Data Characteristics
Procurement bidding regulation data in the biopharmaceutical sector primarily originates from national pharmaceutical centralized procurement platforms, official medical insurance bureau websites, and local public resource trading centers. This data is frequently updated, typically monthly or quarterly, with incremental changes for new batches, products, or policy adjustments. Document structures are mainly structured or semi-structured, such as Excel spreadsheets, PDF announcements, and web pages. Excel spreadsheets often contain fields like product name, manufacturer, listed price, listed province, effective date, and procurement cycle. PDF announcements and web pages may include unstructured information such as policy interpretations, detailed rules, and technical requirements. Price units are usually "RMB/box" or "RMB/unit," and procurement quantity units are "box" or "unit." The data is characterized by its timeliness and strong policy correlation; any policy change can lead to extensive data updates or invalidations.
Constraints on Source and Traceability
High timeliness of procurement bidding data requires RAG retrieval results to accurately point to the latest document versions, avoiding outdated policies or pricing information. Heterogeneous data formats (tables, PDFs, web pages) from multiple sources increase the complexity of text extraction and chunking. This necessitates optimizing preprocessing for different document types. For example, the row and column relationships in tabular data must remain intact during chunking to ensure accurate association of critical information like prices and companies. Policy documents often have large text volumes and contain many specialized terms, demanding finer chunking granularity. Each chunk must retain a complete semantic unit. Additionally, frequent data updates require rapid response from indexing or incremental update strategies to ensure that cited sources always point to currently valid regulatory documents. Incorrect citations can lead to significant discrepancies for companies in bidding or price negotiations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Balances the integrity of semantic units in policy documents with processing efficiency for individual chunks, preventing critical information from being cut off. |
Overlap Length | 100 characters | Ensures contextual continuity, especially at policy clause junctions, improving recall accuracy. |
Similarity Threshold | 0.75–0.85 | Improves recall precision for policy texts with many specialized terms and high text similarity. |
Recall Count | Top 5 | Balances recall breadth with subsequent re-ranking efficiency; most relevant information is covered within this range. |
Rerank Return Count | Top 3 | Focuses on the most relevant and core regulatory clauses, reducing redundant information that could interfere with the final answer. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates the possibility of single bidding announcements or policy documents containing numerous attachments or detailed descriptions, ensuring smooth file uploads. |
Common Pitfalls
- The response includes links to outdated documents irrelevant to the current query. This occurs when the knowledge base is not updated promptly or indexing strategies fail to differentiate document versions.
- The AI platform response lacks citations for specific listed prices or policy terms. This happens when tabular data chunking does not preserve row and column associations, leading to critical values being disconnected from their context.
- Retrieval results are empty or inaccurate when querying specific policy documents. This indicates that PDF content was not parsed correctly, or OCR errors resulted in incomplete text extraction.
Verification Steps
- Query procurement bidding information on the same topic at different times to verify if cited sources point to the latest effective policy documents.
- Randomly select procurement bidding documents containing tabular data. Query specific product prices or manufacturers within them, then check if the cited sources accurately pinpoint the corresponding rows and columns in the table.
- Simulate user questions to verify if the policy clause citation links in the AI platform's response are clickable and navigate to the correct location in the original document.
- Check log outputs for
PARSE_FILE_TIMEOUT_SECONDSerrors orOCR_FAILUREwarnings during file parsing.
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.