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83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
import os
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import tiktoken
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.vectorstores import FAISS
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from retry import retry
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# from langchain.embeddings import HuggingFaceEmbeddings
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# from langchain.embeddings import HuggingFaceInstructEmbeddings
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# from langchain.embeddings import CohereEmbeddings
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def num_tokens_from_string(string: str, encoding_name: str) -> int:
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# Function to convert string to tokens and estimate user cost.
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encoding = tiktoken.get_encoding(encoding_name)
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num_tokens = len(encoding.encode(string))
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total_price = ((num_tokens / 1000) * 0.0004)
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return num_tokens, total_price
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@retry(tries=10, delay=60)
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def store_add_texts_with_retry(store, i):
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store.add_texts([i.page_content], metadatas=[i.metadata])
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# store_pine.add_texts([i.page_content], metadatas=[i.metadata])
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def call_openai_api(docs, folder_name, task_status):
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# Function to create a vector store from the documents and save it to disk.
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# create output folder if it doesn't exist
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if not os.path.exists(f"{folder_name}"):
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os.makedirs(f"{folder_name}")
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from tqdm import tqdm
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docs_test = [docs[0]]
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docs.pop(0)
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c1 = 0
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store = FAISS.from_documents(docs_test, OpenAIEmbeddings(openai_api_key=os.getenv("EMBEDDINGS_KEY")))
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# Uncomment for MPNet embeddings
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# model_name = "sentence-transformers/all-mpnet-base-v2"
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# hf = HuggingFaceEmbeddings(model_name=model_name)
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# store = FAISS.from_documents(docs_test, hf)
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s1 = len(docs)
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for i in tqdm(docs, desc="Embedding 🦖", unit="docs", total=len(docs),
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bar_format='{l_bar}{bar}| Time Left: {remaining}'):
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try:
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task_status.update_state(state='PROGRESS', meta={'current': int((c1 / s1) * 100)})
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store_add_texts_with_retry(store, i)
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except Exception as e:
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print(e)
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print("Error on ", i)
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print("Saving progress")
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print(f"stopped at {c1} out of {len(docs)}")
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store.save_local(f"{folder_name}")
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break
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c1 += 1
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store.save_local(f"{folder_name}")
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def get_user_permission(docs, folder_name):
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# Function to ask user permission to call the OpenAI api and spend their OpenAI funds.
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# Here we convert the docs list to a string and calculate the number of OpenAI tokens the string represents.
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# docs_content = (" ".join(docs))
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docs_content = ""
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for doc in docs:
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docs_content += doc.page_content
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tokens, total_price = num_tokens_from_string(string=docs_content, encoding_name="cl100k_base")
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# Here we print the number of tokens and the approx user cost with some visually appealing formatting.
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print(f"Number of Tokens = {format(tokens, ',d')}")
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print(f"Approx Cost = ${format(total_price, ',.2f')}")
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# Here we check for user permission before calling the API.
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user_input = input("Price Okay? (Y/N) \n").lower()
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if user_input == "y":
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call_openai_api(docs, folder_name)
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elif user_input == "":
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call_openai_api(docs, folder_name)
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else:
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print("The API was not called. No money was spent.")
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