Source code for langchain_community.vectorstores.milvus

from __future__ import annotations

import logging
from typing import Any, Iterable, List, Optional, Tuple, Union
from uuid import uuid4

import numpy as np
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore

from langchain_community.vectorstores.utils import maximal_marginal_relevance

logger = logging.getLogger(__name__)

DEFAULT_MILVUS_CONNECTION = {
    "host": "localhost",
    "port": "19530",
    "user": "",
    "password": "",
    "secure": False,
}


[docs]@deprecated( since="0.2.0", removal="0.3.0", alternative_import="langchain_milvus.MilvusVectorStore", ) class Milvus(VectorStore): """`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. Args: 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 metadata field. Defaults to None. When metadata_field is specified, the document's metadata will store as json. 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: .. code-block:: python 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. """
[docs] def __init__( self, 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.""" try: from pymilvus import Collection, utility except ImportError: raise ImportError( "Could not import pymilvus python package. " "Please install it with `pip install pymilvus`." ) # Default search params when one is not provided. self.default_search_params = { "IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, "IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}}, "IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, "HNSW": {"metric_type": "L2", "params": {"ef": 10}}, "RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}}, "RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}}, "RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}}, "IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}}, "ANNOY": {"metric_type": "L2", "params": {"search_k": 10}}, "SCANN": {"metric_type": "L2", "params": {"search_k": 10}}, "AUTOINDEX": {"metric_type": "L2", "params": {}}, "GPU_CAGRA": { "metric_type": "L2", "params": { "itopk_size": 128, "search_width": 4, "min_iterations": 0, "max_iterations": 0, "team_size": 0, }, }, "GPU_IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, "GPU_IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, } self.embedding_func = embedding_function self.collection_name = collection_name self.collection_description = collection_description self.collection_properties = collection_properties self.index_params = index_params self.search_params = search_params self.consistency_level = consistency_level self.auto_id = auto_id # In order for a collection to be compatible, pk needs to be varchar self._primary_field = primary_field # In order for compatibility, the text field will need to be called "text" self._text_field = text_field # In order for compatibility, the vector field needs to be called "vector" self._vector_field = vector_field self._metadata_field = metadata_field self._partition_key_field = partition_key_field self.fields: list[str] = [] self.partition_names = partition_names self.replica_number = replica_number self.timeout = timeout self.num_shards = num_shards # Create the connection to the server if connection_args is None: connection_args = DEFAULT_MILVUS_CONNECTION self.alias = self._create_connection_alias(connection_args) self.col: Optional[Collection] = None # Grab the existing collection if it exists if utility.has_collection(self.collection_name, using=self.alias): self.col = Collection( self.collection_name, using=self.alias, ) if self.collection_properties is not None: self.col.set_properties(self.collection_properties) # If need to drop old, drop it if drop_old and isinstance(self.col, Collection): self.col.drop() self.col = None # Initialize the vector store self._init( partition_names=partition_names, replica_number=replica_number, timeout=timeout, )
@property def embeddings(self) -> Embeddings: return self.embedding_func def _create_connection_alias(self, connection_args: dict) -> str: """Create the connection to the Milvus server.""" from pymilvus import MilvusException, connections # Grab the connection arguments that are used for checking existing connection host: str = connection_args.get("host", None) port: Union[str, int] = connection_args.get("port", None) address: str = connection_args.get("address", None) uri: str = connection_args.get("uri", None) user = connection_args.get("user", None) # Order of use is host/port, uri, address if host is not None and port is not None: given_address = str(host) + ":" + str(port) elif uri is not None: if uri.startswith("https://"): given_address = uri.split("https://")[1] elif uri.startswith("http://"): given_address = uri.split("http://")[1] else: logger.error("Invalid Milvus URI: %s", uri) raise ValueError("Invalid Milvus URI: %s", uri) elif address is not None: given_address = address else: given_address = None logger.debug("Missing standard address type for reuse attempt") # User defaults to empty string when getting connection info if user is not None: tmp_user = user else: tmp_user = "" # If a valid address was given, then check if a connection exists if given_address is not None: for con in connections.list_connections(): addr = connections.get_connection_addr(con[0]) if ( con[1] and ("address" in addr) and (addr["address"] == given_address) and ("user" in addr) and (addr["user"] == tmp_user) ): logger.debug("Using previous connection: %s", con[0]) return con[0] # Generate a new connection if one doesn't exist alias = uuid4().hex try: connections.connect(alias=alias, **connection_args) logger.debug("Created new connection using: %s", alias) return alias except MilvusException as e: logger.error("Failed to create new connection using: %s", alias) raise e def _init( self, embeddings: Optional[list] = None, metadatas: Optional[list[dict]] = None, partition_names: Optional[list] = None, replica_number: int = 1, timeout: Optional[float] = None, ) -> None: if embeddings is not None: self._create_collection(embeddings, metadatas) self._extract_fields() self._create_index() self._create_search_params() self._load( partition_names=partition_names, replica_number=replica_number, timeout=timeout, ) def _create_collection( self, embeddings: list, metadatas: Optional[list[dict]] = None ) -> None: from pymilvus import ( Collection, CollectionSchema, DataType, FieldSchema, MilvusException, ) from pymilvus.orm.types import infer_dtype_bydata # Determine embedding dim dim = len(embeddings[0]) fields = [] if self._metadata_field is not None: fields.append(FieldSchema(self._metadata_field, DataType.JSON)) else: # Determine metadata schema if metadatas: # Create FieldSchema for each entry in metadata. for key, value in metadatas[0].items(): # Infer the corresponding datatype of the metadata dtype = infer_dtype_bydata(value) # Datatype isn't compatible if dtype == DataType.UNKNOWN or dtype == DataType.NONE: logger.error( ( "Failure to create collection, " "unrecognized dtype for key: %s" ), key, ) raise ValueError(f"Unrecognized datatype for {key}.") # Dataype is a string/varchar equivalent elif dtype == DataType.VARCHAR: fields.append( FieldSchema(key, DataType.VARCHAR, max_length=65_535) ) else: fields.append(FieldSchema(key, dtype)) # Create the text field fields.append( FieldSchema(self._text_field, DataType.VARCHAR, max_length=65_535) ) # Create the primary key field if self.auto_id: fields.append( FieldSchema( self._primary_field, DataType.INT64, is_primary=True, auto_id=True ) ) else: fields.append( FieldSchema( self._primary_field, DataType.VARCHAR, is_primary=True, auto_id=False, max_length=65_535, ) ) # Create the vector field, supports binary or float vectors fields.append( FieldSchema(self._vector_field, infer_dtype_bydata(embeddings[0]), dim=dim) ) # Create the schema for the collection schema = CollectionSchema( fields, description=self.collection_description, partition_key_field=self._partition_key_field, ) # Create the collection try: if self.num_shards is not None: # Issue with defaults: # https://github.com/milvus-io/pymilvus/blob/59bf5e811ad56e20946559317fed855330758d9c/pymilvus/client/prepare.py#L82-L85 self.col = Collection( name=self.collection_name, schema=schema, consistency_level=self.consistency_level, using=self.alias, num_shards=self.num_shards, ) else: self.col = Collection( name=self.collection_name, schema=schema, consistency_level=self.consistency_level, using=self.alias, ) # Set the collection properties if they exist if self.collection_properties is not None: self.col.set_properties(self.collection_properties) except MilvusException as e: logger.error( "Failed to create collection: %s error: %s", self.collection_name, e ) raise e def _extract_fields(self) -> None: """Grab the existing fields from the Collection""" from pymilvus import Collection if isinstance(self.col, Collection): schema = self.col.schema for x in schema.fields: self.fields.append(x.name) def _get_index(self) -> Optional[dict[str, Any]]: """Return the vector index information if it exists""" from pymilvus import Collection if isinstance(self.col, Collection): for x in self.col.indexes: if x.field_name == self._vector_field: return x.to_dict() return None def _create_index(self) -> None: """Create a index on the collection""" from pymilvus import Collection, MilvusException if isinstance(self.col, Collection) and self._get_index() is None: try: # If no index params, use a default HNSW based one if self.index_params is None: self.index_params = { "metric_type": "L2", "index_type": "HNSW", "params": {"M": 8, "efConstruction": 64}, } try: self.col.create_index( self._vector_field, index_params=self.index_params, using=self.alias, ) # If default did not work, most likely on Zilliz Cloud except MilvusException: # Use AUTOINDEX based index self.index_params = { "metric_type": "L2", "index_type": "AUTOINDEX", "params": {}, } self.col.create_index( self._vector_field, index_params=self.index_params, using=self.alias, ) logger.debug( "Successfully created an index on collection: %s", self.collection_name, ) except MilvusException as e: logger.error( "Failed to create an index on collection: %s", self.collection_name ) raise e def _create_search_params(self) -> None: """Generate search params based on the current index type""" from pymilvus import Collection if isinstance(self.col, Collection) and self.search_params is None: index = self._get_index() if index is not None: index_type: str = index["index_param"]["index_type"] metric_type: str = index["index_param"]["metric_type"] self.search_params = self.default_search_params[index_type] self.search_params["metric_type"] = metric_type def _load( self, partition_names: Optional[list] = None, replica_number: int = 1, timeout: Optional[float] = None, ) -> None: """Load the collection if available.""" from pymilvus import Collection, utility from pymilvus.client.types import LoadState timeout = self.timeout or timeout if ( isinstance(self.col, Collection) and self._get_index() is not None and utility.load_state(self.collection_name, using=self.alias) == LoadState.NotLoad ): self.col.load( partition_names=partition_names, replica_number=replica_number, timeout=timeout, )
[docs] def add_texts( self, 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. Args: 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. should be less than 65535 bytes. Required and work when auto_id is False. 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 Raises: MilvusException: Failure to add texts Returns: List[str]: The resulting keys for each inserted element. """ from pymilvus import Collection, MilvusException texts = list(texts) if not self.auto_id: assert isinstance( ids, list ), "A list of valid ids are required when auto_id is False." assert len(set(ids)) == len( texts ), "Different lengths of texts and unique ids are provided." assert all( len(x.encode()) <= 65_535 for x in ids ), "Each id should be a string less than 65535 bytes." try: embeddings = self.embedding_func.embed_documents(texts) except NotImplementedError: embeddings = [self.embedding_func.embed_query(x) for x in texts] if len(embeddings) == 0: logger.debug("Nothing to insert, skipping.") return [] # If the collection hasn't been initialized yet, perform all steps to do so if not isinstance(self.col, Collection): kwargs = {"embeddings": embeddings, "metadatas": metadatas} if self.partition_names: kwargs["partition_names"] = self.partition_names if self.replica_number: kwargs["replica_number"] = self.replica_number if self.timeout: kwargs["timeout"] = self.timeout self._init(**kwargs) # Dict to hold all insert columns insert_dict: dict[str, list] = { self._text_field: texts, self._vector_field: embeddings, } if not self.auto_id: insert_dict[self._primary_field] = ids # type: ignore[assignment] if self._metadata_field is not None: for d in metadatas: # type: ignore[union-attr] insert_dict.setdefault(self._metadata_field, []).append(d) else: # Collect the metadata into the insert dict. if metadatas is not None: for d in metadatas: for key, value in d.items(): keys = ( [x for x in self.fields if x != self._primary_field] if self.auto_id else [x for x in self.fields] ) if key in keys: insert_dict.setdefault(key, []).append(value) # Total insert count vectors: list = insert_dict[self._vector_field] total_count = len(vectors) pks: list[str] = [] assert isinstance(self.col, Collection) for i in range(0, total_count, batch_size): # Grab end index end = min(i + batch_size, total_count) # Convert dict to list of lists batch for insertion insert_list = [ insert_dict[x][i:end] for x in self.fields if x in insert_dict ] # Insert into the collection. try: res: Collection timeout = self.timeout or timeout res = self.col.insert(insert_list, timeout=timeout, **kwargs) pks.extend(res.primary_keys) except MilvusException as e: logger.error( "Failed to insert batch starting at entity: %s/%s", i, total_count ) raise e return pks
[docs] def similarity_search_by_vector( self, 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. Args: 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: Collection.search() keyword arguments. Returns: List[Document]: Document results for search. """ if self.col is None: logger.debug("No existing collection to search.") return [] timeout = self.timeout or timeout res = self.similarity_search_with_score_by_vector( embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs ) return [doc for doc, _ in res]
[docs] def similarity_search_with_score( self, 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 Args: 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: Collection.search() keyword arguments. Returns: List[float], List[Tuple[Document, any, any]]: """ if self.col is None: logger.debug("No existing collection to search.") return [] # Embed the query text. embedding = self.embedding_func.embed_query(query) timeout = self.timeout or timeout res = self.similarity_search_with_score_by_vector( embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs ) return res
[docs] def similarity_search_with_score_by_vector( self, 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 Args: 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: Collection.search() keyword arguments. Returns: List[Tuple[Document, float]]: Result doc and score. """ if self.col is None: logger.debug("No existing collection to search.") return [] if param is None: param = self.search_params # Determine result metadata fields with PK. output_fields = self.fields[:] output_fields.remove(self._vector_field) timeout = self.timeout or timeout # Perform the search. res = self.col.search( data=[embedding], anns_field=self._vector_field, param=param, limit=k, expr=expr, output_fields=output_fields, timeout=timeout, **kwargs, ) # Organize results. ret = [] for result in res[0]: data = {x: result.entity.get(x) for x in output_fields} doc = self._parse_document(data) pair = (doc, result.score) ret.append(pair) return ret
[docs] def max_marginal_relevance_search_by_vector( self, 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. Args: 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: 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: Collection.search() keyword arguments. Returns: List[Document]: Document results for search. """ if self.col is None: logger.debug("No existing collection to search.") return [] if param is None: param = self.search_params # Determine result metadata fields. output_fields = self.fields[:] output_fields.remove(self._vector_field) timeout = self.timeout or timeout # Perform the search. res = self.col.search( data=[embedding], anns_field=self._vector_field, param=param, limit=fetch_k, expr=expr, output_fields=output_fields, timeout=timeout, **kwargs, ) # Organize results. ids = [] documents = [] scores = [] for result in res[0]: data = {x: result.entity.get(x) for x in output_fields} doc = self._parse_document(data) documents.append(doc) scores.append(result.score) ids.append(result.id) vectors = self.col.query( expr=f"{self._primary_field} in {ids}", output_fields=[self._primary_field, self._vector_field], timeout=timeout, ) # Reorganize the results from query to match search order. vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors} ordered_result_embeddings = [vectors[x] for x in ids] # Get the new order of results. new_ordering = maximal_marginal_relevance( np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult ) # Reorder the values and return. ret = [] for x in new_ordering: # Function can return -1 index if x == -1: break else: ret.append(documents[x]) return ret
[docs] def delete( # type: ignore[no-untyped-def] self, 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. Args: ids: List of ids to delete. expr: Boolean expression that specifies the entities to delete. kwargs: Other parameters in Milvus delete api. """ if isinstance(ids, list) and len(ids) > 0: if expr is not None: logger.warning( "Both ids and expr are provided. " "Ignore expr and delete by ids." ) expr = f"{self._primary_field} in {ids}" else: assert isinstance( expr, str ), "Either ids list or expr string must be provided." return self.col.delete(expr=expr, **kwargs) # type: ignore[union-attr]
[docs] @classmethod def from_texts( cls, texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = "LangChainCollection", connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION, 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. Args: 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. Returns: Milvus: Milvus Vector Store """ if isinstance(ids, list) and len(ids) > 0: auto_id = False else: auto_id = True vector_db = cls( embedding_function=embedding, collection_name=collection_name, connection_args=connection_args, consistency_level=consistency_level, index_params=index_params, search_params=search_params, drop_old=drop_old, auto_id=auto_id, **kwargs, ) vector_db.add_texts(texts=texts, metadatas=metadatas, ids=ids) return vector_db
def _parse_document(self, data: dict) -> Document: return Document( page_content=data.pop(self._text_field), metadata=data.pop(self._metadata_field) if self._metadata_field else data, )
[docs] def get_pks(self, expr: str, **kwargs: Any) -> List[int] | None: """Get primary keys with expression Args: expr: Expression - E.g: "id in [1, 2]", or "title LIKE 'Abc%'" Returns: List[int]: List of IDs (Primary Keys) """ from pymilvus import MilvusException if self.col is None: logger.debug("No existing collection to get pk.") return None try: query_result = self.col.query( expr=expr, output_fields=[self._primary_field] ) except MilvusException as exc: logger.error("Failed to get ids: %s error: %s", self.collection_name, exc) raise exc pks = [item.get(self._primary_field) for item in query_result] return pks
[docs] def upsert( self, ids: Optional[List[str]] = None, documents: List[Document] | None = None, **kwargs: Any, ) -> List[str] | None: """Update/Insert documents to the vectorstore. Args: ids: IDs to update - Let's call get_pks to get ids with expression \n documents (List[Document]): Documents to add to the vectorstore. Returns: List[str]: IDs of the added texts. """ from pymilvus import MilvusException if documents is None or len(documents) == 0: logger.debug("No documents to upsert.") return None if ids is not None and len(ids): try: self.delete(ids=ids) except MilvusException: pass try: return self.add_documents(documents=documents, **kwargs) except MilvusException as exc: logger.error( "Failed to upsert entities: %s error: %s", self.collection_name, exc ) raise exc