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feat: Add milvus vector store (#2634)

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Jael Gu 2024-07-13 03:47:55 +08:00 • committed by GitHub
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27
docs/docs/components/vector-stores.mdx
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@ -167,6 +167,33 @@ For more details, see the [FAISS Component Documentation](https://faiss.ai/index
---
### Milvus
`Milvus` builds a vector store based on Milvus or Zilliz Cloud service to support document ingestion and search.
**Parameters:**
- **Input:** Documents or Data.
- **Embedding:** Model used in Milvus vector store.
- **Collection Name:** Collection identifier in Milvus.
- **Connection URI:** URI used to connect Milvus or Zilliz Cloud service.
---
### Milvus Search
`MilvusSearch` searches a Milvus Vector Store for documents similar to the input, using advanced similarity metrics.
**Parameters:**
- **Input Value:** Search query.
- **Connection URI:** URI used to connect Milvus or Zilliz Cloud service.
- **Embedding:** Model used in Milvus vector store.
- **Collection Name:** Collection identifier in Milvus.
- **Number of Results:** Number of search results to return.
---
### MongoDB Atlas
`MongoDBAtlas` builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents.

184
poetry.lock generated
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@ -1057,13 +1057,13 @@ uvicorn = {version = ">=0.18.3", extras = ["standard"]}
[[package]]
name = "clarifai"
version = "10.5.3"
version = "10.5.4"
description = "Clarifai Python SDK"
optional = false
python-versions = ">=3.8"
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[package.dependencies]
@ -2069,6 +2069,27 @@ typing-extensions = ">=4.7.0"
[package.extras]
dev = ["coverage", "pytest (>=7.4.4)"]
[[package]]
name = "environs"
version = "9.5.0"
description = "simplified environment variable parsing"
optional = false
python-versions = ">=3.6"
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python-dotenv = "*"
[package.extras]
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django = ["dj-database-url", "dj-email-url", "django-cache-url"]
lint = ["flake8 (==4.0.1)", "flake8-bugbear (==21.9.2)", "mypy (==0.910)", "pre-commit (>=2.4,<3.0)"]
tests = ["dj-database-url", "dj-email-url", "django-cache-url", "pytest"]
[[package]]
name = "exceptiongroup"
version = "1.2.1"
@ -3305,61 +3326,61 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
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{file = "grpcio-1.63.0.tar.gz", hash = "sha256:f3023e14805c61bc439fb40ca545ac3d5740ce66120a678a3c6c2c55b70343d1"},
]
[package.extras]
protobuf = ["grpcio-tools (>=1.64.1)"]
protobuf = ["grpcio-tools (>=1.63.0)"]
[[package]]
name = "grpcio-health-checking"
@ -4683,6 +4704,22 @@ files = [
groq = ">=0.4.1,<1"
langchain-core = ">=0.2.2,<0.3"
[[package]]
name = "langchain-milvus"
version = "0.1.1"
description = "An integration package connecting Milvus and LangChain"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_milvus-0.1.1-py3-none-any.whl", hash = "sha256:af382659041623441dae72e22ed53c6e73305c8b48d91b4481d92954cd6a3cd1"},
{file = "langchain_milvus-0.1.1.tar.gz", hash = "sha256:27dbabde6a003f5d0aa58379ebe26c32b1ca015f37ce80cf5be36b36318c4391"},
]
[package.dependencies]
langchain-core = ">=0.2.0,<0.3.0"
pymilvus = ">=2.4.3,<3.0.0"
scipy = ">=1.7,<2.0"
[[package]]
name = "langchain-mistralai"
version = "0.1.9"
@ -5389,6 +5426,22 @@ files = [
[package.dependencies]
requests = "*"
[[package]]
name = "milvus-lite"
version = "2.4.8"
description = "A lightweight version of Milvus wrapped with Python."
optional = false
python-versions = ">=3.7"
files = [
{file = "milvus_lite-2.4.8-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:b7e90b34b214884cd44cdc112ab243d4cb197b775498355e2437b6cafea025fe"},
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]
[package.dependencies]
tqdm = "*"
[[package]]
name = "minijinja"
version = "2.0.1"
@ -7563,6 +7616,31 @@ files = [
[package.extras]
windows-terminal = ["colorama (>=0.4.6)"]
[[package]]
name = "pymilvus"
version = "2.4.4"
description = "Python Sdk for Milvus"
optional = false
python-versions = ">=3.8"
files = [
{file = "pymilvus-2.4.4-py3-none-any.whl", hash = "sha256:073b76bc36f6f4e70f0f0a0023a53324f0ba8ef9a60883f87cd30a44b6c6f2b5"},
{file = "pymilvus-2.4.4.tar.gz", hash = "sha256:50c53eb103e034fbffe936fe942751ea3dbd2452e18cf79acc52360ed4987fb7"},
]
[package.dependencies]
environs = "<=9.5.0"
grpcio = ">=1.49.1,<=1.63.0"
milvus-lite = {version = ">=2.4.0,<2.5.0", markers = "sys_platform != \"win32\""}
pandas = ">=1.2.4"
protobuf = ">=3.20.0"
setuptools = ">=67"
ujson = ">=2.0.0"
[package.extras]
bulk-writer = ["azure-storage-blob", "minio (>=7.0.0)", "pyarrow (>=12.0.0)", "requests"]
dev = ["black", "grpcio (==1.62.2)", "grpcio-testing (==1.62.2)", "grpcio-tools (==1.62.2)", "pytest (>=5.3.4)", "pytest-cov (>=2.8.1)", "pytest-timeout (>=1.3.4)", "ruff (>0.4.0)"]
model = ["milvus-model (>=0.1.0)"]
[[package]]
name = "pymongo"
version = "4.8.0"
@ -9029,7 +9107,7 @@ tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (
name = "scipy"
version = "1.14.0"
description = "Fundamental algorithms for scientific computing in Python"
optional = true
optional = false
python-versions = ">=3.10"
files = [
{file = "scipy-1.14.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7e911933d54ead4d557c02402710c2396529540b81dd554fc1ba270eb7308484"},
@ -11326,4 +11404,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.13"
content-hash = "e4d237d2f128824e707fb5e2c0ba645495fc0947ba868099aac39a3e4424ac5d"
content-hash = "d7d4b2967b85dc9871076366efec3105138157245447a24317a925fd1bf5b8b0"

1
pyproject.toml
View file

@ -99,6 +99,7 @@ json-repair = "^0.25.2"
langchain-nvidia-ai-endpoints = "^0.1.2"
langchain-google-calendar-tools = "^0.0.1"
langchain-milvus = "^0.1.1"

123
src/backend/base/langflow/components/vectorstores/Milvus.py Normal file
View file

@ -0,0 +1,123 @@
from typing import List
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import (
DataInput,
StrInput,
IntInput,
FloatInput,
BoolInput,
DictInput,
MultilineInput,
DropdownInput,
SecretStrInput,
HandleInput,
)
from langflow.schema import Data
class MilvusVectorStoreComponent(LCVectorStoreComponent):
"""Milvus vector store with search capabilities"""
display_name: str = "Milvus"
description: str = "Milvus vector store with search capabilities"
documentation = "https://python.langchain.com/docs/integrations/vectorstores/milvus"
name = "Milvus"
icon = "Milvus"
inputs = [
StrInput(name="collection_name", display_name="Collection Name", value="langflow"),
StrInput(name="collection_description", display_name="Collection Description", value=""),
StrInput(
name="uri",
display_name="Connection URI",
value="http://localhost:19530",
),
SecretStrInput(
name="password",
display_name="Connection Password",
value="",
info="Ignore this field if no password is required to make connection.",
),
DictInput(name="connection_args", display_name="Other Connection Arguments", advanced=True),
StrInput(name="primary_field", display_name="Primary Field Name", value="pk"),
StrInput(name="text_field", display_name="Text Field Name", value="text"),
StrInput(name="vector_field", display_name="Vector Field Name", value="vector"),
DropdownInput(
name="consistency_level",
display_name="Consistencey Level",
options=["Bounded", "Session", "Strong", "Eventual"],
value="Session",
advanced=True,
),
DictInput(name="index_params", display_name="Index Parameters", advanced=True),
DictInput(name="search_params", display_name="Search Parameters", advanced=True),
BoolInput(name="drop_old", display_name="Drop Old Collection", value=False, advanced=True),
FloatInput(name="timeout", display_name="Timeout", advanced=True),
MultilineInput(name="search_query", display_name="Search Query"),
DataInput(
name="ingest_data",
display_name="Ingest Data",
is_list=True,
),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
IntInput(
name="number_of_results",
display_name="Number of Results",
info="Number of results to return.",
value=4,
advanced=True,
),
]
def build_vector_store(self):
try:
from langchain_milvus.vectorstores import Milvus as LangchainMilvus
except ImportError:
raise ImportError(
"Could not import Milvus integration package. " "Please install it with `pip install langchain-milvus`."
)
self.connection_args.update(uri=self.uri, token=self.password)
milvus_store = LangchainMilvus(
embedding_function=self.embedding,
collection_name=self.collection_name,
collection_description=self.collection_description,
connection_args=self.connection_args,
consistency_level=self.consistency_level,
index_params=self.index_params,
search_params=self.search_params,
drop_old=self.drop_old,
auto_id=True,
primary_field=self.primary_field,
text_field=self.text_field,
vector_field=self.vector_field,
timeout=self.timeout,
)
documents = []
for _input in self.ingest_data or []:
if isinstance(_input, Data):
documents.append(_input.to_lc_document())
else:
documents.append(_input)
if documents:
milvus_store.add_documents(documents)
return milvus_store
def search_documents(self) -> List[Data]:
vector_store = self.build_vector_store()
if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
docs = vector_store.similarity_search(
query=self.search_query,
k=self.number_of_results,
)
data = docs_to_data(docs)
self.status = data
return data
else:
return []