added ollama embeddigs parameters for nomic-embed-text (#214)

* added ollama embeddigs parameters for nomic-embed-text

* fixed Space missing after colon.

* refactoring

* lint fix

* lint fix

* added embed_now generation

* added embed_later generation

* Adding embeddings callback and tests

* Linting

* Updating embeddings docs and cassettes.

---------

Co-authored-by: Justin Bowen <JusBowen@gmail.com>

Author: nicolangr

CLAUDE.md

@@ -1299,3 +1299,4 @@ When updating documentation:
 
 - VCR cassettes need to be removed and tests run again to record new cassettes when the request params change
 
+- Do not hardcode examples and make sure to use vscode regions and vite-press code snippets imports

docs/docs/framework/embeddings.md

@@ -0,0 +1,384 @@
+# Embeddings
+
+Embeddings are numerical representations of text that capture semantic meaning, enabling similarity searches, clustering, and other vector-based operations. ActiveAgent provides a unified interface for generating embeddings across all supported providers.
+
+## Overview
+
+Embeddings transform text into high-dimensional vectors that represent semantic meaning. Similar texts produce similar vectors, enabling powerful features like:
+
+- **Semantic Search** - Find related content by meaning, not just keywords
+- **Clustering** - Group similar documents automatically
+- **Classification** - Categorize text based on similarity to examples
+- **Recommendation** - Suggest related content based on embeddings
+- **Anomaly Detection** - Identify outliers in text data
+
+## Basic Usage
+
+### Generating Embeddings
+
+Use the `embed_now` method to generate embeddings synchronously:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_sync_generation {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/embedding-agent-test.rb-test-generates-embeddings-synchronously-with-embed-now.md -->
+:::
+
+### Async Embeddings
+
+Generate embeddings in background jobs:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_async_generation {ruby:line-numbers}
+
+## Embedding Callbacks
+
+Use callbacks to process embeddings before and after generation:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_with_callbacks {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/embedding-agent-test.rb-test-processes-embeddings-with-callbacks.md -->
+:::
+
+## Provider Configuration
+
+Each provider supports different embedding models and configurations:
+
+### OpenAI
+
+Configure OpenAI-specific embedding models:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_openai_model_config {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/embedding-agent-test.rb-test-uses-configured-openai-embedding-model.md -->
+:::
+
+### Ollama
+
+Configure Ollama for local embedding generation:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_ollama_provider_test {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/embedding-agent-test.rb-test-generates-embeddings-with-Ollama-provider.md -->
+:::
+
+### Error Handling
+
+ActiveAgent provides proper error handling for connection issues:
+
+<<< @/../test/generation_provider/ollama_provider_test.rb#ollama_provider_embed {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/ollama-provider-test.rb-test-embed-method-works-with-ollama-provider.md -->
+:::
+
+## Working with Embeddings
+
+### Similarity Search
+
+Find similar documents using cosine similarity:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_similarity_search {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/embedding-agent-test.rb-test-performs-similarity-search-with-embeddings.md -->
+:::
+
+### Batch Processing
+
+Process multiple embeddings efficiently:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_batch_processing {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/embedding-agent-test.rb-test-processes-multiple-embeddings-in-batch.md -->
+:::
+
+### Embedding Dimensions
+
+Different models produce different embedding dimensions:
+
+<<< @/../test/agents/embedding_agent_test.rb#embedding_dimension_test {ruby:line-numbers}
+
+::: details Response Example
+<!-- @include: @/parts/examples/embedding-agent-test.rb-test-verifies-embedding-dimensions-for-different-models.md -->
+:::
+
+## Advanced Patterns
+
+### Caching Embeddings
+
+Cache embeddings to avoid regenerating them:
+
+```ruby
+class CachedEmbeddingAgent < ApplicationAgent
+  def get_embedding(text)
+    cache_key = "embedding:#{Digest::SHA256.hexdigest(text)}"
+    
+    Rails.cache.fetch(cache_key, expires_in: 30.days) do
+      generation = self.class.with(message: text).prompt_context
+      generation.embed_now.message.content
+    end
+  end
+end
+```
+
+### Multi-Model Embeddings
+
+Use different models for different purposes:
+
+```ruby
+class MultiModelEmbeddingAgent < ApplicationAgent
+  def generate_semantic_embedding(text)
+    # High-quality semantic embedding
+    self.class.generate_with :openai, 
+      embedding_model: "text-embedding-3-large"
+    
+    generation = self.class.with(message: text).prompt_context
+    generation.embed_now
+  end
+  
+  def generate_fast_embedding(text)
+    # Faster, smaller embedding for real-time use
+    self.class.generate_with :openai,
+      embedding_model: "text-embedding-3-small"
+    
+    generation = self.class.with(message: text).prompt_context
+    generation.embed_now
+  end
+end
+```
+
+## Vector Databases
+
+Store and query embeddings using vector databases:
+
+### PostgreSQL with pgvector
+
+```ruby
+class PgVectorAgent < ApplicationAgent
+  def store_document(text)
+    # Generate embedding
+    generation = self.class.with(message: text).prompt_context
+    embedding = generation.embed_now.message.content
+    
+    # Store in PostgreSQL with pgvector
+    Document.create!(
+      content: text,
+      embedding: embedding  # pgvector column
+    )
+  end
+  
+  def search_similar(query, limit: 10)
+    query_embedding = get_embedding(query)
+    
+    # Use pgvector's <-> operator for cosine distance
+    Document
+      .order(Arel.sql("embedding <-> '#{query_embedding}'"))
+      .limit(limit)
+  end
+end
+```
+
+### Pinecone Integration
+
+```ruby
+class PineconeAgent < ApplicationAgent
+  def initialize
+    super
+    @pinecone = Pinecone::Client.new(api_key: ENV['PINECONE_API_KEY'])
+    @index = @pinecone.index('documents')
+  end
+  
+  def upsert_document(id, text, metadata = {})
+    embedding = get_embedding(text)
+    
+    @index.upsert(
+      vectors: [{
+        id: id,
+        values: embedding,
+        metadata: metadata.merge(text: text)
+      }]
+    )
+  end
+  
+  def query_similar(text, top_k: 10)
+    embedding = get_embedding(text)
+    
+    @index.query(
+      vector: embedding,
+      top_k: top_k,
+      include_metadata: true
+    )
+  end
+end
+```
+
+## Testing Embeddings
+
+Test embedding functionality with comprehensive test coverage including callbacks, similarity search, and batch processing as shown in the examples above.
+
+## Performance Optimization
+
+### Batch Processing
+
+Process embeddings in batches for better performance:
+
+```ruby
+class BatchOptimizedAgent < ApplicationAgent
+  def process_documents(documents)
+    documents.each_slice(100) do |batch|
+      Parallel.each(batch, in_threads: 5) do |doc|
+        generation = self.class.with(message: doc.content).prompt_context
+        doc.embedding = generation.embed_now.message.content
+        doc.save!
+      end
+    end
+  end
+end
+```
+
+### Caching Strategy
+
+Implement intelligent caching:
+
+```ruby
+class SmartCacheAgent < ApplicationAgent
+  def get_or_generate_embedding(text)
+    # Check cache first
+    cached = fetch_from_cache(text)
+    return cached if cached
+    
+    # Generate if not cached
+    embedding = generate_embedding(text)
+    
+    # Cache based on text length and importance
+    if should_cache?(text)
+      cache_embedding(text, embedding)
+    end
+    
+    embedding
+  end
+  
+  private
+  
+  def should_cache?(text)
+    text.length > 100 || text.include?("important")
+  end
+end
+```
+
+## Best Practices
+
+1. **Choose the Right Model** - Balance quality, speed, and cost
+2. **Normalize Text** - Preprocess consistently before embedding
+3. **Cache Aggressively** - Embeddings are expensive to generate
+4. **Batch When Possible** - Process multiple texts together
+5. **Monitor Dimensions** - Different models produce different sizes
+6. **Use Callbacks** - Process embeddings consistently
+7. **Handle Failures** - Implement retry logic and fallbacks
+8. **Version Embeddings** - Track which model generated each embedding
+
+## Common Use Cases
+
+### Semantic Search
+
+```ruby
+class SemanticSearchAgent < ApplicationAgent
+  def build_search_index(documents)
+    documents.each do |doc|
+      generation = self.class.with(message: doc.content).prompt_context
+      doc.update!(embedding: generation.embed_now.message.content)
+    end
+  end
+  
+  def search(query)
+    query_embedding = get_embedding(query)
+    
+    Document
+      .select("*, embedding <-> '#{query_embedding}' as distance")
+      .order("distance")
+      .limit(10)
+  end
+end
+```
+
+### Content Recommendations
+
+```ruby
+class RecommendationAgent < ApplicationAgent
+  def recommend_similar(article)
+    article_embedding = article.embedding || generate_embedding(article.content)
+    
+    Article
+      .where.not(id: article.id)
+      .select("*, embedding <-> '#{article_embedding}' as similarity")
+      .order("similarity")
+      .limit(5)
+  end
+end
+```
+
+### Clustering
+
+```ruby
+class ClusteringAgent < ApplicationAgent
+  def cluster_documents(documents, num_clusters: 5)
+    # Generate embeddings
+    embeddings = documents.map do |doc|
+      get_embedding(doc.content)
+    end
+    
+    # Use k-means or other clustering algorithm
+    clusters = perform_clustering(embeddings, num_clusters)
+    
+    # Assign documents to clusters
+    documents.zip(clusters).each do |doc, cluster_id|
+      doc.update!(cluster_id: cluster_id)
+    end
+  end
+end
+```
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Dimension Mismatch** - Ensure all embeddings use the same model
+2. **Memory Issues** - Large embedding vectors can consume significant RAM
+3. **Rate Limits** - Implement exponential backoff for API limits
+4. **Cost Management** - Monitor embedding API usage and costs
+5. **Connection Errors** - Handle network issues with Ollama and other providers
+
+### Debugging
+
+```ruby
+class DebuggingAgent < ApplicationAgent
+  def debug_embedding(text)
+    generation = self.class.with(message: text).prompt_context
+    
+    Rails.logger.info "Generating embedding for: #{text[0..100]}..."
+    Rails.logger.info "Provider: #{generation_provider.class.name}"
+    Rails.logger.info "Model: #{generation_provider.embedding_model}"
+    
+    response = generation.embed_now
+    embedding = response.message.content
+    
+    Rails.logger.info "Dimensions: #{embedding.size}"
+    Rails.logger.info "Range: [#{embedding.min}, #{embedding.max}]"
+    Rails.logger.info "Mean: #{embedding.sum / embedding.size}"
+    
+    embedding
+  end
+end
+```
+
+## Related Documentation
+
+- [Generation Provider Overview](/docs/framework/generation-provider)
+- [OpenAI Provider](/docs/generation-providers/openai-provider)
+- [Ollama Provider](/docs/generation-providers/ollama-provider)
+- [Callbacks](/docs/active-agent/callbacks)
+- [Generation](/docs/active-agent/generation)