Zep Vector Store: Insert consultants

Q: What is Zep and how is it different from other vector databases like Pinecone or Qdrant?

Zep is specifically designed for AI application memory. While Pinecone and Qdrant are general-purpose vector databases, Zep includes built-in document embedding (so you do not need a separate embedding API), session-based memory management for chatbots, and automatic summarisation of long conversation histories. It is also self-hostable, which matters for data privacy.

Q: Does Zep handle embedding generation, or do I need a separate embedding model?

Zep can generate embeddings internally using its built-in embedding service. You send raw text to the Zep Vector Store: Insert node, and Zep handles the vectorisation. This simplifies your n8n workflow because you do not need to add a separate embedding node before the vector store insert step, though you can use external embeddings if you prefer.

Q: Can I use Zep for both document storage and conversation memory in the same application?

Yes, that is one of Zep's key strengths. You can use the vector store for RAG-based document retrieval while also using Zep's session memory to track ongoing conversations. This means your AI chatbot can both reference your knowledge base and remember what the user said earlier in the conversation.

Q: How do I update or replace documents in Zep when my source data changes?

You can delete existing documents from a Zep collection and re-insert updated versions. In n8n, you would build a workflow that identifies changed records in your source system, removes the old document chunks from Zep by their metadata identifiers, and then re-runs the ingestion pipeline for the updated content.

Q: Is Zep self-hosted or cloud-only?

Zep offers both options. Zep Open Source can be self-hosted using Docker, giving you full control over your data. Zep Cloud is a managed service that handles infrastructure for you. For organisations with data sovereignty requirements, the self-hosted option means your documents and conversation data never leave your own infrastructure.

Q: How many documents can Zep handle before performance degrades?

Zep is built on PostgreSQL with pgvector, which handles millions of vectors efficiently. For most business use cases — knowledge bases with thousands to tens of thousands of documents — performance is not an issue. If you are working with very large datasets, proper indexing configuration and chunking strategy matter more than raw document count.

We can help you automate your business with Zep Vector Store: Insert and hundreds of other systems to improve efficiency and productivity. Get in touch if you’d like to discuss implementing Zep Vector Store: Insert.

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About Zep Vector Store: Insert

The Zep Vector Store: Insert node in n8n writes document embeddings into a Zep vector database, which is a purpose-built memory store for AI applications. Zep handles both the embedding generation and storage in a single service, so you do not need to manage a separate embedding model and a separate vector database — Zep does both.

Vector stores are the foundation of retrieval-augmented generation (RAG) systems. When you want an AI chatbot or agent to answer questions about your specific business data — your policies, products, support history, or internal documentation — you first embed that data into vectors and store them. When a user asks a question, the system converts the question into a vector, finds the most similar stored documents, and passes those to the LLM as context. The result is an AI that actually knows your content rather than just generating generic responses.

In n8n, the Zep Vector Store: Insert node sits at the end of your document ingestion pipeline. A typical flow pulls data from a source (API, database, file), runs it through a document loader and text splitter to create chunks, and then inserts those chunks into Zep. Zep also maintains long-term memory for conversational AI, tracking user sessions and message history, which makes it particularly useful for building chatbots that remember previous interactions.

If you are building an AI system that needs to work with your own business data, our AI agent development services can help you set up the full RAG pipeline — from data ingestion through Zep to a working AI agent that your team can query.

Zep Vector Store: Insert FAQs

Frequently Asked Questions

Common questions about how Zep Vector Store: Insert consultants can help with integration and implementation

What is Zep and how is it different from other vector databases like Pinecone or Qdrant?

Does Zep handle embedding generation, or do I need a separate embedding model?

Can I use Zep for both document storage and conversation memory in the same application?

How do I update or replace documents in Zep when my source data changes?

Is Zep self-hosted or cloud-only?

How many documents can Zep handle before performance degrades?

How it works

We work hand-in-hand with you to implement Zep Vector Store: Insert

As Zep Vector Store: Insert consultants we work with you hand in hand build more efficient and effective operations. Here’s how we will work with you to automate your business and integrate Zep Vector Store: Insert with integrate and automate 800+ tools.

Step 1

Set up your Zep instance

Either sign up for Zep Cloud or deploy Zep Open Source locally using Docker (docker compose up). For self-hosted setups, Zep runs as a container alongside PostgreSQL. Note your Zep server URL and API key — you will need these to configure the n8n credential. Verify the instance is running by accessing the Zep API endpoint in your browser.

Step 2

Create a Zep credential in n8n

In n8n, go to Credentials and add a new Zep API credential. Enter your Zep server URL (for example, http://localhost:8000 for self-hosted or the Zep Cloud endpoint) and your API key. Test the connection to make sure n8n can reach your Zep instance before building the workflow.

Step 3

Build the document ingestion pipeline

Add your data source node (HTTP Request for APIs, Read Binary File for documents, a database node, etc.) followed by a Document Loader node (such as the JSON Input Loader or PDF Loader) to convert the raw data into Document objects. Then add a Text Splitter node to chunk the documents into smaller pieces, typically 500-1000 characters with some overlap.

Step 4

Configure the Zep Vector Store: Insert node

Add the Zep Vector Store: Insert node after your Text Splitter. Select your Zep credential and specify the collection name where documents should be stored. If the collection does not exist, Zep will create it. The node will take each document chunk and insert it into the vector store, where Zep handles the embedding generation.

Step 5

Add metadata for filtering

Configure your Document Loader or a preceding Set node to attach metadata to each document — such as the source URL, document category, date, or department. This metadata is stored alongside the vector in Zep and lets you filter search results later. For example, your AI agent could search only documents tagged with ‘hr-policy’ instead of the entire collection.

Step 6

Test retrieval with a query

After inserting documents, test the pipeline by building a simple retrieval workflow. Add a Zep Vector Store: Retrieve node with a sample question and check that it returns relevant document chunks. If results are poor, adjust your chunking strategy (try smaller or larger chunks) or review the source data quality. Once retrieval works well, connect it to an LLM chain for full RAG.

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