Pinecone Assistant
Pinecone Assistant leverages vector databases for intelligent retrieval and context understanding, boosting AI performance across various applications.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add pinecone-assistant npx -- -y @trustedskills/pinecone-assistant
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"pinecone-assistant": {
"command": "npx",
"args": [
"-y",
"@trustedskills/pinecone-assistant"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The pinecone-assistant skill enables AI agents to interact with Pinecone, a vector database. It allows agents to perform operations like indexing data, querying embeddings, and retrieving relevant information based on semantic similarity. This facilitates building applications that require efficient similarity search and retrieval of large datasets.
When to use it
- Semantic Search: Build an agent capable of answering questions by searching through a knowledge base stored in Pinecone.
- Recommendation Systems: Develop agents that recommend products or content based on user preferences represented as embeddings in Pinecone.
- Image/Video Retrieval: Create applications where users can search for similar images or videos using embedding-based similarity search within Pinecone.
- Chatbots with Memory: Enhance chatbots by storing conversation history and relevant context in Pinecone to improve response accuracy and personalization.
Key capabilities
- Indexing data into a Pinecone index.
- Querying a Pinecone index for similar vectors.
- Retrieving information from a Pinecone index based on semantic similarity.
Example prompts
- "Find the top 5 most similar documents to this query: [query text]"
- "Index these data points into my 'product_catalog' Pinecone index: [data]"
- "Retrieve all entries in the 'customer_support_logs' index with a similarity score greater than 0.8."
Tips & gotchas
- Requires an active Pinecone account and API key to function correctly.
- The quality of results depends heavily on the embeddings used for indexing data; ensure they accurately represent the semantic meaning.
Tags
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