Self Internet hosting RAG Purposes On Edge Gadgets

Introduction

In a comfortable nook of a tech fanatic’s workshop, the search started: may a tiny Raspberry Pi deal with the ability of superior AI? This text follows that journey, exhibiting remodel this small machine right into a succesful software for sensible doc processing. We’ll information you thru establishing the Raspberry Pi, putting in the wanted software program, and constructing a system to deal with doc ingestion and QnA duties. By the tip, you’ll see how even the smallest tech devices can obtain spectacular outcomes with a little bit of creativity and energy.

Studying Targets

• Discover ways to configure a Raspberry Pi for headless operation.
• Perceive the method of putting in and managing dependencies like Ollama.
• Uncover develop a system for loading and processing PDF paperwork.
• Achieve abilities in making a Retrieval-Augmented Era (RAG) pipeline for answering queries.
• Be taught strategies for verifying the performance of your software.
• Discover put together your software for deployment with FastAPI and construct a consumer interface with Reflex.

This text was revealed as part of the Information Science Blogathon.

Setting Up Your Raspberry Pi

First we have to setup our Raspberry Pi for the applying to be working. Step one is to have a correct OS setup within the machine. If you have already got this accomplished, you may skip this. We will likely be utilizing Ubuntu server picture for this demo, however the default OS may also be used. All you want is a microSD of minimal 16GB to flash the OS picture.

Steps to Flash SD card

Comply with the steps under to flash the SD card with the OS picture:

• Obtain Raspberry Pi Imager based mostly in your system from this hyperlink, and set up it. All default settings are really useful.
• Open the Imager software and first choose the machine.
• Then choose the OS that you just need to set up. On this case, Ubuntu Server 24.04.
• Then selected the storage the place the picture will likely be flashed. This would be the SD card the place picture is to be flashed.

• Click on on subsequent. You’ll be prompted to edit the settings for brand new machine. We will likely be setting the Raspberry Pi for headless improvement, through SSH.

• Add the username and password for the consumer to login to. Then add an WiFi SSID and it’s password, for the machine to robotically login to that WiFi community on boot up. That is essential because the we’ll want the machine to connect with the community robotically for us to have the ability to connect with it through SSH.

• On the Providers tab, verify the Allow SSH and choose the Use username and password choice. Click on Save.

• Then press the Write button and look forward to the flashing to finish.

As soon as the SD card is flashed with the OS, insert it into the machine and begin it. It ought to take couple of minutes on first boot as much as full preliminary setup. The machine will robotically connect with WiFi after booting. Now from one other machine, ideally utilizing a laptop computer or desktop, login to your Raspberry Pi utilizing it’s IP. In case you are unsure what it’s IP is, you should utilize Fing android/ios app to loo-up for the IP.

Upon getting the IP, SSH into your machine utilizing the username and password you used in the course of the setup. The command for me
could be:

ssh [email protected]

Updating the Packages

Now, we have to replace the packages for all issues to be put in appropriately. Replace your system utilizing the next instructions:

sudo apt replace
sudo apt improve

Subsequent, want to put in Ollama to make use of the Language mannequin and the Embedding mannequin in our machine. Set up Ollama utilizing the next command:

curl -fsSL https://ollama.com/set up.sh | sh

If you happen to see any error when working the above command, run the next command to put in curl and check out once more:

sudo apt set up curl

As soon as Ollama is put in, use the next instructions to obtain Phi3 mannequin and Embeddings mannequin:

ollama pull phi3
ollama pull nomic-embed-text

After the 2 fashions are downloaded, create a venture inside any listing of your alternative and begin constructing the Utility.

Constructing the Utility Spine

Now that we’ve the machine setup, we’ll transfer ahead with constructing the RAG software.

Step1: Atmosphere Setup

First we may have the surroundings setup. Create a digital surroundings and set up the next packages:

deeplake
boto3==1.34.144
botocore==1.34.144
fastapi==0.110.3
gunicorn==22.0.0
httpx==0.27.0
huggingface-hub==0.23.4
langchain==0.2.6
langchain-community==0.2.6
langchain-core==0.2.11
langchain-experimental==0.0.62
langchain-text-splitters==0.2.2
langsmith==0.1.83
marshmallow==3.21.3
numpy==1.26.4
pandas==2.2.2
pydantic==2.8.2
pydantic_core==2.20.1
PyMuPDF==1.24.7
PyMuPDFb==1.24.6
python-dotenv==1.0.1
pytz==2024.1
PyYAML==6.0.1
reflex==0.5.6
requests==2.32.3

Step2: Creating config.py File

Subsequent, create a config.py file and add the next fields in it:

LANGUAGE_MODEL_NAME = "phi3"
EMBEDDINGS_MODEL_NAME = "nomic-embed-text"
OLLAMA_URL = "http://localhost:11434"

We now have setup our venture with needed packages and configs for the Utility.

Step3: Creating Ingestion Class

Now, let’s create the Ingestion class that will likely be used to ingest doc into the vector retailer. Under is the code for whole Ingestion class:

import os
import config as cfg

from pinecone import Pinecone
from langchain.vectorstores.deeplake import DeepLake
from langchain.embeddings.ollama import OllamaEmbeddings
from .doc_loader import PDFLoader

class Ingestion:
    """Doc Ingestion pipeline."""
    def __init__(self):
        attempt:
            self.embeddings = OllamaEmbeddings(
                mannequin=cfg.EMBEDDINGS_MODEL_NAME,
                base_url=cfg.OLLAMA_URL,
                show_progress=True,
            )
            self.vector_store = DeepLake(
                dataset_path="knowledge/text_vectorstore",
                embedding=self.embeddings,
                num_workers=4,
                verbose=False,
            )
        besides Exception as e:
            elevate RuntimeError(f"Did not initialize Ingestion system. ERROR: {e}")

    async def create_and_add_embeddings(
        self,
        file: str,
    ):
        attempt:
            loader = PDFLoader(
                file_path=file,
            )

            chunks = await loader.load_document()
            dimension = await self.vector_store.aadd_documents(paperwork=chunks)
            return len(dimension)
        besides (ValueError, RuntimeError, KeyError, TypeError) as e:
            elevate Exception(f"ERROR: {e}")

Within the above class, we outline the init technique the place the embeddings mannequin and the vector retailer situations are initialized. We additionally outlined an asynchronous technique create_and_add_embeddings that may take the file path, load it, chunk it and ingest it into the doc. We now have used a PDFLoader class to load the PDF file and chunk it. This class is outlined to make a separate chunking logic based mostly on necessities.

Step4: Code for PDFLoader

Under is the code for PDFLoader:

import os
from langchain.schema import Doc
from langchain.document_loaders.pdf import PyMuPDFLoader
from langchain.text_splitter import CharacterTextSplitter


class PDFLoader():
    def __init__(self, file_path: str) -> None:
        self.file_path = file_path

    async def load_document(self):
        self.file_name = os.path.basename(self.file_path)
        loader = PyMuPDFLoader(file_path=self.file_path)

        text_splitter = CharacterTextSplitter(
            separator="n",
            chunk_size=1000,
            chunk_overlap=200,
        )
        pages = await loader.aload()
        total_pages = len(pages)
        chunks = []
        for idx, web page in enumerate(pages):
            chunks.append(
                Doc(
                    page_content=web page.page_content,
                    metadata=dict(
                        {
                            "file_name": self.file_name,
                            "page_no": str(idx + 1),
                            "total_pages": str(total_pages),
                        }
                    ),
                )
            )

        final_chunks = text_splitter.split_documents(chunks)
        return final_chunks

Clarification

Let’s breakdown the PDFLoader class. We first initialize the file_path parameter utilizing an init technique. We
have outlined a load_document technique that does the next issues:

• Extracts the filename from the file_path.
• Masses the PDF file utilizing PyMuPDFLoader from Langchain. Notice that another PDFLoader may also be used at this level.
• Initializes a CharacterTextSplitter to separate the doc into a number of smaller chunks of outlined dimension.
• Then we asynchronously load the doc utilizing the PyMuPDFLoader object occasion.
• We then iterate over the pages and create Doc object utilizing the web page content material. We additionally add customized metadata fields of alternative. On this case will add file_name, page_no and the total_pages fields.
• Then we take all of the chunks and cut up them utilizing CharacterTextSplitter object occasion and eventually return the chunks.

Now that we’ve the Ingestion pipeline prepared, we’ll begin working with the QnA pipeline.

Step5: Code for QnA Pipeline

Under is the code for QnA pipeline:

import os
import config as cfg

from pinecone import Pinecone
from langchain.vectorstores.deeplake import DeepLake
from langchain.embeddings.ollama import OllamaEmbeddings
from langchain_community.llms.ollama import Ollama
from .doc_loader import PDFLoader

class QnA:
    """Doc Ingestion pipeline."""
    def __init__(self):
        attempt:
            self.embeddings = OllamaEmbeddings(
                mannequin=cfg.EMBEDDINGS_MODEL_NAME,
                base_url=cfg.OLLAMA_URL,
                show_progress=True,
            )
            self.mannequin = Ollama(
                mannequin=cfg.LANGUAGE_MODEL_NAME,
                base_url=cfg.OLLAMA_URL,
                verbose=True,
                temperature=0.2,
            )
            self.vector_store = DeepLake(
                dataset_path="knowledge/text_vectorstore",
                embedding=self.embeddings,
                num_workers=4,
                verbose=False,
            )
            self.retriever = self.vector_store.as_retriever(
                search_type="similarity",
                search_kwargs={
                    "ok": 10,
                },
            )
        besides Exception as e:
            elevate RuntimeError(f"Did not initialize Ingestion system. ERROR: {e}")

    def create_rag_chain(self):
        attempt:
            system_prompt = """<Directions>nnContext: {context}"
            """
            immediate = ChatPromptTemplate.from_messages(
                [
                    ("system", system_prompt),
                    ("human", "{input}"),
                ]
            )
            question_answer_chain = create_stuff_documents_chain(self.mannequin, immediate)
            rag_chain = create_retrieval_chain(self.retriever, question_answer_chain)

            return rag_chain
        besides Exception as e:
            elevate RuntimeError(f"Did not create retrieval chain. ERROR: {e}")

Let’s break down the QnA class. Within the init technique, we initialize the embeddings occasion, the mannequin occasion and the vector retailer occasion. Utilizing the vector retailer occasion, we outline the retriever occasion. That is the place we are able to outline metadata filters and ok, the max variety of chunks to be fetched. Within the create_rag_chain technique, we outline the system_prompt, the place all of the directions for the Language
mannequin is outlined. This technique immediate may be outlined based mostly in your use-case. We then create the chat immediate utilizing the system immediate.

Lastly, utilizing the mannequin and immediate we create doc chain, and utilizing the retriever and doc chain we create the retrieval chain and return it. This retrieval chain will likely be used to invoke the mannequin
utilizing consumer question.

Now let’s check out each the Ingestion and QnA pipelines to verify all the pieces works as meant. We are going to use Apple 2023 10K doc for this experiment. First let’s implement the Ingestion pipeline to ingest the doc into the vector retailer.

Code Implementation

from backend.src.ingestion import Ingestion

ingestion = Ingestion()
file = "knowledge/Apple-10K-2023.pdf"

ids = await ingestion.create_and_add_embeddings(file=file)

Notice that the ingestion takes quiet lengthy, which will depend on the ingesting doc dimension.

Step6: Utilizing Rag Chain to Ask Queries

Subsequent, let’s use the rag chain to ask queries on the ingested doc.

from backend.src.qna import QnA

qna = QnA()
rag_chain = qna.create_rag_chain()
for chunk in rag_chain.decide("reply").stream({"enter": "What is that this doc about?"}):
    print(chunk, finish="")

This code generates response for the question in a stream, identical to the way you see in ChatGPT interface. Though the response time is big, the response generated by the mannequin is correct.

With this, we’ve accomplished implementation of our Raspberry Pi RAG software setup. Within the subsequent a part of this text, we’ll wrap our app utilizing FastAPI and construct an UI utilizing Reflex. Reflex is a Python solely UI library to construct net software for cell and desktop.

Conclusion

We now have now accomplished the setup of Raspberry Pi and efficiently applied the spine of a RAG software. From putting in the OS and getting ready the surroundings to constructing the Ingestion and QnA
pipelines, every step has been important in creating the doc query answering system. By following the steps, you’ve outfitted your Raspberry Pi to deal with complicated doc ingestion and question duties. Within the subsequent a part of this text, we’ll deal with wrapping our software utilizing FastAPI and creating an interactive consumer interface with Reflex, a Python-based UI library. It will improve the usability and accessibility of the RAG software, making it prepared for real-world purposes. Keep tuned for the subsequent
steps!

Key Takeaways

• We discovered to arrange put together and configure a Raspberry Pi for working a RAG software.
• Putting in and managing dependencies like Ollama and mannequin downloads.
• Constructing a system to ingest and course of PDF paperwork right into a vector retailer.
• Implementing a Retrieval-Augmented Era chain for answering queries.
• Verifying the applying performance and getting ready for deployment with FastAPI and Reflex.

Ceaselessly Requested Query

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