Vector Model and Indexing for High-Value Consumables

Data Characteristics for This Category

Registration and declaration documents for high-value consumables typically exist in formats such as PDF, Word, and Excel. These documents include product technical requirements, inspection reports, clinical evaluation reports, instructions for use, labels, and manufacturing process flows. Data sources primarily consist of internal R&D, production, and quality inspection department documents, along with regulatory documents and guidelines issued by the National Medical Products Administration (NMPA). These documents have a relatively low update frequency, with updates mainly occurring during regulatory adjustments or product iterations. The document structure is highly standardized, adhering to specific templates for national medical device registration and declaration. Fields involve material composition, performance parameters, scope of application, contraindications, and adverse events. Units are precise, using common engineering units such as micrometers, milligrams, and Newtons.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The standardized document structure of high-value consumables declaration materials requires vector models to effectively process structured and semi-structured text. Models must accurately understand semantic relationships between different sections. Low update frequency means that after initial index construction, frequent incremental updates are less critical. The focus shifts to the comprehensiveness and accuracy of the initial build. The large number of technical parameters and precise units demand higher semantic understanding capabilities from vector models. Models must differentiate between similar numerical values and unit differences to avoid recall errors due to subtle variations. Documents often contain numerous tables and images. This requires indexing strategies to effectively handle non-textual information, either by pre-processing through OCR/table parsing to convert it into vectorizable text. Furthermore, regulatory documents are dense with specialized terminology, requiring models to have a high command of domain-specific vocabulary.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters`	Balances contextual completeness with vector model processing efficiency, avoiding overly long or short segments.
`Recall count` (Recall Count)	`Top 8–12 items`	Ensures coverage of multiple potential technical points or regulatory clauses in declaration documents.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements, typically fine-tune within `0.75–0.85`	Balances recall rate and accuracy, reducing interference from irrelevant information.
`Rerank result count` (Rerank Return Count)	`Top 3–5 items`	Selects the most relevant results for engineers, reducing manual filtering burden.
`embeddingModel`	`bge-large-zh-v1.5` or `Doubao-embedding-large`	Optimized for Chinese medical device domain text, providing more precise semantic representation.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates parsing time for large PDF or Word documents, preventing file processing failures due to timeouts.

Three Common Pitfalls

After enabling the index model, clicking "test" results in an error message: "Invalid custom request address or API Key." This usually indicates an incorrect Request Address (request address) or an incorrectly configured or expired API Key.
Retrieval results contain a large number of irrelevant or low-relevance document snippets, leading to information overload. This might be due to a Similarity threshold (similarity threshold) set too low, or an unreasonable Chunk size (chunk length) causing individual snippets to lack semantic focus.
Some important technical parameters or tabular information are not recalled, affecting the completeness of declaration materials. This could be because non-textual content was not effectively extracted during the document pre-processing stage, or the vector model's understanding of specific structured data is insufficient.

How to Verify Correct Configuration

Upload a typical high-value consumables registration and declaration PDF file. Check if the file parsing status is normal, with no timeout or parsing failure messages.
For specific technical requirements or regulatory clauses within the declaration materials, perform keyword or phrase searches. Observe if the Recall count (recall count) meets expectations and examine the distribution of similarity scores for the recalled snippets.
Use documents containing tables or complex graphics and text for retrieval. Verify that relevant information can be effectively indexed and recalled.
Simulate common declaration questions. Evaluate whether the document snippets cited in FastGPT's answers are accurate, complete, highly relevant, and effectively support the answer.

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.