Create IBM Granite Embedding Models MCP Server

## Overview
Create a comprehensive MCP Server for IBM Granite Embedding Models supporting semantic search, document retrieval, similarity analysis, and vector database integration with enterprise-grade capabilities for enhanced user intent understanding.

## Server Specifications

### **Server Details**
- **Name**: `granite-embedding-server`
- **Language**: Python 3.11+
- **Location**: `mcp-servers/python/granite_embedding_server/`
- **Purpose**: Provide semantic embedding capabilities via IBM Granite embedding models

### **Supported Models**
From [IBM Granite Embedding Models on Hugging Face](https://huggingface.co/ibm-granite):
- **Text Embeddings**: granite-embedding-768, granite-embedding-1024
- **Code Embeddings**: granite-code-embedding-v1, granite-multilingual-code
- **Document Embeddings**: granite-document-embedding, granite-legal-embedding
- **Multilingual**: granite-multilingual-embedding, granite-cross-lingual

### **Provider Support**
- **Ollama**: Local inference with embedding models
- **watsonx.ai**: IBM's enterprise embedding services
- **Hugging Face**: Direct access to Granite embedding models
- **Custom Endpoints**: Integration with vector databases and search engines

### **Vector Database Integration**
- **ChromaDB**: Open-source vector database
- **Pinecone**: Managed vector database service
- **Weaviate**: Open-source vector search engine
- **Qdrant**: Vector similarity search engine
- **Elasticsearch**: Full-text and vector search
- **Custom**: Generic vector database integration

### **Tools Provided**

#### 1. `generate_embeddings`
Generate high-quality semantic embeddings for text and documents
```python
@dataclass
class EmbeddingGenerationRequest:
    texts: List[str]  # Text content to embed
    model: str = "granite-embedding-768"
    provider: str = "ollama"
    embedding_type: str = "semantic"  # semantic, code, document, multilingual
    normalize_embeddings: bool = True
    batch_size: int = 32
    pooling_strategy: str = "mean"  # mean, cls, max
    output_format: str = "numpy"  # numpy, list, tensor
```

#### 2. `semantic_search`
Perform semantic search across document collections
```python
@dataclass
class SemanticSearchRequest:
    query: str
    model: str = "granite-embedding-768"
    provider: str = "watsonx"
    collection_id: str  # Vector database collection
    top_k: int = 10
    similarity_threshold: float = 0.7
    include_scores: bool = True
    include_metadata: bool = True
    rerank_results: bool = False
```

#### 3. `compute_similarity`
Calculate semantic similarity between texts
```python
@dataclass
class SimilarityRequest:
    text_pairs: List[Tuple[str, str]]  # Pairs of texts to compare
    model: str = "granite-embedding-1024"
    provider: str = "huggingface"
    similarity_metric: str = "cosine"  # cosine, euclidean, dot_product
    batch_processing: bool = True
    include_matrix: bool = False  # Full similarity matrix
```

#### 4. `cluster_documents`
Document clustering based on semantic similarity
```python
@dataclass
class DocumentClusteringRequest:
    documents: List[str]
    model: str = "granite-document-embedding"
    provider: str = "ollama"
    num_clusters: Optional[int] = None  # Auto-determine if None
    clustering_algorithm: str = "kmeans"  # kmeans, hierarchical, dbscan
    min_cluster_size: int = 2
    include_cluster_summaries: bool = True
```

#### 5. `find_duplicates`
Detect near-duplicate documents using embeddings
```python
@dataclass
class DuplicateDetectionRequest:
    documents: List[str]
    model: str = "granite-embedding-768"
    provider: str = "watsonx"
    similarity_threshold: float = 0.95
    deduplication_strategy: str = "threshold"  # threshold, clustering, hierarchical
    preserve_originals: bool = True
    include_similarity_scores: bool = True
```

#### 6. `build_vector_index`
Create and manage vector database indexes
```python
@dataclass
class VectorIndexRequest:
    documents: List[Dict[str, Any]]  # Documents with metadata
    model: str = "granite-embedding-1024"
    provider: str = "huggingface"
    vector_db: str = "chromadb"  # chromadb, pinecone, weaviate, qdrant
    index_name: str
    metadata_fields: List[str]
    batch_size: int = 100
    create_if_not_exists: bool = True
```

#### 7. `recommend_content`
Content recommendation based on user preferences
```python
@dataclass
class ContentRecommendationRequest:
    user_profile: str  # User preferences or history
    candidate_items: List[str]  # Items to recommend from
    model: str = "granite-embedding-768"
    provider: str = "ollama"
    recommendation_strategy: str = "similarity"  # similarity, collaborative, hybrid
    top_k: int = 10
    diversity_factor: float = 0.2  # Balance between relevance and diversity
```

#### 8. `analyze_text_relationships`
Analyze semantic relationships within text collections
```python
@dataclass
class TextRelationshipRequest:
    texts: List[str]
    model: str = "granite-multilingual-embedding"
    provider: str = "watsonx"
    relationship_types: List[str] = ["similarity", "clustering", "outliers"]
    visualization: bool = True
    include_statistics: bool = True
    output_format: str = "graph"  # graph, matrix, hierarchical
```

### **Implementation Requirements**

#### Directory Structure
```
mcp-servers/python/granite_embedding_server/
├── src/
│   └── granite_embedding_server/
│       ├── __init__.py
│       ├── server.py
│       ├── providers/
│       │   ├── __init__.py
│       │   ├── ollama_embedding.py
│       │   ├── watsonx_embedding.py
│       │   ├── huggingface_embedding.py
│       │   └── custom_endpoint.py
│       ├── models/
│       │   ├── __init__.py
│       │   ├── granite_embedding_models.py
│       │   └── model_capabilities.py
│       ├── vector_db/
│       │   ├── __init__.py
│       │   ├── chromadb_client.py
│       │   ├── pinecone_client.py
│       │   ├── weaviate_client.py
│       │   ├── qdrant_client.py
│       │   └── base_client.py
│       ├── processing/
│       │   ├── __init__.py
│       │   ├── embedding_processor.py
│       │   ├── similarity_calculator.py
│       │   ├── clustering.py
│       │   └── search_engine.py
│       ├── tools/
│       │   ├── __init__.py
│       │   ├── embedding_tools.py
│       │   ├── search_tools.py
│       │   ├── similarity_tools.py
│       │   └── recommendation_tools.py
│       └── utils/
│           ├── __init__.py
│           ├── text_preprocessing.py
│           ├── vector_operations.py
│           └── visualization.py
├── tests/
├── requirements.txt
├── README.md
└── examples/
    ├── semantic_search.py
    ├── document_clustering.py
    └── recommendation_system.py
```

#### Dependencies
```python
# requirements.txt
mcp>=1.0.0
transformers>=4.35.0
torch>=2.1.0
sentence-transformers>=2.2.2
numpy>=1.24.0
scipy>=1.11.0
scikit-learn>=1.3.0
chromadb>=0.4.0
pinecone-client>=2.2.0
weaviate-client>=3.25.0
qdrant-client>=1.7.0
elasticsearch>=8.11.0
faiss-cpu>=1.7.4
plotly>=5.17.0
umap-learn>=0.5.4
requests>=2.31.0
pydantic>=2.5.0
ollama>=0.1.7
ibm-watson-machine-learning>=1.0.325
```

### **Configuration**
```yaml
# config.yaml
providers:
  ollama:
    base_url: "http://localhost:11434"
    embedding_models_enabled: true
    timeout: 180
    
  watsonx:
    url: "https://us-south.ml.cloud.ibm.com"
    apikey: "${WATSONX_API_KEY}"
    project_id: "${WATSONX_PROJECT_ID}"
    embedding_endpoint: "/ml/v1/embeddings"
    
  huggingface:
    api_key: "${HF_API_KEY}"
    cache_dir: "./hf_embedding_cache"
    device: "auto"

models:
  default_text: "granite-embedding-768"
  default_code: "granite-code-embedding-v1"
  default_document: "granite-document-embedding"
  default_multilingual: "granite-multilingual-embedding"

vector_databases:
  chromadb:
    persist_directory: "./chromadb_data"
    collection_prefix: "granite_"
    
  pinecone:
    api_key: "${PINECONE_API_KEY}"
    environment: "${PINECONE_ENVIRONMENT}"
    index_dimension: 768
    
  weaviate:
    url: "${WEAVIATE_URL}"
    api_key: "${WEAVIATE_API_KEY}"
    
  qdrant:
    host: "${QDRANT_HOST}"
    port: 6333
    api_key: "${QDRANT_API_KEY}"

processing:
  max_batch_size: 64
  max_text_length: 8192
  default_similarity_threshold: 0.7
  enable_gpu: true
  
search:
  default_top_k: 10
  max_results: 1000
  enable_reranking: true
  
clustering:
  algorithms: ["kmeans", "hierarchical", "dbscan", "spectral"]
  max_clusters: 100
  min_cluster_size: 2
```

### **Usage Examples**

#### Generate Document Embeddings
```python
# Generate embeddings for a collection of documents
result = await mcp_client.call_tool("generate_embeddings", {
    "texts": [
        "Artificial intelligence is transforming healthcare",
        "Machine learning algorithms improve diagnostic accuracy", 
        "Deep learning models process medical imaging data"
    ],
    "model": "granite-embedding-768",
    "provider": "ollama",
    "embedding_type": "document",
    "normalize_embeddings": True,
    "output_format": "numpy"
})
```

#### Semantic Search
```python
# Search for relevant documents
result = await mcp_client.call_tool("semantic_search", {
    "query": "AI applications in medical diagnosis",
    "model": "granite-embedding-1024",
    "provider": "watsonx",
    "collection_id": "medical_papers",
    "top_k": 15,
    "similarity_threshold": 0.75,
    "include_scores": True,
    "rerank_results": True
})
```

#### Document Similarity Analysis
```python
# Compare document similarity
result = await mcp_client.call_tool("compute_similarity", {
    "text_pairs": [
        ("Research paper on neural networks", "Deep learning study"),
        ("Climate change analysis", "Weather prediction model"),
        ("Financial market trends", "Stock price forecasting")
    ],
    "model": "granite-embedding-768",
    "provider": "huggingface",
    "similarity_metric": "cosine",
    "batch_processing": True
})
```

#### Document Clustering
```python
# Cluster related documents
result = await mcp_client.call_tool("cluster_documents", {
    "documents": [
        "Introduction to machine learning algorithms",
        "Neural network architecture design",
        "Climate change impact studies",
        "Weather forecasting techniques",
        "Financial risk assessment models",
        "Investment portfolio optimization"
    ],
    "model": "granite-document-embedding",
    "provider": "ollama",
    "clustering_algorithm": "kmeans",
    "num_clusters": 3,
    "include_cluster_summaries": True
})
```

#### Build Vector Index
```python
# Create vector database index
result = await mcp_client.call_tool("build_vector_index", {
    "documents": [
        {
            "text": "Research paper content...",
            "title": "AI in Healthcare",
            "author": "Dr. Smith",
            "category": "medical"
        },
        # ... more documents
    ],
    "model": "granite-embedding-1024",
    "provider": "watsonx",
    "vector_db": "chromadb",
    "index_name": "research_papers",
    "metadata_fields": ["title", "author", "category"],
    "batch_size": 50
})
```

#### Content Recommendation
```python
# Recommend content based on user profile
result = await mcp_client.call_tool("recommend_content", {
    "user_profile": "Interested in AI, machine learning, and data science applications",
    "candidate_items": [
        "Advanced neural network architectures",
        "Statistical analysis methods", 
        "Computer vision applications",
        "Natural language processing",
        "Database optimization techniques"
    ],
    "model": "granite-embedding-768",
    "provider": "huggingface",
    "top_k": 3,
    "diversity_factor": 0.3
})
```

#### Duplicate Detection
```python
# Find near-duplicate documents
result = await mcp_client.call_tool("find_duplicates", {
    "documents": [
        "Machine learning is a subset of artificial intelligence",
        "ML represents a branch of AI technology",
        "Natural language processing enables human-computer interaction",
        "NLP allows computers to understand human language",
        "Deep learning uses neural networks for pattern recognition"
    ],
    "model": "granite-embedding-768",
    "provider": "ollama",
    "similarity_threshold": 0.85,
    "include_similarity_scores": True
})
```

### **Advanced Features**
- **Multi-modal Embeddings**: Support for text, code, and document embeddings
- **Cross-lingual Search**: Multilingual semantic understanding
- **Hierarchical Clustering**: Multi-level document organization
- **Temporal Embeddings**: Time-aware semantic representations
- **Domain Adaptation**: Fine-tuned embeddings for specific domains
- **Federated Search**: Search across multiple vector databases

### **Enterprise Features**
- **High-Performance Computing**: GPU acceleration for large-scale embedding
- **Scalable Architecture**: Handle millions of documents
- **API Integration**: RESTful endpoints for enterprise applications
- **Security**: Encrypted embedding storage and transmission
- **Monitoring**: Performance metrics and usage analytics
- **Compliance**: Data governance and audit trails

### **Vector Database Ecosystem**
- **Multi-database Support**: Work with various vector database platforms
- **Index Optimization**: Efficient vector storage and retrieval
- **Distributed Storage**: Scale across multiple database instances
- **Backup and Recovery**: Data persistence and disaster recovery
- **Migration Tools**: Move embeddings between database systems

### **Performance Optimizations**
- **Batch Processing**: Efficient bulk embedding generation
- **Caching**: Intelligent caching of embeddings and search results
- **Model Quantization**: Optimized models for deployment
- **Parallel Processing**: Multi-threaded embedding computation
- **Memory Management**: Efficient memory usage for large datasets

## Acceptance Criteria
- [ ] Python MCP server with 8+ Granite embedding model tools
- [ ] Support for all major Granite embedding models
- [ ] Multi-provider integration (Ollama, watsonx.ai, Hugging Face)
- [ ] Semantic search and similarity analysis capabilities
- [ ] Document clustering and duplicate detection
- [ ] Vector database integration (ChromaDB, Pinecone, Weaviate, Qdrant)
- [ ] Content recommendation system
- [ ] Batch embedding generation and processing
- [ ] Multi-format output support (NumPy, lists, tensors)
- [ ] Performance optimization for large-scale processing
- [ ] Comprehensive test suite with sample documents (>90% coverage)
- [ ] Complete documentation with semantic search examples

## Priority
High - Essential for modern AI applications requiring semantic understanding

## Use Cases
- Semantic search and information retrieval
- Document similarity and clustering
- Content recommendation systems
- Duplicate detection and deduplication
- Knowledge base organization
- Customer support automation
- Legal document analysis
- Research paper categorization
- Code similarity and search
- Multi-language content processing
- E-commerce product matching
- Enterprise knowledge management

Create IBM Granite Embedding Models MCP Server #911

Description

Overview

Server Specifications

Server Details

Supported Models

Provider Support

Vector Database Integration

Tools Provided

1. generate_embeddings

2. semantic_search

3. compute_similarity

4. cluster_documents

5. find_duplicates

6. build_vector_index

7. recommend_content

8. analyze_text_relationships

Implementation Requirements

Directory Structure

Dependencies

Configuration

Usage Examples

Generate Document Embeddings

Semantic Search

Document Similarity Analysis

Document Clustering

Build Vector Index

Content Recommendation

Duplicate Detection

Advanced Features

Enterprise Features

Vector Database Ecosystem

Performance Optimizations

Acceptance Criteria

Priority

Use Cases

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions

1. `generate_embeddings`

2. `semantic_search`

3. `compute_similarity`

4. `cluster_documents`

5. `find_duplicates`

6. `build_vector_index`

7. `recommend_content`

8. `analyze_text_relationships`