What the Data for This Category Looks Like
Historical query record data originates from natural language interaction logs generated by end users within the terminal. Updates are generated in real time. Each record maps to a single user query action. Core fields for each record include: original query text, query trigger timestamp, user session identifier, terminal device identifier, and associated retrieval result set ID. The length of a single record varies with the length of the user’s query text. It typically falls between 10 and 500 characters. There is no fixed batch aggregation structure. Each independent interaction corresponds to one complete record.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
The real-time update requirement means the index must support incremental writing and updates. This eliminates performance overhead from full index rebuilding. The wide fluctuation in single-record length requires the vector model to deliver stable semantic encoding for both short and medium-length text. It also requires the index to avoid forced truncation of original query text. The multi-field structure requires the index to support precise filtering by fields such as session ID and user identifier. This prevents invalid recall across users or sessions. Historical query records have strong timeliness needs. The index’s recall sorting logic must include timestamp weighting. This prioritizes recently generated interaction records.
How to Set the Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Select M3E or Qwen3-Embedding-8B. For local deployment, fill in the container access address | Historical query records are mostly short to medium-length natural language queries. These models deliver stable short-text encoding effects, and support local Docker deployment for private scenarios |
index_chunk_size | Do not truncate the original text | Historical query records do not require chunking. Use a single complete record as the index unit directly. This avoids damaging query semantics |
recall_filter_fields | ["session_id", "user_id"] | Filter recall results by session or user identifier. This prevents invalid retrieval across users or sessions |
index_refresh_interval | Real-time | Historical query records must be synchronized to the index in real time. This ensures the timeliness and accuracy of retrieval |
similarity_threshold | 0.75–0.85 | Filter irrelevant historical queries with low similarity. This prevents recalling records unrelated to the current query |
max_recall_count | Top 10 | Terminal display space is limited. Reasonably control the number of recalled entries to fit interface display requirements |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When configuring a locally Docker-deployed
M3Emodel, the interface displays a model loading failure prompt. Cause: The local model access address was not configured correctly, or the container port was not opened for FastGPT to call. - Symptom: After adding an embedding model with the same name, existing configurations are overwritten. Cause: FastGPT’s model management interface uses the model name as the unique identifier by default, and no version differentiation field was configured.
- Symptom: The retrieval result format of the public network deployment version differs from that of the local deployment version. Cause: The
response_formatparameter was not configured correctly, or the default rendering rules of the public network version differ from those of the local deployment.
How to Confirm the Configuration Is Complete
- Initiate a natural language query within the terminal. Check whether the system generates a corresponding historical record entry. Confirm that the index was successfully written.
- Enter the model management interface. Verify that the configuration path for
embedding_modelmatches the local Docker-deployed model address, and no duplicate model overwrite prompts appear. - Switch to different user sessions. Verify that
recall_filter_fieldscorrectly filters historical records from non-current sessions. - Adjust the value of
similarity_threshold. Observe changes in the number of recalled results. Confirm that the parameter configuration takes effect.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.