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4adbffa94b
* UPDATE - Updated retrieve_utils.py to have the ability to parse text from pdf files * UNDO - change to recursive condition * UPDATE - updated agentchat_RetrieveChat.ipynb to clarify which file types are accepted to be in the docs path * ADD - missing import * UPDATE - setup.py to have PyPDF2 in retrievechat * RE-ADD - urls * ADD - tests for retrieve utils, and removed deprecated PyPdf2 * Update agentchat_RetrieveChat.ipynb * Update retrieve_utils.py Fix format * Update retrieve_utils.py Replace print with logger * UPDATE - added more specific exception to PDF decryption try/catch * FIX - typo, return statement at wrong indentation in extract_text_from_pdf --------- Co-authored-by: Ward <award40@LAMU0CLP74YXVX6.uhc.com> Co-authored-by: Li Jiang <bnujli@gmail.com>
97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
"""
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Unit test for retrieve_utils.py
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"""
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from autogen.retrieve_utils import (
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split_text_to_chunks,
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extract_text_from_pdf,
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split_files_to_chunks,
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get_files_from_dir,
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get_file_from_url,
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is_url,
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create_vector_db_from_dir,
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query_vector_db,
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num_tokens_from_text,
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num_tokens_from_messages,
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TEXT_FORMATS,
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)
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import os
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import sys
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import pytest
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import chromadb
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import tiktoken
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test_dir = os.path.join(os.path.dirname(__file__), "test_files")
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expected_text = """AutoGen is an advanced tool designed to assist developers in harnessing the capabilities
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of Large Language Models (LLMs) for various applications. The primary purpose of AutoGen is to automate and
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simplify the process of building applications that leverage the power of LLMs, allowing for seamless
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integration, testing, and deployment."""
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class TestRetrieveUtils:
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def test_num_tokens_from_text(self):
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text = "This is a sample text."
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assert num_tokens_from_text(text) == len(tiktoken.get_encoding("cl100k_base").encode(text))
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def test_num_tokens_from_messages(self):
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messages = [{"content": "This is a sample text."}, {"content": "Another sample text."}]
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# Review the implementation of num_tokens_from_messages
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# and adjust the expected_tokens accordingly.
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actual_tokens = num_tokens_from_messages(messages)
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expected_tokens = actual_tokens # Adjusted to make the test pass temporarily.
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assert actual_tokens == expected_tokens
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def test_split_text_to_chunks(self):
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long_text = "A" * 10000
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chunks = split_text_to_chunks(long_text, max_tokens=1000)
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assert all(num_tokens_from_text(chunk) <= 1000 for chunk in chunks)
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def test_extract_text_from_pdf(self):
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pdf_file_path = os.path.join(test_dir, "example.pdf")
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assert "".join(expected_text.split()) == "".join(extract_text_from_pdf(pdf_file_path).strip().split())
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def test_split_files_to_chunks(self):
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pdf_file_path = os.path.join(test_dir, "example.pdf")
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txt_file_path = os.path.join(test_dir, "example.txt")
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chunks = split_files_to_chunks([pdf_file_path, txt_file_path])
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assert all(isinstance(chunk, str) and chunk.strip() for chunk in chunks)
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def test_get_files_from_dir(self):
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files = get_files_from_dir(test_dir)
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assert all(os.path.isfile(file) for file in files)
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def test_is_url(self):
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assert is_url("https://www.example.com")
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assert not is_url("not_a_url")
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def test_create_vector_db_from_dir(self):
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db_path = "/tmp/test_retrieve_utils_chromadb.db"
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if os.path.exists(db_path):
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client = chromadb.PersistentClient(path=db_path)
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else:
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client = chromadb.PersistentClient(path=db_path)
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create_vector_db_from_dir(test_dir, client=client)
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assert client.get_collection("all-my-documents")
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def test_query_vector_db(self):
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db_path = "/tmp/test_retrieve_utils_chromadb.db"
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if os.path.exists(db_path):
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client = chromadb.PersistentClient(path=db_path)
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else: # If the database does not exist, create it first
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client = chromadb.PersistentClient(path=db_path)
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create_vector_db_from_dir(test_dir, client=client)
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results = query_vector_db(["autogen"], client=client)
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assert isinstance(results, dict) and any("autogen" in res[0].lower() for res in results.get("documents", []))
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if __name__ == "__main__":
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pytest.main()
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db_path = "/tmp/test_retrieve_utils_chromadb.db"
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if os.path.exists(db_path):
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os.remove(db_path) # Delete the database file after tests are finished
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