What Data Looks Like for This Category
Data sources include internal government and enterprise compliance document libraries and public policy platforms of industry regulatory authorities. Update frequency adjusts dynamically with official policy releases. Core regulatory documents are synchronized in bulk every quarter. New special policies are added immediately. Document structure includes fields such as unified document number, issuing authority, release date, main body content, attachment links. Individual document lengths range from hundreds to tens of thousands of characters. Some attachments are in PDF or scanned format. OCR recognition is required to convert these attachments into searchable text.
Constraints Imposed on Vector Models and Indexing
First, data sources include internal compliance documents and public policy platforms. The indexing system must support permission-level data isolation. Only authorized users may access internal document searches. Second, the update rhythm combines bulk synchronization and immediate entry. Vector indexes must support incremental update modes. This avoids resource consumption from full index rebuilding. Third, individual document lengths vary widely, from hundreds to tens of thousands of characters. Chunking logic must adapt to different text lengths. This avoids splitting policy clause associations too short, or exceeding maxToken limits due to excessive length. Fourth, documents include structured metadata such as document numbers and issuing authorities. Indexes must support precise filtering based on metadata. This narrows search scope to improve the accuracy of policy searches.
Configuration Guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of government and enterprise policy documents. Avoids splitting policy clause associations too short, or exceeding vector model input limits due to excessive length |
| Overlap Length | 100–200 characters | Preserves semantic continuity between adjacent chunks. Avoids policy clauses being truncated at chunk boundaries leading to missed search results |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance policy text. Balances search recall rate and the rigor of policy content. Adjust based on actual measurements |
maxToken | 4096 token | Matches the context window limits of mainstream open-source vector models. Adapts to semantic input after policy document chunking |
defaultToken | 2048 token | Configures the default retrieval context length. Balances retrieval accuracy and system resource usage |
| Single Search Limit | Top 10 results | Controls the number of policy documents returned per query. Avoids system overload. May be adjusted based on business scenarios |
Three Common Configuration Mistakes
- Phenomenon: 404 error returned when configuring baidu-embedding-v1. Cause: Incorrect matching of the embedding model's interface path and parameter format. Requests fail to be recognized by the target model service.
- Phenomenon: Unable to add the m3e vector model in OneAPI after deployment without a GPU environment. Cause: The CPU running mode of the model was not enabled, or container-mounted resources were not allocated sufficient CPU quota.
- Phenomenon: Excessively long index update delay after full synchronization of policy documents. Cause: Incremental indexing mode was not configured. Each synchronization performs full vector rebuilding, leading to excessive resource usage.
How to Confirm Configuration Is Valid
- Check network connectivity between the FastGPT environment and the vector model service. Confirm that the model service address can be accessed normally.
- Upload a test policy document. After performing a search, verify the matching degree of returned results. Adjust the similarity threshold to a range that meets business requirements.
- View the update log in the index management interface. Confirm that incremental synchronization tasks can be triggered and completed normally.
- Verify the metadata filtering function. Attempt to filter search results by issuing authority or release date. Confirm that the configuration takes effect.
The values provided are common starting points and should be assessed against in-house sample datasets.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.