dakera 0.8.1

Python SDK for Dakera - AI memory platform

Dakera Python SDK

Official Python client for Dakera — a high-performance vector database for AI agent memory.

Installation

pip install dakera

For async support:

pip install dakera[async]

Quick Start

Synchronous

from dakera import DakeraClient

client = DakeraClient("http://localhost:3000")

# Upsert vectors
client.upsert("my-namespace", vectors=[
    {"id": "vec1", "values": [0.1, 0.2, 0.3], "metadata": {"label": "a"}},
    {"id": "vec2", "values": [0.4, 0.5, 0.6], "metadata": {"label": "b"}},
])

# Query similar vectors
results = client.query("my-namespace", vector=[0.1, 0.2, 0.3], top_k=10)
for result in results.results:
    print(f"{result.id}: {result.score}")

Async

import asyncio
from dakera import AsyncDakeraClient

async def main():
    async with AsyncDakeraClient("http://localhost:3000") as client:
        # Upsert vectors
        await client.upsert("my-namespace", vectors=[
            {"id": "vec1", "values": [0.1, 0.2, 0.3], "metadata": {"label": "a"}},
            {"id": "vec2", "values": [0.4, 0.5, 0.6], "metadata": {"label": "b"}},
        ])

        # Query similar vectors
        results = await client.query("my-namespace", vector=[0.1, 0.2, 0.3], top_k=10)
        for result in results.results:
            print(f"{result.id}: {result.score}")

asyncio.run(main())

Features

• Vector Operations: Upsert, query, delete, fetch vectors
• Full-Text Search: Index documents and perform BM25 search
• Hybrid Search: Combine vector and text search with configurable weights
• Namespace Management: Create, list, delete namespaces
• Metadata Filtering: Filter queries by metadata fields
• Async Support: AsyncDakeraClient built on httpx for non-blocking I/O
• Agent Memory: Store, recall, and manage memories for AI agents
• Type Hints: Full type annotation support
• Context Manager: Automatic connection cleanup (sync and async)

Usage Examples

Vector Operations

from dakera import DakeraClient, Vector

client = DakeraClient("http://localhost:3000")

# Using dataclass
vectors = [
    Vector(id="vec1", values=[0.1, 0.2, 0.3], metadata={"category": "A"}),
    Vector(id="vec2", values=[0.4, 0.5, 0.6], metadata={"category": "B"}),
]
client.upsert("my-namespace", vectors)

# Using dictionaries
client.upsert("my-namespace", vectors=[
    {"id": "vec3", "values": [0.7, 0.8, 0.9]},
])

# Query with metadata filter
results = client.query(
    "my-namespace",
    vector=[0.1, 0.2, 0.3],
    top_k=5,
    filter={"category": {"$eq": "A"}},
    include_metadata=True,
)

# Batch query
batch_results = client.batch_query("my-namespace", queries=[
    {"vector": [0.1, 0.2, 0.3], "top_k": 5},
    {"vector": [0.4, 0.5, 0.6], "top_k": 3},
])

# Delete vectors
client.delete("my-namespace", ids=["vec1", "vec2"])
client.delete("my-namespace", filter={"category": {"$eq": "obsolete"}})

Full-Text Search

# Index documents
client.index_documents("my-namespace", documents=[
    {"id": "doc1", "content": "Machine learning is transforming industries"},
    {"id": "doc2", "content": "Vector databases enable semantic search"},
])

# Search
results = client.fulltext_search(
    "my-namespace",
    query="machine learning",
    top_k=10,
)

for result in results:
    print(f"{result.id}: {result.score}")

Hybrid Search

# Combine vector and text search
results = client.hybrid_search(
    "my-namespace",
    vector=[0.1, 0.2, 0.3],  # Embedding of query
    query="machine learning",  # Text query
    top_k=10,
    alpha=0.7,  # 0 = pure vector, 1 = pure text
)

for result in results:
    print(f"{result.id}: score={result.score}, vector={result.vector_score}, text={result.text_score}")

Namespace Management

# Create namespace with specific configuration
client.create_namespace(
    "embeddings",
    dimensions=384,
    index_type="hnsw",
)

# List all namespaces
namespaces = client.list_namespaces()
for ns in namespaces:
    print(f"{ns.name}: {ns.vector_count} vectors")

# Get namespace info
info = client.get_namespace("embeddings")
print(f"Dimensions: {info.dimensions}, Index: {info.index_type}")

# Delete namespace
client.delete_namespace("old-namespace")

Metadata Filtering

Dakera supports rich metadata filtering:

# Equality
filter = {"status": {"$eq": "active"}}

# Comparison
filter = {"price": {"$gt": 100, "$lt": 500}}

# In list
filter = {"category": {"$in": ["electronics", "books"]}}

# Logical operators
filter = {
    "$and": [
        {"status": {"$eq": "active"}},
        {"price": {"$lt": 1000}},
    ]
}

results = client.query(
    "products",
    vector=query_embedding,
    filter=filter,
    top_k=20,
)

Async Operations

Use AsyncDakeraClient (requires pip install dakera[async]) for non-blocking I/O with asyncio:

import asyncio
from dakera import AsyncDakeraClient

async def main():
    async with AsyncDakeraClient("http://localhost:3000") as client:
        # All methods are coroutines — await each call
        await client.upsert("my-namespace", vectors=[
            {"id": "vec1", "values": [0.1, 0.2, 0.3], "metadata": {"category": "A"}},
        ])

        # Parallel queries with asyncio.gather
        results_a, results_b = await asyncio.gather(
            client.query("my-namespace", vector=[0.1, 0.2, 0.3], top_k=5,
                         filter={"category": {"$eq": "A"}}),
            client.hybrid_search("my-namespace",
                                 vector=[0.1, 0.2, 0.3],
                                 query="machine learning",
                                 top_k=5, alpha=0.7),
        )

asyncio.run(main())

Async Agent Memory

import asyncio
from dakera import AsyncDakeraClient

async def main():
    async with AsyncDakeraClient("http://localhost:3000") as client:
        # Store a memory
        stored = await client.store_memory(
            "my-agent",
            "The user prefers concise responses",
            importance=0.8,
            metadata={"source": "feedback"},
        )

        # Recall relevant memories
        memories = await client.recall("my-agent", "user preferences", top_k=5)
        for mem in memories:
            print(f"{mem['id']}: {mem['content']} (score: {mem.get('score', 'N/A')})")

asyncio.run(main())

Error Handling

from dakera import (
    DakeraClient,
    NotFoundError,
    ValidationError,
    RateLimitError,
)

client = DakeraClient("http://localhost:3000")

try:
    results = client.query("nonexistent", vector=[0.1, 0.2])
except NotFoundError as e:
    print(f"Namespace not found: {e}")
except ValidationError as e:
    print(f"Invalid request: {e}")
except RateLimitError as e:
    print(f"Rate limited, retry after {e.retry_after} seconds")

Context Manager

# Synchronous
with DakeraClient("http://localhost:3000") as client:
    client.upsert("my-namespace", vectors=[...])
    results = client.query("my-namespace", vector=[...])

# Async
import asyncio
from dakera import AsyncDakeraClient

async def main():
    async with AsyncDakeraClient("http://localhost:3000") as client:
        await client.upsert("my-namespace", vectors=[...])
        results = await client.query("my-namespace", vector=[...])

asyncio.run(main())

Authentication

# With API key
client = DakeraClient(
    "http://localhost:3000",
    api_key="your-api-key",
)

# With custom headers
client = DakeraClient(
    "http://localhost:3000",
    headers={"X-Custom-Header": "value"},
)

Configuration

Parameter	Type	Default	Description
base_url	str	required	Dakera server URL
api_key	str	None	API key for authentication
timeout	float	30.0	Request timeout in seconds
max_retries	int	3	Max retries for failed requests
headers	dict	None	Additional HTTP headers

API Reference

Both DakeraClient (sync) and AsyncDakeraClient (async) expose identical interfaces. Async methods are prefixed with await.

Vector Operations

• upsert(namespace, vectors) - Insert or update vectors
• query(namespace, vector, top_k, filter, ...) - Query similar vectors
• delete(namespace, ids, filter, delete_all) - Delete vectors
• fetch(namespace, ids) - Fetch vectors by ID
• batch_query(namespace, queries) - Execute multiple queries

Full-Text Operations

• index_documents(namespace, documents) - Index documents
• fulltext_search(namespace, query, top_k, filter) - BM25 text search
• hybrid_search(namespace, vector, query, alpha, ...) - Hybrid vector + text search

Namespace Operations

• list_namespaces() - List all namespaces
• get_namespace(namespace) - Get namespace info
• create_namespace(namespace, dimensions, index_type) - Create namespace
• delete_namespace(namespace) - Delete namespace

Agent Memory Operations

• store_memory(agent_id, content, importance, metadata, ...) - Store a memory
• recall(agent_id, query, top_k, ...) - Recall relevant memories
• get_memory(agent_id, memory_id) - Fetch a specific memory
• update_memory(agent_id, memory_id, content, ...) - Update a memory
• forget(agent_id, memory_id) - Delete a memory
• search_memories(agent_id, query, top_k, ...) - Search memories

Admin Operations

• health() - Check server health
• get_index_stats(namespace) - Get index statistics
• compact(namespace) - Trigger compaction
• flush(namespace) - Flush pending writes

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Type checking
mypy src/dakera

# Linting
ruff check src/

Related Repositories

Repository	Description
dakera	Core vector database engine (Rust)
dakera-js	TypeScript/JavaScript SDK
dakera-go	Go SDK
dakera-rs	Rust SDK
dakera-cli	Command-line interface
dakera-mcp	MCP Server for AI agent memory
dakera-dashboard	Admin dashboard (Leptos/WASM)
dakera-docs	Documentation
dakera-deploy	Deployment configs and Docker Compose

License

MIT License - see LICENSE for details.