Sim

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, 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 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's specialized vector database. Generate embeddings from text and perform semantic similarity searches with customizable filtering options.

Tools

pinecone_generate_embeddings

Generate embeddings from text using Pinecone

Input

ParameterTypeRequiredDescription
modelstringYesModel to use for generating embeddings
inputsarrayYesArray of text inputs to generate embeddings for
apiKeystringYesPinecone API key

Output

ParameterTypeDescription
dataarrayGenerated embeddings data with values and vector type
modelstringModel used for generating embeddings
vector_typestringType of vector generated (dense/sparse)
usageobjectUsage statistics for embeddings generation

pinecone_upsert_text

Insert or update text records in a Pinecone index

Input

ParameterTypeRequiredDescription
indexHoststringYesFull Pinecone index host URL
namespacestringYesNamespace to upsert records into
recordsarrayYesRecord or array of records to upsert, each containing _id, text, and optional metadata
apiKeystringYesPinecone API key

Output

ParameterTypeDescription
statusTextstringStatus of the upsert operation
upsertedCountnumberNumber of records successfully upserted

pinecone_search_text

Search for similar text in a Pinecone index

Input

ParameterTypeRequiredDescription
indexHoststringYesFull Pinecone index host URL
namespacestringNoNamespace to search in
searchQuerystringYesText to search for
topKstringNoNumber of results to return
fieldsarrayNoFields to return in the results
filterobjectNoFilter to apply to the search
rerankobjectNoReranking parameters
apiKeystringYesPinecone API key

Output

ParameterTypeDescription
matchesarraySearch results with ID, score, and metadata

pinecone_search_vector

Search for similar vectors in a Pinecone index

Input

ParameterTypeRequiredDescription
indexHoststringYesFull Pinecone index host URL
namespacestringNoNamespace to search in
vectorarrayYesVector to search for
topKnumberNoNumber of results to return
filterobjectNoFilter to apply to the search
includeValuesbooleanNoInclude vector values in response
includeMetadatabooleanNoInclude metadata in response
apiKeystringYesPinecone API key

Output

ParameterTypeDescription
matchesarrayVector search results with ID, score, values, and metadata
namespacestringNamespace where the search was performed

pinecone_fetch

Fetch vectors by ID from a Pinecone index

Input

ParameterTypeRequiredDescription
indexHoststringYesFull Pinecone index host URL
idsarrayYesArray of vector IDs to fetch
namespacestringNoNamespace to fetch vectors from
apiKeystringYesPinecone API key

Output

ParameterTypeDescription
matchesarrayFetched vectors with ID, values, metadata, and score

Notes

  • Category: tools
  • Type: pinecone
Pinecone