Self Internet hosting RAG Purposes On Edge Gadgets with Langchain

Introduction 

Within the second a part of our collection on constructing a RAG utility on a Raspberry Pi, we’ll broaden on the inspiration we laid within the first half, the place we created and examined the core pipeline. Within the first half, we created the core pipeline and examined it to make sure every part labored as anticipated. Now, we’re going to take issues a step additional by constructing a FastAPI utility to serve our RAG pipeline and making a Reflex app to offer customers a easy and interactive solution to entry it. This half will information you thru establishing the FastAPI back-end, designing the front-end with Reflex, and getting every part up and working in your Raspberry Pi. By the top, you’ll have an entire, working utility that’s prepared for real-world use.

Studying Targets

• Arrange a FastAPI back-end to combine with the prevailing RAG pipeline and course of queries effectively.
• Design a user-friendly interface utilizing Reflex to work together with the FastAPI back-end and the RAG pipeline.
• Create and check API endpoints for querying and doc ingestion, guaranteeing easy operation with FastAPI.
• Deploy and check the whole utility on a Raspberry Pi, guaranteeing each back-end and front-end parts perform seamlessly.
• Perceive the mixing between FastAPI and Reflex for a cohesive RAG utility expertise.
• Implement and troubleshoot FastAPI and Reflex parts to supply a completely operational RAG utility on a Raspberry Pi.

For those who missed the earlier version, you should definitely test it out right here: Self-Internet hosting RAG Purposes on Edge Gadgets with Langchain and Ollama – Half I.

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

Creating Python Atmosphere

Earlier than we begin with creating the appliance we have to setup the setting. Create an setting and set up the under dependencies:

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
reflex==0.5.6
reflex-hosting-cli==0.1.13

As soon as the required packages are put in, we have to have the required fashions current within the system. We are going to do that utilizing Ollama. Comply with the steps from Half-1 of this text to obtain each the language and embedding fashions. Lastly, create two directories for the back-end and front-end functions.

As soon as the fashions are pulled utilizing Ollama, we’re able to construct the ultimate utility.

Creating the Again-Finish with FastAPI

Within the Half-1 of this text, we now have constructed the RAG pipeline having each the Ingestion and QnA modules. Now we have examined each the pipelines utilizing some paperwork and so they have been completely working. Now we have to wrap the pipeline with FastAPI to create consumable API. This can assist us combine it with any front-end utility like Streamlit, Chainlit, Gradio, Reflex, React, Angular and so forth. Let’s begin by constructing a construction for the appliance. Following the construction is totally optionally available, however be certain that to verify the dependency imports if you happen to observe a unique construction to create the app.

Under is the tree construction we’ll observe:

backend
├── app.py
├── necessities.txt
└── src
    ├── config.py
    ├── doc_loader
    │   ├── base_loader.py
    │   ├── __init__.py
    │   └── pdf_loader.py
    ├── ingestion.py
    ├── __init__.py
    └── qna.py

Let’s begin with the config.py. This file will include all of the configurable choices for the appliance, just like the Ollama URL, LLM identify and the embeddings mannequin identify. Under is an instance:

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

The base_loader.py file incorporates the mother or father doc loader class that will probably be inherited by kids doc loader. On this utility we’re solely working with PDF information, so a Baby PDFLoader class will probably be
created that can inherit the BaseLoader class.

Under are the contents of base_loader.py and pdf_loader.py:

# base_loader.py
from abc import ABC, abstractmethod

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

    @abstractmethod
    async def load_document(self):
        go


# pdf_loader.py
import os

from .base_loader import BaseLoader
from langchain.schema import Doc
from langchain.document_loaders.pdf import PyMuPDFLoader
from langchain.text_splitter import CharacterTextSplitter


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

Now we have mentioned the working of pdf_loader within the Half-1 of the article.

Subsequent, let’s construct the Ingestion class. That is identical because the one we constructed within the Half-1 of this text.

Code for 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:
    """Document 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="data/text_vectorstore",
                embedding=self.embeddings,
                num_workers=4,
                verbose=False,
            )
        besides Exception as e:
            elevate RuntimeError(f"Failed to 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()
            measurement = await self.vector_store.aadd_documents(paperwork=chunks)
            return len(measurement)
        besides (ValueError, RuntimeError, KeyError, TypeError) as e:
            elevate Exception(f"ERROR: {e}")

Now that we now have setup the Ingestion class, we’ll go ahead with creating the QnA class. This too is identical because the one we created within the Half-1 of this text.

Code for QnA Class

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:
    """Document 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="data/text_vectorstore",
                embedding=self.embeddings,
                num_workers=4,
                verbose=False,
            )
            self.retriever = self.vector_store.as_retriever(
                search_type="similarity",
                search_kwargs={
                    "k": 10,
                },
            )
        besides Exception as e:
            elevate RuntimeError(f"Failed to initialize Ingestion system. ERROR: {e}")

    def create_rag_chain(self):
        attempt:
            system_prompt = """<Instructions>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"Failed to create retrieval chain. ERROR: {e}")

With this we now have completed creating the code functionalities of the RAG app. Now let’s wrap the app with FastAPI.

Code for the FastAPI Software

import sys
import os
import uvicorn

from src import QnA, Ingestion
from fastapi import FastAPI, Request, File, UploadFile
from fastapi.responses import StreamingResponse

app = FastAPI()

ingestion = Ingestion()
chatbot = QnA()
rag_chain = chatbot.create_rag_chain()


@app.get("https://www.analyticsvidhya.com/")
def hi there():
    return {"message": "API Running in server 8089"}


@app.publish("/query")
async def ask_query(request: Request):
    information = await request.json()
    query = information.get("question")

    async def event_generator():
        for chunk in rag_chain.choose("answer").stream({"input": query}):
            yield chunk

    return StreamingResponse(event_generator(), media_type="text/plain")


@app.publish("/ingest")
async def ingest_document(file: UploadFile = File(...)):
    attempt:
        os.makedirs("files", exist_ok=True)
        file_location = f"files/{file.filename}"
        with open(file_location, "wb+") as file_object:
            file_object.write(file.file.learn())

        measurement = await ingestion.create_and_add_embeddings(file=file_location)
        return {"message": f"File ingested! Document count: {size}"}
    besides Exception as e:
        return {"message": f"An error occured: {e}"}


if __name__ == "__main__":
    attempt:
        uvicorn.run(app, host="0.0.0.0", port=8089)
    besides KeyboardInterrupt as e:
        print("App stopped!")

Let’s breakdown the app by every endpoints:

• First we initialize the FastAPI app, the Ingestion and the QnA objects. We then create a RAG chain utilizing the create_rag_chain methodology of QnA class.
• Our first endpoint is a straightforward GET methodology. This can assist us know whether or not the app is wholesome or not. Consider it like a ‘Hello World’ endpoint.
• The second is the question endpoint. This can be a POST methodology and will probably be used to run the chain. It takes in a request parameter, from which we extract the consumer’s question. Then we create a asynchronous methodology that acts as an asynchronous wrapper across the chain.stream perform name. We have to do that to permit FastAPI to deal with the LLM’s stream perform name, to get a ChatGPT-like expertise within the chat interface. We then wrap the asynchronous methodology with StreamingResponse class and return it.
• The third endpoint is the ingestion endpoint. It is also a POST methodology that takes in the whole file as bytes as enter. We retailer this file within the native listing after which ingest it utilizing the create_and_add_embeddings methodology of Ingestion class.

Lastly, we run the app utilizing uvicorn package deal, utilizing host and port. To check the app, merely run the appliance utilizing the next command:

python app.py

Use a API testing IDE like Postman, Insomnia or Bruno for testing the appliance. You may also use Thunder Consumer extension to do the identical.

Testing the Ingestion endpoint:

Testing the question endpoint:

Designing the Entrance-Finish with Reflex

Now we have efficiently created a FastAPI app for the backend of our RAG utility. It’s time to construct our front-end. You may selected any front-end library for this, however for this specific article we’ll construct the front-end utilizing Reflex. Reflex is a python-only front-end library, created to construct net functions, purely utilizing python. It proves us with templates for widespread functions like calculator, picture technology and chatbot. We are going to use the chatbot utility template as a begin for our consumer interface. Our closing app can have the next construction, so let’s have it right here for reference.

Frontend Listing

We can have a frontend listing for this:

frontend
├── belongings
│   └── favicon.ico
├── docs
│   └── demo.gif
├── chat
│   ├── parts
│   │   ├── chat.py
│   │   ├── file_upload.py
│   │   ├── __init__.py
│   │   ├── loading_icon.py
│   │   ├── modal.py
│   │   └── navbar.py
│   ├── __init__.py
│   ├── chat.py
│   └── state.py
├── necessities.txt
├── rxconfig.py
└── uploaded_files

Steps for Remaining App

Comply with the steps to arrange the grounding for the ultimate app.

Step1: Clone the chat template repository within the frontend listing

git clone https://github.com/reflex-dev/reflex-chat.git .

Step2: Run the next command to initialize the listing as a reflex app

reflex init

This can setup the reflex app and will probably be able to run and develop. 

Step3: Check the app, use the next command from contained in the frontend listing

reflex run

Let’s begin modifying the parts. First let’s modify the chat.py file.

Under is the code for a similar:

import reflex as rx
from reflex_demo.parts import loading_icon
from reflex_demo.state import QA, State

message_style = dict(
    show="inline-block",
    padding="0 10px",
    border_radius="8px",
    max_width=["30em", "30em", "50em", "50em", "50em", "50em"],
)


def message(qa: QA) -> rx.Element:
    """A single query/reply message.

    Args:
        qa: The query/reply pair.

    Returns:
        A element displaying the query/reply pair.
    """
    return rx.field(
        rx.field(
            rx.markdown(
                qa.query,
                background_color=rx.colour("mauve", 4),
                colour=rx.colour("mauve", 12),
                **message_style,
            ),
            text_align="right",
            margin_top="1em",
        ),
        rx.field(
            rx.markdown(
                qa.reply,
                background_color=rx.colour("accent", 4),
                colour=rx.colour("accent", 12),
                **message_style,
            ),
            text_align="left",
            padding_top="1em",
        ),
        width="100%",
    )


def chat() -> rx.Element:
    """List all the messages in a single conversation."""
    return rx.vstack(
        rx.field(rx.foreach(State.chats[State.current_chat], message), width="100%"),
        py="8",
        flex="1",
        width="100%",
        max_width="50em",
        padding_x="4px",
        align_self="center",
        overflow="hidden",
        padding_bottom="5em",
    )


def action_bar() -> rx.Element:
    """The action bar to send a new message."""
    return rx.middle(
        rx.vstack(
            rx.chakra.kind(
                rx.chakra.form_control(
                    rx.hstack(
                        rx.enter(
                            rx.enter.slot(
                                rx.tooltip(
                                    rx.icon("info", measurement=18),
                                    content material="Enter a question to get a response.",
                                )
                            ),
                            placeholder="Type something...",
                            id="question",
                            width=["15em", "20em", "45em", "50em", "50em", "50em"],
                        ),
                        rx.button(
                            rx.cond(
                                State.processing,
                                loading_icon(top="1em"),
                                rx.textual content("Send", font_family="Ubuntu"),
                            ),
                            sort="submit",
                        ),
                        align_items="center",
                    ),
                    is_disabled=State.processing,
                ),
                on_submit=State.process_question,
                reset_on_submit=True,
            ),
            rx.textual content(
                "ReflexGPT may return factually incorrect or misleading responses. Use discretion.",
                text_align="center",
                font_size=".75em",
                colour=rx.colour("mauve", 10),
                font_family="Ubuntu",
            ),
            rx.emblem(margin_top="-1em", margin_bottom="-1em"),
            align_items="center",
        ),
        place="sticky",
        backside="0",
        left="0",
        padding_y="16px",
        backdrop_filter="auto",
        backdrop_blur="lg",
        border_top=f"1px solid {rx.color('mauve', 3)}",
        background_color=rx.colour("mauve", 2),
        align_items="stretch",
        width="100%",
    )

The adjustments are minimal from the one current natively within the template.

Subsequent, we’ll edit the chat.py app. That is the primary chat element.

Code for Primary Chat Element

Under is the code for it:

import reflex as rx
from reflex_demo.parts import chat, navbar, upload_form
from reflex_demo.state import State


@rx.web page(route="/chat", title="RAG Chatbot")
def chat_interface() -> rx.Element:
    return rx.chakra.vstack(
        navbar(),
        chat.chat(),
        chat.action_bar(),
        background_color=rx.colour("mauve", 1),
        colour=rx.colour("mauve", 12),
        min_height="100vh",
        align_items="stretch",
        spacing="0",
    )


@rx.web page(route="https://www.analyticsvidhya.com/", title="RAG Chatbot")
def index() -> rx.Element:
    return rx.chakra.vstack(
        navbar(),
        upload_form(),
        background_color=rx.colour("mauve", 1),
        colour=rx.colour("mauve", 12),
        min_height="100vh",
        align_items="stretch",
        spacing="0",
    )


# Add state and web page to the app.
app = rx.App(
    theme=rx.theme(
        look="dark",
        accent_color="jade",
    ),
    stylesheets=["https://fonts.googleapis.com/css2?family=Ubuntu&display=swap"],
    type={
        "font_family": "Ubuntu",
    },
)
app.add_page(index)
app.add_page(chat_interface)

That is the code for the chat interface. Now we have solely added the Font household to the app config, the remainder of the code is identical.

Subsequent let’s edit the state.py file. That is the place the frontend will make name to the API endpoints for response.

Enhancing state.py File

import requests
import reflex as rx


class QA(rx.Base):
    query: str
    reply: str


DEFAULT_CHATS = {
    "Intros": [],
}


class State(rx.State):
    chats: dict[str, list[QA]] = DEFAULT_CHATS
    current_chat = "Intros"
    url: str = "http://localhost:8089/query"
    query: str
    processing: bool = False
    new_chat_name: str = ""

    def create_chat(self):
        """Create a new chat."""
        # Add the brand new chat to the checklist of chats.
        self.current_chat = self.new_chat_name
        self.chats[self.new_chat_name] = []

    def delete_chat(self):
        """Delete the current chat."""
        del self.chats[self.current_chat]
        if len(self.chats) == 0:
            self.chats = DEFAULT_CHATS
        self.current_chat = checklist(self.chats.keys())[0]

    def set_chat(self, chat_name: str):
        """Set the identify of the present chat.

        Args:
            chat_name: The identify of the chat.
        """
        self.current_chat = chat_name

    @rx.var
    def chat_titles(self) -> checklist[str]:
        """Get the checklist of chat titles.

        Returns:
            The checklist of chat names.
        """
        return checklist(self.chats.keys())

    async def process_question(self, form_data: dict[str, str]):
        # Get the query from the shape
        query = form_data["question"]

        # Test if the query is empty
        if query == "":
            return

        mannequin = self.openai_process_question

        async for worth in mannequin(query):
            yield worth

    async def openai_process_question(self, query: str):
        """Get the response from the API.

        Args:
            form_data: A dict with the present query.
        """
        # Add the query to the checklist of questions.
        qa = QA(query=query, reply="")
        self.chats[self.current_chat].append(qa)
        payload = {"question": query}

        # Clear the enter and begin the processing.
        self.processing = True
        yield

        response = requests.publish(self.url, json=payload, stream=True)

        # Stream the outcomes, yielding after each phrase.
        for answer_text in response.iter_content(chunk_size=512):
            # Guarantee answer_text shouldn't be None earlier than concatenation
            answer_text = answer_text.decode()
            if answer_text shouldn't be None:
                self.chats[self.current_chat][-1].reply += answer_text
            else:
                answer_text = ""
                self.chats[self.current_chat][-1].reply += answer_text
            self.chats = self.chats
            yield

        # Toggle the processing flag.
        self.processing = False

On this file, we now have outlined the URL for the question endpoint. Now we have additionally modified the openai_process_question methodology to ship a POST request to the question endpoint and get the streaming
response, which will probably be displayed within the chat interface.

Writing Contents of the file_upload.py File

Lastly, let’s write the contents of the file_upload.py file. This element will probably be displayed at first which is able to permit us to add the file for ingestion.

import reflex as rx
import os
import time

import requests


class UploadExample(rx.State):
    importing: bool = False
    ingesting: bool = False
    progress: int = 0
    total_bytes: int = 0
    ingestion_url = "http://127.0.0.1:8089/ingest"

    async def handle_upload(self, information: checklist[rx.UploadFile]):
        self.ingesting = True
        yield
        for file in information:
            file_bytes = await file.learn()
            file_name = file.filename
            information = {
                "file": (os.path.basename(file_name), file_bytes, "multipart/form-data")
            }
            response = requests.publish(self.ingestion_url, information=information)
            self.ingesting = False
            yield
            if response.status_code == 200:
                # yield rx.redirect("/chat")
                self.show_redirect_popup()

    def handle_upload_progress(self, progress: dict):
        self.importing = True
        self.progress = spherical(progress["progress"] * 100)
        if self.progress >= 100:
            self.importing = False

    def cancel_upload(self):
        self.importing = False
        return rx.cancel_upload("upload3")


def upload_form():
    return rx.vstack(
        rx.add(
            rx.flex(
                rx.textual content(
                    "Drag and drop file here or click to select file",
                    font_family="Ubuntu",
                ),
                rx.icon("upload", measurement=30),
                route="column",
                align="center",
            ),
            id="upload3",
            border="1px solid rgb(233, 233,233, 0.4)",
            margin="5em 0 10px 0",
            background_color="rgb(107,99,246)",
            border_radius="8px",
            padding="1em",
        ),
        rx.vstack(rx.foreach(rx.selected_files("upload3"), rx.textual content)),
        rx.cond(
            ~UploadExample.ingesting,
            rx.button(
                "Upload",
                on_click=UploadExample.handle_upload(
                    rx.upload_files(
                        upload_id="upload3",
                        on_upload_progress=UploadExample.handle_upload_progress,
                    ),
                ),
            ),
            rx.flex(
                rx.spinner(measurement="3", loading=UploadExample.ingesting),
                rx.button(
                    "Cancel",
                    on_click=UploadExample.cancel_upload,
                ),
                align="center",
                spacing="3",
            ),
        ),
        rx.alert_dialog.root(
            rx.alert_dialog.set off(
                rx.button("Continue to Chat", color_scheme="green"),
            ),
            rx.alert_dialog.content material(
                rx.alert_dialog.title("Redirect to Chat Interface?"),
                rx.alert_dialog.description(
                    "You will be redirected to the Chat Interface.",
                    measurement="2",
                ),
                rx.flex(
                    rx.alert_dialog.cancel(
                        rx.button(
                            "Cancel",
                            variant="soft",
                            color_scheme="gray",
                        ),
                    ),
                    rx.alert_dialog.motion(
                        rx.button(
                            "Continue",
                            color_scheme="green",
                            variant="solid",
                            on_click=rx.redirect("/chat"),
                        ),
                    ),
                    spacing="3",
                    margin_top="16px",
                    justify="end",
                ),
                type={"max_width": 450},
            ),
        ),
        align="center",
    )

This element will permit us to add a file and ingest it into the vector retailer. It makes use of the ingest endpoint of our FastAPI app to add and ingest the file. After ingestion, the consumer can merely transfer
to the chat interface for asking queries.

With this we now have accomplished constructing the front-end for our utility. Now we might want to check the appliance utilizing some doc.

Testing and Deployment

Now let’s check the appliance on some manuals or paperwork. To make use of the appliance, we have to run each the back-end app and the reflex app individually. Run the back-end app from it’s listing utilizing the
following command:

python app.py

Look forward to the FastAPI to start out working. Then in one other terminal occasion run the front-end app utilizing the next command:

reflex run

One the apps are up and working, acquired to the following URL to entry the reflex app. Initially we’d be within the File Add web page. Add a file and press the add button.

The file will probably be uploaded and ingested. This can take some time relying on the doc measurement and
the system specs. As soon as it’s completed, click on on the ‘Continue to Chat’ button to maneuver to the chat interface. Write your question and press Ship.

Conclusion

On this two half collection, you’ve now constructed an entire and useful RAG utility on a Raspberry Pi, from creating the core pipeline to wrapping it with a FastAPI back-end and creating a Reflex-based front-end. With these instruments, your RAG pipeline is accessible and interactive, offering real-time question processing by means of a user-friendly net interface. By mastering these steps, you’ve gained invaluable expertise in constructing and deploying end-to-end functions on a compact, environment friendly platform. This setup opens the door to numerous prospects for deploying AI-driven functions on resource-constrained gadgets just like the Raspberry Pi, making cutting-edge expertise extra accessible and sensible for on a regular basis use.

Key Takeaways

• An in depth information is supplied on establishing the event setting, together with putting in mandatory dependencies and fashions utilizing Ollama, guaranteeing the appliance is prepared for the ultimate construct.
• The article explains how you can wrap the RAG pipeline in a FastAPI utility, together with establishing endpoints for querying the mannequin and ingesting paperwork, making the pipeline accessible through an online API.
• The front-end of the RAG utility is constructed utilizing Reflex, a Python-only front-end library. The article demonstrates how you can modify the chat utility template to create a user-friendly interface for interacting with the RAG pipeline.
• The article guides on integrating the FastAPI backend with the Reflex front-end and deploying the whole utility on a Raspberry Pi, guaranteeing seamless operation and consumer accessibility.
• Sensible steps are supplied for testing each the ingestion and question endpoints utilizing instruments like Postman or Thunder Consumer, together with working and testing the Reflex front-end to make sure the whole utility features as anticipated.

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