Hanx Knowledge Base

Hanx Knowledge Base & RAG Skill

Build searchable knowledge bases from diverse document types with advanced RAG (Retrieval-Augmented Generation) capabilities.

Overview

This skill provides a complete document ingestion and search pipeline:

1. Document Processing: Extract text from PDF, DOCX, MD, TXT, CSV, HTML, JSON
2. Intelligent Chunking: Split documents with semantic awareness
3. Vector Embeddings: Generate embeddings using local or OpenAI models
4. Persistent Storage: Store in ChromaDB vector database
5. Semantic Search: Find relevant content using natural language queries
6. Category Management: Organize documents by topic

When to Use This Skill

Automatic Triggers

• User says "ingest this document" or "add to knowledge base"
• User requests "search my documents" or "find information about"
• User mentions "build knowledge base from" a directory
• User wants to "query the knowledge base"

Manual Invocation

# Add single document
python scripts/knowledge_base.py add document.pdf --category technical

# Add directory of documents
python scripts/knowledge_base.py add-batch ./documents --category research

# Search knowledge base
python scripts/search_kb.py "What is RAG?" --limit 5

# List all documents
python scripts/knowledge_base.py list

Core Capabilities

1. Document Ingestion

Supported Formats:

• PDF (.pdf): Full text extraction with page numbers
• Word (.docx, .doc): Text, tables, and document properties
• Markdown (.md): With YAML frontmatter support
• Text (.txt): Plain text files
• CSV (.csv): Structured data with headers
• HTML (.html, .htm): Web pages with metadata extraction
• JSON (.json): Structured JSON data

Features:

• Automatic format detection
• Metadata extraction (author, title, dates, etc.)
• Batch processing for directories
• Category-based organization
• Progress tracking

2. Intelligent Chunking

Chunking Strategy:

Document
    ↓
[Extract Text]
    ↓
[Split by Semantic Boundaries]
    ↓
[Chunks with Overlap]
    ↓
Vector Embeddings

Features:

• Recursive character splitting
• Semantic boundary detection (paragraphs, sentences)
• Configurable chunk size (default: 1000 characters)
• Overlap between chunks (default: 200 characters)
• Metadata preservation per chunk

3. Vector Embeddings

Local Embeddings (Default - FREE):

• Model: all-MiniLM-L6-v2 (SentenceTransformers)
• Dimensions: 384
• Speed: Fast (local inference)
• Cost: $0 (no API costs)
• Privacy: Data stays local

Alternative Models:

• all-mpnet-base-v2: Better quality, 768 dimensions
• multi-qa-MiniLM-L6-cos-v1: Optimized for Q&A
• OpenAI embeddings: Highest quality (requires API key)

4. Vector Storage (ChromaDB)

Features:

• Persistent Storage: Documents saved to disk
• Metadata Filtering: Filter by category, author, date
• Multiple Distance Metrics: Cosine, L2, Inner Product
• Efficient Indexing: Fast similarity search
• CRUD Operations: Add, search, update, delete

Storage Structure:

knowledge_base/
├── documents/           # Original documents (categorized)
│   ├── technical/
│   ├── business/
│   ├── reference/
│   └── ...
├── metadata/           # Document metadata (JSON)
├── vector_store/       # ChromaDB vector database
└── exports/           # Exported metadata

5. Semantic Search

Search Capabilities:

• Natural Language Queries: "How do I implement RAG?"
• Similarity Scoring: Relevance scores (0.0 to 1.0)
• Metadata Filtering: Filter by category, file type, date
• Top-K Retrieval: Get most relevant chunks
• Contextual Results: Full text + metadata + score

Example Search:

$ python search_kb.py "vector database implementation"

================================================================================
Result 1
================================================================================
Relevance: 0.8742 ████████████████████
Category: technical
Source: chromadb_guide.pdf

Content:
--------------------------------------------------------------------------------
Vector databases are specialized databases designed to store and search
vector embeddings efficiently. ChromaDB provides an open-source solution
with features like persistent storage, metadata filtering, and cosine
similarity search. Implementation involves three key steps: 1) Generate
embeddings using a model like SentenceTransformers, 2) Store embeddings
in the vector database, 3) Query using similarity search.
--------------------------------------------------------------------------------

Workflow Examples

Example 1: Build Knowledge Base from Directory

User: "Build a knowledge base from all PDF files in ./research"

Skill Actions:

1. Scan directory for PDF files
2. Process each PDF (extract text + metadata)
3. Chunk documents intelligently
4. Generate embeddings for all chunks
5. Store in ChromaDB vector database
6. Report statistics

Output:

================================================================================
Knowledge Base Batch Ingestion
================================================================================

Directory: ./research
Category: research
Recursive: True
File Filter: .pdf

[1/15] Processing: machine_learning_survey.pdf
[KB] Adding document: machine_learning_survey.pdf
[KB] Category: research
[KB] Processing document...
[KB] Metadata saved: machine_learning_survey.json
[KB] Adding to RAG system...
[KB] ✅ Document added successfully in 2.34s
[KB] Document ID: doc_1730123456789

[2/15] Processing: neural_networks_intro.pdf
...

[15/15] Processing: transformer_architecture.pdf

================================================================================
BATCH INGESTION COMPLETE
================================================================================

Total Files: 15
Successfully Added: 15
Failed: 0
Total Time: 45.2s
Average Time per Document: 3.0s

Knowledge base ready for search!

Example 2: Semantic Search with Filters

User: "Search my technical documents for information about embeddings with minimum relevance 0.7"

Command:

python search_kb.py "embeddings" --category technical --min-score 0.7 --limit 10

Output:

================================================================================
Knowledge Base Search
================================================================================

Knowledge Base: ./data/knowledge_base
Query: "embeddings"
Category Filter: technical
Max Results: 10
Min Score: 0.7

[SEARCH] Searching knowledge base...
[SEARCH] Found 8 results

================================================================================
Result 1
================================================================================
Relevance: 0.8921 ██████████████████
Category: technical
Source: rag_implementation_guide.md

Content:
--------------------------------------------------------------------------------
Embeddings are dense vector representations of text that capture semantic
meaning. Modern embedding models like OpenAI's text-embedding-3-small or
SentenceTransformers produce vectors of 384 to 3072 dimensions. These
embeddings enable semantic search by measuring similarity through cosine
distance or dot product calculations.
--------------------------------------------------------------------------------

[... more results ...]

Example 3: Document Management

List all documents:

python scripts/knowledge_base.py list --category technical

Output:

Knowledge Base Documents
Total: 23 documents in 'technical' category

1. rag_implementation_guide.md (45.2 KB)
   Modified: 2025-11-01T10:30:45
   Metadata: {"author": "John Doe", "topics": ["RAG", "embeddings"]}

2. chromadb_setup.pdf (1.2 MB)
   Modified: 2025-10-28T14:22:10
   Metadata: {"author": "Tech Team", "version": "2.0"}

[... 21 more documents ...]

Remove document:

python scripts/knowledge_base.py remove chromadb_setup.pdf --category technical

Export metadata:

python scripts/knowledge_base.py export --output kb_metadata.json

Technical Specifications

Embedding Models Comparison

Performance Characteristics

Document Ingestion (1000-page PDF):

• Text extraction: ~5 seconds
• Chunking: <1 second
• Embedding generation: ~10 seconds (local), ~3 seconds (API)
• Database storage: ~2 seconds
• Total: ~18 seconds (local), ~11 seconds (API)

Search Performance:

• Query embedding: ~100ms (local), ~200ms (API)
• Vector search (10K chunks): ~50ms
• Result formatting: <10ms
• Total: ~160ms (local), ~260ms (API)

Storage Requirements (per 1000 documents):

• Original documents: ~500 MB (varies by type)
• ChromaDB vectors: ~100 MB
• Metadata: ~10 MB
• Total: ~610 MB

Chunking Configuration

Default Settings:

CHUNK_SIZE = 1000         # characters per chunk
CHUNK_OVERLAP = 200       # overlap between chunks
SEPARATORS = [
    "\n\n",              # Paragraphs
    "\n",                # Lines
    ". ",                # Sentences
    " ",                 # Words
    ""                   # Characters
]

Customization:

from rag_utils import DocumentChunker

chunker = DocumentChunker(
    chunk_size=1500,       # Larger chunks for technical docs
    chunk_overlap=300,     # More overlap for context
    separators=["\n\n", "\n", ". "]  # Custom separators
)

Usage Instructions

Setup (One-time)

1. Install Dependencies:

cd .claude/skills/hanx-knowledge-base
pip install -r scripts/requirements.txt

2. Verify Installation:

python scripts/rag_utils.py  # Runs test
python scripts/document_processor.py  # Tests processors

3. Optional: Configure OpenAI Embeddings:

# Create .env file
echo "OPENAI_API_KEY=sk-your-key-here" > .env

Basic Usage

Initialize Knowledge Base:

from knowledge_base import KnowledgeBase

kb = KnowledgeBase("./data/my_kb")

Add Documents:

# Single document
kb.add_document("document.pdf", category="technical")

# Batch add from directory
kb.add_documents_batch(
    "./documents",
    category="research",
    recursive=True,
    file_extensions=['.pdf', '.docx', '.md']
)

Search:

results = kb.search("What is RAG?", limit=5)

for result in results:
    print(f"Score: {result.score:.4f}")
    print(f"Text: {result.text[:200]}...")
    print(f"Source: {result.metadata['source']}")

Advanced Usage

Custom RAG System:

from rag_utils import VectorStore, LocalEmbeddings, DocumentChunker, RAGSystem

# Configure components
embeddings = LocalEmbeddings(model_name="all-mpnet-base-v2")
vector_store = VectorStore(
    persist_directory="./my_vector_db",
    embedding_function=embeddings
)
chunker = DocumentChunker(chunk_size=1500, chunk_overlap=300)

# Create RAG system
rag = RAGSystem(vector_store=vector_store, chunker=chunker)

# Use it
rag.add_text("Document content here...", metadata={"source": "custom.txt"})
results = rag.query("search query", limit=10)

Metadata Filtering:

# Search only recent documents
results = kb.search(
    "machine learning",
    limit=5,
    metadata_filter={"category": "research", "year": 2025}
)

Programmatic Document Processing:

from document_processor import process_document, process_directory

# Process single document
content, metadata = process_document("document.pdf")

# Process directory
results = process_directory(
    "./documents",
    recursive=True,
    file_extensions=['.pdf', '.md']
)

for content, metadata in results:
    print(f"Processed: {metadata['file_name']}")
    print(f"Length: {len(content)} characters")

Best Practices

Document Organization

Categories:

• ✅ Use meaningful categories (technical, business, reference)
• ✅ Keep category names lowercase and simple
• ✅ Create custom categories as needed
• ❌ Don't over-categorize (use metadata for fine-grained classification)

Metadata:

• ✅ Add relevant metadata (author, date, topic, version)
• ✅ Use consistent metadata keys across documents
• ✅ Include source URLs for web content
• ❌ Don't duplicate information already in content

Chunking Strategy

When to use larger chunks (1500-2000 chars):

• Technical documentation with code examples
• Academic papers with complex concepts
• Documents requiring more context

When to use smaller chunks (500-800 chars):

• Q&A documents
• Short articles or blog posts
• Documents with discrete topics

Overlap considerations:

• More overlap (300-400 chars): Better context preservation
• Less overlap (100-200 chars): More efficient storage
• Default 200 chars works well for most content

Embedding Selection

Use Local Embeddings when:

• Privacy is critical (data cannot leave your system)
• Cost is a concern (embeddings are free)
• You have many documents to process
• Embedding quality is "good enough"

Use OpenAI Embeddings when:

• Highest quality is required
• Processing time is critical (API is faster than some local models)
• You need consistency with other OpenAI tools
• Cost is acceptable (~$0.02 per 1M tokens)

Search Optimization

Query Formulation:

• ✅ Use natural language questions
• ✅ Be specific rather than general
• ✅ Include key terms from your domain
• ❌ Don't use single keywords (use phrases)

Relevance Thresholds:

• 0.8+: Highly relevant, near-exact matches
• 0.7-0.8: Very relevant, strong semantic match
• 0.5-0.7: Relevant, moderate semantic match
• <0.5: Marginal relevance, consider filtering out

Result Limits:

• Start with 5 results for most queries
• Increase to 10-20 for broad exploratory queries
• Use 1-3 for highly specific questions

Limitations

Current Limitations

• Languages: Best results with English (models are English-optimized)
• Image Content: Text-only extraction (no OCR or image analysis)
• Tables: Basic table extraction (formatting may be lost)
• Code: Extracted as text (no syntax highlighting or execution)
• Updates: Documents must be re-ingested after changes
• Scale: ChromaDB optimized for <1M documents (beyond that, consider alternatives)

Performance Constraints

• Large PDFs (>1000 pages) may be slow to process
• First-time embedding model download (~100MB) required
• Local embeddings require CPU/GPU resources
• Memory usage scales with number of documents

Troubleshooting

Common Issues

Issue: "Module not found" errors

# Solution: Install dependencies
pip install -r scripts/requirements.txt

Issue: "No results found" for valid queries

# Solution 1: Check if documents are loaded
python scripts/knowledge_base.py list

# Solution 2: Lower minimum score threshold
python search_kb.py "query" --min-score 0.0

# Solution 3: Rebuild knowledge base
python scripts/knowledge_base.py rebuild

Issue: "Out of memory" during embedding generation

# Solution: Use smaller embedding model
from rag_utils import LocalEmbeddings

embeddings = LocalEmbeddings(model_name="all-MiniLM-L6-v2")  # Smaller, 384 dim

Issue: "Slow search performance"

# Solution: Reduce chunk size or document count
# Or: Switch to more efficient embedding model
# Or: Consider upgrading hardware

Issue: "PDF text extraction failed"

# Solution: Check if PDF is text-based (not scanned image)
# For scanned PDFs, use OCR preprocessing:
# tesseract input.pdf output -l eng pdf

Integration Examples

With LangChain

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from knowledge_base import KnowledgeBase

kb = KnowledgeBase("./data/kb")
retriever = kb.rag_system.vector_store.vectorstore.as_retriever()

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=retriever
)

answer = qa.run("What is RAG?")

With Claude API

import anthropic
from knowledge_base import KnowledgeBase

kb = KnowledgeBase("./data/kb")
results = kb.search("RAG implementation", limit=3)

# Build context from search results
context = "\n\n".join([r.text for r in results])

# Query Claude with context
client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": f"Based on this context:\n\n{context}\n\nQuestion: How do I implement RAG?"
    }]
)

print(message.content[0].text)

Reference Documentation

• RAG Utils Reference: Complete API documentation
• Document Processors: Supported formats and extraction details
• Chunking Strategies: Best practices for document chunking
• Embedding Models: Comparison and selection guide

Examples

• Basic Workflow: Step-by-step basic usage
• Batch Ingestion: Process multiple documents
• Advanced Search: Complex queries and filtering
• Integration Examples: Use with LangChain, Claude, etc.

Version: 1.0.0 Created: 2025-11-01 Status: Production Ready Task: 022 Dependencies: sentence-transformers, chromadb, langchain

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