Database and Operations for Structured Parsing of Bidding

Data Characteristics

Bidding and listing documents in the biomedical sector originate from government procurement platforms, pharmaceutical centralized procurement platforms, and industry association websites. These documents update frequently, typically weekly or monthly. Formats vary, primarily PDF, Word, and Excel, with occasional scanned images. Structurally, these documents usually contain fixed fields such as project name, procurement number, procuring entity, budget amount, bidding scope, technical requirements, qualification requirements, bid opening time, and supplier instructions. They also include specific technical parameters, specifications, and units (e.g., milligrams, tablets, milliliters, units, sets) for drugs or medical devices, often presented in tables or unstructured text.

Constraints Imposed by Data Characteristics on Database and Operations

The multi-source nature and high update frequency of bidding and listing documents require the database to have efficient data ingestion and incremental update capabilities to ensure information timeliness. Document format diversity and the presence of unstructured content challenge the file parsing module, which must support multiple file types and extract key information from complex layouts. Accurate identification and structuring of technical parameters, specifications, and units for drugs or devices are crucial for database field design and vector embedding precision. Missing or incorrectly parsed specific fields directly impact downstream intelligent comparison and analysis functions. Therefore, the database design must support flexible field extensibility to accommodate unique attributes of different bidding document categories and enable precise queries on numerical and text fields. Operationally, high-concurrency document parsing and vectorization tasks can strain computing resources, requiring attention to resource scheduling and task queue management.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Bidding and listing documents often contain extensive text and tables, leading to larger file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDFs or scanned documents require longer parsing times; this prevents task failures due to timeouts.
`Chunk size` (Segment Length)	`800–1200 characters`	Retains sufficient contextual information while preventing overly long segments from affecting vector recall precision.
`Recall count` (Recall Count)	`Top 10`	Ensures multiple document fragments relevant to the query intent are covered during initial retrieval.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjust according to the balance needed between recall accuracy and recall rate in the specific business scenario.
`Vector Database Index Type`	`HNSW`	Suitable for storing and retrieving large-scale vector data while ensuring query efficiency.

Three Common Mistakes

After uploading a document, some critical fields (e.g., "Budget Amount" or "Bid Opening Time") appear empty in the parsing results. This can happen if the field's expression in the document does not match predefined patterns or due to OCR errors.
After local deployment of FastGPT, a database connection failure error occurs during login. The logs typically show database connection error. This is caused by incorrect database service configuration or port conflicts in the docker-compose.yml file.
Directly modifying data in the vector database using external management tools leads to inaccurate FastGPT knowledge base query results. Queries return unexpected or missing results because direct vector database operations do not synchronize with FastGPT's internal metadata index.

How to Verify Configuration

Upload bidding and listing documents in various formats (PDF, Word, Excel). Check if core fields like project name, procurement number, and budget amount are accurately extracted in the parsing results. Verify that extracted technical parameters and units match the original text.
Simulate high-concurrency document upload and parsing tasks. Review system logs and resource monitoring to confirm no parsing task timeouts and that system resource (CPU, memory) utilization remains within acceptable limits.
Perform fuzzy queries for technical parameters of specific drugs or devices. Observe the distribution of similarity scores in the recall results. Manually verify if the recalled document fragments are highly relevant to the query intent to assess the reasonableness of the Similarity threshold (similarity threshold).

Note: The values provided are common starting points. They should be measured against specific 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.