mirror of
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Support Vald secure connection (#13269)
**Description:** When using Vald, only insecure grpc connection was supported, so secure connection is now supported. In addition, grpc metadata can be added to Vald requests to enable authentication with a token. <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->
This commit is contained in:
Masanori Taniguchi
GitHub
parent
54355b651a
commit
235bdb9fa7
@ -149,6 +149,156 @@
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"source": [
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"db.max_marginal_relevance_search(query, k=2, fetch_k=10)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7dc7ce16-35af-49b7-8009-7eaadb7abbcb",
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"metadata": {},
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"source": [
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"## Example of using secure connection\n",
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"In order to run this notebook, it is necessary to run a Vald cluster with secure connection.\n",
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"\n",
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"Here is an example of a Vald cluster with the following configuration using [Athenz](https://github.com/AthenZ/athenz) authentication.\n",
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"\n",
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"ingress(TLS) -> [authorization-proxy](https://github.com/AthenZ/authorization-proxy)(Check athenz-role-auth in grpc metadata) -> vald-lb-gateway"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6894c02d-7a86-4600-bab1-f7e9cce79333",
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"metadata": {},
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"outputs": [],
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"source": [
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"import grpc\n",
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"\n",
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"with open(\"test_root_cacert.crt\", \"rb\") as root:\n",
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" credentials = grpc.ssl_channel_credentials(root_certificates=root.read())\n",
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"\n",
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"# Refresh is required for server use\n",
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"with open(\".ztoken\", \"rb\") as ztoken:\n",
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" token = ztoken.read().strip()\n",
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"\n",
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"metadata = [(b\"athenz-role-auth\", token)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc15c20b-485d-435e-a2ec-c7dcb9db40b5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"from langchain.embeddings import HuggingFaceEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Vald\n",
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"\n",
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"raw_documents = TextLoader(\"state_of_the_union.txt\").load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"documents = text_splitter.split_documents(raw_documents)\n",
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"embeddings = HuggingFaceEmbeddings()\n",
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"\n",
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"db = Vald.from_documents(\n",
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" documents,\n",
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" embeddings,\n",
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" host=\"localhost\",\n",
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" port=443,\n",
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" grpc_use_secure=True,\n",
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" grpc_credentials=credentials,\n",
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" grpc_metadata=metadata,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "069b96c6-6db2-46ce-a820-24e8933156a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = db.similarity_search(query, grpc_metadata=metadata)\n",
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"docs[0].page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8327accb-6776-4a20-a325-b5da92e3a049",
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"metadata": {},
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"source": [
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"### Similarity search by vector"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d0ab2a97-83e4-490d-81a5-8aaa032d8811",
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"metadata": {},
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"outputs": [],
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"source": [
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"embedding_vector = embeddings.embed_query(query)\n",
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"docs = db.similarity_search_by_vector(embedding_vector, grpc_metadata=metadata)\n",
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"docs[0].page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f3f987bd-512e-4e29-acb3-e110e74b51a2",
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"metadata": {},
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"source": [
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"### Similarity search with score"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "88dd39bc-8764-4a8c-ac89-06e2341aefa6",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs_and_scores = db.similarity_search_with_score(query, grpc_metadata=metadata)\n",
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"docs_and_scores[0]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fef1bd41-484e-4845-88a9-c7f504068db0",
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"metadata": {},
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"source": [
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"### Maximal Marginal Relevance Search (MMR)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6cf08477-87b0-41ac-9536-52dec1c5d67f",
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"metadata": {},
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"outputs": [],
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"source": [
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"retriever = db.as_retriever(\n",
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" search_kwargs={\"search_type\": \"mmr\", \"grpc_metadata\": metadata}\n",
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")\n",
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"retriever.get_relevant_documents(query, grpc_metadata=metadata)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f994fa57-53e4-4fe6-9418-59a5136c6fe8",
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"metadata": {},
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"source": [
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"Or:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2111ce42-07c7-4ccc-bdbf-459165e3a410",
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"metadata": {},
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"outputs": [],
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"source": [
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"db.max_marginal_relevance_search(query, k=2, fetch_k=10, grpc_metadata=metadata)"
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]
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}
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],
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"metadata": {
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@ -167,7 +317,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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@ -41,19 +41,40 @@ class Vald(VectorStore):
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("grpc.keepalive_time_ms", 1000 * 10),
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("grpc.keepalive_timeout_ms", 1000 * 10),
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),
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grpc_use_secure: bool = False,
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grpc_credentials: Optional[Any] = None,
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):
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self._embedding = embedding
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self.target = host + ":" + str(port)
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self.grpc_options = grpc_options
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self.grpc_use_secure = grpc_use_secure
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self.grpc_credentials = grpc_credentials
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@property
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def embeddings(self) -> Optional[Embeddings]:
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return self._embedding
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def _get_channel(self) -> Any:
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try:
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import grpc
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except ImportError:
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raise ValueError(
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"Could not import grpcio python package. "
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"Please install it with `pip install grpcio`."
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)
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return (
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grpc.secure_channel(
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self.target, self.grpc_credentials, options=self.grpc_options
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)
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if self.grpc_use_secure
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else grpc.insecure_channel(self.target, options=self.grpc_options)
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)
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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grpc_metadata: Optional[Any] = None,
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skip_strict_exist_check: bool = False,
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**kwargs: Any,
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) -> List[str]:
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@ -62,7 +83,6 @@ class Vald(VectorStore):
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skip_strict_exist_check: Deprecated. This is not used basically.
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"""
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try:
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import grpc
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import upsert_pb2_grpc
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except ImportError:
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@ -71,7 +91,7 @@ class Vald(VectorStore):
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"Please install it with `pip install vald-client-python`."
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)
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channel = grpc.insecure_channel(self.target, options=self.grpc_options)
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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@ -82,7 +102,10 @@ class Vald(VectorStore):
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embs = self._embedding.embed_documents(list(texts))
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for text, emb in zip(texts, embs):
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vec = payload_pb2.Object.Vector(id=text, vector=emb)
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res = stub.Upsert(payload_pb2.Upsert.Request(vector=vec, config=cfg))
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res = stub.Upsert(
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payload_pb2.Upsert.Request(vector=vec, config=cfg),
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metadata=grpc_metadata,
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)
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ids.append(res.uuid)
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channel.close()
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@ -92,6 +115,7 @@ class Vald(VectorStore):
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self,
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ids: Optional[List[str]] = None,
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skip_strict_exist_check: bool = False,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> Optional[bool]:
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"""
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@ -99,7 +123,6 @@ class Vald(VectorStore):
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skip_strict_exist_check: Deprecated. This is not used basically.
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"""
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try:
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import grpc
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import remove_pb2_grpc
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except ImportError:
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@ -111,7 +134,7 @@ class Vald(VectorStore):
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if ids is None:
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raise ValueError("No ids provided to delete")
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channel = grpc.insecure_channel(self.target, options=self.grpc_options)
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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@ -120,7 +143,9 @@ class Vald(VectorStore):
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for _id in ids:
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oid = payload_pb2.Object.ID(id=_id)
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_ = stub.Remove(payload_pb2.Remove.Request(id=oid, config=cfg))
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_ = stub.Remove(
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payload_pb2.Remove.Request(id=oid, config=cfg), metadata=grpc_metadata
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)
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channel.close()
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return True
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@ -132,10 +157,11 @@ class Vald(VectorStore):
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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docs_and_scores = self.similarity_search_with_score(
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query, k, radius, epsilon, timeout
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query, k, radius, epsilon, timeout, grpc_metadata
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)
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docs = []
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@ -151,11 +177,12 @@ class Vald(VectorStore):
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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emb = self._embedding.embed_query(query)
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docs_and_scores = self.similarity_search_with_score_by_vector(
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emb, k, radius, epsilon, timeout
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emb, k, radius, epsilon, timeout, grpc_metadata
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)
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return docs_and_scores
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@ -167,10 +194,11 @@ class Vald(VectorStore):
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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docs_and_scores = self.similarity_search_with_score_by_vector(
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embedding, k, radius, epsilon, timeout
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embedding, k, radius, epsilon, timeout, grpc_metadata
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)
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docs = []
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@ -186,10 +214,10 @@ class Vald(VectorStore):
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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try:
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import grpc
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import search_pb2_grpc
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except ImportError:
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@ -198,7 +226,7 @@ class Vald(VectorStore):
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"Please install it with `pip install vald-client-python`."
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)
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channel = grpc.insecure_channel(self.target, options=self.grpc_options)
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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@ -207,7 +235,10 @@ class Vald(VectorStore):
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num=k, radius=radius, epsilon=epsilon, timeout=timeout
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)
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res = stub.Search(payload_pb2.Search.Request(vector=embedding, config=cfg))
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res = stub.Search(
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payload_pb2.Search.Request(vector=embedding, config=cfg),
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metadata=grpc_metadata,
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)
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docs_and_scores = []
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for result in res.results:
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@ -225,6 +256,7 @@ class Vald(VectorStore):
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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emb = self._embedding.embed_query(query)
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@ -236,6 +268,7 @@ class Vald(VectorStore):
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epsilon=epsilon,
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timeout=timeout,
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lambda_mult=lambda_mult,
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grpc_metadata=grpc_metadata,
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)
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return docs
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@ -249,10 +282,10 @@ class Vald(VectorStore):
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radius: float = -1.0,
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epsilon: float = 0.01,
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timeout: int = 3000000000,
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grpc_metadata: Optional[Any] = None,
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**kwargs: Any,
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) -> List[Document]:
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try:
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import grpc
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from vald.v1.payload import payload_pb2
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from vald.v1.vald import object_pb2_grpc
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except ImportError:
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@ -260,15 +293,19 @@ class Vald(VectorStore):
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"Could not import vald-client-python python package. "
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"Please install it with `pip install vald-client-python`."
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)
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channel = grpc.insecure_channel(self.target, options=self.grpc_options)
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channel = self._get_channel()
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# Depending on the network quality,
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# it is necessary to wait for ChannelConnectivity.READY.
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# _ = grpc.channel_ready_future(channel).result(timeout=10)
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stub = object_pb2_grpc.ObjectStub(channel)
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docs_and_scores = self.similarity_search_with_score_by_vector(
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embedding, fetch_k=fetch_k, radius=radius, epsilon=epsilon, timeout=timeout
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embedding,
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fetch_k=fetch_k,
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radius=radius,
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epsilon=epsilon,
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timeout=timeout,
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grpc_metadata=grpc_metadata,
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)
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docs = []
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@ -277,7 +314,8 @@ class Vald(VectorStore):
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vec = stub.GetObject(
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payload_pb2.Object.VectorRequest(
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id=payload_pb2.Object.ID(id=doc.page_content)
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)
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),
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metadata=grpc_metadata,
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)
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embs.append(vec.vector)
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docs.append(doc)
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@ -304,6 +342,9 @@ class Vald(VectorStore):
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("grpc.keepalive_time_ms", 1000 * 10),
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("grpc.keepalive_timeout_ms", 1000 * 10),
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),
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grpc_use_secure: bool = False,
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grpc_credentials: Optional[Any] = None,
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grpc_metadata: Optional[Any] = None,
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skip_strict_exist_check: bool = False,
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**kwargs: Any,
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) -> Vald:
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@ -316,11 +357,14 @@ class Vald(VectorStore):
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host=host,
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port=port,
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grpc_options=grpc_options,
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grpc_use_secure=grpc_use_secure,
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grpc_credentials=grpc_credentials,
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**kwargs,
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)
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vald.add_texts(
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texts=texts,
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metadatas=metadatas,
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grpc_metadata=grpc_metadata,
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skip_strict_exist_check=skip_strict_exist_check,
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)
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return vald
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|
||||
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