Toggle Menu
Prev Up Next

# `langchain_community.vectorstores.milvus`.Milvus

class langchain_community.vectorstores.milvus.Milvus(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', collection_description: str = '', collection_properties: Optional[dict[str, Any]] = None, connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: Optional[bool] = False, auto_id: bool = False, *, primary_field: str = 'pk', text_field: str = 'text', vector_field: str = 'vector', metadata_field: Optional[str] = None, partition_key_field: Optional[str] = None, partition_names: Optional[list] = None, replica_number: int = 1, timeout: Optional[float] = None, num_shards: Optional[int] = None)

  Milvus vector store.

  You need to install pymilvus and run Milvus.

  See the following documentation for how to run a Milvus instance: https://milvus.io/docs/install_standalone-docker.md

  If looking for a hosted Milvus, take a look at this documentation: https://zilliz.com/cloud and make use of the Zilliz vectorstore found in this project.

  IF USING L2/IP metric, IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.

  Parameters

    - embedding_function (Embeddings) – Function used to embed the text.

    - collection_name (str) – Which Milvus collection to use. Defaults to “LangChainCollection”.

    - collection_description (str) – The description of the collection. Defaults to “”.

    - collection_properties (Optional[dict[str, any]]) – The collection properties. Defaults to None. If set, will override collection existing properties. For example: {“collection.ttl.seconds”: 60}.

    - connection_args (Optional[dict[str, any]]) – The connection args used for this class comes in the form of a dict.

    - consistency_level (str) – The consistency level to use for a collection. Defaults to “Session”.

    - index_params (Optional[dict]) – Which index params to use. Defaults to HNSW/AUTOINDEX depending on service.

    - search_params (Optional[dict]) – Which search params to use. Defaults to default of index.

    - drop_old (Optional[bool]) – Whether to drop the current collection. Defaults to False.

    - auto_id (bool) – Whether to enable auto id for primary key. Defaults to False. If False, you needs to provide text ids (string less than 65535 bytes). If True, Milvus will generate unique integers as primary keys.

    - primary_field (str) – Name of the primary key field. Defaults to “pk”.

    - text_field (str) – Name of the text field. Defaults to “text”.

    - vector_field (str) – Name of the vector field. Defaults to “vector”.

    - metadata_field (str) – Name of the metadta field. Defaults to None. When metadata_field is specified, the document’s metadata will store as json.

    - partition_key_field (Optional[str]) –

    - partition_names (Optional[list]) –

    - replica_number (int) –

    - timeout (Optional[float]) –

    - num_shards (Optional[int]) –

  The connection args used for this class comes in the form of a dict, here are a few of the options:

  > address (str): The actual address of Milvus

  >   instance. Example address: “localhost:19530”

  > uri (str): The uri of Milvus instance. Example uri:

  >   “http://randomwebsite:19530”, “tcp:foobarsite:19530”, “https://ok.s3.south.com:19530”.

  > host (str): The host of Milvus instance. Default at “localhost”,

  >   PyMilvus will fill in the default host if only port is provided.

  > port (str/int): The port of Milvus instance. Default at 19530, PyMilvus

  >   will fill in the default port if only host is provided.

  > user (str): Use which user to connect to Milvus instance. If user and

  >   password are provided, we will add related header in every RPC call.

  > password (str): Required when user is provided. The password

  >   corresponding to the user.

  > secure (bool): Default is false. If set to true, tls will be enabled. client_key_path (str): If use tls two-way authentication, need to

  > > write the client.key path.

  > client_pem_path (str): If use tls two-way authentication, need to

  >   write the client.pem path.

  > ca_pem_path (str): If use tls two-way authentication, need to write

  >   the ca.pem path.

  > server_pem_path (str): If use tls one-way authentication, need to

  >   write the server.pem path.

  > server_name (str): If use tls, need to write the common name.

  Example

  from langchain_community.vectorstores import Milvus from langchain_community.embeddings import OpenAIEmbeddings

  embedding = OpenAIEmbeddings() # Connect to a milvus instance on localhost milvus_store = Milvus(

  > embedding_function = Embeddings, collection_name = “LangChainCollection”, drop_old = True, auto_id = True

  )

  Raises

    ValueError – If the pymilvus python package is not installed.

  Parameters

    - embedding_function (Embeddings) –

    - collection_name (str) –

    - collection_description (str) –

    - collection_properties (Optional[dict[str, Any]]) –

    - connection_args (Optional[dict[str, Any]]) –

    - consistency_level (str) –

    - index_params (Optional[dict]) –

    - search_params (Optional[dict]) –

    - drop_old (Optional[bool]) –

    - auto_id (bool) –

    - primary_field (str) –

    - text_field (str) –

    - vector_field (str) –

    - metadata_field (Optional[str]) –

    - partition_key_field (Optional[str]) –

    - partition_names (Optional[list]) –

    - replica_number (int) –

    - timeout (Optional[float]) –

    - num_shards (Optional[int]) –

  Initialize the Milvus vector store.

  Attributes

  `embeddings` | Access the query embedding object if available.

  Methods

  `__init__`(embedding_function[, ...]) | Initialize the Milvus vector store.
  `aadd_documents`(documents, **kwargs) | Run more documents through the embeddings and add to the vectorstore.
  `aadd_texts`(texts[, metadatas]) | Run more texts through the embeddings and add to the vectorstore.
  `add_documents`(documents, **kwargs) | Run more documents through the embeddings and add to the vectorstore.
  `add_texts`(texts[, metadatas, timeout, ...]) | Insert text data into Milvus.
  `adelete`([ids]) | Delete by vector ID or other criteria.
  `afrom_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `afrom_texts`(texts, embedding[, metadatas]) | Return VectorStore initialized from texts and embeddings.
  `amax_marginal_relevance_search`(query[, k, ...]) | Return docs selected using the maximal marginal relevance.
  `amax_marginal_relevance_search_by_vector`(...) | Return docs selected using the maximal marginal relevance.
  `as_retriever`(**kwargs) | Return VectorStoreRetriever initialized from this VectorStore.
  `asearch`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `asimilarity_search`(query[, k]) | Return docs most similar to query.
  `asimilarity_search_by_vector`(embedding[, k]) | Return docs most similar to embedding vector.
  `asimilarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1], asynchronously.
  `asimilarity_search_with_score`(*args, **kwargs) | Run similarity search with distance asynchronously.
  `delete`([ids, expr]) | Delete by vector ID or boolean expression.
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts, embedding[, metadatas, ...]) | Create a Milvus collection, indexes it with HNSW, and insert data.
  `get_pks`(expr, **kwargs) | Get primary keys with expression
  `max_marginal_relevance_search`(query[, k, ...]) | Perform a search and return results that are reordered by MMR.
  `max_marginal_relevance_search_by_vector`(...) | Perform a search and return results that are reordered by MMR.
  `search`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `similarity_search`(query[, k, param, expr, ...]) | Perform a similarity search against the query string.
  `similarity_search_by_vector`(embedding[, k, ...]) | Perform a similarity search against the query string.
  `similarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1].
  `similarity_search_with_score`(query[, k, ...]) | Perform a search on a query string and return results with score.
  `similarity_search_with_score_by_vector`(embedding) | Perform a search on a query string and return results with score.
  `upsert`([ids, documents]) | Update/Insert documents to the vectorstore.

  __init__(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', collection_description: str = '', collection_properties: Optional[dict[str, Any]] = None, connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: Optional[bool] = False, auto_id: bool = False, *, primary_field: str = 'pk', text_field: str = 'text', vector_field: str = 'vector', metadata_field: Optional[str] = None, partition_key_field: Optional[str] = None, partition_names: Optional[list] = None, replica_number: int = 1, timeout: Optional[float] = None, num_shards: Optional[int] = None)

    Initialize the Milvus vector store.

    Parameters

      - embedding_function (Embeddings) –

      - collection_name (str) –

      - collection_description (str) –

      - collection_properties (Optional[dict[str, Any]]) –

      - connection_args (Optional[dict[str, Any]]) –

      - consistency_level (str) –

      - index_params (Optional[dict]) –

      - search_params (Optional[dict]) –

      - drop_old (Optional[bool]) –

      - auto_id (bool) –

      - primary_field (str) –

      - text_field (str) –

      - vector_field (str) –

      - metadata_field (Optional[str]) –

      - partition_key_field (Optional[str]) –

      - partition_names (Optional[list]) –

      - replica_number (int) –

      - timeout (Optional[float]) –

      - num_shards (Optional[int]) –

  async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]

    Run more documents through the embeddings and add to the vectorstore.

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]

    Run more texts through the embeddings and add to the vectorstore.

    Parameters

      - texts (Iterable[str]) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      List[str]

  add_documents(documents: List[Document], **kwargs: Any) → List[str]

    Run more documents through the embeddings and add to the vectorstore.

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, timeout: Optional[float] = None, batch_size: int = 1000, *, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

    Insert text data into Milvus.

    Inserting data when the collection has not be made yet will result in creating a new Collection. The data of the first entity decides the schema of the new collection, the dim is extracted from the first embedding and the columns are decided by the first metadata dict. Metadata keys will need to be present for all inserted values. At the moment there is no None equivalent in Milvus.

    Parameters

      - texts (Iterable[str]) – The texts to embed, it is assumed that they all fit in memory.

      - metadatas (Optional[List[dict]]) – Metadata dicts attached to each of the texts. Defaults to None.

      - False. (should be less than 65535 bytes. Required and work when auto_id is) –

      - timeout (Optional[float]) – Timeout for each batch insert. Defaults to None.

      - batch_size (int, optional) – Batch size to use for insertion. Defaults to 1000.

      - ids (Optional[List[str]]) – List of text ids. The length of each item

      - kwargs (Any) –

    Raises

      MilvusException – Failure to add texts

    Returns

      The resulting keys for each inserted element.

    Return type

      List[str]

  async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

    Delete by vector ID or other criteria.

    Parameters

      - ids (Optional[List[str]]) – List of ids to delete.

      - **kwargs (Any) – Other keyword arguments that subclasses might use.

    Returns

      True if deletion is successful, False otherwise, None if not implemented.

    Return type

      Optional[bool]

  async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - embedding (Embeddings) –

      - kwargs (Any) –

    Return type

      VST

  async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

    Return VectorStore initialized from texts and embeddings.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      VST

  async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

    Parameters

      - query (str) – Text to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - fetch_k (int) –

      - lambda_mult (float) –

      - kwargs (Any) –

    Return type

      List[Document]

  as_retriever(**kwargs: Any) → VectorStoreRetriever

    Return VectorStoreRetriever initialized from this VectorStore.

    Parameters

      - search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

      - search_kwargs (Optional[Dict]) –

        Keyword arguments to pass to the search function. Can include things like:

        > k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

        > > for similarity_score_threshold

        > fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

        > > 1 for minimum diversity and 0 for maximum. (Default: 0.5)

        > filter: Filter by document metadata

      - kwargs (Any) –

    Returns

      Retriever class for VectorStore.

    Return type

      VectorStoreRetriever

    Examples:

        # Retrieve more documents with higher diversity
        # Useful if your dataset has many similar documents
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 6, 'lambda_mult': 0.25}
        )

        # Fetch more documents for the MMR algorithm to consider
        # But only return the top 5
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 5, 'fetch_k': 50}
        )

        # Only retrieve documents that have a relevance score
        # Above a certain threshold
        docsearch.as_retriever(
            search_type="similarity_score_threshold",
            search_kwargs={'score_threshold': 0.8}
        )

        # Only get the single most similar document from the dataset
        docsearch.as_retriever(search_kwargs={'k': 1})

        # Use a filter to only retrieve documents from a specific paper
        docsearch.as_retriever(
            search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
        )

  async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

    Return docs most similar to query.

    Parameters

      - query (str) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

    Return docs most similar to embedding vector.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs and relevance scores in the range [0, 1], asynchronously.

    0 is dissimilar, 1 is most similar.

    Parameters

      - query (str) – input text

      - k (int) – Number of Documents to return. Defaults to 4.

      - **kwargs (Any) –

        kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

        > filter the resulting set of retrieved docs

    Returns

      List of Tuples of (doc, similarity_score)

    Return type

      List[Tuple[Document, float]]

  async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

    Run similarity search with distance asynchronously.

    Parameters

      - args (Any) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]

  delete(ids: Optional[List[str]] = None, expr: Optional[str] = None, **kwargs: str)

    Delete by vector ID or boolean expression. Refer to [Milvus documentation](https://milvus.io/docs/delete_data.md) for notes and examples of expressions.

    Parameters

      - ids (Optional[List[str]]) – List of ids to delete.

      - expr (Optional[str]) – Boolean expression that specifies the entities to delete.

      - kwargs (str) – Other parameters in Milvus delete api.

  classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - embedding (Embeddings) –

      - kwargs (Any) –

    Return type

      VST

  classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'LangChainCollection', connection_args: dict[str, Any] = {'host': 'localhost', 'password': '', 'port': '19530', 'secure': False, 'user': ''}, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: bool = False, *, ids: Optional[List[str]] = None, **kwargs: Any) → Milvus

    Create a Milvus collection, indexes it with HNSW, and insert data.

    Parameters

      - texts (List[str]) – Text data.

      - embedding (Embeddings) – Embedding function.

      - metadatas (Optional[List[dict]]) – Metadata for each text if it exists. Defaults to None.

      - collection_name (str, optional) – Collection name to use. Defaults to “LangChainCollection”.

      - connection_args (dict[str, Any], optional) – Connection args to use. Defaults to DEFAULT_MILVUS_CONNECTION.

      - consistency_level (str, optional) – Which consistency level to use. Defaults to “Session”.

      - index_params (Optional[dict], optional) – Which index_params to use. Defaults to None.

      - search_params (Optional[dict], optional) – Which search params to use. Defaults to None.

      - drop_old (Optional[bool], optional) – Whether to drop the collection with that name if it exists. Defaults to False.

      - ids (Optional[List[str]]) – List of text ids. Defaults to None.

      - kwargs (Any) –

    Returns

      Milvus Vector Store

    Return type

      Milvus

  get_pks(expr: str, **kwargs: Any) → Optional[List[int]]

    Get primary keys with expression

    Parameters

      - expr (str) – Expression - E.g: “id in [1, 2]”, or “title LIKE ‘Abc%’”

      - kwargs (Any) –

    Returns

      List of IDs (Primary Keys)

    Return type

      List[int]

  max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document]

    Perform a search and return results that are reordered by MMR.

    Parameters

      - query (str) – The text being searched.

      - k (int, optional) – How many results to give. Defaults to 4.

      - fetch_k (int, optional) – Total results to select k from. Defaults to 20.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5

      - param (dict, optional) – The search params for the specified index. Defaults to None.

      - expr (str, optional) – Filtering expression. Defaults to None.

      - timeout (float, optional) – How long to wait before timeout error. Defaults to None.

      - kwargs (Any) – Collection.search() keyword arguments.

    Returns

      Document results for search.

    Return type

      List[Document]

  max_marginal_relevance_search_by_vector(embedding: list[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document]

    Perform a search and return results that are reordered by MMR.

    Parameters

      - embedding (str) – The embedding vector being searched.

      - k (int, optional) – How many results to give. Defaults to 4.

      - fetch_k (int, optional) – Total results to select k from. Defaults to 20.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5

      - param (dict, optional) – The search params for the specified index. Defaults to None.

      - expr (str, optional) – Filtering expression. Defaults to None.

      - timeout (float, optional) – How long to wait before timeout error. Defaults to None.

      - kwargs (Any) – Collection.search() keyword arguments.

    Returns

      Document results for search.

    Return type

      List[Document]

  search(query: str, search_type: str, **kwargs: Any) → List[Document]

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  similarity_search(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document]

    Perform a similarity search against the query string.

    Parameters

      - query (str) – The text to search.

      - k (int, optional) – How many results to return. Defaults to 4.

      - param (dict, optional) – The search params for the index type. Defaults to None.

      - expr (str, optional) – Filtering expression. Defaults to None.

      - timeout (int, optional) – How long to wait before timeout error. Defaults to None.

      - kwargs (Any) – Collection.search() keyword arguments.

    Returns

      Document results for search.

    Return type

      List[Document]

  similarity_search_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Document]

    Perform a similarity search against the query string.

    Parameters

      - embedding (List[float]) – The embedding vector to search.

      - k (int, optional) – How many results to return. Defaults to 4.

      - param (dict, optional) – The search params for the index type. Defaults to None.

      - expr (str, optional) – Filtering expression. Defaults to None.

      - timeout (int, optional) – How long to wait before timeout error. Defaults to None.

      - kwargs (Any) – Collection.search() keyword arguments.

    Returns

      Document results for search.

    Return type

      List[Document]

  similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs and relevance scores in the range [0, 1].

    0 is dissimilar, 1 is most similar.

    Parameters

      - query (str) – input text

      - k (int) – Number of Documents to return. Defaults to 4.

      - **kwargs (Any) –

        kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

        > filter the resulting set of retrieved docs

    Returns

      List of Tuples of (doc, similarity_score)

    Return type

      List[Tuple[Document, float]]

  similarity_search_with_score(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Perform a search on a query string and return results with score.

    For more information about the search parameters, take a look at the pymilvus documentation found here: https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md

    Parameters

      - query (str) – The text being searched.

      - k (int, optional) – The amount of results to return. Defaults to 4.

      - param (dict) – The search params for the specified index. Defaults to None.

      - expr (str, optional) – Filtering expression. Defaults to None.

      - timeout (float, optional) – How long to wait before timeout error. Defaults to None.

      - kwargs (Any) – Collection.search() keyword arguments.

    Return type

      List[float], List[Tuple[Document, any, any]]

  similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Perform a search on a query string and return results with score.

    For more information about the search parameters, take a look at the pymilvus documentation found here: https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md

    Parameters

      - embedding (List[float]) – The embedding vector being searched.

      - k (int, optional) – The amount of results to return. Defaults to 4.

      - param (dict) – The search params for the specified index. Defaults to None.

      - expr (str, optional) – Filtering expression. Defaults to None.

      - timeout (float, optional) – How long to wait before timeout error. Defaults to None.

      - kwargs (Any) – Collection.search() keyword arguments.

    Returns

      Result doc and score.

    Return type

      List[Tuple[Document, float]]

  upsert(ids: Optional[List[str]] = None, documents: Optional[List[Document]] = None, **kwargs: Any) → Optional[List[str]]

    Update/Insert documents to the vectorstore.

    Parameters

      - ids (Optional[List[str]]) – IDs to update - Let’s call get_pks to get ids with expression

      - documents (List[Document]) – Documents to add to the vectorstore.

      - kwargs (Any) –

    Returns

      IDs of the added texts.

    Return type

      List[str]

# Examples using Milvus

- Milvus

- This example only specifies a relevant query

- This will only get documents for Ankush

- Zilliz

- replace