Vector Models and Indexing for Property Management

What the Data for This Category Looks Like

Data for property management due diligence reports comes primarily from property project basic archives, property fee collection ledgers, public area operation and maintenance logs, owner request work orders, and fire protection and security inspection records. There are two update cadences: basic archive information is updated quarterly or annually. Real-time data such as operation and maintenance logs and work orders is updated daily. Most documents are Excel tables, supplemented by PDF annual operation and maintenance summaries. Fields include project number, property fee unit price (yuan/square meter·month), repair response duration (hours), and owner satisfaction score. Some fields have clear units of measurement.

Constraints on Vector Models and Indexing

Massive row-level tabular data creates a large number of chunks per file. Chunk granularity must be precisely controlled to avoid redundant splitting or overly long single chunks. Numeric fields with clear units require vector models to support multimodal feature encoding. This prevents vector matching deviations caused by unit ambiguity. Real-time updated operation and maintenance work order data requires indexes that support incremental updates. This avoids resource consumption from full reindexing. Long documents such as annual operation and maintenance reports may have content exceeding 1024 tokens per page. Chunks must be split while retaining field association relationships, to ensure the accuracy of vector recall.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Adapts to the 1024-token input limit of most vector models, covers the field length of property management data, and avoids over-splitting or overly long single chunks
`chunk_overlap`	`100–150 characters`	Retains field association between adjacent chunks, such as contextual connection between the chunk containing property fee unit price and the chunk containing project name, and avoids losing key information during recall
`vector_model_max_tokens`	`1024`	Matches the input upper limit of mainstream open-source vector models, and adapts to the single-chunk data length requirements of property management due diligence reports
`recall_top_k`	`Top 8–12 entries`	Covers the multi-dimensional characteristics of property management data, and ensures that recall results include valid information from categories such as fee payment, operation and maintenance, and owner feedback
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Supports single upload of annual operation and maintenance ledger summary files, and avoids chunk transmission exceptions after splitting
`similarity_threshold`	`0.72–0.78`	Filters low-relevance chunks, avoids introducing non-target operation and maintenance records or fee payment data, and adapts to scenarios with multi-field mixed matching

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 the configuration.

Three Common Mistakes

Phenomenon: When uploading Excel files larger than 10 MB, the interface displays vectorization failed errors after generating 1000+ chunks, and normalcy returns after retrying. Cause: Chunk length exceeds the 1024-token limit set by vector_model_max_tokens. Some long text chunks cannot be processed by the model. Retrying allows chunks to be re-split, restoring normal functionality.
Phenomenon: The number of recall results during RAG classification matching does not match the set recall_top_k, with either too few or too many results. Cause: similarity_threshold is not adjusted based on the multi-field characteristics of property management data. An overly high threshold filters valid chunks, while an overly low threshold introduces irrelevant data.
Phenomenon: After locally deploying a model on an Ubuntu system, knowledge base uploads have no response, and the interface displays a connection timeout error. Cause: The local model service port is not mapped to the container's public port, so FastGPT cannot access the local vector model service.

How to Confirm Configuration is Correct

Upload a test Excel file with 1000 rows, check the matching between the number of generated chunks and the set chunk_size, and confirm that the chunk granularity meets expectations.
Manually split a long text chunk, test the vectorization process, and confirm that no vectorization failed errors occur.
Initiate a classification matching request, verify that the number of recalled chunks matches the set recall_top_k, and that the results include property management data fields from different dimensions.
Upload a test file larger than 10 MB, confirm that the number of generated chunks is reasonable and no abnormal errors occur.

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.