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import os
"""
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0, model_name="gpt-4-turbo")
from langchain_ollama.llms import OllamaLLM
llm = OllamaLLM(temperature=0,model="llama3.2")
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
llm = HuggingFaceInferenceAPI(temperature=0.2, model_name="meta-llama/Llama-3.2-1B")
HF_TOKEN= os.environ["HF_TOKEN"]
from llama_index.llms.litellm import LiteLLM
llm = LiteLLM("huggingface/meta-llama/Llama-3.2-1B")
"""
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from langchain_groq import ChatGroq
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain.chains import GraphQAChain
from langchain_core.documents import Document
from langchain_community.graphs.networkx_graph import NetworkxEntityGraph
GROQ_API_KEY = os.environ.get('GROQ_API_KEY')
# Set up LLM and Flux client
llm = ChatGroq(temperature=0, model_name='llama-3.1-8b-instant', groq_api_key=GROQ_API_KEY)
customer="Low APR and great customer service. I would highly recommend if you’re looking for a great credit card company and looking to rebuild your credit. I have had my credit limit increased annually and the annual fee is very low."
text="""
A business model is a combination of things: it's what you sell, how you deliver it, how you acquire customers, and how you make money from them.
Acquisition: how do users become aware of you?
Activation: Do drive-by visitors subscribe and use?
Retention: does a one-time user become engaged?
Referral: Do users tell others?
Revenue: How do you make money?
"""
question=f"Create marketing campaign that can improve customer acquisition, activation, retention and referral for this persona: {customer}"
def knowledge_graph(text):
documents = [Document(page_content=text)]
llm_transformer_filtered = LLMGraphTransformer(llm=llm)
# allowed_nodes=["Need", "Issue", "Product"],
# allowed_relationships=["WANT", "WITH", "USING", "RECOMMEND"]
graph_documents_filtered = llm_transformer_filtered.convert_to_graph_documents(documents)
graph = NetworkxEntityGraph()
for node in graph_documents_filtered[0].nodes:
graph.add_node(node.id)
for edge in graph_documents_filtered[0].relationships:
graph._graph.add_edge(
edge.source.id,
edge.target.id,
relation=edge.type
)
return graph, graph_documents_filtered
def reasoning(text, question):
try:
print("Generate Knowledgegraph...")
graph, graph_documents_filtered = knowledge_graph(text)
print("GraphQAChain...")
graph_rag = GraphQAChain.from_llm(
llm=llm,
graph=graph,
verbose=True
)
print("Answering through GraphQAChain...")
answer = graph_rag.invoke(question)
return answer['result']
except Exception as e:
print(f"An error occurred in process_text: {str(e)}")
import traceback
traceback.print_exc()
return str(e)
def marketing(text, question):
try:
print("Generate Knowledgegraph...")
graph, graph_documents_filtered = knowledge_graph(text)
print("GraphQAChain...")
graph_rag = GraphQAChain.from_llm(
llm=llm,
graph=graph,
verbose=True
)
print("Answering through GraphQAChain...")
answer = graph_rag.invoke(f"""Create
marketing campaign that can improve customer acquisition, activation, retention and referral for this persona: {question}""")
return answer['result']
except Exception as e:
print(f"An error occurred in process_text: {str(e)}")
import traceback
traceback.print_exc()
return str(e)
if __name__=="__main__":
pass |