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Building a meeting summarizer means connecting two separate AI tasks: transcribing the recording into text, then asking a chat model to turn that transcript into useful notes. Spring AI supplies a shared transcription API and model integrations for Spring applications, but it does not make a meeting-minutes format or a particular transcription provider automatic.
This walkthrough explains the architecture and the decisions to make before wiring it into a Spring Boot app. The exact dependency coordinates and supported transcription models depend on the Spring AI release you choose, so pin a version and use its matching documentation rather than mixing examples from different release lines.
What the application does
A summarizer for a completed meeting recording follows a straightforward pipeline:
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- Accept an audio file as a Spring
Resource. - Send it to a speech-to-text model and receive a transcript.
- Pass the transcript to a chat model with instructions for the notes you want.
- Return or store the generated meeting notes.
These are distinct operations. The transcription model turns audio into text; the chat model interprets that text. Spring AI describes itself as “an application framework for AI engineering” on its project page, and its model APIs provide integration points for both stages. The application still needs to handle file validation, errors, privacy, and the output contract.
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Choose the Spring AI version before adding dependencies
Spring AI’s current general reference displays stable version 2.0.1 and lists audio transcription among its model APIs. Its transcription reference describes a shared TranscriptionModel interface that takes an AudioTranscriptionPrompt and returns an AudioTranscriptionResponse; it also offers a convenience method for transcribing a Spring Resource. Provider selection is configured through Spring Boot. See the Spring AI API reference and the Transcription API.
Provider-neutral does not mean every provider option or upstream model is interchangeable. The detailed Spring AI OpenAI transcription page available here belongs to the 1.0 reference line. It documents a particular starter and properties and says that line uses whisper-1. Do not assume those details apply to 2.0.1 or another release. Check the reference for the exact release pinned in your project before copying dependency coordinates or model settings.
OpenAI configuration in the Spring AI 1.0 reference
For orientation only, the Spring AI 1.0 OpenAI transcription page lists the spring-ai-starter-model-openai starter and the spring.ai.openai.api-key configuration property. It also documents transcription settings for model, response format, prompt, language, temperature, and timestamp granularities. These are details of that version’s documentation, not a verified configuration recipe for every current Spring AI release. Consult the Spring AI 1.0 OpenAI Transcriptions page alongside the documentation for your chosen version.
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Transcribe a completed meeting recording
For batch processing, treat the recording as a completed file and pass it through the transcription model. Spring AI’s transcription API accepts an AudioTranscriptionPrompt; its convenience method can take a Spring Resource. A typical service therefore needs to load or receive the resource, invoke the configured transcription model, and extract the returned transcript before calling the summarization stage. The API reference explains the shared interface and provider configuration, but exact construction and provider options should follow your pinned release.
OpenAI’s file transcription guide describes a 25 MB limit for the documented file flow. It lists supported formats including MP3, MP4, M4A, WAV, and WebM. Validate size and type before submitting the file; if a recording exceeds the documented limit, your application needs a deliberate handling strategy rather than assuming the request will succeed. Consult the current OpenAI speech-to-text guide for file-flow details.
That guide distinguishes completed-file transcription from realtime transcription for audio that is still arriving, such as a live microphone or call. A meeting summarizer that works on an uploaded recording can use the batch path; a live captioning or running-notes feature needs a realtime transcription design instead.
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Choose transcript output for the review workflow
Plain transcript text is usually the simplest handoff to a summarization prompt. Other formats can help when people need to review timing or identify speakers, but supported formats depend on the transcription model and integration:
- Plain text: a simple input for a chat model to summarize.
- JSON variants: useful when the application needs machine-readable transcription data; available combinations vary by model.
- SRT or VTT: subtitle formats that retain timing information where supported.
- Diarized output: speaker-labeled segments from a model/API combination that supports diarization.
OpenAI’s Create transcription API reference documents multiple model IDs and output formats, with diarization available through a specialized model. That upstream list does not establish that every model or option is exposed by your Spring AI release. Check both the provider API and the Spring AI version-specific documentation before designing around a feature.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Summarize the transcript with an explicit contract
Once transcription returns text, send it to a chat model with a prompt that defines what useful notes mean for your audience. Spring AI provides the model integration; the prompt and schema below are application choices, not built-in meeting-summary features:
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Summarize the meeting transcript below using only information stated in it.
Return:
- Summary: the main purpose and outcome
- Decisions: each decision explicitly made
- Action items: task, owner (or "unassigned"), and due date (or "not stated")
- Open questions: unresolved questions raised in the meeting
Do not infer owners, dates, or decisions. If a detail is absent, mark it as not stated.
Transcript:
{transcript}In a production application, format the prompt using the chosen Spring AI release’s APIs rather than concatenating arbitrary user text into a fixed string. If downstream systems need predictable fields, define a structured response contract and validate the model output before saving or displaying it. A prompt can ask the model not to invent details, but the application should still make the transcript available for verification.
Design around the main implementation choices
| Choice | Use it when | Trade-off |
|---|---|---|
| Completed file or realtime audio | File processing fits uploaded recordings; realtime processing fits audio that is still arriving. | The file guide documents a 25 MB limit for its described flow; realtime needs a streaming-oriented design. See OpenAI’s speech-to-text guide. |
| Shared API or provider-specific options | Use Spring AI’s TranscriptionModel interface for a common integration point; use provider-specific settings when you need provider features. |
The shared API helps decouple application code, but does not erase differences in model capabilities or options. See the Transcription API. |
| Plain text or richer transcript | Use plain text for a basic summary; retain timestamps, subtitles, or diarized segments when review or attribution matters. | Format and feature availability depend on the model and the Spring AI integration. See the OpenAI transcription API reference. |
Handle failures and protect meeting content
A useful implementation needs more than a successful model call. Treat meeting audio and transcripts as potentially sensitive data, and decide how long each is retained, who can access it, and whether it may be sent to the configured provider. The cited model references describe APIs, not your organization’s consent, retention, or compliance requirements.
- Reject unsupported file types and files outside the applicable size limit before calling the provider.
- Handle transcription and chat-model errors separately so a failed summary does not obscure whether a transcript was produced.
- Keep the transcript or a suitable diagnostic identifier available for troubleshooting, subject to your retention policy.
- Make it clear that generated notes are derived from the transcript and can omit context; let users check them against the source when accuracy matters.
- Return a controlled error or retry only where appropriate; avoid automatically resubmitting large recordings without considering cost and duplicate work.
What Spring AI contributes—and what remains yours
Spring AI supplies a Spring-oriented interface for transcription, configuration-driven provider selection, and integrations with AI models. The application design remains responsible for choosing a compatible release and provider, deciding between batch and realtime audio, defining the meeting-notes format, validating generated output, and setting data-handling rules. Separating transcription from summarization makes those responsibilities visible and gives you room to change one stage without treating it as the other.
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