semvec/pypi-packages huggingface dataset:
sbcli-numa 1.0.2
CLI for managing SimplyBlock cluster


# SBCLI (SimplyBlock CLI)

Simplyblock provides a feature-rich CLI (command line interface) to deploy SimplyBlock Cluster and to manage the SimplyBlock Control plane

## Install

The CLI can be installed using pypi package [sbctl](https://pypi.org/project/sbctl/)

```
pip install --upgrade sbctl
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
The list of all the available CLI options can be here [CLI docs](./simplyblock_cli/README.md)

## SimplyBlock Web API

Web app that provides API to manage the cluster. More about this in [README.md](./simplyblock_web/README.md)


### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```
setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.

```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
cloudhands 1.0.6
Python SDK for CloudHands API
Requires Python: >=3.6

# CloudHands SDK

CloudHands SDK is a Python library for interacting with the CloudHands API. It provides tools for authentication, charging events, and retrieving transaction details.

## Installation

```bash
pip install cloudhands
```

## Usage

```python
# CloudHands is your more general interface with cloudhands.ai and its apis
ch = CloudHands(api_key=YOUR_API_KEY)
ch.text_post(title="Posting Sample Usage", content="This shows how you can use the SDK to make a post on cloudhands.ai")

# CloudHandsPayment is used for dealing with a particular transaction/auth flow for a user to pay you
chPay = CloudHandsPayment(author_key=YOUR_AUTHOR_KEY)
auth_url = chPay.get_authorization_url()
# you must give this auth url to the user you want to pay you
# they will visit and be prompted to confirm the auth, then will be given a code to return to you with
chPay.exchange_code_for_token(code)
# Now authenticated, can charge cloudhands credits for the user
result = chPay.charge(charge=1.0, event_name="Sample cloudhands charge")
```

## Publish on pypi
pip install setuptools wheel twine
rm -rf src/cloudhands.egg-info/ dist
python -m build
twine upload dist/*
sbcli-foo 19.1.3
CLI for managing SimplyBlock cluster


# SBCLI (SimplyBlock CLI)

Simplyblock provides a feature-rich CLI (command line interface) to deploy SimplyBlock Cluster and to manage the SimplyBlock Control plane

## Install

The CLI can be installed using pypi package [sbctl](https://pypi.org/project/sbctl/)

```
pip install --upgrade sbctl
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
The list of all the available CLI options can be here [CLI docs](./simplyblock_cli/README.md)

## SimplyBlock Web API

Web app that provides API to manage the cluster. More about this in [README.md](./simplyblock_web/README.md)


### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```
setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.

```
sudo docker compose -f docker-compose-dev.yml up --build -d
```
llama-index-vector-stores-db2 0.1.0
llama-index vector_stores db2 database integration
Requires Python: <3.13,>=3.9

# LlamaIndex VectorStore Integration for IBM Db2

For a detailed guide look at https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/db2llamavs.ipynb

`pip install llama-index-vector-stores-db2`

# A sample example

```python
from typing import TYPE_CHECKING
import sys
from llama_index.core.schema import Document, TextNode
from llama_index.vector_stores.db2 import DB2LlamaVS, DistanceStrategy
from llama_index.vector_stores.db2 import base as db2llamavs

if TYPE_CHECKING:
    import ibm_db

"""
Create connection to Db2 database instance

The following sample code will show how to connect to Db2 Database. Besides the dependencies above, you will need a Db2 database instance (with version v12.1.2+, which has the vector datatype support) running.
"""

import ibm_db_dbi

database = ""
username = ""
password = ""

try:
    connection = ibm_db_dbi.connect(database, username, password)
    print("Connection successful!")
except Exception as e:
    print("Connection failed!", e)


"""
Create Db2 vector store
"""

vectorstore = DB2LlamaVS.from_documents(
    client=conn,
    docs=chunks_with_mdata,
    table_name="db2vs",
    distance_strategy=DistanceStrategy.DOT_PRODUCT,
)

"""
Perform Similarity search
"""

# Similarity search
query = VectorStoreQuery(query_embedding=[1.0, 1.0], similarity_top_k=3)
results = vectorstore.query(query=query)
print(f"\n\n\nSimilarity search results for vector store: {results}\n\n\n")
```