Knowledge Base Retrieval for Tender Bidding Quality

Data Characteristics

Tender bidding quality documents in the biopharmaceutical industry originate from provincial and municipal drug and medical device centralized procurement platforms, official medical insurance bureau websites, and internal enterprise reporting systems. These documents update frequently, typically with policy adjustments, batch updates, or new product launches. Monthly or quarterly updates are common. Document structures are often PDF, Word, or Excel formats. Content includes basic product information, registration certificate numbers, manufacturers, quality standards, batch inspection reports, winning bid prices, and listing status. Fields include general product information and biopharmaceutical-specific identifiers such as "GMP certificate number," "drug administration filing number," and "production license number." Units involve dosage units (mg/ml), packaging specifications (box/piece), price units (yuan/box), and validity period units (month/year). This information is critical for drug compliance and traceability.

Constraints on Knowledge Base Retrieval and Recall

The high update frequency of tender bidding documents requires the knowledge base to support efficient incremental updates and version management. This ensures retrieved information reflects the latest listing status and compliance requirements. Diverse document formats and complex tabular data challenge document parsing capabilities. Accurate extraction of structured information is necessary to avoid field misalignment or data loss. Biopharmaceutical-specific fields make standardized entity recognition and term matching critical for precise queries, such as compliance for specific product batches. Data format variations across provinces and procurement platforms increase the complexity of data cleaning and unified modeling, affecting the consistency of recall results. Numerical information like prices and specifications often involves range queries or comparisons during retrieval, requiring robust numerical processing capabilities from the knowledge base.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–700 characters` (characters)	Balances document context with information density in a single text block, avoiding fragmentation or redundancy.
`Chunk overlap` (Segment Overlap)	`50–100 characters` (characters)	Ensures contextual continuity between segments, minimizing loss of key information due to segmentation.
`Recall count` (Recall Count)	`8–12 entries` (items)	Controls the number of recall results while ensuring coverage, reducing subsequent processing load.
`Similarity threshold` (Similarity Threshold)	`Calibrate by actual measurement` (Calibrated by actual measurement)	Ensures relevance of recall results, preventing interference from low-relevance documents.
`Rerank result count` (Rerank Return Count)	`3–5 entries` (items)	Re-sorts recall results to focus on the most relevant content, improving user experience.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large PDFs or complex tabular files can take significant time, preventing parsing failures due to timeouts.

Common Pitfalls

Retrieval results contain many documents irrelevant to the query. This is due to a Similarity threshold (Similarity Threshold) set too low, leading to an overly broad recall scope.
After uploading large Excel files, training data is incomplete or some fields are missing. This occurs because parsing configurations are not optimized for table structures, or the PARSE_FILE_TIMEOUT_SECONDS value is insufficient to process the file.
AI responses cite outdated or revoked listing document information. The Listing Status field in the returned documents shows "expired," but the model still references them. This may be because the knowledge base update mechanism failed to synchronize the latest data promptly, or Listing Status was not used as a filter during retrieval.

Verification of Configuration

Select a batch of representative query questions. Manually verify that the recall results include all relevant tender bidding documents and check if their Listing Status field is up-to-date.
Upload tender bidding files of different formats and sizes (e.g., PDF, Word, Excel). Observe the knowledge base's parsing progress and the completeness of the final ingested data, especially whether key fields like Registration Certificate Number and Manufacturer are correctly extracted.
Query for specific batch numbers or product names. Verify the accuracy of numerical information such as Batch Inspection Report and Winning Bid Price in the returned results by comparing them with the original documents.
Check system logs for PARSE_FILE_TIMEOUT_SECONDS timeout error codes during file parsing or field missing warnings during data ingestion. This helps assess the robustness of the parsing configuration.

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.