What This Category of Data Looks Like
The data related to basic chemical administrative inquiries and responses comes primarily from internal enterprise process manuals, quality inspection reports, raw material procurement standards, government compliance documents, and tabulated Excel files and multi-dimensional meeting minutes in collaboration tools such as Feishu. Data update rhythms vary: process manuals and compliance documents are updated quarterly or annually, while batch-level quality inspection reports and procurement ledgers generate new content daily. Document structures include long-text process specifications, structured Excel ledgers with fields such as raw material number, purity, and test values, and multi-dimensional documents with question-and-answer records. Fields and units include professional chemical units such as MPa, wt%, and ppm, as well as identification fields such as batch number and production date.
Constraints on Vector Models and Indexing Workflows
The multi-type, varied update rhythms, and specialized field characteristics of basic chemical data impose multiple constraints on the vector indexing process. The difference in length between long-text process specifications and short-text quality inspection ledgers requires chunking strategies tailored to different text lengths, to avoid disrupting semantic coherence or wasting vector model input quotas. Data with different update frequencies requires separate full and incremental indexing configurations, to avoid repeatedly processing low-frequency change compliance documents during daily batch updates. Differences in specialized units and fields require the indexing process to support semantic mapping of structured fields, to prevent retrieval result deviations caused by unit confusion. The precision requirements of administrative inquiry scenarios require controlling the number and relevance threshold of recall results, to avoid irrelevant content interfering with final responses.
Configuration Settings
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to mixed content of basic chemical long-text process specifications and short-text ledgers, and matches the input length limits of mainstream embedding models |
| Overlap Length | 100–150 characters | Prevents chunking from cutting continuous semantic units such as process steps and raw material ratios, and improves retrieval context coherence |
maxToken | 8192 | Adapts to the maximum token limit of mainstream embedding models, and meets the vector generation requirements of long-text process manuals |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance compliance or process content in basic chemical scenarios, and ensures the precision of administrative inquiry responses |
| Single Search Limit | Top 5 entries | Matches the rapid response requirements of administrative inquiry scenarios, and avoids excessive redundant results interfering with judgment |
defaultToken | 4096 | Adapts to short-text field vector generation for structured Excel ledgers, and balances computing resources and semantic coverage |
Three Common Configuration Mistakes
- Symptom: After importing Excel files or multi-dimensional documents from a Feishu folder, the indexing task displays "parsing failed", and the log returns "field format not supported". Cause: Structured field automatic mapping configuration is not enabled, and multi-unit fields such as MPa and ppm in basic chemical ledgers are not correctly identified, causing the vector model to fail to generate valid semantic vectors.
- Symptom: In an environment where the PostgreSQL database is deployed via Docker, indexing establishment times out. The host has 8 cores and 16 GB of memory without a GPU, and the log shows that peak memory usage exceeds the virtual machine configuration. Cause: Chunk length and the number of documents processed in a single batch are not limited. When batch importing batch-level quality inspection reports for basic chemicals, excessive memory usage triggers a timeout.
- Symptom: Embedding model calls via OneAPI report errors, and vectors cannot be generated normally. Cause: A dedicated embedding model for indexing is not separately configured, and reusing the global model configuration triggers OneAPI rate limits or permission verification failures.
How to Verify Proper Configuration
- Upload a basic chemical process specification Excel file and multi-dimensional meeting minutes, then check the indexing task run logs to confirm that structured fields are correctly parsed, with no format errors.
- Submit an administrative inquiry question such as "What are the quality inspection standards for a certain raw material", then check that the number of retrieved results matches the single search limit configuration.
- Test the dedicated indexing embedding model call separately, then check that the returned vector dimensions match the model parameters, with no connection or permission errors.
- Upload an updated quality inspection report, then check that the incremental indexing task executes normally, with no prompt for full reconstruction.
The values provided are common starting points and should be measured against samples specific to the deployment context.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.