Data Characteristics for This Category
Tender listing data originates from provincial and municipal drug procurement platforms, medical consumables procurement platforms, and government announcements. Data updates frequently, often weekly or daily, with new policies, product batches, and bidding results. Document types vary, including PDF notices, Excel quotation or bidding catalogs, Word application templates, and web-based public announcements. These documents typically contain key fields such as product name, specifications, manufacturer, registration number, listed price, procurement cycle, and effective date. Price fields may involve different units (e.g., "yuan/box," "yuan/piece") and require specific decimal precision. Some documents may include image-based supporting materials or charts.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval
High update frequency requires efficient document synchronization and indexing mechanisms to ensure timely retrieval results. Diverse document formats, especially PDF and Excel, challenge parsing capabilities, requiring accurate extraction of text and tabular data. Accurate identification and extraction of key fields, such as registration numbers and listed prices, are fundamental for precise retrieval. The variety of measurement units and decimal requirements for prices affect the standardization and querying of numerical data. Indexing image content may involve image recognition technology to extract text from images. Furthermore, retaining and retrieving historical versions is crucial for tracking policy changes and price adjustments.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Accommodates policy documents and table row data, ensuring contextual completeness |
Chunk overlap (Segment Overlap) | 100–200 characters | Prevents critical information from being truncated by segment boundaries |
Recall count (Retrieval Count) | Top 10–15 items | Covers multiple sources, improving relevance recall |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Filters low-relevance results, balancing recall and precision |
Rerank result count (Reranked Return Count) | Top 5 items | Refines final output, focusing on the most relevant information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing time for large PDF or complex Excel files |
Common Pitfalls
- Retrieval results provide general, irrelevant information instead of specific answers. This occurs due to overly coarse segmentation strategies, leading to knowledge point confusion, or insufficient semantic matching between queries and document content.
- Updated listed prices or policies are not retrieved promptly. This happens when the knowledge base synchronization mechanism does not match the source data update frequency, or when indexes are not rebuilt or incrementally updated in time.
- Retrieval of certain fields (e.g., registration numbers) is incomplete or missing. This is caused by insufficient recognition capabilities of the document parser for specific formats (e.g., image text in scanned PDFs, complex table structures), failing to accurately extract key fields.
How to Validate Configuration
- Select a batch of representative queries covering policies, prices, and product information. Check if retrieval results accurately point to relevant document segments.
- Simulate recent policy or price updates. Perform incremental synchronization and verify if new information is immediately retrievable and distinguishable from older versions.
- Manually verify the accuracy of key field extraction for different document types (PDF, Excel, Word). Confirm that information like registration numbers and listed prices is correct.
- Adjust the
Similarity threshold(Similarity Threshold) to observe changes in the quantity and relevance of retrieval results. Determine a threshold that balances recall and precision.
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.