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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 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 concepts in the 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)
llm='llama-3.1-8b-instant' #"llama3.2", #
"""
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(q, input) -> KnowledgeGraph:
return client.chat.completions.create(
model=llm,
max_retries=5,
messages=[
{
"role": "user",
"content": f"Help me understand the following by describing it as a detailed knowledge graph: ### Question: {q} ### Context: {input}",
}
],
response_model=KnowledgeGraph,
)
class Issue(BaseModel):
"Break down Issue as sub issues"
question: str
class IssuePlan(BaseModel):
"List of Issue"
issue_graph: List[Issue] = []
def expandIssue(input) -> IssuePlan:
return client.chat.completions.create(
model=llm,
max_retries=10,
messages=[
{
"role": "system",
"content": "As a Mckinsey Consultant create a framework that relevant to the topic, list all issues.",
}, {
"role": "user",
"content": f"Question: {input}",
},
],
response_model=IssuePlan,
)
def graph(query):
queryx = expandIssue(query)
ctx = ", ".join([q.question for q in queryx.issue_graph])
graph = generate_graph(query, ctx)
return graph.json() |