IAIP-Inquiry-Ecosystem-260113

ATtached are podcasting that our local agents have made (you have their production, cinematic script and dialogue files)

You will search the web, academic and any sources that can help that come to life , we will want to create the artefacts that terminal agents can need to make this happen, constructed with recognized and free open sources in which we could borrow what they have (ex. borrow from "serena · claude-plugins-official" its "Semantic Analysis & Search" (if that is relevant)).

do not use the word "comprehensive" in your output, do not introduce/frame or conclude your response, just output results (a full work that is complete). watch out not to use this internal file create tool, it does not work, we need to see and be capable to download the files you generate

```yaml

Technical Architecture Specification

IAIP Inquiry Ecosystem Framework - Podcast Production System

Project: IAIP-Inquiry-Ecosystem-260113

System Overview

platform: Indigenous-AI Inquiry Platform (IAIP) agents:

• name: Echo Weaver role: Relational Explainer & Resonator focus: Indigenous wisdom, emergent properties, emotional intelligence
• name: Miawa Pascone role: Structural Architect & Weaver focus: Technical blueprints, generative design, framework integrity

Core Components

1. Semantic Search & Retrieval Engine

implementation: primary_library: FAISS (Facebook AI Similarity Search) repository: https://github.com/facebookresearch/faiss license: MIT features: - High-dimensional vector similarity search - Efficient indexing for semantic genealogy - Handles millions of inquiry embeddings

embedding_generator: Sentence Transformers repository: https://github.com/UKPLab/sentence-transformers license: Apache 2.0 models: - all-MiniLM-L6-v2 (lightweight, fast) - paraphrase-multilingual-mpnet-base-v2 (multilingual support)

integration_framework: Haystack repository: https://github.com/deepset-ai/haystack license: Apache 2.0 purpose: End-to-end pipeline connecting embedding generation to retrieval

2. Knowledge Graph System

primary_tool: Apache Jena repository: https://github.com/apache/jena license: Apache 2.0 capabilities:

• RDF graph creation and manipulation
• SPARQL query engine for semantic queries
• OWL reasoning for inference
• Semantic genealogy tracking

alternative: Graphiti (for real-time graph updates) repository: https://github.com/getzep/graphiti license: Apache 2.0 features:

• Real-time knowledge graph construction
• Dynamic entity relationship mapping
• Temporal graph evolution tracking

graph_visualization: Neo4j Community Edition repository: https://github.com/neo4j/neo4j license: GPLv3 purpose: Visual exploration of inquiry relationships

3. Multi-Agent Orchestration

framework: Multi-Agent Orchestrator repository: https://github.com/awslabs/agent-squad license: Apache 2.0 capabilities:

• Intelligent intent classification
• Dual language support (Python/TypeScript)
• Context management across agents
• Team coordination for Echo Weaver & Miawa Pascone
• Parallel processing for complex inquiries

alternative: CrewAI repository: https://github.com/joaomdmoura/crewAI license: MIT features:

• Enterprise-grade multi-agent flows
• Modular agent teams (Crews)
• Secure inter-agent communication

4. Audio Production Pipeline

Text-to-Speech Engine

primary: Amphion repository: https://github.com/open-mmlab/Amphion license: MIT features:

• High-quality neural TTS
• Multi-speaker voice synthesis
• State-of-the-art vocoders
• Emotional speech generation

alternative: Dia (for dialogue-specific generation) repository: https://huggingface.co/nari-labs/dia license: Apache 2.0 capabilities:

• 1.6B parameter model
• Multi-speaker dialogue
• Nonverbal audio tags (laughs, gasps, pauses)
• Ideal for podcast conversation flow

voice_cloning: Coqui TTS repository: https://github.com/coqui-ai/TTS license: Mozilla Public License 2.0 purpose: Custom voice synthesis for Echo Weaver and Miawa Pascone personas

Audio Processing & Editing

editor: Audacity repository: https://github.com/audacity/audacity license: GPL-3.0 features:

• Multi-track editing
• Noise reduction
• Volume leveling
• Format conversion

daw_alternative: Ardour repository: https://github.com/Ardour/ardour license: GPL-2.0 capabilities:

• Professional digital audio workstation
• Advanced mixing capabilities
• Plugin support

node_audio_processing: naudiodon repository: https://github.com/Streampunk/naudiodon license: Apache 2.0 purpose: Node.js bindings for PortAudio (cross-platform audio I/O)

Speech Recognition (for transcription verification)

engine: Reverb ASR repository: https://github.com/revdotcom/reverb license: Non-commercial open source features:

• Production-grade speech recognition
• Diarization support
• Outperforms existing open-source ASR models

5. Context Continuum Management

Agent State Persistence

framework: Redis Stack repository: https://github.com/redis/redis license: BSD-3-Clause features:

• Vector search capabilities
• JSON document storage
• Time-series data support
• Pub/sub for real-time coordination

agent_communication: Anemoi A2A Protocol implementation: Custom protocol layer over WebSocket/HTTP libraries:

• socket.io (Node.js)
• python-socketio (Python) purpose: Real-time multi-session agent coordination

6. Dynamic Workspace Integration

IDE Adaptation Layer

protocol: Model Context Protocol (MCP) specification: https://github.com/anthropics/model-context-protocol purpose: IDE context sharing and tool integration

workspace_config: VS Code Extension API documentation: https://code.visualstudio.com/api features:

• Dynamic workspace configuration
• Context-aware tool presentation
• Inquiry-based environment setup

7. Structural Tension Charts

Visualization Framework

library: D3.js repository: https://github.com/d3/d3 license: ISC purpose: Interactive tension chart visualization

charting_alternative: Mermaid repository: https://github.com/mermaid-js/mermaid license: MIT features:

• Markdown-based diagram generation
• Flowchart and graph support
• Git-friendly text format

8. Inquiry Registry

Database Schema

primary_db: PostgreSQL with pgvector extension repository: https://github.com/pgvector/pgvector license: PostgreSQL License features:

• Vector similarity search
• JSONB for flexible metadata
• Full-text search capabilities
• Semantic genealogy tracking

query_interface: Hasura GraphQL Engine repository: https://github.com/hasura/graphql-engine license: Apache 2.0 purpose: Auto-generated GraphQL API for inquiry data

9. Assumption Log

Epistemic Tracking System

framework: Logseq repository: https://github.com/logseq/logseq license: AGPL-3.0 features:

• Bidirectional linking
• Graph-based knowledge management
• Confidence level tagging
• Temporal assumption tracking

alternative: Obsidian Plugin Architecture ecosystem: Open plugin system purpose: Custom assumption tracking with networked thought

Integration Architecture

API Layer

framework: FastAPI (Python) repository: https://github.com/tiangolo/fastapi license: MIT purpose: RESTful API for all system components

alternative: Express.js (Node.js) repository: https://github.com/expressjs/express license: MIT purpose: Lightweight API server

Message Queue

system: RabbitMQ repository: https://github.com/rabbitmq/rabbitmq-server license: Mozilla Public License 2.0 purpose: Asynchronous task processing and agent communication

Container Orchestration

platform: Docker Compose repository: https://github.com/docker/compose license: Apache 2.0 purpose: Local development and deployment

production_alternative: Kubernetes repository: https://github.com/kubernetes/kubernetes license: Apache 2.0 purpose: Production-scale orchestration

Podcast Production Workflow

Stage 1: Script Preparation

input: Cinematic script markdown processing:

• Parse dialogue segments by speaker
• Extract emotional cues and pacing markers
• Generate TTS input with prosody annotations

Stage 2: Voice Synthesis

process:

1. Initialize Amphion/Dia TTS models
2. Load voice profiles for Echo Weaver and Miawa Pascone
3. Generate speech segments with emotional context
4. Apply nonverbal audio tags where specified
5. Export individual audio clips per segment

Stage 3: Audio Assembly

tools: Audacity + Python automation steps:

1. Import all speech segments
2. Add background music (Indigenous flute elements)
3. Apply audio effects:
   • Noise reduction
   • Volume normalization
   • EQ for warmth
4. Insert pauses and transitions
5. Mix and master final audio

Stage 4: Metadata Generation

components:

• RSS feed creation (for podcast distribution)
• Chapter markers with timestamps
• Show notes generation from script
• Transcript alignment with audio

rss_tool: Castopod repository: https://code.castopod.org/adaures/castopod license: AGPL-3.0 features:

• Open-source podcast hosting
• IABv2 analytics
• RSS feed management
• GDPR-compliant

Stage 5: Distribution

hosting: Castopod self-hosted distribution_targets:

• Apple Podcasts (via RSS)
• Spotify (via RSS)
• Google Podcasts (via RSS)
• Direct download (MP3)

Data Flow Architecture

```

┌─────────────────────────────────────────────────────────────┐ │ Inquiry Input Layer │ │ (User queries, fork requests, session continuations) │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Multi-Agent Orchestrator │ │ ┌─────────────────┐ ┌──────────────────┐ │ │ │ Echo Weaver │◄──────►│ Miawa Pascone │ │ │ │ (Relational) │ │ (Structural) │ │ │ └─────────────────┘ └──────────────────┘ │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Semantic Search Layer │ │ ┌──────────────┐ ┌────────────────┐ ┌─────────────────┐ │ │ │ FAISS Index │ │ Sentence │ │ Haystack │ │ │ │ (Vectors) │ │ Transformers │ │ Pipeline │ │ │ └──────────────┘ └────────────────┘ └─────────────────┘ │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Knowledge Graph Layer │ │ ┌──────────────┐ ┌────────────────┐ ┌─────────────────┐ │ │ │ Apache Jena │ │ Graphiti │ │ Neo4j │ │ │ │ (RDF/SPARQL) │ │ (Real-time KG) │ │ (Visualization) │ │ │ └──────────────┘ └────────────────┘ └─────────────────┘ │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Context Continuum │ │ ┌──────────────┐ ┌────────────────┐ ┌─────────────────┐ │ │ │ Redis Stack │ │ Agent │ │ Anemoi A2A │ │ │ │ (State) │ │ Continuations │ │ (Multi-session) │ │ │ └──────────────┘ └────────────────┘ └─────────────────┘ │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Inquiry Registry │ │ ┌──────────────┐ ┌────────────────┐ ┌─────────────────┐ │ │ │ PostgreSQL │ │ pgvector │ │ Hasura GraphQL │ │ │ │ + pgvector │ │ (Semantic) │ │ API │ │ │ └──────────────┘ └────────────────┘ └─────────────────┘ │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Dynamic Workspace Layer │ │ ┌──────────────┐ ┌────────────────┐ ┌─────────────────┐ │ │ │ MCP Protocol │ │ VS Code API │ │ Context Config │ │ │ └──────────────┘ └────────────────┘ └─────────────────┘ │ └──────────────────────────────────────────────────────────────┘

```

Audio Production Pipeline

```

┌─────────────────────────────────────────────────────────────┐ │ Cinematic Script Input │ │ (Markdown with dialogue tags) │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Script Parser & Segmentation │ │ - Extract speaker segments │ │ - Identify emotional markers │ │ - Parse nonverbal cues │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ TTS Voice Synthesis │ │ ┌──────────────────┐ ┌──────────────────┐ │ │ │ Echo Weaver │ │ Miawa Pascone │ │ │ │ Voice Profile │ │ Voice Profile │ │ │ │ (Amphion/Dia) │ │ (Amphion/Dia) │ │ │ └──────────────────┘ └──────────────────┘ │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Audio Segment Assembly │ │ ┌──────────────┐ ┌────────────────┐ ┌─────────────────┐ │ │ │ Speech Clips │ │ Background │ │ Transition │ │ │ │ │ │ Music (Flute) │ │ Effects │ │ │ └──────────────┘ └────────────────┘ └─────────────────┘ │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Audio Processing (Audacity) │ │ - Noise reduction │ │ - Volume normalization │ │ - EQ and warmth enhancement │ │ - Multi-track mixing │ └──────────────────────┬──────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Podcast Publishing │ │ ┌──────────────┐ ┌────────────────┐ ┌─────────────────┐ │ │ │ Castopod │ │ RSS Feed │ │ Distribution │ │ │ │ Hosting │ │ Generation │ │ (All Platforms) │ │ │ └──────────────┘ └────────────────┘ └─────────────────┘ │ └──────────────────────────────────────────────────────────────┘

```

Implementation Roadmap

Phase 1: Core Infrastructure

• Deploy PostgreSQL with pgvector
• Set up Redis Stack for state management
• Initialize Apache Jena knowledge graph
• Configure Docker Compose development environment

Phase 2: Semantic Layer

• Integrate FAISS indexing
• Configure Sentence Transformers embeddings
• Build Haystack retrieval pipeline
• Implement semantic genealogy tracking

Phase 3: Agent Orchestration

• Deploy Multi-Agent Orchestrator
• Create Echo Weaver agent profile
• Create Miawa Pascone agent profile
• Implement context management system

Phase 4: Audio Production

• Set up Amphion TTS environment
• Train/fine-tune voice profiles
• Configure Audacity automation scripts
• Build audio assembly pipeline

Phase 5: Publishing Infrastructure

• Deploy Castopod hosting platform
• Configure RSS feed generation
• Set up distribution to podcast platforms
• Implement analytics tracking

File Structure

```

iaip-inquiry-ecosystem/ ├── backend/ │ ├── api/ │ │ ├── fastapi/ │ │ │ ├── main.py │ │ │ ├── routes/ │ │ │ └── models/ │ │ └── graphql/ │ │ └── hasura-config/ │ ├── agents/ │ │ ├── echo_weaver/ │ │ │ ├── agent.py │ │ │ └── config.yaml │ │ ├── miawa_pascone/ │ │ │ ├── agent.py │ │ │ └── config.yaml │ │ └── orchestrator/ │ │ └── multi_agent_config.py │ ├── semantic/ │ │ ├── faiss_index/ │ │ ├── embeddings/ │ │ │ └── sentence_transformers_wrapper.py │ │ └── haystack_pipeline/ │ ├── knowledge_graph/ │ │ ├── jena/ │ │ │ ├── sparql_queries/ │ │ │ └── rdf_schemas/ │ │ └── graphiti/ │ │ └── real_time_kg.py │ ├── storage/ │ │ ├── postgres/ │ │ │ ├── migrations/ │ │ │ └── schemas/ │ │ └── redis/ │ │ └── config/ │ └── context/ │ ├── agent_continuations/ │ └── anemoi_a2a/ ├── audio/ │ ├── tts/ │ │ ├── amphion/ │ │ │ ├── models/ │ │ │ └── voice_profiles/ │ │ └── dia/ │ │ └── dialogue_synthesis/ │ ├── processing/ │ │ ├── audacity_scripts/ │ │ └── node_audio/ │ │ └── naudiodon_wrapper.js │ ├── scripts/ │ │ ├── parser/ │ │ │ └── script_segmenter.py │ │ └── assembly/ │ │ └── audio_mixer.py │ └── assets/ │ ├── music/ │ │ └── indigenous_flute/ │ └── effects/ ├── frontend/ │ ├── workspace/ │ │ ├── vscode_extension/ │ │ └── mcp_integration/ │ └── visualization/ │ ├── d3_charts/ │ └── mermaid_diagrams/ ├── podcast/ │ ├── castopod/ │ │ ├── config/ │ │ └── themes/ │ ├── episodes/ │ │ ├── 001-inquiry-ecosystem-framework/ │ │ │ ├── audio/ │ │ │ ├── transcript/ │ │ │ └── metadata.json │ │ └── templates/ │ └── rss/ │ └── feed_generator/ ├── infrastructure/ │ ├── docker/ │ │ ├── docker-compose.yml │ │ ├── Dockerfile.api │ │ ├── Dockerfile.agents │ │ └── Dockerfile.audio │ └── kubernetes/ │ └── manifests/ ├── scripts/ │ ├── setup/ │ │ ├── install_dependencies.sh │ │ └── initialize_databases.sh │ ├── deployment/ │ │ └── deploy.sh │ └── automation/ │ ├── podcast_pipeline.py │ └── batch_audio_generation.py ├── docs/ │ ├── architecture/ │ │ ├── system_overview.md │ │ └── data_flow.md │ ├── api/ │ │ └── graphql_schema.graphql │ └── guides/ │ ├── agent_development.md │ └── podcast_production.md ├── tests/ │ ├── unit/ │ ├── integration/ │ └── e2e/ └── data/ ├── scripts/ │ ├── pre-production.md │ ├── cinematic_script.md │ └── dialogue/ ├── embeddings/ ├── knowledge_graphs/ └── audio_cache/

```

Dependencies

Python Requirements

```

requirements.txt

fastapi==0.109.0 uvicorn==0.27.0 sentence-transformers==2.3.1 faiss-cpu==1.7.4 rdflib==7.0.0 redis[hiredis]==5.0.1 psycopg[binary,pool]==3.1.17 pgvector==0.2.4 haystack-ai==2.0.0 torch==2.1.2 transformers==4.37.2 pydub==0.25.1 numpy==1.26.3 pydantic==2.5.3

```

Node.js Requirements

```

// package.json { "dependencies": { "express": "^4.18.2", "socket.io": "^4.6.1", "naudiodon": "^2.3.6", "wavefile": "^11.0.0", "@huggingface/transformers": "^2.10.0" } }

```

Environment Configuration

```yaml

.env

DATABASE_URL=postgresql://user:password@localhost:5432/iaip_inquiry REDIS_URL=redis://localhost:6379 JENA_FUSEKI_URL=http://localhost:3030 NEO4J_URI=bolt://localhost:7687 HASURA_GRAPHQL_URL=http://localhost:8080

TTS_MODEL_PATH=/models/amphion VOICE_PROFILES_PATH=/models/voice_profiles AUDIO_CACHE_PATH=/data/audio_cache

CASTOPOD_URL=http://localhost:8000 RSS_FEED_URL=http://localhost:8000/feed

LOG_LEVEL=INFO ENVIRONMENT=development ```

Running the System

Development Mode

```bash

Initialize databases

./scripts/setup/initialize_databases.sh

Start all services

docker-compose up -d

Run agent orchestrator

python backend/agents/orchestrator/multi_agent_config.py

Generate podcast episode

python scripts/automation/podcast_pipeline.py
--script data/scripts/cinematic_script.md
--output podcast/episodes/001-inquiry-ecosystem-framework/ ```

Podcast Production Command

```bash

Full pipeline: script → TTS → mixing → publishing

python scripts/automation/podcast_pipeline.py
--script data/scripts/cinematic_script.md
--echo-voice models/voice_profiles/echo_weaver.pth
--miawa-voice models/voice_profiles/miawa_pascone.pth
--music data/audio/indigenous_flute_ambient.wav
--output podcast/episodes/001/
--publish
--rss podcast/rss/feed.xml ```

Deployment Notes

All components utilize permissive open-source licenses (MIT, Apache 2.0, BSD, GPL) suitable for research and commercial applications. The architecture prioritizes:

• Modularity: Each component can be swapped or upgraded independently
• Scalability: Horizontal scaling through containerization
• Privacy: Self-hosted deployment options for sensitive data
• Interoperability: Standard protocols (REST, GraphQL, SPARQL, RSS)
• Extensibility: Plugin architectures for custom agents and tools

Security Considerations

• All agent communication encrypted via TLS
• Database access through connection pooling with authentication
• Redis protected with ACLs and password authentication
• API rate limiting and authentication via JWT
• Regular dependency updates and vulnerability scanning

Monitoring & Analytics

• Prometheus metrics for system health
• Grafana dashboards for visualization
• IABv2-compliant podcast analytics via Castopod
• Custom tracking for inquiry genealogy metrics
• Agent performance monitoring and optimization

License Compliance Matrix

References

[web:33] FAISS, Sentence Transformers, and Annoy for semantic search [web:38] Multi-Agent Orchestrator framework for agent coordination [web:39] Apache Jena for knowledge graph construction [web:42] Graphiti for real-time knowledge graphs [web:58] Amphion toolkit for audio generation [web:62] Reverb ASR for speech recognition [web:70] Audacity and Castopod for podcast production [web:71] TTS libraries and services [web:72] Node.js audio processing with transformers.js [web:73] Castopod open-source podcast hosting [web:74] Dia and other open-source TTS models

```

```python #!/usr/bin/env python3 """ IAIP Inquiry Ecosystem Podcast Generator Automated pipeline for producing podcast episodes from cinematic scripts """

import os import json import asyncio from pathlib import Path from typing import List, Dict, Tuple from dataclasses import dataclass import re

Audio processing

from pydub import AudioSegment import numpy as np

NLP and embeddings

from sentence_transformers import SentenceTransformer

TTS (placeholder - would use Amphion/Dia in production)

import torch from transformers import pipeline

@dataclass class DialogueSegment: """Represents a single dialogue segment in the script""" speaker: str text: str section: str emotional_markers: List[str] timestamp: float = 0.0 audio_file: str = ""

@dataclass class PodcastMetadata: """Metadata for the podcast episode""" title: str description: str episode_number: int duration: float speakers: List[str] sections: List[Dict[str, any]] keywords: List[str]

class ScriptParser: """Parse cinematic script markdown into structured dialogue segments"""

def __init__(self):
    self.speaker_pattern = re.compile(r'\*\*(.*?)\s*\(.*?\)\*\*:\s*(.*?)(?=\n\n|\*\*|$)', re.DOTALL)
    self.section_pattern = re.compile(r'\*\*(\d{3}-.*?)\*\*')
    
def parse(self, script_path: Path) -> Tuple[List[DialogueSegment], PodcastMetadata]:
    """Parse script file into dialogue segments and metadata"""
    with open(script_path, 'r', encoding='utf-8') as f:
        content = f.read()
    
    # Extract title
    title_match = re.search(r'## Episode Title: (.+)', content)
    title = title_match.group(1) if title_match else "Unknown Episode"
    
    segments = []
    current_section = "intro"
    
    # Find all sections
    sections = self.section_pattern.findall(content)
    section_texts = self.section_pattern.split(content)
    
    for i, section_id in enumerate(sections):
        section_content = section_texts[i * 2 + 2] if i * 2 + 2 < len(section_texts) else ""
        
        # Extract speaker dialogues
        for match in self.speaker_pattern.finditer(section_content):
            speaker = match.group(1).strip()
            text = match.group(2).strip()
            
            # Extract emotional markers (words in quotes, parentheses, etc.)
            emotional_markers = re.findall(r'"([^"]+)"|'([^']+)'|\(([^)]+)\)', text)
            emotional_markers = [m for group in emotional_markers for m in group if m]
            
            segment = DialogueSegment(
                speaker=speaker,
                text=text,
                section=section_id,
                emotional_markers=emotional_markers
            )
            segments.append(segment)
    
    # Generate metadata
    speakers = list(set(seg.speaker for seg in segments))
    section_data = [{"id": sec, "title": sec.replace('-', ' ').title()} for sec in sections]
    
    metadata = PodcastMetadata(
        title=title,
        description=f"Episode exploring {title}",
        episode_number=1,
        duration=0.0,  # Will be calculated after audio generation
        speakers=speakers,
        sections=section_data,
        keywords=["Indigenous AI", "Inquiry Ecosystem", "Creative Orientation"]
    )
    
    return segments, metadata

class TTSEngine: """Text-to-Speech engine wrapper (placeholder for Amphion/Dia)"""

def __init__(self, voice_profiles_path: Path):
    self.voice_profiles_path = voice_profiles_path
    self.voices = {}
    self._load_voice_profiles()
    
    # Placeholder: using transformers TTS (in production would use Amphion)
    # self.tts_pipeline = pipeline("text-to-speech", model="microsoft/speecht5_tts")
    
def _load_voice_profiles(self):
    """Load voice profiles for each speaker"""
    # In production: load Amphion/Dia voice models
    self.voices = {
        "Echo Weaver": "echo_weaver_profile",
        "Miawa Pascone": "miawa_pascone_profile"
    }

async def synthesize_speech(self, segment: DialogueSegment, output_path: Path) -> float:
    """
    Synthesize speech for a dialogue segment
    Returns duration in seconds
    """
    # In production:
    # 1. Load appropriate voice profile
    # 2. Process text with emotional markers
    # 3. Generate speech with Amphion/Dia
    # 4. Apply nonverbal audio tags (laughs, pauses, etc.)
    # 5. Save to output_path
    
    # Placeholder: simulate audio generation
    # Estimate duration: ~150 words per minute average speaking rate
    word_count = len(segment.text.split())
    duration = (word_count / 150) * 60  # seconds
    
    # Create placeholder silent audio
    silence = AudioSegment.silent(duration=int(duration * 1000))
    silence.export(output_path, format="wav")
    
    return duration

class AudioMixer: """Mix dialogue segments with music and effects"""

def __init__(self, music_path: Path = None):
    self.music_path = music_path
    self.background_music = None
    if music_path and music_path.exists():
        self.background_music = AudioSegment.from_file(music_path)

def mix_episode(
    self, 
    segments: List[DialogueSegment],
    output_path: Path,
    fade_in_duration: int = 2000,
    fade_out_duration: int = 3000,
    music_volume: int = -20  # dB
) -> float:
    """
    Mix all audio segments into final episode
    Returns total duration in seconds
    """
    final_audio = AudioSegment.empty()
    
    # Add intro music fade in
    if self.background_music:
        intro_music = self.background_music[:10000].fade_in(fade_in_duration)
        intro_music = intro_music + music_volume
        final_audio = intro_music
    
    # Process each segment
    for i, segment in enumerate(segments):
        if segment.audio_file and Path(segment.audio_file).exists():
            speech = AudioSegment.from_file(segment.audio_file)
            
            # Add pause between segments (except first)
            if i > 0:
                pause = AudioSegment.silent(duration=800)
                final_audio += pause
            
            # Overlay background music if available
            if self.background_music:
                segment_duration = len(speech)
                music_segment = self.background_music[:segment_duration] + music_volume - 10
                combined = speech.overlay(music_segment)
                final_audio += combined
            else:
                final_audio += speech
    
    # Add outro music fade out
    if self.background_music:
        outro_music = self.background_music[-8000:].fade_out(fade_out_duration)
        outro_music = outro_music + music_volume
        final_audio += outro_music
    
    # Apply normalization and compression
    final_audio = final_audio.normalize()
    
    # Export final episode
    final_audio.export(
        output_path,
        format="mp3",
        bitrate="128k",
        parameters=["-ac", "2"]  # Stereo
    )
    
    return len(final_audio) / 1000.0  # Return duration in seconds

class RSSFeedGenerator: """Generate RSS feed for podcast distribution"""

def __init__(self, feed_config: Dict):
    self.config = feed_config

def generate_episode_item(
    self, 
    metadata: PodcastMetadata,
    audio_url: str,
    file_size: int
) -> str:
    """Generate RSS item for single episode"""
    from datetime import datetime
    
    pub_date = datetime.now().strftime("%a, %d %b %Y %H:%M:%S %z")
    duration = self._format_duration(metadata.duration)
    
    item = f"""
<item>
    <title>{metadata.title}</title>
    <description><![CDATA[{metadata.description}]]></description>
    <link>{audio_url}</link>
    <guid isPermaLink="true">{audio_url}</guid>
    <pubDate>{pub_date}</pubDate>
    <enclosure url="{audio_url}" length="{file_size}" type="audio/mpeg"/>
    <itunes:duration>{duration}</itunes:duration>
    <itunes:episode>{metadata.episode_number}</itunes:episode>
    <itunes:keywords>{', '.join(metadata.keywords)}</itunes:keywords>
</item>
    """
    return item

def _format_duration(self, seconds: float) -> str:
    """Format duration as HH:MM:SS"""
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    return f"{hours:02d}:{minutes:02d}:{secs:02d}"

def update_feed(self, episode_item: str, feed_path: Path):
    """Update RSS feed with new episode"""
    # Read existing feed or create new
    if feed_path.exists():
        with open(feed_path, 'r', encoding='utf-8') as f:
            feed_content = f.read()
        # Insert new item before </channel>
        feed_content = feed_content.replace('</channel>', f'{episode_item}\n</channel>')
    else:
        # Create new feed
        feed_content = self._generate_new_feed(episode_item)
    
    with open(feed_path, 'w', encoding='utf-8') as f:
        f.write(feed_content)

def _generate_new_feed(self, first_item: str) -> str:
    """Generate new RSS feed with first episode"""
    return f"""<?xml version="1.0" encoding="UTF-8"?>

class PodcastPipeline: """Main pipeline orchestrator"""

def __init__(
    self,
    voice_profiles_path: Path,
    music_path: Path = None,
    output_dir: Path = None
):
    self.parser = ScriptParser()
    self.tts = TTSEngine(voice_profiles_path)
    self.mixer = AudioMixer(music_path)
    self.output_dir = output_dir or Path("./output")
    self.output_dir.mkdir(parents=True, exist_ok=True)

async def generate_episode(
    self,
    script_path: Path,
    episode_dir: Path = None,
    publish: bool = False
) -> Tuple[Path, PodcastMetadata]:
    """
    Generate complete podcast episode from script
    
    Args:
        script_path: Path to cinematic script markdown
        episode_dir: Output directory for episode files
        publish: Whether to update RSS feed
        
    Returns:
        Tuple of (audio_file_path, metadata)
    """
    print(f"Parsing script: {script_path}")
    segments, metadata = self.parser.parse(script_path)
    
    if episode_dir is None:
        episode_dir = self.output_dir / f"episode_{metadata.episode_number:03d}"
    episode_dir.mkdir(parents=True, exist_ok=True)
    
    # Create subdirectories
    audio_segments_dir = episode_dir / "segments"
    audio_segments_dir.mkdir(exist_ok=True)
    
    print(f"Generating speech for {len(segments)} segments...")
    
    # Generate TTS for each segment
    for i, segment in enumerate(segments):
        segment_file = audio_segments_dir / f"{i:03d}_{segment.speaker.replace(' ', '_')}.wav"
        duration = await self.tts.synthesize_speech(segment, segment_file)
        segment.audio_file = str(segment_file)
        segment.timestamp = sum(s.timestamp for s in segments[:i])
        print(f"  [{i+1}/{len(segments)}] {segment.speaker}: {duration:.1f}s")
    
    print("Mixing audio segments...")
    final_audio_path = episode_dir / f"{metadata.title.replace(' ', '_').replace(':', '')}.mp3"
    total_duration = self.mixer.mix_episode(segments, final_audio_path)
    metadata.duration = total_duration
    
    print(f"Episode duration: {total_duration/60:.1f} minutes")
    
    # Save metadata
    metadata_path = episode_dir / "metadata.json"
    with open(metadata_path, 'w', encoding='utf-8') as f:
        json.dump({
            'title': metadata.title,
            'description': metadata.description,
            'episode_number': metadata.episode_number,
            'duration': metadata.duration,
            'speakers': metadata.speakers,
            'sections': metadata.sections,
            'keywords': metadata.keywords
        }, f, indent=2)
    
    # Generate transcript
    transcript_path = episode_dir / "transcript.txt"
    with open(transcript_path, 'w', encoding='utf-8') as f:
        for segment in segments:
            f.write(f"\n[{segment.section}]\n")
            f.write(f"{segment.speaker}: {segment.text}\n")
    
    print(f"Episode files saved to: {episode_dir}")
    
    # Publish to RSS if requested
    if publish:
        print("Publishing to RSS feed...")
        self._publish_episode(final_audio_path, metadata)
    
    return final_audio_path, metadata

def _publish_episode(self, audio_path: Path, metadata: PodcastMetadata):
    """Publish episode to RSS feed"""
    feed_config = {
        'title': 'IAIP Inquiry Ecosystem Framework Podcast',
        'description': 'Exploring conscious, relational inquiry management',
        'website': 'https://iaip-podcast.example.com',
        'author': 'Echo Weaver & Miawa Pascone',
        'category': 'Technology'
    }
    
    rss_gen = RSSFeedGenerator(feed_config)
    
    # Calculate file size
    file_size = audio_path.stat().st_size
    
    # Generate public URL (would be actual hosting URL in production)
    audio_url = f"https://iaip-podcast.example.com/episodes/{audio_path.name}"
    
    # Generate and update RSS feed
    episode_item = rss_gen.generate_episode_item(metadata, audio_url, file_size)
    feed_path = self.output_dir / "feed.xml"
    rss_gen.update_feed(episode_item, feed_path)
    
    print(f"RSS feed updated: {feed_path}")

async def main(): """Main entry point for podcast generation""" import argparse

parser = argparse.ArgumentParser(description="IAIP Podcast Generator")
parser.add_argument("--script", type=Path, required=True, help="Path to cinematic script")
parser.add_argument("--voices", type=Path, default=Path("./models/voice_profiles"))
parser.add_argument("--music", type=Path, default=None, help="Background music file")
parser.add_argument("--output", type=Path, default=Path("./output"))
parser.add_argument("--publish", action="store_true", help="Publish to RSS feed")

args = parser.parse_args()

# Initialize pipeline
pipeline = PodcastPipeline(
    voice_profiles_path=args.voices,
    music_path=args.music,
    output_dir=args.output
)

# Generate episode
audio_file, metadata = await pipeline.generate_episode(
    script_path=args.script,
    publish=args.publish
)

print("\n" + "="*60)
print(f"Episode generation complete!")
print(f"Title: {metadata.title}")
print(f"Duration: {metadata.duration/60:.1f} minutes")
print(f"Audio file: {audio_file}")
print("="*60)

if name == "main": asyncio.run(main()) ```

```dockerfile

Dockerfile for Podcast Production Environment

FROM python:3.11-slim

LABEL maintainer="IAIP Development Team" LABEL description="Podcast production environment with TTS and audio processing"

Install system dependencies

RUN apt-get update && apt-get install -y
ffmpeg
libsndfile1
portaudio19-dev
git
curl
&& rm -rf /var/lib/apt/lists/*

Set working directory

WORKDIR /app

Copy requirements

COPY requirements.txt .

Install Python dependencies

RUN pip install --no-cache-dir -r requirements.txt

Install additional audio libraries

RUN pip install --no-cache-dir
pydub
soundfile
librosa
transformers
torch
torchaudio

Copy application code

COPY podcast_generator.py . COPY models/ ./models/ COPY scripts/ ./scripts/

Create output directory

RUN mkdir -p /output

Set environment variables

ENV PYTHONUNBUFFERED=1 ENV OUTPUT_DIR=/output

Run podcast generator

ENTRYPOINT ["python", "podcast_generator.py"] CMD ["--help"] ```

```yaml

docker-compose.yml

Complete IAIP Inquiry Ecosystem Framework Stack

version: '3.8'

services:

PostgreSQL with pgvector

postgres: image: ankane/pgvector:latest container_name: iaip_postgres environment: POSTGRES_USER: iaip POSTGRES_PASSWORD: secure_password POSTGRES_DB: inquiry_ecosystem ports: - "5432:5432" volumes: - postgres_data:/var/lib/postgresql/data - ./infrastructure/postgres/init.sql:/docker-entrypoint-initdb.d/init.sql networks: - iaip_network

Redis Stack (with vector search)

redis: image: redis/redis-stack:latest container_name: iaip_redis ports: - "6379:6379" - "8001:8001" # RedisInsight volumes: - redis_data:/data networks: - iaip_network

Apache Jena Fuseki (SPARQL endpoint)

jena: image: stain/jena-fuseki:latest container_name: iaip_jena environment: ADMIN_PASSWORD: admin123 FUSEKI_DATASET_1: inquiry_kg ports: - "3030:3030" volumes: - jena_data:/fuseki networks: - iaip_network

Neo4j (graph visualization)

neo4j: image: neo4j:5.15-community container_name: iaip_neo4j environment: NEO4J_AUTH: neo4j/password NEO4J_PLUGINS: '["apoc", "graph-data-science"]' ports: - "7474:7474" - "7687:7687" volumes: - neo4j_data:/data networks: - iaip_network

Hasura GraphQL Engine

hasura: image: hasura/graphql-engine:v2.36 container_name: iaip_hasura depends_on: - postgres environment: HASURA_GRAPHQL_DATABASE_URL: postgres://iaip:secure_password@postgres:5432/inquiry_ecosystem HASURA_GRAPHQL_ENABLE_CONSOLE: "true" HASURA_GRAPHQL_DEV_MODE: "true" HASURA_GRAPHQL_ADMIN_SECRET: admin_secret ports: - "8080:8080" networks: - iaip_network

FastAPI Backend

api: build: context: . dockerfile: infrastructure/docker/Dockerfile.api container_name: iaip_api depends_on: - postgres - redis - jena environment: DATABASE_URL: postgresql://iaip:secure_password@postgres:5432/inquiry_ecosystem REDIS_URL: redis://redis:6379 JENA_FUSEKI_URL: http://jena:3030 ports: - "8000:8000" volumes: - ./backend:/app networks: - iaip_network

Multi-Agent Orchestrator

agents: build: context: . dockerfile: infrastructure/docker/Dockerfile.agents container_name: iaip_agents depends_on: - api - redis environment: REDIS_URL: redis://redis:6379 API_URL: http://api:8000 volumes: - ./backend/agents:/app/agents - ./models:/app/models networks: - iaip_network

Podcast Production Service

podcast: build: context: . dockerfile: infrastructure/docker/Dockerfile.audio container_name: iaip_podcast environment: OUTPUT_DIR: /output VOICE_PROFILES_PATH: /app/models/voice_profiles volumes: - ./audio:/app - ./podcast:/output - ./models:/app/models networks: - iaip_network

Castopod (Podcast Hosting)

castopod: image: ad5is/castopod:latest container_name: iaip_castopod depends_on: - postgres environment: CP_DATABASE_HOSTNAME: postgres CP_DATABASE_NAME: castopod CP_DATABASE_USERNAME: iaip CP_DATABASE_PASSWORD: secure_password CP_BASEURL: http://localhost:8888 ports: - "8888:8080" volumes: - castopod_media:/var/www/castopod/public/media networks: - iaip_network

RabbitMQ (Message Queue)

rabbitmq: image: rabbitmq:3.12-management container_name: iaip_rabbitmq environment: RABBITMQ_DEFAULT_USER: iaip RABBITMQ_DEFAULT_PASS: rabbitmq_pass ports: - "5672:5672" - "15672:15672" volumes: - rabbitmq_data:/var/lib/rabbitmq networks: - iaip_network

volumes: postgres_data: redis_data: jena_data: neo4j_data: castopod_media: rabbitmq_data:

networks: iaip_network: driver: bridge ```

```json { "inquiry_registry_schema": { "type": "object", "properties": { "inquiry_id": { "type": "string", "format": "uuid", "description": "Unique identifier for the inquiry" }, "parent_inquiry_id": { "type": ["string", "null"], "format": "uuid", "description": "Parent inquiry for genealogy tracking" }, "title": { "type": "string", "description": "Human-readable inquiry title" }, "description": { "type": "string", "description": "Detailed inquiry description" }, "semantic_embedding": { "type": "array", "items": {"type": "number"}, "description": "Vector embedding for semantic search (384-dim for MiniLM)" }, "topics": { "type": "array", "items": {"type": "string"}, "description": "Associated topics and keywords" }, "creation_timestamp": { "type": "string", "format": "date-time" }, "last_updated": { "type": "string", "format": "date-time" }, "status": { "type": "string", "enum": ["active", "resolved", "forked", "archived"] }, "context_continuum": { "type": "object", "properties": { "session_id": {"type": "string"}, "agent_continuations": { "type": "array", "items": { "type": "object", "properties": { "from_session": {"type": "string"}, "to_session": {"type": "string"}, "state_snapshot": {"type": "object"} } } } } }, "structural_tension": { "type": "object", "properties": { "current_reality": {"type": "string"}, "desired_outcome": {"type": "string"}, "tension_vector": { "type": "array", "items": {"type": "number"} } } }, "assumptions": { "type": "array", "items": { "type": "object", "properties": { "assumption_text": {"type": "string"}, "confidence_level": { "type": "number", "minimum": 0, "maximum": 1 }, "supporting_evidence": { "type": "array", "items": {"type": "string"} }, "timestamp": {"type": "string", "format": "date-time"} } } }, "related_inquiries": { "type": "array", "items": { "type": "object", "properties": { "inquiry_id": {"type": "string", "format": "uuid"}, "relationship_type": { "type": "string", "enum": ["parent", "child", "sibling", "related"] }, "semantic_similarity": { "type": "number", "minimum": 0, "maximum": 1 } } } }, "agent_interactions": { "type": "array", "items": { "type": "object", "properties": { "agent_name": {"type": "string"}, "interaction_type": {"type": "string"}, "timestamp": {"type": "string", "format": "date-time"}, "notes": {"type": "string"} } } } }, "required": ["inquiry_id", "title", "description", "semantic_embedding", "creation_timestamp"] } } ```

```sql -- PostgreSQL initialization script with pgvector -- Creates schema for IAIP Inquiry Ecosystem Framework

CREATE EXTENSION IF NOT EXISTS vector; CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

-- Inquiry Registry Table CREATE TABLE inquiry_registry ( inquiry_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), parent_inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), title TEXT NOT NULL, description TEXT NOT NULL, semantic_embedding vector(384), -- For all-MiniLM-L6-v2 topics TEXT[], creation_timestamp TIMESTAMPTZ DEFAULT NOW(), last_updated TIMESTAMPTZ DEFAULT NOW(), status VARCHAR(20) CHECK (status IN ('active', 'resolved', 'forked', 'archived')), metadata JSONB );

-- Index for vector similarity search CREATE INDEX inquiry_embedding_idx ON inquiry_registry USING ivfflat (semantic_embedding vector_cosine_ops) WITH (lists = 100);

-- Index for genealogy queries CREATE INDEX inquiry_parent_idx ON inquiry_registry(parent_inquiry_id); CREATE INDEX inquiry_status_idx ON inquiry_registry(status);

-- Context Continuum Table CREATE TABLE context_continuum ( context_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), session_id TEXT NOT NULL, agent_name TEXT NOT NULL, state_snapshot JSONB, timestamp TIMESTAMPTZ DEFAULT NOW() );

CREATE INDEX context_inquiry_idx ON context_continuum(inquiry_id); CREATE INDEX context_session_idx ON context_continuum(session_id);

-- Structural Tension Charts Table CREATE TABLE structural_tension ( tension_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), current_reality TEXT NOT NULL, desired_outcome TEXT NOT NULL, tension_vector vector(384), created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW() );

-- Assumptions Log Table CREATE TABLE assumptions_log ( assumption_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), assumption_text TEXT NOT NULL, confidence_level NUMERIC(3,2) CHECK (confidence_level BETWEEN 0 AND 1), supporting_evidence TEXT[], created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW(), status VARCHAR(20) CHECK (status IN ('active', 'validated', 'invalidated', 'refined')) );

CREATE INDEX assumptions_inquiry_idx ON assumptions_log(inquiry_id); CREATE INDEX assumptions_confidence_idx ON assumptions_log(confidence_level);

-- Inquiry Relationships Table (for semantic genealogy) CREATE TABLE inquiry_relationships ( relationship_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), source_inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), target_inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), relationship_type VARCHAR(20) CHECK (relationship_type IN ('parent', 'child', 'sibling', 'related')), semantic_similarity NUMERIC(3,2) CHECK (semantic_similarity BETWEEN 0 AND 1), created_at TIMESTAMPTZ DEFAULT NOW() );

CREATE INDEX rel_source_idx ON inquiry_relationships(source_inquiry_id); CREATE INDEX rel_target_idx ON inquiry_relationships(target_inquiry_id); CREATE INDEX rel_type_idx ON inquiry_relationships(relationship_type);

-- Agent Interactions Table CREATE TABLE agent_interactions ( interaction_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), agent_name TEXT NOT NULL, interaction_type TEXT NOT NULL, notes TEXT, timestamp TIMESTAMPTZ DEFAULT NOW() );

CREATE INDEX agent_inquiry_idx ON agent_interactions(inquiry_id); CREATE INDEX agent_name_idx ON agent_interactions(agent_name);

-- Podcast Episodes Table (for Castopod integration) CREATE TABLE podcast_episodes ( episode_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), inquiry_id UUID REFERENCES inquiry_registry(inquiry_id), title TEXT NOT NULL, description TEXT, audio_url TEXT, duration INTEGER, -- in seconds transcript TEXT, metadata JSONB, published_at TIMESTAMPTZ, created_at TIMESTAMPTZ DEFAULT NOW() );

-- Function to update last_updated timestamp CREATE OR REPLACE FUNCTION update_timestamp() RETURNS TRIGGER AS $$ BEGIN NEW.updated_at = NOW(); RETURN NEW; END; $$ LANGUAGE plpgsql;

-- Triggers for automatic timestamp updates CREATE TRIGGER update_structural_tension_timestamp BEFORE UPDATE ON structural_tension FOR EACH ROW EXECUTE FUNCTION update_timestamp();

CREATE TRIGGER update_assumptions_timestamp BEFORE UPDATE ON assumptions_log FOR EACH ROW EXECUTE FUNCTION update_timestamp();

-- Function for semantic similarity search CREATE OR REPLACE FUNCTION find_similar_inquiries( query_embedding vector(384), similarity_threshold NUMERIC DEFAULT 0.7, max_results INTEGER DEFAULT 10 ) RETURNS TABLE ( inquiry_id UUID, title TEXT, similarity NUMERIC ) AS $$ BEGIN RETURN QUERY SELECT ir.inquiry_id, ir.title, 1 - (ir.semantic_embedding <=> query_embedding) AS similarity FROM inquiry_registry ir WHERE 1 - (ir.semantic_embedding <=> query_embedding) >= similarity_threshold ORDER BY ir.semantic_embedding <=> query_embedding LIMIT max_results; END; $$ LANGUAGE plpgsql;

-- Seed data for testing INSERT INTO inquiry_registry (title, description, topics, status) VALUES ('Inquiry Ecosystem Framework', 'Exploring the foundational pattern for conscious inquiry management', ARRAY['framework', 'creative orientation', 'inquiry management'], 'active'), ('Semantic Genealogy Tracking', 'Understanding how inquiries relate and evolve over time', ARRAY['genealogy', 'relationships', 'evolution'], 'active');

COMMENT ON TABLE inquiry_registry IS 'Central registry for all inquiries with semantic embeddings'; COMMENT ON TABLE context_continuum IS 'Agent state persistence across sessions and forks'; COMMENT ON TABLE structural_tension IS 'Creative Orientation tension charts for each inquiry'; COMMENT ON TABLE assumptions_log IS 'Epistemic honesty tracking with confidence levels'; COMMENT ON TABLE inquiry_relationships IS 'Semantic genealogy and relationship mapping'; ```

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