What the data for this category looks like
This type of marketing content serves enterprises that produce and sell coatings and inks in partnership with financial institutions. Data primarily comes from enterprise-owned product manuals, promotional materials from partner distributors, industry compliance announcements, and e-commerce channel product detail pages. Update cadence adjusts based on new product development progress, compliance requirement changes, and marketing campaign adjustments.
Document structures include both structured parameters and unstructured scenario descriptions. Structured fields cover product model, execution standards, packaging specifications, and core performance indicators, with units including mass percentage, viscosity units (mPa·s), weather resistance years, and others. Unstructured sections mostly include product application cases and construction guidance instructions.
What constraints these characteristics impose on vector models and indexing
The presence of structured parameters and specialized units requires vector models to complete unit standardization and field normalization before encoding, to avoid vector space misalignment caused by unit differences.
Unstructured descriptions for targeted chemical application scenarios require selecting vector models adapted to basic chemical industry terminology, to improve recall accuracy for scenario-related content.
The irregular update cadence of marketing materials requires the indexing system to support incremental updates, to avoid resource consumption from full reindexing.
Diverse formats of e-commerce channel materials require format standardization before chunking, to ensure chunk length matches content integrity requirements.
Some product parameters involve compliance requirements, so the index must retain the association between parameters and compliance documents to facilitate subsequent traceability.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Coatings and inks marketing content includes long-text application scenario descriptions and structured parameters. This range balances single-block semantic completeness and vector recall accuracy, and adapts to multi-dimensional content needs for customer acquisition scenarios |
embedding_model | text-embedding-v3 | This model’s encoding effect on basic chemical industry terminology has been verified through testing, adapts to content understanding needs for targeted fields, and improves matching accuracy for customer acquisition content |
recall_top_k | Top 8–12 results | Coatings and inks marketing content covers multiple scenarios including automotive coatings, packaging printing, and others. This recall volume covers multi-scenario customer acquisition needs and avoids redundant results |
similarity_threshold | 0.72–0.78 | There are differences in semantic weight between structured parameters and unstructured scenario content. This range filters low-relevance general materials and retains accurately matched customer acquisition content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some product manuals include multi-page structured parameters and long-text application instructions. This duration avoids parsing timeouts and ensures normal processing of long documents |
incremental_index_enabled | Enabled | Marketing material update cadence changes with business needs. Incremental indexing avoids resource consumption from full reindexing, adapting to flexible customer acquisition content update requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- The symptom is that the vector model cannot be invoked after starting the service, and the log prompts that the API key is not configured. The cause is that the
CHAT_API_KEYenvironment variable was not correctly added to the docker-compose configuration, or the container was not recreated to make the configuration take effect. - The symptom is that the vector encoding interface returns
503 No available channels for model text-embedding-v3 under current group default. The cause is that the API key or channel for the corresponding model was not configured, or the channel quota is exhausted. - The symptom is that retrieval results cannot adjust indexing parameters directly through the retrieval page, and modifications must be made on the knowledge base content page. The cause is confusing the retrieval debugging entry and the indexing configuration entry, and the corresponding index management interface was not found.
How to confirm the configuration is correct
- Upload a single marketing document that includes both structured parameters and unstructured scenario content, and verify that the number of parsed chunks matches the
chunk_sizeconfiguration. - Call the vector encoding interface, and check that the returned vector dimensions match the standard dimensions of the selected model.
- Run a retrieval test, and verify that the number of recall results meets the
recall_top_kconfiguration requirements. - Upload updated marketing materials, and check that the index completes incremental updates automatically without triggering full reindexing.
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.