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import json, os, time, uuid
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from transformers import AutoTokenizer, AutoModel
import chromadb
from chromadb.config import Settings
from chromadb.utils import embedding_functions
from InstructorEmbedding import INSTRUCTOR
from langchain.vectorstores import Chroma
'''
If there is a transformers install error:
pip install transformers==4.29.2
Python 3.8 and above will need to upgrade the transformers to 4.2x.xx
https://github.com/huggingface/transformers/issues/11799
The goal is to creat a domain knowledge database based on existing transcribed labels.
I modify the domain knowledge (an xlsx file) so that each row is embedded in a way that most closely
resembles the raw OCR output, since that is what will be used to query against the db.
Once the closest row is found, I use the id to go back to the xlsx and take the whole row, converting
it into a dictionary similar to the desired output from the LLM.
This dict is then added to the prompt as a hint for the LLM.
'''
'''
pip uninstall protobuf
pip install protobuf==3.19.5
'''
class VoucherVisionEmbedding:
# def __init__(self, db_name, path_domain_knowledge, logger, build_new_db=False, model_name="hkunlp/instructor-xl", device="cuda"):
# DB_DIR = os.path.join(os.path.dirname(__file__), db_name)
# client_settings = chromadb.config.Settings(
# chroma_db_impl="duckdb+parquet",
# persist_directory=DB_DIR,
# anonymized_telemetry=False
# )
# embeddings = embedding_functions.InstructorEmbeddingFunction(model_name=model_name, device=device)
# self.collection = Chroma(
# collection_name="langchain_store",
# embedding_function=embeddings,
# client_settings=client_settings,
# persist_directory=DB_DIR,
# )
# total_rows = len(self.domain_knowledge)
# for index, row in self.domain_knowledge.iterrows():
# try:
# self.logger.info(f"[Creating New Embedding DB] --- Adding Row {index+1}/{total_rows}")
# except:
# print(f"Row {index+1}/{total_rows}")
# id = str(row[0])
# document = str(' '.join(row[1:][row[1:].notna()].astype(str)))
# self.collection.add_texts(document, None, id, embedding=embeddings)
# self.collection.persist()
# print(self.collection)
def __init__(self, db_name, path_domain_knowledge, logger, build_new_db=False, model_name="hkunlp/instructor-xl", device="cuda"):
DB_DIR = os.path.join(os.path.dirname(__file__), db_name)
self.logger = logger
self.path_domain_knowledge = path_domain_knowledge
self.client = chromadb.PersistentClient(path=DB_DIR,
settings=Settings(anonymized_telemetry=False))
ef = embedding_functions.InstructorEmbeddingFunction(model_name=model_name, device=device)
self.domain_knowledge = pd.read_excel(path_domain_knowledge).fillna('').astype(str)
if build_new_db:
self.logger.info(f"Creating new DB from {self.path_domain_knowledge}")
self.collection = self.client.create_collection(name=db_name, embedding_function=ef, metadata={"hnsw:space": "cosine"})
self.create_db_from_xlsx()
else:
try:
self.collection = self.client.get_collection(name=db_name, embedding_function=ef)
except:
self.logger.error(f"Embedding database not found! Creating new DB from {self.path_domain_knowledge}")
self.collection = self.client.create_collection(name=db_name, embedding_function=ef, metadata={"hnsw:space": "cosine"})
self.create_db_from_xlsx()
def add_document(self, document, metadata, id):
id = str(id)
existing_documents = self.collection.get()
if id not in existing_documents['ids']:
try:
self.collection.add(documents=[document], ids=[id])
except Exception as e:
self.logger.error(f"Error while adding document {id}: {str(e)}")
# try:
# self.collection.add(documents=[document], ids=[id])
# except:
# try:
# time.sleep(0.1)
# self.collection.add(documents=[document], ids=[id])
# except:
# try:
# self.logger.info(f"[Embedding Add Doc] --- Failed, skipping: {id}")
# except:
# print(f"Failed, skipping: {id}")
else:
try:
self.logger.info(f"[Embedding Add Doc] --- ID already exists in the collection: {id}")
except:
print(f"ID already exists in the collection: {id}")
def query_db(self, query_text, n_results):
results = self.collection.query(query_texts=[query_text], n_results=n_results)
self.similarity = round(results['distances'][0][0],3)
self.similarity_exact = results['distances'][0][0]
try:
self.logger.info(f"[Embedding Search] --- Similarity (close to zero is best) {self.similarity}")
except:
print(f"Similarity (close to zero is best) --- {self.similarity}")
self.domain_knowledge.iloc[:, 0] = self.domain_knowledge.iloc[:, 0].astype(str)
# Initialize an empty list to hold dictionaries
for id in results['ids']:
row_dicts = self._get_row_from_df(id)
if not row_dicts:
# try:
# self.logger.info(f"[Embedding Search] --- Similar Dictionary\n{row_dicts}")
# except:
# print(row_dicts)
# else:
try:
self.logger.info(f"[Embedding Search] --- No row found for id {id}")
except:
print(f"No row found for id {id}")
# Return the list of dictionaries if n_results > 1, else return single dictionary
if n_results > 1:
return row_dicts
else:
return row_dicts[0] if row_dicts else None
def create_db_from_xlsx(self):
total_rows = len(self.domain_knowledge)
for index, row in self.domain_knowledge.iterrows():
try:
self.logger.info(f"[Creating New Embedding DB] --- Adding Row {index+1}/{total_rows}")
except:
print(f"Row {index+1}/{total_rows}")
id = str(row.iloc[0])
document = str(' '.join(row[0:][row[0:].notna()].astype(str)))
self.add_document(document, None, id)
def get_similarity(self):
return self.similarity_exact
def _get_row_from_df(self, ids):
row_dicts = [] # initialize an empty list to hold dictionaries
for id in ids:
row = self.domain_knowledge[self.domain_knowledge.iloc[:, 0] == id]
if not row.empty:
row_dict = row.iloc[0].to_dict()
row_dict.pop('Catalog Number', None)
for key in row_dict:
if pd.isna(row_dict[key]):
row_dict[key] = ''
row_dicts.append(row_dict) # append the dictionary to the list
return row_dicts if row_dicts else None # return the list of dictionaries or None if it's empty
# def _get_row_from_df(self, ids):
# for id in ids:
# row = self.domain_knowledge[self.domain_knowledge.iloc[:, 0] == id]
# if not row.empty:
# row_dict = row.iloc[0].to_dict()
# row_dict.pop('Catalog Number', None)
# for key in row_dict:
# if pd.isna(row_dict[key]):
# row_dict[key] = ''
# return row_dict
# return None
class VoucherVisionEmbeddingTest:
def __init__(self, ground_truth_dir, llm_output_dir, model_name="hkunlp/instructor-xl"):
self.ground_truth_dir = ground_truth_dir
self.llm_output_dir = llm_output_dir
self.model_name = model_name
self.model = INSTRUCTOR(model_name, device="cuda")
self.instruction = "Represent the Science json dictionary document:"
def compare_texts(self, ground_truth_text, predicted_text):
# Convert the texts to embeddings using the given model
ground_truth_embedding = self.model.encode([[self.instruction,ground_truth_text]])
predicted_embedding = self.model.encode([[self.instruction,predicted_text]])
# Compute the cosine similarity between the two embeddings
similarity = cosine_similarity(ground_truth_embedding, predicted_embedding)
return similarity[0][0]
@staticmethod
def json_to_text(json_dict):
return str(json_dict)
def get_max_difference(self, similarities):
differences = [abs(1 - sim) for sim in similarities]
return max(differences)
def evaluate(self):
# Get a list of all ground truth and LLM output files
ground_truth_files = os.listdir(self.ground_truth_dir)
llm_output_files = os.listdir(self.llm_output_dir)
# Ensure file lists are sorted so they match up correctly
ground_truth_files.sort()
llm_output_files.sort()
similarities = []
key_similarities = [] # List to store key similarity
for ground_truth_file, llm_output_file in zip(ground_truth_files, llm_output_files):
# Read the files and convert them to text
with open(os.path.join(self.ground_truth_dir, ground_truth_file), 'r') as f:
ground_truth_dict = json.load(f)
ground_truth_text = self.json_to_text(ground_truth_dict)
with open(os.path.join(self.llm_output_dir, llm_output_file), 'r') as ff:
llm_output_dict = json.load(ff)
llm_output_text = self.json_to_text(llm_output_dict)
# Compute the similarity between the ground truth and the LLM output
similarity = self.compare_texts(ground_truth_text, llm_output_text)
# Clip and round to mitigate/smudge floating-point precision limitations
similarity = np.clip(similarity, -1.0, 1.0)
similarity = np.round(similarity, 6)
similarities.append(similarity)
# Compare keys
ground_truth_keys = ', '.join(sorted(ground_truth_dict.keys()))
llm_output_keys = ', '.join(sorted(llm_output_dict.keys()))
key_similarity = self.compare_texts(ground_truth_keys, llm_output_keys)
key_similarity = np.clip(key_similarity, -1.0, 1.0)
key_similarity = np.round(key_similarity, 6)
key_similarities.append(key_similarity)
# Compute the mean similarity
mean_similarity = np.mean(similarities)
mean_key_similarity = np.mean(key_similarities)
max_diff = self.get_max_difference(similarities)
max_diff_key = self.get_max_difference(key_similarities)
return mean_similarity, max_diff, similarities, mean_key_similarity, max_diff_key, key_similarities
if __name__ == '__main__':
# db_name = "VV_all_asia_minimal"
db_name = "all_asia_minimal"
path_domain_knowledge = 'D:/Dropbox/LeafMachine2/leafmachine2/transcription/domain_knowledge/AllAsiaMinimalasof25May2023_2__FOR-EMBEDDING.xlsx'
# path_domain_knowledge = 'D:/Dropbox/LeafMachine2/leafmachine2/transcription/domain_knowledge/AllAsiaMinimalasof25May2023_2__TRIMMEDtiny.xlsx'
build_new_db = False
VVE = VoucherVisionEmbedding(db_name, path_domain_knowledge, build_new_db)
test_query = "Golden Thread\nHerbaria of Michigan State University (MSC) and\nUniversiti Kebangsaan Malaysia, Sabah Campus (UKMS)\nUNITED STATES\n3539788\nNATIONAL HERBARIUM\nPLANTS OF BORNEO\nBrookea tomentosa Benth.\nMalaysia. Sabah. Beaufort District: Beaufort Hill. 5°22'N,\n115°45'E. Elev. 200 m. Burned logged dipterocarp forest.\nCrocker Formation. Small tree, corolla cream.\nDet. at K, 1986\n28 August 1983\nWith: Reed S. Beaman and Teofila E. Beamann\nJohn H. Beaman 6844"
domain_knowledge_example = VVE.query_db(test_query, 1) |