Voyage AI

Step 1: Embed all customer support documentation using Voyage AI's embedding model.

Step 2: When a customer submits a query, embed the query using the same model.

Step 3: Perform a similarity search between the query embedding and the document embeddings.

Step 4: Return the documents with the highest similarity scores to the customer.

Step 1: Split the knowledge base into smaller chunks of text.

Step 2: Generate embeddings for each chunk using Voyage AI.

Step 3: Store the embeddings in a vector database.

Step 4: When a user searches, generate an embedding for their search query and retrieve the most similar chunks from the vector database.

Step 1: Embed code snippets from your codebase using Voyage AI's code-optimized model.

Step 2: When a developer searches for a code snippet, embed their search query.

Step 3: Perform a similarity search to find the most relevant code snippets.

Step 4: Display the search results to the developer.

Step 1: Embed all legal documents using Voyage AI's domain-specific model for legal text.

Step 2: Embed the query related to a specific legal problem.

Step 3: Search for the most similar documents to the query.

Step 4: Extract and present the relevant legal information from the top search results.

Step 1: Embed product descriptions using Voyage AI's embedding model.

Step 2: Embed customer purchase histories or browsing behavior.

Step 3: Calculate similarity between customer embeddings and product embeddings.

Step 4: Recommend products with high similarity scores to the customer.