Vector Models and Indexing for Residential Development

Data Characteristics for This Category

Residential development investment research data sources include project land acquisition feasibility reports, planning and design documents, construction cost ledgers, public documents from housing and urban-rural development departments, competitor project filing data, and industry research reports. Data updates follow project phases. Feasibility and planning data are updated during the project initiation phase. Cost and sales data are synchronized during construction and pre-sale phases. Policy data is updated immediately upon release of new industry policies. Data falls into two categories: structured and unstructured. Structured fields include project ID, land area, floor area ratio, construction cost, and other fields, with clear attached units. Unstructured content mostly consists of dozens of pages of argumentative PDF or Word documents, primarily long text.

Constraints Imposed by These Characteristics on Vector Models and Indexing

Structured fields have clear attached units. Vector models must support encoding text with units to avoid recall bias caused by unit confusion. Unstructured text is mostly long documents. Segmentation strategies must adapt to per-chunk token limits to avoid truncating key argumentative content. Data updates have two types: phased and sudden. Indexes must support incremental synchronization instead of full reconstruction to reduce resource consumption. Investment research scenarios require filtering recall results by attributes such as project location and type. Indexes must support fast metadata filtering.

Configuration Recommendations

Configuration Item	Recommended Range	Rationale
`chunk_size`	800–1200 characters	Adapts to the length of core information per segment in residential development feasibility reports, aligns with the token limits of mainstream Chinese vector models
`chunk_overlap`	100–150 characters	Prevents key argumentative content from breaking after long text segmentation, ensures contextual coherence
`vector_model`	`bge-large-zh-1.5`	Delivers accurate encoding for Chinese engineering terminology and policy documents, matches the text characteristics of industry investment research scenarios
`index_refresh_interval`	300 seconds	Balances the real-time performance of project phase updates and system resource usage, adapts to the regular update rhythm of investment research data
`filterable_metadata_fields`	`project_id, approval_date, project_type`	Covers the high-frequency need to filter recall results by project attributes in investment research scenarios
`recall_top_k`	10–15 results	Meets the requirement of referencing 3–5 similar cases for single-project investment research, the redundancy aligns with context window limits

The parameter values provided on this page are all common recommendations used to establish a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.

Three Common Configuration Mistakes

Symptom: After uploading multi-field structured data, only one set of vectors is generated, and recall results cannot match field dimensions. Cause: Multi-field vector generation configuration is not enabled. Only full-text vectors are generated, and structured fields are not encoded separately.
Symptom: A single large PDF file remains in the indexing state with no progress updates. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter is not set, or its value exceeds the system resource threshold, resulting in parsing timeout without triggering retries. This issue can be fixed by adjusting the parameter threshold in FastGPT v4.8.7.
Symptom: Vectors generated by a local model cannot be recalled and matched on a remote platform. Cause: Embedding dimension consistency is not verified, or vector normalization methods are not unified, resulting in vector space drift.

How to Confirm Proper Configuration

Upload a single piece of structured project data, check the vector generation log to confirm that the number of generated vectors matches the number of configured encoding fields.
Upload a feasibility report with more than 10,000 characters, check the number of segmented text blocks to confirm they fall within the chunk_size configuration range.
Modify the cost data of a project to trigger incremental indexing, wait for the index_refresh_interval to elapse, then query recall results to confirm the updated data is included.
Call the vector query interface, pass fields from filterable_metadata_fields as filter conditions, confirm that recall results only match documents with the specified attributes.

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.