What the data for this category looks like
Data related to internal office and employee services for state-owned industrial funds comes from multiple sources. These include Feishu and DingTalk documents from internal office systems, research reports, investment decision meeting minutes, fund operation reports, employee handbooks, and departmental policy documents. Update schedules vary. Monthly operation reports are updated on a fixed schedule. Research reports are updated irregularly. Employee handbooks are updated quarterly or annually. Document structures include structured tables and unstructured text. Fields include project codes, employee IDs, department names, and more. Units include ten thousand yuan, months, years, and others.
What constraints these characteristics impose on vector models and indexes
The data includes both structured and unstructured formats. This requires indexes to support precise matching for structured fields and vector retrieval for unstructured text. Document lengths vary widely. Long research reports can reach tens of thousands of characters. This requires adapting chunking and encoding rules for long texts. Data sources include Excel files and multi-dimensional documents in Feishu folders. This requires the index process to adapt to the parsing logic of Feishu data sources. Update frequencies are uneven. This requires flexible configuration for on-demand incremental indexing and scheduled full indexing. The FastGPT v4.17.0 vector index module can cover these requirements. However, parameters must be adjusted for the data characteristics of industrial funds.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the long text structure of industrial fund research reports and meeting minutes, avoids single chunks crossing business logic boundaries |
| Overlap length | 50–100 characters | Retains contextual connections across chunks, prevents core information of long documents from being split outside chunks |
| Similarity threshold | 0.72–0.85 | Matches the density of technical terms in internal documents of industrial funds, filters low-relevance search results |
| Maximum search results per request | Top 10 results | Controls the volume of search returns for internal office scenarios, avoids information overload |
maxToken | 8192 | Adapts to the vector encoding requirements of long documents, covers the length of most internal documents for industrial funds |
defaultToken | Calibrated via actual testing | Adapts to the rate limits of external API vector model calls, avoids overage charges |
Three common configuration mistakes
- Symptom: The vector model configuration page shows "connection timed out" or "API call failed". Local vector models without GPUs cannot be added in OneAPI. Cause: Local port mapping for the vector model was not configured correctly, or the model access format of OneAPI does not match the requirements of FastGPT.
- Symptom: Exported index data only contains the entire knowledge base content. It cannot be exported split by business category or data type. Cause: Directory-specific indexing configuration for the knowledge base was not enabled. Only the global knowledge base export rule was activated.
- Symptom: Excel files and multi-dimensional documents in Feishu folders cannot be successfully parsed and indexed. Cause: The parsing plugins for Feishu Excel and multi-dimensional documents were not enabled in FastGPT data source configuration, or the Feishu application permissions do not cover the target folders.
How to confirm the configuration is correct
- Upload a single industrial fund research report. Check if the chunked text covers complete business logic, with no obvious content truncation.
- Initiate a search related to internal office work. Verify that the similarity and business relevance of returned results meet preset expectations.
- Trigger an incremental index. Check if updated documents can be correctly retrieved, with no missing or duplicate entries.
- View the vector model call logs. Confirm that the
maxTokenanddefaultTokenconfigurations do not trigger overage limits.
The values listed here are common starting points. They 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.