What this category of data looks like
Data for this category comes from state-owned assets supervision and administration commission investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements. Daily documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, post-investment evaluation reports. Most document structures include fields such as project background, investment calculation, approval nodes, post-evaluation indicators. Update frequency is quarterly. Data volume accumulates as individual projects progress. Single core documents are lengthy, containing multi-dimensional business and supervision-related information.
Constraints imposed on vector models and indexing workflow
This category’s data characteristics create multiple constraints on the vector model and indexing workflow. The quarterly update schedule requires support for incremental indexing to avoid resource consumption from full reindexing. Single core documents are lengthy, so reasonable chunking parameters must be set to cover key relevant information across chunks, while adapting to the token limits of vector models. Heterogeneous data from multiple sources requires a unified preprocessing workflow to ensure consistent vectorization. Additionally, the data contains sensitive content related to state-owned assets supervision and investment decision-making. The indexing process must ensure the accuracy of retrieval results, avoiding omission of key approval or post-evaluation nodes.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Core documents contain multiple sections of professional content. This length can cover complete investment calculation or approval node paragraphs, while adapting to the context window of vector models |
| Chunk Overlap Length | 100–150 characters | Relevant information across chunks in long documents (such as investment estimates and post-evaluation indicators) needs to be covered by adjacent chunks to avoid retrieval gaps |
maxToken | 8192 | Adapts to the context window of mainstream open-source vector models, ensuring complete vectorization of chunked content |
| Similarity Threshold | 0.72–0.80 | Business data contains a large number of professional terms. This range can filter low-relevance non-project documents while retaining core relevant content |
| Single Search Limit | Top 8 results | Reference information required for investment review meetings and post-evaluation is usually concentrated in a small number of core documents. Too many results increase decision-making burden |
| Reranking Switch | Enabled | Vectorization quality varies across data from multiple sources. Reranking can improve the sorting priority of highly relevant results |
Three Common Mistakes
- Phenomenon: When uploading a single feasibility study or post-evaluation report larger than 10MB, some chunks fail vectorization after chunking, and the interface displays a "vectorization exception" prompt. Cause: No reasonable chunk overlap length is set for long documents, leading to key terms at the start and end of chunks being truncated, exceeding the
defaultTokenlimit of the vector model, triggering the exception. - Phenomenon: After batch uploading multiple project documents causes server resource exhaustion and restart, the existing knowledge base is in a "not ready" state, cannot automatically trigger the indexing process, and there is no progress prompt on the interface. Cause: No automatic recovery mechanism for incremental indexing is configured, and the batch upload task queue is not persisted. After restart, incomplete indexing tasks are not automatically resumed.
- Phenomenon: After upgrading FastGPT from version 4.9 to version 4.10, the existing investment project knowledge base created with old indexing rules cannot return valid retrieval results, and search results are empty. Cause: The embedding dimension or indexing format of the vector model changed after the version update. The existing knowledge base was not re-vectorized, leading to incompatibility between old and new indexes.
How to Confirm Correct Configuration
- Upload a typical annual investment plan document, confirm that the number of chunked chunks matches the preset chunk length, with no obviously truncated professional paragraphs.
- Initiate a query for "project investment estimation", verify that the sorting priority of retrieval results meets business requirements, with no irrelevant non-project documents.
- Trigger an incremental indexing task, confirm that after quarterly data updates, the system automatically completes vectorization and indexing of newly uploaded documents without manual intervention.
- View the vector model logs for FastGPT v4.17.0, confirm that chunked content does not exceed the limit set by
maxToken, with no overflow errors.
The values provided 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.