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from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from operator import itemgetter
from pinecone import Pinecone, ServerlessSpec
def load_split_file(file_path):
loader = PyPDFLoader(file_path)
pages = loader.load_and_split()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=10)
docs = text_splitter.split_documents(pages)
return docs
def create_index(index_name, PINECONE_API_KEY):
pc = Pinecone(api_key=PINECONE_API_KEY)
if index_name in pc.list_indexes().names():
pc.delete_index(index_name) # To avoid any conflicts in retrieval
pc.create_index(
name=index_name,
dimension=384,
metric='cosine',
spec=ServerlessSpec(
cloud="aws",
region="us-east-1"
)
)
return index_name
def final_response(index, question, model):
retriever = index.as_retriever()
parser = StrOutputParser()
chain = model | parser
template = """
You must provide an answer based strictly on the context below.
The answer is highly likely to be found within the given context, so analyze it thoroughly before responding.
Only if there is absolutely no relevant information, respond with "I don't know".
Do not make things up.
Context: {context}
Question: {question}
"""
prompt = PromptTemplate.from_template(template)
prompt.format(context="Here is some context", question="Here is a question")
chain = (
{
"context": itemgetter("question") | retriever,
"question": itemgetter("question"),
}
| prompt
| model
| parser
)
matching_results=index.similarity_search(question,k=2)
return f"Answer: {chain.invoke({'question': question})}", matching_results
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