semvec/pypi-packages huggingface dataset:
contexere-ai 0.3.1
LLM Tracing and Context Engineering for Production AI Agents
Requires Python: >=3.8

# Contexere

**LLM Tracing and Context Engineering for Production AI Agents**

Contexere is a lightweight Python library for tracing LLM applications and agent workflows in production.

## Installation

```bash
pip install contexere
```

## Quick Start

```python
import contexere as conte
from openai import OpenAI

# Initialize Contexere
conte.init(
    api_key="ck_...",  # Your Contexere API key
    project_name="my-agent"
)

# Instrument your OpenAI client
client = conte.instrument_openai(OpenAI(api_key="sk_..."))

# Trace function executions
@conte.op()
def process_query(question):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": question}]
    )
    return response.choices[0].message.content

# All calls are now traced automatically
result = process_query("What is the capital of France?")
```

## Features

- **Function-level tracing**: Use `@conte.op()` to trace any function
- **LLM call tracing**: Automatic instrumentation for OpenAI calls
- **Captures everything**: Inputs, outputs, exceptions, latency, token usage
- **Project grouping**: Organize traces by project name
- **Non-blocking**: Never breaks your code, even if tracing fails

## API Reference

### `conte.init(api_key, project_name="default", endpoint=None, enabled=True)`

Initialize the Contexere SDK.

- `api_key`: Your Contexere API key
- `project_name`: Logical project name for grouping traces
- `endpoint`: Custom backend endpoint (optional)
- `enabled`: Enable/disable tracing

### `@conte.op(name=None)`

Decorator to trace function execution.

- `name`: Optional custom name for the span (defaults to function name)

### `conte.instrument_openai(client)`

Wrap an OpenAI client to trace all LLM calls.

- `client`: An OpenAI client instance

Returns an instrumented client that traces all calls.

## License

MIT
qutepandas 0.1.0
A pandas-like library for kdb+/q using pykx
Requires Python: >=3.7
Keywords: kdb+, q, pandas, data analysis, timeseries, finance

# qutePandas

A pandas-like library for kdb+/q powered by [PyKX](https://github.com/KxSystems/pykx).

While Pandas is the gold standard for data manipulation in Python, it often struggles with memory overhead and execution speed when handling multi-gigabyte datasets or complex time-series operations. `qutePandas` addresses these limitations by offloading compute-intensive operations to the kdb+/q engine, leveraging its columnar architecture and high-performance vector processing. By providing a familiar pandas-like API, it allows Python engineers to achieve kdb+ speeds without leaving the Python ecosystem.

## Installation

```bash
pip install qutePandas
```

*Note: Requires a kdb+ license and the PyKX library.*

## Quick Start

```python
import qutePandas as qpd
import pandas as pd

# Convert a pandas DataFrame to a qutePandas table
df = pd.DataFrame({
    "time": pd.date_range("2023-01-01", periods=1000000, freq="ms"),
    "price": pd.Series(range(1000000))
})
q_table = qpd.DataFrame(df)

# Perform high-performance aggregation
# Computation happens in kdb+
res = qpd.apply(q_table, "sum", axis=0, return_type="p")
print(res)
```

## Documentation

Full documentation, including API reference and performance benchmarks, is available at:
[https://ishapatro.github.io/qutePandas/](https://ishapatro.github.io/qutePandas/)

## Author

Created by [Isha Patro](https://ishapatro.in)