Reference only; verify against the target FastGPT release and deployment documentation.
Data characteristics for this category
Party building learning data in the commodity trade field originates from internal enterprise party committee issued study manuals, industry party building special reports, and policy documents released by superior party organizations. Updates follow a quarterly routine schedule, with temporary additions during major party building activity nodes. Most documents are in PDF or Word format, with fixed fields including topic, issuing unit, release date, and chapter number. Individual document length ranges from thousands to tens of thousands of characters, and individual document size ranges from 1 to 10 MB.
Constraints on knowledge base retrieval and recall
The above data characteristics impose three constraints on knowledge base retrieval and recall. First, the data includes both internal confidential and public content. Collection IDs are used to isolate exclusive party building data to prevent permission leaks. Second, individual document length is long. Chunk length is controlled during chunked recall to avoid context fragmentation. Third, the data combines the commodity trade industry scenario. Matching covers both general party building terminology and industry-specific vocabulary. The retrieval mode balances semantic and keyword matching.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Party building materials have high semantic similarity. This range filters irrelevant recall results while retaining core relevant content |
limit | Top 8 results | Commodity trade party building documents have long length. Too many recall results will exceed the context window and reduce answer coherence |
searchMode | Semantic + keyword hybrid retrieval | Balances matching of general party building terminology and commodity trade industry-specific vocabulary, covering scenario-based learning questions |
usingReRank | Enabled | Long document chunks have semantic dispersion issues. Reranking enables secondary screening of the most relevant chunked content |
datasetSearchUsingExtensionQuery | Enabled | Party building learning questions have multiple expression methods. Extended queries can cover more relevant documents |
collectionIds | Bind exclusive collection ID for commodity trade party building data | Isolates internal party building data from other category content to ensure precise retrieval scope |
Three common mistakes
- Issue: An error
cannot fetch internal urlappears when creating a party building knowledge base using an internal enterprise URL. Cause: The internal network access whitelist for FastGPT is not configured, so party building document resources stored in enterprise internal storage cannot be retrieved. - Issue: Chunk preview fails to read content when uploading party building PDF files. Cause: The PDF contains encrypted layers or embedded commodity trade industry-specific fonts, so the built-in parsing plugin cannot fully extract text.
- Issue: PPT and PDF files cannot be synchronized after configuring a Feishu party building knowledge base. Cause: The open reading permission for Feishu documents is not enabled, or the bound Feishu application has not been granted access permission for the corresponding document types.
How to verify correct configuration
- Launch scenario-based test questions, such as "How do commodity trade enterprises integrate party building work into supply chain management", and check whether recall results include party building content combined with the industry.
- View retrieval logs to confirm that similarity scores of recall results fall within the 0.72–0.80 range, with no abnormal scores that are too low or too high.
- Upload multiple party building related documents to verify that batch upload is supported and no transcoding timeout or network failure errors occur.
- Switch the retrieval mode to confirm that the hybrid retrieval mode can match both keywords and semantic content, with recall results that better fit question requirements.
The values provided here are common starting points, and should be evaluated against the user'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.