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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" | |
} | |
) | |