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Sycamore
Sycamore
  • Installing and Getting Started With Sycamore
    • Sycamore Concepts
    • Recommended resources to run Sycamore
    • AI Configuration
  • Using Jupyter notebooks
  • Transforms
    • Embed
    • Explode
    • ExtractEntity
    • Extract Schema
    • Filter
    • FlatMap
    • LLM Query
    • Map
    • MapBatch
    • Materialize
    • Merge
    • Partition
    • Sketch
    • Summarize
  • Connectors
    • DuckDB
    • Weaviate
    • Pinecone
    • Elasticsearch
    • OpenSearch
    • Neo4j
    • Qdrant
  • Querying Data with Sycamore
  • Vector Database Ingestion Examples
    • Loading Pinecone with Sycamore
    • Loading OpenSearch with Sycamore
    • Loading Weaviate with Sycamore
  • Sycamore APIs
    • Context
    • DocSet
    • DocSetReader
    • DocSetWriter
    • LLM
    • Prompts
    • Document
    • Functions
    • Node
    • Query
    • Low-Level Transforms (for Sycamore development)
      • Assign Doc Properties
      • Augment Text
      • Basics
      • Bbox Merge
      • Embed
      • Explode
      • Extract Entity
      • Extract Schema
      • Extract Table
      • Extract Table Properties
      • LLM Map
      • LLM Query
      • Map
      • Mark Misc
      • Merge Elements
      • Partition
      • Query
      • Random Sample
      • Regex Replace
      • Sketcher
      • Split Elements
      • Spread Properties
      • Standarizer
      • Summarize
      • Summarize Images
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Recommended resources to run SycamoreΒΆ

We recommend at least 4GB of RAM to run Sycamore.

We recommend using the Aryn Partitioner for the highest quality PDF processing. By default, the Aryn Partitioner is configured to use Aryn DocParse, a GPU-backed serverless endpoint. You can sign up for free here, and use it with your Sycamore job.

If you choose to configure your Aryn Partitioner to run locally, your performance will vary without using an NVIDIA GPU.

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AI Configuration
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Sycamore Concepts
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