AWS Architecture for LLM, GenAI, RAG, and Graph

 

                                                                    AWS

Here's a concise breakdown of what’s in the AWS contact center RAG architecture and modern AWS innovations/tools you can consider adding/enhancing for LLM, GenAI, RAG, and Graph-based use cases:

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✅ Current Architecture Summary

• Core Interaction:

  • Amazon Connect + Lex: Voice/chat → Lex bot

  • AWS Lambda: Fulfillment logic → interacts with LLMs & KB

  • Amazon Bedrock: Claude & Cohere embedding

  • Amazon OpenSearch Serverless: RAG KB indexing

  • Amazon S3: Document storage

  • Amazon SageMaker: LLM testing

  • CloudWatch + Athena + QuickSight: Analytics, logs, and dashboards

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🚀 Modern AWS Additions to Enhance This Architecture

1. Amazon Knowledge Bases for Amazon Bedrock (NEW)

• Built-in RAG: No manual embedding/indexing needed.

• Direct integration with Claude, Titan, Mistral, Llama2.

• Secure and scalable for contact center FAQs, SOPs.

→ Replace OpenSearch + manual embed with this for simplicity.

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2. Amazon Titan Models

• Titan Text G1, Titan Embeddings G1:

  • Optimized for AWS native workloads

  • Good accuracy & cost-performance

  • Can replace Cohere embeddings in Bedrock

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3. Amazon Neptune + Bedrock for Graph RAG

• Use Amazon Neptune (Graph DB):

  • Create context-aware responses using entities/relationships.

  • Ideal for contact center use cases like product recommendations, account histories, support dependencies.

• Use Neptune ML + Bedrock for hybrid Graph-RAG workflows.

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4. Agent Assist with Amazon Q (for internal agents)

• Auto-suggested answers to agents.

• Integrate Amazon Q Developer Agent for internal workflow orchestration.

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5. Vector Search with Amazon Aurora PostgreSQL + pgvector

• Use Aurora PostgreSQL with pgvector to enable hybrid RAG + transactional DB in a single place.

• Ideal if customer/CRM data is already in Aurora.

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6. Amazon AppFabric (for SaaS integration)

• Integrate Zendesk, Salesforce, ServiceNow logs for richer LLM context (ideal in contact center scenarios).

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7. Guardrails for Amazon Bedrock

• Native prompt filters, PII blocking, toxicity checks.

• Useful to extend security in fulfillment function layer.

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8. Amazon DataZone + Bedrock for Enterprise Context

• Federated data access to S3, Redshift, RDS etc.

• Feed knowledge into LLMs securely via Bedrock.

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9. Amazon PartyRock (for internal prototyping)

• Quickly test RAG flows or new ideas before deployment.

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📌 Suggestions for Improvement

Area	Suggestion
LLM Evaluation	Add PromptBench (open-source) or Amazon SageMaker Clarify to evaluate LLM responses.
RAG Indexing	Migrate to Amazon Knowledge Bases to reduce complexity.
Graph Context	Use Amazon Neptune + LLM for better connected Q&A.
Storage Index	Use Amazon S3 Object Lambda to trigger smart preprocessing on upload.
Agent UX	Add Amazon Q for contact center agents (internal-facing copilot).
Observability	Add Amazon CloudTrail Lake for deeper insight beyond CloudWatch.

---

Here’s a focused breakdown of Amazon Bedrock in the context of this architecture, including what it does, how it fits, and what more you can do with it using the latest AWS innovations:

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✅ Current Role of Amazon Bedrock in This Architecture

• Models Used:

  • Anthropic Claude (Haiku, Sonnet): For generating answers

  • Cohere Embed v3: For semantic embedding of documents

• Tasks:

  • Response generation

  • Embedding via Cohere

  • Guardrails and evaluation logic via Lambda

---

🚀 Modern Capabilities in Amazon Bedrock You Can Leverage

1. Amazon Knowledge Bases for Bedrock (NEW - 2024)

• Built-in RAG with no manual vector DB or embedding setup.

• Supports ingestion from S3, websites, Salesforce, etc.

• Automatically uses Titan or Claude behind the scenes.

Replace this part of your diagram:

Amazon S3 + OpenSearch + manual Cohere embedding
→ Use Amazon Knowledge Base with Bedrock instead

---

2. Amazon Bedrock Guardrails (NEW)

• Define prohibited topics, allowed responses, and safety filters.

• Supports moderation at prompt input & response output.

• Integrates with Claude, Titan, and Mistral models.

Use case: In fulfillment Lambda → Add Bedrock Guardrails for:

• Hallucination control

• PII filtering

• Bias/toxicity control

---

3. Agents for Amazon Bedrock

• Lets you create multi-step conversational agents.

• Define tools/functions (e.g., API calls, DB reads).

• Automate complex business flows using LLM.

Use case: Replace manual Lambda orchestration with Bedrock agent that can:

• Read user input

• Trigger APIs

• Retrieve from RAG

• Generate final output

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4. Supported Foundation Models (FMs)

Use new FMs directly from Bedrock:

• Anthropic Claude 3

• Meta Llama 2/3

• Mistral

• Amazon Titan

• Cohere Command-R

Pick based on: Cost, latency, token limits, reasoning.

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5. Streaming + Low-latency Inference

• Supports streaming responses for faster UX

• Useful in real-time contact center flows

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🔁 Optional Enhancements with Bedrock

Enhancement	Tool	Impact
Native RAG	Bedrock + Knowledge Base	Simpler setup, high performance
Safety	Bedrock Guardrails	Reduce hallucination & risk
Conversational Flow	Bedrock Agents	No manual Lambda FSM
Graph-enhanced answers	Neptune + Bedrock	Better context for complex queries
Response speed	Bedrock Streaming	Better UX for chat-like UIs

---

If you need to integrate a graph database into this architecture on AWS (especially for context-rich LLM responses, entity linking, or decision reasoning), the go-to service is:

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🧠 Amazon Neptune – AWS Managed Graph Database

✅ Use Cases in LLM + RAG Context

• Entity linking: Resolve ambiguous terms via graph traversal.

• Personalized answers: Use graph to tailor based on user profile/intent.

• Relationship-aware Q&A: Example: “Which agents have handled both Product A and B cases?”

• Knowledge Graph RAG: Supplement text-based RAG with structured semantic relationships.

---

📌 How to Integrate into Your Architecture

1. Document Upload / Knowledge Source Ingestion:

   • When uploading documents to S3, extract entities & relationships using:

     • Amazon Comprehend (or custom model)

     • Amazon SageMaker + spaCy or LangChain parser

   • Push triples into Neptune.

2. Link Neptune with Bedrock (via Lambda):

   • At runtime, fetch related facts from Neptune

   • Inject into prompt via LangChain Tool / Function Call

   • Claude/Titan gets graph context + retrieved docs = better answers

3. Store Conversation Graph:

   • Create user-session graphs showing what was asked, which intents resolved

   • Analyze later in QuickSight or SageMaker

---

🛠 Tools to Use

• Amazon Neptune ML: To do graph embeddings & reasoning (built-in SageMaker)

• LangChain: Built-in Neptune integration

• Neptune Streams: Real-time updates to apps when graph changes

• IAM + VPC: Ensure private access from Lambda/Bedrock to Neptune

---

🧩 Sample Add-On Workflow

Customer question → Amazon Lex → Lambda (calls Neptune for entity resolution)
→ Lambda queries RAG (via Bedrock) + enriches with graph facts
→ LLM response includes accurate, structured insight

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Here’s how you can enhance the existing contact center RAG solution architecture with new AWS innovations (as of 2024–2025):

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🔹 1. Amazon Bedrock Enhancements

• New Foundation Models (FMs) now available:

  • Cohere Command R+ / Embed v3: Great for multilingual semantic search and summarization.

  • Stability AI: Enable image generation (e.g., visual summaries or diagrams in contact center dashboards).

  • Claude 3 / Sonnet / Haiku: Improved reasoning, lower hallucination, better for safety-critical workflows.

✅ What to add:

• Use Claude 3 Sonnet for improved hallucination detection and conversational quality.

• Add Cohere Embed v3 to handle multilingual RAG and user queries.

• Optionally add Stability AI for visual response generation (e.g., charts/diagrams in product support).

---

🔹 2. Amazon Q Integration

• Amazon Q: GenAI assistant now integrated with Amazon Connect and other AWS services.

  • Use Q for Agents: Automatically summarize calls, suggest actions, and provide live answers.

  • Use Q for Developers: Help internal teams debug and deploy improvements in Lambda, Lex, and Bedrock workflows.

✅ What to add:

• Integrate Amazon Q Agent Assist in Amazon Connect flow for real-time suggestions and summarization.

• Embed Q analytics to enhance QuickSight dashboards with natural language insights.

---

🔹 3. Graph Reasoning with Amazon Neptune

• Add Amazon Neptune for:

  • Knowledge Graph + RAG Hybrid.

  • Entity resolution, reasoning, semantic memory, user-persona linking.

• Combine Neptune ML + Bedrock via Lambda or LangChain.

✅ What to add:

• Store structured knowledge as RDF/triples.

• During RAG, fetch graph facts and add to prompt before LLM call.

---

🔹 4. New LangChain + AWS Integrations

• Use LangChain AWS Toolkit for:

  • Neptune integration for structured reasoning.

  • Bedrock tool calling, agents, and multi-hop chains.

✅ What to add:

• Use LangGraph + Bedrock to orchestrate complex dialogues across Lex, Bedrock, Neptune, and OpenSearch.

---

🔹 5. Vector Store Upgrade

• Consider replacing OpenSearch with Amazon Aurora PostgreSQL + pgvector for:

  • Faster semantic search.

  • Tighter integration with RDS ecosystem.

Optional Enhancement.

---

🧠 Final Enhanced Stack (Key Adds)

• Amazon Bedrock:

  • Claude 3 Sonnet, Cohere Embed v3, Stability AI

• Amazon Q:

  • Agent Assist in Amazon Connect

• Amazon Neptune:

  • Graph-based reasoning, persona modeling

• LangChain + LangGraph:

  • Complex workflows, agent memory

• Optionally:

  • Use Aurora + pgvector for hybrid RAG

---

Check my several template codes to start or customise your solution here.

Why Use Graph Transformers in GenAI

 

                                                            generate by meta ai

Graph Transformers in GenAI: A High-Level Overview

Introduction

GenAI: A subfield of AI focused on generating new data that resembles existing data
Graph Transformers: A type of neural network architecture that combines graph neural networks and transformers

Key Components

Graphs
Representation of data as nodes and edges
Captures complex relationships and dependencies
Transformers
Self-attention mechanisms for parallelization and scalability
Effective in handling sequential data
Graph Transformers
Integration of graph neural networks and transformers
Enables parallelization and scalability in graph-structured data

Advantages in GenAI

Handling Complex Dependencies
Graph Transformers can model intricate relationships between data entities
Enables generation of more realistic and coherent data
Scalability and Parallelization
Graph Transformers can handle large graphs and sequential data
Facilitates efficient generation of high-quality data
Flexibility and Expressiveness
Graph Transformers can be applied to various domains (e.g., molecules, images, text)
Allows for incorporation of domain-specific knowledge and constraints

Applications in GenAI

Molecular Generation
Graph Transformers can generate novel molecules with desired properties
Applications in drug discovery and materials science
Image Generation
Graph Transformers can generate images with complex scene graphs
Applications in computer vision and robotics
Text Generation
Graph Transformers can generate coherent text with complex dependencies
Applications in natural language processing and dialogue systems

Conclusion

Graph Transformers offer a powerful tool for GenAI tasks

Their ability to handle complex dependencies, scalability, and flexibility make them an attractive choice for various applications.


Let's make a deep dive into more details.

Positional Encodings

In standard Transformers used for language tasks, positional encoding is essential because sentences are processed in parallel—without inherent word order. To address this, Transformers embed positional information to distinguish word sequences. This enables the model to interpret meaning correctly in phrases like “dog bites man” vs. “man bites dog”, despite identical words. These absolute encodings follow a fixed order, helping the model capture the linear structure of text.

In contrast, Graph Transformers operate on data without a natural sequence—graphs lack a fixed node order. Here, positional encodings shift focus: instead of capturing order, they capture structural relationships between nodes. These encodings reflect a node's relative position, highlighting aspects like proximity, connectivity, and hierarchy. Given the wide variability of graph structures—from social networks to molecules—designing effective graph positional encodings remains a complex and evolving research challenge. In fact, due to its significance, we explore various strategies for Graph Positional Encoding in a dedicated section later in this blog.

How to Get More Help from Graph Transformers in Multi-Agentic GenAI Applications

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1. Why Use Graph Transformers in GenAI (Especially Multi-Agentic Systems)?

Graph Transformers are powerful for modeling complex relationships, interactions, and structures, making them ideal for:

• Agent communication modeling (who talks to whom, when, how).

• Knowledge graphs (structured domain knowledge for reasoning).

• Task graphs (dependencies between subtasks in multi-agent coordination).

• State/action graphs (representing environment dynamics in planning or simulation).

---

2. Useful Libraries

Here are the top Graph Transformer libraries with Python/GenAI ecosystem integration:

• PyTorch Geometric (PyG)
  https://pytorch-geometric.readthedocs.io/
  ✅ Built-in Graph Transformer models
  ✅ Easy integration with PyTorch/LLMs
  ✅ Supports batching, dynamic graphs, GNN layers

• Deep Graph Library (DGL)
  https://www.dgl.ai/
  ✅ Supports both PyTorch and TensorFlow
  ✅ Modular transformer-based GNNs
  ✅ Scales well on large graphs

• HuggingFace Transformers + Graphormer
  https://huggingface.co
  ✅ Use pretrained transformer-based GNNs
  ✅ Fine-tune on custom graph tasks
  ✅ Combine with LLMs easily

• GraphGPS, Graphormer, TokenGT
  ✅ Cutting-edge research models for Graph Transformers
  ✅ Often available via GitHub (research code) or HuggingFace
  ✅ Good for tasks like molecular prediction, scene understanding, knowledge modeling

---

3. Integration with Multi-Agent GenAI

Here’s how to use them in a multi-agent GenAI setup:

A. Representing Agent States and Interactions

• Build a graph of agents, tasks, tools, or knowledge units.

• Nodes = agents or entities, edges = communication/interaction.

• Use Graph Transformer to encode this structure for reasoning or planning.

B. Integrating with LLMs or Chain-of-Thought

• Use Graph Transformer outputs as input embeddings to an LLM.

• LLMs can attend over the graph-based context, improving multi-agent memory, coordination, or planning.

C. Planning and Tool Use

• Use graphs to model task dependencies.

• Graph Transformer helps LLMs select optimal execution order or tool usage flow.

---

4. Example Pipeline (PyTorch + LangChain/OpenAI)

# Example: Agent Interaction Graph → LLM Prompt Embedding

import torch
from torch_geometric.nn import TransformerConv
from langchain.embeddings import OpenAIEmbeddings

# Build graph data
edge_index = torch.tensor([[0, 1], [1, 0]], dtype=torch.long)  # simple edge
x = torch.rand((2, 128))  # node features

# Graph Transformer layer
conv = TransformerConv(in_channels=128, out_channels=128)
x = conv(x, edge_index)

# Use with LLM or GenAI
embeddings = OpenAIEmbeddings()
prompt_embedding = embeddings.embed_query("Task graph reasoning")
# Combine with x or use x to generate better prompts

---

5. Applications in Multi-Agent AI

• Tool selection: Which agent/tool is best for the task?

• Dialogue routing: Which agent should respond next?

• Resource allocation: Task graph → optimal agent assignment

• Chain-of-thought tracking: Maintain structured reasoning memory across agents

---

Here’s a minimal demo pipeline using LangChain, LangGraph, and a Graph Transformer layer (via PyTorch Geometric) to model agent interactions and influence the LLM prompt generation:

---

✅ Setup Requirements

pip install langchain langgraph openai torch torch-geometric networkx

---

🧠 Use Case: Multi-agent task planner with graph-enhanced reasoning

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🧩 Demo Code

# graph_demo.py

from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langgraph.graph import StateGraph, END
import torch
from torch_geometric.nn import TransformerConv
from torch_geometric.data import Data
import networkx as nx

# ---------- 1. Graph Transformer Prep (Simple Agent Graph) ----------
def get_graph_embedding():
    # Sample agent interaction graph (2 agents interacting)
    edge_index = torch.tensor([[0, 1], [1, 0]], dtype=torch.long)
    x = torch.rand((2, 64))  # Random node features for 2 agents

    conv = TransformerConv(in_channels=64, out_channels=64)
    x_out = conv(x, edge_index)
    return x_out.mean(dim=0)  # Aggregated graph feature

# ---------- 2. LangGraph State + Node Definition ----------
def graph_state_node(state):
    graph_embedding = get_graph_embedding()
    embedding_str = ", ".join([f"{v:.2f}" for v in graph_embedding[:5]])  # Truncate for demo
    state["graph_embedding"] = embedding_str
    return state

# ---------- 3. LLM Node that uses Graph Context ----------
def llm_node(state):
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

    prompt = PromptTemplate.from_template("""
    You are a multi-agent planner. Use this graph context: {graph_embedding}
    Based on this, suggest which agent should handle a 'data cleaning' task and why.
    """)

    final_prompt = prompt.format(graph_embedding=state["graph_embedding"])
    response = llm.predict(final_prompt)
    state["llm_response"] = response
    return state

# ---------- 4. Build LangGraph ----------
workflow = StateGraph()

workflow.add_node("graph_encoder", graph_state_node)
workflow.add_node("llm_reasoning", llm_node)

workflow.set_entry_point("graph_encoder")
workflow.add_edge("graph_encoder", "llm_reasoning")
workflow.add_edge("llm_reasoning", END)

app = workflow.compile()

# ---------- 5. Run the Graph ----------
if __name__ == "__main__":
    final_state = app.invoke({})
    print(">> LLM Output:")
    print(final_state["llm_response"])

---

🧪 Expected Output

>> LLM Output:
Agent 1 should handle the 'data cleaning' task because it is more centrally connected...

---

✅ What This Shows

• GraphTransformer helps model agent structure.

• Embedding is passed into LangGraph state.

• LLM uses graph-derived insight to make multi-agent decisions.

• Can be extended to dynamic graphs, task graphs, or knowledge graphs.

---

Here’s the extended version that:

• Uses multiple agents with roles

• Models a task assignment graph

• Enhances LangGraph state with graph transformer output

• Lets the LLM assign tasks intelligently

---

✅ Extended Setup (3 Agents, Task Assignment)

# multi_agent_graph_demo.py

from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langgraph.graph import StateGraph, END
import torch
from torch_geometric.nn import TransformerConv
import networkx as nx
from torch_geometric.data import Data

# ---------- 1. Define Graph Transformer to model agents ----------
def get_graph_embedding(agent_roles):
    agent_count = len(agent_roles)

    # Simulate interaction edges (e.g., all-to-all)
    edge_index = torch.tensor([[i, j] for i in range(agent_count) for j in range(agent_count) if i != j],
                              dtype=torch.long).t().contiguous()

    # Role-based features (naively encode: analyst=0.1, planner=0.5, executor=0.9)
    role_map = {"analyst": 0.1, "planner": 0.5, "executor": 0.9}
    role_vals = [role_map.get(role, 0.0) for role in agent_roles]
    x = torch.tensor([[v]*64 for v in role_vals], dtype=torch.float)

    conv = TransformerConv(in_channels=64, out_channels=64)
    x_out = conv(x, edge_index)
    return x_out.mean(dim=0)  # Aggregate

# ---------- 2. LangGraph Node: Add agent context ----------
def graph_encoder_node(state):
    state["agent_roles"] = ["analyst", "planner", "executor"]
    graph_embedding = get_graph_embedding(state["agent_roles"])
    state["graph_embedding"] = ", ".join([f"{v:.2f}" for v in graph_embedding[:5]])
    return state

# ---------- 3. LangGraph Node: Let LLM assign the task ----------
def llm_assignment_node(state):
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

    prompt = PromptTemplate.from_template("""
    You are a multi-agent task router. 
    Agents: {agent_roles}
    Graph Embedding (agent structure/context): {graph_embedding}
    
    Assign the task: "prepare data pipeline for customer segmentation"
    Explain which agent should do it and why.
    """)

    final_prompt = prompt.format(
        agent_roles=", ".join(state["agent_roles"]),
        graph_embedding=state["graph_embedding"]
    )

    response = llm.predict(final_prompt)
    state["llm_response"] = response
    return state

# ---------- 4. LangGraph Flow ----------
workflow = StateGraph()
workflow.add_node("graph_encoder", graph_encoder_node)
workflow.add_node("llm_assigner", llm_assignment_node)

workflow.set_entry_point("graph_encoder")
workflow.add_edge("graph_encoder", "llm_assigner")
workflow.add_edge("llm_assigner", END)

app = workflow.compile()

# ---------- 5. Run ----------
if __name__ == "__main__":
    final_state = app.invoke({})
    print(">> Task Assignment:")
    print(final_state["llm_response"])

---

✅ Sample Output

>> Task Assignment:
The "planner" agent should handle the task of preparing the data pipeline...
Because...

---

📌 Next Steps You Can Add

• Add memory using LangGraph’s state (e.g., store past tasks per agent).

• Use actual agent modules to carry out tasks (tool use, web search, DB update).

• Add task graph for sub-task reasoning (e.g., pipeline → ingest → clean → store).

• Connect this to your LCNC GenAI backend for task orchestration.

Here's how you can connect this multi-agent LangGraph + Graph Transformer pipeline to your GenAI LCNC application backend with admin and user flows:

---

✅ System Design Extension

1. Admin Panel

• Uploads:

  • Business category

  • Page templates

  • Agent roles and capabilities

• Stored in: SQLite/PostgreSQL + Vector DB (Chroma)

2. User Chat Flow

• User describes the app they want → parsed into tasks

• LangGraph uses GraphTransformer embeddings + agent graph

• LLM assigns tasks to agents based on graph structure

• Agents execute via tools / code / API generation

---

🧩 Modified LangGraph Pipeline (Integration Ready)

# user_task_flow.py

from langgraph.graph import StateGraph, END
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
import torch
from torch_geometric.nn import TransformerConv

# Fake DB pulled from admin input
agent_roles = ["frontend", "backend", "database"]
role_to_embedding = {"frontend": 0.1, "backend": 0.5, "database": 0.9}

# ---------- 1. Embed agent structure ----------
def graph_encoder_node(state):
    task = state["user_task"]  # e.g., "build login page"
    
    edge_index = torch.tensor([[0, 1], [1, 2], [2, 0]], dtype=torch.long)
    x = torch.tensor([[role_to_embedding[role]] * 64 for role in agent_roles], dtype=torch.float)
    
    conv = TransformerConv(in_channels=64, out_channels=64)
    graph_features = conv(x, edge_index)
    state["graph_embedding"] = ", ".join([f"{v:.2f}" for v in graph_features.mean(dim=0)[:5]])
    return state

# ---------- 2. LLM: Task-to-agent mapper ----------
def agent_selector_node(state):
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

    prompt = PromptTemplate.from_template("""
    Agents: {agent_roles}
    Task: {task}
    Graph Embedding: {graph_embedding}
    
    Which agent should handle this and why?
    """)

    filled = prompt.format(
        agent_roles=", ".join(agent_roles),
        task=state["user_task"],
        graph_embedding=state["graph_embedding"]
    )

    state["assignment"] = llm.predict(filled)
    return state

# ---------- 3. Final Flow ----------
flow = StateGraph()
flow.add_node("graph_embed", graph_encoder_node)
flow.add_node("assign_llm", agent_selector_node)

flow.set_entry_point("graph_embed")
flow.add_edge("graph_embed", "assign_llm")
flow.add_edge("assign_llm", END)

app = flow.compile()

# ---------- 4. Trigger this via API (Flask or FastAPI wrapper) ----------
if __name__ == "__main__":
    final = app.invoke({"user_task": "create user registration form with OTP verification"})
    print(">> Assignment:\n", final["assignment"])

---

🔌 How to Connect With Your LCNC Backend

• Wrap this flow in a FastAPI endpoint /assign-task

• Accept user_task and agent_roles (optional, fallback to DB)

• Feed result into:

  • Page code generator

  • LangChain code generation chain

  • UI builder recommender (pages/components)

---

🔄 Example Workflow in LCNC

1. User: “I want a payment form”

2. → LLM breaks into tasks (frontend UI, backend API, DB table)

3. → Graph transformer ranks agents

4. → LLM assigns frontend agent

5. → Agent generates code → saved to LCNC builder folder

6. → Result shown in UI with editable components

---

Multi Agent Graph Application

 

                                                image credit: langchain langgraph

Integrating Gemma 3 with LangGraph and Neo4j allows you to build sophisticated AI applications that leverage the strengths of each technology:

• Gemma 3:
  • Provides powerful language understanding and generation capabilities.
  • Its multimodal abilities and increased context window are very powerful for complex information processing.
• LangGraph:
  • Enables you to create stateful, multi-actor applications by defining workflows as graphs.
  • This is ideal for building complex AI agents and applications that require multiple steps and interactions.
• Neo4j:
  • A graph database that excels at storing and querying connected data.
  • It's perfect for knowledge graphs and applications that require understanding relationships between entities.

Here's a conceptual overview of how you can combine these technologies:

1. Data Representation in Neo4j:

• Store your data as a graph in Neo4j, where nodes represent entities and edges represent relationships.
• This allows you to capture the rich semantic relationships between your data.
• Neo4j now has vector indexing capabilities, so you can also store vector embeddings within the Neo4j graph, allowing for hybrid searches.

2. LangGraph Workflows:

• Use LangGraph to define the workflow of your AI application.
• This workflow can include steps such as:
  • Receiving user input.
  • Querying Neo4j to retrieve relevant information.
  • Using Gemma 3 to process the retrieved information and generate a response.
  • Updating the Neo4j graph based on the interaction.
  • Using Gemma 3's function calling to interact with tools, or API's.
• LangGraph can also be used to create memory within the agent, and Neo4j can be used to store that memory in graph format, for long term memory storage.

3. Gemma 3 Integration:

• Use Gemma 3 to:
  • Understand user queries and generate Cypher queries to retrieve information from Neo4j.
  • Process the results from Neo4j and generate natural language responses.
  • Extract entities and relationships from text to update the Neo4j graph.
  • Use its multimodal abilities to process image and text data, that can then be used to query, or update the Neo4j Graph.

Key Use Cases:

• Knowledge Graph-Based Question Answering:
  • Build a system that can answer complex questions by querying a knowledge graph stored in Neo4j.
  • Gemma 3 can be used to understand the question and generate a Cypher query, and then to generate a natural language response based on the results.
• AI Agents with Memory:
  • Create AI agents that can remember past interactions and use that information to provide more relevant responses.
  • Neo4j can be used to store the agent's memory as a graph, and LangGraph can be used to manage the agent's state.
• Information Extraction and Graph Enrichment:
  • Use Gemma 3 to extract entities and relationships from text and then use that information to enrich the Neo4j graph.
• Visual Question Answering:
  • Use Gemma 3's multimodal abilities to process images, and then use the information gained from the image to query a Neo4j database.

Important Considerations:

• Cypher Query Generation:
  • Accurately generating Cypher queries from natural language is crucial.
  • You may need to use techniques such as prompt engineering and few-shot learning to improve the accuracy of Gemma 3's Cypher generation.
• Data Consistency:
  • Ensure that the data in your Neo4j graph is consistent and accurate.
• Performance:
  • Optimize your Neo4j queries and Gemma 3 interactions for performance, especially for large datasets.

By combining Gemma 3, LangGraph, and Neo4j, you can create powerful and intelligent AI applications that can understand and reason about complex information.

Example code

from langchain_google_genai import ChatGoogleGenerativeAI

from langgraph.prebuilt import create_react_agent

from langgraph_supervisor import create_supervisor

from neo4j import GraphDatabase

import os

# Neo4j Configuration

NEO4J_URI = os.getenv("NEO4J_URI", "bolt://localhost:7687")  # Replace with your Neo4j URI

NEO4J_USER = os.getenv("NEO4J_USER", "neo4j")  # Replace with your Neo4j username

NEO4J_PASSWORD = os.getenv("NEO4J_PASSWORD", "password")  # Replace with your Neo4j password

# Gemma 3 Model

model = ChatGoogleGenerativeAI(model="gemini-1.0-pro")  # or "gemini-1.0-pro-vision" for multimodal

# Neo4j Interaction Function

def query_neo4j(query: str) -> str:

    """Queries Neo4j and returns the result."""

    try:

        with GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD)) as driver:

            with driver.session() as session:

                result = session.run(query)

                records = [record.data() for record in result]

                return str(records)

    except Exception as e:

        return f"Error querying Neo4j: {e}"

# Example Neo4j Cypher query Function.

def get_faang_headcount() -> str:

    """Retrieves FAANG headcount from Neo4j."""

    query = """

    MATCH (company:Company)-[:HAS_HEADCOUNT]->(headcount:Headcount)

    WHERE company.name IN ['Facebook', 'Apple', 'Amazon', 'Netflix', 'Google']

    RETURN company.name, headcount.value

    """

    return query_neo4j(query)

def sum_list_of_ints(list_of_ints: list) -> int:

    """Sums a list of integers."""

    try:

        return sum(list_of_ints)

    except Exception as e:

        return f"Error summing the list: {e}"

# Agents

neo4j_agent = create_react_agent(

    model=model,

    tools=[get_faang_headcount],

    name="neo4j_expert",

    prompt="You are an expert at querying Neo4j. Only use the neo4j tool. Do not do any math."

)

math_agent = create_react_agent(

    model=model,

    tools=[sum_list_of_ints],

    name="math_expert",

    prompt="You are a math expert. Only use the math tool. Do not use the neo4j tool."

)

# Supervisor Workflow

workflow = create_supervisor(

    agents=[neo4j_agent, math_agent],

    model=model,

    prompt="You are a team supervisor managing a Neo4j expert and a math expert. For data retrieval, use neo4j_expert. For math problems, use math_expert."

)

# Compile and Run

app = workflow.compile()

result = app.invoke({

    "messages": [

        {

            "role": "user",

            "content": "what's the combined headcount of the FAANG companies in 2024?"

        }

    ]

})

print(result)

 

If you want to run locally with Ollama

from langchain_community.chat_models import ChatOllama

from langchain.agents import AgentType, initialize_agent, Tool

from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder

from langchain.schema import SystemMessage

from neo4j import GraphDatabase

import os

# Neo4j Configuration

NEO4J_URI = os.getenv("NEO4J_URI", "bolt://localhost:7687")  # Replace with your Neo4j URI

NEO4J_USER = os.getenv("NEO4J_USER", "neo4j")  # Replace with your Neo4j username

NEO4J_PASSWORD = os.getenv("NEO4J_PASSWORD", "password")  # Replace with your Neo4j password

# Ollama Gemma Model

model = ChatOllama(model="gemma")  # Ensure you have Gemma model downloaded in Ollama

# Neo4j Interaction Function

def query_neo4j(query: str) -> str:

    """Queries Neo4j and returns the result."""

    try:

        with GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD)) as driver:

            with driver.session() as session:

                result = session.run(query)

                records = [record.data() for record in result]

                return str(records)

    except Exception as e:

        return f"Error querying Neo4j: {e}"

# Example Neo4j Cypher query Function.

def get_faang_headcount() -> str:

    """Retrieves FAANG headcount from Neo4j."""

    query = """

    MATCH (company:Company)-[:HAS_HEADCOUNT]->(headcount:Headcount)

    WHERE company.name IN ['Facebook', 'Apple', 'Amazon', 'Netflix', 'Google']

    RETURN company.name, headcount.value

    """

    return query_neo4j(query)

def sum_list_of_ints(list_of_ints: list) -> int:

    """Sums a list of integers."""

    try:

        return sum(list_of_ints)

    except Exception as e:

        return f"Error summing the list: {e}"

# Tools

tools = [

    Tool(

        name="Neo4j_Query",

        func=get_faang_headcount,

        description="Useful for querying Neo4j graph databases.",

    ),

    Tool(

        name="Math_Sum",

        func=sum_list_of_ints,

        description="Useful for summing a list of integers.",

    ),

]

# Prompt template

prompt = ChatPromptTemplate.from_messages(

    [

        SystemMessage(

            content="You are a helpful assistant that can query Neo4j and perform math calculations."

        ),

        MessagesPlaceholder(variable_name="chat_history"),

        ("user", "{input}"),

        MessagesPlaceholder(variable_name="agent_scratchpad"),

    ]

)

# Initialize Agent

agent = initialize_agent(

    tools,

    model,

    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,

    verbose=True,

    memory=None,

    prompt=prompt,

)

# Run Agent

result = agent.run("what's the combined headcount of the FAANG companies in 2024?")

print(result)

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