What Data Looks Like for This Category
Data comes primarily from official brand filing public documents, industry association published research reports, third-party ingredient testing reports, and public product parameter pages from e-commerce platforms. Updates align with new ingredient filings, regulatory policy changes, and quarterly industry trends. Most documents combine structured tables and written explanations. Core fields include ingredient name, concentration labeling (often using percentage or milligrams per gram as units), efficacy descriptions, filing numbers, and publication dates. Some documents include short segments of laboratory testing data.
Constraints Imposed on Vector Models and Indexing Workflows
Cosmetic research reports have many structured fields, clearly labeled but with varying unit formats, irregular update schedules, and short testing data segments. These traits create multiple constraints for the vector model and indexing workflow. Numeric concentration fields require precise encoding logic to avoid recall errors caused by unit ambiguity. Structured fields need layered index configuration: inverted indexes for unique identifier fields like filing numbers, and vector indexes for text fields. Irregular update schedules require incremental index update processes to reduce resource usage from full index rebuilds. Short testing data segments need adjusted chunking thresholds to prevent semantic splitting breaks.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
EMBEDDING_MODEL_NAME | qwen3-embedding-8b | Adapts to specialized semantic scenarios in cosmetic research reports, such as ingredients and efficacy claims, and supports accurate encoding of Chinese terminology |
CHUNK_SIZE | 800–1200 characters | Cosmetic research reports mostly combine structured paragraphs and short testing data. This range preserves semantic integrity for ingredient and efficacy information |
INDEX_UPDATE_MODE | Incremental update | Research report updates follow no fixed schedule. Incremental updates reduce resource consumption from index rebuilds |
RECALL_TOP_K | Top 8–12 results | Core information in cosmetic research reports is concentrated. An appropriate number of recalled results balances retrieval accuracy and response speed |
SIMILARITY_THRESHOLD | Calibrated via actual testing | Semantic similarity for specialized terms requires balancing precision and recall coverage. Adjust based on actual retrieval scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Some research reports include multiple sets of testing data. Sufficient time is needed to complete structured parsing and vectorization tasks |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test with your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The automatically generated index for manually uploaded cosmetic research report knowledge bases disappears automatically after several hours. Cause: The threshold for the
INDEX_AUTO_CLEANUPparameter is not configured, or an overly short expiration time is set. This causes the system to clean up temporary indexes not associated with active retrieval. - Symptom: After connecting
qwen3-embedding-8bin FastGPT v4.9.11, the file status remains stuck on "Indexing". Cause: The deployment address for the corresponding model is not configured inEMBEDDING_MODEL_PLATFORM, or server resources are insufficient to support batch vectorization tasks for the 8B model. - Symptom: Retrieval results include content with incorrect ingredient concentration matches. Cause: Separate numeric encoding rules are not configured for concentration fields. The vector model treats unit format differences as irrelevant text, leading to semantic matching errors.
How to Verify Proper Configuration
- Upload a single cosmetic research report document, and check if the backend
INDEX_STATUSfield shows "Completed", with no persistent timed-out "Indexing" status. - Search for a known cosmetic ingredient name, and verify that the returned
chunk_textincludes core fields such as the ingredient's concentration and efficacy description, with matching logic aligned with the configured settings. - Upload an updated research report document, and check if only the new content is updated in the index, with no full index rebuild triggered.
- View the index statistics panel in the vector database, and confirm that the storage capacity of the dedicated index matches the total character count of the uploaded documents.
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.