Reference only; verify against the target FastGPT release and deployment documentation.
What This Type of Data Looks Like
Data mainly comes from internal party building learning materials, policy documents issued by superior party organizations, and party building activity ledgers of gas station sites. It includes plain-text policy documents, scanned learning e-books, and activity records with structured fields. The update schedule adds quarterly learning materials each month, and synchronizes site activity data every quarter. Document fields include file number, issuing unit, release date, applicable site scope, and units involve stations, batches, dates, and similar items.
Constraints Imposed on Knowledge Base Retrieval and Recall
Scanned learning e-books require an OCR recognition process before retrieval to ensure unstructured scanned documents can be retrieved. The low-frequency monthly/quarterly update rhythm requires the knowledge base incremental synchronization mechanism to adapt to this rhythm, avoiding invalid frequent synchronization that occupies resources. The structured field of applicable site scope requires support for filtering recall results by business dimension during retrieval, ensuring that site users can only access party building content adapted to their own situation. The feature of large differences in document length requires the retrieval chunking logic to adapt to texts of varying lengths, avoiding single chunks that are too long to affect embedding accuracy, or too short leading to loss of semantic information.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Party building policy text semantic matching requires high precision to filter low-relevance recall results |
limit | Top 8 entries | Control the number of results returned per time to ensure party building learning content focuses on core key points |
searchMode | Semantic retrieval | Adapt to the policy-oriented and systematic characteristics of party building texts to improve recall relevance |
usingReRank | Enabled | Perform secondary sorting on initial recall results to optimize content display order |
datasetSearchUsingExtensionQuery | Enabled | Perform semantic expansion on user queries to adapt to multiple expression forms of party building terminology |
collectionIds | Fill in according to the party building knowledge base collection ID | Limit the retrieval scope to avoid mixing non-party building business data |
Three Common Misconfigurations
- Phenomenon: After uploading scanned party building learning e-books, the retrieval result is empty. Cause: OCR recognition configuration is not enabled, and the image content of the scanned document is not converted into retrievable text.
- Phenomenon: An error prompt pops up when trying to create a party building knowledge base, and the configuration cannot be completed. Cause: The associated parameters of the knowledge base are not filled in correctly, or the uploaded document format does not meet the specifications supported by the platform.
- Phenomenon: When entering a long party building learning-related query, the relevance of retrieval results decreases. Cause: The long query is not properly split, causing the embedding model processing to exceed the carrying limit and affecting recall accuracy.
How to Confirm the Configuration Is Complete
- Upload a scanned party building learning document, perform OCR recognition, then retrieve keywords within the document to confirm that the corresponding content can be recalled.
- Initiate a party building learning query that conforms to the business scenario, and check whether the number of returned results meets the requirements of the configured
limitparameter. - Check the matching accuracy of retrieval results to confirm whether it falls within the threshold range set by the configured
similarityparameter. - View the knowledge base synchronization logs to confirm that the newly added content via the interface has been successfully synchronized to the retrieval index.
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.