What the data for this category looks like
Documents and materials for urban investment platforms mainly come from internal project approval archives, financing declaration documents, government announcements and public notices, and daily office documents. Data updates follow project progress and office workflows. Project documents are updated in batches quarterly, while daily official documents are added in real time along with approval processes. Documents uniformly include standard fields such as document number, issuing entity, drafting date, main body content, and attachment identifier. Some project documents have additional structured fields including project number, affiliated region, and budget amount. Most monetary fields use ten thousand yuan as the unit.
What constraints these characteristics impose on the vector model and indexing link
Urban investment platform documents contain both structured fields and unstructured main text, and update rhythms combine batch and real-time updates. This requires the vector indexing link to support multi-field mixed vectorization and incremental updates. Project documents updated in batches have large volumes, so indexing scheduling logic adapted for large-batch imports must be configured. Real-time addition of daily office documents requires the indexing pipeline to support low-latency incremental synchronization. Some documents include attachments, so the indexing link must automatically parse attachment text and associate it with the main document vector. Structured fields such as budget amounts must be combined with the semantic vector of the main text to avoid business relevance deviations caused by relying solely on text matching. Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | The main text of urban investment platform documents is mostly coherent paragraphs. This length preserves complete business semantic units and avoids vector calculation deviations caused by overly long chunks |
| Overlap Length | 100–200 characters | Compensates for semantic fragmentation after chunking, and ensures contextual coherence of cross-chunk content |
maxToken | 8192 token | Adapts to the input limit of mainstream vector models, and matches the average content length of urban investment platform documents |
| Similarity Threshold | Calibrated via actual testing | Adapts to the business semantic matching accuracy of urban investment documents, and avoids recalling irrelevant documents or missing valid content |
| Single Search Limit | Top 8 results | Covers the main associated scope of urban investment project documents, and reduces interference from redundant results |
defaultToken | 4096 token | Sets a standard context window, balancing the completeness of generated content and response speed |
Three common mistakes
- Symptom: After switching to a Doubao vector model, the similarity values returned by search are 10000+, and cannot be filtered via the conventional 0-1 range threshold. Cause: The normalization configuration of the vector model is not adjusted, so the similarity calculation results are not scaled to the standard interval.
- Symptom: After importing a text dataset, the knowledge base status continuously displays as indexing, and initialization cannot be completed. Cause: The number of documents imported in batches exceeds the concurrent processing limit of the current indexing node, or the scheduling threshold for incremental indexing is not configured.
- Symptom: An error prompts that no available channel is available when using the corresponding model, and the indexing task cannot be started. Cause: The API call channel for the corresponding model is not added in the platform configuration, or the channel permission is not enabled.
How to confirm the configuration is correct
- Upload one typical urban investment platform document, and check whether the indexing status updates to ready within a reasonable time frame
- Initiate a query related to the business of the document, and check whether the semantic relevance of the returned results matches business judgments
- Test batch import of documents of the same type, and confirm that the indexing task can be completed automatically without abnormal errors
- Switch to the target vector model, and verify that the similarity calculation results fit the filtering rules of the current configuration interval
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.