AI Notes

Tag: LLM

  • Agentic AI Tools

    Agentic refers to a type of artificial intelligence system that can operate autonomously to achieve specific goals. Unlike traditional AI, which typically follows pre-programmed instructions, can perceive its environment, reason about complex situations, make decisions, and take actions with limited or no direct human intervention. These systems often leverage large language models (LLMs) and other AI capabilities to understand context, develop plans, and execute multi-step tasks.
    An agentic AI toolset comprises the various software, frameworks, and platforms that enable developers and businesses to build and deploy these autonomous AI systems. These toolsets often include components that facilitate:

    • Agent Creation and Configuration: Tools for defining the goals, instructions, and capabilities of individual AI agents. This might involve specifying the to be used, providing initial prompts, and defining the agent’s role and responsibilities. Examples include the “Agents” feature in OpenAI’s new tools for building agents.
    • Task Planning and Execution: Frameworks that allow agents to break down complex goals into smaller, manageable steps and execute them autonomously. This often involves reasoning, decision-making, and the ability to adapt plans based on the environment and feedback.
    • Tool Integration: Mechanisms for AI agents to interact with external tools, APIs, and services to gather information, perform actions, and achieve their objectives. This can include accessing databases, sending emails, interacting with web applications, or controlling physical devices. Examples include the tool-use capabilities in OpenAI’s Assistants and the integration capabilities of platforms like Moveworks.
    • Multi-Agent Collaboration: Features that enable multiple AI agents to work together to solve complex problems. These frameworks facilitate communication, coordination, and the intelligent transfer of control between agents. Examples include Microsoft AutoGen and CrewAI.
    • State Management and Workflows: Tools for managing the state of interactions and defining complex, stateful workflows. LangGraph is specifically designed for mastering such workflows.
    • Safety and Control: Features for implementing guardrails and safety checks to ensure that AI agents operate responsibly and ethically. This includes input and output validation mechanisms.
    • Monitoring and Observability: Tools for visualizing the execution of AI agents, debugging issues, and optimizing their performance. OpenAI’s new tools include tracing and observability features.
      Examples of Agentic AI Toolsets and Platforms (as of April 2025):
    • Microsoft AutoGen: A framework designed for building applications that involve multiple AI agents that can converse and collaborate to solve tasks.
    • LangChain: A popular framework for building AI-powered applications, offering components to create sophisticated AI agents with memory, tool use, and planning capabilities.
    • LangGraph: Extends LangChain to build stateful, multi-actor AI workflows.
    • Microsoft Semantic Kernel: A framework for integrating intelligent reasoning into software applications, enabling the creation of AI agents that can leverage plugins and skills.
    • CrewAI: A framework focused on enabling AI teamwork, allowing developers to create teams of AI agents with specific roles and objectives.
    • Moveworks: An enterprise-grade AI Assistant platform that uses agentic AI to automate employee support and complex workflows across various organizational systems.
    • OpenAI Tools for Building Agents: A new set of APIs and tools, including the Responses API, Agents, Handoffs, and Guardrails, designed to simplify the development of agentic applications.
    • Adept: Focuses on building AI agents capable of interacting with and automating tasks across various software applications through UI understanding and control.
    • AutoGPT: An open-source AI platform that aims to create continuous AI agents capable of handling a wide range of tasks autonomously.
    • AskUI: Provides tools for building AI agents that can interact with and automate tasks based on understanding user interfaces across different applications.
      These toolsets are rapidly evolving as the field of agentic AI advances, offering increasingly sophisticated capabilities for building autonomous and intelligent systems. They hold the potential to significantly impact various industries by automating complex tasks, enhancing productivity, and enabling new forms of human-AI collaboration.

  • Comparing various Time Series Databases

    A (TSDB) is a type of database specifically designed to handle sequences of data points indexed by time. This is in contrast to traditional relational databases that are optimized for transactional data and may not efficiently handle the unique characteristics of time-stamped data.

    Here’s a comparison of key aspects of Time Series Databases:

    Key Features of Time Series Databases:

    • Optimized for Time-Stamped Data: TSDBs are architectured with time as a primary index, allowing for fast and efficient storage and retrieval of data based on time ranges.
    • High Ingestion Rates: They are built to handle continuous and high-volume data streams from various sources like sensors, applications, and infrastructure.
    • Efficient Time-Range Queries: TSDBs excel at querying data within specific time intervals, a common operation in time series analysis.
    • Data Retention Policies: They often include mechanisms to automatically manage data lifecycle by defining how long data is stored and when it should be expired or downsampled.
    • Data Compression: TSDBs employ specialized compression techniques to reduce storage space and improve query performance over large datasets.
    • Downsampling and Aggregation: They often provide built-in functions to aggregate data over different time windows (e.g., average hourly, daily summaries) to facilitate analysis at various granularities.
    • Real-time Analytics: Many TSDBs support real-time querying and analysis, enabling immediate insights from streaming data.
    • Scalability: Modern TSDBs are designed to scale horizontally (adding more nodes) to handle growing data volumes and query loads.

    Comparison of Popular Time Series Databases:

    Here’s a comparison of some well-known time series databases based on various criteria:

    FeatureTimescaleDBInfluxDBPrometheusClickHouse
    Database ModelRelational (PostgreSQL extension)Custom NoSQL, ColumnarPull-based metrics systemColumnar
    Query LanguageSQLInfluxQL, Flux, SQLPromQLSQL-like
    Data ModelTables with time-based partitioningMeasurements, Tags, FieldsMetrics with labelsTables with time-based organization
    ScalabilityVertical, Horizontal (read replicas)Horizontal (clustering in enterprise)Vertical, Horizontal (via federation)Horizontal
    Data IngestionPushPushPull (scraping)Push (various methods)
    Data RetentionSQL-based managementRetention policies per database/bucketConfigurable retention timeSQL-based management
    Use CasesDevOps, IoT, Financial, General TSDevOps, IoT, AnalyticsMonitoring, Alerting, KubernetesAnalytics, Logging, IoT
    CommunityStrong PostgreSQL communityActive InfluxData communityLarge, active, cloud-native focusedGrowing, strong for analytics
    LicensingOpen Source (Timescale License)Open Source (MIT), EnterpriseOpen Source (Apache 2.0)Open Source (Apache 2.0)
    Cloud OfferingTimescale CloudInfluxDB CloudVarious managed Prometheus servicesClickHouse Cloud, various providers

    Key Differences Highlighted:

    • Query Language: SQL compatibility in TimescaleDB and ClickHouse can be advantageous for users familiar with relational databases, while InfluxDB and Prometheus have their own specialized query languages (InfluxQL/Flux and PromQL respectively).
    • Data Model: The way data is organized and tagged differs significantly, impacting query syntax and flexibility.
    • Data Collection: Prometheus uses a pull-based model where it scrapes metrics from targets, while InfluxDB and TimescaleDB typically use a push model where data is sent to the database.
    • Scalability Approach: While all aim for scalability, the methods (clustering, federation, partitioning) and ease of implementation can vary.
    • Focus: Prometheus is heavily geared towards monitoring and alerting in cloud-native environments, while InfluxDB and TimescaleDB have broader applicability in IoT, analytics, and general time series data storage.

    Choosing the Right TSDB:

    The best time series database for a particular use case depends on several factors:

    • Data Volume and Ingestion Rate: Consider how much data you’ll be ingesting and how frequently.
    • Query Patterns and Complexity: What types of queries will you be running? Do you need complex joins or aggregations?
    • Scalability Requirements: How much data do you anticipate storing and querying in the future?
    • Existing Infrastructure and Skills: Consider your team’s familiarity with different database types and query languages.
    • Monitoring and Alerting Needs: If monitoring is a primary use case, Prometheus might be a strong contender.
    • Long-Term Storage Requirements: Some TSDBs are better suited for long-term historical data storage and analysis.
    • Cost: Consider the costs associated with self-managed vs. cloud-managed options and any enterprise licensing fees.

    By carefully evaluating these factors against the strengths and weaknesses of different time series databases, you can choose the one that best fits your specific needs.

  • Building a Personalized Banking Chat Agent with React.js, RAG, LLM, and Redis with sample code

    Here we outline a more detailed structure with conceptual sample code snippets for each layer of a conceptual personalized bank FAQ chat agent. Keep in mind that this is a simplified illustration, and a production-ready system would involve more robust error handling, security measures, and integration logic.

    I. Knowledge Base Preparation:

    Step 1: Data Collection & Structuring

    Assume you have your bank’s FAQs in a structured format, perhaps JSON files where each entry has a question and an answer, or markdown files.

    JSON

    [
  {
    "question": "What are your current mortgage rates?",
    "answer": "Our current mortgage rates vary depending on the loan type and your credit score. Please visit our mortgage page or contact a loan officer for personalized rates."
  },
  {
    "question": "How do I reset my online banking password?",
    "answer": "To reset your online banking password, please click on the 'Forgot Password' link on the login page and follow the instructions."
  },
  // ... more FAQs
]

    Step 2: Chunking

    For larger documents (like policy documents), you’ll need to break them into smaller chunks. A simple approach is to split by paragraphs or sentences, ensuring context isn’t lost.

    def chunk_text(text, chunk_size=512, overlap=50):
    chunks = []
    stride = chunk_size - overlap
    for i in range(0, len(text), stride):
        chunk = text[i:i + chunk_size]
        chunks.append(chunk)
    return chunks

# Example for a policy document
policy_text = """
This is a long banking policy document... It contains important information about accounts... and transaction limits...
Another paragraph discussing security measures... and fraud prevention...
"""
policy_chunks = chunk_text(policy_text)
print(f"Number of policy chunks: {len(policy_chunks)}")

    Step 3: Embedding Generation

    You’ll use an embedding model (e.g., from OpenAI, Sentence Transformers) to convert your FAQ answers and document chunks into vector embeddings.

    Python

    from sentence_transformers import SentenceTransformer
import numpy as np

embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

faq_data = [
    {"question": "...", "answer": "Answer 1"},
    {"question": "...", "answer": "Answer 2"},
    # ...
]

faq_embeddings = embedding_model.encode([item["answer"] for item in faq_data])
print(f"Shape of FAQ embeddings: {faq_embeddings.shape}")

policy_chunks = ["chunk 1 of policy", "chunk 2 of policy"]
policy_embeddings = embedding_model.encode(policy_chunks)
print(f"Shape of policy embeddings: {policy_embeddings.shape}")

    Step 4: Storing Embeddings in

    You’ll use Redis with a vector search module (like Redis Stack) to store and index these embeddings.

    Python

    import redis
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType

REDIS_HOST = "localhost"
REDIS_PORT = 6379
REDIS_PASSWORD = None
INDEX_NAME = "bank_faq_embeddings"
VECTOR_DIM = 384  # Dimension of all-MiniLM-L6-v2 embeddings
NUM_VECTORS = len(faq_data) + len(policy_chunks)

r = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD)

# Define the schema for the Redis index
schema = (
    TextField("content"),  # Store the original text chunk
    VectorField("embedding", "FLAT", {"TYPE": "FLOAT32", "DIM": VECTOR_DIM, "DISTANCE_METRIC": "COSINE"})
)

# Define the index
definition = IndexDefinition(prefix=["faq:", "policy:"], index_type=IndexType.FLAT)

try:
    r.ft(INDEX_NAME).info()
    print(f"Index '{INDEX_NAME}' already exists.")
except:
    r.ft(INDEX_NAME).create_index(fields=schema, definition=definition)
    print(f"Index '{INDEX_NAME}' created.")

# Store FAQ embeddings
for i, item in enumerate(faq_data):
    key = f"faq:{i}"
    embedding = faq_embeddings[i].astype(np.float32).tobytes()
    r.hset(key, mapping={"content": item["answer"], "embedding": embedding})

# Store policy chunk embeddings
for i, chunk in enumerate(policy_chunks):
    key = f"policy:{i}"
    embedding = policy_embeddings[i].astype(np.float32).tobytes()
    r.hset(key, mapping={"content": chunk, "embedding": embedding})

print(f"Stored {r.ft(INDEX_NAME).info()['num_docs']} vectors in Redis.")

    II. Implementation (Backend – Python/Node.js with a Framework like Flask/Express):

    Python

    from flask import Flask, request, jsonify
from sentence_transformers import SentenceTransformer
import redis
from redis.commands.search.query import Query

app = Flask(__name__)
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
r = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD)
INDEX_NAME = "bank_faq_embeddings"
VECTOR_DIM = 384
LLM_API_KEY = "YOUR_LLM_API_KEY" # Replace with your actual  key

def retrieve_relevant_documents(query, top_n=3):
    query_embedding = embedding_model.encode(query).astype(np.float32).tobytes()
    redis_query = (
        Query("*=>[KNN $topK @embedding $query_vector AS score]")
        .sort_by("score")
        .return_fields("content", "score")
        .dialect(2)
    )
    results = r.ft(INDEX_NAME).search(
        redis_query,
        query_params={"query_vector": query_embedding, "topK": top_n}
    )
    return [{"content": doc.content, "score": doc.score} for doc in results.docs]

def generate_response(query, context_documents):
    context = "\n".join([doc["content"] for doc in context_documents])
    prompt = f"""You are a helpful bank assistant. Use the following information to answer the user's question.
    If you cannot find the answer in the provided context, truthfully say "I'm sorry, I don't have the information to answer that question."

    Context:
    {context}

    Question: {query}
    Answer:"""

    import openai
    openai.api_key = LLM_API_KEY
    try:
        response = openai.Completion.create(
            model="gpt-3.5-turbo-instruct", # Choose an appropriate 
            prompt=prompt,
            max_tokens=200,
            temperature=0.2,
            n=1,
            stop=None
        )
        return response.choices[0].text.strip()
    except Exception as e:
        print(f"Error calling LLM: {e}")
        return "An error occurred while generating the response."

@app.route('/chat', methods=['POST'])
def chat():
    user_query = request.json.get('query')
    if not user_query:
        return jsonify({"error": "Missing query"}), 400

    # --- Personalization Layer (Conceptual) ---
    user_profile = get_user_profile(request.headers.get('Authorization')) # Example: Fetch user data
    personalized_context = get_personalized_context(user_profile) # Example: Fetch relevant account info

    # Augment query with personalized context (optional)
    augmented_query = f"{user_query} Regarding my {personalized_context}." if personalized_context else user_query

    relevant_documents = retrieve_relevant_documents(augmented_query)
    response = generate_response(user_query, relevant_documents)

    return jsonify({"response": response})

def get_user_profile(auth_token):
    # In a real application, you would authenticate the token and fetch user data
    # from your bank's user .
    # For this example, let's return a mock profile.
    if auth_token == "Bearer valid_token":
        return {"account_type": "checking", "recent_transactions": [...] }
    return None

def get_personalized_context(user_profile):
    if user_profile and user_profile.get("account_type"):
        return f"my {user_profile['account_type']} account"
    return None

if __name__ == '__main__':
    app.run(debug=True)

    III. LLM Integration (within the Backend):

    The generate_response function in the backend code snippet demonstrates the integration with an LLM (using OpenAI’s API as an example). You would replace "gpt-3.5-turbo-instruct" with your chosen model and handle the API interactions accordingly.

    IV. Redis Integration (within the Backend):

    The backend code shows how Redis is used for:

    • Storing Embeddings: The store_embeddings_in_redis section in the Knowledge Base Preparation.
    • Retrieving Relevant Documents: The retrieve_relevant_documents function uses Redis’s vector search capabilities to find the most similar document embeddings to the user’s query embedding.

    V. React.js Front-End Development:

    JavaScript

    import React, { useState } from 'react';

function ChatAgent() {
  const [messages, setMessages] = useState([]);
  const [inputText, setInputText] = useState('');
  const [isLoading, setIsLoading] = useState(false);

  const sendMessage = async () => {
    if (!inputText.trim()) return;

    const userMessage = { text: inputText, sender: 'user' };
    setMessages([...messages, userMessage]);
    setInputText('');
    setIsLoading(true);

    try {
      const response = await fetch('/chat', {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
          'Authorization': 'Bearer valid_token' // Example: Pass user token if authenticated
        },
        body: JSON.stringify({ query: inputText }),
      });

      if (!response.ok) {
        throw new Error(`HTTP error! status: ${response.status}`);
      }

      const data = await response.json();
      const botMessage = { text: data.response, sender: 'bot' };
      setMessages([...messages, botMessage]);
    } catch (error) {
      console.error("Error sending message:", error);
      const errorMessage = { text: "Sorry, I encountered an error.", sender: 'bot' };
      setMessages([...messages, errorMessage]);
    } finally {
      setIsLoading(false);
    }
  };

  return (
    <div className="chat-container">
      <div className="message-list">
        {messages.map((msg, index) => (
          <div key={index} className={`message ${msg.sender}`}>
            {msg.text}
          </div>
        ))}
        {isLoading && <div className="message bot">Loading...</div>}
      </div>
      <div className="input-area">
        <input
          type="text"
          value={inputText}
          onChange={(e) => setInputText(e.target.value)}
          placeholder="Ask a question..."
          onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
        />
        <button onClick={sendMessage} disabled={isLoading}>Send</button>
      </div>
    </div>
  );
}

export default ChatAgent;

    VI. Personalization Layer:

    The personalization aspect is touched upon in the backend (/chat route and the get_user_profile, get_personalized_context functions). In a real-world scenario, this layer would involve:

    • User Authentication: Securely identifying the user.
    • Data Fetching: Retrieving relevant user data from your bank’s systems based on their identity (e.g., account details, transaction history, past interactions).
    • Contextualization: Using the fetched data to:
      • Filter/Boost Knowledge Base Results: Prioritize FAQs or document sections relevant to the user’s situation.
      • Augment the Query: Add context to the user’s query before retrieval (as shown in the backend example).
      • Tailor the Prompt: Include personalized information in the prompt sent to the LLM.

    VII. Evaluation and Improvement:

    This is an ongoing process that involves:

    • Tracking Metrics: Monitor user engagement, satisfaction, and the accuracy of the ‘s responses.
    • User Feedback Collection: Implement mechanisms for users to provide feedback on the chatbot’s answers.
    • Analysis: Analyze the data and feedback to identify areas where the chatbot can be improved (e.g., gaps in the knowledge base, poor-performing prompts).
    • Iteration: Continuously update the knowledge base, refine the RAG pipeline, and adjust the LLM prompts based on the evaluation results.

    Important Considerations:

    • Security: Implement robust security measures at every layer, especially when handling user data and API keys.
    • Error Handling: Implement comprehensive error handling to gracefully manage unexpected issues.
    • Scalability: Design your system to handle a growing number of users and data.
    • Cost Management: Be mindful of the costs associated with LLM API usage and Redis hosting.
    • User Experience: Focus on creating a smooth and intuitive chat interface.
    • Compliance: Ensure your chatbot complies with all relevant banking regulations and privacy policies.

    This detailed breakdown with sample code provides a solid foundation for building your personalized bank FAQ chat agent. Remember to adapt and expand upon this based on your specific requirements and the complexity of your bank’s information. Good luck!

  • Building a Personalized Bank FAQ Chat Agent with React.js, RAG, LLM, and Redis

    Providing efficient and informative customer support is crucial for any financial institution. A well-designed FAQ chat agent can significantly enhance the user experience by offering instant answers to common queries. This article provides a comprehensive guide to building a personalized bank FAQ chat agent using React.js for the frontend, Retrieval-Augmented Generation () and a Large Language Model () for intelligent responses, and for robust session management and personalized chat history.

    I. The Power of Intelligent Chat for Bank FAQs

    Traditional FAQ pages can be cumbersome. An intelligent chat agent offers a more interactive and efficient way to find answers by understanding natural language queries and providing contextually relevant information drawn from the bank’s knowledge base. Leveraging Redis for session management allows for personalized interactions by remembering past conversations within a session.

    II. Core Components

    1. Frontend (React.js): User interface for interaction.
    2. Backend ( with Flask): Orchestrates RAG, LLM, and session/chat history (Redis).
    3. Knowledge Source: Bank’s FAQ documents, policies, website content.
    4. Embedding Model: Converts text to vectors (e.g., OpenAI Embeddings).
    5. Vector : Stores and indexes vector embeddings (e.g., ChromaDB).
    6. Large Language Model (LLM): Generates responses (e.g., OpenAI’s GPT models).
    7. Redis: In-memory data store for sessions and chat history.
    8. Flask-Session: Flask extension for Redis-backed session management.
    9. LangChain: Framework for streamlining RAG and LLM interactions.

    III. Backend Implementation (Python with Flask, Redis, and RAG)

    Python

    from flask import Flask, request, jsonify, session
from flask_session import Session
from redis import Redis
import uuid
import json
from flask_cors import CORS
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.document_loaders import DirectoryLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os

# --- Configuration ---
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
REDIS_HOST = 'localhost'
REDIS_PORT = 6379
REDIS_DB = 0
VECTOR_DB_PATH = "./bank_faq_db"
FAQ_DOCS_PATH = "./bank_faq_docs"

app = Flask(__name__)
CORS(app)
app.config&lsqb;"SESSION_TYPE"] = "redis"
app.config&lsqb;"SESSION_PERMANENT"] = True
app.config&lsqb;"SESSION_REDIS"] = Redis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB)
app.secret_key = "your_bank_faq_secret_key"  # Replace with a strong key
sess = Session(app)

# --- Initialize RAG Components ---
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
if not os.path.exists(VECTOR_DB_PATH):
    # --- Data Ingestion (Run once to create the vector database) ---
    if not os.path.exists(FAQ_DOCS_PATH):
        os.makedirs(FAQ_DOCS_PATH)
        print(f"Please place your bank's FAQ documents (e.g., .txt files) in '{FAQ_DOCS_PATH}' and rerun the backend to process them.")
        vectordb = None
    else:
        loader = DirectoryLoader(FAQ_DOCS_PATH, glob="**/*.txt", loader_cls=TextLoader)
        documents = loader.load()
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        chunks = text_splitter.split_documents(documents)
        vectordb = Chroma.from_documents(chunks, embeddings, persist_directory=VECTOR_DB_PATH)
        vectordb.persist()
else:
    vectordb = Chroma(persist_directory=VECTOR_DB_PATH, embedding_function=embeddings)

qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=OPENAI_API_KEY), chain_type="stuff", retriever=vectordb.as_retriever() if vectordb else None)

# --- Redis Helper Functions ---
def store_message(session_id, sender, text):
    redis_client = app.config&lsqb;"SESSION_REDIS"]
    key = f"bank_faq_chat:{session_id}"
    message = {"sender": sender, "text": text}
    redis_client.rpush(key, json.dumps(message))

def get_history(session_id):
    redis_client = app.config&lsqb;"SESSION_REDIS"]
    key = f"bank_faq_chat:{session_id}"
    history_bytes = redis_client.lrange(key, 0, -1)
    return &lsqb;json.loads(hb.decode('utf-8')) for hb in history_bytes]

# ---  Endpoints ---
@app.route('/create_session')
def create_session():
    if 'bank_faq_session_id' not in session:
        session_id = str(uuid.uuid4())
        session&lsqb;'bank_faq_session_id'] = session_id
        return jsonify({"session_id": session_id})
    else:
        return jsonify({"session_id": session&lsqb;'bank_faq_session_id']})

@app.route('/get_chat_history')
def get_chat_history():
    if 'bank_faq_session_id' not in session:
        return jsonify({"history": &lsqb;]})
    session_id = session&lsqb;'bank_faq_session_id']
    history = get_history(session_id)
    return jsonify({"history": history})

@app.route('/bank_faq/chat', methods=&lsqb;'POST'])
def bank_faq_chat():
    if 'bank_faq_session_id' not in session:
        return jsonify({"error": "No active session."}), 401

    session_id = session&lsqb;'bank_faq_session_id']
    data = request.get_json()
    user_message = data.get('message')

    if not user_message:
        return jsonify({"error": "Message is required"}), 400

    store_message(session_id, "user", user_message)

    try:
        if qa_chain:
            response = qa_chain.run(user_message)
            store_message(session_id, "agent", response)
            return jsonify({"response": response})
        else:
            error_message = "Bank FAQ knowledge base not initialized. Please ensure FAQ documents are present and the backend is run to process them."
            store_message(session_id, "agent", error_message)
            return jsonify({"error": error_message}), 500

    except Exception as e:
        error_message = f"Sorry, I encountered an error: {str(e)}"
        store_message(session_id, "agent", error_message)
        return jsonify({"error": error_message}), 500

if __name__ == '__main__':
    print("Make sure you have your OpenAI API key set as an environment variable (OPENAI_API_KEY).")
    print(f"Place bank FAQ documents in '{FAQ_DOCS_PATH}' for processing.")
    app.run(debug=True)

    IV. Frontend Implementation (React.js)

    JavaScript

    import React, { useState, useEffect, useRef } from 'react';

function BankFAQChat() {
  const &lsqb;messages, setMessages] = useState(&lsqb;]);
  const &lsqb;inputValue, setInputValue] = useState('');
  const &lsqb;isLoading, setIsLoading] = useState(false);
  const chatWindowRef = useRef(null);
  const &lsqb;sessionId, setSessionId] = useState(null);

  useEffect(() => {
    const fetchSessionAndHistory = async () => {
      try {
        const sessionResponse = await fetch('/create_session');
        if (sessionResponse.ok) {
          const sessionData = await sessionResponse.json();
          setSessionId(sessionData.session_id);
          if (sessionData.session_id) {
            const historyResponse = await fetch('/get_chat_history');
            if (historyResponse.ok) {
              const historyData = await historyResponse.json();
              setMessages(historyData.history);
            } else {
              console.error('Failed to fetch chat history:', historyResponse.status);
            }
          }
        } else {
          console.error('Failed to create/retrieve session:', sessionResponse.status);
        }
      } catch (error) {
        console.error('Error fetching session and history:', error);
      }
    };

    fetchSessionAndHistory();
  }, &lsqb;]);

  useEffect(() => {
    if (chatWindowRef.current) {
      chatWindowRef.current.scrollTop = chatWindowRef.current.scrollHeight;
    }
  }, &lsqb;messages]);

  const sendMessage = async () => {
    if (inputValue.trim() && sessionId) {
      const newMessage = { sender: 'user', text: inputValue };
      setMessages(&lsqb;...messages, newMessage]);
      setInputValue('');
      setIsLoading(true);

      try {
        const response = await fetch('/bank_faq/chat', {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({ message: inputValue }),
        });

        if (response.ok) {
          const data = await response.json();
          const agentMessage = { sender: 'agent', text: data.response };
          setMessages(&lsqb;...messages, newMessage, agentMessage]);
        } else {
          console.error('Error sending message:', response.status);
          const errorMessage = { sender: 'agent', text: 'Sorry, I encountered an error.' };
          setMessages(&lsqb;...messages, newMessage, errorMessage]);
        }
      } catch (error) {
        console.error('Error sending message:', error);
        const errorMessage = { sender: 'agent', text: 'Sorry, I encountered an error.' };
      } finally {
        setIsLoading(false);
      }
    }
  };

  return (
    <div className="chat-container" style={styles.chatContainer}>
      <div ref={chatWindowRef} className="message-list" style={styles.messageList}>
        {messages.map((msg, index) => (
          <div key={index} className={`message ${msg.sender}`} style={msg.sender === 'user' ? styles.userMessage : styles.agentMessage}>
            {msg.text}
          </div>
        ))}
        {isLoading && <div className="message agent" style={styles.agentMessage}>Thinking...</div>}
      </div>
      <div className="input-area" style={styles.inputArea}>
        <input
          type="text"
          value={inputValue}
          onChange={(e) => setInputValue(e.target.value)}
          onKeyPress={(event) => event.key === 'Enter' && sendMessage()}
          placeholder="Ask a bank FAQ..."
          style={styles.input}
        />
        <button onClick={sendMessage} disabled={isLoading} style={styles.button}>Send</button>
      </div>
    </div>
  );
}

const styles = {
  chatContainer: { width: '400px', margin: '20px auto', border: '1px solid #ccc', borderRadius: '5px', overflow: 'hidden', display: 'flex', flexDirection: 'column' },
  messageList: { flexGrow: 1, padding: '10px', overflowY: 'auto' },
  userMessage: { backgroundColor: '#e0f7fa', padding: '8px', borderRadius: '5px', marginBottom: '5px', alignSelf: 'flex-end', maxWidth: '70%', wordBreak: 'break-word' },
  agentMessage: { backgroundColor: '#f5f5f5', padding: '8px', borderRadius: '5px', marginBottom: '5px', alignSelf: 'flex-start', maxWidth: '70%', wordBreak: 'break-word' },
  inputArea: { padding: '10px', borderTop: '1px solid #eee', display: 'flex' },
  input: { flexGrow: 1, padding: '8px', borderRadius: '3px', border: '1px solid #ddd', marginRight: '10px' },
  button: { padding: '8px 15px', borderRadius: '3px', border: 'none', backgroundColor: '#00bcd4', color: 'white', cursor: 'pointer', fontWeight: 'bold', '&:disabled': { backgroundColor: '#ccc', cursor: 'not-allowed' } },
};

export default BankFAQChat;

    V. Running the Application

    1. Install Backend Dependencies: pip install Flask flask-session redis flask-cors langchain openai chromadb
    2. Set Up OpenAI API Key: Ensure you have an OpenAI API key and set it as an environment variable named OPENAI_API_KEY.
    3. Prepare Bank FAQ Documents: Create a directory ./bank_faq_docs and place your bank’s FAQ documents (as .txt files) inside.
    4. Run Backend (Initial Data Ingestion): Run the backend script once. It will attempt to create the vector database if it doesn’t exist. Ensure your FAQ documents are in the specified directory.
    5. Ensure Redis is Running: Start your Redis server.
    6. Run the Backend: Execute the backend script.
    7. Running the React Frontend
    8. Here are the instructions to get the React frontend of the Bank FAQ Chat Agent running:
    9. Navigate to your React project directory in your terminal. If you haven’t created a React project yet, you can do so using Create React App or a similar tool: Bashnpx create-react-app bank-faq-frontend cd bank-faq-frontend
    10. Install Dependencies: If you started with a fresh React project, you’ll need to install any necessary dependencies (though this example uses built-in React features like useState and useEffect). If you have a pre-existing project, ensure you have react and react-dom installed. Bashnpm install # Or yarn install
    11. Replace src/App.js (or your main component file): Open the src/App.js file (or the main component where you want to place the chat agent) and replace its entire content with the React code provided in the previous section. You might need to adjust the import path if your component is named differently or located in a different directory. For example, if you save the code in a file named BankFAQChat.js within a components folder, you would import it in App.js like this: JavaScriptimport BankFAQChat from './components/BankFAQChat'; function App() { return ( <div> <BankFAQChat /> </div> ); } export default App;
    12. Start the Development Server: Run the React development server from your terminal within the React project directory: Bashnpm start # Or yarn start This command will typically open your React application in a new tab in your web browser, usually at http://localhost:3000.
    13. Interact with the Chat Agent: Once the frontend is running, you should see the chat interface. You can type your bank-related questions in the input field and click the “Send” button (or press Enter) to send them to the backend. The agent’s responses and the conversation history will be displayed in the chat window.
    14. Important Notes for the Frontend:
    15. Backend URL: Ensure that the fetch calls in the BankFAQChat component (/create_session and /bank_faq/chat) are pointing to the correct URL where your Flask backend is running. If your backend is running on a different host or port than http://localhost:5000, you’ll need to update these URLs accordingly.
    16. Styling: The provided styles object in the React component offers basic styling. You can customize this further or use a CSS-in-JS library (like Styled Components) or a CSS framework (like Tailwind CSS or Material UI) to enhance the visual appearance of the chat agent.
    17. Error Handling: The frontend includes basic console.error logging for API request failures. You might want to implement more user-friendly error messages within the UI.
    18. Session Management: The frontend automatically fetches or creates a session on mount. The sessionId is managed in the component’s state.
    19. Create React App: Create a new React application if you haven’t already.
    20. Replace Frontend Code: Replace the content of your main React component file with the provided BankFAQChat component code.
    21. Start Frontend: Run your React development server. For Detail see below
    Running the React Frontend

    Here are the instructions to get the React frontend of the Bank FAQ Chat Agent running:
    Navigate to your React project directory in your terminal. If you haven’t created a React project yet, you can do so using Create React App or a similar tool:
    Bash
    npx create-react-app bank-faq-frontend
    cd bank-faq-frontend


    Install Dependencies: If you started with a fresh React project, you’ll need to install any necessary dependencies (though this example uses built-in React features like useState and useEffect). If you have a pre-existing project, ensure you have react and react-dom installed.
    Bash
    npm install  # Or yarn install


    Replace src/App.js (or your main component file): Open the src/App.js file (or the main component where you want to place the chat agent) and replace its entire content with the React code provided in the previous section. You might need to adjust the import path if your component is named differently or located in a different directory. For example, if you save the code in a file named BankFAQChat.js within a components folder, you would import it in App.js like this:
    JavaScript
    import BankFAQChat from ‘./components/BankFAQChat’;

    function App() {
      return (
        <div>
          <BankFAQChat />
        </div>
      );
    }

    export default App;


    Start the Development Server: Run the React development server from your terminal within the React project directory:
    Bash
    npm start  # Or yarn start

    This command will typically open your React application in a new tab in your web browser, usually at http://localhost:3000.


    Interact with the Chat Agent: Once the frontend is running, you should see the chat interface. You can type your bank-related questions in the input field and click the “Send” button (or press Enter) to send them to the backend. The agent’s responses and the conversation history will be displayed in the chat window.


    Important Notes for the Frontend:
    Backend URL: Ensure that the fetch calls in the BankFAQChat component (/create_session and /bank_faq/chat) are pointing to the correct URL where your Flask backend is running. If your backend is running on a different host or port than http://localhost:5000, you’ll need to update these URLs accordingly.


    Styling: The provided styles object in the React component offers basic styling. You can customize this further or use a CSS-in-JS library (like Styled Components) or a CSS framework (like Tailwind CSS or Material UI) to enhance the visual appearance of the chat agent.


    Error Handling: The frontend includes basic console.error logging for API request failures. You might want to implement more user-friendly error messages within the UI.


    Session Management: The frontend automatically fetches or creates a session on mount. The sessionId is managed in the component’s state.
    By following these instructions, you should be able to run the React frontend and interact with the Bank FAQ Chat Agent, provided that your Flask backend is also running and correctly configured.

    This setup provides a functional bank FAQ chat agent with personalized history within a session, powered by RAG and an LLM. Remember to replace placeholders and configure API keys and file paths according to your specific environment and data.

  • Intelligent Chat Agent UI with Retrieval-Augmented Generation (RAG) and a Large Language Model (LLM) using Amazon OpenSearch

    In today’s digital age, providing efficient and accurate customer support is paramount. Intelligent chat agents, powered by the latest advancements in Natural Language Processing (NLP), offer a promising avenue for addressing user queries effectively. This comprehensive article will guide you through the process of building a sophisticated Chat Agent UI application that leverages the power of Retrieval-Augmented Generation () in conjunction with a Large Language Model (), specifically tailored to answer questions based on product manuals stored and indexed using Amazon OpenSearch. We will explore the architecture, key components, and provide a practical implementation spanning from backend development with FastAPI and interaction with OpenSearch and Hugging Face Transformers, to a basic HTML/JavaScript frontend for user interaction.

    I. The Synergy of RAG and LLMs for Product Manual Queries

    Traditional chatbots often rely on predefined scripts or keyword matching, which can be limited in their ability to understand nuanced user queries and extract information from complex documents like product manuals. Retrieval-Augmented Generation offers a significant improvement by enabling the agent to:

    • Understand Natural Language: Leverage the semantic understanding capabilities of embedding models to grasp the intent behind user questions.
    • Retrieve Relevant Information: Search through product manuals stored in Amazon OpenSearch to find the most pertinent sections related to the query.
    • Generate Informed Answers: Utilize a Large Language Model to synthesize the retrieved information into a coherent and helpful natural language response.

    By grounding the LLM’s generation in the specific content of the product manuals, RAG ensures accuracy, reduces the risk of hallucinated information, and provides users with answers directly supported by the official documentation.

    +-------------------------------------+
| 1. User Input: Question about a     |
|    specific product manual.          |
|    (e.g., "How do I troubleshoot    |
|    the Widget Pro connection?")      |
|                                     |
|           Frontend (UI)             |
|        (HTML/JavaScript)            |
| +---------------------------------+ |
| | - Input Field                   | |
| | - Send Button                   | |
| +---------------------------------+ |
|               | (HTTP POST)         |
|               v                     |
+-------------------------------------+
               |
               |
+-------------------------------------+
| 2. Backend (API) receives the query |
|    and the specific product name     |
|    ("Widget Pro").                   |
|                                     |
|           Backend (API)             |
|        (FastAPI - )           |
| +---------------------------------+ |
| | - Receives Request              | |
| | - Generates Query Embedding     | |
| |   using Hugging Face Embedding  | |
| |   Model.                        | |
| +---------------------------------+ |
|               |                     |
|               v                     |
+-------------------------------------+
               |
               |
+-------------------------------------+
| 3. Backend queries Amazon           |
|    OpenSearch with the product name  |
|    and the generated query           |
|    embedding to find relevant       |
|    document chunks from the          |
|    "product_manuals" index.          |
|                                     |
|   Amazon OpenSearch (Vector ) |
| +---------------------------------+ |
| | - Stores embedded product manual| |
| |   chunks.                       | |
| | - Performs k-NN (k-Nearest       | |
| |   Neighbors) search based on      | |
| |   embedding similarity.          | |
| +---------------------------------+ |
|               | (Relevant Document Chunks) |
|               v                     |
+-------------------------------------+
               |
               |
+-------------------------------------+
| 4. Backend receives the relevant    |
|    document chunks from             |
|    OpenSearch.                      |
|                                     |
|           Backend (API)             |
|        (FastAPI - Python)           |
| +---------------------------------+ |
| | - Constructs a prompt for the    | |
| |   Hugging Face LLM, including     | |
| |   the retrieved context and the    | |
| |   user's question.               | |
| +---------------------------------+ |
|               | (Prompt)            |
|               v                     |
+-------------------------------------+
               |
               |
+-------------------------------------+
| 5. Backend sends the prompt to the   |
|    Hugging Face LLM for answer       |
|    generation.                      |
|                                     |
|        Hugging Face LLM              |
| +---------------------------------+ |
| | - Processes the prompt and        | |
| |   generates a natural language     | |
| |   answer based on the context.   | |
| +---------------------------------+ |
|               | (Generated Answer)   |
|               v                     |
+-------------------------------------+
               |
               |
+-------------------------------------+
| 6. Backend receives the generated   |
|    answer and the context snippets.  |
|                                     |
|           Backend (API)             |
|        (FastAPI - Python)           |
| +---------------------------------+ |
| | - Formats the answer and context  | |
| |   into a JSON response.          | |
| +---------------------------------+ |
|               | (HTTP Response)      |
|               v                     |
+-------------------------------------+
               |
               |
+-------------------------------------+
| 7. Frontend receives the JSON        |
|    response containing the answer    |
|    and the relevant context          |
|    snippets.                        |
|                                     |
|           Frontend (UI)             |
|        (HTML/JavaScript)            |
| +---------------------------------+ |
| | - Displays the AI's answer in     | |
| |   the chat window.               | |
| | - Optionally displays the         | |
| |   retrieved context for user      | |
| |   transparency.                  | |
| +---------------------------------+ |
+-------------------------------------+

    II. System Architecture

    Our intelligent chat agent application will follow a robust multi-tiered architecture:

    1. Frontend (UI): The user-facing interface for submitting queries and viewing responses.
    2. Backend (API): The core logic layer responsible for orchestrating the RAG pipeline, interacting with OpenSearch for retrieval, and calling the LLM for response generation.
    3. Amazon OpenSearch + Hugging Face LLM: The knowledge base (product manuals indexed in OpenSearch as vector embeddings) and the generative intelligence (LLM from Hugging Face Transformers).

    III. Key Components and Implementation Details

    Let’s delve into the implementation of each component:

    1. Backend (FastAPI – chatbot_opensearch_api.py):

    The backend API, built using FastAPI, will handle user requests and coordinate the RAG process.

    Python

    from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import boto3
import json
from opensearchpy import OpenSearch, RequestsHttpConnection
from requests_aws4auth import AWS4Auth
import os
from transformers import AutoTokenizer, AutoModel
from transformers import AutoModelForCausalLM
from fastapi.middleware.cors import CORSMiddleware

# --- Configuration (Consider Environment Variables for Security) ---
REGION_NAME = os.environ.get("AWS_REGION", "us-east-1")
OPENSEARCH_DOMAIN_ENDPOINT = os.environ.get("OPENSEARCH_ENDPOINT", "your-opensearch-domain.us-east-1.es.amazonaws.com")
OPENSEARCH_INDEX_NAME = os.environ.get("OPENSEARCH_INDEX", "product_manuals")
EMBEDDING_MODEL_NAME = os.environ.get("EMBEDDING_MODEL", "sentence-transformers/all-mpnet-base-v2")
LLM_MODEL_NAME = os.environ.get("LLM_MODEL", "google/flan-t5-large")

# Initialize  credentials (Consider using IAM roles for better security)
credentials = boto3.Session().get_credentials()
awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, REGION_NAME, 'es', session_token=credentials.token)

# Initialize OpenSearch client
os_client = OpenSearch(
    hosts=&lsqb;{'host': OPENSEARCH_DOMAIN_ENDPOINT, 'port': 443}],
    http_auth=awsauth,
    use_ssl=True,
    verify_certs=True,
    ssl_assert_hostname=False,
    ssl_show_warn=False,
    connection_class=RequestsHttpConnection
)

# Initialize Hugging Face tokenizer and model for embeddings
try:
    embedding_tokenizer = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)
    embedding_model = AutoModel.from_pretrained(EMBEDDING_MODEL_NAME)
except Exception as e:
    print(f"Error loading embedding model: {e}")
    embedding_tokenizer = None
    embedding_model = None

# Initialize Hugging Face tokenizer and model for LLM
try:
    llm_tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_NAME)
    llm_model = AutoModelForCausalLM.from_pretrained(LLM_MODEL_NAME)
except Exception as e:
    print(f"Error loading LLM model: {e}")
    llm_tokenizer = None
    llm_model = None

app = FastAPI(title="Product Manual  API (OpenSearch - No Bedrock)")

# Add CORS middleware to allow requests from your frontend
app.add_middleware(
    CORSMiddleware,
    allow_origins=&lsqb;"*"],  # Adjust to your frontend's origin for production
    allow_credentials=True,
    allow_methods=&lsqb;"POST"],
    allow_headers=&lsqb;"*"],
)

class ChatRequest(BaseModel):
    product_name: str
    user_question: str

class ChatResponse(BaseModel):
    answer: str
    context: List&lsqb;str] = &lsqb;]

def get_embedding(text, tokenizer, model):
    """Generates an embedding for the given text using Hugging Face Transformers."""
    if tokenizer and model:
        try:
            inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
            outputs = model(**inputs)
            return outputs.last_hidden_state.mean(dim=1).detach().numpy().tolist()&lsqb;0]
        except Exception as e:
            print(f"Error generating embedding: {e}")
            return None
    return None

def search_opensearch(index_name, product_name, query, tokenizer, embedding_model, k=3):
    """Searches OpenSearch for relevant documents."""
    embedding = get_embedding(query, tokenizer, embedding_model)
    if embedding:
        search_query = {
            "size": k,
            "query": {
                "bool": {
                    "must": &lsqb;
                        {"match": {"product_name": product_name}}
                    ],
                    "should": &lsqb;
                        {
                            "knn": {
                                "embedding": {
                                    "vector": embedding,
                                    "k": k
                                }
                            }
                        },
                        {"match": {"content": query}} # Basic keyword matching as a fallback/boost
                    ]
                }
            }
        }
        try:
            res = os_client.search(index=index_name, body=search_query)
            hits = res&lsqb;'hits']&lsqb;'hits']
            sources = &lsqb;hit&lsqb;'_source']&lsqb;'content'] for hit in hits]
            return sources, &lsqb;hit&lsqb;'_source']&lsqb;'content']&lsqb;:100] + "..." for hit in hits] # Return full content and snippets
        except Exception as e:
            print(f"Error searching OpenSearch: {e}")
            return &lsqb;], &lsqb;]
    return &lsqb;], &lsqb;]

def generate_answer(prompt, tokenizer, model):
    """Generates an answer using the specified Hugging Face LLM."""
    if tokenizer and model:
        try:
            inputs = tokenizer(prompt, return_tensors="pt")
            outputs = model.generate(**inputs, max_length=500)
            return tokenizer.decode(outputs&lsqb;0], skip_special_tokens=True)
        except Exception as e:
            print(f"Error generating answer: {e}")
            return "An error occurred while generating the answer."
    return "LLM model not loaded."

@app.post("/chat/", response_model=ChatResponse)
async def chat_with_manual(request: ChatRequest):
    """Endpoint for querying the product manuals."""
    context_snippets, context_display = search_opensearch(OPENSEARCH_INDEX_NAME, request.product_name, request.user_question, embedding_tokenizer, embedding_model)

    if context_snippets:
        context = "\n\n".join(context_snippets)
        prompt = f"""You are a helpful chatbot assistant for product manuals related to the product '{request.product_name}'. Use the following information from the manuals to answer the user's question. If the information doesn't directly answer the question, try to infer or provide related helpful information. Do not make up information.

        <context>
        {context}
        </context>

        User Question: {request.user_question}
        """
        answer = generate_answer(prompt, llm_tokenizer, llm_model)
        return {"answer": answer, "context": context_display}
    else:
        raise HTTPException(status_code=404, detail="No relevant information found in the product manuals for that product.")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

    2. Frontend (frontend/templates/index.html and frontend/static/style.css):

    frontend/templates/index.html

    <!DOCTYPE html>
<html>
<head>
    <title>Chat Agent</title>
    <link rel="stylesheet" type="text/css" href="{{ url_for('static', path='style.css') }}">
</head>
<body>
    <div class="chat-container">
        <div class="chat-history" id="chat-history">
            <div class="bot-message">Welcome! Ask me anything.</div>
        </div>
        <div class="chat-input">
            <form id="chat-form">
                <input type="text" id="user-input" placeholder="Type your message...">
                <button type="submit">Send</button>
            </form>
        </div>
        <div class="context-display" id="context-display">
            <strong>Retrieved Context:</strong>
            <ul id="context-list"></ul>
        </div>
    </div>

    <script>
        const chatForm = document.getElementById('chat-form');
        const userInput = document.getElementById('user-input');
        const chatHistory = document.getElementById('chat-history');
        const contextDisplay = document.getElementById('context-display');
        const contextList = document.getElementById('context-list');

        chatForm.addEventListener('submit', async (event) => {
            event.preventDefault();
            const message = userInput.value.trim();
            if (message) {
                appendMessage('user', message);
                userInput.value = '';

                const response = await fetch('/chat/', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/x-www-form-urlencoded',
                    },
                    body: new URLSearchParams({ user_input: message }),
                });

                if (response.ok) {
                    const data = await response.json();
                    appendMessage('bot', data.response);
                    displayContext(data.context);
                } else {
                    appendMessage('bot', 'Error processing your request.');
                }
            }
        });

        function appendMessage(sender, text) {
            const messageDiv = document.createElement('div');
            messageDiv.classList.add(`${sender}-message`);
            messageDiv.textContent = text;
            chatHistory.appendChild(messageDiv);
            chatHistory.scrollTop = chatHistory.scrollHeight; // Scroll to bottom
        }

        function displayContext(context) {
            contextList.innerHTML = ''; // Clear previous context
            if (context && context.length > 0) {
                contextDisplay.style.display = 'block';
                context.forEach(doc => {
                    const listItem = document.createElement('li');
                    listItem.textContent = doc;
                    contextList.appendChild(listItem);
                });
            } else {
                contextDisplay.style.display = 'none';
            }
        }
    </script>
</body>
</html>

    frontend/static/style.css

    body {
    font-family: sans-serif;
    margin: 20px;
    background-color: #f4f4f4;
}

.chat-container {
    max-width: 600px;
    margin: 0 auto;
    background-color: #fff;
    border-radius: 8px;
    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
    padding: 20px;
}

.chat-history {
    height: 300px;
    overflow-y: auto;
    padding: 10px;
    margin-bottom: 10px;
    border: 1px solid #ddd;
    border-radius: 4px;
    background-color: #eee;
}

.user-message {
    background-color: #e2f7cb;
    color: #333;
    padding: 8px 12px;
    border-radius: 6px;
    margin-bottom: 8px;
    align-self: flex-end;
    width: fit-content;
    max-width: 80%;
}

.bot-message {
    background-color: #f0f0f0;
    color: #333;
    padding: 8px 12px;
    border-radius: 6px;
    margin-bottom: 8px;
    width: fit-content;
    max-width: 80%;
}

.chat-input {
    display: flex;
}

.chat-input input&lsqb;type="text"] {
    flex-grow: 1;
    padding: 10px;
    border: 1px solid #ccc;
    border-radius: 4px 0 0 4px;
}

.chat-input button {
    padding: 10px 15px;
    border: none;
    background-color: #007bff;
    color: white;
    border-radius: 0 4px 4px 0;
    cursor: pointer;
}

.context-display {
    margin-top: 20px;
    padding: 10px;
    border: 1px solid #ddd;
    border-radius: 4px;
    background-color: #f9f9f9;
    display: none; /* Hidden by default */
}

.context-display ul {
    list-style-type: none;
    padding: 0;
}

.context-display li {
    margin-bottom: 5px;
}

    3. Knowledge Base and Vector Database (Amazon OpenSearch):

    Before running the chat agent, you need to ingest your product manuals into Amazon OpenSearch. This involves the following steps, typically performed by an ingestion script (ingestion_opensearch.py):

    • Extract Text from Manuals: Read PDF files from a source (e.g., Amazon S3) and extract their text content.
    • Chunk the Text: Divide the extracted text into smaller, manageable chunks.
    • Generate Embeddings: Use the same embedding model (sentence-transformers/all-mpnet-base-v2 in our example) to generate vector embeddings for each text chunk.
    • Index into OpenSearch: Create an OpenSearch index with a knn_vector field and index each text chunk along with its embedding and associated metadata (e.g., product name).

    (The ingestion_opensearch.py script provided in the earlier response details this process.)

    4. LLM (Hugging Face Transformers):

    The backend API utilizes a pre-trained LLM (google/flan-t5-large in the example) from Hugging Face Transformers to generate the final answer based on the retrieved context and the user’s question.

    IV. Running the Complete Application:

    1. Set up AWS and OpenSearch: Ensure you have an AWS account and an Amazon OpenSearch domain configured.
    2. Upload Manuals to S3: Place your product manual PDF files in an S3 bucket.
    3. Run Ingestion Script: Execute the ingestion_opensearch.py script (after configuring the AWS credentials, S3 bucket name, and OpenSearch endpoint) to process your manuals and index them into OpenSearch.
    4. Save Frontend Files: Create the frontend folder with the static/style.css and templates/index.html files.
    5. Install Backend Dependencies: Navigate to the directory containing chatbot_opensearch_api.py and install the required Python libraries: Bashpip install fastapi uvicorn boto3 opensearch-py requests-aws4auth transformers
    6. Run Backend API: Execute the FastAPI application: Bashpython chatbot_opensearch_api.py The API will typically start at http://localhost:8000.
    7. Open Frontend: Open your web browser and navigate to http://localhost:8000. You should see the chat interface. Enter the product name and your question, and the will query OpenSearch, retrieve relevant information, and generate an answer.

    V. Conclusion and Future Enhancements:

    This comprehensive guide has outlined the architecture and implementation of an intelligent Chat Agent UI application specifically designed to answer questions based on product manuals using the powerful combination of RAG, Amazon OpenSearch, and open-source LLMs from Hugging Face Transformers. By leveraging semantic search over indexed product manual content and employing a language model for natural language generation, this approach provides a robust and scalable solution for enhancing customer support and user experience.

    To further enhance this application, consider implementing the following:

    • More Sophisticated Chunking Strategies: Explore advanced techniques for splitting documents to improve retrieval relevance.
    • Metadata Filtering in OpenSearch: Allow users to filter searches by specific manual sections or product versions.
    • Improved Prompt Engineering: Experiment with different prompt structures to optimize the LLM’s answer quality and style.
    • User Feedback Mechanism: Integrate a way for users to provide feedback on the AI’s responses to facilitate continuous improvement.
    • More Advanced UI Features: Enhance the user interface with features like conversation history persistence, different response formats, and clearer display of retrieved context.
    • Integration with User Authentication: Secure the application and potentially personalize the experience based on user roles or product ownership.
    • Handling of Different Document Formats: Extend the ingestion pipeline to support other document types beyond PDF.

    By continuously refining these aspects, you can build a highly effective and user-friendly chat agent that significantly improves access to information within your product manuals.

  • Loading manuals into a vector database


    Here’s a breakdown of how to load manuals into a vector , focusing on the key steps and considerations:

    1. Choose a Vector Database:

    Several vector databases are available, each with its own strengths and weaknesses.1 Some popular options include:

    • Pinecone: A fully managed, cloud-native vector database known for its ease of use and scalability.2
    • Weaviate: An open-source, cloud-native vector database with built-in machine learning modules.3
    • Milvus: An open-source vector database designed for large-scale vector data management.4
    • Qdrant: A vector similarity search engine and database with a focus on ease of use and production readiness.5
    • Chroma: An open-source vector database specifically designed for building applications.
    • pgvector: An open-source extension for PostgreSQL that adds vector data type and indexing.6

    Consider factors like scalability, ease of use, cost, integration with your existing stack, and specific features when making your choice.

    2. Extract Text from Manuals:

    Most manuals are in PDF format. You’ll need to extract the text content from these files. libraries like PyPDF2, pdfminer.six, or unstructured can be used for this purpose.7 Be mindful of complex layouts, tables, and images, which might require more sophisticated extraction techniques.

    3. Chunk the Text:

    Large documents like manuals need to be split into smaller, manageable chunks. This is crucial for several reasons:

    • LLM Context Window Limits: Language models have limitations on the amount of text they can process at once.8
    • Relevance: Smaller chunks are more likely to contain focused and relevant information for a given query.
    • Vector Embeddings: Generating embeddings for very long sequences can be less effective.

    Common chunking strategies include:

    • Fixed-size chunking: Splitting text into chunks of a predefined number of tokens or characters.9 Overlapping chunks can help preserve context across boundaries.
    • Sentence-based chunking: Splitting text at sentence boundaries.
    • Paragraph-based chunking: Splitting text at paragraph breaks.
    • Semantic chunking: Using NLP techniques to identify semantically meaningful units.
    • Content-aware chunking: Tailoring chunking strategies based on the document structure (e.g., splitting by headings, subheadings).

    The optimal chunk size and strategy often depend on the specific characteristics of your manuals and the capabilities of your chosen embedding model and LLM. Experimentation is key.

    4. Generate Vector Embeddings:

    Once you have your text chunks, you need to convert them into vector embeddings. These embeddings are numerical representations of the semantic meaning of the text. You can use various embedding models for this, such as:

    • Sentence Transformers: Pre-trained models that produce high-quality sentence and paragraph embeddings.10
    • OpenAI Embeddings : Provides access to powerful embedding models.11
    • Hugging Face Transformers: Offers a wide range of pre-trained models that you can use.12

    Choose an embedding model that aligns with your desired level of semantic understanding and the language of your manuals.

    5. Load Embeddings and Text into the Vector Database:

    Finally, you’ll load the generated vector embeddings along with the corresponding text chunks and any relevant metadata (e.g., manual name, page number, chunk number) into your chosen vector database. Each record in the database will typically contain:

    • Vector Embedding: The numerical representation of the text chunk.
    • Text Chunk: The original text segment.
    • Metadata: Additional information to help with filtering and context.13

    Most vector databases offer client libraries (e.g., Python clients) that simplify the process of connecting to the database and inserting data. You’ll iterate through your processed manual chunks, generate embeddings, and then use the database’s API to add each embedding, text, and its associated metadata as a new entry.

    Example Workflow (Conceptual – Python with Pinecone and Sentence Transformers):

    Python

    from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import pinecone

# --- Configuration ---
PDF_PATH = "path/to/your/manual.pdf"
PINECONE_API_KEY = "YOUR_PINECONE_API_KEY"
PINECONE_ENVIRONMENT = "YOUR_PINECONE_ENVIRONMENT"
PINECONE_INDEX_NAME = "manual-index"
EMBEDDING_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
CHUNK_SIZE = 512
CHUNK_OVERLAP = 100

# --- Initialize Pinecone and Embedding Model ---
pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
if PINECONE_INDEX_NAME not in pinecone.list_indexes():
    pinecone.create_index(PINECONE_INDEX_NAME, dimension=768) # Adjust dimension
index = pinecone.Index(PINECONE_INDEX_NAME)
embedding_model = SentenceTransformer(EMBEDDING_MODEL_NAME)

# --- Function to Extract Text from PDF ---
def extract_text_from_pdf(pdf_path):
    text = ""
    with open(pdf_path, 'rb') as file:
        pdf_reader = PdfReader(file)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

# --- Function to Chunk Text ---
def chunk_text(text, chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP):
    chunks = &lsqb;]
    start = 0
    while start < len(text):
        end = min(start + chunk_size, len(text))
        chunk = text&lsqb;start:end]
        chunks.append(chunk)
        start += chunk_size - chunk_overlap
    return chunks

# --- Main Processing ---
text = extract_text_from_pdf(PDF_PATH)
chunks = chunk_text(text)
embeddings = embedding_model.encode(chunks)

# --- Load into Vector Database ---
batch_size = 100
for i in range(0, len(chunks), batch_size):
    i_end = min(len(chunks), i + batch_size)
    batch_chunks = chunks&lsqb;i:i_end]
    batch_embeddings = embeddings&lsqb;i:i_end]
    metadata = &lsqb;{"text": chunk, "manual": "your_manual_name", "chunk_id": f"{i+j}"} for j, chunk in enumerate(batch_chunks)]
    vectors = zip(range(i, i_end), batch_embeddings, metadata)
    index.upsert(vectors=vectors)

print(f"Successfully loaded {len(chunks)} chunks into Pinecone.")

    Remember to replace the placeholder values with your actual API keys, environment details, file paths, and adjust chunking parameters and metadata as needed. You’ll also need to adapt this code to the specific client library of the vector database you choose.

  • Building a Product Manual Chatbot with Amazon OpenSearch and Open-Source LLMs

    This article guides you through building an intelligent that can answer questions based on your product manuals, leveraging the power of Amazon OpenSearch for semantic search and open-source Large Language Models (LLMs) for generating informative responses. This approach provides a cost-effective and customizable solution without relying on Amazon Bedrock.

    The Challenge:

    Navigating through lengthy product manuals can be time-consuming and frustrating for users. A chatbot that understands natural language queries and retrieves relevant information directly from these manuals can significantly improve user experience and support efficiency.1

    Our Solution: OpenSearch and Open-Source LLMs

    This article demonstrates how to build such a chatbot using the following key components:

    1. Amazon OpenSearch Service: A scalable search and analytics service that we’ll use as a vector to store document embeddings and perform semantic search.2
    2. Hugging Face Transformers: A powerful library providing access to thousands of pre-trained language models, including those for generating text embeddings.3
    3. Open-Source Large Language Model (): We’ll outline how to integrate with an open-source LLM (running locally or via an ) to generate answers based on the retrieved information.
    4. FastAPI: A modern, high-performance web framework for building the chatbot API.4
    5. SDK for Python (Boto3): Used for interacting with Amazon S3 (where product manuals are stored) and OpenSearch.5

    Architecture:

    The architecture consists of two main parts:

    1. Ingestion Pipeline:
    • Product manuals (in PDF format) are stored in an Amazon S3 bucket.
    • A Python script (ingestion_opensearch.py) extracts text content from these PDFs.
    • It uses a Hugging Face Transformer model to generate vector embeddings for the extracted text.
    • The text content, associated product name, and the generated embeddings are indexed into an Amazon OpenSearch cluster.
    1. Chatbot API:
    • A FastAPI application (chatbot_opensearch_api.py) exposes a /chat/ endpoint.
    • When a user sends a question (along with the product name), the API:
    • Uses the same Hugging Face Transformer model to generate an embedding for the user’s query.
    • Queries the Amazon OpenSearch index to find the most semantically similar document snippets for the given product.
    • Constructs a prompt containing the retrieved context and the user’s question.
    • Sends this prompt to an open-source LLM (you’ll need to integrate your chosen LLM here).
    • Returns the LLM’s generated answer to the user.

    Step-by-Step Implementation:

    1. Prerequisites:

    • AWS Account: You need an active AWS account.
    • Amazon OpenSearch Cluster: Set up an Amazon OpenSearch domain.
    • Amazon S3 Bucket: Create an S3 bucket and upload your product manuals (in PDF format) into it.
    • Python Environment: Ensure you have Python 3.6 or later installed, along with pip.
    • Install Necessary Libraries:
      Bash
      pip install fastapi uvicorn boto3 opensearch-py requests-aws4auth transformers PyPDF2 # Or your preferred PDF library

    2. Ingestion Script (ingestion_opensearch.py):

    Python

    # (See the `ingestion_opensearch.py` code from the previous response)

    Key points in the ingestion script:

    • OpenSearch Client Initialization: Configured to connect to your OpenSearch domain. Remember to replace the placeholder endpoint.
    • Hugging Face Model Loading: Loads a pre-trained sentence transformer model for generating embeddings.
    • OpenSearch Index Creation: Creates an index with a knn_vector field to store embeddings. The dimension of the vector field is determined by the chosen embedding model.
    • PDF Text Extraction: You need to implement the actual PDF parsing logic using a library like PyPDF2 or pdfminer.six within the ingest_pdfs_from_s3 function. The provided code has a placeholder.
    • Embedding Generation: Uses the Hugging Face model to create embeddings for the extracted text.
    • Indexing into OpenSearch: Stores the product name, content, and embedding in the OpenSearch index.

    3. Chatbot API (chatbot_opensearch_api.py):

    Key points in the API script:

    • OpenSearch Client Initialization: Configured to connect to your OpenSearch domain. Remember to replace the placeholder endpoint.
    • Hugging Face Model Loading: Loads the same embedding model as the ingestion script for generating query embeddings.
    • search_opensearch Function:
    • Generates an embedding for the user’s question.
    • Constructs an OpenSearch query that combines keyword matching (on product name and content) with a k-nearest neighbors (KNN) search on the embeddings to find semantically similar documents.
    • generate_answer Function: This is a placeholder. You need to integrate your chosen open-source LLM here. This could involve:
    • Running an LLM locally using Hugging Face Transformers (requires significant computational resources).
    • Using an API for an open-source LLM hosted elsewhere.
    • API Endpoint (/chat/): Retrieves relevant context from OpenSearch and then uses the generate_answer function to respond to the user’s query.

    4. Running the Application:

    1. Run the Ingestion Script: Execute python ingestion_opensearch.py to process your product manuals and index them into OpenSearch.
    2. Run the Chatbot API: Execute python chatbot_opensearch_api.py to start the API server:
      Bash
      uvicorn chatbot_opensearch_api:app –reload
      The API will be accessible at http://localhost:8000.

    5. Interacting with the Chatbot API:

    You can send POST requests to the /chat/ endpoint with the product_name and user_question in the JSON body. For example, using curl:


    Integrating an Open-Source LLM (Placeholder):

    The most crucial part to customize is the generate_answer function in chatbot_opensearch_api.py. Here are some potential approaches:

    • Hugging Face Transformers for Local LLM:
      Python
      from transformers import AutoModelForCausalLM, AutoTokenizer

      llm_model_name = “google/flan-t5-large” # Example open-source LLM
      llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
      llm_model = AutoModelForCausalLM.from_pretrained(llm_model_name)

      def generate_answer(prompt):
          inputs = llm_tokenizer(prompt, return_tensors=”pt”)
          outputs = llm_model.generate(**inputs, max_length=500)
          return llm_tokenizer.decode(outputs[0], skip_special_tokens=True)

      Note: Running large LLMs locally can be very demanding on your hardware (CPU/GPU, RAM).
    • API for Hosted Open-Source LLMs: Explore services that provide APIs for open-source LLMs. You would make HTTP requests to their endpoints within the generate_answer function.

    Conclusion:

    Building a product manual chatbot with Amazon OpenSearch and open-source LLMs offers a powerful and flexible alternative to managed platforms. By leveraging OpenSearch for efficient semantic search and integrating with the growing ecosystem of open-source LLMs, you can create an intelligent and cost-effective solution to enhance user support and accessibility to your product documentation. Remember to carefully choose and integrate an LLM that meets your performance and resource constraints.

  • Integrating Documentum with an Amazon Bedrock Chatbot API for Product Manuals

    This article outlines the process of building a product manual using Amazon Bedrock, with a specific focus on integrating content sourced from a Documentum repository. By leveraging the power of vector embeddings and Large Language Models (LLMs) within Bedrock, we can create an intelligent and accessible way for users to find information within extensive product documentation managed by Documentum.

    The Need for Integration:

    Many organizations manage their critical product documentation within enterprise content management systems like Documentum. To make this valuable information readily available to users through modern conversational interfaces, a seamless integration with -powered platforms like Amazon Bedrock is essential. This allows users to ask natural language questions and receive accurate, contextually relevant answers derived from the product manuals.

    Architecture Overview:

    The proposed architecture involves the following key components:

    1. Documentum Repository: The central content management system storing the product manuals.
    2. Document Extraction Service: A custom-built service responsible for accessing Documentum, retrieving relevant product manuals and their content, and potentially extracting associated metadata.
    3. Amazon S3: An object storage service used as an intermediary staging area for the extracted documents. Bedrock’s Knowledge Base can directly ingest data from S3.
    4. Amazon Bedrock Knowledge Base: A managed service that ingests and processes the documents from S3, creates vector embeddings, and enables efficient semantic search.
    5. Chatbot API (FastAPI): A -based API built using FastAPI, providing endpoints for users to query the product manuals. This API interacts with the Bedrock Knowledge Base for retrieval and an for answer generation.
    6. Bedrock LLM: A Large Language Model (e.g., Anthropic Claude) within Amazon Bedrock used to generate human-like answers based on the retrieved context.

    Step-by-Step Implementation:

    1. Documentum Extraction Service:

    This is a crucial custom component. The implementation will depend on your Documentum environment and preferred programming language.

    • Accessing Documentum: Utilize the Documentum Content Server API (DFC) or the Documentum REST API to establish a connection. This will involve handling authentication and session management.
    • Document Retrieval: Implement logic to query and retrieve the specific product manuals intended for the chatbot. You might filter based on document types, metadata (e.g., product name, version), or other relevant criteria.
    • Content Extraction: Extract the actual textual content from the retrieved documents. This might involve handling various file formats (PDF, DOCX, etc.) and ensuring clean text extraction.
    • Metadata Extraction (Optional): Extract relevant metadata associated with the documents. While Bedrock primarily uses content for embeddings, this metadata could be useful for future enhancements or filtering within the extraction process.
    • Data Preparation: Structure the extracted content and potentially metadata. You can save each document as a separate file or create structured JSON files.
    • Uploading to S3: Use the AWS SDK for Python (boto3) to upload the prepared files to a designated S3 bucket in your AWS account. Organize the files logically within the bucket (e.g., by product).

    Conceptual Python Snippet (Illustrative – Replace with actual Documentum interaction):

    Python

    import boto3
# Assuming you have a library or logic to interact with Documentum

# AWS Configuration
REGION_NAME = "us-east-1"
S3_BUCKET_NAME = "your-bedrock-ingestion-bucket"
s3_client = boto3.client('s3', region_name=REGION_NAME)

def extract_and_upload_document(documentum_document_id, s3_prefix="documentum/"):
    """
    Conceptual function to extract content from Documentum and upload to S3.
    Replace with your actual Documentum interaction.
    """
    # --- Replace this with your actual Documentum API calls ---
    content = f"Content of Document {documentum_document_id} from Documentum."
    filename = f"{documentum_document_id}.txt"
    # --- End of Documentum interaction ---

    s3_key = os.path.join(s3_prefix, filename)
    try:
        s3_client.put_object(Bucket=S3_BUCKET_NAME, Key=s3_key, Body=content.encode('utf-8'))
        print(f"Uploaded {filename} to s3://{S3_BUCKET_NAME}/{s3_key}")
        return True
    except Exception as e:
        print(f"Error uploading {filename} to S3: {e}")
        return False

if __name__ == "__main__":
    documentum_ids_to_ingest = &lsqb;"product_manual_123", "installation_guide_456"]
    for doc_id in documentum_ids_to_ingest:
        extract_and_upload_document(doc_id)

    2. Amazon S3 Configuration:

    Ensure you have an S3 bucket created in your AWS account where the Documentum extraction service will upload the product manuals.

    3. Amazon Bedrock Knowledge Base Setup:

    • Navigate to the Amazon Bedrock service in the AWS Management Console.
    • Create a new Knowledge Base.
    • When configuring the data source, select “Amazon S3” as the source type.
    • Specify the S3 bucket and the prefix (e.g., documentum/) where the Documentum extraction service uploads the files.
    • Configure the synchronization settings for the data source. You can choose on-demand synchronization or set up a schedule for periodic updates.
    • Bedrock will then process the documents in the S3 bucket, chunk them, generate vector embeddings, and build an index for efficient retrieval.

    4. Chatbot API (FastAPI):

    Create a Python-based API using FastAPI to handle user queries and interact with the Bedrock Knowledge Base.

    Python

    # chatbot_api.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import boto3
import json
import os

# Configuration
REGION_NAME = "us-east-1"  # Replace with your AWS region
KNOWLEDGE_BASE_ID = "kb-your-knowledge-base-id"  # Replace with your Knowledge Base ID
LLM_MODEL_ID = "anthropic.claude-v3-opus-20240229"  # Replace with your desired LLM model ID

bedrock_runtime = boto3.client("bedrock-runtime", region_name=REGION_NAME)
bedrock_knowledge = boto3.client("bedrock-agent-runtime", region_name=REGION_NAME)

app = FastAPI(title="Product Manual Chatbot API")

class ChatRequest(BaseModel):
    product_name: str  # Optional: If you have product-specific manuals
    user_question: str

class ChatResponse(BaseModel):
    answer: str

def retrieve_pdf_context(knowledge_base_id, product_name, user_question, max_results=3):
    """Retrieves relevant document snippets from the Knowledge Base."""
    query = user_question # The Knowledge Base handles semantic search across all ingested data
    if product_name:
        query = f"Information about {product_name} related to: {user_question}"

    try:
        response = bedrock_knowledge.retrieve(
            knowledgeBaseId=knowledge_base_id,
            retrievalConfiguration={
                "vectorSearchConfiguration": {
                    "query": {
                        "text": query
                    }
                }
            },
            retrieveMaxResults=max_results
        )
        results = response.get("retrievalResults", &lsqb;])
        if results:
            context_texts = &lsqb;result.get("content", {}).get("text", "") for result in results]
            return "\n\n".join(context_texts)
        else:
            return None
    except Exception as e:
        print(f"Error during retrieval: {e}")
        raise HTTPException(status_code=500, detail="Error retrieving context")

def generate_answer(prompt, model_id=LLM_MODEL_ID):
    """Generates an answer using the specified Bedrock LLM."""
    try:
        if model_id.startswith("anthropic"):
            body = json.dumps({"prompt": prompt, "max_tokens_to_sample": 500, "temperature": 0.6, "top_p": 0.9})
            mime_type = "application/json"
        elif model_id.startswith("ai21"):
            body = json.dumps({"prompt": prompt, "maxTokens": 300, "temperature": 0.7, "topP": 1})
            mime_type = "application/json"
        elif model_id.startswith("cohere"):
            body = json.dumps({"prompt": prompt, "max_tokens": 300, "temperature": 0.7, "p": 0.7})
            mime_type = "application/json"
        else:
            raise HTTPException(status_code=400, detail=f"Model ID '{model_id}' not supported")

        response = bedrock_runtime.invoke_model(body=body, modelId=model_id, accept=mime_type, contentType=mime_type)
        response_body = json.loads(response.get("body").read())

        if model_id.startswith("anthropic"):
            return response_body.get("completion").strip()
        elif model_id.startswith("ai21"):
            return response_body.get("completions")&lsqb;0].get("data").get("text").strip()
        elif model_id.startswith("cohere"):
            return response_body.get("generations")&lsqb;0].get("text").strip()
        else:
            return None

    except Exception as e:
        print(f"Error generating answer with model '{model_id}': {e}")
        raise HTTPException(status_code=500, detail=f"Error generating answer with LLM")

@app.post("/chat/", response_model=ChatResponse)
async def chat_with_manual(request: ChatRequest):
    """Endpoint for querying the product manuals."""
    context = retrieve_pdf_context(KNOWLEDGE_BASE_ID, request.product_name, request.user_question)

    if context:
        prompt = f"""You are a helpful chatbot assistant for product manuals. Use the following information to answer the user's question. If the information doesn't directly answer, try to infer or provide related helpful information. Do not make up information.

        <context>
        {context}
        </context>

        User Question: {request.user_question}
        """
        answer = generate_answer(prompt)
        if answer:
            return {"answer": answer}
        else:
            raise HTTPException(status_code=500, detail="Could not generate an answer")
    else:
        raise HTTPException(status_code=404, detail="No relevant information found")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

    5. Bedrock LLM for Answer Generation:

    The generate_answer function in the API interacts with a chosen LLM within Bedrock (e.g., Anthropic Claude) to formulate a response based on the retrieved context from the Knowledge Base and the user’s question.

    Deployment and Scheduling:

    • Document Extraction Service: This service can be deployed as a scheduled job (e.g., using AWS Lambda and CloudWatch Events) to periodically synchronize content from Documentum to S3, ensuring the Knowledge Base stays up-to-date.
    • Chatbot API: The FastAPI application can be deployed on various platforms like AWS ECS, AWS Lambda with API Gateway, or EC2 instances.

    Conclusion:

    Integrating Documentum with an Amazon Bedrock chatbot API for product manuals offers a powerful way to unlock valuable information and provide users with an intuitive and efficient self-service experience. By building a custom extraction service to bridge the gap between Documentum and Bedrock’s data source requirements, organizations can leverage the advanced AI capabilities of Bedrock to create intelligent conversational interfaces for their product documentation. This approach enhances accessibility, improves user satisfaction, and reduces the reliance on manual document searching. Remember to carefully plan the Documentum extraction process, considering factors like scalability, incremental updates, and error handling to ensure a robust and reliable solution.

  • Distinguish the use cases for the primary vector database options on AWS:

    Here we try to distinguish the use cases for the primary vector options on :

    1. Amazon OpenSearch Service (with Vector Engine):

    • Core Strength: General-purpose, highly scalable, and performant vector database with strong integration across the AWS ecosystem.1 Offers a balance of flexibility and managed services.2
    • Ideal Use Cases:
      • Large-Scale Semantic Search: When you have a significant volume of unstructured text or other data (documents, articles, product descriptions) and need users to find information based on meaning and context, not just keywords. This includes enterprise search, knowledge bases, and content discovery platforms.
      • Retrieval Augmented Generation () for Large Language Models (LLMs): Providing LLMs with relevant context from a vast knowledge base to improve the accuracy and factual grounding of their responses in chatbots, question answering systems, and content generation tools.3
      • Recommendation Systems: Building sophisticated recommendation engines that suggest items (products, movies, music) based on semantic similarity to user preferences or previously interacted items.4 Can handle large catalogs and user bases.
      • Anomaly Detection: Identifying unusual patterns or outliers in high-dimensional data by measuring the distance between data points in the vector space.5 Useful for fraud detection, cybersecurity, and predictive maintenance.6
      • Image and Video Similarity Search: Finding visually similar images or video frames based on their embedded feature vectors.7 Applications include content moderation, image recognition, and video analysis.
      • Multi-Modal Search: Combining text, images, audio, and other data types into a unified vector space to enable search across different modalities.8

    2. Amazon Bedrock Knowledge Bases (with underlying vector store choices):

    • Core Strength: Fully managed service specifically designed to simplify the creation and management of knowledge bases for RAG applications with LLMs.9 Abstracts away much of the underlying infrastructure and integration complexities.
    • Ideal Use Cases:
      • Rapid Prototyping and Deployment of RAG Chatbots: Quickly building conversational agents that can answer questions and provide information based on your specific data.
      • Internal Knowledge Bases for Employees: Creating searchable repositories of company documents, policies, and procedures to improve employee productivity and access to information.
      • Customer Support Chatbots: Enabling chatbots to answer customer inquiries accurately by grounding their responses in relevant product documentation, FAQs, and support articles.
      • Building Generative AI Applications Requiring Context: Any application where an needs access to external, up-to-date information to generate relevant and accurate content.10
    • Considerations: While convenient, it might offer less granular control over the underlying vector store compared to directly using OpenSearch or other options. The choice of underlying vector store (Aurora with pgvector, Neptune Analytics, OpenSearch Serverless, Pinecone, Enterprise Cloud) will further influence performance and cost characteristics for specific RAG workloads.

    3. Amazon Aurora PostgreSQL/RDS for PostgreSQL (with pgvector):

    • Core Strength: Integrates vector search capabilities within a familiar relational database. Suitable for applications that already rely heavily on PostgreSQL and have vector search as a secondary or tightly coupled requirement.
    • Ideal Use Cases:
      • Hybrid Search Applications: When you need to combine traditional SQL queries with vector similarity search on the same data. For example, filtering products by category and then ranking them by semantic similarity to a user’s query.
      • Smaller to Medium-Scale Vector Search: Works well for datasets that fit comfortably within a PostgreSQL instance and don’t have extremely demanding low-latency requirements.
      • Applications with Existing PostgreSQL Infrastructure: Leveraging your existing database infrastructure to add vector search functionality without introducing a new dedicated vector database.
      • Geospatial Vector Search: pgvector has extensions that can efficiently handle both vector embeddings and geospatial data.

    4. Amazon Neptune Analytics (with Vector Search):

    • Core Strength: Combines graph database capabilities with vector search, allowing you to perform semantic search on interconnected data and leverage relationships for more contextually rich results.
    • Ideal Use Cases:
      • Knowledge Graphs with Semantic Search: When your data is highly interconnected, and you want to search not only based on keywords or relationships but also on the semantic meaning of the nodes and edges.
      • Recommendation Systems Based on Connections and Similarity: Suggesting items based on both user interactions (graph relationships) and the semantic similarity of items.
      • Complex Information Retrieval on Linked Data: Navigating and querying intricate datasets where understanding the relationships between entities is crucial for effective search.
      • Drug Discovery and Biomedical Research: Analyzing relationships between genes, proteins, and diseases, combined with semantic similarity of research papers or biological entities.11

    5. Vector Search for Amazon MemoryDB for Redis:

    • Core Strength: Provides extremely low-latency, in-memory vector search for real-time applications.
    • Ideal Use Cases:
      • Real-time Recommendation Engines: Generating immediate and personalized recommendations based on recent user behavior or context.
      • Low-Latency Semantic Caching: Caching semantically similar results to improve the speed of subsequent queries.12
      • Real-time Anomaly Detection: Identifying unusual patterns in streaming data with very low latency requirements.
      • Features Stores for Real-time ML Inference: Quickly retrieving semantically similar features for machine learning models during inference.13
    • Considerations: In-memory nature can be more expensive for large datasets compared to disk-based options.14 Data durability might be a concern for some applications.

    6. Vector Search for Amazon DocumentDB:

    • Core Strength: Adds vector search capabilities to a flexible, JSON-based NoSQL database.
    • Ideal Use Cases:
      • Applications Already Using DocumentDB: Easily integrate semantic search into existing document-centric applications without migrating data.15
      • Flexible Schema Semantic Search: When your data schema is evolving or semi-structured, and you need to perform semantic search across documents with varying fields.
      • Content Management Systems with Semantic Search: Enabling users to find articles, documents, or other content based on their meaning within a flexible document store.
      • Personalization and Recommendation within Document Databases: Recommending content or features based on the semantic similarity of user profiles or document content.

    By understanding these distinct use cases and the core strengths of each AWS vector database option, you can make a more informed decision about which service best fits your specific application requirements. Remember to also consider factors like scale, performance needs, existing infrastructure, and cost when making your final choice.

  • Language Models vs Embedding Models

    In the ever-evolving landscape of Artificial Intelligence, two types of models stand out as fundamental building blocks for a vast array of applications: Language Models (LLMs) and Embedding Models. While both deal with text, their core functions, outputs, and applications differ significantly. Understanding these distinctions is crucial for anyone venturing into the world of natural language processing and -powered solutions.

    At their heart, Language Models (LLMs) are designed to comprehend and produce human-like text. These sophisticated models operate by predicting the probability of a sequence of words, allowing them to engage in tasks that require both understanding and generation. Think of them as digital wordsmiths capable of: crafting essays, answering intricate questions, translating languages fluently, summarizing lengthy documents, completing partially written text coherently, and understanding context to respond appropriately. The magic behind their abilities lies in their training on massive datasets, allowing them to learn intricate patterns and relationships between words. Architectures like the Transformer enable them to weigh the importance of different words within a context. The primary output of an is text.

    In contrast, Embedding Models focus on converting text into numerical representations known as vectors. These vectors act as a mathematical fingerprint of the text’s semantic meaning. A key principle is that semantically similar texts will have vectors located close together in a high-dimensional vector space. The primary output of an embedding model is a vector (a list of numbers). This numerical representation enables various applications: performing semantic search to find information based on meaning, measuring text similarity, enabling clustering of similar texts, and powering recommendation systems based on textual descriptions. These models are trained to map semantically related text to nearby points in the vector space, often leveraging techniques to understand contextual relationships.

    In frameworks like Langchain, both model types are crucial. LLMs are central for generating responses, reasoning, and decision-making within complex chains and agents. Meanwhile, embedding models are vital for understanding semantic relationships, particularly in tasks like Retrieval-Augmented Generation (), where they retrieve relevant documents from a vector store to enhance the LLM’s knowledge.

    In essence, Language Models excel at understanding and generating human language, while Embedding Models are masters at representing the meaning of text numerically, allowing for sophisticated semantic operations. This powerful synergy drives much of the innovation in modern AI applications.