semvec/pypi-packages huggingface dataset:
sourcelocation 1.1.8
Simple data structures for representing source code locations
License: MIT
Requires Python: >=3.9
Keywords: data structure, source code, program, analysis

.. -*-restructuredtext-*-

sourcelocation
==============

.. image:: https://badge.fury.io/py/sourcelocation.svg
    :target: https://badge.fury.io/py/sourcelocation

.. image:: https://img.shields.io/pypi/pyversions/sourcelocation.svg
    :target: https://pypi.org/project/sourcelocation


A simple collection of reusable data structures for representing source code
locations such as character offsets, character ranges, lines, etc.
httpquick 0.1.1
A HttpQuick that executes on the command line to make http requests
License: MIT
Requires Python: <4.0,>=3.8

# HttpQuick
# EN
A HttpQuick that executes on the command line to make http requests.

I am not responsible for any damages whatsoever. Please use at your own risk.

Write the command line in an http file.
execute_http_file reads the file and sends the request.
Reads the http file, splitting the text with "###" to recognize it as a separate command.

Specify the http file as an argument and optionally specify the path to the output folder.

The received data is output as GET_[domain]_response to the output destination (the current directory if the output destination folder is not specified).
The folder must exist.
To append the time to the output file name, append "-d" to the argument.

TOKEN can also be replaced.
For example, http file as specify GET {{token}}/data, create *.env.json in the current directory and save it as {"token": "test"}. In this case, the file is interpreted as GET test/data. (If the extension is .env.json, it will be read).

## Example

file:test.http
```

GET {{access_url}}/user/userlist/json  

###

GET {{access_url}}/user/data  
Name: Alice  
Data: {{data_type}}
```

file:.env.json  
```
{  
    "access_url": "localhost"  
    "data_type": "Age"  
}  

```
console  
```
HttpQuick test.http
```

{{token_name}} will be read from .env.json and repressed and interpreted.

# JP
HttpQuickはコマンドラインで実行してhttpリクエストを行うツール。

いかなる損害についても、私は責任を負いません。自己責任でご利用ください。

コマンドラインを http ファイルに記述する。
execute_http_fileはファイルを読み込んでリクエストを送信する。
httpファイルを読み込む、"###"でテキストを分割することで別のコマンドと認識する。

引数にhttpファイルを指定し、オプションで出力フォルダのパスを指定する。

受信したデータはGET_[domain]_responseとして出力先（出力先フォルダが指定されていない場合はカレントディレクトリ）に出力される。
フォルダが存在する必要があります。
出力ファイル名に時刻を付加するには、引数に"-d "を付加する。

TOKENを置き換えることもできる。
例えば、httpファイルにGET {{token}}/dataと用意し、カレントディレクトリに*.env.jsonを作成して{"token"： 「test"}として保存します。この場合、ファイルはGET test/dataとして解釈されます。(拡張子が.env.jsonの場合に読み込まれます）。

## Example

file:test.http
```

GET {{access_url}}/user/userlist/json  

###

GET {{access_url}}/user/data  
Name: Alice  
Data: {{data_type}}
```

file:.env.json  
```
{  
    "access_url": "localhost"  
    "data_type": "Age"  
}  

```
console  
```
HttpQuick test.http
```

{{トークン名}}としておくと、.env.jsonから読み込んでそれぞれリプレスして解釈します。
langchain-nexa-ai 0.1.5
An integration package connecting NexaAI and LangChain
License: MIT
Requires Python: <4.0,>=3.8.1

# langchain-nexa-ai

This package contains the LangChain integration with [Nexa AI](https://www.nexa4ai.com/).

## Installation

```bash
pip install -U langchain-nexa-ai
```

And you should get an api key from [Nexa Hub](https://hub.nexa4ai.com/api_keys) and set it as an environment variable (`NEXA_API_KEY`)

## LLMs

`NexaAILLM` class exposes Octopus LLMs from NexaAI. We currently support four catogories: `shopping`, `conference`, `streaming`, and `travel`. See our [langchain-nexa-ai tutorial](./docs/tutorial.ipynb) and [Nexa AI documentation](https://docs.nexa4ai.com/docs/overview) for more details!

```python
from langchain_nexa_ai import NexaAILLM

octopus_llm = NexaAILLM()
result = octopus_llm.invoke("Show recommended products for electronics.", category="shopping")
print(result)
```

If `NEXA_API_KEY` is not set in env, you can also pass api_key as an argument when initializing:

```python
octopus_llm = NexaAILLM(api_key=api_key)
```

You can also pass a list of catogories (corresponding to each of your prompts) when using `generate` method of `NexaAILLM`.

```python
result = octopus_llm.generate(
    prompts=[
        "Show recommended products for electronics.",
        "Find a Hotel to stay in Toyko from June 1 to June 10."
    ],
    categories=[
        "shopping",
        "travel"
    ]
)
```

### Exploit LCEL

```python
from langchain_nexa_ai import NexaAILLM
from langchain_core.output_parsers import JsonOutputParser

octopus_llm = NexaAILLM()
parser = JsonOutputParser()

chain = octopus_llm | parser

result = chain.invoke(
    input="Show me recommended electronics products",
    config={
        "llms": {
            "NexaAILLM": {
                "categories": ["shopping"]
            }
        }
    }
)
print(result)
```

## Chat Models

Coming soon.

## TODO

- [ ] streaming
- [ ] docs