API & Clients

This guide highlights the core workflow.

tip

See full API reference here.

App setup

Python Client

Install API client package

pip install llama-cloud 

Import and configure client

from llama_cloud.client import LlamaCloud

client = LlamaCloud(token='<llama-cloud-api-key>')

TypeScript Client

Install API client package

npm install @llamaindex/cloud

Import and configure client

import { LlamaCloudApiClient } from '@llamaindex/cloud';

const client = new LlamaCloudApiClient({
  token: apiKey
});

Create new index

Upload files

Python Client

with open('test.pdf', 'rb') as f:
  file = client.files.upload_file(upload_file=f)

TypeScript Client

import fs from "fs"

const filePath = "node_modules/llamaindex/examples/abramov.txt";

file = client.files.uploadFile(project.id, fs.createReadStream(filePath))

tip

See Files API for full details on file management.

Configure data sources

Python Client

from llama_cloud.types import CloudS3DataSource

ds = {
  'name': 's3',
  'source_type': 'S3',
  'component': CloudS3DataSource(bucket='test-bucket')
}
data_source = client.data_sources.create_data_source(request=ds)

TypeScript Client

const s3 = {
    'name': 's3',
    'sourceType': 'S3', 
    'component': {
        'bucket': 'test-bucket'
    }
}
data_source = await client.dataSources.createDataSource({
  projectId: projectId,
  body: s3
})

tip

See Data Sources API for full details on data source management.

See full list of data sources and specifications.

Configure data sinks

Python Client

from llama_cloud.types import CloudPineconeVectorStore

ds = {
'name': 'pinecone',
'sink_type': 'PINECONE',
'component': CloudPineconeVectorStore(api_key='test-key', index_name='test-index')
}
data_sink = client.data_sinks.create_data_sink(request=ds)

TypeScript Client

const pinecone = {
    'name': 'pinecone',
    'sinkType': 'PINECONE',
    'component': {
        'api_key': 'test-key',
        'index_name': 'test-index'
    }
}
data_sink = client.dataSinks.createDataSink({
  projectId: projectId,
  body: pinecone
})

tip

See Data Sinks API for full details on data sink management.

See full list of data sinks and specifications.

Setup transformation and embedding config

tip

See Parsing & Transformation for full details on transformation guide.

# Embedding config
embedding_config = {
    'type': 'OPENAI_EMBEDDING',
    'component': {
        'api_key': '<YOUR_API_KEY_HERE>', # editable
        'model_name': 'text-embedding-ada-002' # editable
    }
}

# Transformation auto config
transform_config = {
    'mode': 'auto',
    'config': {
        'chunk_size': 1024, # editable
        'chunk_overlap': 20 # editable
    }
}

Create index (i.e. pipeline)

Python Client

pipeline = {
    'name': 'test-pipeline',
    'embedding_config': embedding_config,
    'transform_config': transform_config,
    'data_sink_id': data_sink.id
}

pipeline = client.pipelines.upsert_pipeline(request=pipeline)

TypeScript Client

const pipeline = {
    'name': 'test-pipeline',
    "embedding_config": embedding_config,
    "transform_config": transform_config,
    'dataSinkId': data_sink.id
}

await client.pipelines.upsertPipeline({
projectId: projectId,
body: pipeline
})

tip

See Pipeline API for full details on index (i.e. pipeline) management.

Add files to index

Python Client

files = [
  {'file_id': file.id}
]

pipeline_files = client.pipelines.add_files_to_pipeline(pipeline.id, request=files)

TypeScript Client

const files = [
  {'file_id': file.id}
]


pipeline_files = client.pipelines.addFilesToPipeline(pipeline.id, files)

Add data sources to index

Python Client

data_sources = [
  {
    'data_source_id': data_source.id,
    'sync_interval': 43200.0 # Optional, scheduled sync frequency in seconds. In this case, every 12 hours.
  }
]

pipeline_data_sources = client.pipelines.add_data_sources_to_pipeline(pipeline.id, request=data_sources)

TypeScript Client

const data_sources = [
  {
    'data_source_id': data_source.id,
    'sync_interval': 43200.0 // Optional, scheduled sync frequency in seconds. In this case, every 12 hours.
  }
]

pipeline_data_sources = client.pipelines.addDataSourcesToPipeline(pipeline.id, data_sources)

tip

For more details on scheduled sync, including how the sync timing works, and available sync frequencies, refer to Scheduled sync.

Add documents to index

Python Client

from llama_cloud.types import CloudDocumentCreate

documents = [
CloudDocumentCreate(
text='test-text',
metadata={
'test-key': 'test-val'
}
)
]

documents = client.pipelines.create_batch_pipeline_documents(pipeline.id, request=documents)

TypeScript Client

const documents = [
  {
      'text': 'test-text',
      'metadata': {
          'test-key': 'test-val'
      }
  }
]

documents = client.pipelines.createBatchPipelineDocuments(pipeline.id, documents)

Observe ingestion status & history

Get index status

Python Client

status = client.pipelines.get_pipeline_status(pipeline.id)

TypeScript Client

status = client.pipelines.getPipelineStatus(pipeline.id)

Get ingestion job history

Python Client

jobs = client.pipelines.list_pipeline_jobs(pipeline.id)

TypeScript Client

jobs = client.pipelines.listPipelineJobs(pipeline.id)

Run search (i.e. retrieval endpoint)

Python Client

results = client.pipelines.run_search(pipeline.id, query='test-query')

TypeScript Client

results = client.pipelines.runSearch(pipeline.id, {
  query: 'test-query'
})