larkintuckerllc

from langchain.agents.middleware import dynamic_prompt
from langchain.agents import create_agent
from langchain_chroma import Chroma
from langchain_openai import ChatOpenAI, OpenAIEmbeddings


def main():
    embeddings = OpenAIEmbeddings(
        model="Qwen/Qwen3-Embedding-0.6B",
        openai_api_base="http://localhost:8000/v1",

larkintuckerllc

from langchain.agents.middleware import dynamic_prompt
from langchain_anthropic import ChatAnthropic
from langchain.agents import create_agent
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings


def main():
    embeddings = OpenAIEmbeddings(
        model="Qwen/Qwen3-Embedding-0.6B",

larkintuckerllc

from langchain_anthropic import ChatAnthropic
from langchain.agents import create_agent
from langchain.tools import tool
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings


def main():
    embeddings = OpenAIEmbeddings(
        model="Qwen/Qwen3-Embedding-0.6B",

larkintuckerllc

from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter


def main():
    loader = DirectoryLoader("./data/", glob="**/*.txt", loader_cls=TextLoader)
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(

larkintuckerllc

from langchain.agents.middleware import dynamic_prompt
from langchain_anthropic import ChatAnthropic
from langchain.agents import create_agent
from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings


def main():
    embeddings = OllamaEmbeddings(model="llama3")
    vector_store = Chroma(

larkintuckerllc

from langchain_anthropic import ChatAnthropic
from langchain.agents import create_agent
from langchain.tools import tool
from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings


def main():
    embeddings = OllamaEmbeddings(model="llama3")
    vector_store = Chroma(

larkintuckerllc

from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter


def main():
    loader = DirectoryLoader("./data/", glob="**/*.txt", loader_cls=TextLoader)
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(

larkintuckerllc

from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter


def main():
    loader = DirectoryLoader("./data/", glob="**/*.txt", loader_cls=TextLoader)
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,

larkintuckerllc

from langchain_community.document_loaders import DirectoryLoader, TextLoader


def main():
    loader = DirectoryLoader("./data/", glob="**/*.txt", loader_cls=TextLoader)
    documents = loader.load()
    print(f"Total characters: {len(documents[0].page_content)}")
    print(documents[0].page_content[:500])

if __name__ == "__main__":

larkintuckerllc

from langchain_anthropic import ChatAnthropic
from langchain.agents import create_agent
from langchain.tools import tool


@tool
def get_weather(city: str) -> str:
    """Get weather for a given city.
    
    Args: