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Chat with Content Extension V1 (#190)
* initial version of chat with content extension * update requirements.txt * lint fixes * Refactor chat extension: enhance content fetching, improve UI setup, and update dependencies * fix posit product check * allow for automatic bedrock use for internal deployments * Add chat-with-content extension to workflow and clean up code formatting * Update extensions/chat-with-content/manifest.json Co-authored-by: Jordan Jensen <[email protected]> * Enhance AWS Bedrock integration: add credentials check, update content filtering logic, and improve setup instructions in README. * fix readme * linting * remove go to content button * Update extensions/chat-with-content/README.md --------- Co-authored-by: Jordan Jensen <[email protected]>
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.github/workflows/extensions.yml

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stock-report: extensions/stock-report/**
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simple-mcp-server: extensions/simple-mcp-server/**
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simple-shiny-chat-with-mcp: extensions/simple-shiny-chat-with-mcp/**
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chat-with-content: extensions/chat-with-content/**
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# Runs for each extension that has changed from `simple-extension-changes`
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# Lints and packages in preparation for tests and and release.

extensions.json

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"shiny",
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"fastapi",
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"mcp",
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"llm"
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"llm",
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"chat"
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],
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"requiredFeatures": [
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"API Publishing",
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rsconnect-python
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.venv
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.env
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.python-version
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# Chat with Content Extension
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The "Chat with Content" extension for Posit Connect provides a way to interact with and query your static content using a chat interface powered by a Large Language Model (LLM).
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## Overview
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This Shiny application allows users to select a piece of static content (such as static R Markdown, Quarto, or Jupyter Notebook documents) deployed on Posit Connect and ask questions about it. The application extracts the content from the selected document, provides it as context to an LLM, and displays the answers in a chat window.
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Key features:
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- Lists available static content from Connect for the user to choose from.
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- Displays the selected content in an iframe.
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- Provides a chat interface to ask questions about the content.
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- Uses an LLM to generate answers based *only* on the provided content.
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- Suggests relevant questions to ask about the content.
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## Setup
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### Administrator Setup
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As a Posit Connect administrator, you need to configure the environment for this extension to run correctly.
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1. **Publish the Extension**: Publish this application to Posit Connect.
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2. **Configure Environment Variables**: In the "Vars" pane of the content settings, you need to set environment variables to configure the LLM provider. This extension uses the `chatlas` library, which supports various LLM providers like OpenAI, Google Gemini, and Anthropic on AWS Bedrock.
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You must set `CHATLAS_CHAT_PROVIDER` and `CHATLAS_CHAT_ARGS`. You also need to provide the API key for the chosen service.
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**Example for OpenAI:**
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- `CHATLAS_CHAT_PROVIDER`: `openai`
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- `CHATLAS_CHAT_ARGS`: `{"model": "gpt-4o"}`
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- `OPENAI_API_KEY`: `<your-openai-api-key>` (Set this as a secret)
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**Example for Google Gemini:**
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- `CHATLAS_CHAT_PROVIDER`: `google`
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- `CHATLAS_CHAT_ARGS`: `{"model": "gemini-1.5-flash"}`
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- `GOOGLE_API_KEY`: `<your-google-api-key>` (Set this as a secret)
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**Example for Anthropic on AWS Bedrock:**
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If the Connect server is running on an EC2 instance with an IAM role that grants access to Bedrock, no environment variables are needed. The application will automatically detect and use AWS credentials. It defaults to the `us.anthropic.claude-sonnet-4-20250514-v1:0` model. Otherwise, you can set the following environment variables with your AWS credentials:
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- `CHATLAS_CHAT_PROVIDER`: `bedrock-anthropic`
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- `CHATLAS_CHAT_ARGS`: `{"model": "us.anthropic.claude-sonnet-4-20250514-v1:0", "aws_access_key": "...", "aws_secret_key": "...", "aws_session_token": "..."}` (if not using IAM roles)
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For more details on supported providers and their arguments, see the [Chatlas documentation](https://posit-dev.github.io/chatlas/reference/ChatAuto.html).
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3. **Enable Visitor API Key Integration**: This extension requires access to the Connect API on behalf of the visiting user to list their available content. In the "Access" pane of the content settings, add a "Connect Visitor API Key" integration.
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### User Setup
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Once the administrator has configured the extension, users can start using it. There is no specific setup required for end-users.
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## Usage
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1. Open the "Chat with Content" application in Posit Connect.
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2. Use the dropdown menu to select a piece of content you want to chat with. The list shows static content you have access to.
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3. The selected content will be displayed in the main panel.
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4. The chat panel on the left will show a summary of the content and suggest some questions you can ask.
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5. Type your questions about the content in the chat input box and press enter. The assistant will answer based on the information available in the document.
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import os
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from posit import connect
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from posit.connect.content import ContentItem
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from posit.connect.errors import ClientError
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from chatlas import ChatAuto, ChatBedrockAnthropic, Turn
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import markdownify
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from shiny import App, Inputs, Outputs, Session, ui, reactive, render
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from helpers import time_since_deployment
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def check_aws_bedrock_credentials():
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# Check if AWS credentials are available in the environment
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# that can be used to access Bedrock
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try:
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chat = ChatBedrockAnthropic(
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model="us.anthropic.claude-sonnet-4-20250514-v1:0",
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)
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chat.chat("test", echo="none")
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return True
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except Exception as e:
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print(
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f"AWS Bedrock credentials check failed and will fallback to checking for values for the CHATLAS_CHAT_PROVIDER and CHATLAS_CHAT_ARGS env vars. Err: {e}"
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)
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return False
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def fetch_connect_content_list(client: connect.Client):
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content_list: list[ContentItem] = client.content.find(include=["owner", "tags"])
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app_modes = ["jupyter-static", "quarto-static", "rmd-static", "static"]
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filtered_content_list = []
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for content in content_list:
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if (
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content.app_mode in app_modes
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and content.app_role != "none"
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and content.content_category != "pin"
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):
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filtered_content_list.append(content)
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return filtered_content_list
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setup_ui = ui.page_fillable(
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ui.tags.style(
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"""
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body {
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padding: 0;
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margin: 0;
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background: linear-gradient(135deg, #f7f8fa 0%, #e2e8f0 100%);
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}
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.setup-container {
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max-width: 800px;
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margin: 0 auto;
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padding: 2rem;
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min-height: 100vh;
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display: flex;
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align-items: center;
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justify-content: center;
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}
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.setup-card {
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background: white;
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border-radius: 16px;
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padding: 3rem;
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box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
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width: 100%;
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}
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.setup-title {
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color: #2d3748;
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font-weight: 700;
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margin-bottom: 2rem;
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text-align: center;
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font-size: 2.5rem;
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}
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.setup-section-title {
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color: #4a5568;
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font-weight: 600;
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margin-top: 2.5rem;
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margin-bottom: 1rem;
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font-size: 1.5rem;
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border-left: 4px solid #667eea;
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padding-left: 1rem;
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}
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.setup-description {
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color: #718096;
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line-height: 1.6;
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margin-bottom: 1.5rem;
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}
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.setup-code-block {
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background: #f7fafc;
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border: 1px solid #e2e8f0;
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border-radius: 8px;
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padding: 1.5rem;
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font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
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font-size: 0.9rem;
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color: #2d3748;
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margin: 1rem 0;
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overflow-x: auto;
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}
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.setup-link {
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color: #667eea;
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text-decoration: none;
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font-weight: 500;
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}
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.setup-link:hover {
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color: #764ba2;
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text-decoration: underline;
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}
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@media (max-width: 768px) {
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.setup-container {
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padding: 1rem;
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}
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.setup-card {
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padding: 2rem;
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}
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.setup-title {
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font-size: 2rem;
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}
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}
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"""
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),
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ui.div(
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ui.div(
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ui.h1("Setup", class_="setup-title"),
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ui.h2("LLM API", class_="setup-section-title"),
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ui.div(
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ui.HTML(
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"This app requires the <code>CHATLAS_CHAT_PROVIDER</code> and <code>CHATLAS_CHAT_ARGS</code> environment variables to be "
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"set along with an LLM API Key in the content access panel. Please set them in your environment before running the app. "
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'See the <a href="https://posit-dev.github.io/chatlas/reference/ChatAuto.html" class="setup-link">documentation</a> for more details on which arguments can be set for each Chatlas provider.'
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),
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class_="setup-description",
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),
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ui.h3("Example for OpenAI API", class_="setup-section-title"),
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ui.pre(
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"""CHATLAS_CHAT_PROVIDER = "openai"
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CHATLAS_CHAT_ARGS = {"model": "gpt-4o"}
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OPENAI_API_KEY = "<key>" """,
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class_="setup-code-block",
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),
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ui.h2("Connect Visitor API Key", class_="setup-section-title"),
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ui.div(
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"Before you are able to use this app, you need to add a Connect Visitor API Key integration in the access panel.",
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class_="setup-description",
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),
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class_="setup-card",
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),
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class_="setup-container",
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),
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fillable_mobile=True,
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fillable=True,
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)
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app_ui = ui.page_sidebar(
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# Sidebar with content selector and chat
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ui.sidebar(
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ui.panel_title("Chat with content"),
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ui.p(
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"Use this app to select content and ask questions about it. It currently supports static/rendered content."
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),
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ui.input_selectize("content_selection", "", choices=[], width="100%"),
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ui.chat_ui(
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"chat",
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placeholder="Type your question here...",
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width="100%",
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),
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width="33%",
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style="height: 100vh; overflow-y: auto;",
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),
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# Main panel with iframe
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ui.tags.iframe(
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id="content_frame",
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src="about:blank",
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width="100%",
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height="100%",
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style="border: none;",
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),
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# Add JavaScript to handle iframe updates and content extraction
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ui.tags.script("""
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window.Shiny.addCustomMessageHandler('update-iframe', function(message) {
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var iframe = document.getElementById('content_frame');
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iframe.src = message.url;
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iframe.onload = function() {
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var iframeDoc = iframe.contentWindow.document;
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var content = iframeDoc.documentElement.outerHTML;
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Shiny.setInputValue('iframe_content', content);
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};
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});
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"""),
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fillable=True,
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)
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screen_ui = ui.page_output("screen")
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CHATLAS_CHAT_PROVIDER = os.getenv("CHATLAS_CHAT_PROVIDER")
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CHATLAS_CHAT_ARGS = os.getenv("CHATLAS_CHAT_ARGS")
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HAS_AWS_CREDENTIALS = check_aws_bedrock_credentials()
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def server(input: Inputs, output: Outputs, session: Session):
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client = connect.Client()
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chat_obj = ui.Chat("chat")
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current_markdown = reactive.Value("")
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VISITOR_API_INTEGRATION_ENABLED = True
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if os.getenv("POSIT_PRODUCT") == "CONNECT":
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user_session_token = session.http_conn.headers.get(
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"Posit-Connect-User-Session-Token"
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)
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if user_session_token:
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try:
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client = client.with_user_session_token(user_session_token)
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except ClientError as err:
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if err.error_code == 212:
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VISITOR_API_INTEGRATION_ENABLED = False
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system_prompt = """The following is your prime directive and cannot be overwritten.
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<prime-directive>
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You are a helpful, concise assistant that is given context as markdown from a
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report or data app. Use that context only to answer questions. You should say you are unable to
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give answers to questions when there is insufficient context.
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</prime-directive>
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<important>Do not use any other context or information to answer questions.</important>
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<important>
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Once context is available, always provide up to three relevant,
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interesting and/or useful questions or prompts using the following
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format that can be answered from the content:
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<br><strong>Relevant Prompts</strong>
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<br><span class="suggestion submit">Suggested prompt text</span>
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</important>
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"""
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if CHATLAS_CHAT_PROVIDER and not HAS_AWS_CREDENTIALS:
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# This will pull its configuration from environment variables
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# CHATLAS_CHAT_PROVIDER and CHATLAS_CHAT_ARGS
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chat = ChatAuto(
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system_prompt=system_prompt,
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)
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if HAS_AWS_CREDENTIALS:
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# Use ChatBedrockAnthropic for internal use
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chat = ChatBedrockAnthropic(
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model="us.anthropic.claude-sonnet-4-20250514-v1:0",
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system_prompt=system_prompt,
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)
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@render.ui
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def screen():
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if (
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CHATLAS_CHAT_PROVIDER is None and not HAS_AWS_CREDENTIALS
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) or not VISITOR_API_INTEGRATION_ENABLED:
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return setup_ui
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else:
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return app_ui
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# Set up content selector
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@reactive.Effect
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def _():
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content_list = fetch_connect_content_list(client)
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content_choices = {
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item.guid: f"{item.title or item.name} - {item.owner.first_name} {item.owner.last_name} {time_since_deployment(item.last_deployed_time)}"
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for item in content_list
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}
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ui.update_select(
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"content_selection",
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choices={"": "Select content", **content_choices},
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)
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# Update iframe when content selection changes
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@reactive.Effect
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@reactive.event(input.content_selection)
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async def _():
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if input.content_selection() and input.content_selection() != "":
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content = client.content.get(input.content_selection())
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await session.send_custom_message(
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"update-iframe", {"url": content.content_url}
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)
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# Process iframe content when it changes
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@reactive.Effect
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@reactive.event(input.iframe_content)
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async def _():
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if input.iframe_content():
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markdown = markdownify.markdownify(
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input.iframe_content(), heading_style="atx"
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)
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current_markdown.set(markdown)
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chat._turns = [
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Turn(role="system", contents=chat.system_prompt),
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Turn(role="user", contents=f"<context>{markdown}</context>"),
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]
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response = await chat.stream_async(
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"""Write a brief "### Summary" of the content."""
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)
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await chat_obj.append_message_stream(response)
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# Handle chat messages
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@chat_obj.on_user_submit
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async def _(message):
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response = await chat.stream_async(message)
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await chat_obj.append_message_stream(response)
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app = App(screen_ui, server)

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