What this category’s data looks like
Data sources for iron ore intelligent due diligence reports include bulk commodity spot trading platforms, public ledgers from port operators, cross-border maritime logistics systems, and public materials from industry research institutions. Update cadences are tiered: spot trading data is updated daily, port inventory data every three days, cross-border transportation data is synchronized with flight dynamics, and industry research reports are released monthly. The document structure of a single report includes spot trading details, regional inventory distribution, cross-border transportation link information, and qualification information of upstream and downstream cooperation entities. Fields include trading target name, storage location, transportation cycle, and qualification level of cooperating parties, with corresponding units being tons, days, days, and level identifiers respectively.
What constraints these characteristics impose on vector models and indexing
Daily updated spot data requires vector indexes to support high-frequency incremental writes, avoiding resource consumption and delays caused by full index rebuilds. Multi-source heterogeneous data structures require unified field mapping rules to ensure semantic consistency for text from different sources during vector embedding. A single report contains multiple segmented fields, so layered index weights must be set for different fields to improve recall accuracy for specific dimensions. Large-volume monthly industry reports require segmented embedding parameters optimized for long texts, avoiding context breaks that degrade vector quality. Geographically related transportation fields also need to be combined with spatial vector models to optimize recall scope and improve matching efficiency for cross-regional data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_batch_size | 32–64 items/batch | Adapts to the number of fields and text length per batch of iron ore data, avoiding memory overflow |
index_incremental_mode | Enabled | Adapts to daily updated spot data, reducing resource consumption from full index rebuilds |
chunk_size | 800–1200 characters | Adapts to the segmented structure of iron ore due diligence reports, balancing context completeness and vector embedding accuracy |
recall_top_k | Top 8–12 items | Adapts to recall requirements for mixed multi-field data, avoiding excessive redundant results that interfere with subsequent processing |
vector_db_management_via_mongodb | Enable metadata synchronization | Supports managing vector database CRUD operations via MongoDB, adapting to multi-system collaboration requirements |
vector_db_security_mode | Enable fine-grained permission control | Adapts to multi-role management requirements, supporting permission allocation for non-root users |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: A vector model accessed via Ollama returns a
403 Forbiddenerror, while curl testing passes normally. Cause: FastGPT's vector service is not configured with Ollama request headers or proxy parameters correctly, leading to permission verification failure. - Symptom: CRUD operations on the vector database cannot be synchronized to the MongoDB management system, and batch updates cannot be completed via third-party systems. Cause: The vector database's metadata synchronization switch is not enabled, and the interface permissions of the third-party management system are not bound.
- Symptom: The recall results contain a large number of empty field entries, and the total number of entries does not match the configured
recall_top_kparameter. Cause: Filter rules are not set for the multi-field structure of iron ore reports, leading to recall of invalid vectors.
How to verify correct configuration
- Run an incremental write test, upload a daily updated iron ore spot data set, and check if only the corresponding entries are added to the vector database without triggering a full index rebuild.
- Call the vector retrieval interface, pass keywords from an iron ore due diligence report, and verify that the number of returned results matches the configured
recall_top_kparameter. - Attempt to perform CRUD operations via the MongoDB client, and check if the vector database metadata is synchronized and updated.
- Switch to a domestic vector model, run an embedding test, and confirm that the vector dimension returned by the model matches the configured expected value.
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.