embrix 0.2.1

Local-first AI toolkit for vector search, workflows, agents, RAG, and database querying

Embrix for Python

Embrix is a local-first AI application toolkit for Python.

It brings together the pieces you usually need for practical AI workflows in one package:

• vector storage and semantic search
• stateful workflow orchestration
• tool-using agents
• retrieval-augmented generation (RAG)
• database vectorization and natural-language querying
• an optional REST API for local services

By default, Embrix works locally with SQLite and a bring-your-own-model approach.

Install from PyPI

Core package:

pip install embrix

Useful extras:

pip install "embrix[openai]"
pip install "embrix[sentence]"
pip install "embrix[all]"

What the Python package includes

Component	Purpose
VectorStore	Store vectors and run similarity search
StateGraph	Build multi-step workflows with state transitions
Agent	Run tool-using agents with your chosen model
RAGPipeline	Ingest documents, retrieve context, and answer questions
DBVectorizer + DBQueryEngine	Turn database rows into searchable context and query them in plain language

Quick start

Vector search

from embrix import VectorStore

store = VectorStore()
store.upsert(
    "docs",
    [
        {
            "_id": "intro",
            "values": [0.1, 0.2, 0.3],
            "text": "Embrix runs local AI workflows with simple building blocks.",
            "metadata": {"topic": "overview"},
        }
    ],
)

results = store.query("docs", [0.1, 0.2, 0.3], top_k=1)
print(results)
store.close()

Stateful workflow

from embrix import END, START, StateGraph

def retrieve(state):
    return {"context": f"facts for: {state['question']}"}

def respond(state):
    return {"answer": f"Answer from {state['context']}"}

graph = StateGraph()
graph.add_node("retrieve", retrieve)
graph.add_node("respond", respond)
graph.add_edge(START, "retrieve")
graph.add_edge("retrieve", "respond")
graph.add_edge("respond", END)

result = graph.compile().invoke({"question": "What is retrieval?"})
print(result["answer"])

Agent with your own provider

from embrix import Agent, OpenAICompatibleChatModel
from embrix.agents import Tool

def calculator(expression: str) -> str:
    return str(eval(expression, {"__builtins__": {}}, {}))

agent = Agent(
    tools=[Tool("calculator", "Evaluate arithmetic expressions", calculator)],
    llm=OpenAICompatibleChatModel(
        model="gpt-4o-mini",
        api_key="your-api-key",
    ),
)

print(agent.run("Use the calculator tool to compute 12 * 9."))

RAG pipeline

from embrix import RAGPipeline
from embrix.embeddings import SentenceEmbedder
from embrix.llms import OllamaChatModel

pipeline = RAGPipeline(
    embedder=SentenceEmbedder(),
    llm=OllamaChatModel(model="llama3.1"),
)

pipeline.ingest([
    "Embrix supports retrieval, workflows, agents, and database tools."
], namespace="docs")

print(pipeline.query("What does Embrix support?", namespace="docs"))

Database search and question answering

from embrix import DBQueryEngine, DBVectorizer, VectorStore
from embrix.db import connect as db_connect

connector = db_connect("sqlite", db_path="products.db")
vectorizer = DBVectorizer(connector, embedder, store=VectorStore(backend="memory", dimension=embedder.dimension))
vectorizer.vectorize_table("products", namespace="products", text_columns=["name", "description"])

engine = DBQueryEngine(vectorizer=vectorizer, llm=chat_model, connector=connector)
hits = engine.search("budget running shoes", namespace="products")
rows = engine.sql_query("Show products under 100 dollars", table="products")

Model provider approach

Embrix does not bundle a hosted model service.

You plug in your own provider, key, gateway, or local runtime.

Built-in chat adapters include:

• OpenAICompatibleChatModel
• AnthropicChatModel
• GeminiChatModel
• OllamaChatModel
• CustomChatModel

That means the same app structure can work with hosted APIs, internal gateways, or local model runtimes.

REST API

Start the API server locally:

uvicorn embrix.api.server:app --reload --port 8080

Main endpoints:

• POST /vectors/upsert
• POST /vectors/query
• POST /vectors/fetch
• POST /vectors/delete
• GET /vectors/list
• GET /describe_index_stats
• GET /namespaces
• GET /health

Package layout

embrix/
├── agents/
├── api/
├── db/
├── embeddings/
├── graph/
├── llms/
├── rag/
└── vector/

Common use cases

• local AI prototypes
• notebook experiments
• backend services that need retrieval or agent steps
• document Q&A
• database exploration with semantic search
• lightweight internal AI tools

License

MIT