Rework optimizers

README.md

@@ -205,8 +205,7 @@ Once you've got the basics working, there's more:
 ```python
 extractor.optimize(
     texts=your_examples,
-    expected_results=expected_outputs,
-    num_trials=50
+    expected_results=expected_outputs
 )
 ```
 

docs/src/content/docs/examples/legal-contracts.mdx

@@ -66,11 +66,11 @@ Create an extractor for legal document analysis:
 extractor = LangStruct(
     schema=LegalContractSchema,
     model="gemini/gemini-2.5-flash-lite",  # Fast and reliable for legal analysis
-    optimize=True,
     use_sources=True,  # Critical for legal document traceability
     temperature=0.1,   # Lower temperature for consistency
     max_retries=3      # Ensure reliability
 )
+# Later: extractor.optimize(training_texts, expected_results)
 
 # Example contract text
 contract_text = """

docs/src/content/docs/examples/scientific-papers.mdx

@@ -76,11 +76,11 @@ Create an extractor for research paper analysis:
 extractor = LangStruct(
     schema=ScientificPaperSchema,
     model="gemini/gemini-2.5-flash-lite",  # Fast and reliable for academic content
-    optimize=True,
     use_sources=True,  # Track where information was found
     temperature=0.2,   # Slightly higher for nuanced interpretation
     max_retries=3
 )
+# Later: extractor.optimize(training_texts, expected_results)
 
 # Example research paper text (excerpt)
 paper_text = """

docs/src/content/docs/optimization.mdx

@@ -9,28 +9,28 @@ Make your extraction more accurate with automatic optimization. LangStruct learn
 
 ## The Easy Way
 
-**Enable optimization (configure optimizer) and then optimize with your data:**
+**Create an extractor (optionally choose the optimizer) and call `optimize()` when you're ready:**
 
 ```python
 from langstruct import LangStruct
 
-# Create extractor with optimization enabled
 extractor = LangStruct(
   example={
     "name": "Dr. Sarah Johnson",
     "age": 34,
     "occupation": "data scientist"
   },
-  optimize=True  # sets up optimizer; run .optimize(...) to train
+  optimizer="miprov2",  # default optimizer
 )
+
+# Later, once you have training data:
+# extractor.optimize(texts=training_texts, expected_results=good_results)
 ```
 
-**Default behavior (faster startup, good baseline accuracy):**
+**Quick experiments (skip optimization entirely):**
 
 ```python
-# No optimization - good for quick experiments
 extractor = LangStruct(example={"name": "John", "age": 30})
-# optimize=False by default - enables faster startup
 ```
 
 ## When You Have Training Data
@@ -74,8 +74,19 @@ Optimization can significantly improve accuracy on real-world tasks:
 
 ## Persisting Results
 
-Saving/loading an optimized extractor is not yet implemented.
-For now, re-run `optimize()` when you start up, or persist your training data and configuration.
+Save and load optimized extractors to reuse them without re-running optimization:
+
+```python
+# Save after optimization
+extractor.save("./my_extractor")
+
+# Load later
+from langstruct import LangStruct
+loaded = LangStruct.load("./my_extractor")
+
+# Use immediately - optimization is preserved
+result = loaded.extract("new text")
+```
 
 ## Advanced (If You Need It)
 
@@ -86,7 +97,6 @@ Most users don't need this, but if you want more control:
 extractor.optimize(
     texts=training_texts,
     expected_results=good_results,
-    num_trials=50,        # More trials = better results (takes longer)
     validation_split=0.3  # Use 30% for testing improvements
 )
 ```
@@ -110,26 +120,26 @@ extractor.optimize(
 
 ## Common Questions
 
-**Q: Do I always need training data?**  
-A: No! Optimization can work without training data, but providing examples improves results significantly.
+**Q: Do I always need training data?**
+A: You need example texts, but not necessarily expected outputs. If you don't provide `expected_results`, LangStruct uses the LLM's confidence ratings to optimize. Providing expected outputs significantly improves accuracy.
 
-**Q: How long does optimization take?**  
+**Q: How long does optimization take?**
 A: Usually 1-5 minutes for typical datasets (10-100 examples).
 
-**Q: Can I optimize an already optimized extractor?**  
-A: Yes! You can keep optimizing with new data as you get it.
+**Q: Can I optimize an already optimized extractor?**
+A: Yes, you can continue optimizing with new data as you collect it.
 
-**Q: Will this make my extractions slower?**  
-A: No - optimization happens once during training. Production extraction speed is the same.
+**Q: Will this make my extractions slower?**
+A: No - optimization happens once during training. Production extraction speed is unchanged.
 
-**Q: What happens when I switch models?**  
-A: Just change the model and re-optimize! Same training data, same accuracy - zero prompt rewriting needed.
+**Q: What happens when I switch models?**
+A: Change the model and re-optimize with the same training data. No prompt rewriting needed.
 
 ## Next Steps
 
 <CardGrid>
 	<Card title="Try It Now" icon="laptop">
-		Create a LangStruct extractor and enable optimization when you need accuracy!
+		Create a LangStruct extractor and enable optimization when you need accuracy.
 	</Card>
 	<Card title="Source Grounding" icon="document">
 		[Track where information comes from](/source-grounding/)

docs/src/content/docs/persistence.mdx

@@ -46,10 +46,9 @@ print(result.entities)
 ```python
 from langstruct import LangStruct
 
-# Create extractor with optimization
+# Create extractor
 extractor = LangStruct(
     example={"name": "John", "age": 30, "role": "engineer"},
-    optimize=True
 )
 
 # Train the extractor
@@ -58,8 +57,7 @@ expected_results = [{"name": "Expected outputs..."}]
 
 extractor.optimize(
     texts=training_texts,
-    expected_results=expected_results,
-    num_trials=50
+    expected_results=expected_results
 )
 
 # Save optimized state
@@ -215,7 +213,7 @@ Common error scenarios:
 
 ```python
 # Development: Train and save
-extractor = LangStruct(schema=MySchema, optimize=True)
+extractor = LangStruct(schema=MySchema)
 extractor.optimize(training_data, expected_results)
 extractor.save("./production_extractor")
 

docs/src/content/docs/query-parsing.mdx

@@ -44,22 +44,22 @@ This single query contains **three distinct types of information**:
     - Quarter: Q3 2024 (exact match)
     - Revenue: > $100B (numeric comparison)
     - Sector: Technology (category match)
-    
+
     These need **database-style filtering**, not semantic search
   </Card>
   <Card title="Semantic Content" icon="magnifier">
     **Conceptual topics for similarity search:**
     - "financial reports" (could be 10-K, earnings, statements)
     - "AI investments" (could be ML, artificial intelligence, neural networks)
-    
+
     These need **embedding-based semantic search**
   </Card>
   <Card title="Implicit Context" icon="information">
     **Assumed context from natural language:**
     - "Show me" implies retrieval intent
     - "companies" implies corporate entities
     - Plural suggests multiple results expected
-    
+
     These provide **query understanding context**
   </Card>
 </CardGrid>
@@ -86,30 +86,30 @@ results = vector_db.similarity_search(query_embedding)
 <Tabs>
   <TabItem label="Semantic Terms">
     **What they are:** Conceptual topics that benefit from semantic understanding
-    
+
     **Examples:**
     - "artificial intelligence" ≈ "AI" ≈ "machine learning"
     - "financial performance" ≈ "earnings" ≈ "fiscal results"
     - "customer satisfaction" ≈ "user happiness" ≈ "client feedback"
-    
+
     **How they work:** Converted to embeddings for similarity matching
-    
+
     **Best for:**
     - Finding conceptually related content
     - Handling synonyms and variations
     - Discovering relevant but not exact matches
   </TabItem>
   <TabItem label="Structured Filters">
     **What they are:** Exact constraints that must be precisely matched
-    
+
     **Examples:**
     - Date/Time: "Q3 2024", "after 2023", "last 30 days"
     - Numbers: "revenue > $100M", "5-10 employees", "top 3"
     - Categories: "tech sector", "approved status", "high priority"
     - Entities: "Apple Inc.", "California", "John Smith"
-    
+
     **How they work:** Converted to database-style filter operations
-    
+
     **Best for:**
     - Enforcing hard constraints
     - Filtering by exact values
@@ -129,7 +129,7 @@ Let's see how different queries naturally decompose:
 - **Structured filters:** `{"quarter": "Q3 2024", "sector": "Technology", "profitable": true}`
 - **Why it matters:** You want companies that ARE profitable (filter), not just ones that DISCUSS profitability
 
-#### Healthcare Query  
+#### Healthcare Query
 > "Patient records over 65 years old with diabetes showing improvement"
 
 - **Semantic terms:** `["showing improvement", "better outcomes"]`
@@ -216,7 +216,7 @@ print("📖 Explanation:", result.explanation)
     'revenue': {'$gte': 100.0}
 }
 💯 Confidence: 91.5%
-📖 Explanation: 
+📖 Explanation:
 Searching for: tech companies
 With filters:
   • quarter = Q3 2024
@@ -270,30 +270,30 @@ class EnhancedRAGSystem:
         # Same schema for both extraction and parsing!
         self.langstruct = LangStruct(example=schema_example)
         self.vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
-    
+
     def index_document(self, text: str):
         """Extract metadata and index document"""
         # Extract structured metadata
         extraction = self.langstruct.extract(text)
-        
+
         # Index with both text and metadata
         self.vectorstore.add_texts(
             texts=[text],
             metadatas=[extraction.entities]
         )
-    
+
     def natural_query(self, query: str, k: int = 5):
         """Query using natural language"""
         # Parse query into components
         parsed = self.langstruct.query(query)
-        
+
         # Perform hybrid search
         results = self.vectorstore.similarity_search(
             query=' '.join(parsed.semantic_terms),
             k=k,
             filter=parsed.structured_filters
         )
-        
+
         return results, parsed.explanation
 
 # Usage
@@ -407,13 +407,13 @@ ls = LangStruct(example=your_schema)
 # Query with natural language
 def smart_search(query: str):
     parsed = ls.query(query)
-    
+
     results = collection.query(
         query_texts=parsed.semantic_terms,
         where=parsed.structured_filters,
         n_results=10
     )
-    
+
     return results
 ```
 
@@ -431,19 +431,19 @@ ls = LangStruct(example=your_schema)
 # Natural language query
 def pinecone_search(query: str):
     parsed = ls.query(query)
-    
+
     # Convert to Pinecone filter format
     pinecone_filter = {
-        f"metadata.{k}": v 
+        f"metadata.{k}": v
         for k, v in parsed.structured_filters.items()
     }
-    
+
     results = index.query(
         vector=embed(parsed.semantic_terms),
         filter=pinecone_filter,
         top_k=10
     )
-    
+
     return results
 ```
 
@@ -497,9 +497,8 @@ domain_ls = LangStruct(
         # Include synonyms in descriptions
         "earnings": 10.5,  # Also covers "profits", "income"
     },
-    # Can optimize for better accuracy
-    optimize=True
 )
+# Call domain_ls.optimize(...) with training examples when ready
 ```
 
 ## Performance Considerations
@@ -512,7 +511,7 @@ from functools import lru_cache
 class CachedLangStruct:
     def __init__(self, schema):
         self.ls = LangStruct(example=schema)
-        
+
     @lru_cache(maxsize=1000)
     def query_cached(self, query: str):
         """Cache frequently used queries"""

docs/src/content/docs/quickstart.mdx

@@ -87,10 +87,8 @@ extractor = LangStruct(example=schema)
 # See optimization in action
 extractor.optimize(
     texts=["training texts..."],
-    expected=[{"expected outputs..."}],
-    num_trials=50  # More trials = better accuracy
+    expected_results=[{"expected outputs..."}]  # Optional - uses confidence if omitted
 )
-print(f"Optimized accuracy: {extractor.score:.1%}")
 ```
 
 ## Process Multiple Documents (with quotas)

docs/src/content/docs/why-dspy.mdx

@@ -119,7 +119,7 @@ extractor = LangStruct(example={
 
 # 2. Let MIPROv2 optimize prompts and examples automatically
 extractor.optimize(
-    training_texts=["Apple reported $125B in Q3...", "Meta earned $40B..."],
+    texts=["Apple reported $125B in Q3...", "Meta earned $40B..."],
     expected_results=[
         {"company": "Apple", "revenue": 125.0, "quarter": "Q3"},
         {"company": "Meta", "revenue": 40.0, "quarter": "Q3"}
@@ -147,17 +147,16 @@ result = extractor.extract("Microsoft announced $65B revenue for Q4")
 extractor = LangStruct(
     example={"company": "Apple", "revenue": 100.0},
     model="gpt-5-mini",
-    optimize=True
 )
-extractor.optimize(training_texts, expected_results)
+extractor.optimize(texts=training_texts, expected_results=expected_results)
 
 # 6 months later, switch to Claude - just two lines!
 extractor.model = "claude-3-7-sonnet-latest"
-extractor.optimize(training_texts, expected_results)  # Auto-reoptimizes prompts
+extractor.optimize(texts=training_texts, expected_results=expected_results)  # Auto-reoptimizes prompts
 
 # Or use local models for privacy
 extractor.model = "ollama/llama3.2"
-extractor.optimize(training_texts, expected_results)  # Works the same way
+extractor.optimize(texts=training_texts, expected_results=expected_results)  # Works the same way
 
 # Same accuracy, zero prompt rewriting, zero vendor lock-in
 ```