Vector Models and Indexing for Bispecific Antibody

Data Characteristics

Regulatory and SOP documents for bispecific antibody research and production originate from internal Quality Management Systems (QMS), Laboratory Information Management Systems (LIMS), and Clinical Trial Management Systems (CTMS). These documents are typically in PDF, Word, or internal knowledge base formats. Update frequency is relatively low, primarily occurring during new drug development, process changes, or regulatory policy adjustments. Document structure is highly standardized, commonly including titles, chapters, clauses, figures, and appendices. In terms of fields and units, SOPs detail operational steps, reagent dosages (e.g., mg/mL, µL), instrument parameters (e.g., rpm, °C), and quality standards (e.g., Purity ≥ 95%). This content is highly specialized and structured.

Constraints from Data Characteristics on Vector Models and Indexing

The highly standardized nature of bispecific antibody regulatory documents means their text content is often long and contains numerous technical terms and numerical information. This requires vector models to understand contextual semantics while effectively distinguishing and encoding these critical specialized entities. Document update frequency is low, but each update may involve extensive revisions. The indexing strategy must therefore support efficient version management and incremental updates, avoiding full rebuilds. Embedded figures and tables within documents contain critical experimental data and quality standards. Direct text extraction may lose structural information. This requires considering structured data extraction and encoding during the preprocessing stage. Strict compliance requirements make traceability and accuracy of query results important considerations. This places higher demands on the precision of similarity calculations and recall strategies.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Balances contextual semantic integrity with information density per chunk. Avoids excessive length that introduces noise or excessive shortness that breaks semantic continuity.
`Overlap Size`	`50–100 characters`	Ensures semantic continuity between paragraphs, especially for specialized terms or key descriptions spanning multiple paragraphs.
`Vector Model` (Vector Model)	`bge-large-zh` or `text-embedding-ada-002`	Suitable for specialized Chinese text. Performs well in semantic understanding and numerical entity recognition.
`Recall count` (Recall Count)	`8–12 items`	Ensures sufficient relevant information points are covered in the initial recall phase, improving the accuracy of subsequent re-ranking.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Determine using a test dataset based on actual query scenarios and business requirements. Ensures a balance between high recall and high precision.
`Rerank result count` (Re-ranked Return Count)	`3–5 items`	Focuses on core content most relevant to the user. Reduces the processing load on large language models and improves response speed.

Common Pitfalls

Knowledge base query results are empty or irrelevant: This often occurs when document chunking granularity is too large or too small. Key information becomes diluted or fragmented, affecting the vector model's semantic understanding.
PARSE_FILE_TIMEOUT_SECONDS error during document import: This typically happens when processing large PDFs or documents with complex tables. File parsing time exceeds the system's preset threshold.
Incorrect numerical values or units in query results: This may occur if tables or specific numerical formats are not structurally extracted during document preprocessing. Instead, they are treated as plain text, leading to loss of semantic properties during vectorization.

How to Verify Configuration

Select typical query questions. Compare FastGPT's recall results with manually identified relevant document snippets. Ensure core information is covered.
Test documents containing tables and complex formats to confirm correct parsing and chunking of their content. Pay particular attention to the extraction of key parameters and standards.
Adjust the Similarity threshold (Similarity Threshold). Observe changes in recall count and relevance until a business-acceptable balance is achieved.
Check log output. Confirm no ERROR level messages during file import, especially those related to file parsing or vector generation failures.

The values provided are common starting points. Measure them against your 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.