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import langchain
from langchain import HuggingFaceHub
from langchain.embeddings import HuggingFaceHubEmbeddings
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.question_answering import load_qa_chain
def llm_conv(filename):
document_loader = PyPDFLoader(filename)
chunks = document_loader.load_and_split()
embeddings = HuggingFaceHubEmbeddings()
db = FAISS.from_documents(chunks, embeddings)
return db, chunks
def similarity(filename, repo_id, model_kwargs, query):
db, chunks = llm_conv(filename)
docs = db.similarity_search(query)
chain = load_qa_chain(
HuggingFaceHub(
repo_id=repo_id,
model_kwargs=model_kwargs
),
chain_type="stuff"
)
question = f"<|system|>\nYou are a intelligent chatbot and expertise in {chunks[0].page_content}.</s>\n<|user|>\n{query}.\n<|assistant|>"
# question = f"""
# Answer the question based on the context, if you don't know then output "Out of Context".
# Context: \n {chunks[0].page_content} \n
# Question: \n {query} \n
# Answer:
# """
result = chain.run(
input_documents=docs,
question=question
)
return result
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