> ## Documentation Index > Fetch the complete documentation index at: https://docs.jinba.io/llms.txt > Use this file to discover all available pages before exploring further. # Pinecone > Vector database for semantic search and RAG applications ## Overview Pinecone is a fully managed vector database that enables semantic search and RAG (Retrieval-Augmented Generation) applications. The Pinecone tool allows you to query vector indexes for similar vectors using semantic similarity. ## Key Features * `PINECONE_CREATE_INDEX` * Create new serverless vector indexes * Configure vector dimension and distance metric * Select cloud provider and deployment region * Optional deletion protection * Wait-until-ready option for immediate use * `PINECONE_QUERY` * Search for similar vectors using semantic similarity * **Dual-mode operation**: Vector query OR text query with integrated inference * Support for top-K results retrieval * Metadata filtering with Pinecone filter syntax * Namespace isolation for data partitioning * Similarity threshold filtering (minScore) * Optional inclusion of metadata and vector values * `PINECONE_UPSERT` * Insert or update vectors in Pinecone index * **Dual-mode operation**: Vector upsert OR document upsert with integrated inference * Batch operations (up to 100 vectors/documents per request) * Metadata support for rich vector annotations * Namespace isolation for data organization ## Authentication You need a Pinecone API key to use this tool. You can obtain one from the [Pinecone Console](https://app.pinecone.io/). **Note**: Treat API keys as sensitive information and never commit them to public repositories. ## Examples ### Example: Create Index and Upsert Documents (Integrated Inference) This example shows the complete workflow using Pinecone's integrated inference feature - no external embedding tool needed! ```yaml theme={null} # Step 1: Create a serverless index - id: create_index name: create_index tool: PINECONE_CREATE_INDEX config: - name: apiKey value: "{{secrets.PINECONE_API_KEY}}" input: - name: indexName value: knowledge-base - name: dimension value: 1024 - name: metric value: cosine - name: cloud value: aws - name: region value: us-east-1 - name: waitUntilReady value: true # Step 2: Upload documents with automatic embedding - id: upload_docs name: upload_docs tool: PINECONE_UPSERT config: - name: apiKey value: "{{secrets.PINECONE_API_KEY}}" input: - name: indexName value: knowledge-base - name: documents value: | [ { "id": "doc1", "text": "Pinecone is a vector database optimized for AI applications.", "metadata": {"category": "intro", "source": "docs"} }, { "id": "doc2", "text": "Integrated inference automatically generates embeddings from your text.", "metadata": {"category": "features", "source": "docs"} } ] # Step 3: Query with text (automatic embedding) - id: search name: search tool: PINECONE_QUERY config: - name: apiKey value: "{{secrets.PINECONE_API_KEY}}" input: - name: indexName value: knowledge-base - name: query value: "How does Pinecone handle embeddings?" - name: topK value: 3 - name: includeMetadata value: true ``` ### Example: Query with Reranking for Improved Relevance Reranking re-scores initial results to improve relevance. This is especially useful for complex queries. ```yaml theme={null} - id: search_with_rerank name: search_with_rerank tool: PINECONE_QUERY config: - name: apiKey value: "{{secrets.PINECONE_API_KEY}}" input: - name: indexName value: knowledge-base - name: query value: "How does Pinecone handle embeddings?" - name: topK value: 10 - name: includeMetadata value: true - name: rerankModel value: bge-reranker-v2-m3 # Options: cohere-rerank-3.5, bge-reranker-v2-m3, pinecone-rerank-v0 - name: rerankTopN value: 3 - name: rerankRankFields value: '["text"]' - name: fields value: '["category", "text"]' ``` ### Example: Traditional Semantic Search with OpenAI Embeddings ```yaml theme={null} - id: generate_embeddings name: generate_embeddings tool: OPENAI_CREATE_EMBEDDING config: - name: api_key value: "{{secrets.OPENAI_API_KEY}}" input: - name: input value: What is semantic search? - name: model value: text-embedding-3-large - id: search_pinecone name: search_pinecone tool: PINECONE_QUERY config: - name: apiKey value: "{{secrets.PINECONE_API_KEY}}" input: - name: indexName value: my-docs-index - name: vector value: "{{steps.generate_embeddings.result.data[0].embedding}}" - name: topK value: 5 - name: minScore value: 0.7 - name: includeMetadata value: true - id: generate_answer name: generate_answer tool: OPENAI_INVOKE config: - name: version value: gpt-4 input: - name: prompt value: | Answer the following question based on the context provided. Context: {{steps.search_pinecone.result.matches[0].metadata.text}} {{steps.search_pinecone.result.matches[1].metadata.text}} Question: What is semantic search? ``` ### Example: Search with Metadata Filtering ```yaml theme={null} - id: filtered_search name: filtered_search tool: PINECONE_QUERY config: - name: apiKey value: "{{secrets.PINECONE_API_KEY}}" input: - name: indexName value: my-docs-index - name: vector value: "[0.1, 0.2, 0.3, ...]" - name: topK value: 10 - name: filter value: '{"category": {"$eq": "technology"}, "year": {"$gte": 2020}}' - name: namespace value: production ``` ## Parameters ### PINECONE\_CREATE\_INDEX | Parameter | Type | Required | Default | Description | | -------------------- | ------- | -------- | -------- | -------------------------------------------------------------------- | | `indexName` | string | Yes | - | Name of the index (lowercase alphanumeric and hyphens, max 45 chars) | | `dimension` | number | Yes | - | Vector dimension (e.g., 1536 for OpenAI ada-002) | | `metric` | string | No | cosine | Distance metric: cosine, euclidean, or dotproduct | | `cloud` | string | No | aws | Cloud provider: aws, gcp, or azure | | `region` | string | Yes | - | Deployment region (e.g., us-east-1) | | `deletionProtection` | string | No | disabled | Enable deletion protection: enabled or disabled | | `waitUntilReady` | boolean | No | true | Wait until index is ready before returning | ### PINECONE\_QUERY | Parameter | Type | Required | Default | Description | | ----------------- | --------- | -------- | ------- | --------------------------------------------------------- | | `indexName` | string | Yes | - | The name of the Pinecone index to query | | `vector` | number\[] | No\* | - | Query vector as array of numbers | | `query` | string | No\* | - | Text query for integrated inference (automatic embedding) | | `topK` | number | No | 10 | Number of results to return (1-100) | | `namespace` | string | No | - | Namespace to query within the index | | `filter` | object | No | - | Metadata filter using Pinecone filter syntax | | `minScore` | number | No | - | Minimum similarity score threshold (0-1) | | `includeMetadata` | boolean | No | true | Include metadata in results | | `includeValues` | boolean | No | false | Include vector values in results | \*Either `vector` or `query` must be provided, but not both. ### PINECONE\_UPSERT | Parameter | Type | Required | Default | Description | | ----------- | ------ | -------- | ------- | ----------------------------------------------------------------------- | | `indexName` | string | Yes | - | The name of the Pinecone index | | `vectors` | array | No\* | - | Array of vector objects (id, values, metadata) | | `documents` | array | No\* | - | Array of document objects for integrated inference (id, text, metadata) | | `namespace` | string | No | - | Namespace for data organization | \*Either `vectors` or `documents` must be provided, but not both. ### Output The tool returns an object with the following structure: ```json theme={null} { "matches": [ { "id": "vec1", "score": 0.95, "metadata": { "title": "Document Title", "text": "Document content...", "category": "technology" } } ], "namespace": "production", "usage": { "readUnits": 5 } } ``` ## Use Cases ### RAG (Retrieval-Augmented Generation) Combine Pinecone with OpenAI to build RAG applications: 1. Generate embeddings from user query 2. Search Pinecone for relevant documents 3. Use retrieved context in LLM prompts ### Semantic Search Search documents by meaning rather than keywords: * Find similar articles * Recommend related content * Discover relevant information ### Question Answering Build Q\&A systems with context retrieval: * Technical documentation search * Customer support knowledge base * Research paper discovery ## Resources * [Pinecone Documentation](https://docs.pinecone.io/) * [Pinecone Console](https://app.pinecone.io/) * [Vector Embeddings Guide](https://www.pinecone.io/learn/vector-embeddings/) * [RAG Best Practices](https://www.pinecone.io/learn/retrieval-augmented-generation/)