Pinecone
Use Pinecone vector database
Pinecone is a vector database designed for building high-performance vector search applications. It enables efficient storage, management, and similarity search of high-dimensional vector embeddings, making it ideal for AI applications that require semantic search capabilities.
With Pinecone, you can:
- Store vector embeddings: Efficiently manage high-dimensional vectors at scale
- Perform similarity search: Find the most similar vectors to a query vector in milliseconds
- Build semantic search: Create search experiences based on meaning rather than keywords
- Implement recommendation systems: Generate personalized recommendations based on content similarity
- Deploy machine learning models: Operationalize ML models that rely on vector similarity
- Scale seamlessly: Handle billions of vectors with consistent performance
- Maintain real-time indexes: Update your vector database in real-time as new data arrives
In Sim Studio, the Pinecone integration enables your agents to leverage vector search capabilities programmatically as part of their workflows. This allows for sophisticated automation scenarios that combine natural language processing with semantic search and retrieval. Your agents can generate embeddings from text, store these vectors in Pinecone indexes, and perform similarity searches to find the most relevant information. This integration bridges the gap between your AI workflows and vector search infrastructure, enabling more intelligent information retrieval based on semantic meaning rather than exact keyword matching. By connecting Sim Studio with Pinecone, you can create agents that understand context, retrieve relevant information from large datasets, and deliver more accurate and personalized responses to users - all without requiring complex infrastructure management or specialized knowledge of vector databases.
Usage Instructions
Store, search, and retrieve vector embeddings using Pinecone
Tools
pinecone_generate_embeddings
Generate embeddings from text using Pinecone
Input
| Parameter | Type | Required | Description |
|---|---|---|---|
apiKey | string | Yes | Pinecone API key |
model | string | Yes | Model to use for generating embeddings |
inputs | array | Yes | Array of text inputs to generate embeddings for |
Output
| Parameter | Type |
|---|---|
data | string |
model | string |
vector_type | string |
usage | string |
pinecone_upsert_text
Insert or update text records in a Pinecone index
Input
| Parameter | Type | Required | Description |
|---|---|---|---|
apiKey | string | Yes | Pinecone API key |
indexHost | string | Yes | Full Pinecone index host URL |
namespace | string | Yes | Namespace to upsert records into |
records | array | Yes | Record or array of records to upsert, each containing _id, text, and optional metadata |
Output
| Parameter | Type |
|---|---|
statusText | string |
pinecone_search_text
Search for similar text in a Pinecone index
Input
| Parameter | Type | Required | Description |
|---|---|---|---|
apiKey | string | Yes | Pinecone API key |
indexHost | string | Yes | Full Pinecone index host URL |
namespace | string | No | Namespace to search in |
searchQuery | string | Yes | Text to search for |
topK | string | No | Number of results to return |
fields | array | No | Fields to return in the results |
filter | object | No | Filter to apply to the search |
rerank | object | No | Reranking parameters |
Output
| Parameter | Type |
|---|---|
matches | string |
score | string |
metadata | string |
pinecone_search_vector
Search for similar vectors in a Pinecone index
Input
| Parameter | Type | Required | Description |
|---|---|---|---|
apiKey | string | Yes | Pinecone API key |
indexHost | string | Yes | Full Pinecone index host URL |
namespace | string | No | Namespace to search in |
vector | array | Yes | Vector to search for |
topK | number | No | Number of results to return |
filter | object | No | Filter to apply to the search |
includeValues | boolean | No | Include vector values in response |
includeMetadata | boolean | No | Include metadata in response |
Output
| Parameter | Type |
|---|---|
matches | string |
score | string |
values | string |
metadata | string |
pinecone_fetch
Fetch vectors by ID from a Pinecone index
Input
| Parameter | Type | Required | Description |
|---|---|---|---|
apiKey | string | Yes | Pinecone API key |
indexHost | string | Yes | Full Pinecone index host URL |
ids | array | Yes | Array of vector IDs to fetch |
namespace | string | No | Namespace to fetch vectors from |
Output
| Parameter | Type |
|---|---|
matches | string |
values | string |
metadata | string |
score | string |
Block Configuration
Input
| Parameter | Type | Required | Description |
|---|---|---|---|
operation | string | Yes | Operation |
Outputs
| Output | Type | Description |
|---|---|---|
response | object | Output from response |
↳ matches | any | matches of the response |
↳ upsertedCount | any | upsertedCount of the response |
↳ data | any | data of the response |
↳ model | any | model of the response |
↳ vector_type | any | vector_type of the response |
↳ usage | any | usage of the response |
Notes
- Category:
tools - Type:
pinecone