Chat through the standard interface

After uploading files to an assistant, you can chat with the assistant.

You can chat with an assistant using the Pinecone console. Select the assistant to chat with, and use the Assistant playground.

Chat through the standard interface

The standard chat interface can return responses in three different formats:

Default response: The assistant returns a structured response and separate citation information.
Streaming response: The assistant returns the response as a text stream.
JSON response: The assistant returns the response as JSON key-value pairs.

This is the recommended way to chat with an assistant, as it offers more functionality and control over the assistant’s responses and references. However, if you need your assistant to be OpenAI-compatible or need inline citations, use the OpenAI-compatible chat interface.

Default response

The following example sends a message and requests a default response:

The content parameter in the request cannot be empty.

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

from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")
assistant = pc.assistant.Assistant(assistant_name="example-assistant")

msg = Message(role="user", content="Who is the CFO of Netflix?")
response = assistant.chat(messages=[msg])

# Alternatively, you can provide a dictionary as the message:
# msg = {"role": "user", "content": "Who is the CFO of Netflix?"}
# response = assistant.chat(messages=[msg])

print(response)

The example above returns a result like the following:

JSON

{
    'id': '0000000000000000163008a05b317b7b', 
    'model': 'gpt-4o-2024-05-13', 
    'usage': {
        'prompt_tokens': 9259, 
        'completion_tokens': 30, 
        'total_tokens': 9289
        }, 
        'message': {
            'content': 'The Chief Financial Officer (CFO) of Netflix is Spencer Neumann.', 
            'role': '"assistant"'
            }, 
            'finish_reason': 'stop', 
            'citations': [
                {
                    'position': 63, 
                    'references': [
                        {
                            'pages': [78, 72, 79], 
                            'file': {
                                'name': 'Netflix-10-K-01262024.pdf', 
                                'id': '76a11dd1...', 
                                'metadata': {
                                    'company': 'netflix', 
                                    'document_type': 'form 10k'
                                    }, 
                                    'created_on': '2024-12-06T01:29:07.369208590Z', 
                                    'updated_on': '2024-12-06T01:29:50.923493799Z', 
                                    'status': 'Available', 
                                    'percent_done': 1.0, 
                                    'signed_url': 'https://storage.googleapis.com/...', 
                                    "error_message": null, 
                                    'size': 1073470.0
                                }
                            }
                        ]
                    }
                ]
            }

Streaming response

The following example sends a message and requests a streaming response:

The content parameter in the request cannot be empty.

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

from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")

assistant = pc.assistant.Assistant(assistant_name="example-assistant")

msg = Message(role="user", content="What is the inciting incident of Pride and Prejudice?")

response = assistant.chat(messages=[msg], stream=True)

for data in response:
    if data:
        print(data)

The example above returns a result like the following:

data:{"type":"message_start","id":"0000000000000000111b35de85e8a8f9","model":"gpt-4o-2024-05-13","role":"assistant"}

data:{"type":"content_chunk","id":"0000000000000000111b35de85e8a8f9","model":"gpt-4o-2024-05-13","delta":{"content":"The"}}

...

data:{"type":"citation","id":"0000000000000000111b35de85e8a8f9","model":"gpt-4o-2024-05-13","citation":{"position":406,"references":[{"file":{"status":"Available","id":"ae79e447-b89e-4994-994b-3232ca52a654","name":"Pride-and-Prejudice.pdf","size":2973077,"metadata":null,"updated_on":"2024-06-14T15:01:57.385425746Z","created_on":"2024-06-14T15:01:02.910452398Z","percent_done":0.0,"signed_url":"https://storage.googleapis.com/...", "error_message":null},"pages":[1]}]}}

data:{"type":"message_end","id":"0000000000000000111b35de85e8a8f9","model":"gpt-4o-2024-05-13","finish_reason":"stop","usage":{"prompt_tokens":9736,"completion_tokens":102,"total_tokens":9838}}

There are four types of messages in a streaming chat response:

Message start: Includes "role":"assistant", which indicates that the assistant is responding to the user’s message.
Content: Includes a value in the content field (e.g., "content":"The"), which is part of the assistant’s streamed response to the user’s message.
Citation: Includes a citation to the document that the assistant used to generate the response.
Message end: Includes "finish_reason":"stop", which indicates that the assistant has finished responding to the user’s message.

JSON response

The following example uses the json_response parameter to instruct the assistant to return the response as JSON key-value pairs. This is useful if you need to parse the response programmatically.

JSON response cannot be used with the stream parameter.

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

import json
from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")

assistant = pc.assistant.Assistant(assistant_name="example-assistant")

msg = Message(role="user", content="Who is the CFO and CEO of Netflix?")

response = assistant.chat(messages=[msg], json_response=True)

print(json.loads(response))

The example above returns a result like the following:

{
  "finish_reason": "stop",
  "message": {
    "role": "assistant",
    "content": "{\"CFO\": \"Spencer Neumann\", \"CEO\": \"Ted Sarandos and Greg Peters\"}"
  },
  "id": "0000000000000000680c95d2faab7aad",
  "model": "gpt-4o-2024-11-20",
  "usage": {
    "prompt_tokens": 14298,
    "completion_tokens": 42,
    "total_tokens": 14340
  },
  "citations": [
    {
      "position": 24,
      "references": [
        {
          "file": {
            "status": "Available",
            "id": "cbecaa37-2943-4030-b4d6-ce4350ab774a",
            "name": "Netflix-10-K-01262024.pdf",
            "size": 1073470,
            "metadata": {
              "test-key": "test-value"
            },
            "updated_on": "2025-01-24T16:53:17.148820770Z",
            "created_on": "2025-01-24T16:52:44.851577534Z",
            "percent_done": 1,
            "signed_url": "https://storage.googleapis.com/knowledge-prod-files/bf0dcf22...",
            "error_message": null
          },
          "pages": [
            79
          ],
          "highlight": null
        },
    ...
  ]
}

Extract the response content

In the assistant’s response, the message string is contained in the following JSON object:

message.content for the default chat response
delta.content for the streaming chat response
message.content for the JSON response

You can extract the message content and print it to the console:

msg = Message(role="user", content="What is the maximum height of a red pine?")

response = assistant.chat(messages=[msg])

print(str(response.message.content))

This creates output like the following:

A red pine, scientifically known as *Pinus resinosa*, is a medium-sized tree that can grow up to 25 meters high and 75 centimeters in diameter. [1, pp. 1]

msg = Message(role="user", content="What is the maximum height of a red pine?")

response = assistant.chat(messages=[msg])

print(str(response.message.content))

This creates output like the following:

A red pine, scientifically known as *Pinus resinosa*, is a medium-sized tree that can grow up to 25 meters high and 75 centimeters in diameter. [1, pp. 1]

msg = Message(role="user", content="What is the maximum height of a red pine?")

response = assistant.chat(messages=[msg], stream=True)

for data in response:
    if hasattr(data, "delta"):
        print(data.delta.content)

This creates output like the following:

Streaming response

The
 maximum
 height
 of
 a
 red
 pine
 (
Pin
us
 resin
osa
)
 is
 up
 to
 twenty
-five
 meters

 [1, pp. 1]
.

import json

msg = Message(role="user", content="What is the maximum height of a red pine?")

response = assistant.chat(messages=[msg], json_response=True)

print(json.loads(response.message.content))

This creates output like the following:

JSON response

{'red pine': 'A red pine, scientifically known as *Pinus resinosa*, is a medium-sized tree that can grow up to 25 meters high and 75 centimeters in diameter.'}

Choose a model

Pinecone Assistant supports the following models:

gpt-4o (default)
gpt-4.1
o4-mini
claude-3-5-sonnet
claude-3-7-sonnet
gemini-2.5-pro

To choose a non-default model for your assistant, set the model parameter in the request:

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

from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")

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

# Chat with the assistant.
chat_context = [Message(role="user", content="What is the maximum height of a red pine?")]
response = assistant.chat(
    messages=chat_context, 
    model="gpt-4.1"
)

Provide conversation history

Models lack memory of previous requests, so any relevant messages from earlier in the conversation must be present in the messages object.

In the following example, the messages object includes prior messages that are necessary for interpreting the newest message.

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

from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")

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

# Chat with the assistant.
chat_context = [
    Message(content="What is the maximum height of a red pine?", role="user"),
    Message(content="The maximum height of a red pine (Pinus resinosa) is up to 25 meters.", role="assistant"),
    Message(content="What is its maximum diameter?", role="user")
]
response = assistant.chat(messages=chat_context)

The example returns a response like the following:

{
  "finish_reason":"stop",
  "message":{
    "role":"assistant",
    "content":"The maximum diameter of a red pine (Pinus resinosa) is up to 1 meter."
    },
    "id":"0000000000000000236a24a17e55309a",
    "model":"gpt-4o-2024-05-13",
    "usage":{
      "prompt_tokens":21377,
      "completion_tokens":20,
      "total_tokens":21397
      },
      "citations":[...]
}

Filter chat with metadata

You can filter which documents to use for chat completions. The following example filters the responses to use only documents that include the metadata "resource": "encyclopedia".

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

from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")

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

# Chat with the assistant.
chat_context = [Message(role="user", content="What is the maximum height of a red pine?")]
response = assistant.chat(messages=chat_context, stream=True, filter={"resource": "encyclopedia"})

Control the context size

This is available in API versions 2025-04 and later.

To limit the number of input tokens used, you can control the context size by tuning top_k * snippet_size. These parameters can be adjusted by setting context_options in the request:

snippet_size: Controls the max size of a snippet (default is 2048 tokens). Note that snippet size can vary and, in rare cases, may be bigger than the set snippet_size. Snippet size controls the amount of context the model is given for each chunk of text.
top_k: Controls the max number of context snippets sent to the LLM (default is 16). top_k controls the diversity of information sent to the model.

While additional tokens will be used for other parameters (e.g., the system prompt, chat input), adjusting the top_k and snippet_size can help manage token consumption.

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

from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")
assistant = pc.assistant.Assistant(assistant_name="example-assistant")

msg = Message(role="user", content="Who is the CFO of Netflix?")
response = assistant.chat(messages=[msg], context_options={snippet_size=2500, top_k=10})

print(response)

The example will return up to 10 snippets and each snippet will be up to 2500 tokens in size.

To better understand the context retrieved using these parameters, you can retrieve context from an assistant.

Set the sampling temperature

This is available in API versions 2025-04 and later.

Temperature is a parameter that controls the randomness of a model’s predictions during text generation. Lower temperatures (~0.0) yield more consistent, predictable answers, while higher temperatures increase the model’s explanatory power and is generally better for creative tasks.

To control the sampling temperature for a model, set the temperarture parameter in the request. If a model does not support a temperature parameter, the parameter is ignored.

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

from pinecone import Pinecone
from pinecone_plugins.assistant.models.chat import Message

pc = Pinecone(api_key="YOUR_API_KEY")
assistant = pc.assistant.Assistant(assistant_name="example-assistant")

msg = Message(role="user", content="Who is the CFO of Netflix?")
response = assistant.chat(
    messages=[msg], 
    temperature=0.8
)

print(response)

Include citation highlights in the response

Citation highlights are available in the Pinecone console or API versions 2025-04 and later.

When using the standard chat interface, every response includes a citation object. The object includes a reference to the document that the assistant used to generate the response. Additionally, you can include highlights, which are the specific parts of the document that the assistant used to generate the response, by setting the include_highlights parameter to true in the request:

curl

PINECONE_API_KEY="YOUR_API_KEY"
ASSISTANT_NAME="example-assistant"

curl "https://prod-1-data.ke.pinecone.io/assistant/chat/$ASSISTANT_NAME" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-API-Version: 2025-04" \
  -d '{
  "messages": [
    {
      "role": "user",
      "content": "Who is the CFO of Netflix?"
    }
  ],
  "stream": false,
  "model": "gpt-4o",
  "include_highlights": true
}'

The example returns response like the following:

{
  "finish_reason":"stop",
  "message":{
    "role":"assistant",
    "content":"The Chief Financial Officer (CFO) of Netflix is Spencer Neumann."
    },
    "id":"00000000000000006685b07087b1ad42",
    "model":"gpt-4o-2024-05-13",
    "usage":{
      "prompt_tokens":12490,
      "completion_tokens":33,
      "total_tokens":12523
      },
      "citations":[{
        "position":63,
        "references":[{
          "file":{
            "status":"Available",
            "id":"cbecaa37-2943-4030-b4d6-ce4350ab774a",
            "name":"Netflix-10-K-01262024.pdf",
            "size":1073470,
            "metadata":{"test-key":"test-value"},
            "updated_on":"2025-01-24T16:53:17.148820770Z",
            "created_on":"2025-01-24T16:52:44.851577534Z",
            "percent_done":1.0,
            "signed_url":"https://storage.googleapis.com/knowledge-prod-files/b...",
            "error_message":null
            },
            "pages":[78],
            "highlight":{
              "type":"text",
              "content":"EXHIBIT 31.3\nCERTIFICATION OF CHIEF FINANCIAL OFFICER\nPURSUANT TO SECTION 302 OF THE SARBANES-OXLEY ACT OF 2002\nI, Spencer Neumann, certify that:"
              }
            },
            {
              "file":{
                "status":"Available",
                "id":"cbecaa37-2943-4030-b4d6-ce4350ab774a",
                "name":"Netflix-10-K-01262024.pdf",
                "size":1073470,
                "metadata":{"test-key":"test-value"},
                "updated_on":"2025-01-24T16:53:17.148820770Z",
                "created_on":"2025-01-24T16:52:44.851577534Z",
                "percent_done":1.0,
                "signed_url":"https://storage.googleapis.com/knowledge-prod-files/bf...",
                "error_message":null
                },
                "pages":[79],
                "highlight":{
                  "type":"text",
                  "content":"operations of\nNetflix, Inc.\nDated: January 26, 2024  By:  /S/ SPENCER NEUMANN\n  Spencer Neumann\n  Chief Financial Officer"
                }
            }
          ]
        }
      ]
}

Enabling highlights will increase token usage.

Get started

Build an assistant

Upload your data

Chat with an assistant

Evaluate answers

Retrieve context snippets

Integrate with AI agents

Admin

Chat through the standard interface