| from sentence_transformers import SentenceTransformer |
| from sentence_transformers import util |
|
|
| from dora import DoraStatus |
| import os |
| import sys |
| import torch |
| import pyarrow as pa |
|
|
| SHOULD_BE_INCLUDED = [ |
| "webcam.py", |
| "object_detection.py", |
| "plot.py", |
| ] |
|
|
|
|
| |
| 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: |
| |
| sys.path.append(root) |
| |
| raw.append(f.read()) |
| paths.append(path) |
|
|
| return raw, paths |
|
|
|
|
| def search(query_embedding, corpus_embeddings, paths, raw, k=5, file_extension=None): |
| 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): |
| |
| self.model = SentenceTransformer("/home/peiji/bge-large-en-v1.5/") |
| self.encoding = [] |
| |
| path = os.path.dirname(os.path.abspath(__file__)) |
|
|
| self.raw, self.path = get_all_functions(path) |
| |
| 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] |
| send_output( |
| "raw_file", |
| pa.array([{"raw": raw, "path": path, "user_message": 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() |
|
|
