ai_generate.py

import torch
from openai import OpenAI
import os
from transformers import pipeline
from groq import Groq
from langchain_community.document_loaders import PyMuPDFLoader
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import CharacterTextSplitter
from langchain import hub
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain.chains import RetrievalQA
from langchain_groq import ChatGroq
from dotenv import load_dotenv
load_dotenv()

groq_client = Groq(
    api_key=os.environ.get("GROQ_API_KEY"),
)

def create_db_with_langchain(path):
    loader = PyMuPDFLoader(path)
    data = loader.load()
    # split it into chunks
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    docs = text_splitter.split_documents(data)

    # create the open-source embedding function
    embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

    # load it into Chroma
    db = Chroma.from_documents(docs, embedding_function)
    return db


def generate_groq_rag(text, model, path):
    llm = ChatGroq(
        temperature=0,
        model_name=model,
    )
    db = create_db_with_langchain(path)
    retriever = db.as_retriever(search_type="mmr", search_kwargs={"k": 4, "fetch_k": 20})
    prompt = hub.pull("rlm/rag-prompt")

    def format_docs(docs):
        return "\n\n".join(doc.page_content for doc in docs)

    rag_chain = {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | llm
    return rag_chain.invoke(text).content


def generate_groq_base(text, model):
    completion = groq_client.chat.completions.create(
        model=model,
        messages=[
            {"role": "user", "content": text},
            {
                "role": "assistant",
                "content": "Please follow the instruction and write about the given topic in approximately the given number of words",
            },
        ],
        temperature=1,
        max_tokens=1024,
        top_p=1,
        stream=True,
        stop=None,
    )
    response = ""
    for i, chunk in enumerate(completion):
        if i != 0:
            response += chunk.choices[0].delta.content or ""
    return response

def generate_groq(text, model, path):
    if path:
        return generate_groq_rag(text, model, path)
    else:
        return generate_groq_base(text, model)


def generate_openai(text, model, openai_client):
    message = [{"role": "user", "content": text}]
    response = openai_client.chat.completions.create(
        model=model, messages=message, temperature=0.2, max_tokens=800, frequency_penalty=0.0
    )
    return response.choices[0].message.content


def generate(text, model, path, api):
    if model == "Llama 3":
        return generate_groq(text, "llama3-70b-8192", path)
    elif model == "Groq":
        return generate_groq(text, "llama3-groq-70b-8192-tool-use-preview", path)
    elif model == "Mistral":
        return generate_groq(text, "mixtral-8x7b-32768", path)
    elif model == "Gemma":
        return generate_groq(text, "gemma2-9b-it", path)
    elif model == "OpenAI GPT 3.5":
        try:
            openai_client = OpenAI(api_key=api)
            return generate_openai(text, "gpt-3.5-turbo", openai_client)
        except:
            return "Please add a valid API key"
    elif model == "OpenAI GPT 4":
        try:
            openai_client = OpenAI(api_key=api)
            return generate_openai(text, "gpt-4-turbo", openai_client)
        except:
            return "Please add a valid API key"
    elif model == "OpenAI GPT 4o":
        try:
            openai_client = OpenAI(api_key=api)
            return generate_openai(text, "gpt-4o", openai_client)
        except:
            return "Please add a valid API key"