Model Access and Configuration for In-Client Natural

What the Data for This Category Looks Like

The data sources for this category include structured business fields from the client’s embedded business system, natural language search queries entered by users within the client, and terminal context parameters from the current session. Data updates in real time to match the real-time requirements of client operations. The document structure includes structured business identification fields, unstructured user query text, plus session timestamps and terminal device identification fields. Field units follow these rules: milliseconds for timestamps, characters for query text, and business identification fields have no fixed unit.

What Constraints These Characteristics Impose on Model Access and Configuration

Real-time synchronized data sources require model access to support low-latency calls, avoiding client operation freezes. Mixed structured and unstructured data structures require configuring hybrid retrieval parameters to adapt to processing logic for both data formats. Real-time update rhythm requires the vector model index refresh frequency to match the change speed of client business, ensuring the timeliness of search results. Business fields bound to client context require associating corresponding business parameters when configuring prompt templates, narrowing the search scope and improving result matching accuracy.

How to Set Configurations

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–16000 characters`	In-client sessions typically use single-scenario short contexts, adapting to mainstream large model window limits
`retrievalTopK`	`3–5 entries`	Client screen space is limited, avoiding excessive results occupying operational area
`similarityThreshold`	`0.72–0.85`	Financial scenarios require balancing search accuracy and result coverage
`streamResponse`	`false`	In-client interactions require returning complete results immediately, avoiding interface anomalies caused by streaming segmentation
`embeddingBatchSize`	`2–4 entries`	Client deployment resources are typically limited, reducing memory usage of single vector model calls
`modelTimeout`	`60 seconds`	Matches the waiting tolerance threshold for client operations, avoiding interaction interruptions caused by timeouts

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

Phenomenon: The vector model fails to respond normally after deployment via Docker Compose. Container logs return a connection refused error. Cause: Port mapping for the vector model is not configured in docker-compose.yml, so the retrieval service cannot connect to the model container.
Phenomenon: After importing text, search result matching accuracy is poor, and a large number of irrelevant content is recalled. Cause: The chunkSize parameter is not adjusted, and the default segmentation length leads to semantic cutting that does not match the text structure of the business scenario.
Phenomenon: After triggering a search via in-client voice input, the interface displays a transcription timeout error. Cause: The voice model call timeout period is not adjusted in the model configuration, so the search is triggered before the voice transcription process is completed.

How to Confirm the Configuration Is Complete

Access the function entry within the client, enter a natural language query that matches the business fields, and verify that the number of returned results matches the configured retrievalTopK value.
View the model service monitoring dashboard, confirm that the call latency of the vector model and large model meets the preset modelTimeout configuration.
Upload test business text, and verify through the retrieval interface that the returned result similarity falls within the set similarityThreshold range.
Switch to voice input mode, trigger the retrieval process, and confirm that no audioTranscribeFailed type errors are returned.

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.