Reference and Traceability for High-Value Consumable R&D

Data Characteristics

High-value consumable R&D document data primarily originates from internal corporate R&D reports, experimental records, design specifications, risk assessment reports, and registration submission materials. These documents update infrequently, typically revised at critical product development stages or when regulatory requirements change. Document structures are mainly unstructured and semi-structured text, such as PDF experimental reports, Word product manuals, and some CAD drawings with technical parameters. Text content includes extensive specialized terminology, abbreviations, and interdisciplinary units of measurement. Examples include material mechanics strength units like megapascals (MPa), cytotoxicity grades from biocompatibility testing, and micron (μm) level precision for product dimensions. Data may also involve traceability information like specific batch numbers and serial numbers, often embedded in documents as tables.

Constraints from "Reference and Traceability"

The low update frequency of high-value consumable R&D documents means initial data quality is critical for knowledge base construction. Subsequent incremental updates are less frequent, but each update can have a significant impact. The unstructured and semi-structured nature of documents demands high accuracy in text segmentation and information extraction. This requires fine-grained processing to ensure critical data is not overlooked or misunderstood. The large volume of specialized terminology and abbreviations requires models with strong semantic understanding to avoid ambiguity in references. Interdisciplinary units of measurement and precision requirements mean references must ensure unit consistency and numerical accuracy to prevent serious engineering errors. The embedding of batch numbers, serial numbers, and other traceability information requires the referencing system to precisely locate these identifiers to support product traceability and quality management.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Size`	`500–800 characters`	High-value consumable documents have strong logical paragraph structures. This balances context completeness and retrieval efficiency.
`Overlap Size`	`100 characters`	Ensures continuous context at chunk boundaries, reducing the risk of semantic fragmentation.
`Recall Count`	`8–12 items`	Guarantees sufficient relevant context to cover specialized terminology and complex concepts.
`Similarity Threshold`	`0.75–0.85`	Medical device R&D demands high accuracy, preventing interference from low-relevance references.
`Max Reference Tokens`	`2000 tokens`	Balances large model processing capabilities with the rich detail of high-value consumable documents.
`Reference Metadata Fields`	`document_id`, `page_number`, `batch_id`	Ensures references are traceable to specific documents, page numbers, and particular batch information.

Common Pitfalls

The large model provides answers without citing any sources, even when relevant knowledge chunks are recalled. This often occurs when Similarity Threshold is set too high, causing recalled knowledge chunks to be relevant but fail to meet the model's internal minimum threshold for citation.
External systems call the FastGPT API, but the returned content lacks reference information. This might be because the Return References option is not enabled in the publishing channel configuration, or Number of References to Return is set to zero.
The returned answer does not match the knowledge base reference content; the answer seems fabricated by the model. This can result from a Max Reference Tokens value that is too small. The model, with limited reference text, cannot obtain enough information to support a complete answer and instead resorts to factual inference.

How to Verify Correct Configuration

Submit a question containing high-value consumable specialized terminology. Check if the returned answer includes at least 3 explicit knowledge base references and verify if the document_id and page_number in the references point to the accurate location in the original document.
Select key technical parameters from a document, such as a specific material's strength value or dimensional tolerance. Ask a question and check if the numerical values and units in the answer's references strictly match the original document, verifying reference accuracy.
Simulate a traceability question about a product batch or serial number. Check if the returned references include corresponding batch_id or serial_number metadata and verify its traceability.
Through FastGPT's log system, view the Recall Count and Similarity Score for each query. Evaluate whether the configured Similarity Threshold is reasonable and if the recalled content is comprehensive and relevant enough.

The values provided 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 2026-09-21.