semvec/pypi-packages huggingface dataset:
planoai 0.4.8
Python-based CLI tool to manage Plano.
Requires Python: >=3.10

## plano CLI - Local Development

This guide will walk you through setting up the plano CLI for local development using uv.

### Install uv

First, install the uv package manager. This is required for managing dependencies and running the development version of planoai.

**On macOS and Linux:**
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

**On Windows:**
```powershell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```

### Setup

1. **Install dependencies**

   In the cli directory, run:

   ```bash
   uv sync
   ```

   This will create a virtual environment in `.venv` and install all dependencies from `pyproject.toml`.

2. **Install the CLI tool globally (optional)**

   To install planoai as a global tool on your system:

   ```bash
   uv tool install --editable .
   ```

   This installs planoai globally in editable mode, allowing you to run `planoai` commands from anywhere while still using the source code from this directory. Any changes you make to the code will be reflected immediately.

3. **Run plano commands**

   Use `uv run` to execute plano commands with the development version:

   ```bash
   uv run planoai build
   ```

   Or, if you installed globally with `uv tool install .`:

   ```bash
   planoai build
   ```

   Note: `uv run` automatically uses the virtual environment - no activation needed.

### Development Workflow

**Build plano:**
```bash
uv run planoai build
```

**View logs:**
```bash
uv run planoai logs --follow
```

**Run other plano commands:**
```bash
uv run planoai <command> [options]
```

### CI: Keep CLI templates and demos in sync

The CLI templates in `cli/planoai/templates/` are the source of truth for mapped
demo `config.yaml` files.

Use the sync utility to write mapped demo configs from templates:

```bash
uv run python -m planoai.template_sync
```

### Optional: Manual Virtual Environment Activation

While `uv run` handles the virtual environment automatically, you can activate it manually if needed:

```bash
source .venv/bin/activate
planoai build  # No need for 'uv run' when activated
```

**Note:** For end-user installation instructions, see the [Plano documentation](https://docs.planoai.dev).
swarmauri_vectorstore_redis 0.9.2.dev4
Swarmauri Redis Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, redis, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_redis/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_redis" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_redis/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_redis.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_redis/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_redis" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_redis/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_redis" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_redis/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_redis?label=swarmauri_vectorstore_redis&color=green" alt="PyPI - swarmauri_vectorstore_redis"/></a>
</p>

---

# Swarmauri Vectorstore Redis

A Redis-based vector store implementation for the Swarmauri SDK that enables efficient storage and retrieval of document embeddings.

## Installation

```bash
pip install swarmauri_vectorstore_redis
```

## Usage

Basic example of using RedisVectorStore:

```python
from swarmauri.vector_stores.RedisVectorStore import RedisVectorStore
from swarmauri.documents.Document import Document

# Initialize the vector store
vector_store = RedisVectorStore(
    redis_host="localhost",
    redis_port=6379,
    redis_password="your_password",
    embedding_dimension=8000
)

# Add documents
document = Document(
    id="doc1",
    content="Sample document content",
    metadata={"category": "sample"}
)
vector_store.add_document(document)

# Retrieve similar documents
similar_docs = vector_store.retrieve("sample content", top_k=5)
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.
swarmauri_vectorstore_qdrant 0.9.2.dev4
Swarmauri Persistent Qdrant Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, qdrant, persistent, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_qdrant/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_qdrant" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_qdrant/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_qdrant.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_qdrant/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_qdrant" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_qdrant/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_qdrant" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_qdrant/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_qdrant?label=swarmauri_vectorstore_qdrant&color=green" alt="PyPI - swarmauri_vectorstore_qdrant"/></a>
</p>

---

# Swarmauri Vectorstore Qdrant

A vector store implementation using Qdrant as the backend, supporting both persistent local storage and cloud-based operations for document storage and retrieval.

## Installation

```bash
pip install swarmauri_vectorstore_qdrant
```

## Usage

```python
from swarmauri.documents.Document import Document
from swarmauri.vectorstores.PersistentQdrantVectorStore import PersistentQdrantVectorStore
from swarmauri.vector_stores.CloudQdrantVectorStore import CloudQdrantVectorStore

# Local Persistent Storage
local_store = PersistentQdrantVectorStore(
    collection_name="my_collection",
    vector_size=100,
    path="http://localhost:6333"
)
local_store.connect()

# Cloud Storage
cloud_store = CloudQdrantVectorStore(
    api_key="your_api_key",
    collection_name="my_collection",
    vector_size=100,
    url="your_qdrant_cloud_url"
)
cloud_store.connect()

# Add documents
documents = [
    Document(content="sample text 1"),
    Document(content="sample text 2")
]
local_store.add_documents(documents)

# Retrieve similar documents
results = local_store.retrieve("sample query", top_k=5)
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.
swarmauri_vectorstore_pinecone 0.9.3.dev5
Swarmauri Pinecone Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, pinecone, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_pinecone/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_pinecone" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_pinecone/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_pinecone.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_pinecone/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_pinecone" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_pinecone/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_pinecone" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_pinecone/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_pinecone?label=swarmauri_vectorstore_pinecone&color=green" alt="PyPI - swarmauri_vectorstore_pinecone"/></a>
</p>

---

# Swarmauri Vectorstore Pinecone

A vector store implementation using Pinecone as the backend for efficient similarity search and document storage.

## Installation

```bash
pip install swarmauri_vectorstore_pinecone
```

## Usage

```python
from swarmauri.vector_stores.PineconeVectorStore import PineconeVectorStore
from swarmauri.documents.Document import Document

# Initialize vector store
vector_store = PineconeVectorStore(
    api_key="your-pinecone-api-key",
    collection_name="example",
    vector_size=100
)
vector_store.connect()

# Add documents
documents = [
    Document(content="First document"),
    Document(content="Second document"),
]
vector_store.add_documents(documents)

# Retrieve similar documents
results = vector_store.retrieve(query="document", top_k=2)
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.
swarmauri_vectorstore_persistentchromadb 0.9.3.dev5
A Persistent ChromaDB based Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, persistentchromadb, persistent, chromadb, based, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_persistentchromadb/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_persistentchromadb" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_persistentchromadb/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_persistentchromadb.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_persistentchromadb/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_persistentchromadb" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_persistentchromadb/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_persistentchromadb" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_persistentchromadb/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_persistentchromadb?label=swarmauri_vectorstore_persistentchromadb&color=green" alt="PyPI - swarmauri_vectorstore_persistentchromadb"/></a>
</p>

---

# Swarmauri Vectorstore Persistentchromadb

A persistent vector store implementation using ChromaDB for document storage and retrieval with embeddings.

## Installation

```bash
pip install swarmauri_vectorstore_persistentchromadb
```

## Usage

```python
from swarmauri.documents.Document import Document
from swarmauri.vector_stores.PersistentChromaDBVectorStore import PersistentChromaDBVectorStore

# Initialize the vector store
vector_store = PersistentChromaDBVectorStore(
    path="./chromadb_data",
    collection_name="my_collection",
    vector_size=100
)

# Connect to the store
vector_store.connect()

# Add documents
documents = [
    Document(content="first document"),
    Document(content="second document"),
    Document(content="third document")
]
vector_store.add_documents(documents)

# Retrieve similar documents
results = vector_store.retrieve(query="document", top_k=2)
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.
swarmauri_vectorstore_mlm 0.9.3.dev5
Swarmauri MLM Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, mlm, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_mlm/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_mlm" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_mlm/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_mlm.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_mlm/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_mlm" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_mlm/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_mlm" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_mlm/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_mlm?label=swarmauri_vectorstore_mlm&color=green" alt="PyPI - swarmauri_vectorstore_mlm"/></a>
</p>

---

# Swarmauri Vectorstore Mlm

A vector store implementation using MLM (Masked Language Model) embeddings for document storage and retrieval.

## Installation

```bash
pip install swarmauri_vectorstore_mlm
```

## Usage

Here's a basic example of how to use the MLM Vector Store:

```python
from swarmauri.documents.Document import Document
from swarmauri.vector_stores.MlmVectorStore import MlmVectorStore

# Initialize the vector store
vs = MlmVectorStore()

# Create some documents
documents = [
    Document(content="first document"),
    Document(content="second document"),
    Document(content="third document")
]

# Add documents to the store
vs.add_documents(documents)

# Retrieve similar documents
results = vs.retrieve(query="document", top_k=2)
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.
swarmauri_vectorstore_neo4j 0.9.3.dev5
Swarmauri Neo4j Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, neo4j, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_neo4j/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_neo4j" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_neo4j/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_neo4j.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_neo4j/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_neo4j" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_neo4j/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_neo4j" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_neo4j/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_neo4j?label=swarmauri_vectorstore_neo4j&color=green" alt="PyPI - swarmauri_vectorstore_neo4j"/></a>
</p>

---

# Swarmauri Vectorstore Neo4j

A Neo4j-based vector store implementation for Swarmauri SDK, providing document storage and retrieval functionality using Neo4j graph database.

## Installation

```bash
pip install swarmauri_vectorstore_neo4j
```

## Usage

```python
from swarmauri.documents.Document import Document
from swarmauri.vector_stores.Neo4jVectorStore import Neo4jVectorStore

# Initialize the vector store
store = Neo4jVectorStore(
    uri="neo4j://localhost:7687",
    user="neo4j",
    password="your_password"
)

# Add a document
doc = Document(
    id="doc1",
    content="Sample content",
    metadata={"author": "John Doe"}
)
store.add_document(doc)

# Retrieve similar documents
similar_docs = store.retrieve(query="sample", top_k=5)

# Close connection when done
store.close()
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.
swarmauri_vectorstore_cloudweaviate 0.9.3.dev5
Swarmauri Weaviate Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, cloudweaviate, weaviate, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_cloudweaviate/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_cloudweaviate" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_cloudweaviate/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_cloudweaviate.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_cloudweaviate/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_cloudweaviate" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_cloudweaviate/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_cloudweaviate" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_cloudweaviate/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_cloudweaviate?label=swarmauri_vectorstore_cloudweaviate&color=green" alt="PyPI - swarmauri_vectorstore_cloudweaviate"/></a>
</p>

---

# Swarmauri Vectorstore CloudWeaviate

A Weaviate-based vector store implementation for Swarmauri, providing cloud-based document storage and retrieval with vector similarity search capabilities.

## Installation

```bash
pip install swarmauri_vectorstore_cloudweaviate
```

## Usage
Here's a basic example of how to use the CloudWeaviateVectorStore:

```python
from swarmauri.vector_stores.CloudWeaviateVectorStore import CloudWeaviateVectorStore
from swarmauri.documents.Document import Document

# Initialize the vector store
vector_store = CloudWeaviateVectorStore(
    url="your-weaviate-url",
    api_key="your-api-key",
    collection_name="example",
    vector_size=100
)

# Connect to Weaviate
vector_store.connect()

# Add documents
document = Document(
    id="doc-001",
    content="This is a sample document content.",
    metadata={"author": "Alice", "date": "2024-01-01"}
)
vector_store.add_document(document)

# Retrieve similar documents
results = vector_store.retrieve(query="sample content", top_k=5)

# Clean up
vector_store.disconnect()
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.
swarmauri_vectorstore_duckdb 0.9.3.dev5
A DuckDB based Vector Store
Requires Python: <3.13,>=3.10
Keywords: swarmauri, vectorstore, duckdb, based, vector, store


![Swarmauri Logo](https://github.com/swarmauri/swarmauri-sdk/blob/3d4d1cfa949399d7019ae9d8f296afba773dfb7f/assets/swarmauri.brand.theme.svg)

<p align="center">
    <a href="https://pypi.org/project/swarmauri_vectorstore_duckdb/">
        <img src="https://img.shields.io/pypi/dm/swarmauri_vectorstore_duckdb" alt="PyPI - Downloads"/></a>
    <a href="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_duckdb/">
        <img alt="Hits" src="https://hits.sh/github.com/swarmauri/swarmauri-sdk/tree/master/pkgs/community/swarmauri_vectorstore_duckdb.svg"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_duckdb/">
        <img src="https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_duckdb" alt="PyPI - Python Version"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_duckdb/">
        <img src="https://img.shields.io/pypi/l/swarmauri_vectorstore_duckdb" alt="PyPI - License"/></a>
    <a href="https://pypi.org/project/swarmauri_vectorstore_duckdb/">
        <img src="https://img.shields.io/pypi/v/swarmauri_vectorstore_duckdb?label=swarmauri_vectorstore_duckdb&color=green" alt="PyPI - swarmauri_vectorstore_duckdb"/></a>
</p>

---

# Swarmauri Vectorstore Duckdb

A vector store implementation using DuckDB as the backend for efficient document storage and similarity search.

## Installation

```bash
pip install swarmauri_vectorstore_duckdb
```

## Usage

```python
from swarmauri.vector_stores.DuckDBVectorStore import DuckDBVectorStore
from swarmauri.documents.Document import Document

# Initialize vector store
vector_store = DuckDBVectorStore(database_name="my_vectors.db")
vector_store.connect()

# Add documents
doc = Document(
    id="doc1",
    content="The quick brown fox jumps over the lazy dog",
    metadata={"type": "example"}
)
vector_store.add_document(doc)

# Retrieve similar documents
results = vector_store.retrieve("fox jumping", top_k=2)

# Clean up
vector_store.disconnect()
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.