Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
Data for this category comes from China Government Procurement Network, local government procurement websites, fiscal integration systems, procurer budget systems, and supplier credit records. The update frequency is daily. Typical documents include procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, and question and answer responses. Supporting party building learning texts are also included, involving four entities: procurer units, government procurement supervision departments of finance departments, procurement agencies, and review experts. Most data consists of structured procurement process documents, with each document ranging from 500 to 5000 characters. Fields include standardized content such as procurement number, project name, and supplier qualification requirements.
What Constraints Do These Characteristics Impose on the "Knowledge Base Retrieval and Recall" Link
Daily updated procurement and party building data requires the retrieval system to support incremental synchronization. The synchronization interval must not exceed 24 hours. This avoids excessive resource usage from full reindexing. Multiple types of procurement documents have strict process distinctions. Party building learning content must be isolated from procurement scenarios. Retrieval must be limited to specified procurement projects or party building learning materials. This prevents interference from cross-process documents. Standardized procurement fields and fixed party building terminology require high retrieval matching accuracy. This avoids compliance risks from fuzzy matching. Data sources are scattered across multiple government procurement-related systems. A unified knowledge base access method is needed. This ensures retrieval covers all compliant data sources.
Recommended Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Government procurement and party building texts use precise terminology, require high matching accuracy to avoid irrelevant results |
limit | Top 8 entries | A large number of procurement and party building documents, control returned results to avoid redundancy |
searchMode | vector+keyword | Balance the structured field requirements of procurement documents and semantic matching needs of party building texts |
usingReRank | Enabled | Filter procurement process documents unrelated to the current party building learning Q&A |
datasetSearchUsingExtensionQuery | Disabled | Party building Q&A requires strict matching of original text content, no question expansion |
collectionIds | Dataset IDs grouped by procurement project | Limit retrieval scope to prevent cross-project data from interfering with the accuracy of party building learning Q&A |
Three Common Mistakes
- Phenomenon: After deleting a Q&A pair in the knowledge base via the backend, FastGPT retrieval results still include the content. Cause: Incremental synchronization for the dataset was not triggered, cached embedding vectors were not cleared, so old data remains in the retrieval index.
- Phenomenon: Knowledge base file upload occasionally fails and gets stuck at 1 or 2 indexing groups. Cause: When using the local m3e-large model, model loading times out, or non-standard formats of government procurement documents cannot be parsed correctly, leading to failed index construction.
- Phenomenon: Retrieval results contain a large number of irrelevant content matched by pinyin, or documents outside the specified scope. Cause: Keyword matching for
searchModeis not enabled, or retrieval scope is not limited viacollectionIds, causing the system to prioritize matching non-target content with similar pinyin.
How to Confirm the Configuration Is Properly Set Up
- Submit a party building learning-related retrieval request. Verify that all returned document results are party building learning materials or procurement documents within the specified scope, and no irrelevant content matched by pinyin appears.
- Manually delete a test Q&A pair, trigger dataset synchronization, then retrieve again to confirm the content no longer appears in the results.
- Upload a test document with non-compliant format. Check if the upload status returns a format error prompt to verify that the exception handling logic works.
- Adjust the
similaritythreshold to 0.7. Observe changes in retrieval result matching accuracy to confirm that configuration parameters take effect normally.
The provided values are common starting points. They should be evaluated against the samples relevant to the specific deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.