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POST
/
files
/
{assistant_name}
# To use the Python SDK, install the plugin:
# pip install --upgrade pinecone pinecone-plugin-assistant

from pinecone import Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")

# Get the assistant.
assistant = pc.assistant.Assistant(
    assistant_name="example-assistant", 
)

# Upload a file from a local path.
response = assistant.upload_file(
    file_path="/Users/jdoe/Downloads/example_file.txt",
    metadata={"published": "2024-01-01", "document_type": "manuscript"},
    timeout=None
)

# Upload from an in-memory binary stream.
from io import BytesIO

# Create a BytesIO stream with some content.
md_text = "# Title\n\ntext"
stream = BytesIO(md_text.encode("utf-8"))

# Upload the stream.
response_bytes_stream = assistant.upload_bytes_stream(
    stream=stream,
    file_name="example_file.md",
    timeout=None
)

{
  "name": "example-file.txt",
  "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "metadata": "{ 'published': '2024-01-01', 'document_type': 'manuscript' }",
  "created_on": "2023-11-07T05:31:56Z",
  "updated_on": "2023-11-07T05:31:56Z",
  "status": "Processing",
  "percent_done": 50,
  "signed_url": "https://storage.googleapis.com/bucket/file.pdf",
  "error_message": "<string>"
}

Documentation Index

Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt

Use this file to discover all available pages before exploring further.

# To use the Python SDK, install the plugin:
# pip install --upgrade pinecone pinecone-plugin-assistant

from pinecone import Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")

# Get the assistant.
assistant = pc.assistant.Assistant(
    assistant_name="example-assistant", 
)

# Upload a file from a local path.
response = assistant.upload_file(
    file_path="/Users/jdoe/Downloads/example_file.txt",
    metadata={"published": "2024-01-01", "document_type": "manuscript"},
    timeout=None
)

# Upload from an in-memory binary stream.
from io import BytesIO

# Create a BytesIO stream with some content.
md_text = "# Title\n\ntext"
stream = BytesIO(md_text.encode("utf-8"))

# Upload the stream.
response_bytes_stream = assistant.upload_bytes_stream(
    stream=stream,
    file_name="example_file.md",
    timeout=None
)

{
  "name": "example-file.txt",
  "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "metadata": "{ 'published': '2024-01-01', 'document_type': 'manuscript' }",
  "created_on": "2023-11-07T05:31:56Z",
  "updated_on": "2023-11-07T05:31:56Z",
  "status": "Processing",
  "percent_done": 50,
  "signed_url": "https://storage.googleapis.com/bucket/file.pdf",
  "error_message": "<string>"
}

Authorizations

​
Api-Key
string
header
required

Pinecone API Key

Path Parameters

​
assistant_name
string
required

The name of the assistant to upload files to.

Query Parameters

​
metadata
string<json>

Optional JSON-encoded metadata for files.

Body

multipart/form-data

The desired file to be uploaded and processed into the assistant.

​
file
file
required

The file to upload.

Response

File upload has been accepted.

AssistantFileModel is the response format to a successful file upload request.

​
name
string
required
​
id
string<uuid>
required
​
metadata
object
​
created_on
string<date-time>
​
updated_on
string<date-time>
​
status
enum<string>
Available options:
Processing,
Available,
Deleting,
ProcessingFailed
​
percent_done
number<double> | null

The percentage of the file that has been processed

​
signed_url
string | null

A signed URL that provides temporary, read-only access to the underlying file. Anyone with the link can access the file, so treat it as sensitive data. Expires after a short time.

Example:

"https://storage.googleapis.com/bucket/file.pdf?..."

​
error_message
string | null

A message describing any error during file processing, provided only if an error occurs.