Vector Models and Indexing for CDMO Quality Documents

Data Characteristics in this Category

CDMO (Contract Development and Manufacturing Organization) quality documents include batch production records, inspection reports, deviation management, change control, client audit reports, supplier qualification files, and stability study data. These documents originate from various sources, typically as PDF, Word, Excel electronic files, or scanned images. Data updates frequently; for example, batch production records generate in real-time with production batches, while deviation and change records trigger based on events. Document structures are highly standardized, adhering to regulatory requirements such as GMP (Good Manufacturing Practice). They contain numerous tables, charts, and structured text. Fields and units have strong industry-specific attributes, such as batch numbers, product codes, inspection items, results, limits, and units (mg/mL, ppm, %), often accompanied by regulatory citation numbers.

Constraints from these Characteristics on Vector Models and Indexing

The highly structured and standardized nature of CDMO quality documents requires vector models to effectively capture relationships between data when processing tables and nested structures. This avoids loss of context from simple text chunking. For instance, if parameters and results from different steps in a batch production record are segmented improperly, recall might fail to provide complete process information. Second, high update frequency and multiple data sources demand real-time and incremental update capabilities for indexing. This requires support for rapid re-indexing of new or modified documents to ensure knowledge base timeliness. Furthermore, regulatory compliance necessitates that vector models accurately identify and match specific terminology, abbreviations, and regulatory numbers. This prevents recall errors due to semantic ambiguity, which could impact decision accuracy. Finally, common units of measurement and numerical values in documents require vector models to distinguish the meaning of the value itself from its unit context, improving retrieval precision.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`800–1200 characters`	Balances context completeness with vector model processing efficiency, suitable for paragraph lengths in structured documents.
`Chunk Overlap`	`100–200 characters`	Ensures semantic continuity at chunk boundaries, especially useful for documents containing tables and lists.
`Enable Multi-vector`	Enabled	For tables and complex structures, multi-vector models more accurately represent semantic information at different granularities.
`Recall Count`	`10–15 items`	Increases initial recall coverage, providing a richer candidate set for subsequent re-ranking.
`Similarity Threshold`	Calibrated by measurement	Adjusts based on business requirements for recall accuracy and recall rate, through testing with a dataset, typically between `0.75–0.85`.
`Rerank Return Count`	`3–5 items`	Focuses on a few highly relevant, high-quality results, reducing the processing burden on downstream LLMs.

Three Common Mistakes

An "No available index model detected" error after enabling a model often indicates incorrect model service interface configuration or an invalid API Key. This prevents FastGPT from successfully calling external model services for health checks.
Poor retrieval results for tabular data in the knowledge base occur when multi-vector support is not enabled. This typically happens if the multi-vector processing option is not selected or configured in the knowledge base settings.
Retrieval results do not reflect document updates in a timely manner. Common causes include an indexing strategy not configured for automatic incremental updates, or delays in the file synchronization mechanism. This leads to the knowledge base index not reflecting the latest document status.

How to Verify Correct Configuration

Upload a batch of test documents, including batch production records, inspection reports, and deviation records. Test the recall accuracy for key information (e.g., inspection results for a specific batch, deviation numbers, and corrective actions). Evaluate the relevance of recall results with business experts.
Check FastGPT backend logs to confirm successful vector model calls. Verify no HTTP 4xx or 5xx status codes appear, and embedding generation time is within acceptable limits.
Randomly select several documents, modify key values or descriptions, and re-upload them. Ask relevant questions via the conversational Agent to verify if the knowledge base recalls the updated information and check for version consistency in the recalled content.
In the knowledge base management interface, check the index status. Confirm all documents are successfully indexed, and index timestamps roughly match document upload or update times.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.