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import openai | |
from langchain.embeddings.openai import OpenAIEmbeddings | |
from langchain.vectorstores import Pinecone | |
from langchain.llms import OpenAI | |
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings | |
from langchain.schema import Document | |
import pinecone | |
from langchain.vectorstores import FAISS | |
from pypdf import PdfReader | |
from langchain.llms.openai import OpenAI | |
from langchain.chains.summarize import load_summarize_chain | |
from langchain import HuggingFaceHub | |
from langchain.document_loaders import DirectoryLoader | |
#Extract Information from PDF file | |
def get_pdf_text(pdf_doc): | |
text = "" | |
pdf_reader = PdfReader(pdf_doc) | |
for page in pdf_reader.pages: | |
text += page.extract_text() | |
return text | |
# iterate over files in | |
# that user uploaded PDF files, one by one | |
def create_docs(user_pdf_list, unique_id): | |
docs=[] | |
for filename in user_pdf_list: | |
chunks=get_pdf_text(filename) | |
#Adding items to our list - Adding data & its metadata | |
docs.append(Document( | |
page_content=chunks, | |
metadata={"name": filename.name,"id":filename.id,"type=":filename.type,"size":filename.size,"unique_id":unique_id}, | |
)) | |
# Load Files from Directory (Local Version) | |
#loader = DirectoryLoader('./Repository', glob='**/*') | |
#docs1 = loader.load() | |
#final_docs = docs + docs1 | |
return docs | |
#Create embeddings instance | |
def create_embeddings_load_data(): | |
embeddings = OpenAIEmbeddings() | |
#embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | |
return embeddings | |
def close_matches(query,k,docs,embeddings): | |
#https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html#langchain.vectorstores.faiss.FAISS.similarity_search_with_score | |
db = FAISS.from_documents(docs, embeddings) | |
similar_docs = db.similarity_search_with_score(query, int(k)) | |
return similar_docs | |
# Helps us get the summary of a document | |
def get_summary(current_doc): | |
llm = OpenAI(temperature=0) | |
#llm = HuggingFaceHub(repo_id="bigscience/bloom", model_kwargs={"temperature":1e-10}) | |
chain = load_summarize_chain(llm, chain_type="map_reduce") | |
summary = chain.run([current_doc]) | |
return summary |