What the data for this category looks like
State-owned capital investment company document data sources include internal investment decision documents, state-owned assets supervision and administration report materials, group charters, project due diligence reports, annual work reports, and similar materials. Update cycles are divided into real-time updates for daily project documents, quarterly or annual updates for regulatory documents, and updates for major system documents only when group organizational structure changes. Documents include fields such as document number, project number, affiliated group, generation date, confidentiality level, and others. Single document length ranges from approval slips with hundreds of characters to due diligence reports with tens of thousands of characters.
What constraints do these characteristics impose on vector models and indexes
Coexisting documents of multiple lengths require chunking strategies that balance the integrity of short texts and semantic coherence of long texts. Multiple field attributes require indexes to support filtered retrieval based on fields such as confidentiality level and project type. Differences in update frequencies require a hybrid index mechanism configuration that balances real-time incremental updates and periodic full updates. Dense professional terminology requires vector models to adapt to semantic features of the state-owned assets investment field to avoid matching deviations. The presence of confidential data requires index layers to integrate permission verification logic to prevent sensitive information leaks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | State-owned capital investment company documents are mostly professional long texts. This range preserves complete semantic units and avoids splitting critical information |
| Overlap Length | 100–150 characters | Retains contextual connection after chunking long documents to ensure cross-chunk semantic coherence |
| Similarity Threshold | 0.72–0.85 | There are many professional terms in the state-owned assets field. A higher threshold filters irrelevant matching results and improves retrieval accuracy |
| Single Search Limit | Top 8 results | Project documents are mostly associated by theme. 8 results cover most relevant content and avoid redundant information |
maxToken | 8000 | Adapts to the default limit of FastGPT v4.17.0, meeting the context splicing requirements for long documents |
defaultToken | 200 | Controls the number of tokens generated for a single vector segment, balancing semantic accuracy and computing resource consumption |
Three common mistakes
- Symptom: Retrieval returns empty results after connecting a custom Qdrant vector database. Cause: The database access key and corresponding cluster address were not configured in FastGPT, resulting in failure to read existing data.
- Symptom: A 403 permission error is returned when calling the Tencent Hunyuan vector model. Cause: The API key and exclusive access domain of the model channel were not filled in correctly, so the interface verification failed.
- Symptom: Tasks remain unresponsive for a long time after enabling the index model. Cause: The
maxTokenparameter was not adjusted to adapt to vector generation requests for long documents, exceeding the default token limit of the interface.
How to confirm the configuration is complete
- Access the vector database management page in FastGPT, test the connection to the custom database, and check whether the connection status shows normal.
- Upload a typical document from a state-owned capital investment company, trigger the indexing task, and check whether the task log displays the complete process of chunking, vector generation, and storage completion.
- Initiate a retrieval request related to state-owned assets investment, and check whether the matching degree of returned results falls within the preset similarity threshold range.
- Adjust the configuration parameters of the vector model, regenerate test vectors, compare token consumption and retrieval speed before and after, and confirm that the parameters take effect.
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.