File size: 10,636 Bytes
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import os
import sys
from typing import Any, Dict, List
# Add the project root to Python path
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(project_root)
import chromadb
import numpy as np
import torch
from nanoid import generate
from transformers import AutoModel, AutoTokenizer
def append_to_json(new_entries, filename="json_file_record.json"):
"""
Append new entries to an existing JSON array file, or create a new one if it doesn't exist.
Args:
new_entries (list): List of dictionaries to append
filename (str): Name of the JSON file
"""
try:
# Read existing data if file exists
if os.path.exists(filename) and os.path.getsize(filename) > 0:
with open(filename, "r") as f:
data = json.load(f)
if not isinstance(data, list):
data = []
else:
data = []
# # Append new entries
# data.extend(new_entries)
# print(data)
# Write back the updated data
with open(filename, "w") as f:
json.dump(data, f, indent=4)
except json.JSONDecodeError:
# Handle case where file exists but is not valid JSON
data = new_entries
with open(filename, "w") as f:
json.dump(data, f, indent=4)
class BERTEmbedder:
def __init__(self):
self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
self.model = AutoModel.from_pretrained("bert-base-uncased")
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def get_embeddings(self, texts: List[str]) -> np.ndarray:
embeddings = []
self.model.eval()
with torch.no_grad():
for text in texts:
inputs = self.tokenizer(
text,
padding=True,
truncation=True,
max_length=512,
return_tensors="pt",
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
outputs = self.model(**inputs)
embeddings.append(outputs.last_hidden_state.mean(dim=1).cpu().numpy())
return np.vstack(embeddings)
class VectorStore:
def __init__(
self, persist_directory: str = "../chroma_rag", query=False, is_uploaded=False
):
try:
if query == False and is_uploaded == True:
print("Embbeding store mode.")
print(f"Initializing ChromaDB with directory: {persist_directory}")
self.client = chromadb.PersistentClient(path=persist_directory)
print("ChromaDB client created successfully")
self.collection = self.client.get_or_create_collection(
name="documents",
metadata={"hnsw:space": "cosine"},
embedding_function=None, # We're using our own embeddings
)
print(f"Collection 'documents' ready")
self.embedder = BERTEmbedder()
print("BERT embedder initialized")
# Check if collection has documents
content = self.collection.get()
print(f"Collection contains {len(content['documents'])} documents")
self.json_file_path = "json_file_record.json"
else:
print("query mode")
persist_directory = "chroma_rag"
print(f"Initializing ChromaDB with directory: {persist_directory}")
self.client = chromadb.PersistentClient(path=persist_directory)
print("ChromaDB client created successfully")
self.collection = self.client.get_or_create_collection(
name="documents",
metadata={"hnsw:space": "cosine"},
embedding_function=None, # We're using our own embeddings
)
print(f"Collection 'documents' ready")
self.embedder = BERTEmbedder()
print("BERT embedder initialized")
# Check if collection has documents
content = self.collection.get()
print(f"Collection contains {len(content['documents'])} documents")
self.json_file_path = "utils/json_file_record.json"
except Exception as e:
print(f"Error initializing VectorStore: {e}")
raise
def is_collection_empty(self) -> bool:
try:
content = self.collection.get()
return len(content["documents"]) == 0
except Exception as e:
print(f"Error checking collection: {e}")
return True
def add_documents(self, chunks: List[Dict[str, Any]]):
try:
texts = [chunk["content"] for chunk in chunks]
metadatas = [chunk["metadata"] for chunk in chunks]
print(f"Generating embeddings for {len(texts)} documents...")
print(texts)
embeddings = self.embedder.get_embeddings(texts)
id_val = str(generate(size=8))
print(f"Generated ID: {id_val}")
if os.path.exists(self.json_file_path):
with open(self.json_file_path, "r") as f:
data = json.load(f)
for chunk in chunks:
temp = {"id": id_val, "file_path": chunk["metadata"]["source"]}
break
# Append the new entry
data.append(temp)
# Write the updated JSON data back to the file
with open(self.json_file_path, "w") as file:
json.dump(data, file, indent=4)
else:
# Usage in your code would be:
with open(self.json_file_path, "w") as f:
temp = []
for chunk in chunks:
temp.append(
{"id": id_val, "file_path": chunk["metadata"]["source"]}
)
break
# Write the updated JSON data back to the file
with open(self.json_file_path, "w") as file:
json.dump(temp, file, indent=4)
print("*************")
count = 0
ids = []
# Clean metadata
for metadata in metadatas:
metadata["topics"] = str(metadata["topics"])
ids.append(f"{id_val}{count}")
count += 1
print(metadatas)
print("------------------------")
print(len(metadatas))
print(f"Adding {len(texts)} documents to collection...")
self.collection.add(
embeddings=embeddings.tolist(),
documents=texts,
metadatas=metadatas,
ids=ids,
)
# Verify addition
collection_content = self.collection.get()
print(
f"Collection now contains {len(collection_content['documents'])} documents"
)
return True
except Exception as e:
print(f"Error adding documents: {e}")
return False
def query(self, query_text: str, n_results: int = 3) -> Dict:
try:
print(f"Generating embedding for query: {query_text}")
query_embedding = self.embedder.get_embeddings([query_text])
print("Checking collection content:")
collection_content = self.collection.get()
print(
f"Number of documents in collection: {len(collection_content['documents'])}"
)
print("Executing query...")
query_vector = query_embedding.tolist()
results = self.collection.query(
n_results=min(n_results, len(collection_content["documents"])),
query_embeddings=query_vector,
)
# query_texts=[query_text],
print(f"Query results: {json.dumps(results, indent=2)}")
return results
except Exception as e:
print(f"Error during query: {e}")
return {"error": str(e)}
def delete_documents_by_filename(self, file_substring: str):
"""
Delete documents from the collection and JSON file by matching a substring in the file path.
Args:
file_substring (str): Substring to match in the file paths.
json_file (str): Path to the JSON file containing document metadata.
"""
try:
# Load JSON data
print(file_substring)
json_file = self.json_file_path
if not os.path.exists(json_file):
print(f"JSON file {json_file} does not exist.")
return
with open(json_file, "r") as f:
data = json.load(f)
# Find matching records
matching_records = [
record for record in data if file_substring in record["file_path"]
]
if not matching_records:
print(f"No records found matching substring: {file_substring}")
return
# print("record", record)
# Get IDs of matching records
matching_ids = [record["id"] for record in matching_records]
print("maching_ids", matching_ids[0])
# Remove matching records from JSON file
updated_data = [
record for record in data if record["id"] not in matching_ids
]
print("updated data", updated_data)
with open(json_file, "w") as f:
json.dump(updated_data, f, indent=4)
print(f"Deleted {len(matching_records)} records from JSON file.")
# Retrieve all IDs in the collection
all_ids = self.collection.get()["ids"]
# Filter IDs that contain the substring "LDtz9CG5"
ids_to_delete = [id_ for id_ in all_ids if matching_ids[0] in id_]
# Delete those IDs from the collection
if ids_to_delete:
self.collection.delete(ids=ids_to_delete)
print(
f"Deleted {len(ids_to_delete)} records with IDs containing 'LDtz9CG5'."
)
else:
print("No matching IDs found.")
except Exception as e:
print(f"Error deleting documents: {e}")
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