Data Characteristics
R&D document data for bidding and tendering primarily originates from government and enterprise procurement platforms, medical device registration and approval agency websites, and industry association publications. This data updates frequently, often with new announcements released weekly or even daily. Document formats vary, including PDF, Word, and Excel, with PDF being the most prevalent.
Documents typically contain fixed fields such as project name, bidding entity, procurement content, technical requirements, qualification requirements, bid submission deadline, and contact information. However, they also include extensive unstructured or semi-structured descriptions, such as detailed technical parameter specifications, clinical trial protocols, and drug mechanisms of action. Common units include monetary amounts (Yuan), time (days), quantities (units/batches), and performance indicators (e.g., purity %, sensitivity ng/mL). Unit expressions can be inconsistent; for example, "percentage" might appear as "%" or "percent".
Constraints on Knowledge Base Retrieval and Recall
The high update frequency of bidding and tendering documents necessitates efficient incremental update and indexing mechanisms in the knowledge base to ensure timely retrieval results. Diverse document formats, especially the large number of PDF files, challenge text extraction accuracy. OCR errors or layout parsing issues directly impact subsequent structuring and retrieval quality.
The coexistence of fixed fields and unstructured descriptions requires the knowledge base to balance precise matching with semantic understanding. For example, for specific parameters within "technical requirements," the system must extract numerical values and units from ambiguous descriptions for comparison. Inconsistent unit expressions complicate standardization, potentially preventing effective aggregation or comparison of similar information across documents, thereby affecting recall rate and accuracy. Without standardization, a query for "purity greater than 99%" might fail to retrieve documents stating "purity exceeding ninety-nine percent."
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances document structural integrity with retrieval granularity. Avoids long paragraphs diluting key information and short paragraphs losing context. |
Chunk Overlap Length | 80–120 characters | Ensures continuity of context across segment boundaries, improving recall of cross-paragraph information. |
Recall count | 8–12 entries | Limits the number of returned results while ensuring coverage, reducing the burden on subsequent reranking and model processing. |
Similarity threshold | Calibrate by measurement | Requires small-sample testing based on actual business query difficulty and desired recall precision. |
Rerank result count | 3–5 entries | Focuses on a small number of the most relevant documents, improving the accuracy of the final answer and model inference efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time-consuming parsing of large PDF documents, preventing file processing failures due to timeouts. |
Common Pitfalls
- Knowledge base import takes too long, preventing timely retrieval of newly published bidding information. This often results from a lack of parallel optimization for file parsing and vector embedding, or importing too many files at once, exceeding system processing capacity.
- Retrieval results contain numerous irrelevant or low-relevance documents, failing to focus on specific technical parameters or qualification requirements. This may stem from an unreasonable segmentation strategy that dilutes key information, or a similarity threshold set too low, recalling excessive noise.
- Structured fields like "bid submission deadline" are not precisely matched during retrieval, leading to the omission of some eligible documents. This typically occurs due to diverse date formats in original documents that lack standardized processing, or formatting errors during text extraction.
Configuration Validation
- Randomly select 20 newly published bidding documents. After importing them into the knowledge base, use keyword and semantic queries to verify that all key information in these documents can be recalled. Evaluate the ranking order of the recalled results.
- For PDF documents containing complex tables or charts, examine the corresponding text segments in the knowledge base to ensure key data and related descriptions are accurately extracted, with no obvious OCR errors.
- Simulate user queries for specific technical parameters or qualification requirements, such as "injectables with purity above 99.5%." Verify that the recalled results include all eligible documents and check for consistency in numerical values and units.
- Monitor incremental knowledge base update tasks to ensure new document import and index construction complete within the expected timeframe, for example, processing newly added documents daily within
30 minutes.
Note: The values provided are common starting points and should be measured 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.