Using Qdrant for vector search throws Error building Component Qdrant: 'NoneType' object has no attribute 'page_content'

[snagrecha]

Bug Description

I am using a locally hosted instance of Qdrant to store documents via the File Upload component. I am connecting the File Upload Data to the Ingest Data of Qdrant. This seems to work fine with the data getting ingested in the vector db and search query retrieving relevant documents.

However when I try to use the same vector store which already has the data ingested, I get the following error:

Should it not be able to use the vector store to just retrieve the documents without having to ingest?

Reproduction

1. Create an OpenAI Embedding Component
2. Create a Qdrant component which connects to a Qdrant collection with some data already present
3. Connect the embedding component to the Qdrant component
4. Provide a search query in the Qdrant component and run the same

Expected behavior

Expected behaviour is to be able to use the vector store to retrieve relevant chunks without having to ingest the documents every time.

Who can help?

No response

Operating System

Ubuntu 22.04

Langflow Version

1.3.2

Python Version

None

Screenshot

No response

Flow File

No response

[dosubot]

Hello @snagrecha! 👋 I'm here to help you with your bugs, answer your questions, and assist you as you explore contributing. Let me know how I can assist you while you wait for a human maintainer!

To resolve the error "'NoneType' object has no attribute 'page_content'", you need to adjust the content_payload_key setting in the Langflow Qdrant component. Change it from 'page_content' to 'text'. This is necessary because your Qdrant collection's payload structure uses 'text' as the key for content, while Langflow is expecting 'page_content'. Follow these steps:

1. Unhide the advanced controls in the Qdrant component.
2. Change the content_payload_key setting from 'page_content' to 'text'.

This adjustment will align Langflow's expectations with your Qdrant collection's actual payload structure, allowing you to retrieve documents without re-ingesting them ^[1].

To continue talking to Dosu, mention @dosu.

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[snagrecha]

Hi @dosu that is not the problem since 'NoneType' object indicates that it did not fetch any results. Also the same works if I am ingesting some document along with the search. Also below is the screenshot of the Qdrant data, it uses 'page_content' and not 'text'.

[dosubot]

If the Qdrant data indeed uses "page_content" as the key, and you're still encountering a 'NoneType' error, it suggests that the issue might not be with the key itself. Since the retrieval works when ingesting a document, it could be related to how the search operation is being executed when no new data is ingested.

Here are a few things to check:

1. Ensure Data Availability: Verify that the data is correctly stored in the Qdrant collection and accessible. You can do this by querying the Qdrant database directly to ensure the data is present.

2. Search Query Configuration: Double-check the configuration of your search query in the Langflow Qdrant component. Ensure that the query parameters are correctly set and that they match the structure of your stored data.

3. Component Initialization: Make sure that the Qdrant component is properly initialized and connected to the correct collection. Any misconfiguration here could lead to retrieval issues.

4. Logs and Debugging: Check the logs for any additional error messages or warnings that might provide more context about the issue. This can help identify if there's a problem with the connection or query execution.

If these steps don't resolve the issue, it might be helpful to look into any recent changes or updates in the Langflow or Qdrant setup that could affect the retrieval process.

To continue talking to Dosu, mention @dosu.

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[snagrecha]

@dosu I have checked everything that you said. I am unable to use the vector store in a standalone format. Only while ingesting documents.

[dosubot]

The issue you're experiencing might be related to the configuration or initialization of the Qdrant component when used in standalone mode. Here are a few additional steps to consider:

1. Embedding Consistency: Ensure that the embeddings used during the search operation are consistent with those used during ingestion. Any mismatch in embedding configuration could lead to retrieval issues.

2. Search Type and Parameters: Verify that the search_type and other search parameters are correctly set. For instance, ensure that the distance_func and number_of_results are configured appropriately for your use case ^[1].

3. Advanced Configuration: Double-check advanced settings such as host, port, and api_key to ensure they match your Qdrant server's configuration. Any discrepancies here could affect connectivity and search operations ^[1].

4. Direct Qdrant Queries: As a troubleshooting step, try querying the Qdrant collection directly using the Qdrant client to confirm that the data is accessible and the search operation works outside of Langflow.

If these steps don't resolve the issue, it might be worth reviewing any recent changes in your setup or considering potential compatibility issues with the current version of Langflow or Qdrant.

To continue talking to Dosu, mention @dosu.

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