Vector stores are semantic indexes built from your documents. They allow language models to retrieve contextually relevant information, powering workflows like agents, assistants, and context-grounded fine-tuning.

Each chunk of your documents is converted into a vector embedding, making content searchable by semantic similarity rather than keywords.

Create a vector store

1. Navigate to Data Engine > Storage, then select the Vector Stores tab.
2. Click Create Vector Store.
3. Enter the vector store details:
   • Name (required, 100 characters max)
   • Description (optional, 500 characters max)
   • Embedding Model — select from available options
4. Click Create Vector Store.

The new store appears in the vector stores list with its name, file count, total size, and date modified.

Vector store details

Click any vector store to open its details view, which displays:

• ID
• Model – the embedding model used
• Size
• File Count
• Date Created
• Last Modified
• Description – editable after creation
• Files currently embedded in the store
• Option to add more files

Add files to a vector store

1. Open the vector store details view.
2. Click Add Files.
3. In the modal, use Select Files to choose from existing files in File Storage, or switch to Upload Files to upload new files directly.
4. Optionally expand Advanced Embedding Options to configure chunking behavior.
5. Click Add Files to confirm.

Supported file types: .pdf, .docx, .ppt, .md, .json

Upload limits: 20 files at a time, maximum 150MB per file.

ℹ️

.jsonl and .parquet files cannot be added to a vector store.

Advanced Embedding Options

Expand this section to configure how content is segmented before embedding:

• Chunk Size (Tokens) — Maximum token count per chunk. Default: 800.
• Chunk Overlap (Tokens) — Token overlap between adjacent chunks to reduce information loss at boundaries. Default: 400.

File processing

Once you click Add Files, SeekrFlow handles the rest automatically:

1. Ingesting — Converts compatible files to Markdown if needed.
2. Chunking — Breaks content into segments based on your settings.
3. Embedding — Transforms each chunk into a vector.
4. Indexing — Stores vectors into the semantic index.

When processing completes, files appear in the details view with their name, processing status, and size. If a file fails to process, an error is shown with guidance for resolution.

File status

Manage vector stores

From the details view you can add or remove files, monitor file processing status, and rebuild the store when content changes.

Next steps

• AI-Ready Data – Generate context-grounded training data using a vector store
• Create an agent – Attach a vector store to an agent for retrieval-augmented memory