What this category of data looks like
Party building study Q&A data for the steel smelting field primarily comes from party committee archives of local steel enterprises, study minutes of party groups in each production workshop, and party building guidance documents issued by higher-level state-owned assets supervision and administration commissions and group party committees. The data update schedule is monthly updates to monthly study records of workshop party groups, and quarterly updates to higher-level party building policy documents. Most documents are chaptered study manuals, Q&A compilations, and meeting minutes. They include fields such as document number, release date, affiliated production workshop, and topic classification. Single document length varies widely, from hundreds to thousands of characters.
What constraints do these characteristics impose on knowledge base retrieval and recall
Data is scattered across independent datasets per workshop. Retrieval must specify the corresponding workshop’s dataset using collectionIds, to avoid interference from irrelevant cross-workshop party building content. The wide range of single document lengths requires proper chunking of long documents. This avoids semantic loss from overly long chunks, or context breaks from overly short chunks. For scenarios where local images are embedded in markdown files, the knowledge base must support parsing local image links. Otherwise, retrieval results cannot fully present study material content. The monthly and quarterly update schedule requires the retrieval system to support flexible configuration of incremental updates and full retraining. This adapts to different data update frequencies.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Steel smelting party building content contains a large number of technical terms and fixed expressions. This range filters low-match irrelevant results while retaining core relevant content |
limit | Top 8 entries | Workshop party building Q&A scenarios require quick access to core information. Too many results increase context processing burden. This value balances recall scope and response speed |
searchMode | local | Data is divided into independent datasets by production workshop. Local mode accurately matches datasets specified by collectionIds, avoiding redundant results from global retrieval |
usingReRank | Enabled | Retrieval results from split long documents require secondary sorting. This corrects semantic bias in initial recall and improves relevance of final results |
datasetSearchUsingExtensionQuery | Enabled | Party building Q&A often uses synonym substitutions, such as "party group" and "branch study group". Extended queries improve recall rate and cover content with more synonymous expressions |
collectionIds | Fill in the dataset ID list corresponding to each production workshop of the steel smelting enterprise | Dividing party building data by production unit enables precise retrieval by workshop, preventing non-target cross-workshop content from being included in results |
Three common mistakes
- After importing a markdown file with embedded local images, images fail to display correctly in retrieval results. The cause is that the local file storage path of the knowledge base was not configured. Only text content was uploaded, and image resources were not processed synchronously.
- After triggering a full knowledge base retraining, the system returns a timeout error. The cause is that reasonable chunking parameters were not set. The total document data volume processed per batch exceeded the system's default processing limit, causing task interruption.
- Retrieval results have overly high match with the query but insufficient coverage. The cause is that the
similarityparameter was set too high, filtering out valid content that is semantically relevant but has slightly lower literal match.
How to confirm correct configuration
- Upload a test markdown file containing local images. Initiate a retrieval targeting the theme of that file, and check if the returned results contain complete text and image parsing content.
- Initiate a query targeting party building content from a specific production workshop. Verify that the number of returned results matches the value set for
limit. - Trigger an incremental update task. Check if FastGPT v4.17.0 system logs show successful update with no abnormal errors.
- Adjust the
similarityparameter to 0.70 and 0.85. Compare the differences between the two retrieval results, and confirm that the match range meets business requirements.
The configuration values provided are common starting points. They should be evaluated against the specific samples of the target 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.