local-rag-llm 0.0.21

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. It also handles .csv data files.

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, on Windows pg_ctl -D "C:\Program Files\PostgreSQL\16\data" start, depending on where you installed Postgres. 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 (psql -U postgres in Windows), 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.model_setup import instantiate_llm
from local_rag_llm.local_llm import local_llm

# instantiate the LLM
llm = instantiate_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
	n_gpu_layers = 0, # number of GPU layers, 0 for CPU, e.g., 100 if you have a GPU
	context_window = 3900, # working memory of the model in tokens, model-dependent
)

# instantiate the model
model = local_llm(
	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
	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
	memory_limit = 2048, # if using a chat engine, memory limit of the chat engine
	system_prompt = "You are a chatbot." # priming context of the chatbot
)

# setup the Postgres database
model.setup_db(
	user = "<user>",
	password = "<password>",
	table_name = "texts",
	clear_database = False, # whether or not to clear out the vector database
)

# populate the database with your own documents
# you can skip this step if you had 'clear_database' = False in the previous step and the vector db has previously been populated
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",
	llm = llm,
	similarity_top_k = 4, # number of documents/chunks to return/query alongside the prompt
	use_chat_engine = True, # whether or not to use the chat engine, i.e., have a short-term memory of your chat history
	reset_chat_engine = False, # if using a chat engine, whether or not to reset its memory
	chat_mode = "context" # "context" for first searching the vector db with the user's query, putting that information in the context prompt format, then answering based on that and the user's chat history. "condense_plus_context" for condensing the conversation and last user query into a question, searching the vector db with that, then pass the context plus that query to the LLM. If you plan on having follow-up questions query the vector db, "condense_plus_context" is recommended
)

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

# if you set streaming=True in .gen_response, response["response"] will be the streaming agent, not the text response
response["response"].print_response_stream() # to generate it the first time
response["response"].response # to access it after it's been generated

Transferring a vector database

You can transfer a vector database for easy backup and portability.

from local_rag_llm.db_setup import pg_dump, pg_restore

# save a vector database to file
pg_dump(
	host = "<host>" # e.g., "localhost"
	port = <port>, # e.g., 5432
	user = "<user>",
	password = "<password>",
	db_name = "<name of db to dump",
	filename = "<filename.sql>",
)

# restore a vector database
pg_restore(
	host = "<host>" # e.g., "localhost"
	port = <port>, # e.g., 5432
	user = "<user>",
	password = "<password>",
	db_name = "<name of newly restored db",
	filename = "<filename.sql>",
)

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.model_setup import instantiate_llm
from local_rag_llm import local_llm

# instantiate the LLM
llm = instantiate_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
	n_gpu_layers = 0 # number of GPU layers, 0 for CPU, e.g., 100 if you have a GPU
)

# instantiate the model
model = local_llm.local_llm(
	hf_token = None,
	temperature = 0.0,
	max_new_tokens = 512,
)

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

response["response"] # the text of the model's response

Docker usage

CPU only and CUDA

If only using the CPU or an Nvidia GPU, you can do run everything with Docker.

• Download the docker-compose.yml and Dockerfile (for CPU-only) or Dockerfile-gpu (for GPU) files
• Edit the HF_TOKEN to your API token
• Change the volume mappings to your desired local directories
• Navigate to the directory you saved the .yml file and run docker compose up. If you don't have a GPU, make the following edits to the docker-compose.yml file:
  • Change dockerfile: Dockerfile-gpu line to be dockerfile: Dockerfile
  • Delete the deploy: line and everything below it
• Check the name of the local_rag_llm image (not the postgres one) with docker ps -a
• Run your desired script with docker exec -t <image name from previous step> python /app/script.py, being sure to use the container's directory structure in the script. The setup_db() call in your script should look like:

model.setup_db(
    host = "postgres",
    port = "5432",
	user = "postgres",
	password = "secret",
    db_name = "vector_db",
    table_name = "desired_table_name",
)

Apple silicon

If you are using Apple silicon, you won't be able to run everything in Docker because of the lack of MPS drivers. You can still use the pgvector image however.

• Install torch with the mps backend enabled with pip install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
• Install the requirements.txt file as normal with pip install -r requirements.txt, as well as pip install local_rag_llm
• You can check if you successfully installed torch with the MPS backend enabled by running torch.backends.mps.is_available() in Python
• Download the docker-compose.yml and Dockerfile files
• Comment out the environment: and volumes: lines
• Navigate to the directory you saved the .yml file and run docker compose start postgres
• You can now instantiate your LLM with local_rag_llm.model_setup.instantiate_llm, passing n_gpu_layers = 100 or the desired amount
• The setup_db() call in your script should look like:

model.setup_db(
  	host = "localhost",
   	port = "5432",
	user = "postgres",
	password = "secret",
   	db_name = "vector_db",
  	table_name = "desired_table_name",
)

API

You can host the LLM as an API using the documents in the api/ folder. You cannot create new RAG corpora via the API, create and vectorize those in the backend. To host the API, update the following three files:

• db_settings.csv: CSV with postgres and HuggingFace parameters.
• corpora_list.csv: CSV containing the names of the corpora, the name of the corpus's table in postgres (should be the same as the name), and the directory of the txt files comprising the corpus.
• llm_list.csv: CSV containing the names of the LLMs, the size of their context windows, and the local path to the GGUF file

Make sure FastAPI is installed with pip install fastapi. Navigate to the api/ directory and serve the API with fastapi dev api.py

Usage

• get the names of available corpora: http://localhost:8000/api/v1/which_corpora/
• get the names of available LLMs: http://localhost:8000/api/v1/which_llms/
• send a query request: http://localhost:8000/api/v1/query/?prompt=<my prompt>&which_corpus=<desired corpus name>

The response will be a dictionary with response bearing the LLM's response, including the response's text as well as retrieved chunks, if applicable, and chat_history bearing the chat history, in case you want to feed it back to the LLM.

The API supports any of the hyperparameters found in the model.gen_response function. For example, if we wanted to specify similarity_top_k in our call, we would write http://localhost:8000/api/v1/query/?prompt=<my prompt>&which_corpus=<desired corpus name>&similarity_top_k=6