AI Conversation Summarization
Summarize growing conversations without building another service
AI conversations grow long. You need summaries. Building the infrastructure (chunking, retries, error handling) shouldn't be your engineering burden.
Triggers summarize when ready
POST /api/v1/summaryChunks, calls your LLM, returns result
Async. Your app never blocks.
Rolling your own is a maintenance burden
Prompt engineering, chunking logic, retries, error handling, cost monitoring. That's a service to maintain, not a feature to add.
You don't need to build it
DialogueDB handles the orchestration: chunking, retries, token budgets, error recovery. No infrastructure to build, no service to maintain.
It doesn't block your app
The API returns immediately with a summary ID. Processing runs asynchronously in the background, so your application stays responsive while DialogueDB does the work.
Send a query, get a summary
Point it at a conversation and it handles the rest. Chunks the messages, calls your LLM provider, combines the results. You get back a summary.
How DialogueDB Handles It
One API call. No infrastructure to maintain.
One call triggers it, nothing blocks
Send a POST with a dialogue ID. The API returns a 202 immediately with a summary ID. DialogueDB chunks the conversation, calls your LLM provider, handles retries, and combines the results in the background. Poll when you're ready.
Your provider, your prompt, your key
Choose your LLM provider (OpenAI, Anthropic, or others). Customize the summarization prompt to tune output for your specific use case. It's a bring-your-own-key feature: add your API key in project settings, and the LLM calls run on your account. DialogueDB orchestrates; you control costs directly.
The original conversation is never overwritten
Summaries are additive. Your raw messages stay in place, queryable by ID, searchable by content. Future summarizations always work from the original data.
{ "dialogueId": "dlg_abc123" }Chunks messages, calls your LLM, combines results
Returns summary content + stats when complete. Raw messages still accessible.
The integration
One call to summarize, one to check the result
import { DialogueDB } from "dialogue-db"
const db = new DialogueDB({
apiKey: process.env.DIALOGUEDB_API_KEY
})
// Trigger summarization on a conversation
const summary = await db.createSummary({
dialogueId: "dlg_abc123"
})
// → { id: "sum_xyz", status: "processing" }
// Check the result when ready
const result = await db.getSummary(summary.id)
// → { status: "completed", content: "..." }
// Original messages are still accessible
const dialogue = await db.getDialogue("dlg_abc123")
await dialogue.loadMessages()When to call it
Trigger patterns that work in production
You control when summarization runs. That's a feature, not a gap.
Here are the patterns teams use.
On session end
Summarize when a user closes the chat or the dialogue is ended. Clean wrap-up of the full conversation.
At a message-count threshold
When a conversation hits a threshold, summarize the oldest portion. Keep the active context window lean while preserving full history.
Scheduled batch job
Run nightly or hourly summarization of dialogues that grew past a threshold that day. Process in bulk without affecting active conversations.
On-demand by users
Give users a 'Summarize this conversation' button. They get a condensed view, you get lower context window costs on follow-up calls.
How summarization approaches compare
Trade-offs across engineering time, control, and what gets kept
| Approach | Engineering Time | Original Kept | Timing Control |
|---|---|---|---|
| Per-message inline | Low setup, high ongoing | Depends on implementation | None |
| DIY batched service | High (build + maintain) | Depends on implementation | Full |
| Auto-summarization tools | Low | Often no | Limited |
| DialogueDB | Low (one API call) | Always | Full |
The DIY approach gives you full control and keeps the original if you build it that way. It also means maintaining chunking logic, prompt engineering, retries, and error handling yourself.
Data Integrity
What's preserved, what's returned
The biggest concern with summarization is losing access to the source material. DialogueDB is built so that never happens.
When you call summarize, you get back:
- The summary text content
- Message count and range covered
- Token count and chunk statistics
- Processing status and timing
When you call summarize, what does NOT happen:
- Original messages are not deleted
- Original messages are not replaced with the summary
- Future summarizations still use the raw data
- Message search results are not affected
Frequently asked questions
Start free, summarize
as you scale.
Store your conversations now. Add summarization when your dialogues grow. The data is always there when you need it.