LLM and Ollama

Introduction

This is a page about ollama and you guest it LLM. I have downloaded several models and got a UI going over them locally. The plan is to build something like Claude desktop in Typescript to Golang. First some theory in Python from here. Here is the problem I am trying to solve.

Using the Remote Ollama

You can connect by setting the host with

export OLLAMA_HOST=192.blah.blah.blah

Now you can use with

ollama run llama3.2:latest

Taking lamma3.2b as an example

Model Info

• Architecture:llama Who made it
• Parameters:3.2B - Means 3.2 billion parameters (bigger requires more resources)
• Context Length:131072 - Number of tokens it can injest
• Embedding Length:3072 - Size of the vector for each token in the input text
• Quantization:Q4_K_M - Too complex to explain

You can customize the mode with a Modelfile and running create with ollama. For example

FROM llama3.2

# set the temperature where higher is more creative
PARAMETER temperature 0.3

SYSTEM """
   You are Bill, a very smart assistant who answers questions succintly and informatively
"""

Now we can create a copy with

ollama create bill -f ./Modelfile

Rest API Interaction

So we can send questions to llama using the the rest endpoint to 11434

curl http://192.blah.blah.blah:11434/api/generate -d '{ 
  "model": "llama3.2", 
  "prompt": "Why is the sky blue?",
  "stream": false 
}'

We can chat by changing the endpoint and the format by adding format in the playload

curl http://192.blah.blah.blah:11434/api/chat -d '{ 
  "model": "llama3.2", 
  "prompt": "Why is the sky blue?",
  "stream": false,
  "format": "json" 
}'

All of the options are at here

UI Based Client Msty

Seems that Msty was a good choice. You specify a provide and you can then put in 192.blah.blah.blah:11434. It support deepseek and other provider too.

RAG Retrieval-Augmented Generation

This allows us to converse with out own documents/data and solves the bizarre statement LLMs produce

• LLM
• Document Corpus (Knowledge Base)
• Document Embeddings
• Vecto Store (Vector DB, Faiss, Pinecone, Chromadb)
• Retrieval Mechanism

LangChain is a tool to make this easier

• Loading and parsing documents
• Splitting documents
• Generating embeddings
• Provides a unified abstraction for working with LLMs and Apps

This is referred to as a simple RAG system. We shall see

This was a bit of strange experience. The code is at here but really the thing consisted of the boxes in the diagram.

## 1. Ingest PDF Files
# 2. Extract Text from PDF Files and split into small chunks
# 3. Send the chunks to the embedding model
# 4. Save the embeddings to a vector database
# 5. Perform similarity search on the vector database to find similar documents
# 6. retrieve the similar documents and present them to the user
## run pip install -r requirements.txt to install the required packages

from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_community.document_loaders import OnlinePDFLoader

doc_path = "./data/BOI.pdf"
model = "llama3.2"

# Local PDF file uploads
if doc_path:
    loader = UnstructuredPDFLoader(file_path=doc_path)
    data = loader.load()
    print("done loading....")
else:
    print("Upload a PDF file")

    # Preview first page
content = data[0].page_content
# print(content[:100])


# ==== End of PDF Ingestion ====


# ==== Extract Text from PDF Files and Split into Small Chunks ====

from langchain_ollama import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma

# Split and chunk
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1200, chunk_overlap=300)
chunks = text_splitter.split_documents(data)
print("done splitting....")

# print(f"Number of chunks: {len(chunks)}")
# print(f"Example chunk: {chunks[0]}")

# ===== Add to vector database ===
import ollama

ollama.pull("nomic-embed-text")

vector_db = Chroma.from_documents(
    documents=chunks,
    embedding=OllamaEmbeddings(model="nomic-embed-text"),
    collection_name="simple-rag",
)
print("done adding to vector database....")


## === Retrieval ===
from langchain.prompts import ChatPromptTemplate, PromptTemplate
from langchain_core.output_parsers import StrOutputParser

from langchain_ollama import ChatOllama

from langchain_core.runnables import RunnablePassthrough
from langchain.retrievers.multi_query import MultiQueryRetriever

# set up our model to use
llm = ChatOllama(model=model)

# a simple technique to generate multiple questions from a single question and then retrieve documents
# based on those questions, getting the best of both worlds.
QUERY_PROMPT = PromptTemplate(
    input_variables=["question"],
    template="""You are an AI language model assistant. Your task is to generate five
    different versions of the given user question to retrieve relevant documents from
    a vector database. By generating multiple perspectives on the user question, your
    goal is to help the user overcome some of the limitations of the distance-based
    similarity search. Provide these alternative questions separated by newlines.
    Original question: {question}""",
)

retriever = MultiQueryRetriever.from_llm(
    vector_db.as_retriever(), llm, prompt=QUERY_PROMPT
)


# RAG prompt
template = """Answer the question based ONLY on the following context:
{context}
Question: {question}
"""

prompt = ChatPromptTemplate.from_template(template)


chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)


# res = chain.invoke(input=("what is the document about?",))
# res = chain.invoke(
#     input=("what are the main points as a business owner I should be aware of?",)
# )
res = chain.invoke(input=("how to report BOI?",))

print(res)