What this category of data looks like
Document and material data for oil and gas exploration and development primarily comes from formal exploration project reports, drilling logs, trial production data analysis documents, compliance approval documents, and similar sources. Data update frequency changes with the stage of the exploration project. Single upload scales range from individual professional documents to bulk batches of historical data. Document structures include long text paragraphs, structured parameter tables, and mixed-format content. Core fields include well ID, exploration block, well depth, permeability, daily gas production, and similar items. Professional units include meters, millidarcies, cubic meters per day, and similar units.
What constraints do these characteristics create for the vector models and indexing workflow
Mixed document structures of long text and structured parameter tables require the indexing workflow to support both plain text chunking and structured table parsing, to avoid breaking the associative logic of professional parameters during splitting. Frequent use of specialized terminology and fixed units requires vector models to have domain-specific semantic adaptation capabilities; otherwise, low-relevance recall results will occur. Irregular upload demands with fluctuating scale require indexing to support batch task splitting and resumable uploads, to accommodate data processing of varying sizes.
How to configure the settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the professional paragraph length and term association scope of oil and gas exploration documents |
| Overlap Length | 100–200 characters | Preserves context for core parameters such as well ID and permeability across chunks |
| maxToken | 8192 token | Matches the input length limit of mainstream professional vector models |
| defaultToken | 1024 token | Serves as the default context reference value for short document chunking |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance match results for exploration data |
| Maximum Search Results per Query | Top 10 results | Balances the precision of specialized terminology recall and retrieval speed |
Three common mistakes
- Phenomenon: After upgrading to FastGPT v4.17.0, chunking errors occur when uploading drilling parameter tables in CSV format for oil and gas exploration. Cause: The default chunking rules after the upgrade are optimized for long text, and do not support the field separation logic of structured tables.
- Phenomenon: When batch uploading more than 80 exploration reports, indexing progress gets stuck at 90% with no updates. Cause: Resumable upload parameters are not configured, and full indexing tasks are not split into batches, leading to resource exhaustion and a stuck process.
- Phenomenon: When calling knowledge base references, unexpected terminology match results are returned, such as matching "porosity" to the general physical pore concept. Cause: The vector model adapted for the petroleum and petrochemical industry is not switched, and a general base model is still used.
How to confirm the configuration is correct
- Upload a single exploration report of approximately 10,000 characters, and check if the chunked text fragments cover complete associative paragraphs of specialized terminology.
- Adjust the chunk length and overlap length parameters, search for the keyword "reservoir thickness", and verify if the context coherence of the recall results meets business expectations.
- After switching the vector model, verify if the matching precision of specialized terminology in retrieval results is suitable for oil and gas exploration scenarios.
- Trigger a batch indexing task, and confirm that the indexing progress updates normally and displays the completed status.
The values provided here are common starting points, and should be evaluated against the operator'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.