Reference only; verify against the target FastGPT release and deployment documentation.
What Data Looks Like for This Category
Data for this category mainly comes from normative documents issued by state-owned assets supervision departments, internal group merger and acquisition agreements, annual operation reports, state-owned asset transfer plans submitted by subsidiaries, and internally shared Feishu multi-dimensional documents and Excel ledgers. Update rhythms are adjusted according to business nodes. Official documents are updated as projects progress. Monthly operation reports are updated each month. Ledger documents submitted by subsidiaries are updated quarterly.
Most documents follow fixed formats, including document numbers, addressees, main text, and attachment lists. Business documents include fields such as transaction subjects, shareholding ratios, and fund amounts. Units involved include ten thousand yuan and percentage.
What Constraints These Characteristics Impose on the Vector Model and Indexing Link
Data in this category includes multi-format documents, varied update rhythms, fixed structures, and multi-business classification attributes.
Multi-format documents require the indexing link to support parsing capabilities for Feishu Excel, multi-dimensional documents, and structured ledgers.
Fixed structures require chunking to preserve business field associations, to avoid damaging business logic.
Varied update rhythms require support for incremental indexing, to reduce resource consumption from full reindexing.
Documents with multiple business classifications require indexing configurations to limit recall scope. This ensures retrieval results match corresponding business scenarios, and prevents mixed content from different business categories when the entire knowledge base is exported.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to business documents with fixed structures in this category, preserves business logic integrity, and balances vector embedding efficiency |
| Overlap Length | 100–200 characters | Preserves business associations across paragraphs, avoids context breaks |
| Similarity Threshold | 0.75–0.85 | Matches content relevance requirements for professional documents in this category, prevents excessive irrelevant or insufficient valid recall results |
| Single Search Limit | Top 8 entries | Controls the number of retrieval results, adapts to context length requirements in state-owned capital operation scenarios |
defaultToken | 2000 token | Adapts to vector embedding needs for conventional business documents, balances computing resources and embedding accuracy |
maxToken | 12000 token | Accommodates complete business context after a single round of retrieval, meets long document parsing requirements |
Three Common Mistakes
- Exporting the entire knowledge base and importing it again leads to mixed retrieval results across different business category documents, making accurate matching to the current business scenario impossible. This occurs because the index recall scope is not configured by business category, and the full knowledge base index is used directly without distinguishing business classification dimensions.
- When importing Excel files and multi-dimensional documents from a Feishu folder, parsed document business fields are not correctly extracted, and key business information is lost during vector embedding. This occurs because structured document field mapping configuration is not enabled, and business field associations to be retained are not specified.
- When connecting a vector model API and deploying on an ARM soft router, retrieval requests time out and return error status code 504. This occurs because the reasonable value of
defaultTokenis not adjusted, embedding computing resources are insufficient, and the soft router's limited computing power cannot handle vector embedding requests with high token counts.
How to Confirm the Configuration Is Correct
- Import Excel files and multi-dimensional documents from a Feishu folder, check if parsed documents retain business fields such as transaction subjects and shareholding ratios, to confirm structured document parsing configuration is correct.
- Initiate a document retrieval for a specified business category, check if recall results only include documents of the corresponding category, to confirm recall scope configuration meets business requirements.
- Initiate a vector embedding request in the ARM soft router environment connected to a vector model API, check if the request has no timeout errors, to confirm computing power adaptation configuration is reasonable.
- Export the knowledge base for a specified business category and re-import it, check if imported documents are organized by business category, to confirm index configuration matches business classification.
The values listed above are common starting points and should be evaluated 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.