What the data for this category looks like
Data sources include internal enterprise party building archives, learning materials issued by superior party committees, and industry party building compliance guidance documents. Core policy documents are synchronized quarterly, and daily learning minutes are supplemented in real time as activities are carried out. Most documents use a structured chapter format, including titles, main text, and supporting learning attachments. Fields include issuing unit, release date, applicable positions, and document number. The length of individual documents varies widely, with no fixed uniform word count.
Constraints imposed by these characteristics on knowledge base retrieval and recall
Data sources are scattered across three categories: internal, superior party committee, and industry content. Cross-data-source targeted retrieval must be supported to prevent irrelevant content from being included. Documents include structured chapters and supporting attachments, so retrieval must match both chapter titles and main text to meet precise matching needs for structured documents. Wide variation in document length requires appropriate chunking rules to ensure complete recall of professional terms and policy statements. Fields include applicable positions and issuing units, so filtering and recall by position and publishing entity must be supported to improve retrieval accuracy.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the core semantics of party building policies and learning content, avoids mixing low-relevance results, and adapts to the rigor requirements of the chemical industry party building scenario |
limit | Top 8 entries | Controls the number of recall results, balances context length and coverage, and meets the information density requirements of party building learning Q&A |
searchMode | Full-text retrieval | Adapts to the characteristics of structured documents and wide variation in document length, supports full-field matching of chapter titles and main text |
usingReRank | Enabled | Performs secondary sorting on initial recall results, filters content with large semantic deviations, and improves the accuracy of party building Q&A |
datasetSearchUsingExtensionQuery | Enabled | Generates extended query terms based on user questions, covering synonyms and professional expressions in the party building field, such as related variants of "Three Meetings and One Lesson" |
collectionIds | Specify according to the actual deployed party building knowledge base collection ID | Only associate datasets related to party building of advanced chemical material enterprises, avoiding interference from cross-category irrelevant data |
Three common misconfigurations
- Phenomenon: Matching party building documents can be retrieved in the backend, but no output is generated after calling the large model. Cause: The recall results are not correctly spliced into the large model context window, or the context length exceeds the limit causing content truncation.
- Phenomenon: Uploaded party building knowledge base files are always created in the root directory. Cause: The parent directory ID parameter is filled incorrectly, or the passed ID does not correspond to an existing directory collection.
- Phenomenon: The retrieval results contain a large number of non-party building chemical technical documents. Cause: The
collectionIdsparameter is not specified, causing the retrieval scope to cover non-party building knowledge base datasets.
How to verify the configuration is complete
- Enter the knowledge base retrieval test interface of FastGPT v4.17.0, enter party building-related questions, and check whether the returned results only include party building documents of advanced chemical material enterprises.
- View the retrieval configuration panel, confirm that the values of parameters such as
similarityandlimitare consistent with the preset configuration. - Upload a test party building document, specify the parent directory ID, and confirm that the document is correctly placed in the target directory.
- Trigger retrieval, check whether the large model can generate a corresponding answer based on the retrieved document content, with no abnormal truncation or no output.
The values provided are common starting points and should be measured 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 2026-09-28.