What the data for this category looks like
The policy and regulatory basis data for the basic chemical industry mainly comes from official announcements, industry specifications, and departmental rules issued by the Ministry of Ecology and Environment, the Ministry of Emergency Management, and the Ministry of Industry and Information Technology. The update rhythm is adjusted irregularly as new policies are released. The document structure uniformly includes fields such as policy document number, issuing authority, effective date, scope of application, core clauses, etc. The length of individual documents varies widely, ranging from a few hundred characters to several thousand characters. Some normative documents also include attachment tables and penalty details.
What constraints do these characteristics impose on the vector models and indexes link
The differences in length and field diversity of basic chemical industry policy documents require that vector chunking must balance context integrity and retrieval accuracy, avoiding splitting single chunks too finely which would break clause logic. The irregular update rhythm requires the index to support flexible incremental updates and manual trigger mechanisms to adapt to the irregular nature of policy releases. The multi-field metadata structure requires the index to support filtering and recall based on fields such as issuing authority and effective date, narrowing the retrieval scope and improving the accuracy of policy Q&A. The presence of long texts also requires that the input length configuration of the vector model must adapt to the context window limits of large models.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | The length of individual clauses in basic chemical industry policies mostly ranges from 300–800 characters. This range can retain the complete context of clauses |
| Overlap Length | 100–150 characters | Prevents context breaks after long clauses are split, ensuring the coherence of policy logic |
| Similarity Threshold | 0.75–0.85 | Policy Q&A requires accurate matching of clauses. This interval can filter lowly relevant non-target policies |
| Single Search Limit | Top 8 results | Basic chemical industry policy entries are relatively concentrated. Too many results will exceed the context carrying capacity of large models |
maxToken | 4000 token | Adapts to the context window of common large models, ensuring that spliced policy content is fully readable |
| Metadata Filtering | By issuing authority, effective date | Matches the scope of application of policies, narrowing the recall scope and improving retrieval efficiency |
Three Common Mistakes
- Symptom: Abnormal binding relationship between vector data and metadata occurs when attempting to manage the vector database directly via MongoDB. Cause: The metadata of basic chemical industry policy documents includes key associated information such as document number and effective date. Directly operating MongoDB will destroy the preset binding rules between the vector database and metadata, leading to failure to correctly filter valid policies during recall.
- Symptom: FastGPT returns a request rejection when calling a vector model accessed via Ollama, but the curl test returns normal results. Cause: The Ollama API request does not carry the authentication parameters required by FastGPT, or the input content exceeds the configured
maxTokenlimit, causing the interface to be blocked. - Symptom: Duplicate policy clauses appear after knowledge base index merging without deduplication. Cause: The deduplication rule based on policy document number is not enabled during merging, and no duplicate verification is performed on metadata, leading to multiple recalls of different chunks of the same policy.
How to Confirm the Configuration is Correct
- Upload a local basic chemical industry policy document, trigger vector index construction, and check the chunking log to confirm that the chunk length matches the preset range.
- Initiate a policy-related query, and check whether the metadata of the recall results includes the correct issuing authority and effective date.
- Perform an index merging operation, and check whether the number of documents after merging matches the number of original deduplicated documents.
- Call the vector model interface, confirm that the returned response status code is 200, and that the content meets the expectations of policy Q&A.
The values provided here are common starting points and should be measured against the reader's own samples.
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.