What this category of data looks like
Data for this category is primarily sourced from search ad backends, search term reports, bidding ranking data, and landing page data. Most update frequencies are daily or weekly. Daily produced documents include standardized keyword lists, negative keyword tables, bid records, and search term reports. Individual data entries typically contain fields such as keyword text, matching rules, bid values, search volume, conversion-related metrics, and more. There is no fixed, unified long-text structure; most content is in structured tables or list formats.
What constraints do these characteristics impose on the vector model and indexing workflow
This category features structured short text, high update frequency, and scattered data sources. These traits create multiple constraints for the vector model and indexing workflow. For short text scenarios, select a vector model optimized for short-text semantic understanding to avoid redundant computation from long-text optimization. High-frequency updates require indexes to support incremental synchronization or regular full updates, preventing data lag from reducing optimization efficiency. Structured data from multiple sources needs table parsing and field extraction support, to ensure only valid business fields are retained during vector generation and reduce interference from irrelevant information. Additionally, recall results must be strongly linked to ad delivery rules, preventing irrelevant keywords from appearing in retrieval results.
How to Set Configurations
Configuration references for FastGPT v4.17.0 are listed below:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single business data entries for this category, avoids splitting that breaks the semantic integrity of keywords |
| Overlap Length | 50–100 characters | Retains semantic connections between adjacent chunks, prevents keywords from being split so they cannot match retrieval requests |
| Similarity Threshold | 0.75–0.85 | Balances retrieval recall rate and precision, meets the strong matching business requirements of search ad keywords |
| Single Search Limit | Top 20 entries | Controls the number of returned results, aligns with the typical work scope of optimization specialists |
maxToken | 4096 | Adapts to the input length limit of mainstream embedding models, avoids truncation of valid business field content |
defaultToken | Empty string | Fills fields with no valid content, ensures format consistency during vector generation |
Three Common Misconfigurations
- Symptom: After configuring
text-embedding-ada-002and adding it to the configuration file, "no valid" type error messages appear when using the knowledge base. Reason: Failed to correctly associate vector database connection parameters, or failed to select the correct embedding model during knowledge base binding, causing the vector generation process to interrupt. - Symptom: After indexing hundreds of thousands of ad data entries, retrieval speed slows significantly. Reason: Did not set reasonable chunk length and single search limit, leading to an overly large vector database scan range, or did not enable incremental indexing mode, resulting in full data scans for every retrieval.
- Symptom: A large number of irrelevant non-keyword content appears in retrieval results. Reason: Did not filter non-business fields in documents, directly generated vectors from long-text descriptions of landing pages, leading to semantic matching deviating from the retrieval requirements of ad keywords.
How to Verify Successful Configuration
- Check vector database connection logs to confirm there are no connection timeout or permission errors, and verify that the embedding model call link is working properly.
- A single keyword list data entry is used to generate a vector. The output of the embedding model is compared to confirm that field extraction and semantic encoding align with expectations.
- A simulated retrieval request is sent, the number of returned results and similarity distribution are reviewed, and the values of relevant configuration items are adjusted.
- The incremental update function is verified: After adding a new negative keyword table entry, confirm that the retrieval results are updated synchronously.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.