What this category of data looks like
This category of data typically originates from natural resource transaction systems, the national public resource trading platform, property right exchange systems and state-owned asset supervision systems. The update frequency is daily. Commonly processed documents include transfer announcements, transaction notices, bid application documents, margin vouchers, transaction confirmation letters, listing result announcements, property right transfer specifications and similar materials. Core fields include ownership number, transaction target type, margin amount, transaction time, transferee information and similar items. Document formats vary across different source systems.
What constraints do these characteristics impose on the vector models and indexing workflow?
Multi-source, heterogeneous document formats require the vector model preprocessing step to adapt to different document structures, to prevent core ownership information from being incorrectly split or omitted. Daily updated data volumes require the indexing workflow to support incremental synchronization, to avoid additional time consumption caused by full reindexing. Strong semantic associations between core fields require that context integrity be maintained during chunking, to prevent key information such as ownership details and transaction amounts from being truncated across chunks. Transaction data has strict compliance requirements, so the indexing workflow must retain the original document's metadata to support subsequent traceability and verification.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of long documents such as property right transfer specifications and transfer announcements, while controlling the token consumption of single-chunk vectors |
| Overlap Length | 100–200 characters | Retain semantic correlations between adjacent chunks, and prevent ownership information from being truncated across chunks |
defaultToken | 8192 | Adapt to the context window upper limit of most localized vector models, ensuring that spliced long document chunks meet model input requirements |
maxToken | 16384 | Support batch document processing containing multiple transaction vouchers, meeting the multi-document merging requirements for ledger summary |
| Similarity Threshold | 0.75–0.85 | Filter low-match degree ownership information, avoiding ledger errors caused by mismatching |
| Single Search Limit | Top 10 results | Cover the number of associated documents for most transaction targets, balancing recall efficiency and accuracy |
Three common misconfigurations
- Symptom: A 503 status code appears, prompting that the text-embedding model under the current default group is unavailable. Cause: No vector model group routing is configured, or the default group is not bound to an available vector model service.
- Symptom: The indexing task remains in the running state with no progress updates. Cause: No incremental indexing trigger rule is configured, time consumption is too long when fully indexing multiple historical transaction documents, or the docker deployment resource quota is insufficient causing process blocking.
- Symptom: Unable to connect to the vector database, or vector generation results do not meet expectations. Cause: No correct vector database connection parameters are configured, or no vector model adapted to multi-source transaction data formats is selected.
How to verify correct configuration
- Open the vector model configuration page of FastGPT v4.17.0, confirm that the
defaultTokenandmaxTokenparameters match the context window of the selected model. - Run an indexing test on a single property right transfer specification, and check whether the chunking results retain the context of core fields including ownership number and transaction target type.
- Initiate an incremental indexing task, confirm that the task automatically triggers within the set daily update cycle and has no abnormal errors.
- Connect to the vector database, and check whether the indexed document vectors include core field information related to transactions.
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.