Create interact_with_llm.py

interact_with_llm.py

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+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import DeepLake
+from langchain.chat_models import ChatOpenAI
+from langchain.schema.runnable import RunnablePassthrough
+from langchain import hub
+import os
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+
+QUESTION_ANSWER_MAP = {
+    "Question1: Are you usually?": {
+        "A 'Good Mixer with groups of people": "Extrovert",
+        "Rather quiet and reserved": "Introvert"
+    },
+    "Question2: Among your friends, you are?": {
+        "Full of news about everybody": "Extrovert",
+        "One of the last to hear what is going on": "Introvert"
+    },
+    "Question3: In doing something that many other people do, you would rather?": {
+        "Invent a way of your own": "Intuition",
+        "Do it in the accepted way": "Sensing"
+    },
+    "Question4: Do you admire the people who are?": {
+        "Normal-acting to never make themselves the center of attention": "Sensing",
+        "Too original and individual to care whether they are the center of attention or not": "Intuition"
+    },
+    "Question5: Do you more often let?": {
+        "Your heart rule your head": "Feeling",
+        "Your head rule your heart": "Thinking"
+    },
+    "Question6: Do you usually?": {
+        "Value emotion more than logic": "Feeling",
+        "Value logic more than feelings": "Thinking"
+    },
+    "Question7: When you go somewhere for the day, you would rather": {
+        "Plan what you will do and when": "Judging",
+        "Just go": "Perceiving"
+    },
+    "Question8: When you have a special job to do, you like to": {
+        "Organize it carefully before you start": "Judging",
+        "Find out what is necessary as you go along": "Perceiving"
+    }
+}
+
+os.environ['OPENAI_API_KEY']= os.environ.get('OPENAI_API_KEY')
+class Agent():
+    def __init__(self) -> None:
+
+        embeddings = OpenAIEmbeddings()
+        documents = TextLoader("document_0.txt").load()
+        text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+        docs = text_splitter.split_documents(documents)
+        vectorstore = Chroma(embedding_function=OpenAIEmbeddings(), persist_directory="./chroma_db")
+        retriever = vectorstore.as_retriever()
+        llm = ChatOpenAI(model_name="gpt-4", temperature=0)
+        rag_prompt = hub.pull("rlm/rag-prompt")
+        self.rag_chain = (
+            {"context": retriever, "question": RunnablePassthrough()} 
+            | rag_prompt 
+            | llm 
+        )
+
+    def send_msg(self,dic):
+        for k, v in dic.items():
+            original = k
+            question= k.split(":")[1].strip()
+            ans = v
+
+        options = list(QUESTION_ANSWER_MAP[original].items())
+        optionA = options[0][0]
+        optionB = options[1][0]
+        desA = options[0][1]
+        desB = options[1][1]
+
+        result = self.rag_chain.invoke( "Given the question: '{}', the possible answers are: "
+        "A. '{}' and B. '{}'. Choosing A indicates the person is {} "
+        "while B indicates the person is {}. Based on the response: "
+        "{}, "
+        "determine how much this person aligns with being {} on a scale of 0-100. "
+        "0-10 means 'Extremely {}', 11-20 means 'Very {}', 21-45 means '{}', "
+        "55-80 means 'Moderately {}', 81-90 means 'Very {}', "
+        "91-100 means 'Extremely {}'. Give a number as output, don't give any explanation!"
+        "If the answer provided by this person is not related, return 50."
+        .format(question, optionA, optionB, desA, desB, ans,desA, desA, desA, desA, desB, desB, desB))
+
+        return (desA, result.content)
+    def send_post(self, post):
+        result = self.rag_chain.invoke(
+    f"Based on the content of the post '{post}',"
+    "provide a guess using the MBTI dimensions. "
+    "For each dimension, assign a score from 0 to 100 to indicate the likelihood of the author displaying the first trait over the second." 
+    "For example, a score of 60 for 'Extrovert-Introvert' would indicate 60% Extrovert and 40% Introvert." 
+    "Along with the score, provide a brief rationale for your judgment. If the information is not enough, just try to guess, don't worry about the correctness."
+    "Return the results in a structured format that includes both the scores and rationales, suitable for parsing with Python's json library.")
+        return result.content
+