Constructing a RAG-based Question Decision System with LangChain

Companies right now deal with a big quantity of queries from prospects, gross sales groups, and inner stakeholders. Manually responding to those queries is a gradual and inefficient course of, typically resulting in delays and inconsistent solutions. A question decision system powered by AI ensures quick, correct, and scalable responses. It really works by retrieving related info and producing exact solutions utilizing Retrieval-Augmented Era (RAG). On this article, I can be sharing with you my journey of constructing a RAG-based question decision system utilizing LangChain, ChromaDB, and CrewAI.

Why Do We Want an AI-powered Question Decision System?

Now, handbook responses take time and will, subsequently, result in delays. Clients count on on the spot replies, and companies want fast entry to correct info. An AI-driven system automates question dealing with, decreasing workload and enhancing consistency. It enhances productiveness, accelerates decision-making, and gives dependable responses throughout completely different sectors.

An AI-powered question decision system is helpful in buyer assist, the place it automates responses and improves buyer satisfaction. In gross sales and advertising and marketing, it gives real-time product particulars and buyer insights. Industries like finance, healthcare, training, and e-commerce profit from automated question dealing with, guaranteeing clean operations and higher person experiences.

Understanding the RAG Workflow

Earlier than diving into the implementation, let’s first perceive how a Retrieval-Augmented Era (RAG) system works.

The structure consists of three key levels: Indexing, Retrieval, and Era.

1. Constructing a Vector Retailer (Doc Processing & Storage)

The system first processes and shops related paperwork to make them simply searchable. Right here’s how the indexing course of works:

• Paperwork & Chunking: Massive paperwork are damaged into smaller textual content chunks for environment friendly retrieval.
• Embedding Mannequin: These textual content chunks are transformed into vector representations utilizing an AI-based embedding mannequin.
• Vector Retailer: The vectorized knowledge is listed and saved in a database (e.g., ChromaDB) for quick lookup.

2. Question Processing & Retrieval

When a person submits a question, the system retrieves related knowledge earlier than producing a response. Listed below are the steps concerned in question processing and retrieval:

• Consumer Question Enter: The person submits a query or request.
• Vectorization: The question is transformed right into a numerical vector utilizing the embedding mannequin.
• Search & Retrieval: The system searches for essentially the most related chunks within the vector retailer and retrieves them.

3. Augmentation & Response Era

To generate a well-informed response, the system augments the question with retrieved knowledge. Given under are the steps concerned in response technology.

• Increase Question: The retrieved doc chunks are mixed with the unique question.
• LLM Processing: A big language mannequin (LLM) generates a remaining response utilizing each the question and the retrieved context.
• Closing Response: The system gives a factual and context-aware reply to the person.

Now that you understand how RAG methods work, let’s learn to construct a RAG-based question decision system.

Constructing a RAG-based Question Decision System

On this article, I’ll stroll you thru constructing a RAG-based Question Decision System that effectively solutions learner queries utilizing an AI agent. To maintain issues easy, I’ll show a simplified model of the challenge and clarify the way it works.

Deciding on the Proper Information for Question Decision

Earlier than constructing a RAG-based question decision system, crucial issue to think about is knowledge – particularly, the kinds of knowledge required for efficient retrieval. A well-structured data base is important, because the accuracy and relevance of responses depend upon the standard of the information out there. Beneath are the important thing knowledge varieties that needs to be thought-about for various functions:

• Buyer Help Information: FAQs, troubleshooting guides, product manuals, and previous buyer interactions.
• Gross sales & Advertising and marketing Information: Product catalogs, pricing particulars, competitor evaluation, and buyer inquiries.
• Inside Data Base: Firm insurance policies, coaching paperwork, and normal working procedures (SOPs).
• Monetary & Authorized Paperwork: Compliance pointers, monetary reviews, and regulatory insurance policies.
• Consumer-Generated Content material: Discussion board discussions, chat logs, and suggestions varieties that present real-world person queries.

Deciding on the best knowledge sources was essential for our learner question decision system, to make sure correct and related responses. Initially, I experimented with several types of knowledge to find out which offered the most effective outcomes. First, I used PowerPoint slides (PPTs), however they didn’t yield complete solutions as anticipated. Subsequent, I integrated widespread queries, which improved response accuracy however lacked ample context. Then, I examined previous discussions, which helped in making responses extra related by leveraging earlier learner interactions. Nonetheless, the simplest method turned out to be utilizing subtitles from course movies, as they offered structured and detailed content material straight associated to learner queries. This method helps in offering fast and related solutions, making it helpful for e-learning platforms and academic assist methods.

Structuring the Question Decision System

Earlier than coding, you will need to construction the Question Decision System. One of the simplest ways to do that is by defining the important thing duties it must carry out.

The system will deal with three primary duties:

1. Extract and retailer course content material from subtitles (SRT information).
2. Retrieve related course supplies based mostly on learner queries.
3. Use an AI-powered agent to generate structured responses.

To realize this, the system is split into three elements, every dealing with a particular operate. This ensures effectivity and scalability.

The system consists of:

• Subtitle Processing  – Extracts textual content from SRT information, processes it, and shops embeddings in ChromaDB.
• Retrieval  – Searches and retrieves related course supplies based mostly on learner queries.
• Question Answering Agent – Makes use of CrewAI to generate structured and correct responses.

Every element ensures environment friendly question decision, personalised responses, and clean content material retrieval. Now that we’ve got our construction, let’s transfer on to implementation.

Implementation Steps

Now that we’ve got our construction, let’s transfer on to implementation.

1. Importing Libraries

To construct the AI-powered studying assist system, we first must import important libraries.

import pysrt
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Doc
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from crewai import Agent, Process, Crew 
import pandas as pd
import ast

Let’s perceive these libraries.

• pysrt – For extracting textual content from SRT subtitle information.
• langchain.text_splitter.RecursiveCharacterTextSplitter – Splits massive textual content into smaller chunks for higher retrieval.
• langchain.schema.Doc – Represents structured textual content paperwork.
• langchain.embeddings.OpenAIEmbeddings – Converts textual content into numerical vectors for similarity searches.
• langchain.vectorstores.Chroma – Shops embeddings in a vector database for environment friendly retrieval.
• crewai (Agent, Process, Crew) – Defines AI brokers that course of learner queries.
• pandas – Handles structured knowledge within the type of DataFrames.
• ast – Helps in parsing string-based knowledge constructions into Python objects.
• os – Supplies system-level operations like studying atmosphere variables.
• tqdm – Shows progress bars throughout long-running duties.

2. Setting Up the Surroundings

To make use of OpenAI’s API for embeddings, we should load the API key and configure the mannequin settings.

Step 1: Learn the API key from an area textual content file.

with open('/house/janvi/Downloads/openai.txt', 'r') as file:
   openai_api_key = file.learn()

Step 2: Retailer the API key as an atmosphere variable so it may be accessed by different elements.

os.environ['OPENAI_API_KEY'] = openai_api_key

Step3: Specify the OpenAI mannequin for use for processing embeddings.

os.environ["OPENAI_MODEL_NAME"] = 'gpt-4o-mini'

By organising these configurations, we guarantee seamless integration with OpenAI’s API, permitting our system to course of and retailer embeddings effectively.

3. Extracting and Storing Subtitle Information

Subtitles typically comprise helpful insights from video lectures, making them a wealthy supply of structured content material for AI-based retrieval methods. Extracting and processing subtitle knowledge successfully permits for environment friendly search and retrieval of related info when answering learner queries.

To protect academic insights, we’re utilizing pysrt to learn and preprocess textual content from SRT information. This ensures the extracted content material is structured and prepared for additional processing and storage..

def extract_text_from_srt(srt_path):
   """Extracts textual content from an SRT subtitle file utilizing pysrt."""
   subs = pysrt.open(srt_path)
   textual content = " ".be a part of(sub.textual content for sub in subs)
   return textual content

Since programs could have a number of subtitle information, we systematically manage and iterate by way of course supplies saved in predefined folders. This enables for seamless textual content extraction and additional processing.

# Outline course names and their respective folder paths
course_folders = {
   "Introduction to Deep Studying utilizing PyTorch": "C:MCodeGAILearn_queriesSubtitle_Introduction_to_Deep_Learning_Using_Pytorch",
   "Constructing Manufacturing-Prepared RAG methods utilizing LlamaIndex": "C:MCodeGAILearn_queriesSubtitle of Constructing Manufacturing-Prepared RAG methods utilizing LlamaIndex",
   "Introduction to LangChain - Constructing Generative AI Apps & Brokers": "C:MCodeGAILearn_queriesSubtitle_introduction_to_langchain_using_agentic_ai"
}


# Dictionary to retailer course names and their respective .srt file paths
course_srt_files = {}


# Iterate by way of course folder mappings
for course, folder_path in course_folders.objects():
   srt_files = []
  
   # Stroll by way of the listing to search out .srt information
   for root, _, information in os.stroll(folder_path):
       srt_files.lengthen(os.path.be a part of(root, file) for file in information if file.endswith(".srt"))
  
   # Add to dictionary if there are .srt information
   if srt_files:
       course_srt_files[course] = srt_files

This extracted textual content varieties the inspiration of our AI-driven studying assist system, enabling superior retrieval and question decision.

Step 2: Storing Subtitles in ChromaDB

On this half, we’ll break down the method of storing course subtitles in ChromaDB, together with textual content chunking, embedding technology, persistence, and price estimation.

a. Persistent Listing for ChromaDB

The persist_directory is a folder path the place the saved knowledge can be saved, permitting us to retain embeddings even after restarting this system. With out this, the database would reset after every execution.

persist_directory = "./subtitles_db"

ChromaDB is used as a vector database to retailer and retrieve embeddings effectively.

b. Splitting Textual content into Smaller Chunks

Massive paperwork (like whole course subtitles) exceed token limits for embeddings. To deal with this, we use RecursiveCharacterTextSplitter to interrupt textual content into smaller, overlapping chunks to enhance search accuracy.

# Textual content splitter to interrupt paperwork into smaller chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

Every chunk is 1,000 characters lengthy, guaranteeing that the textual content is damaged into manageable items. To keep up context between chunks, 200 characters from the earlier chunk are included within the subsequent one. This overlap helps protect necessary particulars and improves retrieval accuracy.

c. Initializing OpenAI Embeddings and ChromaDB Vector Retailer

We have to convert textual content into numerical vector representations for similarity search. OpenAI’s embeddings enable us to encode our course content material right into a format that may be searched effectively.

# Initialize OpenAI embeddings
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)

Right here, OpenAIEmbeddings() initializes the embedding mannequin utilizing our OpenAI API key (openai_api_key). This ensures that each textual content chunk will get transformed right into a high-dimensional vector illustration.

d. Initializing ChromaDB

Now, we retailer these vector embeddings in ChromaDB.

# Initialize Chroma vectorstore with persistent listing
vectorstore = Chroma(
   collection_name="course_materials",
   embedding_function=embeddings,
   persist_directory=persist_directory
)

The collection_name=”course_materials” creates a devoted assortment in ChromaDB to arrange all course-related embeddings. The embedding_function=embeddings specifies OpenAI embeddings for changing textual content into numerical vectors. The persist_directory=persist_directory ensures that every one saved embeddings stay out there in ./subtitles_db/, even after restarting this system.

Step 3: Estimating Price of Storing Course Information

Earlier than including paperwork to the vector database, it’s important to estimate the price of token utilization. Since OpenAI prices per 1,000 tokens, we calculate the anticipated price to handle bills effectively.

a. Defining Pricing Parameters

Since OpenAI prices per 1,000 tokens, we estimate the fee earlier than including paperwork.

import time


# OpenAI Pricing (regulate based mostly on the mannequin getting used)
COST_PER_1K_TOKENS = 0.0001  # Price per 1K tokens for 'text-embedding-ada-002'
TOKENS_PER_CHUNK_ESTIMATE = 750  # Approximate tokens per 1000-character chunk


# Monitor complete tokens and price
total_tokens = 0
total_cost = 0


# Begin timing
start_time = time.time()

The COST_PER_1K_TOKENS = 0.0001 defines the fee per 1,000 tokens when utilizing OpenAI embeddings. The TOKENS_PER_CHUNK_ESTIMATE = 750 estimates that every 1,000-character chunk accommodates about 750 tokens. The total_tokens and total_cost variables observe the overall processed knowledge and price incurred throughout execution. The start_time variable data the beginning time to measure how lengthy the method takes.

b. Checking and Including Programs to ChromaDB

We wish to keep away from reprocessing programs which can be already saved within the vector database. So for that we’re querying ChromaDB to verify if the course already exists. If the course is just not discovered, we extract and retailer its subtitle knowledge.

# Add new programs to the vectorstore if they do not exist already
for course, srt_list in course_srt_files.objects():
   # Verify if the course already exists within the vectorstore
   existing_docs = vectorstore._collection.get(the place={"course": course})
   if not existing_docs['ids']:
       # Course not discovered, add it
       srt_texts = [extract_text_from_srt(srt) for srt in srt_list]
       course_text = "nnnn".be a part of(srt_texts)  # Be a part of SRT texts with 4 new traces
       doc = Doc(page_content=course_text, metadata={"course": course})
       chunks = text_splitter.split_documents([doc])

The subtitles are extracted utilizing the extract_text_from_srt() operate. A number of subtitle information are then joined collectively utilizing nnnn to enhance readability. A Doc object is created, storing the complete subtitle textual content together with its metadata. Lastly, the textual content is cut up into smaller chunks utilizing text_splitter.split_documents() for environment friendly processing and retrieval.

c. Estimating Token Utilization and Price

Earlier than including the chunks to ChromaDB, we estimate the fee.

      # Estimate price earlier than including paperwork
       chunk_count = len(chunks)
       batch_tokens = chunk_count * TOKENS_PER_CHUNK_ESTIMATE
       batch_cost = (batch_tokens / 1000) * COST_PER_1K_TOKENS
       total_tokens += batch_tokens
       total_cost += batch_cost

The chunk_count represents the variety of chunks generated after splitting the textual content. The batch_tokens estimates the overall variety of tokens based mostly on the chunk rely. The batch_cost calculates the estimated price for processing the present course. The total_tokens and total_cost accumulate values throughout all programs to trace general processing and bills.

d. Including Chunks to ChromaDB

       vectorstore.add_documents(chunks)
       print(f"Added course: {course} (Chunks: {chunk_count}, Price: ${batch_cost:.4f})")
   else:
       print(f"Course already exists: {course}")

The processed chunks are saved in ChromaDB for environment friendly retrieval. A message is displayed, indicating the variety of chunks added and the estimated processing price.

As soon as all programs are processed, we calculate and show the ultimate outcomes.

# Finish timing
end_time = time.time()


# Show price and time
print(f"nCourse Embeddings Replace Accomplished! 🚀")
print(f"Complete Chunks Processed: {total_tokens // TOKENS_PER_CHUNK_ESTIMATE}")
print(f"Estimated Complete Tokens: {total_tokens}")
print(f"Estimated Price: ${total_cost:.4f}")
print(f"Complete Time Taken: {end_time - start_time:.2f} seconds")

The whole processing time is calculated utilizing (end_time – start_time). The system then shows the variety of chunks processed, the estimated token utilization, and the general price. Lastly, it gives a abstract of the complete embedding course of.

Output:

From the output, we are able to see {that a} complete of 739 chunks have been processed in 10 seconds, with an estimated price of $0.0554.

4. Querying and Responding to Learner Queries

As soon as the subtitles are saved in ChromaDB, the system wants a option to retrieve related content material when a learner submits a question. This retrieval course of is dealt with utilizing similarity search, which identifies saved textual content segments that are most related to the enter question.

The way it Works:

1. Question Enter: The learner submits a query associated to the course.
2. Filtering by Course: The system ensures that retrieval is restricted to the related course materials.
3. Similarity Search in ChromaDB: The question is transformed into an embedding, and ChromaDB retrieves essentially the most comparable saved textual content chunks.
4. Returning the Prime Outcomes: The system selects the highest three most related textual content segments.
5. Formatting the Output: The retrieved textual content is formatted and introduced as context for additional processing.

# Outline retrieval device with metadata filtering
def retrieve_course_materials(question: str, course = course):
   """Retrieves course supplies filtered by course title."""
   filter_dict = {"course": course}
   outcomes = vectorstore.similarity_search(question, ok=3, filter=filter_dict)
   return "nn".be a part of([doc.page_content for doc in results])

Instance queries:

course_name = "Introduction to Deep Studying utilizing PyTorch"
query = "What's gradient descent?"
context = retrieve_course_materials(question=query, course= course_name)
print(context)

The output consists of the retrieved content material from ChromaDB, filtered by course title and query, utilizing similarity search to search out essentially the most related info.

Why is Similarity Search Used?

• Semantic Understanding: In contrast to key phrase searches, similarity search finds textual content semantically associated to the question.
• Environment friendly Retrieval: As an alternative of scanning whole paperwork, the system retrieves solely essentially the most related components.
• Improved Reply High quality: By filtering by course and rating outcomes by relevance, learners obtain extremely focused content material.

This mechanism ensures that when a learner submits a query, they obtain related and contextually correct info from saved course supplies.

5. Implementing the AI Question Answering Agent

As soon as related course materials is retrieved from ChromaDB, the following step is to make use of an AI-powered agent to formulate significant responses to learner queries. CrewAI is used to outline an clever agent liable for analyzing queries and producing well-structured responses.

Now, let’s see the way it works.

Step 1: Defining the Agent

The question answering agent is created with a transparent position and backstory to information its conduct when responding to learner queries.

# Outline the agent with a well-structured position and backstory
query_answer_agent = Agent(
   position = "Studying Help Specialist",
   objective = "You assist learners with their queries with the very best response",
   backstory = """You lead the Learners Question decision division of 
   an Ed tech firm focussed on self paced programs on subjects associated to 
   Information Science, Machine Studying and Generative AI. You reply to learner
    queries associated to course content material, assignments, technical and administrative points. 
    You're well mannered, diplomatic and take possession of issues which may very well be 
    imporved in your oragnisation.
  
   """,
   verbose = False,
)

Let’s perceive what is occurring within the code block. Firstly, we’re offering the position as Studying Help Specialist for the reason that agent acts as a digital tutor that solutions scholar queries. Then, we outline the objective, guaranteeing that the agent prioritizes accuracy and readability in its responses. Lastly, we set verbose=False, which retains the execution silent until debugging is required. This well-defined agent position ensures that responses are useful, structured, and aligned with the academic platform’s tone.

Step 2: Defining the Process

After defining the agent, we have to assign it a activity

query_answering_task  = Process(
   description= """
   Reply the learner queries to the most effective of your skills. Attempt to preserve your response concise with lower than 100 phrases.
   Right here is the question: {question}


   Right here is analogous content material from the course extracted from subtitles, which it's best to use solely when required: {relevant_content} .  
   Since this content material is extracted from course subtitles, there could also be spelling errors, ensure that to appropriate these, whereas utilizing this info in your response.


   There could also be some earlier dialogue with the learner on this thread. Right here is the python record of previous discussions: {thread} . 
   On this thread, the content material which begins with 'learner' is the query by the coed and the content material which begins with 'assist' 
   is the response given by you. Use this previous dialogue appropriatly to return with an important reply.


   That is the complete title of the learner: {learner_name}
   Handle every learner by their first title, if you're undecided what the primary title is, merely begin with Hello.
   Additionally point out some applicable and inspiring comforting traces on the finish of the reponse, like "hope you discovered this useful", 
   "I hope this info is helpful. Sustain the good work!", "Glad to help! Be happy to achieve out anytime." and so forth.


   In case you are undecided concerning the reply point out - "Sorry, I'm not positive about this, I'll get again to you"


   """,
   expected_output = "A crisp correct response to the question",
   agent=query_answer_agent)

Let’s break down the duty offered to the AI agent. The question dealing with includes processing {question}, which represents the learner’s query. The response needs to be concise (underneath 100 phrases) and correct. When utilizing course content material, {relevant_content} is extracted from subtitles saved in ChromaDB, and the AI should appropriate any spelling errors earlier than together with the content material in its response.

If previous discussions exist, {thread} helps preserve continuity. Learner queries begin with “learner”, whereas previous responses start with “assist”, permitting the agent to supply context-aware solutions. Personalization is achieved utilizing {learner_name}—the agent  addresses college students by their first title or defaults to “Hello” if unsure.

To make responses extra participating, the AI provides a optimistic closing assertion, similar to “Hope you discovered this useful!” or “Be happy to achieve out anytime.” If the AI is uncertain about a solution, it explicitly states: “Sorry, I’m not positive about this, I’ll get again to you.” This method ensures politeness, readability, and a structured response format, enhancing learner engagement and belief.

Step 3: Initializing the CrewAI Occasion

Now that we’ve got each the agent and the duty, we initialize CrewAI, which permits the agent to course of queries dynamically.

# Create the Crew
response_crew = Crew(
   brokers=[query_answer_agent],
   duties=[query_answering_task],
   verbose=False
)

The brokers=[query_answer_agent] parameter provides the Studying Help Specialist agent to the crew. The duties=[query_answering_task] assigns the question answering activity to this agent. Setting verbose=False retains the output minimal until debugging is required. CrewAI permits the system to course of a number of learner queries concurrently, making it scalable and environment friendly for dynamic question dealing with.

Why Use CrewAI for Question Answering?

• Structured Responses: Ensures that every response is well-organized and informative.
• Context Consciousness: Makes use of retrieved course materials and previous discussions to enhance response high quality.
• Scalability: Can deal with a number of queries dynamically by processing them as duties inside CrewAI.
• Effectivity: Reduces response time by streamlining the question decision workflow.

By implementing this AI-powered answering system, learners obtain well-informed responses tailor-made to their particular queries.

Step 4: Producing Responses for A number of Learner’s Queries

As soon as the AI agent is ready up, it must dynamically course of learner queries saved in a structured dataset.

The under code processes learner queries saved in a CSV file and generates responses utilizing an AI agent. It first masses the dataset containing learner queries, course particulars, and dialogue threads. The reply_to_query operate extracts related particulars just like the learner’s title, course title, and present question. If earlier discussions exist, they’re retrieved for context. If the question accommodates a picture, it’s skipped. The operate then fetches associated course supplies from ChromaDB and sends the question, related content material, and previous discussions to the AI agent for producing a structured response.

df = pd.read_csv(filepath_or_buffer="C:MCodeGAILearn_queries/filtered_data_top3_courses.csv")
def reply_to_query(df_new=df_new, index=1):
   learner_name = df_new.iloc[index]["thread_starter"]
   course_name = df_new.iloc[index]["course"]
   if df_new.iloc[index]['number_of_replies']>1:
       thread = ast.literal_eval(df_new.iloc[index]["modified_thread"])
   else:
       thread = []
   query = df_new.iloc[index]["current_query"]
   if df_new.iloc[index]['has_image'] == True:
       return " "
  


   context = retrieve_course_materials(question = query , course=course_name)


   response_result = response_crew.kickoff(inputs={"question": query, "relevant_content": context, "thread": thread, "learner_name": learner_name})
   print('Q: ', query)
   print('n')
   print('A: ', response_result)
   print('nn')

Testing the operate, it’s executed for one question (index=1)

reply_to_query(df, index=1)

From this we are able to see that it really works fantastic only for one index.

Now, iterating by way of all queries, processing every one whereas dealing with potential errors. This ensures environment friendly automation of question decision, permitting a number of learner queries to be processed dynamically.

for i in vary(len(df)):
   strive:
       reply_to_query(df, index=i)
   besides:
       print("Error in index quantity: ", i)
       proceed

Why is This Step Vital?

• Automates Question Processing: The system can deal with a number of learner queries effectively.
• Ensures Contextual Relevance: Responses are generated based mostly on retrieved course supplies and previous discussions.
• Scalability: The strategy permits the AI agent to course of and reply to hundreds of queries dynamically.
• Improved Studying Help: Learners obtain personalised, data-driven responses to their queries.

This step ensures that each learner question is analyzed, contextualized, and answered successfully, enhancing the general studying expertise.

Output:

From the output we are able to see that the method of replying to the question has turn into automated  adopted by query after which reply.

Future Enhancements

To improve the RAG-Primarily based Question Decision System, a number of enhancements might be made:

• Frequent Questions and Their Options: Implementing a structured FAQ system inside the question decision framework will assist in offering on the spot solutions to regularly requested questions, decreasing dependency on stay assist.
• Picture Processing Capability: Including the potential to investigate and extract related info from photos (similar to screenshots, charts, or scanned paperwork) will improve the system’s versatility, making it extra helpful in academic and buyer assist domains.
• Enhancing the Picture Column Boolean: Refining the logic behind the picture column detection to appropriately determine and course of image-based queries with higher accuracy.
• Semantic Chunking and Completely different Chunking Strategies: Experimenting with varied chunking methods, similar to semantic chunking, fixed-length segmentation, and hybrid approaches, can enhance retrieval accuracy and contextual understanding of responses.

Conclusion

This RAG-Primarily based Question Decision System leverages LangChain, ChromaDB, and CrewAI to automate learner assist effectively. It extracts subtitles, shops them as embeddings in ChromaDB, and retrieves related content material utilizing similarity search. A CrewAI agent processes queries, references previous discussions, and generates structured responses, guaranteeing accuracy and personalization.

The system enhances scalability, retrieval effectivity, and response high quality, making self-paced studying extra interactive. Future enhancements embrace multi-modal assist, higher retrieval optimization, and enhanced response technology. By automating question decision, this technique streamlines studying assist, offering learners with quicker, context-aware responses and enhancing general engagement.

Continuously Requested Questions