What the data for this category looks like
Gas industry research report data comes from public industry association reports, published data from energy regulatory authorities, annual and quarterly reports of listed gas enterprises, and special analysis documents from third-party energy consulting institutions. Updates follow fixed monthly or quarterly regular schedules, plus temporary updates triggered by sudden gas source changes or pipeline network policy adjustments. Most documents include sections for industry supply and demand overview, core operating indicators, regional market breakdown, and policy interpretation. Fields cover publishing organization, release date, total gas supply, LNG ex-factory price, pipeline network coverage length, and more. Common units include physical or currency units such as cubic meters, yuan per cubic meter, and kilometers.
Constraints imposed on vector models and indexes
A large volume of structured quantitative indicators and unstructured policy interpretation text coexist in research reports. Vector models must support vectorization of both long text passages and structured fields. Regular research reports updated on a fixed schedule and temporary ad-hoc documents both exist. This requires indexes to support flexible switching between incremental updates and full reconstruction. Core indicator fields in gas industry research reports have limited unit consistency. Some cross-regional data requires unit conversion. Vector indexes must support fast filtered retrieval based on metadata fields. Long research report passages require segment processing adapted to dense industry terminology, to avoid splitting that breaks professional logical connections.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Gas industry research reports contain extensive professional terminology and long sentences. This range avoids splitting that disrupts industry logic, while matching the input length limits of most open-source vector models |
CHUNK_OVERLAP | 100–200 characters | Supply and demand data in research reports has tight contextual links. Overlapping segments preserve professional contextual information across fragments, avoiding loss of critical logic during recall |
VECTOR_RECALL_TOPK | 20–30 results | In gas industry research report scenarios, core information concentrates in specific sections. A higher recall base covers potentially relevant content while avoiding excessive redundant data |
SIMILARITY_THRESHOLD | 0.72–0.80 | Semantic similarity between gas industry terminology is relatively high. This threshold filters low-relevance non-professional matching results, retaining precise research report content |
INDEX_TYPE | IVF_FLAT (for Zilliz vector database) | Vector data volume for gas industry research reports is moderate. IVF_FLAT balances retrieval speed and recall accuracy, adapting to incremental update requirements |
VECTOR_DB_TYPE | zilliz (for migration scenarios) | Zilliz supports distributed expansion of vector indexes, adapting to storage requirements as gas industry research reports grow over time |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test using in-house samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A
400 Bad Requesterror occurs when migrating vector storage from PGSQL to Zilliz. Cause: The Zilliz cluster address and API key are not correctly configured in the FastGPT backend, preventing vector data from synchronizing to the target database. - Symptom: The number of recall results is far lower than expected, returning only single-digit matching entries. Cause:
SIMILARITY_THRESHOLDis set too high, filtering out a large number of semantically relevant gas industry research report fragments. - Symptom: The private reranking model does not take effect, and returned results show no obvious optimization compared to initial recall content. Cause: The deployment path of the private reranking model is not correctly entered in the FastGPT model management module, resulting in calls to the default general model instead of the energy industry-adapted reranking model.
How to Verify Proper Configuration
- Upload a test segment of a gas industry research report, check the parsed segment results in the backend, and confirm that segment length and overlap parameters match preset configurations.
- Submit a retrieval request targeting gas supply and demand data, check that the number of vector recall results matches the
VECTOR_RECALL_TOPKsetting. - Compare initial recall results with reranked results, confirm that the reranking model is working correctly, and that returned content relevance meets expectations.
- Check the collection data volume in the vector database backend, confirm that incrementally updated research report data has been synced to the target storage repository.
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.