What This Category of Data Looks Like
Document data for state-owned industrial funds primarily comes from internal approval process files, project due diligence archives, compliance review reports, investment agreements, and annual operation summaries. Data updates trigger when projects progress or annual reviews take place. Individual document lengths vary widely, from hundreds-of-word approval documents to tens-of-thousand-word due diligence reports. All documents include standardized fields: document number, decision-making body, involved amount, decision date, and others. The amount field uses ten thousand yuan as its unit. The date field follows standard Gregorian calendar format.
What Constraints These Characteristics Impose on Vector Models and Indexing
Large differences in document length cause significant fluctuations in token usage per text block. This creates clear requirements for defaultToken and maxToken configurations. Structured fields include standardized amounts and dates. These must be indexed separately from unstructured descriptive text to avoid vector confusion. Data updates do not follow a fixed batch cycle. The incremental indexing trigger logic must adapt to project-level and annual update rhythms, instead of using fixed-cycle full reindexing. Compliance documents have high recall accuracy requirements. Indexing recall rules must match the information weight of the business scenario. FastGPT v4.17.0’s indexing module supports separate construction of structured and unstructured data. This module adapts to this type of data characteristics.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of state-owned industrial fund documents, prevents single-block tokens from exceeding model limits |
maxToken | 16384 | Matches the maximum input length of mainstream local vector models, adapts to splicing needs after long document chunking |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Differentiates recall accuracy for compliance and descriptive documents, prevents false recall of irrelevant project materials |
| Single Search Limit | Top 10 entries | Covers core relevant information for a single document, prevents redundant results from disrupting drafting |
defaultToken | 1024 | Serves as the token baseline for default chunks, balances recall accuracy and indexing storage costs |
| Reranking | Enabled | Performs secondary sorting on recall results, corrects semantic bias from vector recall, meets the precision requirements of compliance documents |
Three Common Mistakes
- Phenomenon: Calling the vector model interface returns a 503 status code. The text-embedding model fails to load under the default group. Cause: Model concurrency quotas are not adjusted based on the total document volume and update frequency of state-owned industrial funds. High-concurrency requests exceed node carrying limits.
- Phenomenon: Knowledge base capacity estimation lacks clear basis, and cannot match the storage requirements of existing documents. Cause: The vector storage proportions of structured fields and unstructured text are not distinguished. Direct estimation based on total character count leads to result deviation.
- Phenomenon: Local vector model calls produce recall results with significant semantic bias. Cause: Model adaptation parameters are not adjusted for professional fund document terms such as investment ratio, investment decision process, and compliance clauses. This leads to inaccurate vector semantic matching.
How to Confirm Proper Configuration
- Upload a single typical due diligence report. Check if the length of chunked text blocks matches the preset chunk configuration, with no obviously overlong or overly short blocks.
- Submit a retrieval request based on compliance clauses. Verify that the number of recall results matches the single search limit configuration.
- View real-time indexing storage usage. Confirm that storage costs match the baseline set by the defaultToken configuration.
- Trigger an incremental update task. Check if newly added fund project documents can be properly indexed and recalled.
Additional Note
The configuration values listed above are common starting points. They should be evaluated against the reader's own specific 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.