Milvus integration (#771)

* Initial commit for Milvus integration

* Add latest docstring and tutorial changes

* Updating implementation of Milvus document store

* Add latest docstring and tutorial changes

* Adding tests and updating doc string

* Add latest docstring and tutorial changes

* Fixing issue caught by tests

* Addressing review comments

* Fixing mypy detected issue

* Fixing issue caught in test about sorting of vector ids

* fixing test

* Fixing generator test failure

* update docstrings

* Addressing review comments about multiple network call while fetching embedding from milvus server

* Add latest docstring and tutorial changes

* Ignoring mypy issue while converting vector_id to int

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>

.github/workflows/ci.yml

@@ -17,6 +17,9 @@ jobs:
     - name: Run Elasticsearch
       run: docker run -d -p 9200:9200 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms128m -Xmx128m" elasticsearch:7.9.2
 
+    - name: Run Milvus
+      run: docker run -d -p 19530:19530 -p 19121:19121 milvusdb/milvus:0.10.5-cpu-d010621-4eda95
+
     - name: Run Apache Tika
       run: docker run -d -p 9998:9998 -e "TIKA_CHILD_JAVA_OPTS=-JXms128m" -e "TIKA_CHILD_JAVA_OPTS=-JXmx128m" apache/tika:1.24.1
 

docs/_src/api/api/document_store.md

@@ -346,7 +346,7 @@ None
 #### delete\_all\_documents
 
 ```python
- | delete_all_documents(index: str, filters: Optional[Dict[str, List[str]]] = None)
+ | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
 ```
 
 Delete documents in an index. All documents are deleted if no filters are passed.
@@ -629,6 +629,7 @@ DocumentStore's default index (self.index) will be used.
 - `filters`: Optional filters to narrow down the documents to return.
 Example: {"name": ["some", "more"], "category": ["only_one"]}
 - `return_embedding`: Whether to return the document embeddings.
+- `batch_size`: When working with large number of documents, batching can help reduce memory footprint.
 
 <a name="sql.SQLDocumentStore.get_all_labels"></a>
 #### get\_all\_labels
@@ -763,7 +764,7 @@ the vector embeddings are indexed in a FAISS Index.
 #### \_\_init\_\_
 
 ```python
- | __init__(sql_url: str = "sqlite:///", vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: bool = False, update_existing_documents: bool = False, index: str = "document", similarity: str = "dot_product", **kwargs, ,)
+ | __init__(sql_url: str = "sqlite:///", vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: bool = False, update_existing_documents: bool = False, index: str = "document", similarity: str = "dot_product", embedding_field: str = "embedding", **kwargs, ,)
 ```
 
 **Arguments**:
@@ -796,6 +797,7 @@ added already exists.
 - `index`: Name of index in document store to use.
 - `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
 more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
+- `embedding_field`: Name of field containing an embedding vector.
 
 <a name="faiss.FAISSDocumentStore.write_documents"></a>
 #### write\_documents
@@ -881,7 +883,7 @@ None
 #### delete\_all\_documents
 
 ```python
- | delete_all_documents(index=None)
+ | delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
 ```
 
 Delete all documents from the document store.

haystack/document_store/base.py

@@ -198,6 +198,6 @@ class BaseDocumentStore(ABC):
             logger.error("File needs to be in json or jsonl format.")
 
     @abstractmethod
-    def delete_all_documents(self, index: str, filters: Optional[Dict[str, List[str]]] = None):
+    def delete_all_documents(self, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None):
         pass
 

haystack/document_store/elasticsearch.py

@@ -757,7 +757,7 @@ class ElasticsearchDocumentStore(BaseDocumentStore):
 
             bulk(self.client, doc_updates, request_timeout=300, refresh=self.refresh_type)
 
-    def delete_all_documents(self, index: str, filters: Optional[Dict[str, List[str]]] = None):
+    def delete_all_documents(self, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None):
         """
         Delete documents in an index. All documents are deleted if no filters are passed.
 
@@ -765,6 +765,7 @@ class ElasticsearchDocumentStore(BaseDocumentStore):
         :param filters: Optional filters to narrow down the documents to be deleted.
         :return: None
         """
+        index = index or self.index
         query: Dict[str, Any] = {"query": {}}
         if filters:
             filter_clause = []

haystack/document_store/faiss.py

@@ -41,6 +41,7 @@ class FAISSDocumentStore(SQLDocumentStore):
         update_existing_documents: bool = False,
         index: str = "document",
         similarity: str = "dot_product",
+        embedding_field: str = "embedding",
         **kwargs,
     ):
         """
@@ -72,6 +73,7 @@ class FAISSDocumentStore(SQLDocumentStore):
         :param index: Name of index in document store to use.
         :param similarity: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
                    more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
+        :param embedding_field: Name of field containing an embedding vector.
         """
         self.vector_dim = vector_dim
 
@@ -83,6 +85,7 @@ class FAISSDocumentStore(SQLDocumentStore):
                 self.faiss_index.set_direct_map_type(faiss.DirectMap.Hashtable)
 
         self.return_embedding = return_embedding
+        self.embedding_field = embedding_field
         if similarity == "dot_product":
             self.similarity = similarity
         else:
@@ -154,7 +157,7 @@ class FAISSDocumentStore(SQLDocumentStore):
 
     def _create_document_field_map(self) -> Dict:
         return {
-            self.index: "embedding",
+            self.index: self.embedding_field,
         }
 
     def update_embeddings(self, retriever: BaseRetriever, index: Optional[str] = None, batch_size: int = 10_000):
@@ -275,13 +278,13 @@ class FAISSDocumentStore(SQLDocumentStore):
             embeddings = np.array(embeddings, dtype="float32")
         self.faiss_index.train(embeddings)
 
-    def delete_all_documents(self, index=None):
+    def delete_all_documents(self, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None):
         """
         Delete all documents from the document store.
         """
         index = index or self.index
         self.faiss_index.reset()
-        super().delete_all_documents(index=index)
+        super().delete_all_documents(index=index, filters=filters)
 
     def query_by_embedding(self,
                            query_emb: np.array,

haystack/document_store/milvus.py

@@ -0,0 +1,498 @@
+import logging
+from typing import Any, Dict, Generator, List, Optional, Union
+
+import numpy
+import numpy as np
+
+from milvus import IndexType, MetricType, Milvus, Status
+from scipy.special import expit
+from tqdm import tqdm
+
+from haystack import Document
+from haystack.document_store.sql import SQLDocumentStore
+from haystack.retriever.base import BaseRetriever
+from haystack.utils import get_batches_from_generator
+
+logger = logging.getLogger(__name__)
+
+
+class MilvusDocumentStore(SQLDocumentStore):
+    """
+    Milvus (https://milvus.io/) is a highly reliable, scalable Document Store specialized on storing and processing vectors.
+    Therefore, it is particularly suited for Haystack users that work with dense retrieval methods (like DPR).
+    In contrast to FAISS, Milvus ...
+     - runs as a separate service (e.g. a Docker container) and can scale easily in a distributed environment
+     - allows dynamic data management (i.e. you can insert/delete vectors without recreating the whole index)
+     - encapsulates multiple ANN libraries (FAISS, ANNOY ...)
+
+    This class uses Milvus for all vector related storage, processing and querying.
+    The meta-data (e.g. for filtering) and the document text are however stored in a separate SQL Database as Milvus
+    does not allow these data types (yet).
+
+    Usage:
+    1. Start a Milvus server (see https://milvus.io/docs/v0.10.5/install_milvus.md)
+    2. Init a MilvusDocumentStore in Haystack
+    """
+
+    def __init__(
+            self,
+            sql_url: str = "sqlite:///",
+            milvus_url: str = "tcp://localhost:19530",
+            connection_pool: str = "SingletonThread",
+            index: str = "document",
+            vector_dim: int = 768,
+            index_file_size: int = 1024,
+            similarity: str = "dot_product",
+            index_type: IndexType = IndexType.FLAT,
+            index_param: Optional[Dict[str, Any]] = None,
+            search_param: Optional[Dict[str, Any]] = None,
+            update_existing_documents: bool = False,
+            return_embedding: bool = False,
+            embedding_field: str = "embedding",
+            **kwargs,
+    ):
+        """
+        :param sql_url: SQL connection URL for storing document texts and metadata. It defaults to a local, file based SQLite DB. For large scale
+                        deployment, Postgres is recommended. If using MySQL then same server can also be used for
+                        Milvus metadata. For more details see https://milvus.io/docs/v0.10.5/data_manage.md.
+        :param milvus_url: Milvus server connection URL for storing and processing vectors.
+                           Protocol, host and port will automatically be inferred from the URL.
+                           See https://milvus.io/docs/v0.10.5/install_milvus.md for instructions to start a Milvus instance.
+        :param connection_pool: Connection pool type to connect with Milvus server. Default: "SingletonThread".
+        :param index: Index name for text, embedding and metadata (in Milvus terms, this is the "collection name").
+        :param vector_dim: The embedding vector size. Default: 768.
+        :param index_file_size: Specifies the size of each segment file that is stored by Milvus and its default value is 1024 MB.
+         When the size of newly inserted vectors reaches the specified volume, Milvus packs these vectors into a new segment.
+         Milvus creates one index file for each segment. When conducting a vector search, Milvus searches all index files one by one.
+         As a rule of thumb, we would see a 30% ~ 50% increase in the search performance after changing the value of index_file_size from 1024 to 2048.
+         Note that an overly large index_file_size value may cause failure to load a segment into the memory or graphics memory.
+         (From https://milvus.io/docs/v0.10.5/performance_faq.md#How-can-I-get-the-best-performance-from-Milvus-through-setting-index_file_size)
+        :param similarity: The similarity function used to compare document vectors. 'dot_product' is the default and recommended for DPR embeddings.
+                           'cosine' is recommended for Sentence Transformers, but is not directly supported by Milvus.
+                           However, you can normalize your embeddings and use `dot_product` to get the same results.
+                           See https://milvus.io/docs/v0.10.5/metric.md?Inner-product-(IP)#floating.
+        :param index_type: Type of approximate nearest neighbour (ANN) index used. The choice here determines your tradeoff between speed and accuracy.
+                           Some popular options:
+                           - FLAT (default): Exact method, slow
+                           - IVF_FLAT, inverted file based heuristic, fast
+                           - HSNW: Graph based, fast
+                           - ANNOY: Tree based, fast
+                           See: https://milvus.io/docs/v0.10.5/index.md
+        :param index_param: Configuration parameters for the chose index_type needed at indexing time.
+                            For example: {"nlist": 16384} as the number of cluster units to create for index_type IVF_FLAT.
+                            See https://milvus.io/docs/v0.10.5/index.md
+        :param search_param: Configuration parameters for the chose index_type needed at query time
+                             For example: {"nprobe": 10} as the number of cluster units to query for index_type IVF_FLAT.
+                             See https://milvus.io/docs/v0.10.5/index.md
+        :param update_existing_documents: Whether to update any existing documents with the same ID when adding
+                                          documents. When set as True, any document with an existing ID gets updated.
+                                          If set to False, an error is raised if the document ID of the document being
+                                          added already exists.
+        :param return_embedding: To return document embedding.
+        :param embedding_field: Name of field containing an embedding vector.
+        """
+        self.milvus_server = Milvus(uri=milvus_url, pool=connection_pool)
+        self.vector_dim = vector_dim
+        self.index_file_size = index_file_size
+
+        if similarity == "dot_product":
+            self.metric_type = MetricType.L2
+        else:
+            raise ValueError("The Milvus document store can currently only support dot_product similarity. "
+                             "Please set similarity=\"dot_product\"")
+
+        self.index_type = index_type
+        self.index_param = index_param or {"nlist": 16384}
+        self.search_param = search_param or {"nprobe": 10}
+        self.index = index
+        self._create_collection_and_index_if_not_exist(self.index)
+        self.return_embedding = return_embedding
+        self.embedding_field = embedding_field
+
+        super().__init__(
+            url=sql_url,
+            update_existing_documents=update_existing_documents,
+            index=index
+        )
+
+    def __del__(self):
+        return self.milvus_server.close()
+
+    def _create_collection_and_index_if_not_exist(
+        self,
+        index: Optional[str] = None,
+        index_param: Optional[Dict[str, Any]] = None
+    ):
+        index = index or self.index
+        index_param = index_param or self.index_param
+
+        status, ok = self.milvus_server.has_collection(collection_name=index)
+        if not ok:
+            collection_param = {
+                'collection_name': index,
+                'dimension': self.vector_dim,
+                'index_file_size': self.index_file_size,
+                'metric_type': self.metric_type
+            }
+
+            status = self.milvus_server.create_collection(collection_param)
+            if status.code != Status.SUCCESS:
+                raise RuntimeError(f'Collection creation on Milvus server failed: {status}')
+
+            status = self.milvus_server.create_index(index, self.index_type, index_param)
+            if status.code != Status.SUCCESS:
+                raise RuntimeError(f'Index creation on Milvus server failed: {status}')
+
+    def _create_document_field_map(self) -> Dict:
+        return {
+            self.index: self.embedding_field,
+        }
+
+    def write_documents(
+            self, documents: Union[List[dict], List[Document]], index: Optional[str] = None, batch_size: int = 10_000
+    ):
+        """
+        Add new documents to the DocumentStore.
+
+        :param documents: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
+                                  them right away in Milvus. If not, you can later call update_embeddings() to create & index them.
+        :param index: (SQL) index name for storing the docs and metadata
+        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
+        :return:
+        """
+        index = index or self.index
+        self._create_collection_and_index_if_not_exist(index)
+        field_map = self._create_document_field_map()
+
+        if len(documents) == 0:
+            logger.warning("Calling DocumentStore.write_documents() with empty list")
+            return
+
+        document_objects = [Document.from_dict(d, field_map=field_map) if isinstance(d, dict) else d for d in documents]
+
+        add_vectors = False if document_objects[0].embedding is None else True
+
+        batched_documents = get_batches_from_generator(document_objects, batch_size)
+        with tqdm(total=len(document_objects)) as progress_bar:
+            for document_batch in batched_documents:
+                vector_ids = []
+                if add_vectors:
+                    doc_ids = []
+                    embeddings = []
+                    for doc in document_batch:
+                        doc_ids.append(doc.id)
+                        if isinstance(doc.embedding, np.ndarray):
+                            embeddings.append(doc.embedding.tolist())
+                        elif isinstance(doc.embedding, list):
+                            embeddings.append(doc.embedding)
+                        else:
+                            raise AttributeError(f'Format of supplied document embedding {type(doc.embedding)} is not '
+                                                 f'supported. Please use list or numpy.ndarray')
+
+                    if self.update_existing_documents:
+                        existing_docs = super().get_documents_by_id(ids=doc_ids, index=index)
+                        self._delete_vector_ids_from_milvus(documents=existing_docs, index=index)
+
+                    status, vector_ids = self.milvus_server.insert(collection_name=index, records=embeddings)
+                    if status.code != Status.SUCCESS:
+                        raise RuntimeError(f'Vector embedding insertion failed: {status}')
+
+                docs_to_write_in_sql = []
+                for idx, doc in enumerate(document_batch):
+                    meta = doc.meta
+                    if add_vectors:
+                        meta["vector_id"] = vector_ids[idx]
+                    docs_to_write_in_sql.append(doc)
+
+                super().write_documents(docs_to_write_in_sql, index=index)
+                progress_bar.update(batch_size)
+        progress_bar.close()
+
+        self.milvus_server.flush([index])
+        if self.update_existing_documents:
+            self.milvus_server.compact(collection_name=index)
+
+    def update_embeddings(self, retriever: BaseRetriever, index: Optional[str] = None, batch_size: int = 10_000):
+        """
+        Updates the embeddings in the the document store using the encoding model specified in the retriever.
+        This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).
+
+        :param retriever: Retriever to use to get embeddings for text
+        :param index: (SQL) index name for storing the docs and metadata
+        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
+        :return: None
+        """
+        index = index or self.index
+        self._create_collection_and_index_if_not_exist(index)
+
+        document_count = self.get_document_count(index=index)
+        if document_count == 0:
+            logger.warning("Calling DocumentStore.update_embeddings() on an empty index")
+            return
+
+        logger.info(f"Updating embeddings for {document_count} docs...")
+
+        result = self.get_all_documents_generator(index=index, batch_size=batch_size, return_embedding=False)
+        batched_documents = get_batches_from_generator(result, batch_size)
+        with tqdm(total=document_count) as progress_bar:
+            for document_batch in batched_documents:
+                self._delete_vector_ids_from_milvus(documents=document_batch, index=index)
+
+                embeddings = retriever.embed_passages(document_batch)  # type: ignore
+                embeddings_list = [embedding.tolist() for embedding in embeddings]
+                assert len(document_batch) == len(embeddings_list)
+
+                status, vector_ids = self.milvus_server.insert(collection_name=index, records=embeddings_list)
+                if status.code != Status.SUCCESS:
+                    raise RuntimeError(f'Vector embedding insertion failed: {status}')
+
+                vector_id_map = {}
+                for vector_id, doc in zip(vector_ids, document_batch):
+                    vector_id_map[doc.id] = vector_id
+
+                self.update_vector_ids(vector_id_map, index=index)
+                progress_bar.update(batch_size)
+        progress_bar.close()
+
+        self.milvus_server.flush([index])
+        self.milvus_server.compact(collection_name=index)
+
+    def query_by_embedding(self,
+                           query_emb: np.array,
+                           filters: Optional[dict] = None,
+                           top_k: int = 10,
+                           index: Optional[str] = None,
+                           return_embedding: Optional[bool] = None) -> List[Document]:
+        """
+        Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
+
+        :param query_emb: Embedding of the query (e.g. gathered from DPR)
+        :param filters: Optional filters to narrow down the search space.
+                        Example: {"name": ["some", "more"], "category": ["only_one"]}
+        :param top_k: How many documents to return
+        :param index: (SQL) index name for storing the docs and metadata
+        :param return_embedding: To return document embedding
+        :return:
+        """
+        if filters:
+            raise Exception("Query filters are not implemented for the MilvusDocumentStore.")
+
+        index = index or self.index
+        status, ok = self.milvus_server.has_collection(collection_name=index)
+        if status.code != Status.SUCCESS:
+            raise RuntimeError(f'Milvus has collection check failed: {status}')
+        if not ok:
+            raise Exception("No index exists. Use 'update_embeddings()` to create an index.")
+
+        if return_embedding is None:
+            return_embedding = self.return_embedding
+        index = index or self.index
+
+        query_emb = query_emb.reshape(1, -1).astype(np.float32)
+        status, search_result = self.milvus_server.search(
+            collection_name=index,
+            query_records=query_emb,
+            top_k=top_k,
+            params=self.search_param
+        )
+        if status.code != Status.SUCCESS:
+            raise RuntimeError(f'Vector embedding search failed: {status}')
+
+        vector_ids_for_query = []
+        scores_for_vector_ids: Dict[str, float] = {}
+        for vector_id_list, distance_list in zip(search_result.id_array, search_result.distance_array):
+            for vector_id, distance in zip(vector_id_list, distance_list):
+                vector_ids_for_query.append(str(vector_id))
+                scores_for_vector_ids[str(vector_id)] = distance
+
+        documents = self.get_documents_by_vector_ids(vector_ids_for_query, index=index)
+
+        if return_embedding:
+            self._populate_embeddings_to_docs(index=index, docs=documents)
+
+        for doc in documents:
+            doc.score = scores_for_vector_ids[doc.meta["vector_id"]]
+            doc.probability = float(expit(np.asarray(doc.score / 100)))
+
+        return documents
+
+    def delete_all_documents(self, index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None):
+        """
+        Delete all documents (from SQL AND Milvus).
+        :param index: (SQL) index name for storing the docs and metadata
+        :param filters: Optional filters to narrow down the search space.
+                        Example: {"name": ["some", "more"], "category": ["only_one"]}
+        :return: None
+        """
+        index = index or self.index
+        super().delete_all_documents(index=index, filters=filters)
+        status, ok = self.milvus_server.has_collection(collection_name=index)
+        if status.code != Status.SUCCESS:
+            raise RuntimeError(f'Milvus has collection check failed: {status}')
+        if ok:
+            status = self.milvus_server.drop_collection(collection_name=index)
+            if status.code != Status.SUCCESS:
+                raise RuntimeError(f'Milvus drop collection failed: {status}')
+
+            self.milvus_server.flush([index])
+            self.milvus_server.compact(collection_name=index)
+
+    def get_all_documents_generator(
+        self,
+        index: Optional[str] = None,
+        filters: Optional[Dict[str, List[str]]] = None,
+        return_embedding: Optional[bool] = None,
+        batch_size: int = 10_000,
+    ) -> Generator[Document, None, None]:
+        """
+        Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
+        document store and yielded as individual documents. This method can be used to iteratively process
+        a large number of documents without having to load all documents in memory.
+
+        :param index: Name of the index to get the documents from. If None, the
+                      DocumentStore's default index (self.index) will be used.
+        :param filters: Optional filters to narrow down the documents to return.
+                        Example: {"name": ["some", "more"], "category": ["only_one"]}
+        :param return_embedding: Whether to return the document embeddings.
+        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
+        """
+        index = index or self.index
+        documents = super().get_all_documents_generator(
+            index=index, filters=filters, batch_size=batch_size
+        )
+        if return_embedding is None:
+            return_embedding = self.return_embedding
+
+        for doc in documents:
+            if return_embedding:
+                self._populate_embeddings_to_docs(index=index, docs=[doc])
+            yield doc
+
+    def get_all_documents(
+            self,
+            index: Optional[str] = None,
+            filters: Optional[Dict[str, List[str]]] = None,
+            return_embedding: Optional[bool] = None,
+            batch_size: int = 10_000,
+    ) -> List[Document]:
+        """
+        Get documents from the document store (optionally using filter criteria).
+
+        :param index: Name of the index to get the documents from. If None, the
+                      DocumentStore's default index (self.index) will be used.
+        :param filters: Optional filters to narrow down the documents to return.
+                        Example: {"name": ["some", "more"], "category": ["only_one"]}
+        :param return_embedding: Whether to return the document embeddings.
+        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
+        """
+
+        index = index or self.index
+        result = self.get_all_documents_generator(
+            index=index, filters=filters, return_embedding=return_embedding, batch_size=batch_size
+        )
+        documents = list(result)
+        return documents
+
+    def get_document_by_id(self, id: str, index: Optional[str] = None) -> Optional[Document]:
+        """
+        Fetch a document by specifying its text id string
+
+        :param id: ID of the document
+        :param index: Name of the index to get the documents from. If None, the
+                      DocumentStore's default index (self.index) will be used.
+        """
+        documents = self.get_documents_by_id([id], index)
+        document = documents[0] if documents else None
+        return document
+
+    def get_documents_by_id(
+            self, ids: List[str], index: Optional[str] = None, batch_size: int = 10_000
+    ) -> List[Document]:
+        """
+        Fetch multiple documents by specifying their IDs (strings)
+
+        :param ids: List of IDs of the documents
+        :param index: Name of the index to get the documents from. If None, the
+                      DocumentStore's default index (self.index) will be used.
+        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
+        """
+        index = index or self.index
+        documents = super().get_documents_by_id(ids=ids, index=index)
+        if self.return_embedding:
+            self._populate_embeddings_to_docs(index=index, docs=documents)
+
+        return documents
+
+    def _populate_embeddings_to_docs(self, docs: List[Document], index: Optional[str] = None):
+        index = index or self.index
+        docs_with_vector_ids = []
+        for doc in docs:
+            if doc.meta and doc.meta.get("vector_id") is not None:
+                docs_with_vector_ids.append(doc)
+
+        if len(docs_with_vector_ids) == 0:
+            return
+
+        ids = [int(doc.meta.get("vector_id")) for doc in docs_with_vector_ids] # type: ignore
+        status, vector_embeddings = self.milvus_server.get_entity_by_id(
+            collection_name=index,
+            ids=ids
+        )
+        if status.code != Status.SUCCESS:
+            raise RuntimeError(f'Getting vector embedding by id failed: {status}')
+
+        for embedding, doc in zip(vector_embeddings, docs_with_vector_ids):
+            doc.embedding = numpy.array(embedding, dtype="float32")
+
+    def _delete_vector_ids_from_milvus(self, documents: List[Document], index: Optional[str] = None):
+        index = index or self.index
+        existing_vector_ids = []
+        for doc in documents:
+            if "vector_id" in doc.meta:
+                existing_vector_ids.append(int(doc.meta["vector_id"]))
+        if len(existing_vector_ids) > 0:
+            status = self.milvus_server.delete_entity_by_id(
+                collection_name=index,
+                id_array=existing_vector_ids
+            )
+            if status.code != Status.SUCCESS:
+                raise RuntimeError("E existing vector ids deletion failed: {status}")
+
+    def get_all_vectors(self, index=None) -> List[np.array]:
+        """
+        Helper function to dump all vectors stored in Milvus server.
+
+        :param index: Name of the index to get the documents from. If None, the
+                      DocumentStore's default index (self.index) will be used.
+        :return: List[np.array]: List of vectors.
+        """
+        index = index or self.index
+        status, collection_info = self.milvus_server.get_collection_stats(collection_name=index)
+        if not status.OK():
+            logger.info(f"Failed fetch stats from store ...")
+            return list()
+
+        logger.debug(f"collection_info = {collection_info}")
+
+        ids = list()
+        partition_list = collection_info["partitions"]
+        for partition in partition_list:
+            segment_list = partition["segments"]
+            for segment in segment_list:
+                segment_name = segment["name"]
+                status, id_list = self.milvus_server.list_id_in_segment(
+                    collection_name=index,
+                    segment_name=segment_name)
+                logger.debug(f"{status}: segment {segment_name} has {len(id_list)} vectors ...")
+                ids.extend(id_list)
+
+        if len(ids) == 0:
+            logger.info(f"No documents in the store ...")
+            return list()
+
+        status, vectors = self.milvus_server.get_entity_by_id(collection_name=index, ids=ids)
+        if not status.OK():
+            logger.info(f"Failed fetch document for ids {ids} from store ...")
+            return list()
+
+        return vectors

haystack/document_store/sql.py

@@ -165,6 +165,7 @@ class SQLDocumentStore(BaseDocumentStore):
         :param filters: Optional filters to narrow down the documents to return.
                         Example: {"name": ["some", "more"], "category": ["only_one"]}
         :param return_embedding: Whether to return the document embeddings.
+        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
         """
 
         index = index or self.index
@@ -408,7 +409,7 @@ class SQLDocumentStore(BaseDocumentStore):
         """
 
         if filters:
-            raise NotImplementedError("Delete by filters is not implemented for SQLDocumentStore.")
+            raise NotImplementedError(f"Delete by filters is not implemented for {type(self).__name__}")
         index = index or self.index
         documents = self.session.query(DocumentORM).filter_by(index=index)
         documents.delete(synchronize_session=False)

haystack/generator/transformers.py

@@ -176,7 +176,7 @@ class RAGenerator(BaseGenerator):
             embeddings = self.retriever.embed_passages(docs)
 
         embeddings_in_tensor = torch.cat(
-            [torch.from_numpy(embedding).unsqueeze(0) for embedding in embeddings],
+            [torch.from_numpy(embedding).float().unsqueeze(0) for embedding in embeddings],
             dim=0
         )
 

requirements.txt

@@ -23,3 +23,5 @@ httptools
 nltk
 more_itertools
 networkx
+# Refer milvus version support matrix at https://github.com/milvus-io/pymilvus#install-pymilvus
+pymilvus

test/conftest.py

@@ -6,6 +6,9 @@ from sys import platform
 import pytest
 import requests
 from elasticsearch import Elasticsearch
+from milvus import Milvus
+
+from haystack.document_store.milvus import MilvusDocumentStore
 from haystack.generator.transformers import RAGenerator, RAGeneratorType
 
 from haystack.retriever.sparse import ElasticsearchFilterOnlyRetriever, ElasticsearchRetriever, TfidfRetriever
@@ -78,6 +81,20 @@ def elasticsearch_fixture():
         time.sleep(30)
 
 
+@pytest.fixture(scope="session")
+def milvus_fixture():
+    # test if a Milvus server is already running. If not, start Milvus docker container locally.
+    # Make sure you have given > 6GB memory to docker engine
+    try:
+        milvus_server = Milvus(uri="tcp://localhost:19530", timeout=5, wait_timeout=5)
+        milvus_server.server_status(timeout=5)
+    except:
+        print("Starting Milvus ...")
+        status = subprocess.run(['docker run -d --name milvus_cpu_0.10.5 -p 19530:19530 -p 19121:19121 '
+                                 'milvusdb/milvus:0.10.5-cpu-d010621-4eda95'], shell=True)
+        time.sleep(40)
+
+
 @pytest.fixture(scope="session")
 def tika_fixture():
     try:
@@ -245,21 +262,19 @@ def get_retriever(retriever_type, document_store):
     return retriever
 
 
-@pytest.fixture(params=["elasticsearch", "faiss", "memory", "sql"])
+@pytest.fixture(params=["elasticsearch", "faiss", "memory", "sql", "milvus"])
 def document_store_with_docs(request, test_docs_xs):
     document_store = get_document_store(request.param)
     document_store.write_documents(test_docs_xs)
     yield document_store
-    if request.param == "faiss":
+    document_store.delete_all_documents()
-        document_store.faiss_index.reset()
 
 
-@pytest.fixture(params=["elasticsearch", "faiss", "memory", "sql"])
+@pytest.fixture(params=["elasticsearch", "faiss", "memory", "sql", "milvus"])
 def document_store(request, test_docs_xs):
     document_store = get_document_store(request.param)
     yield document_store
-    if request.param == "faiss":
+    document_store.delete_all_documents()
-        document_store.faiss_index.reset()
 
 
 def get_document_store(document_store_type, embedding_field="embedding"):
@@ -284,6 +299,14 @@ def get_document_store(document_store_type, embedding_field="embedding"):
             index="haystack_test",
         )
         return document_store
+    elif document_store_type == "milvus":
+        document_store = MilvusDocumentStore(
+            sql_url="sqlite://",
+            return_embedding=True,
+            embedding_field=embedding_field,
+            index="haystack_test",
+        )
+        return document_store
     else:
         raise Exception(f"No document store fixture for '{document_store_type}'")
 

test/test_document_store.py

@@ -113,7 +113,7 @@ def test_get_all_documents_generator(document_store):
 
 
 @pytest.mark.elasticsearch
-@pytest.mark.parametrize("document_store", ["elasticsearch", "sql", "faiss"], indirect=True)
+@pytest.mark.parametrize("document_store", ["elasticsearch", "sql", "faiss", "milvus"], indirect=True)
 @pytest.mark.parametrize("update_existing_documents", [True, False])
 def test_update_existing_documents(document_store, update_existing_documents):
     original_docs = [
@@ -177,7 +177,7 @@ def test_write_document_index(document_store):
 
 
 @pytest.mark.elasticsearch
-@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory"], indirect=True)
+@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "milvus"], indirect=True)
 def test_document_with_embeddings(document_store):
     documents = [
         {"text": "text1", "id": "1", "embedding": np.random.rand(768).astype(np.float32)},
@@ -196,7 +196,7 @@ def test_document_with_embeddings(document_store):
 
 
 @pytest.mark.parametrize("retriever", ["dpr", "embedding"], indirect=True)
-@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory"], indirect=True)
+@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "milvus"], indirect=True)
 def test_update_embeddings(document_store, retriever):
     documents = []
     for i in range(23):
@@ -232,17 +232,17 @@ def test_update_embeddings(document_store, retriever):
 
 @pytest.mark.elasticsearch
 def test_delete_all_documents(document_store_with_docs):
-    assert len(document_store_with_docs.get_all_documents(index="haystack_test")) == 3
+    assert len(document_store_with_docs.get_all_documents()) == 3
 
-    document_store_with_docs.delete_all_documents(index="haystack_test")
+    document_store_with_docs.delete_all_documents()
-    documents = document_store_with_docs.get_all_documents(index="haystack_test")
+    documents = document_store_with_docs.get_all_documents()
     assert len(documents) == 0
 
 
 @pytest.mark.elasticsearch
 @pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
 def test_delete_documents_with_filters(document_store_with_docs):
-    document_store_with_docs.delete_all_documents(index="haystack_test", filters={"meta_field": ["test1", "test2"]})
+    document_store_with_docs.delete_all_documents(filters={"meta_field": ["test1", "test2"]})
     documents = document_store_with_docs.get_all_documents()
     assert len(documents) == 1
     assert documents[0].meta["meta_field"] == "test3"

test/test_dpr_retriever.py

@@ -4,13 +4,14 @@ import numpy as np
 
 from haystack import Document
 from haystack.document_store.faiss import FAISSDocumentStore
+from haystack.document_store.milvus import MilvusDocumentStore
 from haystack.retriever.dense import DensePassageRetriever
 
 from transformers import DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast
 
 @pytest.mark.slow
 @pytest.mark.elasticsearch
-@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory"], indirect=True)
+@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "milvus"], indirect=True)
 @pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
 @pytest.mark.parametrize("return_embedding", [True, False])
 def test_dpr_retrieval(document_store, retriever, return_embedding):
@@ -71,7 +72,7 @@ def test_dpr_retrieval(document_store, retriever, return_embedding):
         assert res[0].embedding is None
 
     # test filtering
-    if not isinstance(document_store, FAISSDocumentStore):
+    if not isinstance(document_store, FAISSDocumentStore) and not isinstance(document_store, MilvusDocumentStore):
         res = retriever.retrieve(query="Which philosopher attacked Schopenhauer?", filters={"name": ["0", "2"]})
         assert len(res) == 2
         for r in res:

test/test_embedding_retriever.py

@@ -4,7 +4,7 @@ from haystack import Finder
 
 @pytest.mark.slow
 @pytest.mark.elasticsearch
-@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory"], indirect=True)
+@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "milvus"], indirect=True)
 @pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
 def test_embedding_retriever(retriever, document_store):
 

test/test_faiss_and_milvus.py

@@ -1,4 +1,5 @@
 import os
+from copy import deepcopy
 
 import faiss
 import numpy as np
@@ -63,9 +64,9 @@ def test_faiss_write_docs(document_store, index_buffer_size, batch_size):
 
 @pytest.mark.slow
 @pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
-@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
+@pytest.mark.parametrize("document_store", ["faiss", "milvus"], indirect=True)
 @pytest.mark.parametrize("batch_size", [4, 6])
-def test_faiss_update_docs(document_store, retriever, batch_size):
+def test_update_docs(document_store, retriever, batch_size):
     # initial write
     document_store.write_documents(DOCUMENTS)
 
@@ -82,9 +83,35 @@ def test_faiss_update_docs(document_store, retriever, batch_size):
         assert np.allclose(updated_embedding, stored_doc.embedding, rtol=0.01)
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
-@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
+@pytest.mark.parametrize("document_store", ["milvus", "faiss"], indirect=True)
-def test_faiss_update_with_empty_store(document_store, retriever):
+def test_update_exiting_docs(document_store, retriever):
+    document_store.update_existing_documents = True
+    old_document = Document(text="text_1")
+    # initial write
+    document_store.write_documents([old_document])
+    document_store.update_embeddings(retriever=retriever)
+    old_documents_indexed = document_store.get_all_documents()
+    assert len(old_documents_indexed) == 1
+
+    # Update document data
+    new_document = Document(text="text_2")
+    new_document.id = old_document.id
+    document_store.write_documents([new_document])
+    document_store.update_embeddings(retriever=retriever)
+    new_documents_indexed = document_store.get_all_documents()
+    assert len(new_documents_indexed) == 1
+
+    assert old_documents_indexed[0].id == new_documents_indexed[0].id
+    assert old_documents_indexed[0].text == "text_1"
+    assert new_documents_indexed[0].text == "text_2"
+    assert not np.allclose(old_documents_indexed[0].embedding, new_documents_indexed[0].embedding, rtol=0.01)
+
+
+@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
+@pytest.mark.parametrize("document_store", ["faiss", "milvus"], indirect=True)
+def test_update_with_empty_store(document_store, retriever):
     # Call update with empty doc store
     document_store.update_embeddings(retriever=retriever)
 
@@ -125,8 +152,8 @@ def test_faiss_retrieving(index_factory):
 
 
 @pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
-@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
+@pytest.mark.parametrize("document_store", ["faiss", "milvus"], indirect=True)
-def test_faiss_finding(document_store, retriever):
+def test_finding(document_store, retriever):
     document_store.write_documents(DOCUMENTS)
     finder = Finder(reader=None, retriever=retriever)
 
@@ -136,8 +163,8 @@ def test_faiss_finding(document_store, retriever):
 
 
 @pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
-@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
+@pytest.mark.parametrize("document_store", ["faiss", "milvus"], indirect=True)
-def test_faiss_pipeline(document_store, retriever):
+def test_pipeline(document_store, retriever):
     documents = [
         {"name": "name_1", "text": "text_1", "embedding": np.random.rand(768).astype(np.float32)},
         {"name": "name_2", "text": "text_2", "embedding": np.random.rand(768).astype(np.float32)},

test/test_generator.py

@@ -415,7 +415,7 @@ def test_rag_token_generator(rag_generator):
 @pytest.mark.elasticsearch
 @pytest.mark.parametrize(
     "retriever,document_store",
-    [("embedding", "memory"), ("embedding", "faiss"), ("elasticsearch", "elasticsearch")],
+    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "milvus"), ("elasticsearch", "elasticsearch")],
     indirect=True,
 )
 def test_generator_pipeline(document_store, retriever, rag_generator):

test/test_imports.py

@@ -1,6 +1,10 @@
 def test_module_imports():
     from haystack import Finder
     from haystack.document_store.sql import SQLDocumentStore
+    from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
+    from haystack.document_store.faiss import FAISSDocumentStore
+    from haystack.document_store.milvus import MilvusDocumentStore
+    from haystack.document_store.base import BaseDocumentStore
     from haystack.preprocessor.cleaning import clean_wiki_text
     from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
     from haystack.reader.farm import FARMReader
@@ -10,6 +14,10 @@ def test_module_imports():
 
     assert Finder is not None
     assert SQLDocumentStore is not None
+    assert ElasticsearchDocumentStore is not None
+    assert FAISSDocumentStore is not None
+    assert MilvusDocumentStore is not None
+    assert BaseDocumentStore is not None
     assert clean_wiki_text is not None
     assert convert_files_to_dicts is not None
     assert fetch_archive_from_http is not None

test/test_pipeline.py

@@ -67,7 +67,7 @@ def test_extractive_qa_answers_single_result(reader, retriever_with_docs):
 @pytest.mark.elasticsearch
 @pytest.mark.parametrize(
     "retriever,document_store",
-    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "elasticsearch")],
+    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "milvus"), ("embedding", "elasticsearch")],
     indirect=True,
 )
 def test_faq_pipeline(retriever, document_store):
@@ -97,7 +97,7 @@ def test_faq_pipeline(retriever, document_store):
 @pytest.mark.elasticsearch
 @pytest.mark.parametrize(
     "retriever,document_store",
-    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "elasticsearch")],
+    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "milvus"), ("embedding", "elasticsearch")],
     indirect=True,
 )
 def test_document_search_pipeline(retriever, document_store):

test/test_summarizer.py

@@ -57,7 +57,7 @@ def test_summarization_one_summary(summarizer):
 @pytest.mark.summarizer
 @pytest.mark.parametrize(
     "retriever,document_store",
-    [("embedding", "memory"), ("embedding", "faiss"), ("elasticsearch", "elasticsearch")],
+    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "milvus"), ("elasticsearch", "elasticsearch")],
     indirect=True,
 )
 def test_summarization_pipeline(document_store, retriever, summarizer):
@@ -79,7 +79,7 @@ def test_summarization_pipeline(document_store, retriever, summarizer):
 @pytest.mark.summarizer
 @pytest.mark.parametrize(
     "retriever,document_store",
-    [("embedding", "memory"), ("embedding", "faiss"), ("elasticsearch", "elasticsearch")],
+    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "milvus"), ("elasticsearch", "elasticsearch")],
     indirect=True,
 )
 def test_summarization_pipeline_one_summary(document_store, retriever, summarizer):