Data Characteristics for This Category
Bispecific antibody product data originates from clinical trial reports, patent literature, academic papers, drug inserts, and internal pharmaceutical R&D documents. This data updates frequently, especially during clinical trials, with batches released continuously. Document structures typically include standardized fields such as target information, molecular structure, mechanism of action, pharmacokinetics, pharmacodynamics, toxicology data, clinical indications, dosage and administration, and adverse reactions. Molecular structures often appear as SMILES strings, FASTA sequences, or PDB IDs. Pharmacokinetic and pharmacodynamic data include numerical values with specific units, such as concentration (ng/mL), time (h), and dose (mg/kg).
Constraints Imposed by These Characteristics on Vector Models and Indexing
The complex molecular structures and mechanisms of action of bispecific antibodies result in highly specialized and specific textual descriptions. This requires vector models to effectively capture the semantic interactions between biomacromolecules and distinguish subtle structural differences. The continuous updates to clinical trial data and the large number of numerical fields demand real-time indexing and numerical retrieval capabilities. Sequence data (e.g., FASTA) or structural identifiers (e.g., PDB ID) in documents require specific preprocessing methods to convert them into vectorizable representations. Furthermore, the multi-target and multi-mechanism nature increases information retrieval complexity, requiring vector indexes to support multi-dimensional, high-precision semantic matching to avoid recalling irrelevant product information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–750 characters | Balances context integrity and vectorization precision, preventing truncation of key information |
Chunk Overlap Length (Segment Overlap Length) | 100–150 characters | Ensures semantic continuity between segments, especially when describing mechanisms of action |
Recall count (Recall Count) | 8–12 items | Ensures comprehensive coverage of multi-target, multi-mechanism information, improving initial recall rate |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires adjustment based on the specific model and dataset to balance recall and precision |
Rerank result count (Reranked Return Count) | 3–5 items | Focuses on the most relevant results, reducing the burden on subsequent processing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large clinical trial reports and patent documents can be time-consuming, preventing timeouts |
Three Common Mistakes
- After uploading many documents, some knowledge base statuses show "Not Ready" and are not retrievable: This usually occurs due to exceptions during file parsing or vectorization, and the system does not automatically trigger a retry mechanism.
- Search results contain many irrelevant or low-relevance items: This might be due to a
Similarity threshold(Similarity Threshold) set too low, or aChunk size(Segment Length) that is too long, leading to unfocused segment semantics. - After updating the FastGPT version, knowledge bases with existing indexes cannot perform vector retrieval: This could be due to incompatible vector model versions, preventing the new model from recognizing old index data.
How to Verify Configuration
- Upload representative bispecific antibody product documents and check if all files show "Ready" status.
- Use queries containing keywords such as molecular structure, mechanism of action, and indications to verify the accuracy and relevance of recall results. Check if the
Recall count(Recall Count) meets expectations. - For queries targeting known specific targets or mechanisms of action, observe if the returned results include core information about that target or mechanism. Adjust the
Similarity threshold(Similarity Threshold) to observe changes in results and determine an appropriate threshold range. - Simulate high-concurrency upload tasks to check if
PARSE_FILE_TIMEOUT_SECONDSis sufficient to prevent parsing interruptions.
The values provided are common starting points and should be measured 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.