What Data for This Category Looks Like
Core data for landing page conversion optimization typically comes from landing page access logs, heatmaps, form data, and ad source parameters. Update frequency is usually weekly or per single campaign cycle. Daily processed documents usually include landing page plans, tracking requirement documents, AB test reports, and form field lists. Data fields include page paths, dwell time, form submission status, ad channel identifiers, and more. Text length varies widely, ranging from short fields such as form identifiers to long documents like AB test analysis content.
Constraints Imposed on Vector Models and Indexes
This document is based on the official configuration logic of FastGPT v4.17.0. Since data sources are scattered and include multi-format text, vector models and indexes must support different types of data source inputs. Update frequency is usually weekly or per campaign cycle, so high-frequency real-time indexing is not required. Incremental indexes can be built on a periodic basis. The wide range of text lengths requires vector models to adapt to input content of different lengths. This avoids issues such as short text information being broken or long text exceeding context limits. At the same time, core retrieval scenarios focus on page path analysis, form blockage troubleshooting, and experiment result judgment. These scenarios require precise matching of specific fields and text fragments, with clear requirements for recall accuracy.
How to Set Configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide range of text lengths of landing page plans and AB test reports, balancing single-block information integrity and retrieval efficiency |
| Overlap Length | 100–150 characters | Ensures contextual coherence for short text blocks such as page paths and form fields, avoiding key information being broken during chunking |
| Similarity Threshold | 0.75–0.85 | Enables precise matching for scenarios like landing page path analysis and tracking requirement retrieval, reducing the number of irrelevant recalled results |
maxToken | 8000 token | Adapts to the context splicing needs of long documents such as AB test reports, avoiding exceeding the model's context length limit |
defaultToken | Value configured per model channel | Follows FastGPT official model channel configuration requirements |
Three Common Misconfiguration Issues
- Symptom: Service startup verification fails, or a token unauthorized error is prompted when calling the model. Troubleshooting steps: Check that AIPROXYAPITOKEN is an administrator token consistent with the AI Proxy server configuration, do not use application API keys or model vendor keys as substitutes, and ensure the variable is not empty.
- Symptom: Knowledge base search responses are slow, and page path analysis takes an excessively long time. Cause: Chunk length is set beyond the adaptive range, leading to increased time spent generating and retrieving single-block vectors, and not matching the text span characteristics of landing page data.
- Symptom: Retrieval results fail to match the target form field list or tracking requirement document. Cause: The similarity threshold is set too high or too low, not adapting to the precise retrieval needs of landing page conversion optimization scenarios.
How to Confirm Configurations Are Correct
- Check server environment variable configurations, confirm that AIPROXYAPIENDPOINT (root address of the AI Proxy service) and AIPROXYAPITOKEN (AI Proxy administrator token, consistent with the AI Proxy server configuration and not empty) are correctly set and not empty.
- Upload a landing page plan document, perform keyword retrieval testing, and verify that the matching degree between returned results and input keywords meets the preset threshold.
- Simulate batch import of landing page access log data, check the index building progress, and confirm there are no timeout or error messages.
- Call the application API to initiate a retrieval request, confirm that returned results include core business data such as page paths and form fields.
The values provided are common starting points and should be evaluated 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.