To optimize semantic search for customer support knowledge bases, focus on three key areas: improving content representation, refining search relevance, and enhancing query understanding. Start by structuring your knowledge base content to align with how users ask questions. Convert articles and FAQs into vector embeddings using models like Sentence-BERT or OpenAI’s text-embedding-3-small. These embeddings capture the semantic meaning of text, allowing you to match user queries to relevant content even if the wording differs. For example, a user searching for “can’t reset password” should match an article titled “Troubleshooting Password Recovery Issues” because the embeddings recognize the contextual similarity.
Next, implement a hybrid search approach that combines semantic and keyword-based techniques. Use tools like Elasticsearch or OpenSearch to index both the embeddings and traditional keyword data (e.g., synonyms, common misspellings). For instance, if a user types “login broken,” semantic search might prioritize articles about authentication errors, while keyword matching ensures results include exact phrases like “login page not loading.” Adding filters based on metadata (e.g., product version, date last updated) further narrows results. You can also use reranking models like cross-encoders (e.g., MiniLM-L6) to refine the final order of search results by comparing the query directly to candidate articles, improving precision.
Finally, optimize query processing. Preprocess user inputs to handle typos, expand abbreviations (e.g., “acct” to “account”), and extract key terms. Tools like spaCy or AWS Comprehend can identify entities (product names, error codes) to add context. For example, if a user mentions “error 404,” the system could automatically include related terms like “page not found” in the search. Continuously test and iterate using real support tickets to identify gaps—A/B testing different embedding models or adjusting keyword weights can reveal what works best. Logging search metrics (click-through rates, resolution times) helps quantify improvements, ensuring the system adapts to evolving user needs.