What This Category's Data Looks Like
Oil and gas extraction intelligent due diligence report data sources include geological survey documents, drilling construction logs, trial production operation ledgers, and environmental compliance approval documents. Update frequency varies significantly by project phase. Exploration phase documents are updated quarterly. Drilling phase documents are updated per well completion node. Operations phase documents are updated with daily production data. Most documents are PDF formats with embedded structured tables, containing geological parameters, production metrics, equipment operation records, and compliance content. Fields include well depth (meters), per-well daily crude oil output (cubic meters), drilling cycle (days), porosity (decimal), and permeability (millidarcy). Some documents have custom fields with no unified naming convention.
Constraints Imposed on Vector Models and Indexing
Oil and gas extraction due diligence reports contain mixed structured numerical data and unstructured analytical text. This requires vector models to support both semantic encoding and numerical feature mapping. Generic text embedding models cannot accurately encode structured numerical fields. Embedding models that support multimodal or numerical feature enhancement must be selected. Second, data update frequency varies significantly. Some production data requires real-time synchronization. Full index rebuilding causes retrieval delays. A strategy supporting incremental index updates must be configured. In addition, documents have no unified field specifications and many custom fields. Index metadata storage must support dynamic expansion to avoid restricting data access via fixed schemas. Finally, single document length varies widely, from hundreds of words of equipment logs to dozens of pages of geological reports. Segmentation rules adapted to long texts must be configured to avoid information loss from semantic truncation.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-m3:latest or text-embedding-v3 | Supports multimodal and numerical feature encoding, adapts to mixed structured and unstructured data in oil and gas extraction reports |
index_chunk_size | 800–1200 characters | Balances semantic integrity for long geological reports and retrieval accuracy for short equipment logs |
recall_top_k | 10–15 results | Covers multi-dimensional production and compliance retrieval needs, avoids excessive recall increasing subsequent processing burden |
similarity_threshold | 0.72–0.80 | Adapts to semantic features of oil and gas extraction professional terminology, reduces false recall probability for irrelevant documents |
incremental_index_enable | Enabled | Adapts to real-time update requirements for some production data, avoids delays caused by full index rebuilding |
embedding_api_timeout | 600 seconds | Adapts to vector encoding time for large geological reports, prevents task interruption due to timeout |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. Testing on local samples is recommended before finalizing values.
Three Common Configuration Errors
- Phenomenon: When configuring bge-m3 deployed via ollama as the embedding model, the interface displays "No available embedding model channel" or returns a 404 error. Cause: The ollama embedding model interface address is not configured in FastGPT's model channel management, or the model name and authentication information are not filled correctly.
- Phenomenon: Knowledge base question answering retrieval speed is slow, with single-round retrieval taking more than 10 seconds. Cause: The configured
recall_top_kvalue is too high, or the reranking model is not enabled, and the single-batch vector count is too high due to no reasonableindex_chunk_sizebeing set. - Phenomenon: When adding the Alibaba Cloud Qwen text-embedding-v3 model, after entering the API key, the prompt "No available channel under current group default" is displayed. Cause: The embedding model permission is not enabled for the default group in FastGPT's group management, or the API key is not bound to the access permission for the corresponding model.
How to Verify Successful Configuration
- On the FastGPT model management page, check the embedding model status, confirm it shows "Connected" with no error prompts.
- Upload a typical oil and gas extraction due diligence report, trigger vector index construction, and check that there are no timeout or encoding failure records in the task log.
- Initiate a test retrieval, enter oil and gas extraction professional terminology, and verify that the relevance and quantity of recall results match the configured expectations.
- Add a test production data entry, trigger incremental index update, and confirm that the new data can be retrieved normally.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.