What This Category of Data Looks Like
Three main sources supply state-owned asset management company ledger data: state-owned asset supervision and reporting systems, internal financial accounting systems, and physical asset management card systems. This data follows three common update workflows: daily new asset registration, monthly inventory adjustments, and quarterly financial report submissions. Each individual ledger document includes fields such as asset unique ID, name, specification model, entry date, usage department, book value, accumulated depreciation, storage location, and annual maintenance records. Units used include yuan, square meters, units/sets, kilometers, and other standard units. Some entries include scanned paper copies of maintenance documents as attachments.
Constraints on Vector Models and Indexing
Ledger data’s multi-field structure and mixed format require the vectorization process to balance structured identification and unstructured text content. This prevents loss of key associated information such as asset IDs and book values due to overly fragmented chunking. High daily volumes of new asset data require indexes to support incremental updates, reducing duplicate calculation costs. Scanned paper maintenance document attachments on some entries require vector models to support multimodal input, adapting to supplementary ledger materials in different formats. Downstream requirements for ledger summary and report generation also mandate that retrieval results aggregate by asset ID. This avoids repeated return of multiple chunked entries for the same asset.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of individual ledger entries, avoids including too many unrelated fields in a single chunk |
| Chunk Overlap Length | 100–200 characters | Retains key identifiers such as asset ID and book value across chunks, prevents loss of associated information during retrieval |
| Single Retrieval Limit | Top 15 entries | Matches multi-entry retrieval needs during ledger summary, avoids retrieving excessive redundant data |
maxToken | 8192 | Adapts to the context window of mainstream vector models, supports full vectorization of long ledger text |
defaultToken | 1024 | Serves as the default context quota for basic vectorization, balances calculation accuracy and resource consumption |
Common Misconfigurations
- Vectorization tasks take too long, and the number of results returned by the API does not meet expectations. Batch vectorization configuration remains disabled. Only individual text chunks submit requests, and the batch processing capability of the vectorization service goes unused.
- Relevant content for specified document chunks fails to load, and key cross-chunk fields go missing from returned results. Chunk overlap length configuration remains unset. Cross-chunk identifiers such as asset ID and usage department are not retained.
- After adding the Alibaba multimodal-embedding-v1 model, ledger image attachments cannot vectorize normally. Multimodal support remains disabled in the vector service configuration, or model-adapted input parameters remain incorrectly configured.
How to Verify Proper Configuration
- View vector service logs in FastGPT v4.17.0. Confirm whether batch-submitted text chunk requests exist, and check if the request body includes multiple text entries.
- Retrieve ledger documents for a specified asset ID. Confirm returned results include key cross-chunk fields, and that results aggregate by asset ID.
- Verify API keys and parameters for the Alibaba multimodal-embedding-v1 model fill correctly on the vector model configuration page. Test vectorization tasks for a single uploaded asset maintenance image attachment.
- Test vectorization of a single long ledger text. Confirm returned vector dimensions match the context window configured by
maxToken, with no obvious truncation or format errors.
The values provided 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.