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import pandas as pd
from langchain.schema import Document
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import Chroma
from tqdm import tqdm

janduplessis883

In this walkthrough we will see how to use Pinecone for semantic search.

janduplessis883

import spacy
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from langchain.tools import BaseTool
from typing import Optional, Union, List
from langchain.callbacks.manager import CallbackManagerForToolRun, AsyncCallbackManagerForToolRun

class DataPreprocessingTool(BaseTool):
    name = "DataPreprocessingTool"
    description = "A tool for preprocessing and structuring unstructured data."

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import toml
import pandas as pd
import textwrap
import yaml
from crewai import Agent, Task, Crew, Process, LLM
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource

# Create a knowledge source
from crewai_tools import PDFSearchTool, FileReadTool

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import toml
from crewai_tools import BaseTool
from typing import ClassVar, Union, Dict, Any, List
import requests

# Load the TOML file
with open("notioncrew/config_secrets.toml", "r") as f:
    config_secrets = toml.load(f)

# Load environment variables from streamlit secrets