local-rag-llm 0.0.2

Create and run a local LLM with RAG

local_llm

Create and run a local LLM with RAG. Adaptation of this original article. Works well in conjunction with the nlp_pipeline library which you can use to convert your PDFs and websites to the .txt files the library uses.

Installation

In addition to the libraries in the requirements.txt, Postgres SQL and pgvector need to be installed on your system.

Quick usage

If you want to use RAG, first you have to have Postgres running. E.g., on Mac, brew services start postgresql from the command line. You then need to make sure you have created the users/roles you will enter later on in Postgres. E.g., psql postgres from the command line, then in SQL:

CREATE ROLE <user> WITH LOGIN PASSWORD '<password>';
ALTER ROLE <user> SUPERUSER;

Now you are ready to use the library in Python.

RAG example

from local_rag_llm import local_llm

# instantiate the model
model = local_llm.local_llm(
	llm_url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_K_M.gguf", # the URL of the particular LLM you want to use. If you have the model locally you don't need to pass this
	llm_path = llm_path, # path where the local LLM file is stored or will be downloaded to
	redownload_llm = True, # whether or not to redownload the LLM file
	text_path = text_path, # either a directory where your .txt files are stored, or a list of absolute paths to the .txt files 
	metadata_path = "metadata.csv", # optional in case your .txt files have more metadata about them
	hf_token = None, # hugging face API token. If "HF_AUTH" is in your environment, you don't need to pass
	n_gpu_layers = 0, # number of GPU layers, 0 for CPU
	temperature = 0.0, # 0-1, 0 = more conservative, 1 = more random/creative
	max_new_tokens = 512, # length of new responses, equal to words more or less
	context_window = 3900 # working memory of the model in tokens, model-dependent but max is usually around 4k
)

# setup the Postgres database
model.setup_db(
	user = "<user>",
	password = "<password>"
)

# populate the database with your own documents
model.populate_db(
	chunk_size = 1024 # number of tokens/words to split your documents into
)

# get a response from the model
response = model.gen_response(
	prompt = "prompt",
	similarity_top_k = 4 # number of documents/chunks to return/query alongside the prompt
)

response["response"] # the text response of the model
response["supporting_text_01"] # the text of the chunks the response is largely based on plus its metadata

# you can also convert tabular CSVs in long format into LLM readable ones
from local_rag_llm.db_setup import convert_csv

convert_csv(
	csv_path = "gdp.csv", # CSV to convert
	txt_out_path = "texts/gdp.txt", # where to write the text file to, e.g. the same as your 'text_path'
	sentence_template = "GDP in {country} was {GDP} in {year}" # template sentence, where {col_name} will be replaced with the values in the CSV
)

non-RAG example

A non-RAG model is simpler to set up. The library will infer that the model is non-RAG if you pass nothing for the text_path parameter.

from local_rag_llm import local_llm

# instantiate the model
model = local_llm.local_llm(
	llm_url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_K_M.gguf",
	llm_path = llm_path,
	redownload_llm = True,
	hf_token = None,
	n_gpu_layers = 0,
	temperature = 0.0,
	max_new_tokens = 512,
	context_window = 3900
)

# get a response from the model
response = model.gen_response(
	prompt = "prompt"
)

response # the text of the model's response