txtai-ollama.py

from txtai import Embeddings, LLM

# Data to index
data = [
  "US tops 5 million confirmed virus cases",
  "Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg",
  "Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
  "The National Park Service warns against sacrificing slower friends in a bear attack",
  "Maine man wins $1M from $25 lottery ticket",
  "Make huge profits without work, earn up to $100,000 a day"
]

# Vector store with embeddings via local Ollama server
embeddings = Embeddings(path="ollama/all-minilm", content=True)
embeddings.index(data)

# LLM via local Ollama server
llm = LLM(path="ollama/mistral-openorca")

# Question and context
question = "funny story"
context = "\n".join(x["text"] for x in embeddings.search(question))

# RAG
llm([
    {"role": "system", 
     "content": "You are a friendly assistant. You answer questions from users."},
    {"role": "user", 
     "content": f"""
Answer the following question using only the context below. Only include information
specifically discussed.

question: {question}
context: {context}
"""}
])

# A funny story from the given context is that the National Park Service warned people not to
# sacrifice their slower friends in a bear attack