synapcores 0.3.0

Official Python SDK for SynapCores AI-Native Database

SynapCores Python SDK

Official Python SDK for SynapCores - The AI-Native Database Management System.

0.2.1 — gateway response + graph fixes. Bug-fix release: _handle_response now unwraps the gateway's {"data": …, "meta": …} success envelope (so sql() returns rows instead of an empty result) and tolerates empty 200 bodies from DELETE; QueryResult.columns is populated with column names; and the graph client matches the gateway contract — nodes.create sends labels, edges.create sends src/dst, graph.cypher() sends {"sql": …}, and edges.delete() was added.

0.2.0 — gateway v1.5.0-ce alignment. This release rewires every module against the v1.5.0-ce route surface. AutoML moved from /ai/* to /automl/*, vector ops collapsed onto /vector-algebra/operation, WebSocket auth now uses ticket exchange, API keys are sent via the X-API-Key header (and accept the new ak_* prefix in addition to the legacy aidb_*), and we ship new top-level modules: graph, nl2sql, filesystem, chat, multimodal, system, transactions, mcp, recipes, and schema.

What's new in 0.2.0

from synapcores import SynapCores

client = SynapCores(host="localhost", port=8080, api_key="ak_prod_xxx...")

# 1. Cypher-style graph traversal
friends = client.graph.cypher(
    "MATCH (u:User {id:$id})-[:FRIEND]->(f) RETURN f",
    {"id": "u-123"},
)

# 2. Natural language → SQL (with optional execution)
ans = client.nl2sql.ask(
    "top 10 customers by revenue this quarter",
    execute=True,
)
print(ans["sql"], ans.get("rows"))

# 3. AI chat sessions with streaming
session = client.chat.sessions.create(model="gpt-4o")
for chunk in client.chat.stream(session["id"], "Summarize today's alerts"):
    if chunk.get("delta"):
        print(chunk["delta"], end="", flush=True)

# 4. Filesystem-backed RAG collections with progress
fs = client.filesystem.collections.create(name="docs", path="/data/docs", watch=True)
for evt in client.filesystem.collections.subscribe_progress(fs["id"]):
    print(evt.get("status"), evt.get("progress"), evt.get("filename"))

# 5. Server-side transactions with savepoints
with client.transactions.begin(isolation_level="SERIALIZABLE") as tx:
    tx.execute("UPDATE accounts SET balance = balance - $1 WHERE id = $2", [100, "a"])
    tx.savepoint("mid")
    tx.execute("UPDATE accounts SET balance = balance + $1 WHERE id = $2", [100, "b"])
    # commit() runs implicitly on clean exit; rollback() on exception.

Other new surfaces:

• client.multimodal.{similarity,search,join,embed} for cross-modal retrieval.
• client.system.vision.{get,set,delete,test} for the admin vision config.
• client.mcp.{invoke,batch,info} for the Model Context Protocol gateway.
• client.recipes.list_categories()/list_templates()/execute_template()/list_executions().
• client.schema.list_databases()/preview_table().

Breaking changes vs 0.1.0

• embed() routes to /ai/embeddings (single) or /ai/embeddings/batch.
• automl.* paths moved from /ai/* to /automl/*.
• All vector math goes through POST /vector-algebra/operation with an op discriminator.
• KNN/range/hybrid search routes to /vectors/collections/:name/search with a mode field.
• API keys are sent as the X-API-Key header (was previously Authorization: Bearer).
• WebSocket subscriptions now use ticket exchange via POST /v1/ws/ticket and connect to /ws?token=....
• nlp.analyze() is implemented client-side as parallel calls to /ai/sentiment, /ai/entities, and /ai/summarize since /ai/analyze was removed.
• The client.sql(query, params=...) payload now matches POST /v1/query/execute (positional parameters); params is still accepted as a dict for backwards compatibility.

Features

• AI-Native Operations: Built-in support for embeddings, vector search, and semantic analysis
• Document & Vector Storage: Seamlessly work with both structured and unstructured data
• SQL with AI Extensions: Use familiar SQL syntax enhanced with AI operations
• Real-time Subscriptions: WebSocket support for live data updates
• AutoML Integration: Automated machine learning model training and deployment
• Type-Safe: Full type hints and Pydantic models for better IDE support

Installation

pip install synapcores

Quick Start

from synapcores import SynapCores

# Initialize client with API key authentication
# API keys can be created from your AIDB dashboard at Settings > API Keys
# Note: API keys must start with 'aidb_' prefix
client = SynapCores(
    host="localhost",
    port=8080,
    api_key="aidb_sk_your_api_key_here"  # Replace with your actual API key
)

# Create a collection
collection = client.create_collection(
    name="products",
    schema={
        "name": "string",
        "description": "string",
        "price": "float",
        "embedding": "vector[384]"
    }
)

# Insert documents
collection.insert([
    {
        "name": "Laptop",
        "description": "High-performance laptop for developers",
        "price": 1299.99
    },
    {
        "name": "Mouse",
        "description": "Ergonomic wireless mouse",
        "price": 49.99
    }
])

# Semantic search
results = collection.search(
    query="computer accessories",
    top_k=5
)

# SQL with AI extensions
df = client.sql("""
    SELECT name, price, 
           similarity(embedding, embed('perfect for coding')) as relevance
    FROM products
    WHERE price < 1500
    ORDER BY relevance DESC
    LIMIT 10
""")

# Real-time subscriptions
async def handle_update(change):
    print(f"Document {change.op}: {change.document}")

subscription = await collection.subscribe(
    filter={"price": {"$lt": 100}},
    on_change=handle_update
)

Advanced Features

Vector Operations

# Generate embeddings
embedding = client.embed("High-quality mechanical keyboard")

# Vector similarity search
similar_products = collection.vector_search(
    vector=embedding,
    top_k=10,
    filter={"price": {"$between": [50, 200]}}
)

AutoML

# Train a model
model = client.automl.train(
    collection="sales_data",
    target="revenue",
    features=["product_category", "season", "price"],
    task="regression"
)

# Make predictions
predictions = model.predict({
    "product_category": "electronics",
    "season": "holiday",
    "price": 299.99
})

NLP Analysis

# Analyze text
analysis = client.nlp.analyze(
    text="This product exceeded my expectations. Highly recommend!",
    tasks=["sentiment", "entities", "summary"]
)

print(f"Sentiment: {analysis.sentiment.label} ({analysis.sentiment.score})")
print(f"Entities: {[e.text for e in analysis.entities]}")

Documentation

For detailed documentation, visit https://synapcores.com/developers

License

MIT License - see LICENSE file for details.