Fixing Supabase V4 'Expected A Runnable' Error In Flowise

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Introduction

Hey guys! Are you wrestling with the "Expected a Runnable" error when using Supabase v4 with Flowise? You're not alone! This error pops up specifically when trying to use the Supabase Vector Store as a retriever in either the Retrieval QA Chain or ConversationalRetrievalQAChain. The weird part? Upsert functionality works just fine on its own. This guide will walk you through the ins and outs of this bug, how to reproduce it, and potential solutions.

Understanding the Supabase v4 and Flowise Integration

Before diving deep, let’s establish a basic understanding. Supabase, as a backend-as-a-service platform, provides a PostgreSQL database with several extensions, one of which is pgvector. This extension allows you to store and perform similarity searches on embeddings—crucial for vector stores. Flowise, on the other hand, is a visual tool that allows you to create custom AI workflows. Integrating these two can be incredibly powerful, but it also comes with its challenges. The Supabase Vector Store node in Flowise is designed to act as a bridge, allowing your AI models to retrieve relevant information from your Supabase database.

However, when this integration stumbles, it often manifests as the dreaded "Expected a Runnable" error. This error message indicates that there’s a mismatch or incompatibility in the expected type of object within the Langchain Runnable sequence. Specifically, it means that the chain expected a component that could be executed (like a function or another chain), but instead, it got something it couldn't handle.

Diving into the Bug: The "Expected a Runnable" Error

The core issue lies in how Flowise's retrieval chains interact with the Supabase Vector Store. While the upsert functionality (adding data to your Supabase table) might work flawlessly when triggered manually, the retrieval part of the chain throws a wrench in the gears. It's like your car starts perfectly but refuses to move when you shift into drive. When you set up a workflow to retrieve information using the Retrieval QA Chain or ConversationalRetrievalQAChain, the system chokes and spits out the "Expected a Runnable" error.

This typically occurs because the retriever component isn't correctly recognized or executed within the chain. The retriever is supposed to fetch relevant documents from the Supabase Vector Store based on the query, but somewhere along the line, the expected interface isn't met. This could be due to version mismatches, incorrect configurations, or underlying issues within the Flowise-Supabase integration.

Step-by-Step Guide to Reproducing the Bug

Want to see this bug in action? Follow these steps to reproduce the error and better understand what's happening under the hood. This will also help in systematically troubleshooting the issue.

  1. Set up Supabase with pgvector:
    • First, you'll need a Supabase project. If you don't have one, create a new project on the Supabase platform.
    • Enable the pgvector extension in your Supabase project. You can do this by navigating to the SQL editor in your Supabase dashboard and running CREATE EXTENSION vector;.
  2. Create documents Table and match_documents Function:
    • Use the standard SQL script provided in the Flowise documentation to create the documents table and the match_documents function. This involves defining the table structure to store your embeddings and the function to perform similarity searches.
    • Here’s a sample SQL script (ensure you adapt it to your specific needs):
CREATE TABLE documents (
 id UUID PRIMARY KEY,
 content TEXT,
 embedding VECTOR(1536),
 metadata JSONB
);

CREATE OR REPLACE FUNCTION match_documents(
 query_embedding VECTOR(1536),
 match_count INT,
 filter JSONB DEFAULT '{}'
) RETURNS TABLE(id UUID, content TEXT, metadata JSONB, similarity FLOAT)
AS $
BEGIN
 RETURN QUERY
 SELECT
 id,
 content,
 metadata,
 1 - (embedding <=> query_embedding) AS similarity
 FROM
 documents
 WHERE metadata @> filter
 ORDER BY
 similarity DESC
 LIMIT match_count;
END;
$
LANGUAGE plpgsql IMMUTABLE;
  1. Create a Flowise Workflow:
    • In Flowise, create a new workflow that includes the following nodes:
      • OpenAI Embeddings: To generate embeddings for your queries and documents.
      • ChatOpenAI: To create a chatbot interface.
      • Supabase Vector Store (v4): Configured to connect to your Supabase project.
      • ConversationalRetrievalQAChain: To orchestrate the retrieval and question-answering process.
  2. Configure the Nodes:
    • Configure each node with the appropriate credentials and settings.
      • For the OpenAI Embeddings node, you’ll need your OpenAI API key.
      • For the ChatOpenAI node, configure the model and any other relevant parameters.
      • The Supabase Vector Store node should be pointed to your Supabase project, using the documents table and match_documents function.
  3. Connect the Nodes:
    • Connect the nodes in the following order:
      • OpenAI Embeddings -> Supabase Vector Store
      • Supabase Vector Store (output as Supabase Retriever) -> Vector Store Retriever input of the ConversationalRetrievalQAChain.
      • ChatOpenAI -> ConversationalRetrievalQAChain
  4. Test the Workflow:
    • Ask a question in the chat interface connected to your ConversationalRetrievalQAChain.
    • Observe the error. You should see the "Expected a Runnable" error thrown. Additionally, the Supabase table might remain empty because the upsert isn't triggered correctly in the conversational chain. The agent might also return "I'm not sure" before throwing the internal error, indicating that it couldn't retrieve relevant documents.

Possible Causes and Solutions

So, what's causing this headache, and how can you fix it? Here are a few potential culprits and solutions:

  1. Version Mismatch:
    • Problem: Incompatible versions between Flowise, Supabase client libraries, and the pgvector extension.
    • Solution: Ensure you are using compatible versions of all components. Check the Flowise documentation for recommended versions. Try updating or downgrading packages to align with the suggested configurations.
  2. Incorrect Node Configuration:
    • Problem: Misconfigured Supabase Vector Store node, particularly with incorrect table names or function names.
    • Solution: Double-check the configuration of your Supabase Vector Store node. Ensure the documents table and match_documents function are correctly specified. Verify that the credentials are accurate and have the necessary permissions.
  3. Missing Dependencies:
    • Problem: Some required dependencies might be missing in your Flowise environment.
    • Solution: Check your Flowise environment for missing dependencies. Install any missing packages using npm or yarn. Refer to the Flowise documentation for a list of required dependencies.
  4. Asynchronous Issues:
    • Problem: Asynchronous operations not being handled correctly in the chain.
    • Solution: Ensure that all asynchronous calls are properly awaited. This might involve reviewing the custom code within your Flowise workflow and ensuring that promises are correctly handled.
  5. Type Mismatch in Runnable Sequence:
    • Problem: The "Expected a Runnable" error directly points to a type mismatch. The chain expects a Langchain Runnable (something that can be executed), but it receives something else.
    • Solution: Inspect the output of the Supabase Vector Store node. Ensure that it is indeed a Runnable object that can be consumed by the ConversationalRetrievalQAChain. You might need to add a transformation step to convert the output into the expected format.

Practical Tips and Best Practices

Here are some additional tips and best practices to help you avoid and resolve this issue:

  • Logging and Debugging:
    • Implement detailed logging in your Flowise workflow to trace the data flow and identify where the error occurs. Use console.log statements or Flowise's built-in debugging tools to inspect the output of each node.
  • Simplify the Workflow:
    • Start with a minimal workflow to isolate the issue. Add components incrementally to identify the exact point where the error is introduced.
  • Consult Flowise and Supabase Communities:
    • Engage with the Flowise and Supabase communities. Other users may have encountered similar issues and found solutions.
  • Review Documentation:
    • Carefully review the documentation for both Flowise and Supabase. Pay close attention to the sections on integration and troubleshooting.

Conclusion

The "Expected a Runnable" error in Flowise when using the Supabase v4 node can be a real head-scratcher. However, by understanding the underlying causes and following a systematic approach to troubleshooting, you can overcome this hurdle. Remember to check your versions, configurations, dependencies, and asynchronous operations. Happy coding, and may your chains always run smoothly!