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76 lines
3.0 KiB
Plaintext
76 lines
3.0 KiB
Plaintext
---
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title: Knowledge Base Usage
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description: Common Knowledge Base usage questions
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---
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## Uploaded file content shows garbled characters
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Re-save the file with UTF-8 encoding.
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## What's the difference between the File Processing Model and the Index Model in Knowledge Base settings?
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* **File Processing Model**: Used for **Enhanced Processing** and **Q&A Splitting** during data ingestion. Enhanced Processing generates related questions and summaries; Q&A Splitting generates question-answer pairs.
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* **Index Model**: Used for vectorization — it processes and organizes text data into a structure optimized for fast retrieval.
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## Does the Knowledge Base support Excel files?
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Yes. You can upload xlsx and other spreadsheet formats, not just CSV.
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## How are Knowledge Base tokens calculated?
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All token counts use the GPT-3.5 tokenizer as the standard.
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## I accidentally deleted the rerank model. How do I add it back?
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Add the rerank model configuration in your `config.json` file, then you'll be able to select it again.
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## If I created apps and Knowledge Bases on the cloud platform, will my data be deleted if I don't renew right away?
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On the free plan, Knowledge Base data is cleared after 30 days of inactivity (no login). Apps are not affected. Paid plans automatically downgrade to the free plan upon expiration.
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## The AI stops responding mid-answer when there are too many relevant Knowledge Base results.
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FastGPT calculates the maximum response length as:
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Max Response = min(Configured Max Response, Max Context Window - History)
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For example, with an 18K context model, input + output share the same window. As output grows, available input shrinks.
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To fix this:
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1. Check your configured max response (response limit) setting.
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2. Reduce input to free up space for output — specifically, reduce the number of chat history turns included in the workflow.
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Where to find the max response setting:
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For self-hosted deployments, you can reserve headroom when configuring model context limits. For example, set a 128K model to 120K — the remaining space will be allocated to output.
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## I'm hitting context limit errors before reaching the configured number of chat history turns.
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FastGPT calculates the maximum response length as:
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Max Response = min(Configured Max Response, Max Context Window - History)
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For example, with an 18K context model, input + output share the same window. As output grows, available input shrinks.
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To fix this:
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1. Check your configured max response (response limit) setting.
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2. Reduce input to free up space for output — specifically, reduce the number of chat history turns included in the workflow.
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Where to find the max response setting:
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For self-hosted deployments, you can reserve headroom when configuring model context limits. For example, set a 128K model to 120K — the remaining space will be allocated to output.
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