What the data for this category looks like
Data sources for optoelectronics marketing content include product specification documents, offline exhibition promotional materials, dealer collaboration assets, and online advertising copy. Updates trigger when new products launch, core parameters update, or marketing campaigns adjust. No fixed schedule exists for updates. Most documents include clearly defined technical parameter fields such as brightness, resolution, and size, with matching units attached. Some materials use mixed text-image formats, embedding parameter descriptions and visual content. These materials balance technical rigor and marketing promotional qualities.
What constraints these characteristics impose on knowledge base retrieval and recall
A high share of materials use mixed text-image formats. Retrieval systems must support structured parsing and associated recall of image and text content. This avoids losing parameter information when only text is retrieved. Core parameters have clear unit fields. Unit-aware retrieval matching rules must be configured. This prevents incorrect recall of similar parameters with different units. Updates follow no fixed schedule, and single-update content varies widely. Systems must support incremental indexing and partial updates. This avoids delays from full index rebuilding. Marketing content mixes short copy and long specification documents. A hierarchical recall strategy must be configured to adapt to retrieval needs of different lengths. This balances precise matching and coverage.
Configuration Recommendations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Most optoelectronic product specification documents contain continuous parameter segments. This range preserves the complete association between parameters and scenario descriptions |
overlapRate | 15%–20% | Prevents technical parameters from being split across segments, ensuring semantic coherence of parameters across segments |
similarityThreshold | 0.72–0.85 | Meets the precision requirements for matching optoelectronic parameters, filtering low-relevance general marketing copy |
recallTopK | Top 10–15 results | Covers different types of marketing materials and specification documents, avoiding missed high-match long documents |
rerankTopN | Top 3–5 results | Focuses on core matching results, adapting to the fast retrieval use case for marketing content |
enableImageOCR | Enabled | Parses parameter text from mixed text-image marketing materials, fully recalling information associated with images |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. Testing on deployment-specific samples is recommended before finalizing values.
Three Common Misconfigurations
- Symptom: When calling the API to embed images into the knowledge base, third-party systems cannot display images normally, and returned image links fail to load. Cause: No public access path for knowledge base images is configured. Only local file paths are generated, and no external link conversion is completed.
- Symptom:
maxContextis set to 1500 characters, but knowledge base chunks exceeding this length are still recalled and referenced. Cause: Segment truncation configuration is not enabled, or the segment threshold is not aligned with the context limit. Long documents are not split into chunks that fit the limit. - Symptom: When creating an optoelectronics knowledge base using the open-source version 4.8.11, the task hangs. Ollama logs show
try reducing the size of the batch. Cause: The total size of batched indexed documents exceeds the loading threshold. ThebatchSizeparameter is not adjusted to adapt to large document sets.
How to Verify Correct Configuration
- Upload a mixed text-image marketing material containing technical parameters, run a retrieval test, and confirm whether parameter text from the image is included in returned results.
- Import a long specification document, check whether it is automatically split into chunks matching the configured settings.
- Adjust the similarity threshold, run a retrieval test, and confirm whether the matching precision of returned results meets business expectations.
- View knowledge base index logs, confirm that incremental updates only target newly added or modified documents, and that no full index rebuilding is triggered.
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.