """
+        Delete the entire collection.
+        """
         if self._client.has_collection(self._collection_name):
             self._client.drop_collection(self._collection_name, None)
 
     def text_exists(self, id: str) -> bool:
+        """
+        Check if a text with the given ID exists in the collection.
+        """
         if not self._client.has_collection(self._collection_name):
             return False
 
@@ -128,32 +186,80 @@ class MilvusVector(BaseVector):
 
         return len(result) > 0
 
+    def field_exists(self, field: str) -> bool:
+        """
+        Check if a field exists in the collection.
+        """
+        return field in self._fields
+
+    def _process_search_results(
+        self, results: list[Any], output_fields: list[str], score_threshold: float = 0.0
+    ) -> list[Document]:
+        """
+        Common method to process search results
+
+        :param results: Search results
+        :param output_fields: Fields to be output
+        :param score_threshold: Score threshold for filtering
+        :return: List of documents
+        """
+        docs = []
+        for result in results[0]:
+            metadata = result["entity"].get(output_fields[1], {})
+            metadata["score"] = result["distance"]
+
+            if result["distance"] > score_threshold:
+                doc = Document(page_content=result["entity"].get(output_fields[0], ""), metadata=metadata)
+                docs.append(doc)
+
+        return docs
+
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-        # Set search parameters.
+        """
+        Search for documents by vector similarity.
+        """
         results = self._client.search(
             collection_name=self._collection_name,
             data=[query_vector],
+            anns_field=Field.VECTOR.value,
             limit=kwargs.get("top_k", 4),
             output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
         )
-        # Organize results.
-        docs = []
-        for result in results[0]:
-            metadata = result["entity"].get(Field.METADATA_KEY.value)
-            metadata["score"] = result["distance"]
-            score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if result["distance"] > score_threshold:
-                doc = Document(page_content=result["entity"].get(Field.CONTENT_KEY.value), metadata=metadata)
-                docs.append(doc)
-        return docs
+
+        return self._process_search_results(
+            results,
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            score_threshold=float(kwargs.get("score_threshold") or 0.0),
+        )
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        # milvus/zilliz doesn't support bm25 search
-        return []
+        """
+        Search for documents by full-text search (if hybrid search is enabled).
+        """
+        if not self._hybrid_search_enabled or not self.field_exists(Field.SPARSE_VECTOR.value):
+            logger.warning("Full-text search is not supported in current Milvus version (requires >= 2.5.0)")
+            return []
+
+        results = self._client.search(
+            collection_name=self._collection_name,
+            data=[query],
+            anns_field=Field.SPARSE_VECTOR.value,
+            limit=kwargs.get("top_k", 4),
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+        )
+
+        return self._process_search_results(
+            results,
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            score_threshold=float(kwargs.get("score_threshold") or 0.0),
+        )
 
     def create_collection(
         self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
     ):
+        """
+        Create a new collection in Milvus with the specified schema and index parameters.
+        """
         lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
             collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
@@ -161,7 +267,7 @@ class MilvusVector(BaseVector):
                 return
             # Grab the existing collection if it exists
             if not self._client.has_collection(self._collection_name):
-                from pymilvus import CollectionSchema, DataType, FieldSchema  # type: ignore
+                from pymilvus import CollectionSchema, DataType, FieldSchema, Function, FunctionType  # type: ignore
                 from pymilvus.orm.types import infer_dtype_bydata  # type: ignore
 
                 # Determine embedding dim
@@ -170,16 +276,36 @@ class MilvusVector(BaseVector):
                 if metadatas:
                     fields.append(FieldSchema(Field.METADATA_KEY.value, DataType.JSON, max_length=65_535))
 
-                # Create the text field
-                fields.append(FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535))
+                # Create the text field, enable_analyzer will be set True to support milvus automatically
+                # transfer text to sparse_vector, reference: https://milvus.io/docs/full-text-search.md
+                fields.append(
+                    FieldSchema(
+                        Field.CONTENT_KEY.value,
+                        DataType.VARCHAR,
+                        max_length=65_535,
+                        enable_analyzer=self._hybrid_search_enabled,
+                    )
+                )
                 # Create the primary key field
                 fields.append(FieldSchema(Field.PRIMARY_KEY.value, DataType.INT64, is_primary=True, auto_id=True))
                 # Create the vector field, supports binary or float vectors
                 fields.append(FieldSchema(Field.VECTOR.value, infer_dtype_bydata(embeddings[0]), dim=dim))
+                # Create Sparse Vector Index for the collection
+                if self._hybrid_search_enabled:
+                    fields.append(FieldSchema(Field.SPARSE_VECTOR.value, DataType.SPARSE_FLOAT_VECTOR))
 
-                # Create the schema for the collection
                 schema = CollectionSchema(fields)
 
+                # Create custom function to support text to sparse vector by BM25
+                if self._hybrid_search_enabled:
+                    bm25_function = Function(
+                        name="text_bm25_emb",
+                        input_field_names=[Field.CONTENT_KEY.value],
+                        output_field_names=[Field.SPARSE_VECTOR.value],
+                        function_type=FunctionType.BM25,
+                    )
+                    schema.add_function(bm25_function)
+
                 for x in schema.fields:
                     self._fields.append(x.name)
                 # Since primary field is auto-id, no need to track it
@@ -189,10 +315,15 @@ class MilvusVector(BaseVector):
                 index_params_obj = IndexParams()
                 index_params_obj.add_index(field_name=Field.VECTOR.value, **index_params)
 
+                # Create Sparse Vector Index for the collection
+                if self._hybrid_search_enabled:
+                    index_params_obj.add_index(
+                        field_name=Field.SPARSE_VECTOR.value, index_type="AUTOINDEX", metric_type="BM25"
+                    )
+
                 # Create the collection
-                collection_name = self._collection_name
                 self._client.create_collection(
-                    collection_name=collection_name,
+                    collection_name=self._collection_name,
                     schema=schema,
                     index_params=index_params_obj,
                     consistency_level=self._consistency_level,
@@ -200,12 +331,22 @@ class MilvusVector(BaseVector):
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def _init_client(self, config) -> MilvusClient:
+        """
+        Initialize and return a Milvus client.
+        """
         client = MilvusClient(uri=config.uri, user=config.user, password=config.password, db_name=config.database)
         return client
 
 
 class MilvusVectorFactory(AbstractVectorFactory):
+    """
+    Factory class for creating MilvusVector instances.
+    """
+
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> MilvusVector:
+        """
+        Initialize a MilvusVector instance for the given dataset.
+        """
         if dataset.index_struct_dict:
             class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix
@@ -222,5 +363,6 @@ class MilvusVectorFactory(AbstractVectorFactory):
                 user=dify_config.MILVUS_USER or "",
                 password=dify_config.MILVUS_PASSWORD or "",
                 database=dify_config.MILVUS_DATABASE or "",
+                enable_hybrid_search=dify_config.MILVUS_ENABLE_HYBRID_SEARCH or False,
             ),
         )

api/poetry.lock

@@ -469,13 +469,13 @@ vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
 name = "anyio"
-version = "4.7.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
-    {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
 ]
 
 [package.dependencies]
@@ -485,7 +485,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -856,13 +856,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.35.90"
+version = "1.35.94"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.35.90-py3-none-any.whl", hash = "sha256:51dcbe1b32e2ac43dac17091f401a00ce5939f76afe999081802009cce1e92e4"},
-    {file = "botocore-1.35.90.tar.gz", hash = "sha256:f007f58e8e3c1ad0412a6ddfae40ed92a7bca571c068cb959902bcf107f2ae48"},
+    {file = "botocore-1.35.94-py3-none-any.whl", hash = "sha256:d784d944865d8279c79d2301fc09ac28b5221d4e7328fb4e23c642c253b9932c"},
+    {file = "botocore-1.35.94.tar.gz", hash = "sha256:2b3309b356541faa4d88bb957dcac1d8004aa44953c0b7d4521a6cc5d3d5d6ba"},
 ]
 
 [package.dependencies]
@@ -1966,6 +1966,7 @@ files = [
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+    {file = "cryptography-44.0.0-cp37-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:60eb32934076fa07e4316b7b2742fa52cbb190b42c2df2863dbc4230a0a9b385"},
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@@ -1976,6 +1977,7 @@ files = [
     {file = "cryptography-44.0.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:c5eb858beed7835e5ad1faba59e865109f3e52b3783b9ac21e7e47dc5554e289"},
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+    {file = "cryptography-44.0.0-cp39-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:9abcc2e083cbe8dde89124a47e5e53ec38751f0d7dfd36801008f316a127d7ba"},
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@@ -2322,13 +2324,13 @@ files = [
 
 [[package]]
 name = "elastic-transport"
-version = "8.15.1"
+version = "8.17.0"
 description = "Transport classes and utilities shared among Python Elastic client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "elastic_transport-8.15.1-py3-none-any.whl", hash = "sha256:b5e82ff1679d8c7705a03fd85c7f6ef85d6689721762d41228dd312e34f331fc"},
-    {file = "elastic_transport-8.15.1.tar.gz", hash = "sha256:9cac4ab5cf9402668cf305ae0b7d93ddc0c7b61461d6d1027850db6da9cc5742"},
+    {file = "elastic_transport-8.17.0-py3-none-any.whl", hash = "sha256:59f553300866750e67a38828fede000576562a0e66930c641adb75249e0c95af"},
+    {file = "elastic_transport-8.17.0.tar.gz", hash = "sha256:e755f38f99fa6ec5456e236b8e58f0eb18873ac8fe710f74b91a16dd562de2a5"},
 ]
 
 [package.dependencies]
@@ -2372,27 +2374,6 @@ files = [
 [package.extras]
 dev = ["coverage", "pytest (>=7.4.4)"]
 
-[[package]]
-name = "environs"
-version = "9.5.0"
-description = "simplified environment variable parsing"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "environs-9.5.0-py2.py3-none-any.whl", hash = "sha256:1e549569a3de49c05f856f40bce86979e7d5ffbbc4398e7f338574c220189124"},
-    {file = "environs-9.5.0.tar.gz", hash = "sha256:a76307b36fbe856bdca7ee9161e6c466fd7fcffc297109a118c59b54e27e30c9"},
-]
-
-[package.dependencies]
-marshmallow = ">=3.0.0"
-python-dotenv = "*"
-
-[package.extras]
-dev = ["dj-database-url", "dj-email-url", "django-cache-url", "flake8 (==4.0.1)", "flake8-bugbear (==21.9.2)", "mypy (==0.910)", "pre-commit (>=2.4,<3.0)", "pytest", "tox"]
-django = ["dj-database-url", "dj-email-url", "django-cache-url"]
-lint = ["flake8 (==4.0.1)", "flake8-bugbear (==21.9.2)", "mypy (==0.910)", "pre-commit (>=2.4,<3.0)"]
-tests = ["dj-database-url", "dj-email-url", "django-cache-url", "pytest"]
-
 [[package]]
 name = "esdk-obs-python"
 version = "3.24.6.1"
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+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.67.1)"]
 
 [[package]]
 name = "grpcio-status"
@@ -4217,13 +4198,13 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.5"
+version = "6.5.2"
 description = "Read resources from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -5111,13 +5092,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.23.2"
+version = "3.24.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -5125,7 +5106,7 @@ packaging = ">=17.0"
 
 [package.extras]
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-docs = ["alabaster (==1.0.0)", "autodocsumm (==0.2.14)", "sphinx (==8.1.3)", "sphinx-issues (==5.0.0)", "sphinx-version-warning (==1.1.2)"]
+docs = ["alabaster (==1.0.0)", "autodocsumm (==0.2.14)", "sphinx (==8.1.3)", "sphinx-issues (==5.0.0)"]
 tests = ["pytest", "simplejson"]
 
 [[package]]
@@ -5646,6 +5627,17 @@ files = [
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 ]
 
+[[package]]
+name = "ndjson"
+version = "0.3.1"
+description = "JsonDecoder for ndjson"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "nest-asyncio"
 version = "1.6.0"
@@ -6027,13 +6019,13 @@ files = [
 
 [[package]]
 name = "opencensus-ext-azure"
-version = "1.1.13"
+version = "1.1.14"
 description = "OpenCensus Azure Monitor Exporter"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -6793,13 +6785,13 @@ pydantic = ">=1.9,<3.0"
 
 [[package]]
 name = "posthog"
-version = "3.7.4"
+version = "3.7.5"
 description = "Integrate PostHog into any python application."
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -6841,20 +6833,20 @@ dill = ["dill (>=0.3.9)"]
 
 [[package]]
 name = "primp"
-version = "0.9.2"
+version = "0.10.0"
 description = "HTTP client that can impersonate web browsers, mimicking their headers and `TLS/JA3/JA4/HTTP2` fingerprints"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -7464,13 +7456,13 @@ files = [
 
 [[package]]
 name = "pygments"
-version = "2.18.0"
+version = "2.19.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -7498,21 +7490,21 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pymilvus"
-version = "2.4.9"
+version = "2.5.3"
 description = "Python Sdk for Milvus"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
-environs = "<=9.5.0"
-grpcio = ">=1.49.1"
-milvus-lite = {version = ">=2.4.0,<2.5.0", markers = "sys_platform != \"win32\""}
+grpcio = ">=1.49.1,<=1.67.1"
+milvus-lite = {version = ">=2.4.0", markers = "sys_platform != \"win32\""}
 pandas = ">=1.2.4"
 protobuf = ">=3.20.0"
+python-dotenv = ">=1.0.1,<2.0.0"
 setuptools = ">69"
 ujson = ">=2.0.0"
 
@@ -7916,13 +7908,13 @@ typing-extensions = ">=4.9.0"
 
 [[package]]
 name = "python-dotenv"
-version = "1.0.0"
+version = "1.0.1"
 description = "Read key-value pairs from a .env file and set them as environment variables"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -8701,29 +8693,29 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.8.5"
+version = "0.8.6"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
 dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
-doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
-test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.16.5)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "sentry-sdk"
@@ -9105,23 +9104,23 @@ tornado = ["tornado (>=5)"]
 
 [[package]]
 name = "setuptools"
-version = "75.6.0"
+version = "75.7.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "setuptools-75.6.0-py3-none-any.whl", hash = "sha256:ce74b49e8f7110f9bf04883b730f4765b774ef3ef28f722cce7c273d253aaf7d"},
-    {file = "setuptools-75.6.0.tar.gz", hash = "sha256:8199222558df7c86216af4f84c30e9b34a61d8ba19366cc914424cdbd28252f6"},
+    {file = "setuptools-75.7.0-py3-none-any.whl", hash = "sha256:84fb203f278ebcf5cd08f97d3fb96d3fbed4b629d500b29ad60d11e00769b183"},
+    {file = "setuptools-75.7.0.tar.gz", hash = "sha256:886ff7b16cd342f1d1defc16fc98c9ce3fde69e087a4e1983d7ab634e5f41f4f"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.7.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"]
 core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (>=1.12,<1.14)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
 
 [[package]]
 name = "sgmllib3k"
@@ -9532,13 +9531,13 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "tencentcloud-sdk-python-common"
-version = "3.0.1294"
+version = "3.0.1298"
 description = "Tencent Cloud Common SDK for Python"
 optional = false
 python-versions = "*"
 files = [
-    {file = "tencentcloud-sdk-python-common-3.0.1294.tar.gz", hash = "sha256:a6d079690b69d60e8bfd1e27a65138e36d7f6cc57de7e7549c45a6084bc4743c"},
-    {file = "tencentcloud_sdk_python_common-3.0.1294-py2.py3-none-any.whl", hash = "sha256:bc43fb56e6a9d0f825d74f1cbdf159e0417ff3f4b59b9c75a73eeb6526259329"},
+    {file = "tencentcloud-sdk-python-common-3.0.1298.tar.gz", hash = "sha256:0f0f182410c1ceda5764ff8bcbef27aa6139caf1c5f5985d94ec731a41c8a59f"},
+    {file = "tencentcloud_sdk_python_common-3.0.1298-py2.py3-none-any.whl", hash = "sha256:c80929a0ff57ebee4ceec749dc82d5f2d1105b888e55175a7e9c722afc3a5d7a"},
 ]
 
 [package.dependencies]
@@ -9546,17 +9545,17 @@ requests = ">=2.16.0"
 
 [[package]]
 name = "tencentcloud-sdk-python-hunyuan"
-version = "3.0.1294"
+version = "3.0.1298"
 description = "Tencent Cloud Hunyuan SDK for Python"
 optional = false
 python-versions = "*"
 files = [
-    {file = "tencentcloud-sdk-python-hunyuan-3.0.1294.tar.gz", hash = "sha256:ca7463b26e54bd4dc922c5bce24f728b9fed1494d55a3a0a76594db74f347657"},
-    {file = "tencentcloud_sdk_python_hunyuan-3.0.1294-py2.py3-none-any.whl", hash = "sha256:b53ea5c7880623d649eb235a2a6865312db1276b03bf21d9520d2136d14dadf4"},
+    {file = "tencentcloud-sdk-python-hunyuan-3.0.1298.tar.gz", hash = "sha256:c3d86a577de02046d25682a3804955453555fa641082bb8765238460bded3f03"},
+    {file = "tencentcloud_sdk_python_hunyuan-3.0.1298-py2.py3-none-any.whl", hash = "sha256:f01e33318b6a4152ac88c500fda77f2cda1864eeca000cdd29c41e4f92f8de65"},
 ]
 
 [package.dependencies]
-tencentcloud-sdk-python-common = "3.0.1294"
+tencentcloud-sdk-python-common = "3.0.1298"
 
 [[package]]
 name = "termcolor"
@@ -10107,13 +10106,13 @@ files = [
 
 [[package]]
 name = "unstructured"
-version = "0.16.11"
+version = "0.16.12"
 description = "A library that prepares raw documents for downstream ML tasks."
 optional = false
 python-versions = "<3.13,>=3.9.0"
 files = [
-    {file = "unstructured-0.16.11-py3-none-any.whl", hash = "sha256:a92d5bc2c2b7bb23369641fb7a7f0daba1775639199306ce4cd83ca564a03763"},
-    {file = "unstructured-0.16.11.tar.gz", hash = "sha256:33ebf68aae11ce33c8a96335296557b5abd8ba96eaba3e5a1554c0b9eee40bb5"},
+    {file = "unstructured-0.16.12-py3-none-any.whl", hash = "sha256:bcac29ac1b38fba4228c5a1a7721d1aa7c48220f7c1dd43b563645c56e978c49"},
+    {file = "unstructured-0.16.12.tar.gz", hash = "sha256:c3133731c6edb9c2f474e62cb2b560cd0a8d578c4532ec14d8c0941e401770b0"},
 ]
 
 [package.dependencies]
@@ -10127,6 +10126,7 @@ html5lib = "*"
 langdetect = "*"
 lxml = "*"
 markdown = {version = "*", optional = true, markers = "extra == \"md\""}
+ndjson = "*"
 nltk = "*"
 numpy = "<2"
 psutil = "*"
@@ -10998,13 +10998,13 @@ requests = "*"
 
 [[package]]
 name = "zhipuai"
-version = "2.1.5.20241204"
+version = "2.1.5.20250106"
 description = "A SDK library for accessing big model apis from ZhipuAI"
 optional = false
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 files = [
-    {file = "zhipuai-2.1.5.20241204-py3-none-any.whl", hash = "sha256:063c7527d6741ced82eedb19d53fd24ce61cf43ab835ee3c0262843f59503a7c"},
-    {file = "zhipuai-2.1.5.20241204.tar.gz", hash = "sha256:888b42a83c8f1daf07375b84e560219eedab96b9f9e59542f0329928291db635"},
+    {file = "zhipuai-2.1.5.20250106-py3-none-any.whl", hash = "sha256:ca76095f32db501e36038fc1ac4b287b88ed90c4cdd28902d3b1a9365fff879b"},
+    {file = "zhipuai-2.1.5.20250106.tar.gz", hash = "sha256:45d391be336a210b360f126443f07882fa6d8184a148c46a8c7d0b7607d6d1f8"},
 ]
 
 [package.dependencies]
@@ -11220,4 +11220,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11,<3.13"
-content-hash = "8c74132f2fe0b8dc7318bfbfb1bd3dbf7cd2ecfd4fc430c8924b46edacc8d33e"
+content-hash = "907718f7ca775ad226c1f668f4bb6c6dbfa6cacc556fce43a8ad0b6f3c35095a"

api/pyproject.toml

@@ -71,7 +71,7 @@ pyjwt = "~2.8.0"
 pypdfium2 = "~4.30.0"
 python = ">=3.11,<3.13"
 python-docx = "~1.1.0"
-python-dotenv = "1.0.0"
+python-dotenv = "1.0.1"
 pyyaml = "~6.0.1"
 readabilipy = "0.2.0"
 redis = { version = "~5.0.3", extras = ["hiredis"] }
@@ -157,7 +157,7 @@ opensearch-py = "2.4.0"
 oracledb = "~2.2.1"
 pgvecto-rs = { version = "~0.2.1", extras = ['sqlalchemy'] }
 pgvector = "0.2.5"
-pymilvus = "~2.4.4"
+pymilvus = "~2.5.0"
 pymochow = "1.3.1"
 pyobvector = "~0.1.6"
 qdrant-client = "1.7.3"

api/tests/integration_tests/vdb/milvus/test_milvus.py

@@ -19,9 +19,9 @@ class MilvusVectorTest(AbstractVectorTest):
         )
 
     def search_by_full_text(self):
-        # milvus dos not support full text searching yet in < 2.3.x
+        # milvus support BM25 full text search after version 2.5.0-beta
         hits_by_full_text = self.vector.search_by_full_text(query=get_example_text())
-        assert len(hits_by_full_text) == 0
+        assert len(hits_by_full_text) >= 0
 
     def get_ids_by_metadata_field(self):
         ids = self.vector.get_ids_by_metadata_field(key="document_id", value=self.example_doc_id)

docker/.env.example

@@ -403,6 +403,7 @@ MILVUS_URI=http://127.0.0.1:19530
 MILVUS_TOKEN=
 MILVUS_USER=root
 MILVUS_PASSWORD=Milvus
+MILVUS_ENABLE_HYBRID_SEARCH=False
 
 # MyScale configuration, only available when VECTOR_STORE is `myscale`
 # For multi-language support, please set MYSCALE_FTS_PARAMS with referring to:

docker/docker-compose.yaml

@@ -138,6 +138,7 @@ x-shared-env: &shared-api-worker-env
   MILVUS_TOKEN: ${MILVUS_TOKEN:-}
   MILVUS_USER: ${MILVUS_USER:-root}
   MILVUS_PASSWORD: ${MILVUS_PASSWORD:-Milvus}
+  MILVUS_ENABLE_HYBRID_SEARCH: ${MILVUS_ENABLE_HYBRID_SEARCH:-False}
   MYSCALE_HOST: ${MYSCALE_HOST:-myscale}
   MYSCALE_PORT: ${MYSCALE_PORT:-8123}
   MYSCALE_USER: ${MYSCALE_USER:-default}
@@ -799,7 +800,7 @@ services:
 
   milvus-standalone:
     container_name: milvus-standalone
-    image: milvusdb/milvus:v2.3.1
+    image: milvusdb/milvus:v2.5.0-beta
     profiles:
       - milvus
     command: [ 'milvus', 'run', 'standalone' ]