LlamaIndex integration

llamaindex-dakera (0.3.0) adds a Dakera-backed memory store (DakeraMemoryStore) and a LlamaIndex vector store (DakeraIndexStore) to LlamaIndex pipelines. The server computes embeddings, so no local GPU or embedding API is needed. This page is for Python developers building retrieval or agent pipelines with LlamaIndex.

Quick start

pip install llamaindex-dakera

Requires Python ≥ 3.10, dakera>=0.13.1 (installed automatically) and a running Dakera server. Works with Dakera server v0.12.0 and v0.11.108.

from llama_index_dakera import DakeraMemoryStore, DakeraIndexStore

# Semantic memory
store = DakeraMemoryStore(
    api_url="http://localhost:3000",
    api_key="dk-mykey",
    agent_id="my-agent",
)

# Vector index for RAG
index = DakeraIndexStore(
    api_url="http://localhost:3000",
    api_key="dk-mykey",
    namespace="my-docs",
)

Features

• DakeraMemoryStore — semantic memory with importance scoring and decay
• DakeraIndexStore — vector index compatible with LlamaIndex's VectorStoreQuery
• DakeraSessionManager — session lifecycle tracking
• DakeraKnowledgeGraph — entity-relationship graph operations
• DakeraEntityExtractor — automatic entity extraction
• DakeraNamespaceManager — multi-tenant namespace isolation

Examples

Agent memory

from llama_index_dakera import DakeraMemoryStore

store = DakeraMemoryStore(
    api_url="http://localhost:3000",
    api_key="dk-mykey",
    agent_id="llamaindex-demo",
    recall_k=3,
    default_importance=0.8,
)

store.put("The user prefers Python over JavaScript.")
store.put("Project deadline is next Friday.", memory_type="episodic", importance=0.9)
store.put("The codebase uses FastAPI with SQLAlchemy.", memory_type="semantic")

memories = store.get("programming language preferences")
for m in memories:
    print(f"  [{m['score']:.3f}] {m['content']}")

RAG pipeline with VectorStoreQuery

from llama_index.core.schema import TextNode
from llama_index.core.vector_stores.types import VectorStoreQuery
from llama_index_dakera import DakeraIndexStore

store = DakeraIndexStore(
    api_url="http://localhost:3000",
    api_key="dk-mykey",
    namespace="rag-demo",
)

nodes = [
    TextNode(text="Dakera provides persistent memory for AI agents.", metadata={"topic": "overview"}),
    TextNode(text="Vector search uses cosine similarity over embeddings.", metadata={"topic": "search"}),
    TextNode(text="Server-side embedding removes the need for local GPUs.", metadata={"topic": "architecture"}),
]

ids = store.add(nodes)

query = VectorStoreQuery(query_str="How does Dakera handle embeddings?", similarity_top_k=2)
result = store.query(query)
for node, score in zip(result.nodes, result.similarities):
    print(f"  [{score:.3f}] {node.text}")

API reference

DakeraMemoryStore options

DakeraIndexStore options

Configuration

The package does not read environment variables. To keep the URL and key out of source code, read them yourself:

import os
from llama_index_dakera import DakeraMemoryStore

store = DakeraMemoryStore(
    api_url=os.environ["DAKERA_API_URL"],
    api_key=os.environ.get("DAKERA_API_KEY", ""),
    agent_id="my-agent",
)

Links

• PyPI: llamaindex-dakera
• GitHub: dakera-ai/dakera-llamaindex
• Full integration guide

Other Python integrations: LangChain · CrewAI · AutoGen