What data looks like for this category
Ledger data for state-owned industrial funds primarily comes from state-owned assets supervision systems, fund contribution ledgers, post-investment management systems, and project due diligence documents. Data update cycles include two types: monthly scheduled updates and real-time updates for major projects. Individual ledger documents contain structured fields such as project name, contributing entity, subscribed scale, paid-in amount, contribution milestone, and post-investment rating. They also include unstructured notes such as project background and post-investment status. The length of individual entries varies widely, with no fixed format standards.
What constraints these characteristics impose on vector models and indexing
Multi-source heterogeneous data sources lead to differences in field formats. Field alignment must be completed first before vector representation can be performed. The two update cycles (monthly scheduled and real-time for major projects) require the indexing system to support flexible switching between incremental triggers and full reconstruction. This avoids repeated calculations of full datasets. Mixed content of structured fields and unstructured notes requires retaining the association between fields and content during chunking. This prevents context breaks during retrieval. The wide variation in individual entry lengths requires adjusting chunking parameters to fit different content lengths. This ensures the accuracy of vector representation. Ledgers need to associate full project information, which requires the index to support associative retrieval across multiple documents. This improves the completeness of summary logic.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the average content length of state-owned industrial fund ledgers, balances vector representation accuracy and retrieval efficiency |
| Overlap Length | 100–150 characters | Retains contextual association between chunks, prevents field information breaks across chunks |
| Similarity Threshold | 0.72–0.85 | Calibrated via actual testing, fits the mixed matching needs of structured fields and unstructured notes |
| Single Search Limit | Top 10 entries | Controls retrieval return volume, avoids redundant information interfering with ledger summary logic |
| Maximum Context | 6000 token | Fits the need for multi-document splicing during ledger summary, complies with input length limits of general large models |
defaultToken | 1000 | Ensures contextual completeness for basic retrieval, fits the average token consumption of individual ledger entries |
maxToken | 4096 | Matches the maximum input token limit of the selected vector model, prevents request overflow |
Three common mistakes
- Symptom: After switching vector models, the interface shows no progress and the original model cannot be restored. Cause: Incremental index reconstruction was not performed on the index management page. Only the global configuration was modified, and model vector recalculation was not triggered.
- Symptom: A "404 page not found" error occurs when testing Doubao embedding configuration. Cause: The API access domain and interface path for Doubao embedding were not correctly configured. This caused requests to fail to be received by the target service.
- Symptom: Retrieval results are not updated after new ledger data is uploaded. Cause: Incremental trigger rules matching the data update frequency were not set. Only full indexing was relied on, so new data was not crawled.
How to confirm configurations are set correctly
- Enter the vector model management page of FastGPT v4.17.0. Confirm that the currently active model matches the configured parameters.
- Upload a standard test ledger dataset, run a retrieval test. Check that the number of returned results does not exceed the set single search limit, and that matching degrees fall within the similarity threshold range.
- Run an incremental index update. Check that the index progress page displays normally, with no error logs or abnormal statuses.
- Verify that the core fields of the ledger are included in the retrieval results. Confirm that chunk length and context settings match the expected content splitting logic.
All 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.