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import csv
from langchain.chains.question_answering import load_qa_chain
from langchain_community.embeddings import CohereEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import PromptTemplate
from .chat import chat
csv_file = open("data/books_with_blurbs.csv", "r")
csv_reader = csv.reader(csv_file)
csv_data = list(csv_reader)
parsed_data = [
{
"id": x[0],
"title": x[1],
"author": x[2],
"year": x[3],
"publisher": x[4],
"blurb": x[5],
}
for x in csv_data
]
parsed_data[1]
embeddings = CohereEmbeddings()
docsearch = Chroma.from_texts(
[x["title"] for x in parsed_data], embeddings, metadatas=parsed_data
).as_retriever()
prompt_template = """
{context}
Use the book reccommendations to suggest books for the user to read.
Only use the titles of the books, do not make up titles. Format the response as
a bulleted list prefixed by a relevant message.
User: {message}"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "message"]
)
book_rec_chain = {
"input_documents": lambda x: docsearch.invoke(x["message"]),
"message": lambda x: x["message"],
} | load_qa_chain(chat, chain_type="stuff", prompt=PROMPT)
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