Remove emojis from README

Clean up all emoji characters from README files for better
accessibility and consistency across documentation.

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

Author: Mullassery

README.md

@@ -109,17 +109,17 @@ client = QdrantClient("localhost", port=6333)
 
 # Create a collection
 client.recreate_collection(
-    collection_name="documents",
-    vectors_config={"size": 1536, "distance": "Cosine"}
+ collection_name="documents",
+ vectors_config={"size": 1536, "distance": "Cosine"}
 )
 
 # Add sample embeddings
 vectors = np.random.rand(5, 1536).tolist()
 client.upsert(
-    collection_name="documents",
-    points=[
-        {"id": i, "vector": vec} for i, vec in enumerate(vectors)
-    ]
+ collection_name="documents",
+ points=[
+ {"id": i, "vector": vec} for i, vec in enumerate(vectors)
+ ]
 )
 ```
 
@@ -133,9 +133,9 @@ hound = Hound(db="qdrant", endpoint="localhost:6333")
 
 # Diagnose retrieval quality
 diagnosis = hound.diagnose(
-    query="your search query",
-    top_k=5,
-    expected_docs=["0", "1"]  # optional: docs that should be retrieved
+ query="your search query",
+ top_k=5,
+ expected_docs=["0", "1"] # optional: docs that should be retrieved
 )
 
 # Get actionable report
@@ -153,11 +153,11 @@ kb = OKFDiagnosticKnowledgeBase(Path("./diagnostic_kb"))
 
 # Save diagnosis to KB (automatic)
 kb.record_diagnosis(
-    query_id="query_20260720_001",
-    root_cause=diagnosis.root_cause,
-    confidence=diagnosis.confidence,
-    failure_types=diagnosis.failure_types,
-    recommendations=diagnosis.recommendations
+ query_id="query_20260720_001",
+ root_cause=diagnosis.root_cause,
+ confidence=diagnosis.confidence,
+ failure_types=diagnosis.failure_types,
+ recommendations=diagnosis.recommendations
 )
 
 # Now learn from history
@@ -167,13 +167,13 @@ print(f"Found {len(similar)} similar failures in KB")
 # Get best strategies based on success history
 patterns = kb.extract_patterns(min_frequency=2)
 for pattern in patterns:
-    print(f"{pattern['pattern']}: {pattern['frequency']} of cases, "
-          f"{pattern['avg_success_rate']} success rate")
+ print(f"{pattern['pattern']}: {pattern['frequency']} of cases, "
+ f"{pattern['avg_success_rate']} success rate")
 
 # Recommendations auto-ranked by what worked before
 enhanced = kb.generate_enhanced_recommendations(
-    diagnosis.root_cause,
-    diagnosis.recommendations
+ diagnosis.root_cause,
+ diagnosis.recommendations
 )
 print("Top recommendation (ranked by success):")
 print(f"- {enhanced[0]['strategy']}")
@@ -186,7 +186,7 @@ PyVectorhound tells you exactly what's wrong—**and what worked last time**:
 
 ```
 =======================================================
-              PyVectorhound Diagnosis Report
+ PyVectorhound Diagnosis Report
 =======================================================
 
 Query: "quantum computing"
@@ -196,34 +196,34 @@ COMPONENT BREAKDOWN
 -------------------------------------------------------
 
 EMBEDDING MODEL: WEAK
-  Problem: Your embedding model doesn't understand
-  domain-specific concepts. Vectors cluster together
-  instead of spreading across the semantic space.
-  
-  Metrics:
-  - Isotropy: 45% (should be >70%)
-  - Distinctiveness: 21% (should be >60%)
-  
-  Impact: Vector search can't find semantically
-  similar documents
-
-VECTOR SEARCH: MODERATE  
-  Precision: 62% (should be >85%)
-  Recall: 55% (should be >80%)
-  
-  Impact: 38% of results are irrelevant
+ Problem: Your embedding model doesn't understand
+ domain-specific concepts. Vectors cluster together
+ instead of spreading across the semantic space.
+ 
+ Metrics:
+ - Isotropy: 45% (should be >70%)
+ - Distinctiveness: 21% (should be >60%)
+ 
+ Impact: Vector search can't find semantically
+ similar documents
+
+VECTOR SEARCH: MODERATE 
+ Precision: 62% (should be >85%)
+ Recall: 55% (should be >80%)
+ 
+ Impact: 38% of results are irrelevant
 
 KEYWORD SEARCH (BM25): GOOD
-  Precision: 85%, Recall: 78%
-  
-  Status: Working well, catching many matches
-  that vector search misses
+ Precision: 85%, Recall: 78%
+ 
+ Status: Working well, catching many matches
+ that vector search misses
 
 RERANKER: GOOD
-  Calibration: 91%
-  
-  Status: Helping but limited by weak upstream
-  components
+ Calibration: 91%
+ 
+ Status: Helping but limited by weak upstream
+ components
 
 ROOT CAUSE
 -------------------------------------------------------
@@ -234,27 +234,27 @@ domain-specific corpus.
 RECOMMENDATIONS (Ranked by Historical Success)
 -------------------------------------------------------
 1. HIGHEST PRIORITY: Upgrade Embedding Model
-   Try: text-embedding-3-large OR domain-specific model
-   Expected quality gain: +8-12 F1 points
-   ✅ HISTORICAL SUCCESS: 87% (based on 12 similar cases in KB)
-   Cost impact: +$8/month  
-   Implementation time: 2 hours
-   ROI: High (8-12% improvement for 40% cost increase)
+ Try: text-embedding-3-large OR domain-specific model
+ Expected quality gain: +8-12 F1 points
+ HISTORICAL SUCCESS: 87% (based on 12 similar cases in KB)
+ Cost impact: +$8/month 
+ Implementation time: 2 hours
+ ROI: High (8-12% improvement for 40% cost increase)
 
 2. QUICK WIN: Adjust Hybrid Search Weights
-   Current: BM25 (50%) + Vector (50%)
-   Try: BM25 (40%) + Vector (60%)
-   Expected gain: +2-3 F1 points
-   ✅ HISTORICAL SUCCESS: 65% (based on 4 similar cases in KB)
-   Time: 10 minutes
-   Cost: None
+ Current: BM25 (50%) + Vector (50%)
+ Try: BM25 (40%) + Vector (60%)
+ Expected gain: +2-3 F1 points
+ HISTORICAL SUCCESS: 65% (based on 4 similar cases in KB)
+ Time: 10 minutes
+ Cost: None
 
 3. OPTIONAL: Fine-tune Embedding on Your Corpus
-   Requires: 500+ labeled examples
-   Expected gain: +5-8% quality
-   ✅ HISTORICAL SUCCESS: 92% (based on 11 similar cases in KB)
-   Time: 1-2 days
-   Cost: Training infrastructure
+ Requires: 500+ labeled examples
+ Expected gain: +5-8% quality
+ HISTORICAL SUCCESS: 92% (based on 11 similar cases in KB)
+ Time: 1-2 days
+ Cost: Training infrastructure
 
 KNOWLEDGE BASE INSIGHTS
 -------------------------------------------------------
@@ -266,7 +266,7 @@ Trending: 3 similar failures in last 2 weeks
 
 ## Star If This Helps!
 
-If PyVectorhound solves your retrieval debugging problem, consider giving it a star ⭐ on GitHub. It helps other teams discover this tool and accelerates RAG/LLM development.
+If PyVectorhound solves your retrieval debugging problem, consider giving it a star on GitHub. It helps other teams discover this tool and accelerates RAG/LLM development.
 
 ## Understanding the Output
 
@@ -278,40 +278,40 @@ If PyVectorhound solves your retrieval debugging problem, consider giving it a s
 
 ## FAQ
 
-**Q: Do I need to set up PyVectorhound specially?**  
+**Q: Do I need to set up PyVectorhound specially?** 
 A: No. Install via pip, point it at your existing vector database, and run diagnosis.
 
-**Q: Can PyVectorhound work with my existing vector database?**  
+**Q: Can PyVectorhound work with my existing vector database?** 
 A: Yes. Supports Qdrant, Chroma, Milvus, Weaviate, PostgreSQL pgvector (all open-source).
 
-**Q: Does PyVectorhound modify my data?**  
+**Q: Does PyVectorhound modify my data?** 
 A: No. PyVectorhound is read-only. It analyzes but never modifies your vectors or documents.
 
-**Q: What if I don't have ground truth (expected_docs)?**  
+**Q: What if I don't have ground truth (expected_docs)?** 
 A: Ground truth is optional. Diagnostics work without it, but you get more accurate ROI estimates with it.
 
-**Q: How long does a diagnosis take?**  
+**Q: How long does a diagnosis take?** 
 A: Typically 45ms for small queries. Larger corpus analysis may take seconds.
 
-**Q: Can I use PyVectorhound in production?**  
+**Q: Can I use PyVectorhound in production?** 
 A: Yes. It's designed for production monitoring. Overhead is minimal (<1ms per operation).
 
-**Q: Does PyVectorhound require Rust knowledge?**  
+**Q: Does PyVectorhound require Rust knowledge?** 
 A: No. PyVectorhound is pure Python to use. Rust is only for building from source.
 
-**Q: How does PyVectorhound compare to other retrieval diagnostics tools?**  
+**Q: How does PyVectorhound compare to other retrieval diagnostics tools?** 
 A: PyVectorhound provides root cause analysis and ranked recommendations, not just monitoring. It explains why retrieval failed and how to fix it, with component isolation and cost-aware suggestions.
 
 ## Supported Vector Databases
 
 All database connectors are open-source compliant:
 
--  **Qdrant** — Open-source vector database
--  **Chroma** — Open-source embedding database
--  **Milvus** — Open-source vector database
--  **Weaviate** — Open-source semantic search engine
--  **PostgreSQL (pgvector)** — SQL + open-source pgvector extension
--  **Custom** — Query any database
+- **Qdrant** — Open-source vector database
+- **Chroma** — Open-source embedding database
+- **Milvus** — Open-source vector database
+- **Weaviate** — Open-source semantic search engine
+- **PostgreSQL (pgvector)** — SQL + open-source pgvector extension
+- **Custom** — Query any database
 
 Add more databases by implementing the `VectorDB` protocol.
 
@@ -323,9 +323,9 @@ Rust Core (pyvectorhound._core)
 - Pipeline analysis
 - Drift detection
 - Improvement tracking
-  |
-  (PyO3 bindings)
-  |
+ |
+ (PyO3 bindings)
+ |
 Python Wrapper (pyvectorhound)
 - Hound class (main API)
 ```
@@ -342,20 +342,20 @@ PyVectorHound goes beyond monitoring: it diagnoses retrieval issues, recommends
 
 | Capability | Traditional Tools | PyVectorHound |
 |-----------|---|---|
-| Root cause analysis | ❌ | ✅ |
-| Component isolation | ❌ | ✅ |
-| Ranked recommendations | ❌ | ✅ |
-| Cost-aware suggestions | ❌ | ✅ |
-| Before/after comparison | ❌ | ✅ |
-| Model comparison | ❌ | ✅ |
-| **Persistent knowledge base** | ❌ | ✅ OKF |
-| **Learning from history** | ❌ | ✅ OKF |
-| **Pattern recognition** | ❌ | ✅ OKF |
-| **Autonomous optimization** | ❌ | ✅ OKF |
+| Root cause analysis | | |
+| Component isolation | | |
+| Ranked recommendations | | |
+| Cost-aware suggestions | | |
+| Before/after comparison | | |
+| Model comparison | | |
+| **Persistent knowledge base** | | OKF |
+| **Learning from history** | | OKF |
+| **Pattern recognition** | | OKF |
+| **Autonomous optimization** | | OKF |
 
 **The OKF Difference:**
-- Traditional tools: "Here's what's wrong" → Forget after you close the report
-- PyVectorHound: "Here's what's wrong, here's what worked last time, here's what your team learned" → Improve every diagnosis
+- Traditional tools: "Here's what's wrong"  Forget after you close the report
+- PyVectorHound: "Here's what's wrong, here's what worked last time, here's what your team learned"  Improve every diagnosis
 
 ## Speed Comparison
 
@@ -412,8 +412,8 @@ print(diagnosis.hunt())
 ```python
 # Should you upgrade to a larger embedding model?
 comparison = hound.compare_models(
-    model_type="embedding",
-    candidates=["3-small", "3-large", "cohere-v3"]
+ model_type="embedding",
+ candidates=["3-small", "3-large", "cohere-v3"]
 )
 print(comparison.report())
 # Get: quality metrics, cost impact, ROI analysis
@@ -428,12 +428,12 @@ scorer = hound.quality_scorer()
 # Score embeddings in real-time
 quality = scorer.score(embedding_vector)
 if quality["status"] == "WEAK":
-    alert("Embedding quality degraded")
+ alert("Embedding quality degraded")
 
 # Detect gradual drift
 health = scorer.corpus_health()
 if health["drift"] > 0.15:
-    alert(f"15% quality degradation detected")
+ alert(f"15% quality degradation detected")
 ```
 
 ## Troubleshooting
@@ -485,22 +485,22 @@ comparison = hound.compare_models(model_type="embedding", candidates=[...])
 scorer = hound.quality_scorer()
 
 # Diagnosis methods
-diagnosis.hunt()              # Plain English report
-diagnosis.metrics()           # Raw metrics by component
-diagnosis.recommendations()   # Ranked fixes
-diagnosis.root_cause()        # Root cause explanation
+diagnosis.hunt() # Plain English report
+diagnosis.metrics() # Raw metrics by component
+diagnosis.recommendations() # Ranked fixes
+diagnosis.root_cause() # Root cause explanation
 
 # Comparison methods
-comparison.report()           # Side-by-side comparison
-comparison.metrics()          # Quality/cost/latency data
-comparison.pareto_frontier()  # Optimal models
-comparison.ab_test(...)       # Setup A/B test
+comparison.report() # Side-by-side comparison
+comparison.metrics() # Quality/cost/latency data
+comparison.pareto_frontier() # Optimal models
+comparison.ab_test(...) # Setup A/B test
 
 # Scorer methods
-scorer.score(embedding)       # Score single embedding
-scorer.corpus_health()        # Corpus-wide metrics
-scorer.detect_anomalies(...)  # Find problematic embeddings
-scorer.trend_analysis(...)    # Historical trends
+scorer.score(embedding) # Score single embedding
+scorer.corpus_health() # Corpus-wide metrics
+scorer.detect_anomalies(...) # Find problematic embeddings
+scorer.trend_analysis(...) # Historical trends
 ```
 
 ## Documentation
@@ -586,7 +586,7 @@ from pyvectorhound.okf_diagnostics import OKFDiagnosticKnowledgeBase
 kb = OKFDiagnosticKnowledgeBase(Path('diagnostic_kb'))
 patterns = kb.extract_patterns()
 for p in patterns:
-    print(f\"{p['pattern']}: {p['frequency']} of cases\")
+ print(f\"{p['pattern']}: {p['frequency']} of cases\")
 "
 
 # Your KB is now a git repo—share with your team!
@@ -621,7 +621,7 @@ Built with:
 
 *Diagnose 45ms. Learn forever. Fix autonomously.*
 
-## 🔒 Security & Error Handling
+## Security & Error Handling
 
 PyVectorHound includes: