What this category of data looks like
Data for state-owned asset management company declaration materials and file review primarily originates from internal project declaration systems, state-owned asset record ledgers, and externally returned approval documents. Data updates are triggered as projects advance. A single batch upload can include dozens to hundreds of documents. Most documents have mixed structures, containing standardized fields such as declaration subject, asset category, and approval document number, alongside long-form text for declaration explanations and review case bodies. Some files also include plain text transcribed from scanned documents.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Mixed-structure documents include standardized fields and long-form text, requiring indexing to support field-weighted recall to prevent non-core fields from interfering with matching accuracy. A large number of documents per batch and a high proportion of long text extend single-batch indexing duration, requiring adaptation of chunking parameters and indexing queue scheduling rules. Some text transcribed from scanned documents contains format redundancy, increasing computational load on vector models. Invalid characters must be filtered before indexing. Additionally, declaration material update cycles fluctuate with project progress, requiring incremental indexing trigger logic to adapt to non-fixed update frequencies.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to long-form declaration explanations and case bodies, avoid single chunks exceeding vector model input limits |
| Overlap Length | 100–150 characters | Preserve cross-chunk context, reduce breaks in key review information |
maxToken | 4096 token | Match the input upper limit of mainstream open-source vector models, adapt to the average length of state-owned asset management company documents |
| Similarity Threshold | 0.75–0.85 | Distinguish valid matches from noise matches, adapt to the density of professional terminology in declaration materials |
| Single Recall Limit | Top 10 entries | Control context window length, avoid exceeding model processing capabilities |
Three Common Misconfigurations
- Phenomenon: After upgrading FastGPT v4.17.0, indexing of previously used CSV-format declaration materials throws an error, prompting chunk failure. Cause: The new version adjusted field separator validation rules for CSV files. When no separator is specified, the default uses a comma. Some older templates use semicolons to separate fields.
- Phenomenon: After batch importing hundreds of case files, the indexing status stays at "processing" for a long time, with no index progress bar updates. Cause: No concurrency limit was configured for the indexing queue. Submitting a large number of long-text chunks simultaneously depletes system resources, causing process blocking.
- Phenomenon: After switching vector models, previously indexed historical data cannot be recalled by the new model, and retrieval results are empty. Cause: Vector embeddings for historical data were not regenerated. Vector spaces of old and new models are incompatible. Indexes must be rebuilt.
How to Verify Proper Configuration
- A single typical declaration material is uploaded, manual indexing is triggered, and chunk results are verified to split per the configured length, with no redundant empty blocks.
- A simulated review retrieval is initiated, professional review keywords are entered, and similarity scores of recall results are checked to fall within the set threshold range.
- 10 test case files are batch imported, and the indexing queue is verified to schedule according to the configured concurrency limit, with no process blocking errors.
- After switching vector models, vector embeddings for a single test data set are regenerated, and retrieval results are verified to return normally.
The values provided here are common starting points. They should be evaluated using samples specific to each deployment.
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.