What data for this category looks like
Data primarily comes from internal product technical documents, compliance reports, marketing materials of chemical enterprises integrated with financial institutions, as well as publicly available industry standard materials. The update rhythm adjusts with product iterations and compliance requirements, with no fixed cycle, but single-update documents have long length. Most document structures include fields such as product name, CAS number, purity indicators, packaging specifications, application scenarios, and safety instructions. Units involve professional chemical units including mass percentage, molar concentration, particle size mesh number, and packaging weight.
What constraints these characteristics impose on vector models and indexing
The long length and multi-field characteristics of chemical raw material marketing documents create multiple constraints for the vector processing link. First, single document content is lengthy. Direct embedding will exceed the model context window, so targeted adjustment of segmentation granularity is required to avoid splitting critical information. Second, documents contain unique identifiers and professional units such as CAS numbers and purity. Field association relationships must be retained during preprocessing to prevent confusion between different units of similar indicators during vector matching. In addition, marketing materials and compliance documents coexist. Content types must be distinguished during indexing to avoid accidental recall of non-marketing compliance content into customer acquisition scenarios. Finally, the volume of documents updated in batches is large. The trigger logic for incremental indexing must adapt to batch scenarios to prevent exceeding embedding rate limits.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Chemical raw material marketing documents often contain long paragraphs of professional descriptions. This range retains complete indicator associations and avoids losing critical information during segmentation |
embedding_batch_size | 2–4 | Single document content is lengthy. Excessive batch size triggers interface rate limits. Small batch sizes reduce embedding request pressure |
embedding_threads | 2–3 | Reduces the number of parallel requests to avoid exceeding embedding rate limits, adapting to batch vectorization scenarios |
retrieve_top_k | Top 8–12 results | Marketing content needs to cover parameter requirements for multiple application scenarios. This recall volume balances context length and matching coverage |
field_retrieval_enabled | Enable separate retrieval for CAS number and purity fields | The core identifiers of chemical raw materials are CAS numbers and purity indicators. Separate retrieval improves accurate matching efficiency |
The parameter values provided on this page are conventional recommendations used to determine starting points for configurations. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- The phenomenon is text chunk loss when
chunk_sizeis set to 3000 characters. The cause is that long text segmentation does not adapt to the long paragraph structure of chemical raw material documents, and fragments exceeding the model context limit are automatically discarded. - The phenomenon is a
429 Too Many Requestserror triggered during vectorization. The cause is thatembedding_threadsorembedding_batch_sizevalues are too high, and parallel requests exceed the service provider's interface call quota. - The phenomenon is long response time for knowledge base retrieval. The cause is that field-level retrieval is not enabled. Full vector matching introduces irrelevant compliance document content, increasing matching calculation volume.
How to confirm the configuration is correct
- View vectorization logs to confirm that the values of
embedding_batch_sizeandembedding_threadsdo not trigger interface current limiting errors. - Randomly select multiple long documents to check whether split text chunks retain complete product indicators and unit associations.
- Test query terms containing CAS numbers or purity indicators to confirm that recall results include matching field information.
- Submit a batch update task to check whether the processing progress of the indexing queue meets the expected cycle.
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.