Vector Models and Indexing for Cardiovascular Intervention

Data Characteristics

R&D documents in the cardiovascular intervention field primarily come from clinical trial reports, device design specifications, biocompatibility test reports, regulatory compliance files, and patent literature. Data updates are infrequent, typically occurring with product development phases or regulatory changes. Documents are largely unstructured text, including many charts, images, and scanned documents. Text often mixes specialized terminology, abbreviations, and numerical values, such as device dimensions (mm, Fr), material properties (MPa, N), and clinical indicators (mmHg, bpm). Data also contains product serial numbers, batch numbers, and specific identifiers, along with fields required by different national and regional regulations.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The unstructured nature of cardiovascular intervention R&D documents, particularly the inclusion of charts and scanned images, challenges text extraction and preprocessing. This requires more sophisticated OCR and layout parsing capabilities. The density of specialized terminology and abbreviations demands that vector models deeply understand domain semantics, preventing bias when generalized models recall relevant information. Infrequent updates mean lower maintenance costs after index construction, but the accuracy and completeness of the initial index are critical. The presence of different units and identifiers requires the model to differentiate between numerical values and their associated units, and to match different forms of expression during retrieval. High regulatory compliance requirements demand strict accuracy and traceability for recall results, avoiding semantic drift.

Configuration Settings

Configuration Item	Recommended Value Range	Rationale
`chunk_size`	`512`–`768` characters	Balances contextual completeness and recall precision, adapting to specialized terminology density.
`overlap`	`10`%–`15`%	Ensures contextual continuity across chunks, preventing critical information from being cut off.
`embedding_model`	`m3e` or `bge-large-zh`	Considers both Chinese semantic understanding and specialized domain vocabulary representation capabilities.
`recall_top_k`	`8`–`15` items	Covers more potentially relevant document segments, increasing recall rate.
`rerank_top_n`	`3`–`5` items	Selects the most relevant results, improving the accuracy of the final presentation.
`similarity_threshold`	`0.75`–`0.82`	Filters out low-relevance noise, ensuring recall quality.

Common Pitfalls

A 401 Unauthorized error from the vector model request often indicates an incorrect or expired API Key configuration.
After importing documents into the knowledge base, duplicate content is deleted, leading to disordered index sequencing after custom chunking. This occurs because the system's default deduplication strategy does not align with specific indexing requirements.
After adding a custom channel vector model, requests are still routed to the LLM service, indicated by LLM model error messages. This happens because FastGPT's internal routing rules do not correctly identify the newly added vector model type or the model_type field is misconfigured.

Verification Steps

Upload representative R&D documents. Check if the chunk content in the knowledge base aligns with expectations, especially regarding the extraction of specialized terminology and chart titles.
Perform retrieval tests for core concepts or key numerical values. Compare the score values of the recall_top_k results to confirm that the similarity ranking of relevant document segments meets expectations.
Simulate real-world query scenarios using queries with specialized terminology and abbreviations. Observe whether the rerank_top_n results accurately recall document segments containing this specific information and check their contextual completeness.

The values provided are common starting points. Measure performance against specific 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.