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Ollama

Ollama allows you to run open-source large language models, such as Llama 2, locally.

Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile.

It optimizes setup and configuration details, including GPU usage.

For a complete list of supported models and model variants, see the Ollama model library.

Setup​

First, follow these instructions to set up and run a local Ollama instance:

  • Download
  • Fetch a model via ollama pull <model family>
  • e.g., for Llama-7b: ollama pull llama2 (see full list here)
  • This will download the most basic version of the model typically (e.g., smallest # parameters and q4_0)
  • On Mac, it will download to

~/.ollama/models/manifests/registry.ollama.ai/library/<model family>/latest

  • And we specify a particular version, e.g., for ollama pull vicuna:13b-v1.5-16k-q4_0
  • The file is here with the model version in place of latest

~/.ollama/models/manifests/registry.ollama.ai/library/vicuna/13b-v1.5-16k-q4_0

You can easily access models in a few ways:

1/ if the app is running:

  • All of your local models are automatically served on localhost:11434
  • Select your model when setting llm = Ollama(..., model="<model family>:<version>")
  • If you set llm = Ollama(..., model="<model family") withoout a version it will simply look for latest

2/ if building from source or just running the binary:

  • Then you must run ollama serve
  • All of your local models are automatically served on localhost:11434
  • Then, select as shown above

Usage​

You can see a full list of supported parameters on the API reference page.

from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = Ollama(
    model="llama2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])
)

With StreamingStdOutCallbackHandler, you will see tokens streamed.

llm("Tell me about the history of AI")

Ollama supports embeddings via OllamaEmbeddings:

from langchain.embeddings import OllamaEmbeddings

oembed = OllamaEmbeddings(base_url="http://localhost:11434", model="llama2")
oembed.embed_query("Llamas are social animals and live with others as a herd.")

RAG​

We can use Olama with RAG, just as shown here.

Let's use the 13b model:

ollama pull llama2:13b

Let's also use local embeddings from OllamaEmbeddings and Chroma.

pip install chromadb
# Load web page
from langchain.document_loaders import WebBaseLoader

loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()
# Split into chunks
from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)
all_splits = text_splitter.split_documents(data)
# Embed and store
from langchain.vectorstores import Chroma
from langchain.embeddings import GPT4AllEmbeddings
from langchain.embeddings import OllamaEmbeddings  # We can also try Ollama embeddings

vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
    Found model file at  /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin


    objc[77472]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x17f754208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x17fb80208). One of the two will be used. Which one is undefined.
# Retrieve
question = "How can Task Decomposition be done?"
docs = vectorstore.similarity_search(question)
len(docs)
    4
# RAG prompt
from langchain import hub

QA_CHAIN_PROMPT = hub.pull("rlm/rag-prompt-llama")
# LLM
from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = Ollama(
    model="llama2",
    verbose=True,
    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
)
# QA chain
from langchain.chains import RetrievalQA

qa_chain = RetrievalQA.from_chain_type(
    llm,
    retriever=vectorstore.as_retriever(),
    chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
)
question = "What are the various approaches to Task Decomposition for AI Agents?"
result = qa_chain({"query": question})
     There are several approaches to task decomposition for AI agents, including:
    
    1. Chain of thought (CoT): This involves instructing the model to "think step by step" and use more test-time computation to decompose hard tasks into smaller and simpler steps.
    2. Tree of thoughts (ToT): This extends CoT by exploring multiple reasoning possibilities at each step, creating a tree structure. The search process can be BFS or DFS with each state evaluated by a classifier or majority vote.
    3. Using task-specific instructions: For example, "Write a story outline." for writing a novel.
    4. Human inputs: The agent can receive input from a human operator to perform tasks that require creativity and domain expertise.
    
    These approaches allow the agent to break down complex tasks into manageable subgoals, enabling efficient handling of tasks and improving the quality of final results through self-reflection and refinement.

You can also get logging for tokens.

from langchain.schema import LLMResult
from langchain.callbacks.base import BaseCallbackHandler


class GenerationStatisticsCallback(BaseCallbackHandler):
    def on_llm_end(self, response: LLMResult, **kwargs) -> None:
        print(response.generations[0][0].generation_info)


callback_manager = CallbackManager(
    [StreamingStdOutCallbackHandler(), GenerationStatisticsCallback()]
)

llm = Ollama(
    base_url="http://localhost:11434",
    model="llama2",
    verbose=True,
    callback_manager=callback_manager,
)

qa_chain = RetrievalQA.from_chain_type(
    llm,
    retriever=vectorstore.as_retriever(),
    chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
)

question = "What are the approaches to Task Decomposition?"
result = qa_chain({"query": question})

eval_count / (eval_duration/10e9) gets tok / s

62 / (1313002000 / 1000 / 1000 / 1000)
    47.22003469910937

Using the Hub for prompt management​

Open-source models often benefit from specific prompts.

For example, Mistral 7b was fine-tuned for chat using the prompt format shown here.

Get the model: ollama pull mistral:7b-instruct

# LLM
from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = Ollama(
    model="mistral:7b-instruct",
    verbose=True,
    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
)
from langchain import hub

QA_CHAIN_PROMPT = hub.pull("rlm/rag-prompt-mistral")

# QA chain
from langchain.chains import RetrievalQA

qa_chain = RetrievalQA.from_chain_type(
    llm,
    retriever=vectorstore.as_retriever(),
    chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
)
question = "What are the various approaches to Task Decomposition for AI Agents?"
result = qa_chain({"query": question})
    
    There are different approaches to Task Decomposition for AI Agents such as Chain of thought (CoT) and Tree of Thoughts (ToT). CoT breaks down big tasks into multiple manageable tasks and generates multiple thoughts per step, while ToT explores multiple reasoning possibilities at each step. Task decomposition can be done by LLM with simple prompting or using task-specific instructions or human inputs.