llmquery is a comprehensive framework for interacting with Language Model APIs, such as OpenAI, Anthropic, DeepSeek, Google Gemini, AWS Bedrock, Mistral, Github AI Models, and Ollama. It leverages standard YAML templates for prompt management, validation, and dynamic generation. Designed to streamline complex workflows, it allows developers to integrate, query, and test LLMs with ease.
Whether you’re building a chatbot, generating creative content, or analyzing data, llmquery provides the tools to standardize and optimize LLM interactions.
This is an example where llmquery runs with detect-security-vulnerabilities template to scan application code.
Language models have become integral to modern applications, but efficiently managing and interacting with several providers can be challenging. llmquery solves this by offering:
View the full documentation at the llmquery documentation.
$ pip install llmquery
or manually:
$ git clone https://github.com/mazen160/llmquery.git $ cd llmquery $ python setup.py install
from llmquery import LLMQuery diff = """diff --git a/example.py b/example.py + def insecure_function(password): + print(f"Your password is {password}") + # TODO: Replace with secure logging + + user_password = "12345" + insecure_function(user_password) """ query = LLMQuery( provider="ANTHROPIC", templates_path="templates/", template_id="pr-reviews" variables={"diff": diff}, anthropic_api_key="your-api-key", model="claude-3-5-sonnet-latest" ) response = query.Query() print(response)
from llmquery import LLMQuery variables = {"user_input": "Hello, how are you?"} query = LLMQuery( provider="OPENAI", template_inline=""" system_prompt: "You are a helpful assistant." prompt: "User says: {{ user_input }}" """, variables=variables, openai_api_key="your-api-key", model="gpt-4o-mini", ) response = query.Query() print(response)
$ llmquery -h Welcome to llmquery CLI! Scaling GenAI automation 🚀🌐 ██ ██ ███ ███ ██████ ██ ██ ███████ ██████ ██ ██ ██ ██ ████ ████ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ████ ██ ██ ██ ██ ██ █████ ██████ ████ ██ ██ ██ ██ ██ ██ ▄▄ ██ ██ ██ ██ ██ ██ ██ ███████ ███████ ██ ██ ██████ ██████ ███████ ██ ██ ██ ▀▀ usage: llmquery [-h] [--provider {OPENAI,ANTHROPIC,GOOGLE_GEMINI,AWS_BEDROCK,OLLAMA,DEEPSEEK,MISTRAL,GITHUB_AI}] [--templates-path TEMPLATES_PATH] [--template-id TEMPLATE_ID] [--variables VARIABLES] [--variables-file VARIABLES_FILE] [--model MODEL] [--max-tokens MAX_TOKENS] [--max-length MAX_LENGTH] [--api-key API_KEY] [bold cyan]A CLI for querying LLMs using YAML templates with llmquery.[/bold cyan] options: -h, --help show this help message and exit --provider {OPENAI,ANTHROPIC,GOOGLE_GEMINI,AWS_BEDROCK,OLLAMA,DEEPSEEK,MISTRAL,GITHUB_AI} Specify the LLM provider to use (e.g. OPENAI, ANTHROPIC, GOOGLE_GEMINI, AWS_BEDROCK, OLLAMA, DEEPSEEK, MISTRAL, GITHUB_AI). --templates-path TEMPLATES_PATH Path to the YAML templates directory defining the query. --template-id TEMPLATE_ID Template ID to use when multiple templates exist in the file. --variables VARIABLES JSON string of variables to pass to the template. --variables-file VARIABLES_FILE JSON file of variables to pass to the template. --model MODEL The model to use for the query (e.g., gpt-4). --max-tokens MAX_TOKENS Maximum number of tokens for the response (default: 8192). --max-length MAX_LENGTH Maximum character length for the prompt (default: 2048). --api-key API_KEY API key for the selected provider. If not provided, the environment variable for the provider will be used.
$ llmquery --provider OPENAI --template ./llmquery-templates/chat-template.yaml \ --variables '{"user_input": "What is AI?"}' --api-key your-api-key --model gpt-4
The llmquery CLI provides a command-line interface for interacting with Language Model APIs. The tool simplifies querying large language models by using YAML templates. This can used for various applications such as automation, testing, and scripting.
The llmquery binary is executed from the command line and supports various options for customization and configuration. Below is a detailed breakdown of its options and usage patterns.
--provider
OPENAI, ANTHROPIC, GOOGLE_GEMINI, AWS_BEDROCK, OLLAMA, DEEPSEEK, MISTRAL, GITHUB_AI--provider OPENAI--templates-path
llmquery framework.--templates-path ./llmquery-templates--template-id
--template-id general-query--variables
--variables '{"user_input": "Hello"}'--variables-file
--variables-file ./variables.json--model
LLMQUERY_MODEL environment variable.--model gpt-4--max-tokens
8192--max-tokens 2048--max-length
2048--max-length 1024--api-key
llmquery --provider OPENAI --templates-path ./llmquery-templates \ --template-id basic-query --variables '{"user_input": "What is AI?"}' \ --api-key YOUR_API_KEY --model gpt-4
llmquery --provider ANTHROPIC --templates-path ./llmquery-templates \ --template-id basic-query --variables-file ./vars.json \ --api-key YOUR_API_KEY --model claude-3-5-sonnet-latest
llmquery --provider GOOGLE_GEMINI --templates-path ./llmquery-templates \ --template-id translate-task --variables '{"text": "Hello", "language": "French"}' \ --api-key YOUR_API_KEY --model gemini-latest --max-tokens 1000
code = """ def index @users = User.where("name LIKE '%#{params[:search]}%'") if params[:search].present? @users ||= User.all end """ query = LLMQuery( provider="ANTHROPIC", templates_path=llmquery.TEMPLATES_PATH, template_id="detect-security-vulnerabilities" variables={"code": code}, anthropic_api_key="your-api-key", model="claude-3-5-sonnet-latest" ) print(query.Query())
diff = """diff --git a/example.py b/example.py + def secure_function(password): + hashed_password = hash_password(password) + log("Password successfully hashed") + + user_password = get_password_from_user() + secure_function(user_password) """ query = LLMQuery( provider="GOOGLE_GEMINI", templates_path=llmquery.templates_path, template_id="pr-summary-generator", variables={"diff": diff}, google_gemini_api_key="your-api-key", model="gemini-1.5-flash" ) print(query.Query())
OPENAI_API_KEY
export OPENAI_API_KEY="API_KEY"ANTHROPIC_API_KEY
export ANTHROPIC_API_KEY="API_KEY"GOOGLE_GEMINI_API_KEY
export GOOGLE_GEMINI_API_KEY="API_KEY"AWS_ACCESS_KEY_ID
export AWS_ACCESS_KEY_ID="ACCESS_KEY"AWS_SECRET_ACCESS_KEY
export AWS_SECRET_ACCESS_KEY="SECRET_KEY"AWS_SESSION_TOKEN
export AWS_SESSION_TOKEN="SESSION_TOKEN"AWS_DEFAULT_REGION
export AWS_DEFAULT_REGION="us-east-1"Check the full list of environment variables at example.env.
DEEPSEEK_API_KEY
export DEEPSEEK_API_KEY="API_KEY"MISTRAL_API_KEY
export MISTRAL_API_KEY="API_KEY"GITHUB_TOKEN
export GITHUB_TOKEN="GITHUB_TOKEN"llmquery has a collection of well-tested LLM Prompts Templates for various use-cases, including Application Security, AI Security, Code Reviews, Developer Velocity, and general cases. You can check the templates at the ./llmquery-templates directory. All templates are bundled within llmquery, and can be accessed directly when refrencing the template ID.
Templates are powered by Jinja2, a Turing-complete template engine. This allows for the creation of dynamic and flexible templates through the use of conditional statements, loops, functions, and other advanced constructs.
View the full templates documentation at the llmquery templates documentation.
We're always looking for contributions! Here are some ideas to get started:
Feel free to create issues, submit pull requests, or suggest enhancements on GitHub.
This project is licensed under the MIT License.
Mazin Ahmed
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