What Data Looks Like for This Category
This category of data mainly comes from the national government procurement website, local government procurement websites, the fiscal integration system, the purchaser budget system, and supplier credit records, with a daily update frequency. Daily processed documents include procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, question responses, and more. Most document structures include fields such as project number, procurement content, qualification requirements, scoring criteria, contract clauses, etc. Some documents involve special content such as supplier credit ratings and regulatory penalty records.
Constraints Imposed by These Characteristics on the Vector Models and Indexes Link
The daily updated data source requires the index to support incremental synchronization, avoiding resource consumption caused by full reconstruction. Mixed indexing of multiple types of documents needs to adapt to the field structures of different documents, ensuring that key information such as scoring rules in procurement requirements and winning bid information in winning bid announcements can be accurately vectorized. Mixed storage of short texts such as supplier credit records and long texts such as bidding documents requires the index to support unified vectorization processing of texts of different lengths, while also being compatible with proprietary terms under the government procurement law system to avoid semantic deviation. The data involves regulatory-related content, so the traceability of the index must meet the basic requirements of regulatory verification.
How to Set Configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the core content splitting of long documents such as bidding documents and procurement contracts, avoiding semantic fragmentation of single chunk content |
| Overlap Length | 100–150 characters | Retain semantic association between adjacent chunks, ensuring that key clauses will not be split across two chunks |
| Similarity Threshold | 0.72–0.85 | Adapt to the semantic matching accuracy of proprietary terms for government procurement, filtering low-correlation search results |
| Single Search Limit | Top 10 entries | Balance retrieval efficiency and result coverage, meeting the common clause query needs in inquiry and response |
defaultToken | 8192 | Adapt to the context length limit of most embedding models, ensuring that retrieval results can be fully included in the conversation context |
| Re-ranking | Enabled | Optimize result ranking after long document retrieval, improving the recall accuracy of key information in inquiry and response |
Three Common Mistakes
- Phenomenon: After configuring the Alibaba Qwen text-embedding-v3 model and entering the API key, the interface prompts that no available channel is available. Cause: The access permission for the corresponding embedding model is not enabled in the group configuration, or the default group configuration is not associated with the configured API channel.
- Phenomenon: Response time becomes excessively long after enabling re-ranking during knowledge base question answering. Cause: The knowledge base chunk length is set too large, resulting in too many chunks returned by a single round of retrieval, increasing the computational load on the re-ranking step.
- Phenomenon: Non-target documents unrelated to government procurement rules appear in retrieval results. Cause: The similarity threshold is not adjusted based on government procurement proprietary terms, resulting in accidental recall of low-correlation general documents.
How to Confirm Proper Configuration
- Enter the embedding model configuration page of FastGPT v4.17.0, verify that the added target model and API key status are normal, with no error prompts.
- Upload a typical government procurement bidding document, trigger the chunking test, and check whether the chunking results conform to the preset length and overlap rules.
- Initiate an inquiry about the scoring method in the procurement requirements, and check whether the retrieval results include relevant chunks of the corresponding document.
- After enabling the re-ranking function, compare the response time and result ranking rationality before and after enabling, and adjust the configuration to meet business requirements.
The values provided here are common starting points and should be measured against samples from the actual 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 2026-09-28.