jgwillauthorJan 16, 2026, 09:34 AMIAIP-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
# 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
# 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
# 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
| Component | License | Commercial Use | Attribution Required |
|---|---|---|---|
| FAISS | MIT | Yes | No |
| Sentence Transformers | Apache 2.0 | Yes | No |
| Apache Jena | Apache 2.0 | Yes | No |
| Multi-Agent Orchestrator | Apache 2.0 | Yes | No |
| Amphion | MIT | Yes | No |
| Audacity | GPL-3.0 | Yes | Yes |
| Castopod | AGPL-3.0 | Yes | Yes (if modified) |
| PostgreSQL | PostgreSQL | Yes | No |
| Redis | BSD-3-Clause | Yes | No |
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"?>
<rss version="2.0" xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd">
<channel>
<title>{self.config['title']}</title>
<description>{self.config['description']}</description>
<link>{self.config['website']}</link>
<language>en-us</language>
<itunes:author>{self.config['author']}</itunes:author>
<itunes:category text="{self.config['category']}"/>
{first_item}
</channel>
</rss>
"""
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 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"]
# 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
{
"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"]
}
}
-- 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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<div align="center">⁂</div>Footnotes
-
pre-production.md ↩
-
009-echo_weaver-dynamic_workspaces.txt ↩
-
011-echo_weaver-structural_tension_charts.txt ↩
-
010-miawa_pascone-dynamic_workspaces.txt ↩
-
015-echo_weaver-integration_coherence.txt ↩
-
020-miawa_pascone-future_vision.txt ↩
-
https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2025-109055 ↩
-
https://linkinghub.elsevier.com/retrieve/pii/S1570826814001036 ↩
-
https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0012927600003822 ↩
-
cinematic_script.md ↩
-
001-echo_weaver-intro.txt ↩
-
https://academic.oup.com/database/article-pdf/doi/10.1093/database/bax059/19488573/bax059.pdf ↩
-
https://milvus.io/ai-quick-reference/what-are-the-best-opensource-libraries-for-semantic-search ↩
-
https://dev.to/infrasity-learning/vector-database-tutorial-build-a-semantic-search-engine-27kb ↩
-
https://knowledgegraph.dev/article/Top_10_Knowledge_Graph_Tools_for_Developers.html ↩
-
004-miawa_pascone-core_principles.txt ↩
-
https://www.tigerdata.com/learn/understanding-semantic-search ↩
-
https://www.kubiya.ai/blog/ai-agent-orchestration-frameworks ↩
-
https://pub.towardsai.net/building-a-multilingual-semantic-search-7242950140a5 ↩
-
https://academic.oup.com/ofid/article/doi/10.1093/ofid/ofaf695.195/8420219 ↩
-
002-miawa_pascone-intro.txt ↩
-
https://onepetro.org/SPEAPOG/proceedings/25APOG/25APOG/D021S010R001/791694 ↩
-
https://journals.sagepub.com/doi/10.1177/18747655251390274 ↩
-
003-echo_weaver-core_principles.txt ↩
-
https://www.fame.so/post/the-12-best-editing-software-for-podcasts-in-2026 ↩
-
https://www.thepodcasthost.com/planning/best-podcast-tools/ ↩
-
005-echo_weaver-inquiry_registry.txt ↩
-
https://github.com/awesomelistsio/awesome-podcasting-tools ↩
-
https://huggingface.co/docs/transformers.js/en/guides/node-audio-processing ↩
-
https://www.reddit.com/r/TextToSpeech/comments/1engt02/looking_for_simple_unlimited_free_tts_site/ ↩
-
https://stackoverflow.com/questions/21996275/audio-manipulation-using-node-js ↩
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https://github.blog/open-source/maintainers/5-podcast-episodes-to-help-you-build-with-confidence-in-2026/ ↩
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006-miawa_pascone-inquiry_registry.txt ↩
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https://www.techradar.com/news/the-best-free-text-to-speech-software ↩
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007-echo_weaver-context_continuum.txt ↩