From c8d7f47070bc8ada630e6bc218d7a6b950fe05b0 Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Sat, 13 Jul 2024 03:47:55 +0800 Subject: [PATCH] feat: Add milvus vector store (#2634) Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> --- docs/docs/components/vector-stores.mdx | 27 +++ poetry.lock | 184 +++++++++++++----- pyproject.toml | 1 + .../components/vectorstores/Milvus.py | 123 ++++++++++++ 4 files changed, 282 insertions(+), 53 deletions(-) create mode 100644 src/backend/base/langflow/components/vectorstores/Milvus.py diff --git a/docs/docs/components/vector-stores.mdx b/docs/docs/components/vector-stores.mdx index cf4dc1668..b5ac66992 100644 --- a/docs/docs/components/vector-stores.mdx +++ b/docs/docs/components/vector-stores.mdx @@ -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. diff --git a/poetry.lock b/poetry.lock index a72a8ec51..fb114524f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -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" files = [ - {file = "clarifai-10.5.3-py3-none-any.whl", hash = "sha256:884a0d0ffc36b7e1513a3ebe054716e8f735cd1e6b8ee57397cfa42d81781b24"}, - {file = "clarifai-10.5.3.tar.gz", hash = "sha256:1ed262e84a8a204be4525acfd101753dc1672bf1f2c86ba3302873741c527126"}, + {file = "clarifai-10.5.4-py3-none-any.whl", hash = "sha256:56cc5c0f2a735aa90990c23e9cc2698ce531491d970b0c0f86fb6c107bc1592f"}, + {file = "clarifai-10.5.4.tar.gz", hash = "sha256:bf2d377dfe8fa7704a666cef7b8b564654c6fced4ef3a83a78b38a6c8bb1847c"}, ] [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" +files = [ + {file = 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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" diff --git a/pyproject.toml b/pyproject.toml index 9b6bc7cca..30a3d2323 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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" diff --git a/src/backend/base/langflow/components/vectorstores/Milvus.py b/src/backend/base/langflow/components/vectorstores/Milvus.py new file mode 100644 index 000000000..5e95c6e2d --- /dev/null +++ b/src/backend/base/langflow/components/vectorstores/Milvus.py @@ -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 []