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Data sources

A data source is the connection to where data physically lives. Most people interact with data sources only in the background — they're the plumbing behind search indexes and tools. But there are screens for them, and you'll need them if you're connecting Interakt to something external.

Where to find these screens​

Sidebar → Capabilities → Data Sources.

What a data source is, conceptually​

Think of it as "the cable between Interakt and a body of data." The data source records:

  • What kind of source this is (an Interakt index, an external index, a file store, a database).
  • Where it lives (a URL, a credential, a path).
  • What operations are available on it (search, lookup, inspect, enumerate).
  • How its fields are shaped.

The four built-in tools for each data source — search, lookup, inspect, enumerate — are auto-generated from this metadata. That's the chain: data source → tools → AI experience uses the tools.

The four kinds of data sources​

Search index (internal)​

The most common kind. A data source that points at one of your own Interakt search indexes. When you upload data into an Interakt index, a matching data source is created automatically.

Configuration:

  • Index ID — which Interakt search index this wraps.
  • Operation — which tool operations are exposed (usually all four).
  • Max results — default limit.
  • Response fields — which fields are returned by the tools.
  • Include highlights — whether to highlight matching terms.

You don't usually edit these by hand — the tool generator does it for you. But you can override behaviour here if the auto-generated tools aren't quite right.

External search index​

Points at a search engine that's not hosted by Interakt — your existing Elasticsearch cluster or an Azure AI Search service.

Configuration:

  • Provider — Elasticsearch or Azure AI Search.
  • Connection — URL, index name, auth type and credentials.
  • Search defaults — search type (lexical / vector / hybrid), max results.

Use this when you already have a search backbone and you don't want to copy data into Interakt. Interakt acts as the orchestration layer; the data and the search engine stay where they are.

File store​

A bucket or folder of files (markdown, PDFs, plain text). Interakt extracts the text, chunks it, embeds it, and stores the chunks in its own knowledge base.

Configuration:

  • Chunking strategy — paragraph, fixed-size, semantic.
  • Chunk size / overlap — controls how documents get split for embedding.
  • Embedding provider / model — which AI model embeds the text.
  • Max file size and max total storage — guards against runaway uploads.
  • Allowed file types — md, txt, pdf, docx.
  • Extract metadata — keep file properties (author, date) on the chunks.
  • Extract tables — try to keep tabular structure when extracting from PDFs.

Use this for documentation, knowledge bases, support articles — content that exists as files, not records. The in-app Help Assistant is built on a file store of these very docs.

Database​

Points at a SQL or NoSQL database. Interakt runs queries against it on demand.

Configuration:

  • Connection — connection string with credentials (typically referenced from the Secrets vault).
  • Query template — the SQL or query language used by the tool.

This is for read-only access to a system of record. Useful when you want a chatbot to be able to look up live data (e.g. "what's the status of order #1234") instead of relying on a stale copy.

The list screen​

The usual table-or-cards listing, with:

  • Stats cards — total, active, healthy, total documents across all sources.
  • Filters — by type and by status.
  • Search — by name or slug.

Each row shows the health status — green check if the source can be reached, red alert if not. Click the health value on the detail page to re-check.

The detail page​

The header has the name, type badge, and an Activate / Deactivate toggle plus an Edit button.

Stats strip​

Four numbers: health, status, document count, storage used.

Configuration card​

Type-specific — what you configured at creation. For external sources, this shows the provider, URL, index name, and auth type (the credentials themselves are stored as secrets and not shown).

Metadata card​

Slug, type, created, updated, last health check, health message.

Field schema card​

What fields exist on this data source, with their types and roles. Comes from inspecting the source — Interakt auto-discovers the schema for search indexes and for file stores (after a sample is ingested).

Tools card​

Lists the tools that exist for this data source. The Create Tools button auto-generates the standard set (search, inspect, enumerate, lookup) for any operations that don't yet have a tool. Skipped operations show a reason.

Danger zone​

Delete button. Disabled if any tools or experiences depend on this data source — you have to remove the dependents first.

Creating a data source​

From scratch​

Click New Data Source on the list page. A wizard runs:

  1. Pick the type (Search Index, External Index, File Store, Database).
  2. Fill in basic info — name, slug, description.
  3. Fill in type-specific configuration (see the four kinds above).
  4. Click Create.

Then click Create Tools on the detail page to generate the four standard tools.

Automatically​

When you upload data into an Interakt search index for the first time, a matching internal data source is created behind the scenes. You don't have to do anything — go to the Data Sources list and you'll see it there.

The Initial Setup demo creates a file-store data source for the docs Help Assistant.

Editing a data source​

Two cards in the edit screen:

  • Basic information — name, description.
  • Configuration — type-specific config.

You cannot change the type of a data source after creation. If you need to switch from a file store to a search index, create a new data source.

Common gotchas​

  • The Health check is a real check. If a data source goes unhealthy, the tools using it will fail. Click into the source and use the re-check button to confirm the issue is real, then fix it at the source (e.g. credentials, network).
  • Internal data sources are tied to their search index. Deleting the search index removes the data source and its tools. Don't delete an index that has tools wired into a live experience.
  • File stores re-embed when you change the embedding model. It's not automatic — you have to re-ingest the files.
  • Database tools need careful query design. A bad query template can lock up the DB or leak data. Treat database tools as production code; review them.

Where to go next​

  • Tools — what gets generated from data sources, and how to write custom ones.
  • Search indexes — the most common kind of data source.
  • Secrets — how to reference credentials in data source configurations.