knowledge.py

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)
"""
from openai import OpenAI
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"