216 lines
6.3 KiB
Markdown
216 lines
6.3 KiB
Markdown
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# LLM Functions
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This project empowers you to effortlessly build powerful LLM tools and agents using familiar languages like Bash, JavaScript, and Python.
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Forget complex integrations, **harness the power of [function calling](https://platform.openai.com/docs/guides/function-calling)** to connect your LLMs directly to custom code and unlock a world of possibilities. Execute system commands, process data, interact with APIs – the only limit is your imagination.
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**Tools Showcase**
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**Agents showcase**
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## Prerequisites
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Make sure you have the following tools installed:
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- [argc](https://github.com/sigoden/argc): A bash command-line framework and command runner
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- [jq](https://github.com/jqlang/jq): A JSON processor
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## Getting Started with [AIChat](https://github.com/sigoden/aichat)
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**Currently, AIChat is the only CLI tool that supports `llm-functions`. We look forward to more tools supporting `llm-functions`.**
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### 1. Clone the repository
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```sh
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git clone https://github.com/sigoden/llm-functions
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cd llm-functions
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```
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### 2. Build tools and agents
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#### I. Create a `./tools.txt` file with each tool filename on a new line.
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```
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get_current_weather.sh
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execute_command.sh
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#execute_py_code.py
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```
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<details>
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<summary>Where is the web_search tool?</summary>
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<br>
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The `web_search` tool itself doesn't exist directly, Instead, you can choose from a variety of web search tools.
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To use one as the `web_search` tool, follow these steps:
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1. **Choose a Tool:** Available tools include:
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* `web_search_cohere.sh`
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* `web_search_perplexity.sh`
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* `web_search_tavily.sh`
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* `web_search_vertexai.sh`
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2. **Link Your Choice:** Use the `argc` command to link your chosen tool as `web_search`. For example, to use `web_search_perplexity.sh`:
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```sh
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$ argc link-web-search web_search_perplexity.sh
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```
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This command creates a symbolic link, making `web_search.sh` point to your selected `web_search_perplexity.sh` tool.
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Now there is a `web_search.sh` ready to be added to your `./tools.txt`.
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</details>
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#### II. Create a `./agents.txt` file with each agent name on a new line.
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```
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coder
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todo
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```
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#### III. Build `bin` and `functions.json`
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```sh
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argc build
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```
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#### IV. Ensure that everything is ready (environment variables, Node/Python dependencies, mcp-bridge server)
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```sh
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argc check
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```
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### 3. Link LLM-functions and AIChat
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AIChat expects LLM-functions to be placed in AIChat's **functions_dir** so that AIChat can use the tools and agents that LLM-functions provides.
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You can symlink this repository directory to AIChat's **functions_dir** with:
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```sh
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ln -s "$(pwd)" "$(aichat --info | sed -n 's/^functions_dir\s\+//p')"
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# OR
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argc link-to-aichat
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```
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Alternatively, you can tell AIChat where the LLM-functions directory is by using an environment variable:
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```sh
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export AICHAT_FUNCTIONS_DIR="$(pwd)"
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```
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### 4. Start using the functions
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Done! Now you can use the tools and agents with AIChat.
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```sh
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aichat --role %functions% what is the weather in Paris?
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aichat --agent todo list all my todos
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```
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## Writing Your Own Tools
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Building tools for our platform is remarkably straightforward. You can leverage your existing programming knowledge, as tools are essentially just functions written in your preferred language.
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LLM Functions automatically generates the JSON declarations for the tools based on **comments**. Refer to `./tools/demo_tool.{sh,js,py}` for examples of how to use comments for autogeneration of declarations.
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### Bash
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Create a new bashscript in the [./tools/](./tools/) directory (.e.g. `execute_command.sh`).
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```sh
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#!/usr/bin/env bash
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set -e
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# @describe Execute the shell command.
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# @option --command! The command to execute.
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main() {
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eval "$argc_command" >> "$LLM_OUTPUT"
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}
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eval "$(argc --argc-eval "$0" "$@")"
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```
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### Javascript
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Create a new javascript in the [./tools/](./tools/) directory (.e.g. `execute_js_code.js`).
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```js
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/**
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* Execute the javascript code in node.js.
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* @typedef {Object} Args
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* @property {string} code - Javascript code to execute, such as `console.log("hello world")`
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* @param {Args} args
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*/
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exports.run = function ({ code }) {
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eval(code);
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}
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```
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### Python
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Create a new python script in the [./tools/](./tools/) directory (e.g. `execute_py_code.py`).
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```py
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def run(code: str):
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"""Execute the python code.
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Args:
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code: Python code to execute, such as `print("hello world")`
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"""
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exec(code)
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```
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## Writing Your Own Agents
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Agent = Prompt + Tools (Function Calling) + Documents (RAG), which is equivalent to OpenAI's GPTs.
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The agent has the following folder structure:
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```
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└── agents
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└── myagent
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├── functions.json # JSON declarations for functions (Auto-generated)
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├── index.yaml # Agent definition
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├── tools.txt # Shared tools
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└── tools.{sh,js,py} # Agent tools
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```
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The agent definition file (`index.yaml`) defines crucial aspects of your agent:
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```yaml
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name: TestAgent
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description: This is test agent
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version: 0.1.0
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instructions: You are a test ai agent to ...
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conversation_starters:
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- What can you do?
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variables:
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- name: foo
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description: This is a foo
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documents:
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- local-file.txt
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- local-dir/
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- https://example.com/remote-file.txt
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```
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Refer to [./agents/demo](https://github.com/sigoden/llm-functions/tree/main/agents/demo) for examples of how to implement a agent.
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## MCP (Model Context Protocol)
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- [mcp/server](https://github.com/sigoden/llm-functions/tree/main/mcp/server): Let LLM-Functions tools/agents be used through the Model Context Protocol.
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- [mcp/bridge](https://github.com/sigoden/llm-functions/tree/main/mcp/bridge): Let external MCP tools be used by LLM-Functions.
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## Documents
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- [Tool Guide](https://github.com/sigoden/llm-functions/blob/main/docs/tool.md)
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- [Agent Guide](https://github.com/sigoden/llm-functions/blob/main/docs/agent.md)
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- [Argc Commands](https://github.com/sigoden/llm-functions/blob/main/docs/argcfile.md)
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## License
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The project is under the MIT License, Refer to the [LICENSE](https://github.com/sigoden/llm-functions/blob/main/LICENSE) file for detailed information.
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