keenborder786
commented
1 month ago Well you cannot use OllamaFunctions with AgentExecutor due to unsupported messages type in ChatOllama (ToolAgentAction) but you can still use OllamaFunction to call functions by doing some custom changes as shown below, let me know if you understand the code below because I have made some major changes in OllamaFunctions to make it work. Please try it on your machine and let me know if it works. I would also suggest using React which is being powered by Ollama:
from langchain_experimental.llms.ollama_functions import OllamaFunctions,convert_to_ollama_tool
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import tool
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.pydantic_v1 import (
BaseModel,
Field
)
from langchain_core.messages import AIMessage, ToolCall
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.prompts import SystemMessagePromptTemplate,HumanMessagePromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_community.chat_models.ollama import ChatOllama
from langchain.agents.output_parsers import ToolsAgentOutputParser
class CustomOllamaFunctions(OllamaFunctions):
def _generate(
self,
messages,
stop,
run_manager,
**kwargs,
):
functions = kwargs.get("functions", [])
if "functions" in kwargs:
del kwargs["functions"]
if "function_call" in kwargs:
functions = [
fn for fn in functions if fn["name"] == kwargs["function_call"]["name"]
]
if not functions:
raise ValueError(
"If `function_call` is specified, you must also pass a "
"matching function in `functions`."
)
del kwargs["function_call"]
response_message = ChatOllama(model="llama3", temperature=0, format="json")._generate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
chat_generation_content = response_message.generations[0].text
if not isinstance(chat_generation_content, str):
raise ValueError("OllamaFunctions does not support non-string output.")
try:
parsed_chat_result = json.loads(chat_generation_content)
except json.JSONDecodeError:
raise ValueError(
f"""'{self.model}' did not respond with valid JSON.
Please try again.
Response: {chat_generation_content}"""
)
called_tool_name = parsed_chat_result["tool"]
called_tool = next(
(fn for fn in functions if fn["name"] == called_tool_name), None
)
if called_tool is None:
raise ValueError(
f"Failed to parse a function call from {self.model} output: "
f"{chat_generation_content}"
)
called_tool_arguments = parsed_chat_result["tool_input"]
response_message_with_functions = AIMessage(
content="",
tool_calls=[
ToolCall(
name=called_tool_name,
args=called_tool_arguments if called_tool_arguments else {},
id=f"call_{str(uuid.uuid4()).replace('-', '')}",
)
],
)
return ChatResult(
generations=[ChatGeneration(message=response_message_with_functions)]
)
os.environ["TAVILY_API_KEY"] = ''
class MultiplyInput(BaseModel):
"""Input for the Multiply tool."""
name = "Multiply"
description = "Multiply two integers together."
first_int: int = Field(description="The first integer number to be multiplied e.g 4")
second_int: int = Field(description="The second integer to be multiplied. e.g 7")
class SearchinWebInput(BaseModel):
"""Input for the Multiply tool."""
name = "SearchinWebInput"
description = "Use Tavily to search information in Internet."
query: str = Field(description="The query used to search in Internet. " "e.g. what is the weather in San Francisco?")
def multiply(first_int: int, second_int: int) -> int:
"""Multiply two integers together."""
return first_int * second_int
def search_in_web(query: str) -> str:
"""Use Tavily to search information in Internet."""
search = TavilySearchResults(max_results=2)
context = search.invoke(query)
result = ""
for i in context:
result += f"In site:{i['url']}, context shows:{i['content']}.\n"
return result
llm = CustomOllamaFunctions(model="llama3", temperature=0, format="json")
tools = [MultiplyInput,SearchinWebInput]
tools = [convert_to_ollama_tool(fn) for fn in tools]
system_message = """
You have access to the following tools to answer the user question:
{tools}
You must always select one of the above tools and respond with only a JSON object matching the following schema:
{{
"tool": <name of the selected tool>,
"tool_input": <parameters for the selected tool, matching the tool's JSON schema>
}}
Double check that in your json, you only have `tool` and `tool_input`.
""".format(tools=json.dumps(tools, indent=2))
prompt = ChatPromptTemplate(messages=[SystemMessagePromptTemplate.from_template(system_message,template_format='jinja2'),
HumanMessagePromptTemplate.from_template('{input}')])
all_tools = {'Multiply':multiply,'SearchinWebInput':search_in_web}
llm_with_tools = llm.bind_tools(tools=tools)
agent = prompt | llm_with_tools | ToolsAgentOutputParser()
action = agent.invoke({'input':'Using the search tool tell me Who is elon musk?'})[0]
tool_used,tool_input = action.tool,action.tool_input
print(all_tools[tool_used](**tool_input))
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