2024-07-17 08:46:43 +00:00
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![](../resources/logo.jpeg)
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[English](README.md) | [Chinese](README_zh.md)
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## Function Call
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The CodeGeeX4 model supports 'Function Call', which allows model to select **one** or **multiple** tools from the candidates based on
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the problem.
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## Usage Example
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### 1. Install Dependencies
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```bash
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cd function_call_demo
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pip install -r requirements.txt
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```
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### 2. Run the Script
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```bash
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python main.py
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>>> [{"name": "weather", "arguments": {"location": "Beijing"}}]
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```
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## Explanation
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2024-07-17 08:49:19 +00:00
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### 1. Single Call from Multiple Tools
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2024-07-17 08:46:43 +00:00
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In the example script, only one tool is provided as a candidate. However, you can also provide multiple tools as candidates as needed.
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Here is an example:
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```python
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tool_content = {
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"function": [
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{
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"name": "weather",
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"description": "Use for searching weather at a specific location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"description": "the location need to check the weather",
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"type": "str",
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}
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},
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"required": [
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"location"
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]
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}
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},
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{
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"name": "Cooking/queryDish",
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"description": "Cooking API, providing cooking methods for different cuisines. It queries dish information based on specified parameters.",
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"parameters": {
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"type": "object",
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"properties": {
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"cuisine": {
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"description": "Specify the cuisine to be queried, such as Sichuan cuisine, Cantonese cuisine, Hunan cuisine."
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},
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"dish": {
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"description": "Specify the name of the dish to be queried."
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},
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"difficulty": {
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"description": "Specify the difficulty of the dish to be queried, such as beginner, intermediate, advanced."
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}
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},
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"required": [
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"cuisine"
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]
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}
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}
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]
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}
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response, _ = model.chat(
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tokenizer,
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query="How to make Kung-Pao Chicken",
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history=[{"role": "tool", "content": tool_content}],
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max_new_tokens=1024,
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temperature=0.1
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)
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```
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The following result is obtained
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```text
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[{'name': 'Cooking/queryDish', 'arguments': {'cuisine': 'Sichuan cuisine', 'dish': 'Kung-Pao Chicken'}}]
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```
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2024-07-17 08:49:19 +00:00
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### 2. Multiple Calls from Multiple Tools
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2024-07-17 08:46:43 +00:00
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Additionally, for complex problems, the model has the ability to select and call multiple tools from the candidates. Here is an example:
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```python
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tool_content = {
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"function": [
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{
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"name": "flight_book",
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"description": "Book a flight for a specific route and airlines",
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"parameters": {
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"type": "dict",
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"properties": {
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"from": {
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"type": "string",
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"description": "The departure city in full name."
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},
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"to": {
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"type": "string",
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"description": "The arrival city in full name."
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},
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"airlines": {
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"type": "string",
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"description": "The preferred airline."
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}
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},
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"required": [
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"from",
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"to",
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"airlines"
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]
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}
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},
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{
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"name": "hotel_book",
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"description": "Book a hotel for a specific location for the number of nights",
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"parameters": {
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"type": "dict",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city where the hotel is located."
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},
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"nights": {
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"type": "integer",
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"description": "Number of nights for the stay."
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}
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},
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"required": [
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"location",
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"nights"
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]
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}
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}
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]
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}
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response, _ = model.chat(
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tokenizer,
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query="Book a flight from Seattle to Boston with American Airlines and book a hotel in Boston for 4 nights.",
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history=[{"role": "tool", "content": tool_content}],
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max_new_tokens=1024,
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temperature=0.1
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)
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```
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The following result is obtained
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```text
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[{'name': 'flight_book', 'arguments': {'from': 'Seattle', 'to': 'Boston', 'airlines': 'American Airlines'}}, {'name': 'hotel_book', 'arguments': {'location': 'Boston', 'nights': 4}}]
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```
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