Pinecone Quickstart

Welcome! This skill walks you through your first Pinecone experience using the tools available to you. In this quickstart, you will learn how to do a simple form of semantic search over some example data.

Prerequisites

Before starting either path, verify:

PINECONE_API_KEY is set in the environment. Get a free key at https://app.pinecone.io/?sessionType=signup
For the Database path: the Pinecone MCP server must be configured and available

Step 0: Choose Your Path

Use AskUserQuestion (or any available user-input tool) to ask the user which path they want:

Database – Build a vector search index. Best for developers who want to store and search embeddings. Uses the Pinecone MCP + a Python upsert script.
Assistant – Build a document Q&A assistant. Best for users who want to upload files and ask questions with cited answers. No code required.

Path A: Database Quickstart

For each step, explain to the user what will happen. An overview is here:

Check if MCP is set
Create an integrated index with MCP
Upsert sample data using the bundled script (9 sentences across productivity, health, and nature themes)
Run a semantic search query and explore further queries
Optionally try reranking
Offer the complete standalone script

Step 1 – Verify MCP is Available

Attempt to call list-indexes from the Pinecone MCP. If it fails or tools are unavailable:

Tell the user the MCP server needs to be configured
Point them to: https://docs.pinecone.io/reference/tools/mcp

If it succeeds, proceed.

Step 2 – Create an Integrated Index

Use the MCP create-index-for-model tool to create a serverless index with integrated embeddings:

name: quickstart-skills
cloud: aws
region: us-east-1
embed:
  model: llama-text-embed-v2
  fieldMap:
    text: chunk_text

Explain to the user what's happening:

An integrated index uses a built-in Pinecone embedding model (llama-text-embed-v2)
This means you send plain text and Pinecone handles the embedding automatically
The field_map tells Pinecone which field in your records contains the text to embed

Wait for the index to become ready before proceeding. Waiting a few seconds is sufficient.

Step 3 – Upsert Sample Data

Run the bundled upsert script to seed the index with sample records:

uv run scripts/upsert.py --index quickstart

Explain to the user what's happening:

The script uploads 9 sample records across three themes: productivity (getting work done), health (feeling unwell), and nature (outdoors/wildlife)
The dataset is intentionally varied so semantic search can show its value — the queries below use completely different words than the records, but the right ones still surface
Each record has an _id, a chunk_text field (the text that gets embedded), and a category field
This is the same structure you'd use for your own data — just replace the records

Step 4 – Query with the MCP

Use the MCP search-records tool to run the first semantic search:

index: quickstart-skills
namespace: example-namespace
query:
  topK: 3
  inputs:
    text: "getting things done efficiently"

Display the results in a clean table: ID, score, and chunk_text.

Explain to the user what's happening:

Notice the query shares no keywords with the records — but it surfaces the productivity sentences
That's semantic search: it finds meaning, not just matching words
You sent plain text — Pinecone embedded the query using the same model as the index

Offer to explore further: Ask the user (via AskUserQuestion) if they'd like to try another query to see the effect more clearly:

Option A: "feeling under the weather" — should surface the health records
Option B: "wildlife spotting outside" — should surface the nature records
Option C: No thanks, move on

Run whichever query they choose and display the results the same way. If they want to try both, do both. After each result, point out which theme surfaced and why.

If they decline or are done exploring, proceed to Step 5 or offer to skip ahead to the complete script.

Step 5 – Try Reranking (Optional)

Use AskUserQuestion to ask if they want to try reranking.

If yes, use search-records again with reranking enabled:

rerank:
  model: bge-reranker-v2-m3
  rankFields: [chunk_text]
  topN: 3

Explain: Reranking runs a second-pass model over the results to improve relevance ordering.

Step 6 – Wrap Up

Congratulate the user on completing the quickstart. Ask (via AskUserQuestion) if they'd like a standalone Python script that does everything in one go — create index, upsert, query, and rerank.

If yes, copy it to their working directory:

cp scripts/quickstart_complete.py ./pinecone_quickstart.py

Tell the user:

The script is at ./pinecone_quickstart.py
Run it with: uv run pinecone_quickstart.py
It uses uv inline dependencies — no separate install needed
They can swap in their own records list to build something real

Path B: Assistant Quickstart

Guide the user through the Pinecone Assistant workflow using the existing assistant skills:

Step 1 – Check for Documents

Before anything else, ask the user if they have files to upload. Pinecone Assistant accepts .pdf, .md, .txt, and .docx files — a single file or a folder of files both work.

If they have files: ask for the path and proceed to Step 2.

If they don't have files: offer two options via AskUserQuestion:

Generate sample docs — create a few short markdown files in ./sample-docs/ so they can complete the quickstart right now. Ask what topics they'd like (or default to: a product FAQ, a short how-to guide, and a brief company overview). Write 3 files, each 150–250 words.
Come back later — let them know they can return once they have documents and pick up from Step 2.

Step 2 – Create an Assistant

Invoke pinecone:assistant-create or run:

uv run ../assistant/scripts/create.py --name my-assistant

Explain: The assistant is a fully managed RAG service — upload documents, ask questions, get cited answers.

Step 3 – Upload Documents

Invoke pinecone:assistant-upload or run:

uv run ../assistant/scripts/upload.py --assistant my-assistant --source ./your-docs

Explain: Pinecone handles chunking, embedding, and indexing automatically — no configuration needed.

Step 4 – Chat with the Assistant

Invoke pinecone:assistant-chat or run:

uv run ../assistant/scripts/chat.py --assistant my-assistant --message "What are the main topics in these documents?"

Explain: Responses include citations with source file and page number.

Next Steps for Assistant

Use pinecone:assistant-sync to keep the assistant up to date as documents change
Use pinecone:assistant-context to retrieve raw context snippets for custom workflows
Every assistant is also an MCP server — see https://docs.pinecone.io/guides/assistant/mcp-server

Troubleshooting

PINECONE_API_KEY not set

export PINECONE_API_KEY="your-key-here"

Then restart your IDE/agent session.

MCP tools not available

Verify the Pinecone MCP server is configured in your IDE's MCP settings
Check that PINECONE_API_KEY is set before the MCP server starts

Index already exists

The upsert script is safe to re-run — it will upsert over existing records
Or delete and recreate: use pc index delete -n quickstart via the CLI

uv not installed

curl -LsSf https://astral.sh/uv/install.sh | sh

quickstart

Pinecone Quickstart

Prerequisites

Step 0: Choose Your Path

Path A: Database Quickstart

Step 1 – Verify MCP is Available

Step 2 – Create an Integrated Index

Step 3 – Upsert Sample Data

Step 4 – Query with the MCP

Step 5 – Try Reranking (Optional)

Step 6 – Wrap Up

Path B: Assistant Quickstart

Step 1 – Check for Documents

Step 2 – Create an Assistant

Step 3 – Upload Documents

Step 4 – Chat with the Assistant

Next Steps for Assistant

Troubleshooting

Further Reading