How to Add Custom Fields to cmetadata When Using pgvector in Langflow

[frogFred]

Hello everyone,

I am currently using Langflow with pgvector to store vector data, and I would like to ask whether there is a built-in mechanism to add custom fields into cmetadata when storing vectors.

My specific use case is that I need to store additional business-related information (such as source tags, user IDs, etc.) alongside the vectors, and I hope to include these fields directly within cmetadata for easier retrieval and application in downstream tasks.

Is there an existing API or configuration in Langflow that supports this functionality? Or would you recommend a custom extension approach to achieve this?

Thank you in advance for any guidance or suggestions.

langflow version：1.4.3

[Swapnilpatil03]

🙋‍♂️ Question: How to Add Custom Fields to cmetadata When Using pgvector in Langflow?

Hi @frogFred 👋

Great question — and a very relevant one for many working with Langflow + pgvector! You're looking to store additional business-specific metadata like user_id, tags, or source directly inside cmetadata for better downstream use, and wondering if Langflow supports this out of the box.

Here’s a detailed answer based on Langflow’s architecture and usage patterns as of v1.4.3:

---

✅ Quick Answer: Yes, You Can Add Custom Metadata (with a Bit of Tweaking)

While Langflow doesn't have a dedicated API or setting to customize cmetadata directly, you can still include custom fields by using the metadata parameter when inserting documents into the vector store.

---

🛠️ How to Add Custom Fields in Practice

When you're calling add_texts() or add_documents(), structure your metadata like this:

vectorstore.add_texts(
    texts=["Here is some sample content."],
    metadatas=[{
        "source": "internal_wiki",
        "user_id": "user_123",
        "tags": ["faq", "internal"],
        "custom_field": "business_logic_xyz"
    }]
)

✅ All of these fields will be stored as part of the metadata JSONB column in the underlying pgvector database.

And when you want to filter during a similarity search, you can do:

vectorstore.similarity_search(
    "Search query here",
    filter={"user_id": "user_123"}
)

🔧 Advanced Option: Customize Langflow’s Backend
If your use case needs something more powerful (e.g., indexed SQL queries on custom columns, or a deeply integrated cmetadata schema), you can:

Fork Langflow and modify the database schema to include new fields.

Update the backend logic that writes to the vector DB so it maps your custom fields appropriately.

Or extend your ingestion pipeline to write both to Langflow and an external metadata tracker (e.g., PostgreSQL or MongoDB collection).

💡 Recommendation
For most use cases — including tagging vectors with business-specific context — simply passing custom fields inside the metadata dictionary is the cleanest and most scalable approach. It works great with LangChain + Langflow + pgvector setups.

Unless you need tight SQL-level filtering or indexing on custom fields, there’s no need to modify Langflow’s internal code.

Hope this helps you (and others!) move forward with your implementation. Feel free to ask if you'd like a working end-to-end example!