What the data for this category looks like
Typical data sources for this category include State-owned Assets Supervision and Administration Commission (SASAC) investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements. Daily generated documents cover annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and post-investment evaluation reports. Document structures include professional modules such as project background, financial forecasting, risk assessment, and investment decision opinions. Data is updated quarterly. Core documents are long texts ranging from thousands to tens of thousands of characters. Some fields contain compliance identifiers required by state-owned assets supervision regulations.
What constraints do these data characteristics impose on the vector model and indexing workflow
Inconsistent document formats across multiple systems require text parsing logic adapted to different structures. Quarterly data updates require indexes to support scheduled incremental construction, avoiding excessive system resource usage from full reconstruction. Semantic integrity requirements for long documents demand chunking strategies that balance content coherence and vector encoding efficiency. Compliance fields related to state-owned assets must retain metadata in indexes, ensuring retrieval results can trace their sources. Error consequences include risks such as state-owned asset losses, so index stability and recall accuracy must meet compliance requirements.
How to set configurations
| Configuration Setting | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long documents such as feasibility study reports and post-evaluation reports, prevents key information from being split across chapters |
| Overlap Length | 100–150 characters | Preserves contextual continuity between chunks, prevents technical terms or data from being cut off at chunk boundaries |
| Similarity Threshold | 0.72–0.80 | Matches the technical term density of state-owned asset investment documents, filters low-relevance non-investment content |
| Single Search Limit | Top 8 results | Covers multi-dimensional reference needs for investment decision materials, prevents excessive redundant information from reducing decision efficiency |
defaultToken | 4096 | Adapts to the vector encoding limit for long documents, compatible with the text length of most professional feasibility study reports |
maxToken | 8192 | Matches batch processing limits during index construction, balances performance and content completeness |
Three common mistakes
- Index construction fails, logs display "No available channel for model text-embedding-v3 under current default group". Cause: The embedding model is not configured separately, the one-api channel bound to the chat model is used, and the corresponding embedding interface is not enabled for that channel.
- After starting the container, the configured API key cannot be loaded, or a 503 Service Unavailable error occurs. Cause: The CHATAPIKEY environment variable is not correctly configured in docker-compose.yml, or the container is not rebuilt for the new configuration to take effect.
- Retrieval results include a large number of non-target investment documents, and the matching degree does not meet expectations. Cause: The similarity threshold is set too low, failing to filter generic content unrelated to project approval and post-evaluation.
How to confirm configurations are set correctly
The vector model management page of FastGPT v4.17.0 is accessed, and the embedding model channel configuration is confirmed to match the currently deployed service. A typical feasibility study report is uploaded to trigger index construction, and the task status is checked for successful completion with no error logs. Query terms such as "annual central enterprise investment approval standards" are input to verify that all returned retrieval documents originate from investment data sources and the matching degree meets business requirements. The docker container is accessed internally, and the environment variables are checked for the correct CHATAPIKEY configuration to confirm parameter accuracy.
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.