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graph-cycle.py

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    • doc_output.py 955 B
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    print("----------------------------------------------> Importation des modules")
import os

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma

embedding_function = HuggingFaceEmbeddings()
VECTORS_DIRECTORY = "text_files"  # Change this directory if needed

# Charge des documents locaux

print("----------------------------------------------> Charge des documents locaux")
persist_directory = os.path.join(VECTORS_DIRECTORY, "vector")
new_db = Chroma(persist_directory=persist_directory, embedding_function=embedding_function)
print("Entrez votre requête :")

query = input()
embedding_vector = embedding_function.embed_query(query)

tot_docs = ""
print("----------------------------------------------> Recherche dans la documentation")
docs = new_db.similarity_search_by_vector(embedding_vector, k=3)

for i in range(len(docs)):
    tot_docs += f'data {str(i)} : {docs[i].page_content}\n'

print(tot_docs)