Context and Tokens for Intent Recognition in Private Domain

Data Characteristics

Intent recognition in biopharmaceutical private domain consultation conversion primarily uses text conversation records. These records originate from user interactions across multiple channels, including WeChat Official Accounts, WeChat Work, and in-app chat. The data is largely unstructured text, containing user questions, descriptions, emotional expressions, and consultant replies. Data updates frequently, almost in real-time with user interactions. The document structure is a time-series dialogue, with each record including a timestamp, sender ID, receiver ID, and message content. Message content may involve disease names, symptom descriptions, drug names, treatment plan preferences, and past medical history. Units are typically natural language descriptions, without fixed numerical units.

Constraints Imposed by These Characteristics on "Context and Tokens"

High-frequency updates of dialogue data require the system to process and integrate the latest user input in real-time. This ensures accurate intent recognition. Unstructured text necessitates robust text understanding capabilities to extract key intentions from complex and colloquial expressions. The time-series dialogue structure dictates that context construction must consider dialogue turns and chronological order to maintain semantic coherence. For example, if a user's consultation intent becomes clear over multiple turns, the model must trace back previous dialogue content for accurate judgment. The lack of standardized fields and units limits the efficiency of keyword or rule-based matching. This requires large language models (LLMs) for deep semantic understanding of natural language, directly impacting token consumption and context window management strategies.

Configuration Guidelines

Configuration Item	Recommended Approach	Rationale
`maxContext`	800–1200 characters	Covers 3–5 core dialogue turns, balancing information volume and token consumption
`recall_top_k`	5–8 entries	Ensures retrieval of sufficient relevant historical dialogue segments, preventing loss of critical information
`similarity_threshold`	Calibrate based on actual measurements (e.g., `0.75`)	Validates against specific business scenarios, balancing recall precision and generalization ability
`segment_length`	200–300 characters	Prevents individual text blocks from being too long, leading to information redundancy, or too short, losing semantic meaning
`token_budget_per_request`	1500–2500 tokens	Reserves sufficient space for user input, historical context, and model output
`max_retries`	3 retries	Addresses temporary failures due to network fluctuations or model overload

Common Mistakes

ACCESS_TOKEN invalid or Invalid API Key errors when calling the large language model indicate incorrect or expired API key configurations.
A significant drop in intent recognition accuracy after multiple dialogue turns, where the model fails to understand the user's current intent, often results from maxContext being set too low. This prevents the model from accessing complete historical dialogue information.
Reached the max retries or response timeouts when orchestrating multiple large language model dialogues in a workflow typically occur due to unoptimized token consumption. This leads to individual requests or concurrent requests within a short period exceeding the rate limits of the large language model provider.

Verification

Validate intent recognition results using test cases. Compare the model's identified intent with manually labeled true intent to check if the match rate meets the expected threshold.
Examine token consumption in logs. Ensure token_budget_per_request is not frequently exceeded for each request. Observe if the average response time is within an acceptable range.
Simulate multi-turn dialogues of varying lengths and complexities. Observe the stability of the model's intent recognition in the early, middle, and late stages of the dialogue. Confirm maxContext effectively covers critical information.

The values provided are common starting points and should be measured against specific samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.