WARNING:root:Batch upload failed 1 times. Retrying...WA...

ppayload

WARNING:root:Batch upload failed 1 times. Retrying...
WARNING:root:Batch upload failed 2 times. Retrying...
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804 if "Content-Type" not in headers:
805 headers["Content-Type"] = "application/json"
--> 806 return self.apiclient.request( 807 type=m.InlineResponse2007,
813 content=body,
814 )

File ~/miniconda3/envs/mloflo/lib/python3.12/site-packages/qdrant_client/http/apiclient.py:79, in ApiClient.request(self, type, method, url, path_params, kwargs) 77 kwargs["timeout"] = int(kwargs["params"]["timeout"]) 78 request = self._client.build_request(method, url, kwargs)
---> 79 return self.send(request, type_)

File ~/miniconda3/envs/mloflo/lib/python3.12/site-packages/qdrant_client/http/apiclient.py:96, in ApiClient.send(self, request, type)
95 def send(self, request: Request, type_: Type[T]) -> T:
---> 96 response = self.middleware(request, self.send_inner)
97 if response.status_code in [200, 201, 202]:
98 try:

File ~/miniconda3/envs/mloflo/lib/python3.12/site-packages/qdrant_client/http/api_client.py:205, in BaseMiddleware.call(self, request, call_next)
204 def call(self, request: Request, call_next: Send) -> Response:
--> 205 return call_next(request)

File ~/miniconda3/envs/mloflo/lib/python3.12/site-packages/qdrant_client/http/api_client.py:108, in ApiClient.send_inner(self, request)
106 response = self._client.send(request)
107 except Exception as e:
--> 108 raise ResponseHandlingException(e)
109 return response

ResponseHandlingException: The write operation timed out

@kapa.ai

9 comments

kkapa.ai

Please stand by @payload as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

----------------------
I'm sorry, but the information provided in the knowledge sources does not contain a solution to the issue you're experiencing. The error message you're seeing indicates a timeout during a write operation, possibly due to a network issue or a problem with the Qdrant server.

However, based on the knowledge sources, it's clear that you're using the Qdrant client for LlamaIndex. Make sure that your Qdrant client is properly configured and that the Qdrant server is running and accessible. Here's an example of how to set up a Qdrant client:

Plain Text

client = qdrant_client.QdrantClient(path="qdrant_gemini")
vector_store = QdrantVectorStore(client=client, collection_name="collection")

(Source: LlamaIndex Documentation ())

If you're using a local Qdrant instance, ensure that the host and port are correctly set:

Plain Text

host="localhost",
port=6333

(Source: LlamaIndex Documentation ())

If the issue persists, I recommend reaching out to the maintainers of the Qdrant client or checking their documentation for more information on handling timeouts and retries.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

ppayload

is this error from embeddings model @kapa.ai

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
Based on the error message and the information provided in the knowledge sources, it doesn't appear that this error is directly related to the embeddings model in LlamaIndex. The error message indicates a timeout during a write operation, which is more likely related to network issues or server problems.

In LlamaIndex, embeddings models are used to convert text or other data into numerical vectors that can be used for retrieval and synthesis. They are not directly involved in write operations to a server or database. Here are some examples of how embeddings models are used in LlamaIndex:

Plain Text

# Using a HuggingFaceEmbedding model
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
embeddings = embed_model.get_text_embedding("Hello World!")

(Source: LlamaIndex Documentation ())

Plain Text

# Using a GooglePaLMEmbedding model
model_name = "models/embedding-gecko-001"
api_key = "YOUR API KEY"
embed_model = GooglePaLMEmbedding(model_name=model_name, api_key=api_key)
embeddings = embed_model.get_text_embedding("Google PaLM Embeddings.")

(Source: LlamaIndex Documentation ())

If you're experiencing issues with your embeddings model, it would be more likely to manifest as problems with the generated embeddings, not as a timeout during a write operation. If you're having trouble with your embeddings model, I recommend checking the model configuration and ensuring that the model files are correctly installed and accessible.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

ppayload

@WhiteFang_Jr @Logan M

LLogan M

Increase the timeout?

LLogan M

Pretty sure there's an option to set the timeout on the client

LLogan M

Or decrease the batch size on the qdrant vector store

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