What the data for this category looks like
Residential development research report data sources primarily include public financial reports from real estate enterprises, public project approval documents from housing and construction authorities, market research documents released by industry associations, and special analysis reports from professional consulting institutions. Updates follow a regular quarterly and semi-annual schedule, with temporary supplementary documents issued when industry policies are adjusted or major projects launch. A typical single document includes fields such as project location parameters, land transfer amount, development cycle plan, plot gross floor area, average sales price, and cash flow measurement tables. Units use professional measurement standards including square meters, 100 million yuan, and 10,000 yuan per square meter. Some long documents include cross-chapter analytical content.
Constraints for Vector Models and Indexing
Residential development research reports have a high density of professional terminology, including concepts such as floor area ratio, plot area, and land premium rate. This requires vector models to have industry-specific semantic understanding capabilities. Single documents have relatively long lengths, so segmented processing must balance semantic integrity and retrieval efficiency. Structured content with multiple fields requires targeted vector extraction logic to avoid semantic deviation of professional fields by general models. Additionally, research reports are updated at a stable frequency with clear batches. Indexing must support incremental updates to reduce repeated computing overhead.
This configuration is compatible with FastGPT v4.8.21 and later versions.
How to Set Configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aligns with the paragraph length of residential development research reports, avoids excessive fragmentation of professional analysis context, and controls the load of single-segment vector calculation |
chunk_overlap | 100–150 characters | Retains key connecting information before and after segments, prevents disconnection of professional term associations caused by segment truncation |
embedding_model | text-embedding-v3 | Supports long text input, has general industry semantic understanding capabilities, and adapts to professional terminology scenarios for residential development research reports |
recall_top_k | 10–15 entries | Residential development research reports focus on specific projects or regions. Excessive recall will introduce irrelevant information. This range balances recall coverage and retrieval accuracy |
similarity_threshold | 0.75–0.85 | Filters low-match non-target research report fragments, ensures relevance between recall results and retrieval requirements |
rerank_top_k | 5–8 entries | Performs precise ranking on preliminary recall results, retains the most relevant top results, and reduces redundant load for subsequent processing |
The parameter values provided on this page are all 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. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Configuring
embedding_modelastext-embedding-v3results in a "no available channel" prompt. This occurs when the API key for the model is not configured in the corresponding group, or channel permission verification has not been completed. - Retrieval response time exceeds expected thresholds. This may be caused by
chunk_sizeset above 1200 characters, which increases vector calculation load for single-segment text, orrecall_top_kandrerank_top_kset to overly high values, which increases computing burden for vector recall and reranking. - Recall results include a large number of non-residential development research report fragments. This may be caused by
similarity_thresholdset below 0.75, which fails to effectively filter low-match content, or lack of targeted vector matching configuration for professional fields in research reports.
How to Confirm Successful Configuration
- Access the vector model configuration page, confirm that
embedding_modelis the target model and the API key is configured correctly, and check whether the connection status shows normal. - Upload a sample residential development research report, check whether the parsed segment length falls within the set
chunk_sizerange. - Initiate a retrieval test, verify that the number of recalled entries matches the
recall_top_ksetting, and check whether the number of reranked results matches thererank_top_kvalue. - Observe the index update log, confirm that the update trigger frequency aligns with the configured update strategy, and there are no abnormal interruptions.
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.