Vector database consulting support

• Setup Text/Image Multimodal Search
• Search Through Your Codebase
• Build a Recommendation System with Collaborative Filtering
• Using the Database
• Bulk Upload Vectors
• Create & Restore Snapshots
• Load a HuggingFace Dataset
• Build With Async API
• Automate filtering with LLMs

• Points
• Vectors
• Payload
• Search
• Explore
• Hybrid Queries
• Filtering
• Optimizer
• Storage
• Indexing
• Snapshots

Using sparse-dense vectors involves the following general steps:  

• Create dense vectors using a dense embedding model.
• Create sparse vectors using a sparse embedding model.
•  Create an index with the dotproduct metric.
•  Upsert sparse-dense vectors to your index.
• Search the index using sparse-dense vectors.
• Pinecone returns sparse-dense vectors.

• Upsert data
• Query data
• Fetch data
• Update data
• Delete data
• Import data
• List record IDs
• Target an index
• Understanding metadata
• Manage RAG documents

•  Hybrid search and sparse vectors
• Understanding hybrid search
• Encode sparse vectors
• Upsert sparse-dense vectors
• Query sparse-dense vectors

• Understanding Pinecone Inference
• Generate embeddings
• Rerank documents
• Upsert and search with integrated inference

• Understanding Pinecone Assistant
• Create an assistant
• Manage files
• Chat with an assistant
• Manage assistants
•  Evaluate answers
•  Retrieve context snippets

• Local development
• Automated testing
• Move to production
• Performance tuning
• Security
• Integrate with cloud storage
• Monitoring

• Understanding organizations
• Manage billing
• Manage cost
• Manage organization members