Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data sources for basic telecommunications operation documents include operation and maintenance work orders, customer service correspondence, network optimization plans, standard specification documents, cooperation contracts, tariff publicity documents, and more. Update frequencies fall into three categories: real-time (operation and maintenance work orders, customer feedback), daily (business reports), and on-demand (contract revisions, publicity updates). Document structures include structured metadata fields and unstructured main text. Some documents contain both telecommunications-specific terminology and general explanatory text, with units including minutes, GB, Mbps, and others.
What constraints do these characteristics impose on the vector models and indexes workflow
Mixed structured metadata and unstructured main text require retaining associated metadata during chunking to avoid semantic fragmentation. Otherwise, precise matching of document context during retrieval will not be possible. Real-time generated operation and maintenance work orders and customer feedback require the index to support incremental updates, to avoid performance loss caused by full reconstruction. Document length varies widely, from customer complaints of hundreds of characters to engineering plans of tens of thousands of characters, requiring a flexible chunking strategy. A large number of telecommunications-specific terms are included, so the vector model must adapt to vertical domain semantics. The index must ensure high recall to cover retrieval needs in professional scenarios.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length span of basic telecommunications documents, covering semantic integrity for short complaints and long engineering plans |
| Overlap Length | 100–150 characters | Avoids context fragmentation after chunking, retains semantic association between adjacent chunks |
maxToken | 8192 token | Matches the input limit of Qwen3-Embedding-8B, ensuring normal encoding of long texts |
| Similarity Threshold | 0.72–0.78 | Differentiates between professional terminology matching and general semantic matching, filters low-correlation results |
| Single Search Limit | Top 10 entries | Ensures sufficient reference materials for document drafting, while avoiding interference from redundant information |
defaultToken | 512 token | Adapts to the vector encoding requirements of structured metadata, balancing accuracy and performance |
Three common misconfigurations
- An error "database “postgres” does not exist" occurs when starting the vector database container. This is caused by using an unofficial Compose template with PostgreSQL dependencies, instead of the vector database configuration officially adapted for FastGPT.
- A large number of irrelevant general document fragments are returned in retrieval results. This is caused by not setting a reasonable similarity threshold and failing to filter low-matching content.
- Vector encoding tasks time out. This is caused by the chunk length exceeding the
maxTokenconfiguration limit, preventing the embedding model from processing long text chunks normally.
How to confirm the configuration is correct
- Check the FastGPT vector database connection logs to confirm there are no database connection errors or permission abnormalities.
- Upload a typical basic telecommunications operation and maintenance work order document, verify that the chunked text fragments retain associated structured metadata.
- Submit a test request for document drafting, check that the retrieval results match the professional scenario of the current task.
- View the model configuration page, confirm that the values of
defaultTokenandmaxTokenare consistent with the parameters of the deployed embedding model.
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 2026-09-28.