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import os |
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""" |
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from langchain_openai import ChatOpenAI |
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llm = ChatOpenAI(temperature=0, model_name="gpt-4-turbo") |
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from langchain_ollama.llms import OllamaLLM |
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llm = OllamaLLM(temperature=0,model="llama3.2") |
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from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI |
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llm = HuggingFaceInferenceAPI(temperature=0.2, model_name="meta-llama/Llama-3.2-1B") |
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HF_TOKEN= os.environ["HF_TOKEN"] |
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from llama_index.llms.litellm import LiteLLM |
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llm = LiteLLM("huggingface/meta-llama/Llama-3.2-1B") |
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""" |
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import networkx as nx |
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import matplotlib.pyplot as plt |
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import pandas as pd |
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import numpy as np |
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from langchain_groq import ChatGroq |
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from langchain_experimental.graph_transformers import LLMGraphTransformer |
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from langchain.chains import GraphQAChain |
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from langchain_core.documents import Document |
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from langchain_community.graphs.networkx_graph import NetworkxEntityGraph |
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GROQ_API_KEY = os.environ.get('GROQ_API_KEY') |
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llm = ChatGroq(temperature=0, model_name='llama-3.1-8b-instant', groq_api_key=GROQ_API_KEY) |
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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." |
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text=""" |
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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. |
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Acquisition: how do users become aware of you? |
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Activation: Do drive-by visitors subscribe and use? |
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Retention: does a one-time user become engaged? |
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Referral: Do users tell others? |
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Revenue: How do you make money? |
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""" |
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question=f"Create marketing campaign that can improve customer acquisition, activation, retention and referral for this persona: {customer}" |
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def knowledge_graph(text): |
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documents = [Document(page_content=text)] |
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llm_transformer_filtered = LLMGraphTransformer(llm=llm) |
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graph_documents_filtered = llm_transformer_filtered.convert_to_graph_documents(documents) |
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graph = NetworkxEntityGraph() |
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for node in graph_documents_filtered[0].nodes: |
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graph.add_node(node.id) |
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for edge in graph_documents_filtered[0].relationships: |
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graph._graph.add_edge( |
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edge.source.id, |
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edge.target.id, |
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relation=edge.type |
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) |
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return graph, graph_documents_filtered |
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def reasoning(text, question): |
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try: |
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print("Generate Knowledgegraph...") |
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graph, graph_documents_filtered = knowledge_graph(text) |
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print("GraphQAChain...") |
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graph_rag = GraphQAChain.from_llm( |
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llm=llm, |
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graph=graph, |
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verbose=True |
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) |
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print("Answering through GraphQAChain...") |
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answer = graph_rag.invoke(question) |
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return answer['result'] |
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except Exception as e: |
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print(f"An error occurred in process_text: {str(e)}") |
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import traceback |
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traceback.print_exc() |
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return str(e) |
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