What this category of data looks like
Data for declaration materials and file review from state-owned capital investment companies comes from internal project declaration systems, state-owned assets supervision reporting platforms, and scanned archived paper files. Updates happen in concentrated bulk batches during quarterly declaration cycles, with sporadic file archiving or approval node changes on an ongoing basis.
Document structure includes structured metadata and unstructured main text. Metadata fields include project number, declaring entity, investment amount, approval status, and submission date. Units follow state-owned assets statistics standards such as ten thousand yuan and hundred million yuan. Unstructured main text covers feasibility study reports, audit opinions, and approval opinion letters. Single-document length varies widely.
Constraints on vector models and indexing workflows
The data includes structured metadata and long unstructured text. This requires indexes to support metadata filtering and long text chunking. The concentrated bulk update pattern requires configuring interface call frequencies for batch uploads to avoid rate limits. The wide variation in single-document length requires dynamic adjustment of chunking parameters to prevent semantic fragmentation or information truncation.
The rigor of state-owned asset declaration materials requires that similarity thresholds be calibrated through actual testing to filter out low-relevance non-target file content.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of single feasibility study reports in declaration materials, avoid losing core approval node information due to semantic fragmentation |
| Overlap Length | 100–150 characters | Retain contextual association between chunks, prevent loss of key project information at chunk boundaries |
maxToken | 4096 token | Match the input limit of general embedding models, ensure long text chunks can complete vectorization normally |
| Similarity Threshold | 0.72–0.80, calibrated through actual testing | Filter low-relevance file content, meet the rigorous retrieval requirements of state-owned asset declaration materials |
| Single Search Limit | Top 8 entries | Control the number of returned results, avoid context overload that affects retrieval accuracy |
defaultToken | 1024 token | Unify the embedding input length of short-field metadata, ensure consistency of metadata retrieval |
Three Common Mistakes
- Interface displays "always in indexing state" and no data entries appear after upload. Cause: Incorrect chunk length configuration causes embedding model truncation failure when chunking ultra-long text, and no valid indexable vectors are generated.
- All search return results have very low relevance, and target files cannot be located. Cause: Metadata filtering rules are not configured based on the fields of declaration materials, causing irrelevant system logs or non-target project documents to be included in search results.
- Index progress stalls after calling the pushdata API to upload chunk-mode data. Cause: A reasonable
maxTokenparameter is not set, chunked text exceeds the model input limit, leading to vectorization task timeout and incomplete processing.
How to Confirm Correct Configuration
- Upload a single declaration document of approximately 1000 characters. Check whether the indexing progress completes within 1 minute, and whether the number of generated vector entries matches the expected number of chunks.
- Enter a search term containing a specific project number. Verify that returned results only include file materials for the corresponding project, with no irrelevant content included.
- Test uploading ultra-long text (over 5000 characters). Check whether correct chunked vectors are generated and no truncation-related error prompts appear.
- View embedding model call logs. Confirm that the
maxTokenparameter matches the input limit of the currently used model, with no parameter incompatibility error records.
The values given 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.