Data Characteristics for This Category
Bidding and procurement data originates from various sources: centralized procurement platforms for pharmaceuticals and consumables, official medical institution websites, and third-party information aggregators. Update frequencies vary; some platforms update daily, others weekly or monthly. Document formats are diverse, including PDF bidding announcements, Word procurement documents, and Excel quotation lists or winning bid results. These documents have relatively fixed internal structures. For example, bidding announcements typically contain fields such as project name, procurement number, procuring entity, budget amount, registration deadline, and bid opening time. Excel files often include detailed information like drug name, generic name, dosage form, specification, manufacturer, listed price, and distribution company. Field names can be inconsistent; for instance, "procurement unit" and "bidder" might refer to the same entity. Units are typically "yuan" for currency, and quantities might involve "boxes," "syringes," or "bottles."
Constraints Imposed by These Characteristics on Deployment and Upgrade
The multi-source and heterogeneous nature of bidding and procurement data requires FastGPT to have robust document parsing capabilities during data ingestion. The complex layouts of PDF and Word documents, along with potential merged cells and multi-level headers in Excel spreadsheets, challenge the robustness of the content extraction module. Uncertain data update frequencies necessitate a task scheduling system to periodically trigger data fetching and knowledge base update processes, ensuring information timeliness. Inconsistent field names require standardization or an alias mechanism during knowledge base construction. Additionally, the data volume can be substantial, especially with accumulated historical data, demanding high storage performance and retrieval efficiency from the vector database. Deployment must consider concurrent processing capabilities to handle frequent query requests and ensure real-time pre-screening.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Bidding and procurement documents may contain numerous images or detailed attachments, leading to large file sizes. |
Chunk size (Segment Length) | 800–1200 characters | Ensures that each segment contains complete bidding terms or drug description information. |
Recall count (Recall Count) | 10 | Increases the recall scope, improving the probability of retrieving key information from multiple relevant documents. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall accuracy and relevance, reducing interference from irrelevant information. |
Rerank result count (Rerank Return Count) | 5 | Focuses on the most relevant few results, improving the precision of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the potentially long parsing time for complex PDF/Word documents. |
Common Pitfalls
- Knowledge base creation errors or inability to recognize document types often stem from incomplete
MIMETYPEorFILE_EXTENSIONconfigurations, preventing the system from correctly invoking the appropriate parser. - If the model or vector model pings successfully but local queries yield no results,
OPENAI_API_KEYorVECTOR_MODEL_API_KEYmight be incorrectly configured, leading to token validation failure. - Low answer accuracy, indicating the content extraction module is not working effectively, often occurs when document structures are complex and the segmentation strategy fails to identify key information boundaries, resulting in semantic units being incorrectly split or merged.
Verification Steps
- Upload bidding announcements and procurement documents in various formats (PDF, Word, Excel). Check if files are successfully parsed and imported into the knowledge base, and observe the import status.
- Query for specific project names or drug names. Verify if the returned results include key information from the relevant documents, such as procurement number or listed price, and assess information completeness.
- Simulate multiple concurrent user queries. Monitor system response time and resource utilization to confirm system stability under high load.
- Regularly check the execution logs of knowledge base update tasks. Confirm that data fetching and knowledge base rebuilding processes run at the expected frequency and without significant errors.
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.