dosubot[bot]
commented
2 months ago To resolve the SSL record layer failure when querying the Qdrant vector store in a flow based on the "Vector Store RAG" template, ensure that the location, api_key, and other relevant parameters are correctly provided when initializing the Qdrant client. Here is the relevant code snippet for initializing Qdrant:
def initialize_qdrant(class_object: Type[Qdrant], params: dict):
if not docs_in_params(params):
if "location" not in params and "api_key" not in params:
raise ValueError("Location and API key must be provided in the params")
from qdrant_client import QdrantClient
client_params = {
"location": params.pop("location"),
"api_key": params.pop("api_key"),
"https": params.pop("https", True), # Ensure HTTPS is enabled
"host": params.pop("host", None),
"port": params.pop("port", 6333),
"grpc_port": params.pop("grpc_port", 6334),
"timeout": params.pop("timeout", None),
"url": params.pop("url", None),
}
lc_params = {
"collection_name": params.pop("collection_name"),
"embeddings": params.pop("embedding"),
}
client = QdrantClient(**client_params)
return class_object(client=client, **lc_params)
return class_object.from_documents(**params)Make sure that the location, api_key, and other parameters such as https, host, port, grpc_port, timeout, and url are correctly set in the params dictionary when calling this function. This should help in resolving the SSL record layer failure [1].
Additionally, ensure that the parameters are correctly set in the QdrantComponent class as shown below:
class QdrantComponent(CustomComponent):
display_name = "Qdrant"
description = "Construct Qdrant wrapper from a list of texts."
def build_config(self):
return {
"documents": {"display_name": "Documents"},
"embedding": {"display_name": "Embedding"},
"api_key": {"display_name": "API Key", "password": True, "advanced": True},
"collection_name": {"display_name": "Collection Name"},
"content_payload_key": {"display_name": "Content Payload Key", "advanced": True},
"distance_func": {"display_name": "Distance Function", "advanced": True},
"grpc_port": {"display_name": "gRPC Port", "advanced": True},
"host": {"display_name": "Host", "advanced": True},
"https": {"display_name": "HTTPS", "advanced": True},
"location": {"display_name": "Location", "advanced": True},
"metadata_payload_key": {"display_name": "Metadata Payload Key", "advanced": True},
"path": {"display_name": "Path", "advanced": True},
"port": {"display_name": "Port", "advanced": True},
"prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True},
"prefix": {"display_name": "Prefix", "advanced": True},
"search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
"timeout": {"display_name": "Timeout", "advanced": True},
"url": {"display_name": "URL", "advanced": True},
}
def build(
self,
embedding: Embeddings,
collection_name: str,
documents: Optional[Document] = None,
api_key: Optional[str] = None,
content_payload_key: str = "page_content",
distance_func: str = "Cosine",
grpc_port: int = 6334,
https: bool = False,
host: Optional[str] = None,
location: Optional[str] = None,
metadata_payload_key: str = "metadata",
path: Optional[str] = None,
port: Optional[int] = 6333,
prefer_grpc: bool = False,
prefix: Optional[str] = None,
search_kwargs: Optional[NestedDict] = None,
timeout: Optional[int] = None,
url: Optional[str] = None,
) -> Union[VectorStore, Qdrant, BaseRetriever]:
if documents is None:
from qdrant_client import QdrantClient
client = QdrantClient(
location=location,
url=host,
port=port,
grpc_port=grpc_port,
https=https,
prefix=prefix,
timeout=timeout,
prefer_grpc=prefer_grpc,
metadata_payload_key=metadata_payload_key,
content_payload_key=content_payload_key,
api_key=api_key,
collection_name=collection_name,
host=host,
path=path,
)
vs = Qdrant(
client=client,
collection_name=collection_name,
embeddings=embedding,
)
return vs
else:
vs = Qdrant.from_documents(
documents=documents, # type: ignore
embedding=embedding,
api_key=api_key,
collection_name=collection_name,
content_payload_key=content_payload_key,
distance_func=distance_func,
grpc_port=grpc_port,
host=host,
https=https,
location=location,
metadata_payload_key=metadata_payload_key,
path=path,
port=port,
prefer_grpc=prefer_grpc,
prefix=prefix,
search_kwargs=search_kwargs,
timeout=timeout,
url=url,
)
return vsEnsure that all the necessary parameters are correctly set in the QdrantComponent class to avoid SSL record layer failures [2][3][4].
Additionally, check the docker-compose.yml file for configurations related to Traefik, which is used as a reverse proxy. These settings ensure that HTTPS is enabled, Let's Encrypt is used for SSL certificates, and HTTP requests are redirected to HTTPS:
- traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls=true
- traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls.certresolver=le
- traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.scheme=https
- traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.permanent=trueThese configurations should help in resolving the SSL record layer failure [5].
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carlosrcoelho
Bug Description
When using the Qdrant module in a flow based on the template "Vector Store RAG" I am able to ingest my data into a qdrant instance running locally in docker. When running the RAG flow, querying the vector store fails with an SSL record layer failure message. Screenshot:
Python error
ResponseHandlingException: [SSL] record layer failure (_ssl.c:1000) /Users/peterboucher/projects/langflow/.venv/lib/python3.12/site-packages/pydantic/main.py:1059: PydanticDeprecatedSince20: Thefieldsattribute is deprecated, usemodel_fieldsinstead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.8/migration/ warnings.warn(Qdrant log:
qdrant | 2024-08-02T13:34:38.637910Z ERROR actix_http::h1::dispatcher: stream error: request parse error: invalid Header providedReproduction
Prerequisite: docker container running qdrant vectorDB
dockerfile.yml
Expected behavior
Expected a response from the model based on the ingested document in the vector store.
Who can help?
@carlosrcoelho
Operating System
MacOS Sonoma 14.5 (23F79)
Langflow Version
1.0.14
Python Version
3.12
Screenshot
Flow File
RAG Demo.json