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File size: 2,763 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 | 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
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_command_help.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 `<command_begin> ... </command_end>` segments using regex
pattern = re.compile(r"<command_begin>(.*?)</command_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:
command = re.search(r"<command>(.*?)</command>", match, re.DOTALL).group(1).strip()
help_text = re.search(r"<help_text>(.*?)</help_text>", match, re.DOTALL).group(1).strip()
full_content = match.strip() # Store the complete case
# Create a Document instance
documents.append(Document(
page_content=tokenize(command),
metadata={
"full_content": full_content,
"command": command,
"help_text": help_text
}
))
# 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_command_help")
vectordb.save_local(persist_directory)
print(f"{len(documents)} cases indexed successfully with metadata! Saved at: {persist_directory}")
if __name__ == "__main__":
main()
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