My AI Journey Day 1: Playing with Ollama RAG

Experimenting with Ollama based RAG:

Following the Ollama documentation: https://ollama.com/blog/embedding-models

embedding.py

#!/usr/bin/env python3
import ollama
import chromadb

documents = [
  "Llamas are members of the camelid family meaning they're pretty closely related to vicuñas and camels",
  "Llamas were first domesticated and used as pack animals 4,000 to 5,000 years ago in the Peruvian highlands",
  "Llamas can grow as much as 6 feet tall though the average llama between 5 feet 6 inches and 5 feet 9 inches tall",
  "Llamas weigh between 280 and 450 pounds and can carry 25 to 30 percent of their body weight",
  "Llamas are vegetarians and have very efficient digestive systems",
  "Llamas live to be about 20 years old, though some only live for 15 years and others live to be 30 years old",
]

client = chromadb.PersistentClient(path="./data")
collection = client.create_collection(name="docs")

# store each document in a vector embedding database
for i, d in enumerate(documents):
 response = ollama.embeddings(model="mxbai-embed-large", prompt=d)
 embedding = response["embedding"]
  
 collection.add(
   ids=[str(i)],
   embeddings=[embedding],
   documents=[d]
 )

And for retrieve:

retrieve.py

#!/usr/bin/env python3
import ollama
import chromadb

client = chromadb.PersistentClient(path="./data")
collection = client.get_collection(name="docs")

# an example prompt

prompt = "List the animals llamas are related to and how long they can live. Format the information as JSON with each object having a 'key' and 'value'. The 'key' should be the animal name, and the 'value' should contain the related details. Provide only small JSON objects with no additional explanation."

# generate an embedding for the prompt and retrieve the most relevant doc
response = ollama.embeddings(
  prompt=prompt,
  model="mxbai-embed-large"
)
results = collection.query(
  query_embeddings=[response["embedding"]],
  n_results=1
)
data = results['documents'][0][0]

print(data)

# generate a response combining the prompt and data we retrieved in step 2
output = ollama.generate(
        model="tinyllama:latest",
  prompt=f"Using this data: {data}. Respond to this prompt: {prompt}"
)

print(output['response'])