Use Cases & Examples

from moorcheh_sdk import MoorchehClient

client = MoorchehClient(api_key="your-api-key")

# 1. Upload your knowledge base
documents = [
    {
        "id": "doc-1",
        "text": "Moorcheh uses MIB technology for fast semantic search.",
        "metadata": {"category": "technology", "source": "docs"}
    },
    {
        "id": "doc-2",
        "text": "The API supports 9 AI models including Claude and GPT.",
        "metadata": {"category": "features", "source": "docs"}
    }
]

client.upload_text(namespace_name="knowledge-base", documents=documents)

# 2. Generate contextual answers
answer = client.get_answer(
    namespace="knowledge-base",
    query="What technology does Moorcheh use?",
    ai_model="anthropic.claude-sonnet-4-6",
    top_k=3
)

print(answer["answer"])

# Upload documents with metadata
client.upload_text(
    namespace_name="legal-docs",
    documents=[
        {
            "id": "contract-001",
            "text": "Service Agreement between Party A and Party B...",
            "metadata": {
                "doc_type": "contract",
                "year": "2024",
                "department": "legal"
            }
        }
    ]
)

# Search with metadata filters
results = client.search(
    namespaces=["legal-docs"],
    query="service level agreements #doc_type:contract #year:2024",
    top_k=10
)

for match in results["matches"]:
    print(f"Document: {match['id']}")
    print(f"Relevance: {match['score']}")
    print(f"Type: {match['metadata']['doc_type']}")

# Upload content
client.upload_text(
    namespace_name="articles",
    documents=[
        {"id": "article-1", "text": "Introduction to machine learning..."},
        {"id": "article-2", "text": "Machine learning basics..."},
        {"id": "article-3", "text": "Getting started with ML..."}
    ]
)

# Find similar articles
def find_duplicates(article_id, threshold=0.85):
    # Get the article
    article = client.get_documents(
        namespace_name="articles",
        ids=[article_id]
    )
    
    # Search for similar content
    results = client.search(
        namespaces=["articles"],
        query=article[0]["text"],
        top_k=10
    )
    
    # Filter by similarity threshold
    duplicates = [
        match for match in results["matches"]
        if match["score"] > threshold and match["id"] != article_id
    ]
    
    return duplicates

# Find duplicates
duplicates = find_duplicates("article-1", threshold=0.85)
print(f"Found {len(duplicates)} similar articles")

# Upload products with embeddings
client.upload_vectors(
    namespace_name="products",
    vectors=[
        {
            "id": "prod-001",
            "vector": [0.1, 0.2, ...],  # Product embedding
            "name": "Wireless Headphones",
            "category": "electronics",
            "price": 99.99
        }
    ]
)

# Get recommendations based on user interest
def get_recommendations(user_interest_vector, num_recommendations=5):
    results = client.search(
        namespaces=["products"],
        query=user_interest_vector,
        top_k=num_recommendations
    )
    
    return [
        {
            "name": match["name"],
            "score": match["score"],
            "category": match["category"]
        }
        for match in results["matches"]
    ]

# Get personalized recommendations
recommendations = get_recommendations(user_vector, num_recommendations=5)

# Upload documents in different languages
documents = [
    {"id": "doc-en", "text": "Welcome to Moorcheh", "metadata": {"lang": "en"}},
    {"id": "doc-es", "text": "Bienvenido a Moorcheh", "metadata": {"lang": "es"}},
    {"id": "doc-fr", "text": "Bienvenue à Moorcheh", "metadata": {"lang": "fr"}}
]

client.upload_text(namespace_name="multi-lang-docs", documents=documents)

# Search in specific language
results = client.search(
    namespaces=["multi-lang-docs"],
    query="welcome #lang:en",
    top_k=5
)

# Or search across all languages
results = client.search(
    namespaces=["multi-lang-docs"],
    query="welcome",
    top_k=5
)

# Upload code snippets
code_snippets = [
    {
        "id": "snippet-1",
        "text": "def authenticate(api_key): return validate_key(api_key)",
        "metadata": {"language": "python", "function": "authentication"}
    },
    {
        "id": "snippet-2",
        "text": "function search(query) { return api.search(query); }",
        "metadata": {"language": "javascript", "function": "search"}
    }
]

client.upload_text(namespace_name="code-base", documents=code_snippets)

# Search for code
results = client.search(
    namespaces=["code-base"],
    query="how to authenticate users #language:python",
    top_k=5
)

# Generate code explanation
answer = client.get_answer(
    namespace="code-base",
    query="Explain how authentication works in the codebase",
    ai_model="deepseek.r1-v1:0"  # Best for code
)