What the data for this category looks like
Data sources for pre-sales technical solutions include customer interview records, bidding documents, vendor product materials, and historical project plans. Daily produced documents include technical proposal books, customer demand research forms, quotation lists, and demo scripts. Document structures typically cover modules such as demand matching, technical roadmap, implementation boundaries, and quotation details. Data update frequency is adjusted based on individual project progress, usually once per project or weekly. The content length and number of modules per document vary with project complexity, with no fixed format standard.
What constraints these characteristics impose on the "context and token" workflow
Dispersed data sources and inconsistent formats require adaptation to different document types during chunking. This often leads to fluctuating token consumption. The update frequency of once per project or weekly requires the context window to accommodate the latest solution content and adjusted parameters in a timely manner. The token proportion of different document modules varies greatly. The technical roadmap section usually occupies more tokens. Reasonable allocation of context quotas is needed to cover core content. The length of individual documents fluctuates significantly. Fixed context configurations cannot adapt to all projects. Flexible parameter adjustment must be supported.
How to set configurations
All following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the typical total content length of pre-sales technical solutions, covers complete technical roadmap and quotation detail modules |
quoteMaxToken | 1500–2000 token per quote | Limits the length of a single quoted snippet, prevents individual quotes from exceeding the model's single-segment token limit |
| Maximum single search count | Top 6 results | Matches the typical number of pre-sales solution reference materials, covers major historical project and vendor material content |
| Context overlap length | 200–300 characters | Prevents chunked content from breaking, ensures logical coherence of technical proposals |
| Question optimization switch | Enabled | Optimizes vague demand descriptions in pre-sales solutions, improves retrieval matching accuracy |
Three Common Misconfigurations
- Symptom: When setting chunk length, the character count is mistakenly set as token count, causing chunked snippets to exceed model limits. Cause: Failing to clearly distinguish the units of FastGPT chunk length parameters, not referencing official documentation parameter descriptions.
- Symptom: Partial errors occur when uploading images, with a token limit exceeded prompt. Cause: Image OCR text is converted to tokens and counted towards the context, with no maximum token quota set for image OCR.
- Symptom: Context overflow occurs even after setting a large
maxContextvalue. Cause: Failing to synchronously adjustquoteMaxTokenfor quoted snippets, with multiple long quoted snippets accumulating beyond the total context limit.
How to Confirm Proper Configuration
- Import a typical pre-sales technical solution document, run a chunking test, check if the chunked snippet lengths match the preset units and value ranges.
- Initiate a retrieval based on historical solutions, verify that the number and length of returned quoted snippets match the configured maximum single search count and
quoteMaxToken. - Upload source files in different formats (bidding documents, vendor materials, customer interview records), test the chunking and embedding process for normal operation with no abnormal errors.
- Adjust the
maxContextparameter, verify that the context window can accommodate the complete pre-sales solution content with no automatic truncation prompts.
Note: The configuration values listed above are common starting points. They should be validated against sample data from the target use case.
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.