What Data for This Category Looks Like
This category of data primarily comes from customer interview records, project bidding documents, official vendor product materials, and historical pre-sales project plans. Daily produced documents include technical proposal books, customer requirement research forms, quotation lists, and demo scripts. Each individual data entry includes customer scenario descriptions, technical route planning, quotation configuration items, implementation boundary descriptions, and other content. The data update frequency is adjusted based on project progress, usually once per order or per week. The proposal structure for different projects has personalized adjustments, with no unified mandatory fixed format.
Constraints on Vector Models and Indexes From These Data Characteristics
The data for this category has scattered sources, personalized structures, and a high update frequency. This requires vector indexes to support incremental updates to adapt to data iterations at the weekly or single-project level. The length of individual data entries varies widely, ranging from short requirement entries to complete technical route documents. This means chunking rules need flexible adjustment capabilities. At the same time, pre-sales proposals have strict requirements for technical boundaries and configuration accuracy. Index recall must prioritize matching content related to technical terms, interface parameters, and implementation boundaries, to avoid irrelevant information interfering with retrieval results.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical content length of pre-sales technical proposal books, avoids excessive splitting of semantic blocks |
| Overlap Length | 50–100 characters | Retains overlapping content between adjacent chunks, ensures semantic coherence for technical routes and interface descriptions |
defaultToken | 2048 | Adapts to the typical token usage of technical parameters and interface descriptions in pre-sales proposals, ensures embedded content is complete |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance retrieval results, prioritizes recalling technical content matching current pre-sales requirements |
| Single Search Limit | Top 10 entries | Controls the scale of retrieval results, avoids excessive irrelevant information interfering with proposal boundary judgment |
maxToken | 8192 | Adapts to the context window limit of FastGPT v4.17.0, fully carries key technical information of pre-sales proposals |
Three Common Mistakes
- The exported knowledge base dataset.csv only contains the index field and no content field: Original text synchronization storage is not enabled in the index configuration. Only index metadata is generated, and the actual content of the corresponding document is not associated.
- Long-text pre-sales proposals take too long to parse, and processing efficiency for a 100,000-character technical document is low: Document parsing uses CPU by default, GPU acceleration is not enabled, and resources are insufficient for long-text processing.
- Adding an index model for local deployment results in a load failure error: Compatibility requirements between the current FastGPT version and the index model are not met. Incorrect model path configuration or version mismatch prevents normal loading.
How to Confirm the Configuration Is Correct
- Upload a typical pre-sales technical proposal book, check that the chunked text blocks cover the core content of the document, with no obvious semantic truncation.
- Enter test query terms related to the current pre-sales project, retrieve knowledge base content, and confirm that the matching degree between recall results and query requirements meets preset standards.
- Export the index data file of the knowledge base, check that the file contains complete index and original text content.
- View system operation logs, confirm that the index model loads without abnormal errors, and the task execution process is normal.
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.