Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Party building learning data in the pharmaceutical circulation sector mainly comes from activity minutes from internal enterprise party building platforms, policy documents issued by superior party committees, learning materials from offline theme party day events, and question bank documents used for internal assessments. Data updates follow this rhythm: monthly new activity materials, quarterly updates to industry-related party affairs policy documents, and annual updates to standardized learning courseware. Each data entry includes four fixed fields: title, main body content, issuing department, and issuing date. The main body is stored as continuous paragraphs with no preset segmentation.
What constraints these characteristics impose on the vector models and indexing link
The mixed general party affairs text and professional pharmaceutical circulation policy text requires the vector model to adapt to both types of semantic scenarios, and avoid vector drift of professional terms. The monthly incremental update rhythm requires the indexing system to support periodic incremental construction, reducing resource consumption from full reconstruction. The main body structure with no fixed segmentation requires automatic chunking rules to adapt to texts of different lengths, avoiding truncation of key information. The issuing department and date fields can be used as metadata filtering conditions, requiring the index to support precise recall based on metadata.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of mixed party affairs and pharmaceutical policy texts, avoiding truncation of key professional terms |
| Overlap Length | 100–150 characters | Compensates for context breaks caused by chunk truncation, ensuring semantic coherence for long policy documents |
| Single Search Limit | Top 8 entries | Matches the precise recall requirements of party building Q&A, avoiding redundant results interfering with answer generation |
defaultToken | 4096 | Adapts to the context length requirements of most party building Q&A, ensuring complete loading of original policy documents and Q&A content |
maxToken | 8192 | Handles complex Q&A scenarios containing long policy documents, meeting the input length requirements of large models |
| Index Update Frequency | Daily incremental update | Matches the rhythm of monthly new data, balancing index freshness and construction costs |
Three common mistakes
- Phenomenon: The number of index entries in the background dataset automatically increases with multiple duplicate entries over time. Cause: Deduplication verification for incremental indexing is not enabled, and each update task repeatedly imports existing data and generates new indexes.
- Phenomenon: When calling text-embedding-3-large, the process shows no progress updates for a long time, and restarting the service after commenting out the model configuration does not restore normal operation. Cause: No reasonable timeout limit is set for vector model API requests, and residual blocked processes are not cleaned up, causing tasks to get stuck.
- Phenomenon: After configuring the indexing model, the preset large language model cannot be called normally. Cause: The association relationship between the indexing model and the large language model is not correctly configured, causing the system to overwrite the large language model's call parameters when loading the indexing model.
How to confirm the configuration is correct
- Upload a single test document containing pharmaceutical circulation policies and party affairs content, check whether the chunk results fall within the preset length range with no obvious truncation.
- Initiate a party building Q&A that includes professional terms, verify whether the number of recall results matches the single search limit configuration.
- Trigger an incremental index update task, check whether there are deduplication prompts for duplicate data in the background logs, with no abnormal errors.
- After calling the indexing model to generate vectors, check whether the metadata stored in the vector storage includes fields such as issuing department and issuing date, and can be used for filtering normally.
The values provided here 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.