from sentence_transformers import SentenceTransformer from sentence_transformers import util from dora import DoraStatus import os import sys import inspect import torch import pyarrow as pa SHOULD_NOT_BE_INCLUDED = [ "utils.py", "sentence_transformers_op.py", "chatgpt_op.py", "whisper_op.py", "microphone_op.py", "object_detection_op.py", "webcam.py", ] SHOULD_BE_INCLUDED = ["planning_op.py"] ## Get all python files path in given directory def get_all_functions(path): raw = [] paths = [] for root, dirs, files in os.walk(path): for file in files: if file.endswith(".py"): if file not in SHOULD_BE_INCLUDED: continue path = os.path.join(root, file) with open(path, "r", encoding="utf8") as f: ## add file folder to system path sys.path.append(root) ## import module from path raw.append(f.read()) paths.append(path) return raw, paths def search(query_embedding, corpus_embeddings, paths, raw, k=5, file_extension=None): # TODO: filtering by file extension cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0] top_results = torch.topk(cos_scores, k=min(k, len(cos_scores)), sorted=True) out = [] for score, idx in zip(top_results[0], top_results[1]): out.extend([raw[idx], paths[idx], score]) return out class Operator: """ """ def __init__(self): ## TODO: Add a initialisation step self.model = SentenceTransformer("BAAI/bge-large-en-v1.5") self.encoding = [] # file directory path = os.path.dirname(os.path.abspath(__file__)) self.raw, self.path = get_all_functions(path) # Encode all files self.encoding = self.model.encode(self.raw) def on_event( self, dora_event, send_output, ) -> DoraStatus: if dora_event["type"] == "INPUT": if dora_event["id"] == "query": values = dora_event["value"].to_pylist() query_embeddings = self.model.encode(values) output = search( query_embeddings, self.encoding, self.path, self.raw, ) [raw, path, score] = output[0:3] print( ( score, pa.array([{"raw": raw, "path": path, "query": values[0]}]), ) ) send_output( "raw_file", pa.array([{"raw": raw, "path": path, "query": values[0]}]), dora_event["metadata"], ) else: input = dora_event["value"][0].as_py() index = self.path.index(input["path"]) self.raw[index] = input["raw"] self.encoding[index] = self.model.encode([input["raw"]])[0] return DoraStatus.CONTINUE if __name__ == "__main__": operator = Operator()