Workflow Orchestration for Quality Documents in Bidding and

Data Characteristics for This Category

Data for bidding and listing in the biopharmaceutical industry originates primarily from official platforms like drug regulatory agencies, medical insurance bureaus, and provincial/municipal public resource trading centers, as well as third-party pharmaceutical information service providers. Data updates frequently, especially after the release of provincial centralized procurement and volume-based procurement policies, which lead to intensive updates of related catalogs and price information. Document types are diverse, including enterprise qualification certificates, product registration certificates, quality system documents, inspection reports, clinical trial data, price approval documents, and promotional brochures. These documents typically exist in formats such as PDF, Word, and Excel. Fields and units are highly specialized, for example, the generic name, brand name, dosage form, specification, manufacturer, approval number, medical insurance payment standard, minimum dosage unit, and packaging unit of a drug. Some documents may contain handwritten signatures or seals, which complicates automatic recognition.

Constraints Imposed by These Characteristics on "Workflow Orchestration"

The scattered data sources and inconsistent formats of bidding and listing documents require the workflow to have robust file type parsing capabilities and multi-source data integration capabilities. High-frequency policy updates necessitate flexible trigger mechanisms and version management within the workflow to ensure processing of the latest data. Highly specialized fields and units in documents demand high accuracy from information extraction nodes, requiring validation with domain-specific dictionaries and rules. Image-based content in some documents (e.g., handwritten signatures, seals) requires OCR technology support, which may introduce recognition errors. This mandates the inclusion of manual review or multi-round validation steps in the workflow. Furthermore, sensitive data involving prices and medical insurance standards impose strict requirements on data security and permission management within the workflow to ensure compliance.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`3000 Tokens`	Ensures sufficient context when processing complex documents while avoiding increased costs or performance degradation due to excessively long model inputs.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances text block integrity and model processing efficiency, suitable for paragraph structures in documents like drug instructions and inspection reports.
`Recall count` (Recall Count)	`Top 5 entries` (top 5)	Reduces interference from irrelevant information while ensuring comprehensive information, improving efficiency in subsequent re-ranking and generation stages.
`Similarity threshold` (Similarity Threshold)	`0.75`	Sets a higher threshold for specialized terms like drug names and specifications to ensure the precision of recall results.
`Rerank result count` (Re-ranking Return Count)	`3 entries` (3 items)	Further refines recall results, focusing on the most relevant information and reducing the model's processing burden.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Addresses the time required to parse large PDF documents or scanned copies containing complex charts, preventing timeouts.

Three Common Pitfalls

The text content extraction node prompts a model error, which may manifest as the model returning Invalid token count or Context window exceeded. This typically occurs when the input document is too long or the segmentation strategy is inappropriate, causing a single request to exceed the model's maximum supported context limit.
After calling another workflow within a workflow, subsequent nodes do not execute. The logs show the sub-workflow succeeded, but the main workflow stalls or returns incomplete results. This may happen if the sub-workflow's User Choice node lacks a preset default value or cannot obtain external input during a non-interactive call, leading to a process blockage.
A database query node returns an empty result, but data actually exists in the database. This could be due to inconsistencies between the field names or table names in the query statement and the database definition, or incorrect parameter input format, such as querying a numeric field as a string.

How to Verify Correct Configuration

Select typical bidding and listing document samples (e.g., drug registration certificates, price approval documents). Execute them through the workflow and check if the final output information is complete and accurately covers key fields in the document.
Simulate policy updates or bulk data import scenarios. Observe if the workflow's trigger mechanism starts as expected and verify that the processed data timely reflects the latest status.
Configure log output at each critical node of the workflow (e.g., file parsing, information extraction, data validation). Check the logs for error messages and compare actual processing results with expected results, paying particular attention to the extraction accuracy of specialized fields.

Note: The values provided are common starting points. Measure them against your own samples to determine optimal configurations.

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.