Supabase Vector Workflow Returns Empty Query Results

Below is a list of troubleshooting steps you can follow:

1. Verify the SQL Function and Table Setup

• Check that your Supabase database was set up using the quickstart SQL script exactly. In particular, the SQL script creates a table and the function (usually called match_documents) for vector matching.
• Try running the match_documents function directly in the Supabase SQL editor using a test embedding. This will help determine whether the issue is within Supabase itself or in the n8n workflow.

2. Confirm Parameter Settings in n8n

• Table Name: Make sure that the table name configured in your node exactly matches the one in your Supabase database (case-sensitive).
• Query Name: If you followed the quickstart instructions, the query name should be match_documents. Verify that this value is set in the node options.
• Prompt and Metadata Filter: Double-check that your prompt (search query) is valid and that any metadata filters you might be using aren’t accidentally filtering out all results.

3. Check Your Embeddings

• Ensure that the embeddings generated by OpenAI (usually with a dimension of 1536) match the specification in your table. Any mismatch in dimensions between your inserted embeddings and the function’s expected input can lead to no matches.
• Verify that the documents have been embedded properly. You might test a document’s embedding locally to confirm that it is not null or malformed.

4. Inspect the Workflow’s Input Data

• Since your flow runs without errors but returns an empty output, inspect the data fed to the Supabase node. Make sure that the chat input includes the necessary details (for example, a non-empty chatInput field) that the Retriever expects.
• If using expressions, ensure that they resolve correctly so that you’re not passing a null or empty prompt to Supabase.

By going through these steps, you should be able to pinpoint whether the issue is with the Supabase setup, the node configuration in n8n, or the data being passed through the workflow. Let me know if you have any other questions regarding Firebase, n8n, or LangChain integrations.

Based on your description and knowledge of the Supabase Vector Store node in n8n, here are a few things to check:

1. Verify Table and Function Names

• Table Name: Ensure that the table name in your n8n node parameters exactly matches the documents table you created.
• Query Name: If you followed the quickstart from the LangChain guide on Supabase, your matching function should be named match_documents. Confirm that this is set correctly in the node's options.

2. Check Data Embeddings

• Embedding Process: Make sure that the data was embedded correctly into your documents table. Sometimes an error during embedding (with OpenAI) can lead to records that aren’t searchable.
• Vector Storage: Verify that the vector representation for each document is as expected. An incorrect vector (or none at all) would make your search return empty results.

3. Query Configuration

• Search Query Parameters: Review the parameters (like Prompt and Limit) configured in your node to ensure that the search query is targeting your data properly.
• Metadata Filter: If you’re using a metadata filter, ensure that the query values match the stored metadata for your documents.

4. Supabase Function Deployment

• Confirm that any custom SQL function (like match_documents) is deployed and working as expected on your Supabase instance. If you modified any defaults during table creation, you might need to adjust the function or query accordingly.

Next Steps

• Double-check the node configuration in n8n, including the Table Name, Prompt, and Query Name.
• Verify in Supabase (via the SQL editor or logs) that data, including embedded vectors, has been properly inserted into the table.
• Test retrieving data directly with a simple query in Supabase to rule out issues within n8n’s integration.

If everything matches and the data is as expected in Supabase, you might consider running some direct queries (using the Supabase client or directly in the SQL editor) to ensure that your function returns results before connecting it to your n8n workflow.

These checks should help narrow down whether the issue is with how data is stored in Supabase or how the n8n node is querying it.