Self Internet hosting RAG Purposes On Edge Units 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 muse 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 present customers a easy and interactive option to entry it. This half will information you thru organising 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 a whole, working utility that’s prepared for real-world use.

Studying Targets

• 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 entire utility on a Raspberry Pi, guaranteeing each back-end and front-end parts perform seamlessly.
• Perceive the combination between FastAPI and Reflex for a cohesive RAG utility expertise.
• Implement and troubleshoot FastAPI and Reflex parts to offer a totally operational RAG utility on a Raspberry Pi.

For those who 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 Atmosphere

Earlier than we begin with creating the applying we have to setup the atmosphere. Create an atmosphere and set up the beneath 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’ll do that utilizing Ollama. Observe 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.

Growing the Again-Finish with FastAPI

Within the Half-1 of this text, we have now constructed the RAG pipeline having each the Ingestion and QnA modules. We have now examined each the pipelines utilizing some paperwork they usually have 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 utility like Streamlit, Chainlit, Gradio, Reflex, React, Angular and so on. Let’s begin by constructing a construction for the applying. Following the construction is totally optionally available, however ensure to examine the dependency imports should you observe a special construction to create the app.

Beneath is the tree construction we are going to 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 applying, just like the Ollama URL, LLM identify and the embeddings mannequin identify. Beneath 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 dad or mum doc loader class that shall be inherited by youngsters doc loader. On this utility we’re solely working with PDF information, so a Little one PDFLoader class shall be
created that can inherit the BaseLoader class.

Beneath 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

We have now 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):
        strive:
            self.embeddings = OllamaEmbeddings(
                mannequin=cfg.EMBEDDINGS_MODEL_NAME,
                base_url=cfg.OLLAMA_URL,
                show_progress=True,
            )
            self.vector_store = DeepLake(
                dataset_path="information/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,
    ):
        strive:
            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:
            increase Exception(f"ERROR: {e}")

Now that we have now 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):
        strive:
            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="information/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:
            increase RuntimeError(f"Did not initialize Ingestion system. ERROR: {e}")

    def create_rag_chain(self):
        strive:
            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 we have now 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 hey():
    return {"message": "API Working in server 8089"}


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

        dimension = await ingestion.create_and_add_embeddings(file=file_location)
        return {"message": f"File ingested! Doc depend: {dimension}"}
    besides Exception as e:
        return {"message": f"An error occured: {e}"}


if __name__ == "__main__":
    strive:
        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 ‘Hey World’ endpoint.
• The second is the question endpoint. This can be a POST methodology and shall 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 your complete 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 bundle, utilizing host and port. To check the app, merely run the applying utilizing the next command:

python app.py

Use a API testing IDE like Postman, Insomnia or Bruno for testing the applying. You too can use Thunder Shopper extension to do the identical.

Testing the Ingestion endpoint:

Testing the question endpoint:

Designing the Entrance-Finish with Reflex

We have now efficiently created a FastAPI app for the backend of our RAG utility. It’s time to construct our front-end. You possibly can selected any front-end library for this, however for this explicit article we are going to 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’ll use the chatbot utility template as a begin for our consumer interface. Our ultimate 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
├── 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 Last App

Observe 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 shall 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.

Beneath 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.Part:
    """A single query/reply message.

    Args:
        qa: The query/reply pair.

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


def chat() -> rx.Part:
    """Listing 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.Part:
    """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", dimension=18),
                                    content material="Enter a query to get a response.",
                                )
                            ),
                            placeholder="Sort one thing...",
                            id="query",
                            width=["15em", "20em", "45em", "50em", "50em", "50em"],
                        ),
                        rx.button(
                            rx.cond(
                                State.processing,
                                loading_icon(top="1em"),
                                rx.textual content("Ship", font_family="Ubuntu"),
                            ),
                            kind="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",
                coloration=rx.coloration("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 stable {rx.coloration('mauve', 3)}",
        background_color=rx.coloration("mauve", 2),
        align_items="stretch",
        width="100%",
    )

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

Subsequent, we are going to edit the chat.py app. That is the primary chat part.

Code for Important Chat Part

Beneath 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.Part:
    return rx.chakra.vstack(
        navbar(),
        chat.chat(),
        chat.action_bar(),
        background_color=rx.coloration("mauve", 1),
        coloration=rx.coloration("mauve", 12),
        min_height="100vh",
        align_items="stretch",
        spacing="0",
    )


@rx.web page(route="https://www.analyticsvidhya.com/", title="RAG Chatbot")
def index() -> rx.Part:
    return rx.chakra.vstack(
        navbar(),
        upload_form(),
        background_color=rx.coloration("mauve", 1),
        coloration=rx.coloration("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 have now solely added the Font household to the app config, the remainder of the code is similar.

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

Modifying 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 listing 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 = listing(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) -> listing[str]:
        """Get the listing of chat titles.

        Returns:
            The listing of chat names.
        """
        return listing(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 listing 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 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 have now outlined the URL for the question endpoint. We have now additionally modified the openai_process_question methodology to ship a POST request to the question endpoint and get the streaming
response, which shall 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 part shall be displayed to start with which can 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: listing[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", dimension=30),
                route="column",
                align="heart",
            ),
            id="upload3",
            border="1px stable 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(dimension="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.",
                    dimension="2",
                ),
                rx.flex(
                    rx.alert_dialog.cancel(
                        rx.button(
                            "Cancel",
                            variant="tender",
                            color_scheme="grey",
                        ),
                    ),
                    rx.alert_dialog.motion(
                        rx.button(
                            "Proceed",
                            color_scheme="inexperienced",
                            variant="stable",
                            on_click=rx.redirect("/chat"),
                        ),
                    ),
                    spacing="3",
                    margin_top="16px",
                    justify="finish",
                ),
                model={"max_width": 450},
            ),
        ),
        align="heart",
    )

This part 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 have now accomplished constructing the front-end for our utility. Now we might want to take a look at the applying utilizing some doc.

Testing and Deployment

Now let’s take a look at the applying on some manuals or paperwork. To make use of the applying, 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

Anticipate 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, bought to the following 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 shall be uploaded and ingested. It will take some time relying on the doc dimension and
the system specs. As soon as it’s finished, 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 a whole and useful RAG utility on a Raspberry Pi, from creating the core pipeline to wrapping it with a FastAPI back-end and growing a Reflex-based front-end. With these instruments, your RAG pipeline is accessible and interactive, offering real-time question processing by a user-friendly net interface. By mastering these steps, you’ve gained useful expertise in constructing and deploying end-to-end functions on a compact, environment friendly platform. This setup opens the door to numerous potentialities for deploying AI-driven functions on resource-constrained gadgets 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 offered on organising the event atmosphere, together with putting in needed dependencies and fashions utilizing Ollama, guaranteeing the applying is prepared for the ultimate construct.
• The article explains the way to wrap the RAG pipeline in a FastAPI utility, together with organising 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 the way to 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 entire utility on a Raspberry Pi, guaranteeing seamless operation and consumer accessibility.
• Sensible steps are offered 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 your complete utility features as anticipated.

Often Requested Query

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