Bid Document Preparation: Operations On-Duty and Internal Work Order Q&A – Knowledge Base Retrieval and Recall

What This Category’s Data Looks Like

Data is generated and updated around individual bid projects. Data sources include public tender announcements, bidding documents released by purchasers, internal enterprise qualification libraries, and quotation documents from cooperative suppliers. Core documents produced daily include the main bid document, qualification certification materials, business deviation table, and technical deviation table. Each project’s data is only valid during the corresponding bid cycle. Update frequency is once per project. Data fields include tender number, qualification level, quotation items, clause response content, with no unified fixed unit. Values adjust dynamically based on tender requirements.

Constraints Imposed on Knowledge Base Retrieval and Recall

Data characteristics impose multiple constraints on the knowledge base retrieval and recall link. First, data is updated per project and has strong timeliness. Each retrieval must prioritize recalling exclusive documents for the current bid project. Recall scope must be filtered by project dimension to avoid mixing irrelevant content from other bid projects. Second, core documents include mixed format content such as structured deviation tables and qualification certificates. Some content is presented in table form. Accurate identification of clause alignment and key values within tables is required. Third, internal work order responses require fast retrieval to avoid delaying critical milestones during bid preparation. Fourth, retrieval result accuracy directly relates to bid rejection risk. Strict control of matching precision for recalled content is required to avoid clause response errors or omission of qualification materials.

Configuration Settings

Three Common Misconfigurations

• Retrieval response times out, and the number of returned results is insufficient. Cause: No reasonable limit is set for the limit parameter, or collectionIds are not filtered by project, leading to recall of all historical data.
• The knowledge base contains image links, but Q&A cannot return relevant image content. Cause: No image parser is bound, or extended query is not enabled in the retrieval configuration to identify text information associated with images.
• Retrieval result matching degree is too high or too low. Cause: The similarity parameter value does not adapt to the current project’s document structure, or the re-ranking function is not enabled, leading to incorrect sorting of structured content.

How to Verify Proper Configuration

• Access the knowledge base management interface of FastGPT v4.17.0, verify whether the collectionIds bound to the current project match the target knowledge base collection.
• Initiate a test retrieval, wait no more than 10 seconds, input a clause-related question for the current bid project, verify whether the number of recalled results conforms to the limit parameter setting.
• Upload a test document containing image links, initiate a Q&A test, confirm whether qualification or quotation content corresponding to the image can be associated.
• Adjust the similarity parameter and initiate multiple retrievals, verify whether the matching accuracy of recalled results meets the requirements of the current project.

The values provided here are common starting points and should be evaluated 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 2026-10-19.