llm-quiver 0.3.7

The auxiliary tool is used to invoke the online LLM service API, supporting local caching, prompt rendering, and configuration management.

LLMQuiver

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The auxiliary tool is used to invoke the online LLM service API, supporting local caching, prompt rendering, and configuration management.

Supported

• Support Openai/Azure LLM service providers (or openai-compatible service providers).
• Support vllm service.
• Local caching (based on sqlite).
• Prompt rendering (based on toml file).
• Configuration management (based on toml file).

To Be Done

• Multi service provider support.

Installation

pip install llm-quiver

Basic Usage

1. Direct Call Mode

Assuming you have a configuration file path/to/gpt.toml, you need to fill in your own API_KEY, content as follows:

API_TYPE = "azure_openai"
API_BASE = "https://endpoint.openai.azure.com/"
API_VERSION = "2023-05-15"
API_KEY = "********************************"
MODEL_NAME = "gpt-4o-20240513"
temperature = 0.0
max_tokens = 4096
enable_cache = true
cache_dir = "oai_cache"

Running code:

from llm_quiver import LLMQuiver

# Initialize
llm = LLMQuiver(
    config_path="path/to/gpt.toml",
)

# Text generation mode
prompt_values = ["Who are you?"]
responses = llm.generate(prompt_values)
#   Default role is system
#   ["I am an AI assistant developed by OpenAI, designed to help answer questions, provide information, and complete various tasks. How can I help you?"]

# Chat mode
messages = [[{"role": "user", "content": "Who are you?"}]]
responses = llm.chat(messages)
#   ["I am an AI assistant developed by OpenAI, designed to help answer questions, provide information, and engage in conversations. Feel free to ask me anything!"]

2. Toml Template Call Mode

First, create a TOML template file, for example hello_world.toml:

[hello_world_template]
prompt = "Hello {name}, who are you?"

Then you can use it like this:

from llm_quiver import TomlLLMQuiver

# Specify template during initialization
llm = TomlLLMQuiver(
    config_path="path/to/gpt.toml",
    toml_prompt_name="hello_world_template",
    toml_template_file="path/to/hello_world.toml"
)

# Pass template parameters
prompt_values = [dict(name="GPT")]
responses = llm.generate(prompt_values)

Configuration Guide

There are two ways to configure API keys and other parameters:

1. Through environment variables:

Configuration can be loaded by passing parameter config_path="path/to/config.toml" or setting environment variable "export LLMQUIVER_CONFIG=path/to/config.toml". Parameters like API_TYPE, API_BASE, API_VERSION, API_KEY, MODEL_NAME can also be set in environment variables.

2. Directly passing configuration file path:

llm = TomlLLMQuiver(
    config_path="path/to/config.toml",
    toml_prompt_name="template_name",
    toml_template_file="path/to/template.toml"
)

Configuration file example:

API_TYPE = "azure_openai"
API_BASE = "https://endpoint.openai.azure.com/"
API_VERSION = "2023-05-15"
API_KEY = "********************************"
MODEL_NAME = "gpt-4o-20240513"
temperature = 0.0
max_tokens = 4096
enable_cache = true
cache_dir = "oai_cache"

Return Value Description

• Both generate() and chat() methods return a list of strings
• Each element corresponds to a response for one input prompt

Notes

1. API key must be correctly configured before use
2. Template files must comply with TOML format specifications
3. Input parameters must correspond to placeholders in the template