What the data for this category looks like
Infrastructure construction project financing daily report data is mainly sourced from public project ledgers of housing and urban-rural development authorities, bank credit approval documents, and project financing announcements released by industry associations. The update cadence is one batch of data per working day. Each document includes fields such as project number, project name, affiliated region, total investment amount, financing amount, fund provider type, approval progress, and release date. Units for total investment amount and financing amount are mostly ten thousand yuan or hundred million yuan. The release date uses the YYYY-MM-DD format. The core descriptive text mostly combines project background and financing details.
What constraints do these characteristics impose on the vector models and indexing link
The large number of fields with clear business units requires vector models to encode both text semantics and structured numerical association information, to avoid semantic matching deviations. The fixed working-day update cadence requires indexes to support incremental updates without full reindexing, reducing computing resource consumption. The moderate per-document length but large batch data volume requires indexes to support efficient batch vector writing and retrieval. Some fields have strong business correlations such as region and project type, requiring indexes to support semantic retrieval with field filtering, narrowing the retrieval scope and improving retrieval accuracy.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The core descriptive text of infrastructure construction project financing daily reports mostly falls within this range, avoiding retrieval accuracy degradation caused by semantic fragmentation and overly fine-grained chunking |
vector_batch_size | 16–32 | Adapts to the batch processing limits of most commercial or open-source vector services, reduces the number of interface calls, and improves batch import efficiency |
similarity_threshold | 0.72–0.85 | Filters low-correlation cross-region and non-target type infrastructure projects, ensuring business relevance of retrieval results |
recall_top_k | Top 10 results | Controls the number of results returned per retrieval round, avoiding information overload, and adapting to the conventional reference needs of infrastructure construction financing analysis |
index_refresh_interval | Refresh every hour | Matches the working-day update cadence of financing daily reports, ensures timeliness of retrieval results, while avoiding excessive refresh resource consumption |
embedding_model | Open-source or commercial models that support multi-modal semantic encoding | Adapts to the characteristics of financing daily reports containing text and numerical fields, accurately encodes business correlations of project information |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Vectorization interface returns abnormal results, or batch import performance fails to meet standards. Cause: The
vector_batch_sizeparameter is not configured, and only single-item chunk requests are sent by default, failing to leverage the batch processing capabilities of the vector service. - Phenomenon: Retrieval results include a large number of irrelevant cross-region or non-target type infrastructure projects. Cause: The
similarity_thresholdvalue is set too low, failing to effectively filter low-similarity non-matching content. - Phenomenon: Retrieval results do not include newly released financing daily reports. Cause: The
index_refresh_intervalis configured too long, failing to match the update frequency of financing daily reports, resulting in delayed index updates.
How to Confirm the Configuration Is Correct
- View the vector service monitoring panel to confirm that the number of batch processing requests matches the
vector_batch_sizeconfiguration. - Manually enter a keyword from an infrastructure construction project financing daily report, and check whether the similarity scores of retrieval results fall within the
similarity_thresholdconfiguration range. - Wait for one full
index_refresh_intervalcycle, then retrieve newly released financing daily reports, and confirm that the results include the latest data. - Call the
/api/index/listinterface of FastGPT 4.8.10 to check whether the content of the chunked index matches the chunked content of the original document.
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.