chatbot.py

from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from langchain_pinecone import PineconeVectorStore
from langchain.prompts import PromptTemplate
from langchain_google_genai import GoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from dotenv import load_dotenv, find_dotenv
import os
from pinecone import Pinecone, PodSpec

load_dotenv(find_dotenv())

class Chatbot():

    loader = TextLoader('dataset.txt', autodetect_encoding=True)
    documents = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=4)
    docs = text_splitter.split_documents(documents)

    embeddings = GoogleGenerativeAIEmbeddings(
    model="models/embedding-001", task_type="retrieval_query", google_api_key=os.getenv("GEMINI_API_KEY")
)

    pinecone = Pinecone(
        api_key=os.environ.get("PINECONE_API_KEY")
        # host='gcp-starter'
    )

    index_name = "thehexatechchatbot"

    if index_name not in pinecone.list_indexes().names():
        pinecone.create_index(name=index_name, metric="cosine", dimension=768, spec=PodSpec(environment="gcp-starter"))
        docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name)
    else:
         docsearch = PineconeVectorStore.from_existing_index(index_name, embeddings)


    llm = GoogleGenerativeAI(model="gemini-1.5-flash-latest", google_api_key=os.getenv("GEMINI_API_KEY"))

    template = """
    INSTRUCTION: Act as Personal Assistant chatbot of The Hexatech, an IT Startup that provides core hexa services: design, deployment, web development, ai & ml, software. this is conversation \
    to a user who wants to get his query solved about The Hexatech. Use the CONTEXT to answer in a helpful manner to the QUESTION. \
    If you don't know any ANSWER, say you don't know \
    Always follow general guardrails before generating any response. \
    Always try to keep the conversation in context to The Hexatech. Keep your replies short \
    compassionate and informative.\
    Give the answer from the CONTEXT\
    You should help user to get his query solved and also try to increase engagement and promoting The Hexatech & its services.\
        
    CONTEXT: {context}
    QUESTION: {question}
    ANSWER:
    """

    prompt = PromptTemplate(
    template=template, 
    input_variables=["context", "question"]
    )


    rag_chain = (
    {"context": docsearch.as_retriever(),  "question": RunnablePassthrough()} 
    | prompt 
    | llm
    | StrOutputParser() 
    )