What the Data for This Category Looks Like
Marketing content data for residential development comes from official project brochures, unit parameter specifications, government planning announcement documents, agency marketing materials, and model home presentation scripts. Update frequency fluctuates with project milestones: weekly during the preparation phase, every 3 to 5 days before launch, and monthly or per event after launch. Document types include long-form manuals with tens of thousands of characters per piece, structured unit parameter tables, and supporting facility lists. Fields include project name, land area, gross floor area, unit interior area, supporting facility type, marketing event time, with units such as square meters and maximum event participants.
Constraints for Vector Models and Indexing
The high proportion of long text requires vector models to support long-context encoding, and indexes to support segmented storage while retaining contextual association between adjacent segments. The large number of structured parameters requires indexes to support hybrid retrieval: combining text vector matching and structured field filtering. The volatile update frequency requires indexes to support incremental updates, avoiding resource consumption from full index reconstruction. The strong timeliness of marketing content requires retrieval to allow filtering results by release time, ensuring the timeliness of recalled content.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
max_chunk_size | 800–1200 characters | Ensures semantic completeness of single segments for residential project brochures, avoids truncating critical information |
chunk_overlap_ratio | 10%–15% | Connects contextual association between adjacent segments, ensures semantic coherence for long document retrieval |
vector_model_provider | Determined via actual testing | Adapts to long text encoding requirements, matches the character length of project documents |
retrieve_top_k | 10–15 results | Controls the number of recalled results, balances retrieval efficiency and matching accuracy |
filter_fields | publish_time, building_area | Supports filtering retrieval results by release time and gross floor area parameters, aligns with timeliness and precise matching needs of marketing content |
index_update_mode | Incremental update | Adapts to project node-based update cycles, reduces resource consumption from full index reconstruction |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Mistakes
- When importing a self-built vector database, a connection timeout error appears. The cause is that the IP of the FastGPT deployment node was not added to the database access whitelist, preventing a successful data connection.
- After configuring a vector model, API calls return a 403 error. The cause is that correct API keys and region parameters were not filled in the model channel configuration, leading to permission verification failure.
- After enabling the index model, retrieval returns no results. The cause is that the option to enable vector indexes was not checked on the model provider page, and the corresponding vector model channel was not bound, so retrieval cannot access index data.
How to Confirm Configuration is Complete
- Upload a long residential project document, verify that the number of parsed segments falls within the range set by
max_chunk_size. - Submit a retrieval request, confirm that the number of returned results matches the value set by
retrieve_top_k. - Check that retrieval results include content filtered by the fields configured in
filter_fields, such as results filtered by gross floor area. - Submit a single document update, check that the index update log only displays newly added or modified segments, and does not show records of full index reconstruction.
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.