What the data for this category looks like
Typical data for this category comes from national public resource trading platforms, natural resource trading systems, property right exchange systems and state-owned asset supervision systems, with daily updates as standard. Daily documents include transfer announcements, trading guidelines, bid application documents, margin vouchers, transaction confirmation letters, listing result announcements, property right transfer specifications and more. Most documents include core fields such as ownership descriptions, trading conditions, bidding rules and margin requirements. Some documents have structured tables with content like margin amounts, target locations and transfer base prices.
What constraints these characteristics impose on vector models and indexes
Data for this category comes from scattered sources and is updated frequently, so indexes must support incremental synchronization and rapid updates. Documents contain structured tables and hierarchical content, so multi-vector parsing is required to retain table fields and hierarchical associations. Core business fields including ownership, restriction conditions and margin information directly impact decision-making, so vector recall must accurately match these high-value contents. Transaction data errors have serious consequences, so improper chunking must be avoided to prevent core information from being broken, which would reduce the accuracy of subsequent consulting responses.
How to set the configuration
For the business characteristics of this category, the following vector model and index configurations are recommended:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Chunk Length | 800–1200 characters | Matches the length of single-segment core information in this category's transaction documents, avoiding splitting that breaks complete logic such as ownership and bidding rules |
| Chunk Overlap Length | 100–200 characters | Retains target association information across chunks, preventing context loss caused by chunk breakage |
| Maximum Paragraph Depth | 3 | Adapts to the hierarchical structure of transaction documents, such as subsections under announcements and clause grouping, in line with general configuration experience |
maxToken | 8192 | Adapts to the context limit of mainstream embedding models, balancing the information density and recall efficiency of a single vector |
defaultToken | 1024 | Controls the number of tokens in a single chunk, avoiding exceeding the model's input limit while covering the information length of core business fields |
Three common configuration errors
Common configuration errors and their corresponding causes are as follows:
- After refreshing the knowledge base page, the prompt "No available index model detected" appears. This occurs when the corresponding embedding model is not bound to the knowledge base for this category, or the model configuration has not been saved and activated.
- Table data fails to generate multiple vectors normally. This happens when the table multi-vector parsing switch is not enabled in the knowledge base settings, and structured fields in margin vouchers and transaction confirmation letters are not correctly identified.
- An API call to create an index returns a parameter error. This is because the API input parameters are not adjusted to match the maximum chunk length set in the interface, or the
maxTokensetting exceeds the support range of the currently bound model.
How to confirm the configuration is active
Verify the configuration effectiveness with the following steps:
- Enter the knowledge base settings page of FastGPT v4.17.0, confirm that chunk parameters and embedding model binding status are saved, with no red error prompts.
- Upload a typical transaction document, such as a land transfer announcement, trigger manual indexing, and check that chunk results include complete core fields such as target ownership and transaction conditions.
- Initiate a test conversation, enter "The transfer restriction conditions of a certain mining right", and check whether the recalled chunks match the corresponding content in the document.
- Call the FastGPT API interface to test the index creation process, confirm that the returned status code is 200 and there are no invalid parameter errors.
Note: The configuration values listed above are common starting points. They should be evaluated against the reader's own specific 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.