knowledge graph

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from rag import rbc_product
 from tool import rival_product
 from graphrag import reasoning
-
+from knowledge import graph
 with gr.Blocks() as demo:
   with gr.Tab("RAG"):
     gr.Markdown("""
@@ -122,4 +122,23 @@ Low APR and great customer service. I would highly recommend if you’re looking
     btn_recommend.click(fn=reasoning, inputs=[in_verbatim, in_question], outputs=out_product)
 
 
-demo.launch(allowed_paths=["./xgb","./ts"])
+  with gr.Tab("Knowledge Graph"):
+    gr.Markdown("""
+    Objective: Explain concept in knowledge graph structured output
+    ================================================
+    """)
+    in_verbatim = gr.Textbox(label="Question")
+    out_product = gr.Image(label="Knowledge Graph")
+
+    gr.Examples(
+      [
+        [
+          "Explain me about red flags in transaction pattern for fraud detection"
+          ]
+      ],
+      [in_verbatim]
+    )
+    btn_recommend = gr.Button("Graph It!")
+    btn_recommend.click(fn=graph, inputs=in_verbatim, outputs=out_product)
+
+demo.launch(allowed_paths=["./"])

knowledge.py

@@ -0,0 +1,79 @@
+from openai import OpenAI
+import instructor
+
+from pydantic import BaseModel, Field
+from typing import List
+from graphviz import Digraph
+
+class Node(BaseModel, frozen=True):
+    """
+    Node representing concept in the subject domain
+    """
+    id: int
+    label: str = Field(..., description = "description of the concept concept in the subject domain")
+    color: str
+
+class Edge(BaseModel, frozen=True):
+    """
+    Edge representing relationship between concepts in the subject domain
+    """
+    source: int = Field(..., description = "source representing concept in the subject domain")
+    target: int = Field(..., description = "target representing concept in the subject domain")
+    label: str = Field(..., description = "description representing relationship between concepts in the subject domain")
+    color: str = "black"
+
+class KnowledgeGraph(BaseModel):
+    """
+    graph representation of subject domain
+    """
+    nodes: List[Node] = Field(..., default_factory=list)
+    edges: List[Edge] = Field(..., default_factory=list)
+
+from groq import Groq
+import os
+# Initialize with API key
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+# Enable instructor patches for Groq client
+client = instructor.from_groq(client)
+"""
+client = instructor.from_openai(
+    OpenAI(
+        base_url="http://localhost:11434/v1",
+        api_key="ollama",
+    ),
+    mode=instructor.Mode.JSON,
+)
+"""
+def generate_graph(input) -> KnowledgeGraph:
+    return client.chat.completions.create(
+    model='llama-3.1-8b-instant', #"llama3.2",
+    max_retries=5,
+    messages=[
+        {
+            "role": "user",
+               "content": f"Help me understand the following by describing it as a detailed knowledge graph: {input}",
+        }
+    ],
+    response_model=KnowledgeGraph,
+)
+
+
+def visualize_knowledge_graph(kg: KnowledgeGraph):
+    dot = Digraph(comment="Knowledge Graph")
+
+    # Add nodes
+    for node in kg.nodes:
+        dot.node(str(node.id), node.label, color=node.color)
+
+    # Add edges
+    for edge in kg.edges:
+        dot.edge(str(edge.source), str(edge.target), label=edge.label, color=edge.color)
+
+    # Render the graph
+    dot.render("knowledge_graph", format="png")
+
+def graph(query):
+    graph = generate_graph(query)
+    visualize_knowledge_graph(graph)
+    return "./knowledge_graph.png"

requirements.txt

@@ -11,6 +11,7 @@ llama-index
 faiss-cpu
 tavily-python
 
+#GRAPHRAG
 #llama-index-llms-litellm
 
 #llama-index-llms-huggingface-api
@@ -24,4 +25,9 @@ langchain-community
 pandas
 #gradio-client
 pillow
-numpy
+numpy
+
+#KNOWLEDGE GRAPH
+graphviz
+pydantic
+instructor[groq]