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from langchain.text_splitter import RecursiveCharacterTextSplitter
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 MessagesPlaceholder, ChatPromptTemplate
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 ChatbotMemory():
loader = TextLoader('dataset.txt', autodetect_encoding=True)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=103)
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")
)
index_name = "gdscsou-chatbot"
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-pro", google_api_key=os.getenv("GEMINI_API_KEY"))
def contextualized_question(input: dict):
if input.get("chat_history"):
llm = GoogleGenerativeAI(model="gemini-pro", google_api_key=os.getenv("GEMINI_API_KEY"))
contextualize_q_system_prompt = """Given a chat history and the latest user question \
which might reference context in the chat history, formulate a standalone question \
which can be understood without the chat history. Do NOT answer the question, \
just reformulate it if needed and otherwise return it as is."""
contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", contextualize_q_system_prompt),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
contextualize_q_chain = contextualize_q_prompt | llm | StrOutputParser()
return contextualize_q_chain
else:
return input["question"]
template = """
INSTRUCTION: Act as an AI assistant and sales and marketing expert for Katha - A Tale of Weaves, a brand that bring exclusive collection of sustainable handloom sarees that tells a story of its heritage, this is conversation \
to a customer who came to visit site. Use the CONTEXT to answer in a helpful manner to the QUESTION. \
Don't forget you are an AI assistant and sales and marketing expert for Katha - A Tale of Weaves. \
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 Katha - A Tale of Weaves. 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 for Katha - A Tale of Weaves and also promoting Katha - A Tale of Weaves.\
CONTEXT: {context}
QUESTION: {question}
ANSWER:
"""
prompt = ChatPromptTemplate.from_messages(
[
("system", template),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
rag_chain = (
RunnablePassthrough.assign(
context=contextualized_question | docsearch.as_retriever()
)
| prompt
| llm
) |