What the data for this category looks like
Plastics and rubber category financial report data mainly comes from public periodic reports of listed companies disclosed by domestic and overseas stock exchanges, and production and sales monitoring reports released by industry associations. Update cadence follows: annual reports are disclosed once per year, quarterly reports are updated each quarter, and temporary announcements are released alongside major events. Document structure includes consolidated financial statements, revenue share of plastics and rubber business, production capacity and utilization rate data, raw material procurement cost details, upstream and downstream supply and demand analysis modules, and other sections. Fields include revenue amount, production capacity scale, raw material procurement volume. Units are mostly ten thousand yuan, ten thousand tons, and yuan/ton.
What constraints do these characteristics impose on vector models and indexing?
Data for this category has both structured financial fields and unstructured business analysis content. Update cadence includes both regular bulk updates and temporary incremental updates. This requires the vector model and indexing pipeline to support both structured feature encoding and unstructured text vectorization, while adapting to incremental index update logic. Document length varies widely: a single financial report can cover dozens to hundreds of pages. Chunking must avoid semantic breaks across business segments. Field units use multiple measurement standards, so unified feature normalization processing is required to ensure spatial consistency of vectors of different dimensions during retrieval.
Configuration Recommendations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Plastics and rubber financial reports contain technical terms and long business descriptions. This range preserves complete semantics of a single business module and avoids semantic fragmentation after splitting |
chunk_overlap | 100–150 characters | Financial reports have abundant upstream and downstream related data. Overlapping segments preserve cross-block contextual connections and avoid loss of critical logic during retrieval |
similarity_top_k | Top 10–15 results | Financial report data has many scattered fields. Retrieving too many results increases subsequent processing burden, while retrieving too few may miss critical business data |
vector_store_batch_size | 16–32 items per batch | Adapts to memory usage of general vectorization models, balances single-batch processing efficiency and memory overhead, and avoids batch request timeouts |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single financial report documents have long lengths. Sufficient time must be reserved for parsing and vectorization processing to avoid task interruptions |
embedding_model | Determined based on actual testing | Adapt to technical terms in plastics and rubber financial reports. Select models that support corpus in the chemical industry, such as multimodal-embedding-v1 |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material types, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Vectorization tasks only send requests for individual text slices, triggering batch processing failure errors in the vectorization service. Cause: Batch vectorization configuration in FastGPT is not enabled. The default setting only transmits individual slices, and does not adapt to the batch input requirements of the vectorization service.
- Phenomenon: When calling the FastGPT 4.8.10 version API, the returned chunked index content is empty or has missing fields. Cause: Chunking parameters are not configured correctly, or associated parameters for chunked retrieval are not specified in the API request, resulting in the index not generating correct chunk mappings.
- Phenomenon: A large number of general financial field contents unrelated to plastics and rubber business appear in retrieval results. Cause: The corpus adaptation of the vector model is not adjusted for the technical terms of financial reports, or the number of retrieved items is set too high, introducing low-correlation non-business data.
How to Verify Correct Configuration
- Check the vectorization task logs to confirm that the number of slices carried in each request matches the configured batch processing size requirement.
- Call the chunked index query interface to verify that the returned chunked content matches the business segment division of the original financial report.
- Input search terms related to plastics and rubber business, and check that the relevance and number of retrieved results match the expected configuration.
- On the FastGPT model management page, confirm that the added vector model supports corpus adaptation for the current category.
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.