Data Characteristics for This Category
Tender bidding and listing data in the biopharmaceutical industry originates from various sources. These include centralized procurement platforms for drugs and consumables at all levels, official medical insurance bureau websites, and hospital internal procurement systems. Data updates are frequent, typically weekly or monthly in batches. Policy adjustments or urgent procurements can trigger unscheduled updates. Document structures generally include tender documents, bid winning announcements, listed catalogs, enterprise qualification certificates, product registration certificates, and quality inspection reports. These documents are often in PDF, Word, or Excel formats. Fields and units are highly standardized. Examples include generic drug names, dosage forms, specifications, manufacturers, registration certificate numbers, medical insurance payment standards, bid prices, procurement cycles, and delivery requirements. Measurement units (e.g., mg/tablet, ml/injection) and price units (Yuan) are precise, often to multiple decimal places.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The multi-source nature and high update frequency of tender bidding data require FastGPT deployments to have flexible data access capabilities and efficient incremental update mechanisms. These ensure the knowledge base remains current. Diverse document formats and varying degrees of structure pose challenges for file parsing and information extraction. This necessitates configuring sufficient file processing timeouts and concurrency. Highly standardized fields and precise units mean the knowledge base must accurately identify and match this critical information during recall and answer generation. This prevents price or specification errors due to semantic misunderstandings. Furthermore, the presence of sensitive information, such as product registration certificates, demands higher data security and permission management in the deployment environment.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Tender bidding documents often contain numerous attachments, requiring support for large file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDF files and Word documents with images and tables can be time-consuming. |
Chunk size (Segment Length) | 800–1200 characters | Ensures critical information like tender clauses and technical parameters remain within the same segment, preventing semantic fragmentation. |
Recall count (Recall Count) | Top 8 | Tender bidding queries often require synthesizing multiple relevant document fragments for a complete answer. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures recalled documents are highly relevant to the user's query, filtering out irrelevant general terms. |
CONTEXT_MAX_TOKENS | 4096 | Tender bidding information has strong contextual relevance, requiring a longer context window for follow-up questions. |
Three Common Mistakes
- Symptom: The knowledge base provides incorrect drug prices or incomplete product specification information. Reason: Insufficient file parsing configuration prevents correct extraction or recognition of critical structured information like prices and specifications. Alternatively, the
Chunk size(Segment Length) is too short, truncating key information. - Symptom: After deployment, the model cannot maintain context during continuous questioning, treating each question as a new one. Reason: The
maxContextparameter is configured too small or not applied correctly. This causes session history to be truncated prematurely, preventing the model from maintaining conversational coherence. - Symptom: Uploading large tender documents results in "file processing timeout" or "upload failed" errors. Reason:
UPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSconfigurations are below actual requirements. This prevents processing of complex, large files common in the biopharmaceutical sector.
How to Confirm Correct Configuration
- Upload a typical tender bidding document containing various formats (PDF, Word, Excel). Check if it is successfully parsed and indexed.
- Conduct multi-round, continuous questioning about a specific drug model. Verify if the model maintains context and accurately answers questions about its medical insurance payment price, winning bidder, and supply cycle.
- Randomly select specific clauses from several uploaded documents. Ask questions to verify if the knowledge base recalls accurate and complete relevant fragments. Compare these with the original document content.
Note: The values provided are common starting points. Measure 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.