robert-mcdermott

# Define API key and endpoint
$ApiKey = "sk-12345"
$ApiEndpoint = "http://localhost:11434/v1/chat/completions"

<#
System message.
You can use this to give the AI instructions on what to do, how to act or how to respond to future prompts.
Default value for ChatGPT = "You are a helpful assistant."
#>
$AiSystemMessage = "You are a helpful AI assistant."

robert-mcdermott

#!/bin/bash

base_dir=~/.ollama/models
manifest_dir=$base_dir/manifests/registry.ollama.ai
blob_dir=$base_dir/blobs
publicmodels_dir=~/llm-models

find "$publicmodels_dir" -type l -exec rm {} +

mkdir -p "$publicmodels_dir"

robert-mcdermott

# pip install scikit-learn numpy ollama
import ollama
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

text1 = ollama.embeddings(model='nomic-embed-text', prompt='The sky is blue because of rayleigh scattering')
text2 = ollama.embeddings(model='nomic-embed-text', prompt='The sky is cloudy and grey today')

vec1 = np.array(text1['embedding']).reshape(1, -1)
vec2 = np.array(text2['embedding']).reshape(1, -1)

robert-mcdermott

import argparse
import requests
from langchain.llms import Ollama
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import GPT4AllEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA

def main(args):

robert-mcdermott

# pip install chromadb==0.4.15 # need to pin to this version for current langchain version
from langchain.llms import Ollama
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import GPT4AllEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA

ollama = Ollama(base_url='http://localhost:11434', model='zephyr:latest')

robert-mcdermott

import plotly.express as px
import matplotlib.pyplot as plt
from collections import Counter
import re
import sys
import nltk
from nltk.corpus import stopwords

def plot_word_frequencies_matplot(file_path, top):
    # Load stop words

robert-mcdermott

import os
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse

def is_valid(url, base_url):
    parsed = urlparse(url)
    return bool(parsed.netloc) and parsed.netloc == urlparse(base_url).netloc

def is_binary(url):

robert-mcdermott

from langchain.chat_models import ChatOllama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import HumanMessage

chat_model = ChatOllama(model="mistral", base_url = "http://localhost:11434", callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))
#chat_model = ChatOllama(model="mistral")

messages = [
    HumanMessage(content="Why is the sky blue?")

robert-mcdermott

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode("A fat tuxedo cat")
print(embeddings)

robert-mcdermott

from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')

# Two lists of sentences
sentences1 = ['The cat sits outside',
             'A man is playing guitar',
             'The new movie is awesome',
             'Jim can run very fast',
             'My goldfish is hungry']