Congressional Analytics Pipeline

cap-feature-proposal-constitutional-memory-context.md

Feature: Automated Memory Context Creation and Evolution

Description: The automated memory context creation and evolution feature enables the system to infer and evolve memory contexts based on user interactions and feedback. It allows for the dynamic generation, amendment, and revision of rules within the system, ensuring accurate and contextually relevant information delivery.

Requirements:

• Rule Inference:

  • The system should automatically infer rules based on user interactions and feedback.
  • Rules should be generated to define the initial state of the system's memory context.

• Rule Amendment:

  • Users should have the ability to amend existing rules within the system.
  • Amendments should include clear descriptions of proposed changes and any relevant details or specifications.

• System Revision:

  • The system's memory context should be continuously revised and updated based on amendments made to the rules.
  • Revisions should reflect the latest version of the memory context, incorporating all approved amendments.

• User Feedback:

  • Users should be encouraged to provide feedback on the proposed rules and amendments.
  • The system should facilitate the submission of feedback to improve the clarity, effectiveness, and relevance of the memory context.

• Iterative Process:

  • The process of rule creation, amendment, and system revision should be iterative.
  • The system should learn from user interactions and feedback to enhance its performance and adapt to evolving requirements.

• Privacy and Security:

  • The system should adhere to privacy and security standards to protect user data and interactions.
  • Personal information should be handled in accordance with applicable regulations and best practices.

• User Interface:

  • The user interface should provide a seamless and intuitive experience for users to interact with the system.
  • Clear instructions and guidance should be provided to facilitate user understanding and engagement.

• Documentation and Help:

  • Comprehensive documentation should be available to guide users on how to utilize the automated memory context creation and evolution feature.
  • Help resources should be provided to address common questions and assist users in utilizing the feature effectively.

• Testing and Validation:

  • The system should undergo rigorous testing to ensure the accuracy and effectiveness of rule inference, amendment, and revision processes.
  • Validation mechanisms should be in place to verify the correctness of generated rules and the coherence of the memory context.

• Collaboration and Knowledge Sharing:

  • The system should promote collaboration and knowledge sharing among researchers and users.
  • Mechanisms for sharing experiences, insights, and best practices related to memory context creation and evolution should be facilitated.

• Continuous Improvement:

  • The system should support continuous improvement through ongoing monitoring, analysis, and refinement of the memory context creation and evolution processes.
  • User feedback and system performance evaluations should be utilized to drive enhancements and optimize the feature.

Note: This requirements document outlines the key features and functionalities of the automated memory context creation and evolution feature. It emphasizes the need for accurate rule inference, user-driven amendments, continuous system revision, and user feedback integration. Privacy, security, user interface, documentation, testing, collaboration, and continuous improvement are also important aspects to consider for a successful implementation of this feature.

congressional-analytics-pipeline-rfc.md

Congressional Analytics Pipeline

Status: Draft v0.1.2

Overview

I am designing an AI-powered analytics pipeline to help amplify congressional analysis and discourse called the "Congressional Analytics Pipeline" (CAP). As part of our commitment to developing responsibly and serving the public good, we welcome any constructive feedback from the community.

We intend CAP to analyze transcripts, surface insights, model public opinion, and translate findings into legislative priorities and strategy recommendations - enabling staffers, legislators, journalists and citizens to have more informed policy debates grounded in evidence.

Guiding Tenets

In developing CAP, we aim to:

• Improve policy analysis rigor and efficacy
• Monitor lobbying influences more transparently
• Highlight rhetorical techniques and trends
• Enable access to public debates and discourse

Functionality

Current high-level scope includes:

• Transcripts analysis (rhetoric, language trends)
• Public opinion polling integration
• Geospatial visualizations
• Conversational interfaces for key staff personas
• Strategic recommendations to support policy efficacy

Topic Clustering and Concept Tagging

• Ingest congressional transcripts and related documents
• Computationally detect topics discussed
• Cluster documents and speeches by topic similarity
• Annotate topics with linked concepts from knowledge bases
• Enable slicing and dicing of content by topics and concepts

Sentiment and Emotion Analysis

• Detect expressions of sentiment and emotion in speeches and dialogues
• Categorize sentiment as positive, negative or neutral
• Recognize fine-grained emotions like joy, sadness, trust, fear, etc.
• Associate sentiment and emotions with targets like bills, policies, groups
• Summarize sentiment flows throughout debates and over time

Rhetorical Analysis

• Computationally detect rhetorical devices and patterns in text
• Identify techniques like metaphors, analogies, rhetorical questions
• Analyze speech act patterns (requests, promises, warnings, etc.)
• Model impact of rhetorical choices on audience reception and persuasion
• Compare rhetorical profile by individual, party, state, over time

Entity-Event Timeline Linking

• Extract key entities from congressional transcripts (people, organizations, locations)
• Identify significant events from external data sources (news, social media, public data)
• Link entities to event timeline with confidence scores
• Enable exploratory analysis of entity-event connections over time

User Stories

Congressional Staff

• As a chief of staff, I want to analyze changes in partisan rhetoric over the last 5 years so I can advise on bipartisan policy crafting
• As a legislative aide, I want to compare policy sentiment between committee members so I can identify persuadable targets
• As a communications director, I want to discover impactful speech patterns so I can incorporate them into future press events
• As a district outreach coordinator, I want to match district opinion polls with my member's recent speech so I can provide guidance on connecting with constituents

Legislative Aide

• As a legislative aide, I want to be alerted to bills related to my policy area so I can track likelihood of passage
• As a legislative aide, I want view fine-grained debate transcripts annotated by topic so I can quickly research areas of interest
• As a legislative aide, I want to analyze the rhetorical tactics used by sponsors so I can incorporate effective techniques

Chief of Staff

• As a chief of staff, I want to explore member alignment by committee so I advise my boss on building coalitions
• As a chief of staff, I want to discover vote outcome predictions so I can anticipate pressures on my boss
• As a chief of staff, I want to compare my member's speech patterns by state so I can recommend tailoring messaging

Communications Director

• As a communications director, I want to detect surges in chatter on bills so I can prepare public positions
• As a communications director, I want to uncover phrases resonating with citizens so I can integrate them into talking points
• As a communications director, I want to model how current events impact language so I can advise on responsive rhetoric

Feedback Welcomed

We welcome diverse perspectives to provide input on:

• Priorities for capabilities
• Workflow integration guidance
• Additional functionality requests

congressional-analytics-pipeline-system-architecture.md

Congressional Analytics Pipeline Design

Status: Draft
Last Updated: Dec 16, 2023

This document outlines the high-level design for the Congressional Analytics Pipeline, centered on a graph database architecture.

Overview

The graph database forms the core backbone, interconnecting key congressional data domains like speeches, speakers, committees, and bills. Relationships are created via co-sponsorships, committee memberships, bill authorships, and debates.

This flexible structure allows running targeted graph algorithms for recommendations, similarity search, centrality ranking, and community detection. It also powers various services exposed through APIs and visualization interfaces.

Supplementary pipelines enrich textual transcripts and unstructured data with semantic metadata features for improved analysis.

The system ingests the latest data by scraping Congress.gov and other sources. Purpose-built scrapers handle various formats like text, audio, or video records.

Components

Key Components

• Graph Database: Central data store and computational engine
• Web Scrapers: Gathering raw congressional transcripts, articles, social posts
• APIs: Programmatic interfaces for queries and access
• Visual Explorer: Interactive dashboard for insights

Supporting Components

• Validation and Filtering: Ensuring data quality
• Enrichment Pipelines: Text analysis, metadata extraction
• Caching Layer: Performance and scale

congressional-data-operator-sop.md

Congressional Data Operator Standard Operating Procedures

Version: Draft v1.0
Date: December 16, 2023

Purpose

This document provides the standard operating procedures for the Congressional Data Operator role. It outlines the responsibilities, systems, workflows, tools, and techniques needed to manually fulfill congressional data source requests.

Role Responsibilities

The Congressional Data Operator is responsible for the following core functions:

• Monitoring the manual procurement queue for tasks to compile unavailable congressional data sources
• Researching sources and contacting providers to gain access credentials or procurement methods
• Extracting, transforming, loading needed data from sources into analytics infrastructure
• Testing data extracts thoroughly to ensure reliability and quality
• Documenting all sources, access methods, extraction steps

Workflow Instructions

Overview

When a request for an unavailable congressional data source enters the system, automation attempts compilation first. If unsuccessful after retries, it gets routed to the manual fulfillment queue with priority weighting.

Operators would:

1. Check the queue
2. Investigate the failed request details
3. Research sources
4. Contact providers
5. Procure access
6. Compile data
7. Test extracts
8. Mark request as fulfilled
9. Callback inserts data
10. Notify requestor

Workflow Instructions

Overview

When unavailable requests enter, automation attempts compilation. If unsuccessful after retries, requests get routed to manual queue prioritized by weight.

Manual Queue Detailed Steps

1. Log in to Operator Portal
2. Select "Manual Tasks Queue"
3. Sort by Priority + Due Date
4. Select highest priority

Architecture Diagrams

System Architecture

Workflow Architecture

Troubleshooting Tips

Authentication Issues

Clear cookies and cache before retrying. Verify account permissions.

Explaining the Data Ingestion Process

The process starts with the Requestor submitting a request for data to the Ingestion system. At this point, one of two paths is followed:

1. Automated Compilation

   • If the request can be fulfilled through an automated compilation process:

     • Attempt 1 is made to collect and compile the requested data.

     • If this initial Attempt is a Success, the compiled data is sent directly to the Data Platform.

     • If the first attempt fails, the system checks if the Retry Limit has been reached. If not, additional automated attempts are made to fulfill the request.

2. Manual Task

   • If manual effort is required to fulfill the request:

     • The request is Prioritized & Assigned to the appropriate data specialist(s).

     • The assigned specialist(s) begin Researching Sources to identify where the required data resides.

     • Contact is Made with the various Data Providers to request access to the needed data.

     • If Access is Not Secured initially, repeated contact attempts are made until access is finally granted.

     • Once access is secured, the process moves forward with Extracting and Transforming the Data for compatibility with internal systems and data models.

     • The compiled data set is Staged within the ingestion environment.

     • A Review of the Data Quality takes place, surfacing any issues that need to be addressed.

     • If issues are found, a loop of Troubleshooting & Resolving those issues ensues until all data quality criteria are met.

     • Finally, the fulfillment process is Marked as Complete and the cleaned, transformed data set is loaded into the Data Platform.

     • The Requestor is notified that their request has been successfully fulfilled.