from __future__ import annotations
import logging
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
from uuid import uuid4
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from langchain_milvus.utils.sparse import BaseSparseEmbedding
logger = logging.getLogger(__name__)
DEFAULT_MILVUS_CONNECTION = {
"uri": "http://localhost:19530",
}
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
[docs]def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices."""
if len(X) == 0 or len(Y) == 0:
return np.array([])
X = np.array(X)
Y = np.array(Y)
if X.shape[1] != Y.shape[1]:
raise ValueError(
f"Number of columns in X and Y must be the same. X has shape {X.shape} "
f"and Y has shape {Y.shape}."
)
try:
import simsimd as simd
X = np.array(X, dtype=np.float32)
Y = np.array(Y, dtype=np.float32)
Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
return Z
except ImportError:
logger.debug(
"Unable to import simsimd, defaulting to NumPy implementation. If you want "
"to use simsimd please install with `pip install simsimd`."
)
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
# Ignore divide by zero errors run time warnings as those are handled below.
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
[docs]def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance.
Args:
query_embedding: The query embedding.
embedding_list: The list of embeddings.
lambda_mult: The lambda multiplier. Defaults to 0.5.
k: The number of results to return. Defaults to 4.
Returns:
List[int]: The list of indices.
"""
if min(k, len(embedding_list)) <= 0:
return []
if query_embedding.ndim == 1:
query_embedding = np.expand_dims(query_embedding, axis=0)
similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
most_similar = int(np.argmax(similarity_to_query))
idxs = [most_similar]
selected = np.array([embedding_list[most_similar]])
while len(idxs) < min(k, len(embedding_list)):
best_score = -np.inf
idx_to_add = -1
similarity_to_selected = cosine_similarity(embedding_list, selected)
for i, query_score in enumerate(similarity_to_query):
if i in idxs:
continue
redundant_score = max(similarity_to_selected[i])
equation_score = (
lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idxs
[docs]class Milvus(VectorStore):
"""Milvus vector store integration.
Setup:
Install ``langchain_milvus`` package:
.. code-block:: bash
pip install -qU langchain_milvus
Key init args — indexing params:
collection_name: str
Name of the collection.
collection_description: str
Description of the collection.
embedding_function: Union[Embeddings, BaseSparseEmbedding]
Embedding function to use.
Key init args — client params:
connection_args: Optional[dict]
Connection arguments.
Instantiate:
.. code-block:: python
from langchain_milvus import Milvus
from langchain_openai import OpenAIEmbeddings
URI = "./milvus_example.db"
vector_store = Milvus(
embedding_function=OpenAIEmbeddings(),
connection_args={"uri": URI},
)
Add Documents:
.. code-block:: python
from langchain_core.documents import Document
document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"baz": "baz"})
document_3 = Document(page_content="i will be deleted :(", metadata={"baz": "qux"})
documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)
Delete Documents:
.. code-block:: python
vector_store.delete(ids=["3"])
Search:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'baz': 'baz', 'pk': '2'}]
Search with filter:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'baz': 'baz', 'pk': '2'}]
Search with score:
.. code-block:: python
results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.335463] foo [{'baz': 'bar', 'pk': '1'}]
Async:
.. code-block:: python
# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)
# delete documents
# await vector_store.adelete(ids=["3"])
# search
# results = vector_store.asimilarity_search(query="thud",k=1)
# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.335463] foo [{'baz': 'bar', 'pk': '1'}]
Use as Retriever:
.. code-block:: python
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")
.. code-block:: python
[Document(metadata={'baz': 'baz', 'pk': '2'}, page_content='thud')]
""" # noqa: E501
[docs] def __init__(
self,
embedding_function: Union[Embeddings, BaseSparseEmbedding], # type: ignore
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",
enable_dynamic_field: bool = False,
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,
metadata_schema: Optional[dict[str, Any]] = None,
):
"""Initialize the Milvus vector store."""
try:
from pymilvus import Collection, utility
except ImportError:
raise ValueError(
"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}},
"SPARSE_INVERTED_INDEX": {
"metric_type": "IP",
"params": {"drop_ratio_build": 0.2},
},
"SPARSE_WAND": {"metric_type": "IP", "params": {"drop_ratio_build": 0.2}},
}
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
if metadata_field:
logger.warning(
"DeprecationWarning: `metadata_field` is about to be deprecated, "
"please set `enable_dynamic_field`=True instead."
)
if enable_dynamic_field and metadata_field:
metadata_field = None
logger.warning(
"When `enable_dynamic_field` is True, `metadata_field` is ignored."
)
self.enable_dynamic_field = enable_dynamic_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
self.metadata_schema = metadata_schema
# 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) -> Union[Embeddings, BaseSparseEmbedding]: # type: ignore
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)
db_name = connection_args.get("db_name", "default")
# 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:
given_address = uri # Milvus lite
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)
and (addr.get("db_name", "default") == db_name)
):
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
@property
def _is_sparse_embedding(self) -> bool:
return isinstance(self.embedding_func, BaseSparseEmbedding)
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 # type: ignore
# Determine embedding dim
dim = len(embeddings[0])
fields = []
# If enable_dynamic_field, we don't need to create fields, and just pass it.
# In the future, when metadata_field is deprecated,
# This logical structure will be simplified like this:
# ```
# if not self.enable_dynamic_field and metadatas:
# for key, value in metadatas[0].items():
# ...
# ```
if self.enable_dynamic_field:
# If both dynamic fields and partition key field are enabled
if self._partition_key_field is not None:
# create the partition field
fields.append(
FieldSchema(
self._partition_key_field, DataType.VARCHAR, max_length=65_535
)
)
elif 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():
if key in [
self._vector_field,
self._primary_field,
self._text_field,
]:
logger.error(
(
"Failure to create collection, "
"metadata key: %s is reserved."
),
key,
)
raise ValueError(f"Metadata key {key} is reserved.")
# Infer the corresponding datatype of the metadata
if (
self.metadata_schema
and key in self.metadata_schema # type: ignore
and "dtype" in self.metadata_schema[key] # type: ignore
):
kwargs = self.metadata_schema[key].get("kwargs", {}) # type: ignore
fields.append(
FieldSchema(
name=key,
dtype=self.metadata_schema[key]["dtype"], # type: ignore
**kwargs,
)
)
else:
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}.")
# Datatype is a string/varchar equivalent
elif dtype == DataType.VARCHAR:
fields.append(
FieldSchema(key, DataType.VARCHAR, max_length=65_535)
)
# infer_dtype_bydata currently can't recognize array type,
# so this line can not be accessed.
# This line may need to be modified in the future when
# infer_dtype_bydata can recognize array type.
# https://github.com/milvus-io/pymilvus/issues/2165
elif dtype == DataType.ARRAY:
kwargs = self.metadata_schema[key]["kwargs"] # type: ignore
fields.append(
FieldSchema(name=key, dtype=DataType.ARRAY, **kwargs)
)
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
if self._is_sparse_embedding:
fields.append(FieldSchema(self._vector_field, DataType.SPARSE_FLOAT_VECTOR))
else:
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,
enable_dynamic_field=self.enable_dynamic_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:
if self._is_sparse_embedding:
self.index_params = {
"metric_type": "IP",
"index_type": "SPARSE_INVERTED_INDEX",
"params": {"drop_ratio_build": 0.2},
}
else:
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 # type: ignore
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. "
"You can set `auto_id` to True in this Milvus instance to generate "
"ids automatically, or specify string-type ids for each text."
)
assert len(set(ids)) == len(
texts
), "Different lengths of texts and unique ids are provided."
assert all(isinstance(x, str) for x in ids), "All ids should be strings."
assert all(
len(x.encode()) <= 65_535 for x in ids
), "Each id should be a string less than 65535 bytes."
else:
if ids is not None:
logger.warning(
"The ids parameter is ignored when auto_id is True. "
"The ids will be generated automatically."
)
try:
embeddings: list = 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)
insert_list: list[dict] = []
assert len(texts) == len(
embeddings
), "Mismatched lengths of texts and embeddings."
if metadatas is not None:
assert len(texts) == len(
metadatas
), "Mismatched lengths of texts and metadatas."
for i, text, embedding in zip(range(len(texts)), texts, embeddings):
entity_dict = {}
metadata = metadatas[i] if metadatas else {}
if not self.auto_id:
entity_dict[self._primary_field] = ids[i] # type: ignore[index]
entity_dict[self._text_field] = text
entity_dict[self._vector_field] = embedding
if self._metadata_field and not self.enable_dynamic_field:
entity_dict[self._metadata_field] = metadata
else:
for key, value in metadata.items():
# if not enable_dynamic_field, skip fields not in the collection.
if not self.enable_dynamic_field and key not in self.fields:
continue
# If enable_dynamic_field, all fields are allowed.
entity_dict[key] = value
insert_list.append(entity_dict)
# Total insert count
total_count = len(insert_list)
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)
batch_insert_list = insert_list[i:end]
# Insert into the collection.
try:
res: Collection
timeout = self.timeout or timeout
res = self.col.insert(batch_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
def _collection_search(
self,
embedding: List[float] | Dict[int, float],
k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[float] = None,
**kwargs: Any,
) -> "pymilvus.client.abstract.SearchResult | None": # type: ignore[name-defined] # noqa: F821
"""Perform a search on an embedding and return milvus search results.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md
Args:
embedding (List[float] | Dict[int, 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:
pymilvus.client.abstract.SearchResult: Milvus search result.
"""
if self.col is None:
logger.debug("No existing collection to search.")
return None
if param is None:
param = self.search_params
# Determine result metadata fields with PK.
if self.enable_dynamic_field:
output_fields = ["*"]
else:
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,
)
return res
[docs] def similarity_search(
self,
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.
Args:
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: 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(
query=query, k=k, param=param, expr=expr, timeout=timeout, **kwargs
)
return [doc for doc, _ in res]
[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/v2.4.x/ORM/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] | Dict[int, 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 an embedding 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/v2.4.x/ORM/Collection/search.md
Args:
embedding (List[float] | Dict[int, 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.
"""
col_search_res = self._collection_search(
embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs
)
if col_search_res is None:
return []
ret = []
for result in col_search_res[0]:
data = {x: result.entity.get(x) for x in result.entity.fields}
doc = self._parse_document(data)
pair = (doc, result.score)
ret.append(pair)
return ret
[docs] def max_marginal_relevance_search(
self,
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.
Args:
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: 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 []
embedding = self.embedding_func.embed_query(query)
timeout = self.timeout or timeout
return self.max_marginal_relevance_search_by_vector(
embedding=embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
param=param,
expr=expr,
timeout=timeout,
**kwargs,
)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: list[float] | dict[int, 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 (list[float] | dict[int, float]): 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.
"""
col_search_res = self._collection_search(
embedding=embedding,
k=fetch_k,
param=param,
expr=expr,
timeout=timeout,
**kwargs,
)
if col_search_res is None:
return []
ids = []
documents = []
scores = []
for result in col_search_res[0]:
data = {x: result.entity.get(x) for x in result.entity.fields}
doc = self._parse_document(data)
documents.append(doc)
scores.append(result.score)
ids.append(result.id)
vectors = self.col.query( # type: ignore[union-attr]
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: Union[Embeddings, BaseSparseEmbedding], # type: ignore
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 (Union[Embeddings, BaseSparseEmbedding]): 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:
if self._vector_field in data:
data.pop(self._vector_field)
return Document(
page_content=data.pop(self._text_field),
metadata=data.pop(self._metadata_field) if self._metadata_field else data,
)
[docs] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Run more documents through the embeddings and add to the vectorstore.
Args:
documents: Documents to add to the vectorstore.
Returns:
List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return self.add_texts(texts, metadatas, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Run more documents through the embeddings and add to the vectorstore.
Args:
documents: Documents to add to the vectorstore.
Returns:
List of IDs of the added texts.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return await self.aadd_texts(texts, metadatas, **kwargs)
[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( # type: ignore
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
