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Support Vald secure connection (#13269)

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**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.

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This commit is contained in:
Masanori Taniguchi 2023-11-30 12:07:29 +09:00 committed by GitHub
parent 54355b651a
commit 235bdb9fa7
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 212 additions and 18 deletions
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152
docs/docs/integrations/vectorstores/vald.ipynb
View File

@ -149,6 +149,156 @@
"source": [ "source": [
"db.max_marginal_relevance_search(query, k=2, fetch_k=10)" "db.max_marginal_relevance_search(query, k=2, fetch_k=10)"
] ]
},
{
"cell_type": "markdown",
"id": "7dc7ce16-35af-49b7-8009-7eaadb7abbcb",
"metadata": {},
"source": [
"## Example of using secure connection\n",
"In order to run this notebook, it is necessary to run a Vald cluster with secure connection.\n",
"\n",
"Here is an example of a Vald cluster with the following configuration using [Athenz](https://github.com/AthenZ/athenz) authentication.\n",
"\n",
"ingress(TLS) -> [authorization-proxy](https://github.com/AthenZ/authorization-proxy)(Check athenz-role-auth in grpc metadata) -> vald-lb-gateway"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6894c02d-7a86-4600-bab1-f7e9cce79333",
"metadata": {},
"outputs": [],
"source": [
"import grpc\n",
"\n",
"with open(\"test_root_cacert.crt\", \"rb\") as root:\n",
" credentials = grpc.ssl_channel_credentials(root_certificates=root.read())\n",
"\n",
"# Refresh is required for server use\n",
"with open(\".ztoken\", \"rb\") as ztoken:\n",
" token = ztoken.read().strip()\n",
"\n",
"metadata = [(b\"athenz-role-auth\", token)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc15c20b-485d-435e-a2ec-c7dcb9db40b5",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores import Vald\n",
"\n",
"raw_documents = TextLoader(\"state_of_the_union.txt\").load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"documents = text_splitter.split_documents(raw_documents)\n",
"embeddings = HuggingFaceEmbeddings()\n",
"\n",
"db = Vald.from_documents(\n",
" documents,\n",
" embeddings,\n",
" host=\"localhost\",\n",
" port=443,\n",
" grpc_use_secure=True,\n",
" grpc_credentials=credentials,\n",
" grpc_metadata=metadata,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "069b96c6-6db2-46ce-a820-24e8933156a0",
"metadata": {},
"outputs": [],
"source": [
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"docs = db.similarity_search(query, grpc_metadata=metadata)\n",
"docs[0].page_content"
]
},
{
"cell_type": "markdown",
"id": "8327accb-6776-4a20-a325-b5da92e3a049",
"metadata": {},
"source": [
"### Similarity search by vector"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0ab2a97-83e4-490d-81a5-8aaa032d8811",
"metadata": {},
"outputs": [],
"source": [
"embedding_vector = embeddings.embed_query(query)\n",
"docs = db.similarity_search_by_vector(embedding_vector, grpc_metadata=metadata)\n",
"docs[0].page_content"
]
},
{
"cell_type": "markdown",
"id": "f3f987bd-512e-4e29-acb3-e110e74b51a2",
"metadata": {},
"source": [
"### Similarity search with score"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88dd39bc-8764-4a8c-ac89-06e2341aefa6",
"metadata": {},
"outputs": [],
"source": [
"docs_and_scores = db.similarity_search_with_score(query, grpc_metadata=metadata)\n",
"docs_and_scores[0]"
]
},
{
"cell_type": "markdown",
"id": "fef1bd41-484e-4845-88a9-c7f504068db0",
"metadata": {},
"source": [
"### Maximal Marginal Relevance Search (MMR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6cf08477-87b0-41ac-9536-52dec1c5d67f",
"metadata": {},
"outputs": [],
"source": [
"retriever = db.as_retriever(\n",
" search_kwargs={\"search_type\": \"mmr\", \"grpc_metadata\": metadata}\n",
")\n",
"retriever.get_relevant_documents(query, grpc_metadata=metadata)"
]
},
{
"cell_type": "markdown",
"id": "f994fa57-53e4-4fe6-9418-59a5136c6fe8",
"metadata": {},
"source": [
"Or:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2111ce42-07c7-4ccc-bdbf-459165e3a410",
"metadata": {},
"outputs": [],
"source": [
"db.max_marginal_relevance_search(query, k=2, fetch_k=10, grpc_metadata=metadata)"
]
} }
], ],
"metadata": { "metadata": {
@ -167,7 +317,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.4" "version": "3.11.4"
} }
}, },
"nbformat": 4, "nbformat": 4,

78
libs/langchain/langchain/vectorstores/vald.py
View File

@ -41,19 +41,40 @@ class Vald(VectorStore):
("grpc.keepalive_time_ms", 1000 * 10), ("grpc.keepalive_time_ms", 1000 * 10),
("grpc.keepalive_timeout_ms", 1000 * 10), ("grpc.keepalive_timeout_ms", 1000 * 10),
), ),
grpc_use_secure: bool = False,
grpc_credentials: Optional[Any] = None,
): ):
self._embedding = embedding self._embedding = embedding
self.target = host + ":" + str(port) self.target = host + ":" + str(port)
self.grpc_options = grpc_options self.grpc_options = grpc_options
self.grpc_use_secure = grpc_use_secure
self.grpc_credentials = grpc_credentials
@property @property
def embeddings(self) -> Optional[Embeddings]: def embeddings(self) -> Optional[Embeddings]:
return self._embedding return self._embedding
def _get_channel(self) -> Any:
try:
import grpc
except ImportError:
raise ValueError(
"Could not import grpcio python package. "
"Please install it with `pip install grpcio`."
)
return (
grpc.secure_channel(
self.target, self.grpc_credentials, options=self.grpc_options
)
if self.grpc_use_secure
else grpc.insecure_channel(self.target, options=self.grpc_options)
)
def add_texts( def add_texts(
self, self,
texts: Iterable[str], texts: Iterable[str],
metadatas: Optional[List[dict]] = None, metadatas: Optional[List[dict]] = None,
grpc_metadata: Optional[Any] = None,
skip_strict_exist_check: bool = False, skip_strict_exist_check: bool = False,
**kwargs: Any, **kwargs: Any,
) -> List[str]: ) -> List[str]:
@ -62,7 +83,6 @@ class Vald(VectorStore):
skip_strict_exist_check: Deprecated. This is not used basically. skip_strict_exist_check: Deprecated. This is not used basically.
""" """
try: try:
import grpc
from vald.v1.payload import payload_pb2 from vald.v1.payload import payload_pb2
from vald.v1.vald import upsert_pb2_grpc from vald.v1.vald import upsert_pb2_grpc
except ImportError: except ImportError:
@ -71,7 +91,7 @@ class Vald(VectorStore):
"Please install it with `pip install vald-client-python`." "Please install it with `pip install vald-client-python`."
) )
channel = grpc.insecure_channel(self.target, options=self.grpc_options) channel = self._get_channel()
# Depending on the network quality, # Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY. # it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10) # _ = grpc.channel_ready_future(channel).result(timeout=10)
@ -82,7 +102,10 @@ class Vald(VectorStore):
embs = self._embedding.embed_documents(list(texts)) embs = self._embedding.embed_documents(list(texts))
for text, emb in zip(texts, embs): for text, emb in zip(texts, embs):
vec = payload_pb2.Object.Vector(id=text, vector=emb) vec = payload_pb2.Object.Vector(id=text, vector=emb)
res = stub.Upsert(payload_pb2.Upsert.Request(vector=vec, config=cfg)) res = stub.Upsert(
payload_pb2.Upsert.Request(vector=vec, config=cfg),
metadata=grpc_metadata,
)
ids.append(res.uuid) ids.append(res.uuid)
channel.close() channel.close()
@ -92,6 +115,7 @@ class Vald(VectorStore):
self, self,
ids: Optional[List[str]] = None, ids: Optional[List[str]] = None,
skip_strict_exist_check: bool = False, skip_strict_exist_check: bool = False,
grpc_metadata: Optional[Any] = None,
**kwargs: Any, **kwargs: Any,
) -> Optional[bool]: ) -> Optional[bool]:
""" """
@ -99,7 +123,6 @@ class Vald(VectorStore):
skip_strict_exist_check: Deprecated. This is not used basically. skip_strict_exist_check: Deprecated. This is not used basically.
""" """
try: try:
import grpc
from vald.v1.payload import payload_pb2 from vald.v1.payload import payload_pb2
from vald.v1.vald import remove_pb2_grpc from vald.v1.vald import remove_pb2_grpc
except ImportError: except ImportError:
@ -111,7 +134,7 @@ class Vald(VectorStore):
if ids is None: if ids is None:
raise ValueError("No ids provided to delete") raise ValueError("No ids provided to delete")
channel = grpc.insecure_channel(self.target, options=self.grpc_options) channel = self._get_channel()
# Depending on the network quality, # Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY. # it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10) # _ = grpc.channel_ready_future(channel).result(timeout=10)
@ -120,7 +143,9 @@ class Vald(VectorStore):
for _id in ids: for _id in ids:
oid = payload_pb2.Object.ID(id=_id) oid = payload_pb2.Object.ID(id=_id)
_ = stub.Remove(payload_pb2.Remove.Request(id=oid, config=cfg)) _ = stub.Remove(
payload_pb2.Remove.Request(id=oid, config=cfg), metadata=grpc_metadata
)
channel.close() channel.close()
return True return True
@ -132,10 +157,11 @@ class Vald(VectorStore):
radius: float = -1.0, radius: float = -1.0,
epsilon: float = 0.01, epsilon: float = 0.01,
timeout: int = 3000000000, timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any, **kwargs: Any,
) -> List[Document]: ) -> List[Document]:
docs_and_scores = self.similarity_search_with_score( docs_and_scores = self.similarity_search_with_score(
query, k, radius, epsilon, timeout query, k, radius, epsilon, timeout, grpc_metadata
) )
docs = [] docs = []
@ -151,11 +177,12 @@ class Vald(VectorStore):
radius: float = -1.0, radius: float = -1.0,
epsilon: float = 0.01, epsilon: float = 0.01,
timeout: int = 3000000000, timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any, **kwargs: Any,
) -> List[Tuple[Document, float]]: ) -> List[Tuple[Document, float]]:
emb = self._embedding.embed_query(query) emb = self._embedding.embed_query(query)
docs_and_scores = self.similarity_search_with_score_by_vector( docs_and_scores = self.similarity_search_with_score_by_vector(
emb, k, radius, epsilon, timeout emb, k, radius, epsilon, timeout, grpc_metadata
) )
return docs_and_scores return docs_and_scores
@ -167,10 +194,11 @@ class Vald(VectorStore):
radius: float = -1.0, radius: float = -1.0,
epsilon: float = 0.01, epsilon: float = 0.01,
timeout: int = 3000000000, timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any, **kwargs: Any,
) -> List[Document]: ) -> List[Document]:
docs_and_scores = self.similarity_search_with_score_by_vector( docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, k, radius, epsilon, timeout embedding, k, radius, epsilon, timeout, grpc_metadata
) )
docs = [] docs = []
@ -186,10 +214,10 @@ class Vald(VectorStore):
radius: float = -1.0, radius: float = -1.0,
epsilon: float = 0.01, epsilon: float = 0.01,
timeout: int = 3000000000, timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any, **kwargs: Any,
) -> List[Tuple[Document, float]]: ) -> List[Tuple[Document, float]]:
try: try:
import grpc
from vald.v1.payload import payload_pb2 from vald.v1.payload import payload_pb2
from vald.v1.vald import search_pb2_grpc from vald.v1.vald import search_pb2_grpc
except ImportError: except ImportError:
@ -198,7 +226,7 @@ class Vald(VectorStore):
"Please install it with `pip install vald-client-python`." "Please install it with `pip install vald-client-python`."
) )
channel = grpc.insecure_channel(self.target, options=self.grpc_options) channel = self._get_channel()
# Depending on the network quality, # Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY. # it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10) # _ = grpc.channel_ready_future(channel).result(timeout=10)
@ -207,7 +235,10 @@ class Vald(VectorStore):
num=k, radius=radius, epsilon=epsilon, timeout=timeout num=k, radius=radius, epsilon=epsilon, timeout=timeout
) )
res = stub.Search(payload_pb2.Search.Request(vector=embedding, config=cfg)) res = stub.Search(
payload_pb2.Search.Request(vector=embedding, config=cfg),
metadata=grpc_metadata,
)
docs_and_scores = [] docs_and_scores = []
for result in res.results: for result in res.results:
@ -225,6 +256,7 @@ class Vald(VectorStore):
radius: float = -1.0, radius: float = -1.0,
epsilon: float = 0.01, epsilon: float = 0.01,
timeout: int = 3000000000, timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any, **kwargs: Any,
) -> List[Document]: ) -> List[Document]:
emb = self._embedding.embed_query(query) emb = self._embedding.embed_query(query)
@ -236,6 +268,7 @@ class Vald(VectorStore):
epsilon=epsilon, epsilon=epsilon,
timeout=timeout, timeout=timeout,
lambda_mult=lambda_mult, lambda_mult=lambda_mult,
grpc_metadata=grpc_metadata,
) )
return docs return docs
@ -249,10 +282,10 @@ class Vald(VectorStore):
radius: float = -1.0, radius: float = -1.0,
epsilon: float = 0.01, epsilon: float = 0.01,
timeout: int = 3000000000, timeout: int = 3000000000,
grpc_metadata: Optional[Any] = None,
**kwargs: Any, **kwargs: Any,
) -> List[Document]: ) -> List[Document]:
try: try:
import grpc
from vald.v1.payload import payload_pb2 from vald.v1.payload import payload_pb2
from vald.v1.vald import object_pb2_grpc from vald.v1.vald import object_pb2_grpc
except ImportError: except ImportError:
@ -260,15 +293,19 @@ class Vald(VectorStore):
"Could not import vald-client-python python package. " "Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`." "Please install it with `pip install vald-client-python`."
) )
channel = self._get_channel()
channel = grpc.insecure_channel(self.target, options=self.grpc_options)
# Depending on the network quality, # Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY. # it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10) # _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = object_pb2_grpc.ObjectStub(channel) stub = object_pb2_grpc.ObjectStub(channel)
docs_and_scores = self.similarity_search_with_score_by_vector( docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, fetch_k=fetch_k, radius=radius, epsilon=epsilon, timeout=timeout embedding,
fetch_k=fetch_k,
radius=radius,
epsilon=epsilon,
timeout=timeout,
grpc_metadata=grpc_metadata,
) )
docs = [] docs = []
@ -277,7 +314,8 @@ class Vald(VectorStore):
vec = stub.GetObject( vec = stub.GetObject(
payload_pb2.Object.VectorRequest( payload_pb2.Object.VectorRequest(
id=payload_pb2.Object.ID(id=doc.page_content) id=payload_pb2.Object.ID(id=doc.page_content)
) ),
metadata=grpc_metadata,
) )
embs.append(vec.vector) embs.append(vec.vector)
docs.append(doc) docs.append(doc)
@ -304,6 +342,9 @@ class Vald(VectorStore):
("grpc.keepalive_time_ms", 1000 * 10), ("grpc.keepalive_time_ms", 1000 * 10),
("grpc.keepalive_timeout_ms", 1000 * 10), ("grpc.keepalive_timeout_ms", 1000 * 10),
), ),
grpc_use_secure: bool = False,
grpc_credentials: Optional[Any] = None,
grpc_metadata: Optional[Any] = None,
skip_strict_exist_check: bool = False, skip_strict_exist_check: bool = False,
**kwargs: Any, **kwargs: Any,
) -> Vald: ) -> Vald:
@ -316,11 +357,14 @@ class Vald(VectorStore):
host=host, host=host,
port=port, port=port,
grpc_options=grpc_options, grpc_options=grpc_options,
grpc_use_secure=grpc_use_secure,
grpc_credentials=grpc_credentials,
**kwargs, **kwargs,
) )
vald.add_texts( vald.add_texts(
texts=texts, texts=texts,
metadatas=metadatas, metadatas=metadatas,
grpc_metadata=grpc_metadata,
skip_strict_exist_check=skip_strict_exist_check, skip_strict_exist_check=skip_strict_exist_check,
) )
return vald return vald