What the Data for This Category Looks Like
Property management research report data for financial investment research scenarios originates from official industry regulatory authorities’ property operation guidelines, public property project operation archives, and third-party industry research institutions’ research results. Public research reports are updated quarterly. Project operation data is updated synchronously with monthly operation reports. Each individual document contains structured tables and unstructured text. Structured fields cover project basic attributes, operation data, and compliance clauses. The unstructured section includes case analyses and policy interpretations. Fields include project format, service period, single-project service area, monthly operation and maintenance cost, with corresponding units being none, month, square meter, and yuan respectively.
What Constraints Do These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
Property management research report data for financial investment research has scattered sources and different update cycles. Retrieval systems must support permission isolation and incremental update configuration for multi-source data. This avoids excessive computing resource usage from full refreshes. Documents contain both structured and unstructured content. Systems must support both semantic recall and structured field filtering. This ensures financial investment research personnel can precisely narrow retrieval scope using fields such as project format and service period. The length of individual documents varies widely. A reasonable chunking threshold must be configured. This avoids truncating critical information in overly long text or introducing redundant context. High-frequency retrieval fields such as compliance clauses and operation data have distinct attribute differences. Retrieval weights must be adjusted specifically to improve accurate matching accuracy.
How to Set Configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
chunk_size | 800–1200 characters | Property management research reports contain structured tables and long text. This range balances segmentation integrity and context coherence, avoiding excessive single segment length that impacts recall precision |
recall_top_k | Top 8–12 results | Valid relevant information for property research reports mostly falls within the top 10 results. This value covers core retrieval results while controlling total context length |
similarity_threshold | 0.72–0.80 | Property data contains many professional terms. This threshold filters low-match irrelevant content while retaining valid recall results for targeted scenarios |
incremental_update_interval | 1 hour | Project operation data is updated monthly. Public research reports are updated quarterly. This interval balances real-time performance and computing resource usage |
structured_field_weight | Weighted by field type | Matching priority for structured fields such as project format and service period is higher than plain text. Weighted configuration improves accurate retrieval efficiency |
max_context_token | 12000–15000 | The length of individual research reports varies widely. This value accommodates multiple segments of valid recall content while avoiding exceeding the large model's context limit |
The parameter values provided on this page are conventional recommendations used to establish 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 samples relevant to the target deployment before finalizing settings.
Three Common Mistakes
- Phenomenon: After a document containing Markdown format images is uploaded, embedded explanatory text of the images cannot be correctly associated during retrieval. Cause: The image text extraction configuration in the document parsing link is not enabled, resulting in image-related text not being included in the retrieval corpus.
- Phenomenon: Semantic retrieval returns multiple matching results, but the final AI reply prompts that no relevant answers are found. Cause: The total length of the recalled context exceeds the upper limit supported by the large model, and valid matching content is truncated, making it impossible for the model to read complete information.
- Phenomenon: The content of the AI-generated reply deviates from the document content matched in the knowledge base. Cause: Exclusive retrieval weights are not configured for the structured fields of property data, or the similarity threshold is set unreasonably, introducing low-match irrelevant documents as context.
How to Verify Configuration is Correct
- A test document containing structured tables and Markdown images may be uploaded. Parsed text is checked for included image embedded descriptions and table fields to confirm the image text extraction configuration is effective.
- Operation data of a specified project is retrieved. The number of returned recall results is verified to match the
recall_top_kconfiguration value, confirming the recall count setting is correct. - The similarity threshold is adjusted. The same keyword is retrieved, and matching degree changes of the returned results are compared, confirming the threshold configuration can effectively filter low-match content.
- An incremental update task is triggered. The system is checked to only synchronize and update newly added or modified documents, confirming the incremental update configuration is effective.
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.