What the data for this category looks like
Oil and gas exploration and development policy and regulatory basis data primarily comes from official standard documents issued by the National Energy Administration, Ministry of Natural Resources, and relevant industry authorities. It also includes internal exploration and development operation procedures from oil and gas enterprises. Update timing is adjusted irregularly alongside new policy releases, with no fixed daily update cycle. Most documents are structured formal texts, containing fields such as document number, issuing authority, effective date, clause number, applicable scenario, and term definition. The length of individual documents varies widely.
What constraints these characteristics impose on the vector model and indexing workflow
The formal nature of policy documents requires retrieval results to accurately match clause levels. Chunking must preserve metadata such as clause numbers to avoid breaking core policy logic. Irregular updates require indexes to support incremental updates, without full re-import of all historical documents. Multi-field structured data can be used to build metadata filtering rules to narrow retrieval scope. Wide variation in long text lengths requires chunking and token limits to adapt to document lengths, preventing truncation of key policy content.
How to Set Configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the clause length of oil and gas exploration policy documents, preserving complete core information of a single policy |
| Overlap Length | 100–150 characters | Compensate for context gaps after long text chunking, ensuring adjacent clauses can be associated during retrieval |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meet the rigor requirements of policy documents, filtering low-relevance results |
| Single Search Limit | Top 10 results | Cover business needs for associating multiple policy clauses, while controlling total context length |
| maxToken | 4096 token | Adapt to the input limits of mainstream embedding models, avoiding truncation of core policy content |
| defaultToken | 1024 token | Serve as the token benchmark for default chunking, balancing retrieval accuracy and computational overhead |
Three Common Configuration Mistakes
- Reusing an existing index directly after switching embedding models is not supported. Vector generation must be redone. Phenomenon: Search results for imported knowledge bases remain unchanged, or the system displays a model mismatch prompt. Cause: Vector indexes are strongly bound to embedding models. Vector spaces generated by different models are inconsistent and cannot be directly compatible.
- An error occurs when creating a knowledge base without specifying a compliant embedding model. Phenomenon: The interface shows the
undefined model must match "^(text"error. Cause: No compliant embedding model was selected, or the model name input format does not meet system specifications. - Retrieval result sorting does not align with business expectations, with results not displayed by association priority. Phenomenon: Policy clauses with high semantic relevance are ranked below lower-matching results. Cause: The reranking step was not enabled, or reranking parameter configuration is incorrect, causing the base sorting logic to function abnormally.
How to Verify Correct Configuration
- Upload a single oil and gas exploration-specific policy document, and confirm that chunked fragments retain complete clause numbers and core content.
- After switching the embedding model, run the regenerate index operation, and verify that search results change reasonably with the updated model.
- Manually enter a policy-related query, and check that returned result similarity falls within the preset threshold range, and sorting meets business requirements.
Reference only; verify against the target FastGPT release and deployment documentation.
The values provided are common starting points and should be evaluated 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.