mixtral_agent.py

# 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:instruct",
    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)

# Import things that are needed generically
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool, StructuredTool, tool


global all_sources

# @tool
# def search(query: str) -> str:
#     """Look up things online."""
#     # return "LangChain"
#     data = retriever.invoke(query)
#     meta_data = [i.metadata for i in data]
#     # meta_data += all_sources
#     # all_sources += meta_data
#     all_sources += meta_data
#     # all_sources = []
#     return meta_data

from typing import List, Dict
from datetime import datetime

def format_info_list(info_list: List[Dict[str, str]]) -> str:
    """
    Format a list of dictionaries containing information into a single string.

    Args:
        info_list (List[Dict[str, str]]): A list of dictionaries containing information.

    Returns:
        str: A formatted string containing the information from the list.
    """
    formatted_strings = []
    for info_dict in info_list:
        formatted_string = "|"
        for key, value in info_dict.items():
            if isinstance(value, datetime.date):
                value = value.strftime('%Y-%m-%d')
            formatted_string += f"'{key}': '{value}', "
        formatted_string = formatted_string.rstrip(', ') + "|"
        formatted_strings.append(formatted_string)
    return '\n'.join(formatted_strings)
    
@tool
def search(query: str) -> str:
    """Look up things online."""
    # return "LangChain"
    global all_sources
    data = retriever.invoke(query)
    meta_data = [i.metadata for i in data]
    # meta_data += all_sources
    # all_sources += meta_data
    all_sources += meta_data
    
    # formatted_info = format_info(entry_id, published, title, authors)
    
    # formatted_info = format_info_list(all_sources)
    
    return meta_data.__str__()
    
    # all_sources = []
    # return meta_data

tools = [search]

# 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 #prevents error
    )

# 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"
#     }
# )

# class AgentSample:
    
    
#     def __init__(self, agent_executor_object,*args, **kwargs):
#         self.agent_executor_object = agent_executor_object
#         self.meta_data = []
        
#     def sample_invokex
        
        
    

if __name__ == "__main__":
    
    # global variable for collecting sources
    all_sources =  []    

    input = agent_executor.invoke(
        {
            "input": "How to generate videos from images using state of the art macchine learning models; Using the axriv retriever  " +
            "add the urls of the papers used in the final answer using the metadata from the retriever"
            # f"Please prioritize the newest papers this is the current data {get_current_date()}"
        }
    )
    
    x = 0

    input_1 = agent_executor.invoke(
        {
            "input": "I am looking for a text to 3d model; Using the axriv retriever  " +
            "add the urls of the papers used in the final answer using the metadata from the retriever"
            # f"Please prioritize the newest papers this is the current data {get_current_date()}"
        }
    )

    x = 0