`langchain_milvus.vectorstores.milvus`.Milvus¶

class langchain_milvus.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)[source]¶

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”. or “path/to/local/directory/milvus_demo.db” for Milvus Lite.

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_milvus.vectorstores import Milvus from langchain_openai.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])	Async 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])	Async delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`amax_marginal_relevance_search`(query[, k, ...])	Async return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Async return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`astreaming_upsert`(items, /, batch_size, **kwargs)	[Beta] Upsert documents in a streaming fashion.
`aupsert`(items, /, **kwargs)	[Beta] Add or update documents in the vectorstore.
`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_by_ids`(ids, /)	Get documents by their IDs.
`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 a 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.
`streaming_upsert`(items, /, batch_size, **kwargs)	[Beta] Upsert documents in a streaming fashion.
`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)[source]¶

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][source]¶

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

Parameters

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

Async run more texts through the embeddings and add to the vectorstore.

Parameters

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – vectorstore specific parameters.

Returns

List of ids from adding the texts into the vectorstore.

Raises

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type

List[str]

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

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

Parameters

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

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]¶

Async delete by vector ID or other criteria.

Parameters

ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.
**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¶

Async return VectorStore initialized from documents and embeddings.

Parameters

documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
**kwargs (Any) – Additional keyword arguments.

Returns

VectorStore initialized from documents and embeddings.

Return type

VectorStore

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

Async return VectorStore initialized from texts and embeddings.

Parameters

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – Additional keyword arguments.

Returns

VectorStore initialized from texts and embeddings.

Return type

VectorStore

async aget_by_ids(ids: Sequence[str], /) → List[Document]¶

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters: ids (Sequence[str]) – List of ids to retrieve.
Returns: List of Documents.
Return type: List[Document]

New in version 0.2.11.

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

Async 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. Default is 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.
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]¶

Async return docs selected using the maximal marginal relevance.

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

Parameters

embedding (List[float]) – Embedding 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. Default is 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.
**kwargs (Any) – Arguments to pass to the search method.

Returns

List of Documents selected by maximal marginal relevance.

Return type

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever¶

Return VectorStoreRetriever initialized from this VectorStore.

Parameters

**kwargs (Any) –

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

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]¶

Async return docs most similar to query using a specified search type.

Parameters

query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.

Returns

List of Documents most similar to the query.

Raises

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type

List[Document]

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

Async return docs most similar to query.

Parameters

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns

List of Documents most similar to the query.

Return type

List[Document]

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

Async return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns

List of Documents most similar to the query vector.

Return type

List[Document]

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

Async 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]]

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

Async run similarity search with distance.

Parameters

*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.

Returns

List of Tuples of (doc, similarity_score).

Return type

List[Tuple[Document, float]]

astreaming_upsert(items: AsyncIterable[Document], /, batch_size: int, **kwargs: Any) → AsyncIterator[UpsertResponse]¶

[Beta] Upsert documents in a streaming fashion. Async version of streaming_upsert.

Parameters

items (AsyncIterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
**kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

AsyncIterator[UpsertResponse]

New in version 0.2.11.

Notes

async aupsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse¶

[Beta] Add or update documents in the vectorstore. Async version of upsert.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters

items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.
**kwargs (Any) – Additional keyword arguments.

Returns

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

UpsertResponse

New in version 0.2.11.

Notes

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

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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
**kwargs (Any) – Additional keyword arguments.

Returns

VectorStore initialized from documents and embeddings.

Return type

VectorStore

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'LangChainCollection', connection_args: dict[str, Any] = {'uri': 'http://localhost:19530'}, 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[source]¶

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_by_ids(ids: Sequence[str], /) → List[Document]¶

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters: ids (Sequence[str]) – List of ids to retrieve.
Returns: List of Documents.
Return type: List[Document]

New in version 0.2.11.

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

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][source]¶

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][source]¶

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 a specified search type.

Parameters

query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.

Returns

List of Documents most similar to the query.

Raises

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

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][source]¶

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][source]¶

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]][source]¶

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]][source]¶

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]]

streaming_upsert(items: Iterable[Document], /, batch_size: int, **kwargs: Any) → Iterator[UpsertResponse]¶

[Beta] Upsert documents in a streaming fashion.

Parameters

items (Iterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
**kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead, the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

Iterator[UpsertResponse]

New in version 0.2.11.

Notes

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

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]

langchain_milvus.vectorstores.milvus.Milvus¶

Examples using Milvus¶

`langchain_milvus.vectorstores.milvus`.Milvus¶