Self Hosting RAG Applications On Edge Devices with Langchain and Ollama–Part II

Introduction 

Within the second a part of our collection on constructing a RAG software on a Raspberry Pi, we’ll develop 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 all the pieces labored as anticipated. Now, we’re going to take issues a step additional by constructing a FastAPI software to serve our RAG pipeline and making a Reflex app to offer customers a easy and interactive option to entry it. This half will information you thru establishing the FastAPI back-end, designing the front-end with Reflex, and getting all the pieces up and working in your Raspberry Pi. By the top, you’ll have an entire, working software that’s prepared for real-world use.

Studying Aims

• Arrange a FastAPI back-end to combine with the present 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 take a look at API endpoints for querying and doc ingestion, guaranteeing clean operation with FastAPI.
• Deploy and take a look at the whole software on a Raspberry Pi, guaranteeing each back-end and front-end parts operate seamlessly.
• Perceive the mixing between FastAPI and Reflex for a cohesive RAG software expertise.
• Implement and troubleshoot FastAPI and Reflex parts to supply a completely operational RAG software on a Raspberry Pi.

In the event you missed the earlier version, you’ll want to test it out right here: Self-Internet hosting RAG Purposes on Edge Units with Langchain and Ollama – Half I.

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

Creating Python Setting

Earlier than we begin with creating the appliance we have to setup the atmosphere. Create an atmosphere 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 gadget. We’ll 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 purposes.

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

Growing the Again-Finish with FastAPI

Within the Half-1 of this text, now we have constructed the RAG pipeline having each the Ingestion and QnA modules. We’ve examined each the pipelines utilizing some paperwork and so they had been completely working. Now we have to wrap the pipeline with FastAPI to create consumable API. It will assist us combine it with any front-end software like Streamlit, Chainlit, Gradio, Reflex, React, Angular and so on. Let’s begin by constructing a construction for the appliance. Following the construction is totally optionally available, however ensure to examine the dependency imports in the event you comply with a special construction to create the app.

Under is the tree construction we’ll comply with:

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 accommodates the mum or dad doc loader class that can be inherited by youngsters doc loader. On this software we’re solely working with PDF information, so a Little one PDFLoader class can be
created that may 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):
        move


# 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

We’ve mentioned the working of pdf_loader within the Half-1 of the article.

Subsequent, let’s construct the Ingestion class. That is similar 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:
    """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:
            increase 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()
            measurement = await self.vector_store.aadd_documents(paperwork=chunks)
            return len(measurement)
        besides (ValueError, RuntimeError, KeyError, TypeError) as e:
            increase Exception(f"ERROR: {e}")

Now that now we have setup the Ingestion class, we’ll go ahead with creating the QnA class. This too is similar 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:
    """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={
                    "okay": 10,
                },
            )
        besides Exception as e:
            increase 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:
            increase RuntimeError(f"Did not create retrieval chain. ERROR: {e}")

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

Code for the FastAPI Utility

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 hiya():
    return {"message": "API Operating in server 8089"}


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

    async def event_generator():
        for chunk in rag_chain.decide("reply").stream({"enter": query}):
            yield chunk

    return StreamingResponse(event_generator(), media_type="textual content/plain")


@app.submit("/ingest")
async def ingest_document(file: UploadFile = File(...)):
    attempt:
        os.makedirs("information", exist_ok=True)
        file_location = f"information/{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! Doc depend: {measurement}"}
    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. It will assist us know whether or not the app is wholesome or not. Consider it like a ‘Good day World’ endpoint.
• The second is the question endpoint. It is a POST methodology and can be used to run the chain. It takes in a request parameter, from which we extract the person’s question. Then we create a asynchronous methodology that acts as an asynchronous wrapper across the chain.stream operate name. We have to do that to permit FastAPI to deal with the LLM’s stream operate 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. It’s also possible to use Thunder Shopper extension to do the identical.

Testing the Ingestion endpoint:

Testing the question endpoint:

Designing the Entrance-Finish with Reflex

We’ve efficiently created a FastAPI app for the backend of our RAG software. It’s time to construct our front-end. You may selected any front-end library for this, however for this explicit article we’ll construct the front-end utilizing Reflex. Reflex is a python-only front-end library, created to construct internet purposes, purely utilizing python. It proves us with templates for widespread purposes like calculator, picture technology and chatbot. We’ll use the chatbot software template as a begin for our person interface. Our ultimate app may have the next construction, so let’s have it right here for reference.

Frontend Listing

We may have a frontend listing for this:

frontend
├── property
│   └── 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

It will setup the reflex app and can 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="proper",
            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:
    """Record all of the messages in a single dialog."""
    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="heart",
        overflow="hidden",
        padding_bottom="5em",
    )


def action_bar() -> rx.Element:
    """The motion bar to ship a brand new message."""
    return rx.heart(
        rx.vstack(
            rx.chakra.type(
                rx.chakra.form_control(
                    rx.hstack(
                        rx.enter(
                            rx.enter.slot(
                                rx.tooltip(
                                    rx.icon("information", measurement=18),
                                    content material="Enter a query to get a response.",
                                )
                            ),
                            placeholder="Kind one thing...",
                            id="query",
                            width=["15em", "20em", "45em", "50em", "50em", "50em"],
                        ),
                        rx.button(
                            rx.cond(
                                State.processing,
                                loading_icon(peak="1em"),
                                rx.textual content("Ship", font_family="Ubuntu"),
                            ),
                            sort="submit",
                        ),
                        align_items="heart",
                    ),
                    is_disabled=State.processing,
                ),
                on_submit=State.process_question,
                reset_on_submit=True,
            ),
            rx.textual content(
                "ReflexGPT could return factually incorrect or deceptive responses. Use discretion.",
                text_align="heart",
                font_size=".75em",
                colour=rx.colour("mauve", 10),
                font_family="Ubuntu",
            ),
            rx.brand(margin_top="-1em", margin_bottom="-1em"),
            align_items="heart",
        ),
        place="sticky",
        backside="0",
        left="0",
        padding_y="16px",
        backdrop_filter="auto",
        backdrop_blur="lg",
        border_top=f"1px strong {rx.colour('mauve', 3)}",
        background_color=rx.colour("mauve", 2),
        align_items="stretch",
        width="100%",
    )

The modifications 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 Principal 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="darkish",
        accent_color="jade",
    ),
    stylesheets=["https://fonts.googleapis.com/css2?family=Ubuntu&display=swap"],
    model={
        "font_family": "Ubuntu",
    },
)
app.add_page(index)
app.add_page(chat_interface)

That is the code for the chat interface. We’ve 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/question"
    query: str
    processing: bool = False
    new_chat_name: str = ""

    def create_chat(self):
        """Create a brand 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 present 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"]

        # Examine 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 = {"query": query}

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

        response = requests.submit(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 is just not None earlier than concatenation
            answer_text = answer_text.decode()
            if answer_text is just not 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, now we have outlined the URL for the question endpoint. We’ve additionally modified the openai_process_question methodology to ship a POST request to the question endpoint and get the streaming
response, which can 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 can 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.submit(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 right here or click on to pick out file",
                    font_family="Ubuntu",
                ),
                rx.icon("add", measurement=30),
                course="column",
                align="heart",
            ),
            id="upload3",
            border="1px strong 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(
                "Add",
                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="heart",
                spacing="3",
            ),
        ),
        rx.alert_dialog.root(
            rx.alert_dialog.set off(
                rx.button("Proceed to Chat", color_scheme="inexperienced"),
            ),
            rx.alert_dialog.content material(
                rx.alert_dialog.title("Redirect to Chat Interface?"),
                rx.alert_dialog.description(
                    "You may be redirected to the Chat Interface.",
                    measurement="2",
                ),
                rx.flex(
                    rx.alert_dialog.cancel(
                        rx.button(
                            "Cancel",
                            variant="gentle",
                            color_scheme="grey",
                        ),
                    ),
                    rx.alert_dialog.motion(
                        rx.button(
                            "Proceed",
                            color_scheme="inexperienced",
                            variant="strong",
                            on_click=rx.redirect("/chat"),
                        ),
                    ),
                    spacing="3",
                    margin_top="16px",
                    justify="finish",
                ),
                model={"max_width": 450},
            ),
        ),
        align="heart",
    )

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 person can merely transfer
to the chat interface for asking queries.

With this now we have accomplished constructing the front-end for our software. Now we might want to take a look at the appliance utilizing some doc.

Testing and Deployment

Now let’s take a look at 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

Await 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, received to the next URL to entry the reflex app. Initially we might be within the File Add web page. Add a file and press the add button.

The file can be uploaded and ingested. It will take some time relying on the doc measurement and
the gadget specs. As soon as it’s executed, click on on the ‘Proceed 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 purposeful RAG software 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 way of a user-friendly internet interface. By mastering these steps, you’ve gained precious expertise in constructing and deploying end-to-end purposes on a compact, environment friendly platform. This setup opens the door to numerous prospects for deploying AI-driven purposes on resource-constrained units just like the Raspberry Pi, making cutting-edge know-how extra accessible and sensible for on a regular basis use.

Key Takeaways

• An in depth information is supplied on establishing the event atmosphere, together with putting in crucial dependencies and fashions utilizing Ollama, guaranteeing the appliance is prepared for the ultimate construct.
• The article explains the best way to wrap the RAG pipeline in a FastAPI software, together with establishing endpoints for querying the mannequin and ingesting paperwork, making the pipeline accessible through an internet API.
• The front-end of the RAG software is constructed utilizing Reflex, a Python-only front-end library. The article demonstrates the best way to modify the chat software 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 software on a Raspberry Pi, guaranteeing seamless operation and person accessibility.
• Sensible steps are supplied for testing each the ingestion and question endpoints utilizing instruments like Postman or Thunder Shopper, together with working and testing the Reflex front-end to make sure the whole software capabilities as anticipated.

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