What the data for this category looks like
Plastics and rubber industry due diligence data sources include monthly statistical reports from industry associations, market data from commodity exchanges, and public bidding information from downstream processing enterprises. Data update cycles cover three categories: daily (ex-factory prices, futures market prices), weekly (downstream operating rates), and monthly (industry analysis reports). Document formats include structured parameter tables and unstructured industry analysis texts. Core fields include grade, nominal density, melt flow rate, and tensile breaking strength, with corresponding units of none, g/cm³, g/10min, and MPa respectively.
Constraints on vector models and indexing
The mixed structure, multi-cycle updates, and specialized terminology of plastics and rubber due diligence data impose three constraints on the vector models and indexing link. First, the coexistence of structured parameters and unstructured text requires indexing to support combined multi-field vector storage and full-text vector retrieval, to avoid losing the precise semantics of structured parameters with generic text vectors. Second, the wide span of data update cycles—high-frequency ex-factory price data requires daily synchronization, while low-frequency industry reports only need monthly updates—requires flexible switching between incremental indexing and full indexing, to avoid full rebuild overhead during high-frequency updates. Third, specialized terminology and units exist in sub-segments; for example, the test standards for melt flow rate differ from generic terms, requiring vector models to adapt to professional semantics of basic chemical sub-segments to reduce semantic matching bias.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapt to the segmentation needs of mixed text and parameter tables in plastics and rubber due diligence reports, avoid semantic breaks in single content blocks |
embedding_model | Open-source model fine-tuned for basic chemical sub-segments | Adapt to specialized terminology in sub-segments, improve semantic matching accuracy between structured parameters and industry texts |
index_update_mode | Prioritize incremental updates, trigger full updates weekly | Match update frequencies of different data sources, reduce indexing overhead for high-frequency data |
rerank_top_n | Top 6–10 results | Cover multi-dimensional data required for plastics and rubber due diligence (prices, parameters, policies), avoid missing key information in recall |
similarity_threshold | 0.72–0.85 | Filter low-match generic texts, retain professional semantic matching results for sub-segments |
parse_file_timeout_seconds | 300 seconds | Adapt to parsing and indexing time for tens of thousands of-word industry reports, avoid timeout interruptions |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- When deploying locally, the knowledge base cannot automatically index images, and the log shows "image parsing failed". The cause is that the OCR component required for image parsing is not configured during local deployment, so text content such as plastic grades and parameter tables embedded in images cannot be extracted.
- In version 4.9.0, the Chat model runs normally, but the Embedding model cannot connect to OneAPI, and the interface returns a 502 Bad Gateway error. The cause is that the dedicated port for embedding models is not opened in OneAPI's forwarding configuration, or the locally deployed FastGPT is not configured with the correct OneAPI interface address and API key.
- After enabling the Rerank model, the online recall test does not take effect, and the returned results are not sorted by matching degree and the number of entries does not match the configuration. The cause is that the "return after reranking" switch is not enabled in the recall configuration, or the rerank model interface call times out and the reranking logic is not triggered.
How to confirm the configuration is correct
- Upload a PDF document containing polyethylene grade parameters, check if the parsed text fully extracts the nominal density and melt flow rate fields, and confirm that the segmentation and indexing configurations take effect.
- Call the embedding model interface, pass two parameter texts of the same category but different grades, check if the vector similarity matches the professional semantic difference, and verify the model adaptation effect.
- Initiate an online recall test, enter "2024 polypropylene ex-factory prices", check if the returned results include price data for the corresponding time interval, and that the reranked results are sorted by matching degree.
- Check the index update log, confirm that daily updated ex-factory price data only triggers incremental indexing, and monthly industry reports trigger full indexing, matching the indexupdatemode configuration.
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.