What data in this category looks like
Data for this category mainly comes from audio files awaiting annotation, annotation results submitted by annotation platforms, industry-specific vocabularies, and spot-check verification samples. Daily associated documents include transcription specifications, audio batch lists, quality inspection reports, and speaker annotation forms. Data updates follow a daily or single annotation batch schedule. Single-batch data volume varies with project scale. Data fields include audio unique identifier, speaker tags, structured transcribed text, noise segment markers, quality inspection scores, and corresponding timestamps. Basic units are characters, entries, and seconds.
Constraints Imposed on Database and Operations Workflows by These Characteristics
Multi-source data access requires databases to support hybrid storage structures. Structured quality inspection reports and speaker annotation forms are suitable for relational database storage. Unstructured audio segments and original transcribed text are suitable for document database storage. High-frequency update schedules require database connection pool configurations to support short connection or long connection reuse, to avoid performance loss from frequent connection establishment. Data fields include multi-dimensional markers. Reasonable indexes must be configured for each high-frequency query field, such as audio ID and speaker tags. Without these indexes, efficiency in quality inspection and annotation verification will be reduced. Data errors can cause training deviations and delivery delays. Therefore, data backup and incremental synchronization mechanisms must be enabled to ensure the integrity of single-batch data.
How to Configure Settings
For FastGPT v4.17.0 deployment scenarios, the following configuration recommendations align with database operation and maintenance needs for speech annotation and transcription businesses:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:27017/fastgpt_voice?authSource=admin | Stores unstructured audio segments and original transcribed text. Specifies the business database name and authentication source to ensure data isolation |
PG_URL | postgresql://username:password@host:5432/voice_label?sslmode=require | Stores structured quality inspection reports and speaker annotation forms. Enables SSL to secure data transmission |
DB_MAX_LINK | 20–30 | Adapts to approximately 1000 annotation data updates per daily batch. Balances connection reuse and server resource utilization |
REDIS_URL | redis://:password@host:6379/0 | Caches temporarily generated noise segment judgment results and speaker differentiation intermediate states, reducing high-frequency query pressure on databases |
Three Common Configuration Mistakes
- Deployment fails to start after a power loss. Symptoms include PostgreSQL failing to start normally and MongoDB rejecting connections. Causes include failure to enable database persistent storage, failure to configure an automatic restart script, and corrupted database file integrity from unsynchronized cached data following power loss.
- Workflow verification fails when using a database connection plugin to connect to PostgreSQL. Causes include incorrect database name or authentication parameters specified in the configuration, or unopened database port access permissions.
- MongoDB connection times out. Causes include an undersized database connection pool that cannot handle high-frequency annotation data query requests, or network latency exceeding the default timeout threshold.
How to Verify Successful Configuration
- Run a database connection test script to verify connectivity for
MONGODB_URIandPG_URL, and confirm there are no authentication or network errors. - Check the active connection count of the database connection pool, and confirm that the
DB_MAX_LINKconfiguration matches the current business concurrent request volume. - Simulate a power loss restart scenario, and verify that the database can automatically recover startup with no data loss or file corruption prompts.
- Submit a single batch of test data, query the index acceleration effect of corresponding fields, and confirm that query latency meets business expectations.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.