Vector Model and Index for CSO Registration and Declaration

Data Characteristics

CSO (Contract Sales Organization) registration and declaration document preparation involves data from pharmaceutical companies. This includes original R&D data, clinical trial reports, non-clinical study reports, manufacturing process documents, and domestic and international regulatory databases. Data update frequencies vary. Regulatory updates may occur monthly or weekly, while clinical trial data updates happen in phases as projects progress. Document structures are primarily PDF, Word, and Excel, containing extensive unstructured text, tables, and charts. Pharmaceutical research reports and toxicology reports are often lengthy and dense with specialized terminology. Common and critical fields and units include drug names, CAS numbers, indications, dosages, adverse reactions, batch numbers, manufacturing locations, dose units (mg, μg), and concentration units (g/L, %).

Constraints Imposed by These Characteristics on Vector Models and Indexing

The specialized and diverse nature of CSO registration and declaration documents places specific demands on vector models and indexing. First, a large volume of specialized terminology and abbreviations requires vector models to have strong domain adaptability. This prevents semantic drift or information loss. Second, frequent updates to regulatory documents necessitate an indexing system that supports efficient incremental updates and version management, ensuring timely retrieval results. Complex table and chart structures in documents challenge text extraction and vectorization, requiring specific preprocessing strategies. Since declaration documents involve multiple sub-documents, establishing effective connections between them and ensuring accurate recall of relevant context during retrieval is crucial for index structure and recall strategies. For example, a compound mentioned in a toxicology report must be effectively linked to chemical structure information in a pharmaceutical research report.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`800–1200 characters`	Balances semantic completeness and vector model processing efficiency. Avoids diluting key information in overly long chunks and losing context in overly short chunks.
`Chunk Overlap Length` (Overlap Size)	`100 characters`	Ensures contextual continuity between adjacent chunks, improving retrieval accuracy for information spanning multiple chunks.
`Recall count` (Recall Count)	`Top 5`	Balances retrieval speed and result comprehensiveness. Covers most relevant information while reducing unnecessary computational overhead.
`Similarity threshold` (Similarity Threshold)	`0.75`	For specialized documents, this increases the relevance of recalled results and reduces noise interference. The specific value should be calibrated through actual testing.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses long parsing times for large PDF files, preventing file processing from timing out.
`embeddingModel`	`text-embedding-ada-002`	Offers good general applicability and performance. Can be replaced with a domain-specific model based on actual performance and cost considerations.

Common Pitfalls

Slow knowledge base query responses or "no results" error messages. This may indicate an unavailable or timed-out vector model service.
After uploading large declaration files, a prolonged "indexing" status. This often results from a file parsing timeout setting that is too low or a stuck file processing task.
Retrieval results containing a large amount of irrelevant content, or a failure to recall key information. This may stem from an inappropriate Chunk size (Chunk Size) setting, leading to semantic units being fragmented.

How to Verify Proper Configuration

Upload various types (PDF, Word, Excel) and sizes of typical declaration documents. Observe whether file processing completes normally without timeouts or errors.
Perform retrieval tests on the uploaded documents using specialized terminology and regulatory clauses. Verify the relevance of retrieval results, for example, by checking the distribution of similarity scores.
Use FastGPT's logs or monitoring interface to check the call frequency, response time, and error rate of the vector model and indexing services. This ensures stable service operation.
Select specific key information points from declaration documents and perform precise queries. Compare these with expected results from manual verification to confirm the accuracy of core information recall.

Note: 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.