书接上回
这是2024年5月29日的帖子，当时没认真实验完
curl -fsSL https://ollama.com/install.sh | sh再更新一次
一个月都不到，疯狂刷版本的ollama
模型本身没有更新：
https://ollama.com/library/mistral:7b-instruct-v0.3-q5_K_M
curl http://localhost:11434/api/generate -d '{
"model": "mistral",
"prompt": "[INST] why is the sky blue? [/INST]",
"raw": true,
"stream": false
}'先给mistral发一个请求激活一下模型
list了一下发现有点多
改改
curl http://localhost:11434/api/generate -d '{
"model": "mistral:7b-instruct-v0.3-q5_K_M",
"prompt": "[INST] why is the sky blue? [/INST]",
"raw": true,
"stream": false
}'
ok，拿到了返回了
接着继续
curl http://localhost:11434/api/generate -d '{
"model": "mistral:7b-instruct-v0.3-q5_K_M",
"prompt": "[AVAILABLE_TOOLS] [{\"type\": \"function\", \"function\": {\"name\": \"get_current_weather\", \"description\": \"Get the current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city and state, e.g. San Francisco, CA\"}, \"format\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"], \"description\": \"The temperature unit to use. Infer this from the users location.\"}}, \"required\": [\"location\", \"format\"]}}}][/AVAILABLE_TOOLS][INST] What is the weather like today in San Francisco [/INST]",
"raw": true,
"stream": false
}'注意一下，就是里面的“我都做了转义
{"model":"mistral:7b-instruct-v0.3-q5_K_M","created_at":"2024-06-22T05:16:51.60371924Z",
"response":"[TOOL_CALLS] [{\"name\": \"get_current_weather\",
\"arguments\": {\"location\": \"San Francisco, CA\", \"format\": \"celsius\"}}]",
"done":true,"done_reason":"stop","total_duration":1365854578,"load_duration":35952801,
"prompt_eval_count":137,"prompt_eval_duration":290965000,"eval_count":36,
"eval_duration":991440000}[TOOL_CALLS] [{\"name\": \"get_current_weather\",
\"arguments\": {\"location\": \"San Francisco, CA\", \"format\": \"celsius\"}}]
按官方文档来说，应该给我这个回复
[TOOL_CALLS] [{\"name\": \"get_current_weather\",
\"arguments\": {\"location\": \"San Francisco, CA\", \"format\": \"celsius\"}}]最后无误，这里其实就没啥问题了
===========================================
接下来上langchain
mkdir mistral_tool_call_with_langchain
cd mistral_tool_call_with_langchain
python3 -m venv .venv
source .venv/bin/activatepip install langchain_community
pip install langchain_core
pip install langchain
pip install langchain_experimental
pip install -U duckduckgo-searchfrom langchain_experimental.llms.ollama_functions import OllamaFunctions
from langchain_core.messages import HumanMessage
model = OllamaFunctions(model="mistral:7b-instruct-v0.3-q5_K_M", format="json")
model = model.bind_tools(
tools=[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, " "e.g. San Francisco, CA",
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location."
},
},
"required": ["location", "unit"],
},
}
],
function_call={"name": "get_current_weather"},
)
res = model.invoke("what is the weather in Boston?")
print(res)
#content='' id='run-2d754254-223e-4f42-91f0-4a069f73f522-0'
# tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'Boston, MA', 'unit': 'celsius'},
# 'id': 'call_1674d88b6c1a40d9b25dccb5c4de45f9'}]https://python.langchain.com/v0.2/docs/integrations/chat/ollama_functions/
from langchain_experimental.llms.ollama_functions import OllamaFunctions
from langchain_core.messages import HumanMessage
from langchain_core.messages import AIMessage
from langchain_community.tools.ddg_search.tool import DuckDuckGoSearchRun
import json
search = DuckDuckGoSearchRun()
model = OllamaFunctions(model="mistral:7b-instruct-v0.3-q5_K_M", format="json")
model2 = OllamaFunctions(model="mistral:7b-instruct-v0.3-q5_K_M", format="json")
model = model.bind(
functions=[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, " "e.g. San Francisco, CA",
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["location"],
},
}
],
function_call={"name": "get_current_weather"},
)
# 使用 JsonOutputToolsParser 来试图解析结果
# https://python.langchain.com/docs/modules/model_io/chat/function_calling#binding-functions
# tool_chain = model | JsonOutputToolsParser(name="get_current_weather")
# result = tool_chain.invoke("what is the weather in Boston?")
# print(result)
# 这个方法是废的，因为这是针对OpenAI的
def get_current_weather(location):
res = "真的是在被call哦，get_current_weather:"+location
print(res)
result = search.run("what is the current weather in "+location)
print(result)
return result
result=model.invoke("what is the weather in xi'an?")
print(result.tool_calls)
cur_tool_calls = result.tool_calls
# 判断字符串是否是可调用的函数
#https://stackoverflow.com/questions/59912800/how-do-i-check-if-a-string-is-a-callable-name-in-python
function_call_name = cur_tool_calls[0]['name']
function_call_arguments = cur_tool_calls[0]['args']
print(function_call_arguments)
if callable(locals()[function_call_name]):
print(f"{function_call_name} 是当前文件中可调用的函数。")
location = function_call_arguments["location"]
print("location:",location)
res = eval(function_call_name)(location)
result = model2.invoke(res+"\n\n 根据上下文，并且使用celsius作为单位，使用中文来总结，这个城市的天气是怎样的？")
print("\n")
print(result.content)
print("\n")
else:
print(f"{function_call_name} 不是当前文件中可调用的函数。")紧接着是使用更OK的例子来调用搜索引擎并返回结果的例子
我现在忽然觉得，不需要微调，只需要用提示词工程来给一堆例子，就可以让ollama后面的引擎，给我调出来合适的工具了