InfoChor

Multi-Source Data Ingestion & Trust Scoring Pipeline

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Overview

InfoChor is a modular pipeline that ingests unstructured content from blogs and YouTube, processes it into structured data, and assigns a reliability score using explainable signals.

It is designed as a preprocessing layer for AI systems that depend on high-quality, trustworthy input data.

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Why This System Matters

Most AI systems consume internet data without evaluating its reliability.

This leads to:

• Poor-quality context in retrieval systems
• Unreliable outputs in AI assistants
• No trust signal in data pipelines

InfoChor solves this by introducing:

• Structured content ingestion
• Consistent data transformation
• Explainable trust scoring

This makes it useful as a trust-aware input layer for AI pipelines.

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System Workflow

Input URL ↓ Source Detection (Blog / YouTube) ↓ Scraper Layer ↓ Validation Layer ↓ Processing Layer ↓ Semantic Layer ↓ Trust Engine ↓ Structured Output

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Key Features

Multi-Source Ingestion

• Blog scraping using HTML parsing
• YouTube ingestion via transcript API
• Metadata fallback when transcripts are unavailable

Adaptive Validation

• Source-aware validation logic
• Different thresholds for blogs and YouTube

Content Processing

• Removes UI noise and irrelevant text
• Normalizes extracted content

Semantic Extraction

• TF-IDF based topic extraction
• Filters redundant or weak terms
• Splits content into chunks

Explainable Trust Scoring

• Source credibility
• Content length
• Structure quality
• Language signal

Each score includes a breakdown for transparency.

Robust Failure Handling

• Handles missing transcripts
• Uses fallback strategies
• Returns structured failure responses

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Example Output

{
  "status": "success",
  "document": {
    "title": "...",
    "source": "youtube",
    "word_count": 39
  },
  "insights": {
    "topics": [...],
    "trust_score": 0.49
  },
  "content": {
    "preview": "...",
    "chunks": [...]
  }
}
Design Decisions
Trust Engine
Rule-based for interpretability
Deterministic behavior
Easy to debug
Topic Extraction
TF-IDF for relevance
Heuristics for filtering
Validation Strategy
Blogs → long-form validation
YouTube → short-form fallback validation
Fallback Strategy
Try primary extraction
Fall back to metadata
Ensure pipeline continuity
Folder Structure
src/
 ├── scrapers/
 ├── processing/
 ├── chunking/
 ├── scoring/
 ├── pipeline/
 ├── utils/
 ├── models/
How to Run

Install dependencies:

pip install -r requirements.txt

Run the pipeline:

python main.py
Assignment Coverage
Structured Data Ingestion → Multi-source support
Metadata Extraction → Title, content, topics
Reliability Scoring → Explainable trust engine
Robustness → Handles missing or partial data
Positioning

InfoChor is not just a scraper.

It is a trust-aware data ingestion system designed for AI pipelines.

Author

Ayush Kumar

License

MIT License


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# 🧠 WHAT I FIXED

- Proper headings (`##`, `###`)
- Bullet points everywhere needed
- Removed clutter text
- Fixed broken workflow block
- Clean, readable sections
- No garbage formatting

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# 🚀 NEXT STEP

Now:

```bash
git add README.md
git commit -m "Fix README formatting and clarity"
git push