When a user initiates a query — whether through text input or voice interaction — DAKSH applies a rigorous preprocessing pipeline to ensure that the request is accurately interpreted, semantically aligned, and contextually enriched before it proceeds to retrieval and generation stages. This preprocessing stage is a critical part of the DAKSH inference flow, transforming raw linguistic input into a machine-understandable representation that seamlessly interfaces with the vector memory and generation layers.
The first component of this process is tokenization and language detection. Using the proprietary multilingual tokenizer, the input is segmented into subword tokens while preserving sentence structure and semantic markers. Concurrently, the system performs language identification using phonetic or lexical cues, ensuring that the appropriate language model configuration — including embedding space alignment, prompt language, and reranker — is activated. This is particularly important in code-mixed queries or dialect-heavy speech, where language boundaries are fluid.
Once tokenization is complete, the query undergoes vector embedding generation. DAKSH uses the same embedding encoder applied during knowledgebase ingestion to transform the query into a high-dimensional vector located within the same semantic space as the stored document chunks. This ensures compatibility and accurate similarity scoring when the query is compared against the knowledge memory. The embedding captures not only the literal meaning of the words but also the inferred intent, tone, and structural hints — enabling precise semantic matching.
In situations where the input is brief, ambiguous, or overly generic — such as queries like “fees” or “complaint” — the system triggers an optional query expansion and rerouting mechanism. This module maps the input against a domain-specific synonym graph or keyword ontology to identify potential expansions. For instance, the word “fees” could be contextually expanded to include “application fee,” “late penalty,” “renewal charges,” or “challan amount” based on the active knowledge domain. This process enhances retrieval accuracy by broadening the semantic match space while maintaining contextual precision.
Through this multilayered preprocessing framework, DAKSH ensures that every user query — no matter how brief or complex — is interpreted with clarity, context sensitivity, and multilingual awareness, forming a robust foundation for downstream knowledge retrieval and generation.