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# LangChain supports many other chat models. Here, we're using Ollama
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain.tools.retriever import create_retriever_tool
from langchain_community.utilities import SerpAPIWrapper
from langchain.retrievers import ArxivRetriever
from langchain_core.tools import Tool
from langchain import hub
from langchain.agents import AgentExecutor, load_tools
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.tools.render import render_text_description
import os
import dotenv
dotenv.load_dotenv()
OLLMA_BASE_URL = os.getenv("OLLMA_BASE_URL")
# supports many more optional parameters. Hover on your `ChatOllama(...)`
# class to view the latest available supported parameters
llm = ChatOllama(
model="mistral",
base_url= OLLMA_BASE_URL
)
prompt = ChatPromptTemplate.from_template("Tell me a short joke about {topic}")
# using LangChain Expressive Language chain syntax
# learn more about the LCEL on
# https://python.langchain.com/docs/expression_language/why
chain = prompt | llm | StrOutputParser()
# for brevity, response is printed in terminal
# You can use LangServe to deploy your application for
# production
print(chain.invoke({"topic": "Space travel"}))
retriever = ArxivRetriever(load_max_docs=2)
tools = [
create_retriever_tool(
retriever,
"search arxiv's database for",
"Use this to recomend the user a paper to read Unless stated please choose the most recent models",
# "Searches and returns excerpts from the 2022 State of the Union.",
),
Tool(
name="SerpAPI",
description="A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.",
func=SerpAPIWrapper().run,
)
]
prompt = hub.pull("hwchase17/react-json")
prompt = prompt.partial(
tools=render_text_description(tools),
tool_names=", ".join([t.name for t in tools]),
)
chat_model = llm
# define the agent
chat_model_with_stop = chat_model.bind(stop=["\nObservation"])
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
}
| prompt
| chat_model_with_stop
| ReActJsonSingleInputOutputParser()
)
# instantiate AgentExecutor
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True,
handle_parsing_errors=True
)
# agent_executor.invoke(
# {
# "input": "Who is the current holder of the speed skating world record on 500 meters? What is her current age raised to the 0.43 power?"
# }
# )
# agent_executor.invoke(
# {
# "input": "what are large language models and why are they so expensive to run?"
# }
# )
agent_executor.invoke(
{
"input": "How to generate videos from images using state of the art macchine learning models"
}
)
|