The chat component enables dynamic, conversational interactions with OpenAI. This endpoint allows you to send messages and receive AI-generated responses in a natural dialogue format.

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Parameters

  • source str: The source of the conversation. This parameter is optional.
  • table Optional[str]: The name of the table to query. Optional for PostgreSQL and ClickHouse sources.

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Returns

Returns a Dictionary containing:

  • type: Always “chat”
  • id: Unique component identifier
  • state: Dictionary containing:
    • messages: List of conversation messages
  • config: Dictionary containing:
    • source: The source of the conversation (optional)
    • data: Pandas DataFrame converted to records format (optional)

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Backend Implementation Details

Implemented in services/openai.js

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Authentication

  • Secure API key handling via environment variables (VITE_OPENAI_API_KEY)
  • Error handling for authentication issues

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Message Processing

  • Manages conversation history and context
  • Optimizes message format for API

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Error Handling

  • Graceful fallbacks for API disruptions
  • User-friendly error messages

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API Integration

  • OpenAI chat completions integration
  • GPT-3.5-turbo implementation
  • Conversation threading support

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Basic Usage

Here’s a basic example of using the chat component:

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CSV

from preswald import chat

# Display the chat widget. Make sure source is defined in preswald.toml
chat(source="iris_csv")

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PostgreSQL Source

For PostgreSQL sources, table_name is required:

from preswald import chat

# Read specific table from PostgreSQL
earthquakes_df = chat('eq_pg', 'earthquake_events')

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ClickHouse Source

Similarly for ClickHouse sources, table_name is required:

from preswald import chat

# Read specific table from ClickHouse
events_df = chat('eq_clickhouse', 'events')

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Interactive Source

The chat component can be used with the selectbox component to allow the datasource to be changed interactively:

from preswald import chat, selectbox
import tomllib

# Get all data sources
with open("preswald.toml", "rb") as f:
    config = tomllib.load(f)

source_list = []
for source_path in config["data"]:
    source_list.append(source_path)
 
# Create a selectbox for choosing a column to visualize
source_choice = selectbox(
    label="Choose a dataset as chat source",
    options=source_list,
)

# Create an AI chat window using the selected source!
chat(source_choice)

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Key Features

  • Natural Conversation: Engage in fluid, context-aware dialogues with Claude
  • Data Integration: Seamlessly incorporate your csv data sources into conversations
  • State Management: Automatic handling of conversation history and context