parser-ollama.py

# ruff: noqa: E402
# bring in our LLAMA_CLOUD_API_KEY
import os
from dotenv import load_dotenv

load_dotenv()

import nest_asyncio

nest_asyncio.apply()

# bring in deps
from llama_parse import LlamaParse
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# set up parser
llamaparse_api_key = os.getenv("LLAMA_CLOUD_API_KEY")
parser = LlamaParse(
    api_key=llamaparse_api_key,
    result_type="markdown",  # "markdown" and "text" are available
)

# use SimpleDirectoryReader to parse our file
file_extractor = {".pdf": parser}
documents = SimpleDirectoryReader(
    input_files=["data/gpt4all.pdf"], file_extractor=file_extractor
).load_data()

documents

# len(documents)

# documents[0].text
documents[0].text[:200]

########### Ollama Models ###############

# by default llamaindex uses OpenAI models
from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    # model_name="nomic-embed-text",
    model_name="llama2",
    base_url="http://localhost:11434",
    ollama_additional_kwargs={"mirostat": 0},
)

from llama_index.llms.ollama import Ollama

llm = Ollama(model="llama2", request_timeout=30.0)

from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model

# get the answer out of it
# create an index from the parsed markdown
index = VectorStoreIndex.from_documents(documents)

# create a query engine for the index
query_engine = index.as_query_engine()

# query the engine
from IPython.display import Markdown, display

# query the engine
query = "what is the BoolQ value of GPT4All-J 6B v1.0* model ?"
response = query_engine.query(query)
display(Markdown(f"<b>{response}</b>"))