What the Data for This Category Looks Like
Data sources for brand agency operation intelligent due diligence reports include cooperation qualification documents provided by brand parties, campaign data reports generated during agency operations, social media platform account operation data, and industry regulatory filing information.
Update frequency aligns with the agency operation service cycle. Regular operation data is updated weekly. Qualification documents are updated synchronously when cooperation changes occur.
Document structure is split into structured fields and unstructured attachments. Structured fields include agency operation subject filing information, cooperation cycle, and monthly core operation indicators. Unstructured attachments include monthly review reports, public opinion monitoring summaries, and campaign effect analysis documents.
For field units, operation cycle is measured in months, transaction volume is measured in ten thousand yuan, public opinion mention count is measured in pieces, and qualification document numbers use unified social credit codes or brand filing numbers as identifiers.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Mixed structured and unstructured data requires indexes to support exact matching of structured fields and semantic recall of unstructured text. Configure index rules adapted for multiple content types.
Frequent updates require indexes to support incremental refresh. This avoids resource consumption from full index reconstruction.
Wide variation in document length requires chunking parameters to adapt to both short operation indicator descriptions (hundreds of characters) and long review reports (tens of thousands of characters).
Unit attributes of specific fields require field normalization before vector modeling. This prevents unit differences from impacting similarity calculations of semantic vectors.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to mixed short field descriptions and long review documents. Avoids semantic fragmentation from too-small values, and context redundancy from too-large values |
embedding_model | text-embedding-ada-002 or locally deployed m3e-base | Adapts to mixed Chinese and English content in brand agency operation due diligence reports. Local deployment meets data privacy requirements |
index_refresh_interval | 1 hour | Matches synchronization needs for weekly updated operation data and dynamically updated qualification documents. Balances resource consumption and real-time performance |
top_k | 5–8 entries | Due diligence reports need to cover multi-dimensional operation data and public opinion information. Avoids incomplete information from too few recalled results, and context overload from too many |
similarity_threshold | 0.72–0.8 | Distinguishes valid semantic matches from noise matches. Adapts to scenarios with many professional terms in brand operation data |
structured_field_embedding | Enabled | Structured fields such as brand filing numbers and operation cycles can improve exact matching efficiency through vector modeling. This supplements gaps in unstructured recall |
The parameter values provided on this page are all conventional recommendations used as starting points 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 Mistakes
- Scenario: The knowledge base page remains stuck in the "Indexing" state when using a locally deployed m3e model. Cause: The model’s API access port is not configured, or port permissions are not open. This leads to vector generation requests timing out without a response.
- Scenario: After configuring
text-embedding-ada-002, the system displays "No matching vector data" when calling the knowledge base. Cause: Uploaded due diligence report documents are not properly split into paragraphs, or chunking parameters exceed the maximum token length supported by the model. This prevents valid vectors from being generated. - Scenario: After creating a new data index, the recall results include a large number of irrelevant public opinion data. Cause: The
similarity_thresholdparameter is not set, or the threshold is set too low. This allows low-similarity noise content to be included in recall results.
How to Verify Proper Configuration
- Access the model management page. Confirm that the target vector model has been added and shows a normal running status.
- Upload a sample brand agency operation due diligence report document. Trigger manual indexing. Check if the indexing progress log displays "Vector generation completed".
- Run a knowledge base recall test. Verify that the recalled results include core due diligence fields such as operation cycle and monthly GMV.
- Modify the
similarity_thresholdparameter. Run another recall test. Observe whether the number of recalled results changes as expected.
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.