Spaces:
Running
Running
File size: 3,817 Bytes
7eb1167 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import os
import re
import argparse
from pathlib import Path
from langchain_community.vectorstores import FAISS
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_core.documents import Document
# Function to extract specific fields from text
def extract_field(field_name: str, text: str) -> str:
"""Extracts the specified field from the given text."""
match = re.search(fr"{field_name}:\s*(.*)", text)
return match.group(1).strip() if match else "Unknown"
def tokenize(text: str) -> str:
# Replace underscores with spaces
text = text.replace('_', ' ')
# Insert a space between a lowercase letter and an uppercase letter (global match)
text = re.sub(r'(?<=[a-z])(?=[A-Z])', ' ', text)
return text.lower()
def main():
# Step 1: Parse command-line arguments
parser = argparse.ArgumentParser(
description="Process OpenFOAM case data and store embeddings in FAISS."
)
parser.add_argument(
"--database_path",
type=str,
default=Path(__file__).resolve().parent.parent,
help="Path to the database directory (default: '../../')",
)
args = parser.parse_args()
database_path = args.database_path
print(f"Database path: {database_path}")
# Step 2: Read the input file
database_allrun_path = os.path.join(database_path, "raw/openfoam_tutorials_details.txt")
if not os.path.exists(database_allrun_path):
raise FileNotFoundError(f"File not found: {database_allrun_path}")
with open(database_allrun_path, "r", encoding="utf-8") as file:
file_content = file.read()
# Step 3: Extract `<case_begin> ... </case_end>` segments using regex
pattern = re.compile(r"<case_begin>(.*?)</case_end>", re.DOTALL)
matches = pattern.findall(file_content)
if not matches:
raise ValueError("No cases found in the input file. Please check the file content.")
documents = []
for match in matches:
full_content = match.strip() # Store the complete case
index_match = re.search(r"<index>(.*?)</index>", match, re.DOTALL)
index_content = index_match.group(1).strip() # Extract `<index>` content
# Extract metadata fields
case_name = extract_field("case name", index_content)
case_domain = extract_field("case domain", index_content)
case_category = extract_field("case category", index_content)
case_solver = extract_field("case solver", index_content)
case_directory_structure = re.search(r"<directory_structure>([\s\S]*?)</directory_structure>", full_content).group(1)
detailed_tutorial = re.search(r"<tutorials>([\s\S]*?)</tutorials>", full_content).group(1)
# Create a Document instance
documents.append(Document(
page_content=tokenize(index_content+case_directory_structure),
metadata={
"full_content": full_content, # Store full `<case_begin> ... </case_end>`
"case_name": case_name,
"case_domain": case_domain,
"case_category": case_category,
"case_solver": case_solver,
'dir_structure': case_directory_structure,
'tutorials': detailed_tutorial
}
))
# Step 4: Compute embeddings and store them in FAISS
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
vectordb = FAISS.from_documents(documents, embedding_model)
# Step 5: Save FAISS index locally
persist_directory = os.path.join(database_path, "faiss/openfoam_tutorials_details")
vectordb.save_local(persist_directory)
print(f"{len(documents)} cases indexed successfully with metadata! Saved at: {persist_directory}")
if __name__ == "__main__":
main()
|