What the data for this category looks like
Document data for state-owned asset management companies mainly comes from state-owned asset supervision filing documents, internal asset disposal plans, equity transfer agreements, annual operation reports, and non-performing asset disposal documents. Data update rhythm falls into two categories: project documents are updated in real time with disposal progress, and annual reports are updated regularly per calendar year. Document structure includes fields such as document number, regulatory body, asset details, disposal clauses, and attachment list. Units involved include ten thousand yuan, number of shares, floor area, and others. Single document length varies widely, from hundreds-of-word notices to tens-of-thousand-word comprehensive disposal plans.
What constraints these characteristics impose on the "vector models and indexes" link
Documents contain structured metadata and long-text business content, so indexes must support both vector semantic retrieval and structured field filtering. This avoids irrelevant results caused by relying only on vector matching. Wide variation exists in single document length. Vectorization processing for long-text documents must adapt to different input lengths, to prevent truncation of core business clauses. Data update rhythm has significant differences. Flexible switching between full indexes and incremental indexes is required, to adapt to update needs of real-time project files and regular reports. Documents contain a large number of specialized terms in the state-owned asset field. Vector models must adapt to the semantic characteristics of specialized corpora, to improve retrieval accuracy.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 8192 token | Adapts to the long-text characteristics of state-owned asset management documents, covering the complete semantics of professional clauses |
| Chunk Length | 800–1200 characters | Ensures each chunk covers complete asset details or disposal paragraphs, avoiding semantic fragmentation |
| Overlap Length | 100–150 characters | Connects the semantic boundaries of adjacent chunks, preserving the coherence of key information |
| Similarity Threshold | 0.72–0.85 | Filters low-match irrelevant content, adapting to the specialized term characteristics of state-owned asset documents |
| Single Search Limit | Top 10 results | Controls context length, preventing redundant results from interfering with core retrieval goals |
defaultToken | 2000 token | Matches the core summary length of a single retrieval fragment, ensuring completeness of business information |
Three common errors
- Phenomenon: The exported knowledge base dataset.csv only contains the index field and no content field. Cause: The "export full text content" option was not enabled during configuration, and only index metadata was synced.
- Phenomenon: The number of retrieval return results does not match the set single search limit. Cause: No filtering was performed using structured metadata of state-owned asset documents, such as document number or asset type, leading to early truncation of redundant results.
- Phenomenon: Vector retrieval encounters timeout errors. Cause: The
maxTokenparameter was not adjusted for long-text documents, causing vectorization processing of a single text to exceed interface limits.
How to confirm the configuration is correct
- Upload a standard state-owned asset management company asset disposal agreement, and check that chunked fragments cover complete disposal clauses without semantic fragmentation.
- Run a specialized term retrieval, and confirm that the similarity scores of returned results fall within the set threshold range.
- Export the knowledge base's dataset.csv, and confirm that both index and content fields are included.
- Vectorize a long-text annual operation report, and confirm that the processing process has no abnormal interruptions.
The values provided are common starting points and should be evaluated 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.