What data looks like for this category
Data for this category mainly comes from state-owned assets supervision and administration 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, and post-investment evaluation reports. Data updates follow a quarterly cycle. Involved entities include central enterprise strategic investment departments, project companies, state-owned assets supervision departments, and external consulting and evaluation institutions. Most document structures include core project parameters, financial projections, risk assessments, and implementation progress.
What constraints do these characteristics impose on vector models and indexes
These data characteristics impose multiple constraints on the vector models and indexes link. Multi-source heterogeneous data sources require index systems to support automatic adaptation and unified parsing of multi-format documents, to avoid information loss due to format differences. Document length varies widely: some are quarterly investment plans with thousands of characters, others are feasibility study reports with tens of thousands of characters. Chunking strategies must balance contextual coherence for long documents and integrity for short documents. Fixed quarterly update frequency requires configuring incremental index mechanisms to reduce resource consumption from full refreshes. Additionally, data involves professional content related to state-owned assets, which has high requirements for vector recall accuracy. Matching rules must be adjusted to avoid irrelevant content being included in search results.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the information density per chunk common in documents of this category, avoids including too much irrelevant content in a single chunk |
| Overlap Length | 100–200 characters | Ensures contextual coherence after long documents are split, prevents key information from being truncated by chunking |
maxToken | 8192 token | Adapts to the maximum input length of mainstream open-source embedding models, meets the chunking requirements for long documents in this category |
| Single Search Limit | Top 8 results | Adapts to the volume of core information in investment review meeting materials, avoids excessive redundant content interfering with decision-making |
| Maximum Context | 8000 characters | Adapts to the typical integrated length of core documents in this category, meets the multi-document association needs of post-investment evaluation |
| Reranking | Enabled | Optimizes the sorting accuracy of professional terms, improves search matching performance for long documents such as feasibility study reports |
Three common configuration mistakes
- Symptom: Existing configurations are overwritten when adding an embedding model with the same name. Cause: Different identification tags are not used to distinguish different deployment versions of the same type of model. The system automatically overwrites old configurations based on the unique name rule.
- Symptom: Key information is lost after chunking long documents. Cause: Chunk length is set beyond the
defaultTokenlimit supported by the vector model, resulting in some content being unable to generate valid vectors. - Symptom: The granularity of Excel source data search results does not meet requirements. Cause: Chunking rules are not adjusted for table documents. The default chunking splits content directly by paragraph, and does not retain the association between table headers and corresponding rows.
How to confirm configurations are properly set
- Import a single typical document, such as a complete excerpt from a feasibility study report, and check whether the chunked text matches the expected length and contextual coherence.
- Submit a search request for core project terms, and verify whether the similarity distribution of returned results falls within a reasonable range for the business scenario.
- View the index update log, and confirm that incremental updates only target quarterly new investment plans and post-investment evaluation reports, with no full refresh triggered.
- Test importing multiple configurations for models with the same name, and confirm that the system retains model configurations with different identification tags, with no overwriting occurring.
The values provided here 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.