What This Category of Data Looks Like
Data is primarily sourced from public financing announcements of power enterprises, project financing information disclosed by local energy authorities, and credit granting announcements from cooperating financial institutions. Updates occur daily on workdays. Each daily report typically contains dozens of financing entries. The document structure is standardized, with fields including full name of financing subject, financing amount (unit: ten thousand yuan), financing term, fund investment field, release date, name of credit granting institution. Some entries include project filing numbers.
Constraints on Vector Models and Indexing
High-frequency daily updates require incremental indexing strategies to avoid computational overhead from full reindexing. Each document contains multiple independent financing entries, so configure text splitting at entry granularity to avoid semantic confusion caused by full-document vectorization. Standardized structured fields must be associated with metadata indexing to support quick filtering and recall of results by fields such as financing subject and fund investment. Some fields have unit differences, so uniform formatting must be completed before indexing to prevent vector bias in numeric fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SPLIT_CHUNK_SIZE | 800–1000 characters | Adapts to the length of single financing entries, avoids semantic splitting breaks |
SPLIT_OVERLAP_RATIO | 5%–10% | Retains semantic association between segments, prevents loss of entry context |
ENABLE_INCREMENTAL_INDEX | Enabled | Matches daily workday update frequency, reduces full reindexing overhead |
METADATA_FILTER_FIELDS | Financing subject, fund investment, release date | Aligns with common retrieval filtering dimensions, improves recall accuracy |
EMBEDDING_MODEL | Domain-specific text embedding model for finance | Adapts to semantic alignment requirements for structured financial text, improves vector matching accuracy |
MAX_RECALL_CHUNKS | Adjust based on business needs | Limits the number of segments recalled per round, avoids redundant results |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: When deploying version v4.9.0 locally, no image indexing model option appears when creating a knowledge base and uploading documents. Cause: The
ENABLE_IMAGE_PARSEparameter is not enabled in the deployment configuration. This feature is disabled by default in the community edition. Modifying the configuration file is sufficient to enable it, and it is not exclusive to the commercial edition. - Phenomenon: When deploying version v4.9.0, the Chat model runs normally, but the Embedding model cannot connect to OneAPI, and the interface returns a connection timeout error. Cause: The OneAPI interface address and secret key are not correctly filled in the FastGPT vector model configuration, or network policies restrict access to third-party API gateways.
- Phenomenon: Irrelevant financing entries appear in retrieval results, and the number of recalled entries exceeds expected values. Cause:
METADATA_FILTER_FIELDSis not configured, and recall results are not filtered by financing subject or release date. This causes vector matching to rely solely on semantics, without combining business dimensions.
How to Confirm Configuration Is Complete
- Upload a single test document containing multiple financing entries, review the parsed segment list to confirm reasonable splitting is completed according to entries.
- Manually trigger an incremental indexing task, compare execution duration with full indexing to confirm the incremental indexing function is enabled normally.
- Configure retrieval filtering conditions, enter specific financing subject or release date keywords, verify that recall results can be filtered accurately.
- Call the vector model test interface, enter a single standard financing entry text, confirm that the returned vector data format is correct and no abnormal errors are returned.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.