Auto-Vectorization
Every message vectorized the moment it is saved
DialogueDB embeds every message, dialogue, and memory as it is stored. There is no vector database to run and no embedding pipeline to build, sync, or maintain.
Message stored
I keep getting logged out after about ten minutes of inactivity.
Vectorized automatically
Dense vector · meaning
Sparse vector · keywords
Where this usually breaks
Why keeping your own vector index in sync is harder than it looks
Whether you build it yourself or wire in a managed tool, three things tend to go wrong.
Separate vector indexes drift from your data
When you build it yourself, you write the sync code. When you use a managed service, they handle sync but you can't see how. Either way, your conversation data and the search index fall out of step, and you find out during an incident. DialogueDB manages the sync between your conversation data and the vector index, so they cannot fall behind.
Chunking and embedding choices land on your team
How content is chunked and which embedding model to use are decisions either way. Managed tools make them out of sight; build it yourself and the tuning becomes its own project. DialogueDB uses conversation-tuned defaults and shows what was indexed per record.
Bundled memory tools index a narrow surface
These tools usually only index extracted memories, not full messages or whole conversations. With a DIY setup, you decide what's indexable and most teams under-index by default. DialogueDB indexes messages, dialogues, and memories automatically, so anything in your conversation data is searchable.
How DialogueDB does it
Every message is indexed the moment it is saved
Every record you store is embedded and indexed the moment it is saved, dense and sparse vectors both. By the time your next request arrives, it is ready to retrieve.
Indexed by your next request
Vectorization happens the moment a record is written, not on a scheduled job or a nightly batch. There is no delay baked in, so it is retrievable seconds later.
Conversation-tuned defaults handle the indexing
How content is split and embedded is configured for conversation data, so the index lands well without tuning. Nothing on your team to maintain.
The vector index stays in sync without your code
Storing a message creates its vector. Deleting a message removes it. DialogueDB manages the entire sync between your conversation data and the vector index, so there is no pipeline for your team to build or monitor.
What happens when you write a message
Message written
dialogue.saveMessage() stores the message
Stream triggered
Database stream picks up the new record
Chunked and embedded
Content chunked, dense + sparse vectors generated
Index updated
Vector index updated, message is searchable
All managed by DialogueDB. No external pipeline for you to maintain.
See the code
Write, then search. Nothing in between.
No db.index(). No db.embed(). No db.sync(). The write and the search are the only two calls.
The write and the retrieval are the only two calls. Vectorization happens in between, on its own.
Auto-vectorization on every plan
Every message, dialogue, and memory is indexed the moment you store it, with no vector database to run and no embedding pipeline to configure.
What gets vectorized
Three object types, all vectorized automatically
Every message, dialogue, and memory is embedded and indexed on write. Nothing to configure per type, and nothing to index by hand.
Messages
Every turn is chunked and embedded as it is saved, so the exact moment a user reported an issue or asked a question is indexed.
Dialogues
Whole conversations are indexed, so a thread can be retrieved as a unit, not only its individual messages.
Memories
Persistent facts and user knowledge stored across sessions are embedded the same way, the moment they are written.
What's included
What automatic really means
All of this comes with the database, with no extra setup or configuration required.
Dense and sparse vectors for every record
Each record gets a dense vector for meaning and, on Starter and above, a sparse vector for exact terms, generated for you.
Chunking and embedding tuned for conversations
How content is split and embedded is configured for conversation data, so you never pick a model or tune a chunk size.
The index updates on write, update, and delete
Storing a record indexes it, editing re-indexes it, and deleting removes it, so the index never drifts from your data.
Indexed within seconds of being stored
Vectorization runs the moment a record is written, with no nightly reindex or batch job to wait on.
Per-record index status you can see
The index status, chunk count, and any failure reason are visible per record, so debugging an indexing issue is straightforward.
Messages, dialogues, and memories, all the same way
Every object type is embedded and indexed automatically, with no per-type setup.
How the approaches compare
How DialogueDB compares
Building your own or wiring in a managed service both work. The case isn't that those are bad. It's that they're separate projects, and this isn't.
| Approach | Setup | Sync reliability | Chunking | What's indexed |
|---|---|---|---|---|
| Custom vector database setup | Separate account, index config, pipeline to build | Manual, drift is your problem | You decide | Whatever you index |
| Self-hosted vector database | Infrastructure to run and maintain | Better if managed carefully | You decide | Whatever you index |
| Managed memory service | Low | Usually automatic | Not configurable | Extracted memories only |
| DialogueDB | None | Automatic, no drift | Conversation-tuned defaults | Messages, dialogues, memories |
Frequently asked questions
Your conversations are vectorized
from the first message
No vector database to run. No embedding pipeline to maintain. Every record is indexed the moment it is stored.