Reference only; verify against the target FastGPT release and deployment documentation.
Characteristics of This Category of Data
Data for oil and gas exploration and development primarily comes from raw seismic exploration data, well logging curve reports, drilling construction logs, and reservoir evaluation technical documents. Updates follow a phased schedule tied to project progress, triggered when new wells enter production or regional exploration results are published. Documents include two main formats: structured tables and long-form technical descriptions. Most fields include professional units, such as porosity percentage, permeability millidarcy, and well depth meters. Individual technical reports can reach tens of thousands of characters in length.
Constraints on Vector Models and Indexes
Data characteristics of oil and gas exploration and development create multiple constraints for the vector models and indexes workflow. A high proportion of structured table data requires indexes to support parallel generation of multiple vectors, to avoid cases where a single vector fails to cover associated semantics across multiple table columns. Long-form technical reports have large individual file sizes, requiring chunking strategies adapted to long paragraph splits to prevent semantic breaks. Abundant professional terminology and fixed units require vector models to have domain semantic understanding capabilities, to avoid mapping deviations for technical terms. Phased data updates tied to projects require indexes to support incremental synchronization, to avoid excessive resource usage from full index rebuilds.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the semantic completeness of single paragraphs in oil and gas exploration and development technical reports, to avoid semantic breaks after splitting |
| Overlap Length | 100–200 characters | Retains contextual associations across chunks, to prevent semantic loss after long text splitting |
| Multi-Vector Support | Enabled | Adapts to multi-column data such as structured tables and well logging curve tables, to cover associated semantics across columns |
| Maximum Paragraph Depth | 3 | Matches the standard chapter hierarchy of exploration reports, to optimize semantic aggregation effects |
defaultToken | 4096 | Adapts to the context window range of mainstream professional Embedding models |
| Single Search Limit | Top 5 entries | Focuses on core exploration consultation results, to avoid interference from redundant information during judgment |
Three Common Misconfigurations
- After refreshing the knowledge base page, the prompt "No available index model detected" appears. The cause is that multi-vector support is not enabled, or independent vector generation rules are not configured for structured table data, resulting in the index model failing to load properly.
- When testing access to a multimodal Embedding model, an Invalid error code is returned. The cause is that the model's API key and interface address are not correctly filled in the FastGPT configuration, or the model cannot adapt to the professional terminology semantics of the oil and gas exploration field.
- After uploading an exploration report, retrieval results have severe semantic deviation. The cause is that the chunk length is set outside the reasonable range, resulting in a single chunk containing irrelevant semantics, or the overlap length is insufficient to retain contextual associations across chunks.
How to Verify Correct Configuration
- Enter the knowledge base settings page, check the multi-vector support switch status, and confirm it is enabled.
- Upload an exploration document containing structured tables, wait for index completion, then check the vector generation log to confirm the number of generated vectors matches the document structure.
- Initiate a consultation targeting exploration data, check the number of retrieved results, and confirm it complies with the preset single search limit rule.
- Test the connected Embedding model by initiating a professional terminology query, confirm no errors are returned and normal vector results are generated.
The values listed above 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.