Pinecone Docs
PineconeOfficialPublished by Pinecone
- Skill
- Tool integration
- Data and AI
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Pinecone-docs provides a structured index of official Pinecone documentation and technical specifications. It serves as a reliable lookup tool for developers to access up-to-date guides on index management, search implementation, and data ingestion. By centralizing links to core concepts, API references, and specific data schema requirements, it helps developers avoid reliance on potentially outdated training data when writing code or configuring vector operations.
What it does
- Provides direct links to official Pinecone documentation organized by development task.
- Defines required JSON and JSONL schemas for standard vector upserts.
- Details record formats for integrated inference indexes.
- Outlines specifications for sparse vector structures used in hybrid search.
- Offers guidance on production readiness and error handling.
- Facilitates access to SDK references and example notebooks.
When to use it
- When you need to verify the correct JSON structure for upserting vectors or records.
- When implementing specific search types like hybrid or full-text search.
- When troubleshooting index configuration or production deployment issues.
- When looking for the latest API parameters or SDK usage patterns.
Advantages
- Reduces hallucination by pointing directly to official documentation sources.
- Simplifies data modeling by providing clear schema examples for different index types.
- Organizes complex documentation into actionable developer workflows.
- Ensures developers use current best practices for production environments.
Usage examples
- “What is the correct JSON format for upserting a standard vector into Pinecone?”
- The agent retrieves the standard index vector schema, showing the required 'id', 'values' array, and optional 'metadata' object structure.
- “How do I implement hybrid search in my Pinecone index?”
- The agent provides the link to the hybrid search guide and explains the requirements for combining dense and sparse vector data.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/pinecone-io/pinecone-docs/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip pinecone-docs.zip -d .agents/skills
Documentation by Pinecone
Pinecone Developer Reference
A curated index of Pinecone documentation. Fetch the relevant page(s) for the task at hand rather than relying on training data.
NOTE TO AGENT
Please attempt to fetch the url listed when relevant. If you run into an error, please attempt to append ".md" to the url to retrieve the markdown version of the Docs page.
In case you need it: A full reference to ALL relevant URLs can be found here: https://docs.pinecone.io/llms.txt
Use this as a last resort if you cannot find the relevant page below.
Getting Started
| Topic | URL |
|---|---|
| Quickstart for all languages and coding environments (Cursor, Claude Code, n8n, Python, JavaScript, Java, Go, C#) | https://docs.pinecone.io/guides/get-started/quickstart |
| Pinecone concepts — namespaces, terminology, and key database concepts | https://docs.pinecone.io/guides/get-started/concepts |
| Data modeling for text and vectors | https://docs.pinecone.io/guides/index-data/data-modeling |
| Architecture of Pinecone | https://docs.pinecone.io/guides/get-started/database-architecture |
| Pinecone Assistant overview | https://docs.pinecone.io/guides/assistant/overview |
Indexes
| Topic | URL |
|---|---|
| Create an index | https://docs.pinecone.io/guides/index-data/create-an-index |
| Index types and conceptual overview | https://docs.pinecone.io/guides/index-data/indexing-overview |
| Integrated inference (built-in embedding models) | https://docs.pinecone.io/guides/index-data/indexing-overview#integrated-embedding |
| Dedicated read nodes — predictable low-latency performance at high query volumes | https://docs.pinecone.io/guides/index-data/dedicated-read-nodes |
Upsert & Data
| Topic | URL |
|---|---|
| Upsert vectors and text | https://docs.pinecone.io/guides/index-data/upsert-data |
| Multitenancy with namespaces | https://docs.pinecone.io/guides/index-data/implement-multitenancy |
Search
| Topic | URL |
|---|---|
| Semantic search | https://docs.pinecone.io/guides/search/semantic-search |
| Hybrid search | https://docs.pinecone.io/guides/search/hybrid-search |
| Lexical search | https://docs.pinecone.io/guides/search/lexical-search |
Full-text search (preview) — document-schema FTS indexes with text / query_string / dense / sparse scoring | https://docs.pinecone.io/guides/search/full-text-search |
| Metadata filtering — narrow results and speed up searches | https://docs.pinecone.io/guides/search/filter-by-metadata |
API & SDK Reference
| Topic | URL |
|---|---|
| Python SDK reference | https://docs.pinecone.io/reference/sdks/python/overview |
| Example Colab notebooks | https://docs.pinecone.io/examples/notebooks |
Production
| Topic | URL |
|---|---|
| Production checklist — preparing your index for production | https://docs.pinecone.io/guides/production/production-checklist |
| Common errors and what they mean | https://docs.pinecone.io/guides/production/error-handling |
| Targeting indexes correctly — don't use index names in prod | https://docs.pinecone.io/guides/manage-data/target-an-index#target-by-index-host-recommended |
Data Formats
See references/data-formats.md for vector and record schemas.
Files
- ATTRIBUTION.mdAdded by AutoGineer855 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md3.5 KiB
- references/data-formats.md1.9 KiB
SKILL.md
Pinecone Developer Reference
A curated index of Pinecone documentation. Fetch the relevant page(s) for the task at hand rather than relying on training data.
NOTE TO AGENT
Please attempt to fetch the url listed when relevant. If you run into an error, please attempt to append ".md" to the url to retrieve the markdown version of the Docs page.
In case you need it: A full reference to ALL relevant URLs can be found here: https://docs.pinecone.io/llms.txt
Use this as a last resort if you cannot find the relevant page below.
Getting Started
| Topic | URL |
|---|---|
| Quickstart for all languages and coding environments (Cursor, Claude Code, n8n, Python, JavaScript, Java, Go, C#) | https://docs.pinecone.io/guides/get-started/quickstart |
| Pinecone concepts — namespaces, terminology, and key database concepts | https://docs.pinecone.io/guides/get-started/concepts |
| Data modeling for text and vectors | https://docs.pinecone.io/guides/index-data/data-modeling |
| Architecture of Pinecone | https://docs.pinecone.io/guides/get-started/database-architecture |
| Pinecone Assistant overview | https://docs.pinecone.io/guides/assistant/overview |
Indexes
| Topic | URL |
|---|---|
| Create an index | https://docs.pinecone.io/guides/index-data/create-an-index |
| Index types and conceptual overview | https://docs.pinecone.io/guides/index-data/indexing-overview |
| Integrated inference (built-in embedding models) | https://docs.pinecone.io/guides/index-data/indexing-overview#integrated-embedding |
| Dedicated read nodes — predictable low-latency performance at high query volumes | https://docs.pinecone.io/guides/index-data/dedicated-read-nodes |
Upsert & Data
| Topic | URL |
|---|---|
| Upsert vectors and text | https://docs.pinecone.io/guides/index-data/upsert-data |
| Multitenancy with namespaces | https://docs.pinecone.io/guides/index-data/implement-multitenancy |
Search
| Topic | URL |
|---|---|
| Semantic search | https://docs.pinecone.io/guides/search/semantic-search |
| Hybrid search | https://docs.pinecone.io/guides/search/hybrid-search |
| Lexical search | https://docs.pinecone.io/guides/search/lexical-search |
Full-text search (preview) — document-schema FTS indexes with text / query_string / dense / sparse scoring | https://docs.pinecone.io/guides/search/full-text-search |
| Metadata filtering — narrow results and speed up searches | https://docs.pinecone.io/guides/search/filter-by-metadata |
API & SDK Reference
| Topic | URL |
|---|---|
| Python SDK reference | https://docs.pinecone.io/reference/sdks/python/overview |
| Example Colab notebooks | https://docs.pinecone.io/examples/notebooks |
Production
| Topic | URL |
|---|---|
| Production checklist — preparing your index for production | https://docs.pinecone.io/guides/production/production-checklist |
| Common errors and what they mean | https://docs.pinecone.io/guides/production/error-handling |
| Targeting indexes correctly — don't use index names in prod | https://docs.pinecone.io/guides/manage-data/target-an-index#target-by-index-host-recommended |
Data Formats
See references/data-formats.md for vector and record schemas.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Pinecone
- Publisher
- Pinecone (Official)
- Source
- pinecone-io/skills · skills/pinecone-docs · commit 885dc53
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Pinecone.
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download pinecone-docs.zip