208 lines
7.1 KiB
Python
208 lines
7.1 KiB
Python
# encoding: utf-8
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'''
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@author: 温进
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@file: code_search.py
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@time: 2023/11/21 下午2:35
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@desc:
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'''
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import time
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from loguru import logger
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from collections import defaultdict
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from coagent.db_handler.graph_db_handler.nebula_handler import NebulaHandler
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from coagent.db_handler.vector_db_handler.chroma_handler import ChromaHandler
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from coagent.codechat.code_search.cypher_generator import CypherGenerator
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from coagent.codechat.code_search.tagger import Tagger
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from coagent.embeddings.get_embedding import get_embedding
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from coagent.llm_models.llm_config import LLMConfig
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# from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_MODEL
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# search_by_tag
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VERTEX_SCORE = 10
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HISTORY_VERTEX_SCORE = 5
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VERTEX_MERGE_RATIO = 0.5
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# search_by_description
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MAX_DISTANCE = 1000
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class CodeSearch:
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def __init__(self, llm_config: LLMConfig, nh: NebulaHandler, ch: ChromaHandler, limit: int = 3):
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'''
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init
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@param nh: NebulaHandler
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@param ch: ChromaHandler
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@param limit: limit of result
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'''
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self.llm_config = llm_config
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self.nh = nh
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self.ch = ch
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self.limit = limit
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def search_by_tag(self, query: str):
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'''
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search_code_res by tag
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@param query: str
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@return:
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'''
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tagger = Tagger()
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tag_list = tagger.generate_tag_query(query)
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logger.info(f'query tag={tag_list}')
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# get all verticex
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vertex_list = self.nh.get_vertices().get('v', [])
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vertex_vid_list = [i.as_node().get_id().as_string() for i in vertex_list]
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# update score
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vertex_score_dict = defaultdict(lambda: 0)
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for vid in vertex_vid_list:
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for tag in tag_list:
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if tag in vid:
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vertex_score_dict[vid] += VERTEX_SCORE
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# merge depend adj score
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vertex_score_dict_final = {}
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for vertex in vertex_score_dict:
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cypher = f'''MATCH (v1)-[e]-(v2) where id(v1) == "{vertex}" RETURN v2'''
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cypher_res = self.nh.execute_cypher(cypher, self.nh.space_name)
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cypher_res_dict = self.nh.result_to_dict(cypher_res)
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adj_vertex_list = [i.as_node().get_id().as_string() for i in cypher_res_dict.get('v2', [])]
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score = vertex_score_dict.get(vertex, 0)
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for adj_vertex in adj_vertex_list:
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score += vertex_score_dict.get(adj_vertex, 0) * VERTEX_MERGE_RATIO
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if score > 0:
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vertex_score_dict_final[vertex] = score
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# get most prominent package tag
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package_score_dict = defaultdict(lambda: 0)
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for vertex, score in vertex_score_dict.items():
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if '#' in vertex:
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# get class name first
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cypher = f'''MATCH (v1:class)-[e:contain]->(v2) WHERE id(v2) == '{vertex}' RETURN id(v1) as id;'''
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cypher_res = self.nh.execute_cypher(cypher=cypher, format_res=True)
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class_vertices = cypher_res.get('id', [])
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if not class_vertices:
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continue
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vertex = class_vertices[0].as_string()
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# get package name
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cypher = f'''MATCH (v1:package)-[e:contain]->(v2) WHERE id(v2) == '{vertex}' RETURN id(v1) as id;'''
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cypher_res = self.nh.execute_cypher(cypher=cypher, format_res=True)
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pac_vertices = cypher_res.get('id', [])
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if not pac_vertices:
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continue
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package = pac_vertices[0].as_string()
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package_score_dict[package] += score
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# get respective code
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res = []
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package_score_tuple = list(package_score_dict.items())
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package_score_tuple.sort(key=lambda x: x[1], reverse=True)
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ids = [i[0] for i in package_score_tuple]
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logger.info(f'ids={ids}')
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chroma_res = self.ch.get(ids=ids, include=['metadatas'])
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# logger.info(chroma_res)
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for vertex, score in package_score_tuple:
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index = chroma_res['result']['ids'].index(vertex)
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code_text = chroma_res['result']['metadatas'][index]['code_text']
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res.append({
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"vertex": vertex,
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"code_text": code_text}
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)
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if len(res) >= self.limit:
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break
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logger.info(f'retrival code={res}')
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return res
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def search_by_desciption(self, query: str, engine: str, model_path: str = "text2vec-base-chinese", embedding_device: str = "cpu"):
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'''
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search by perform sim search
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@param query:
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@return:
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'''
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query = query.replace(',', '，')
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query_emb = get_embedding(engine=engine, text_list=[query], model_path=model_path, embedding_device= embedding_device,)
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query_emb = query_emb[query]
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query_embeddings = [query_emb]
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query_result = self.ch.query(query_embeddings=query_embeddings, n_results=self.limit,
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include=['metadatas', 'distances'])
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logger.debug(query_result)
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res = []
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for idx, distance in enumerate(query_result['result']['distances'][0]):
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if distance < MAX_DISTANCE:
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vertex = query_result['result']['ids'][0][idx]
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code_text = query_result['result']['metadatas'][0][idx]['code_text']
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res.append({
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"vertex": vertex,
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"code_text": code_text
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})
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return res
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def search_by_cypher(self, query: str):
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'''
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search by generating cypher
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@param query:
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@param engine:
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@return:
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'''
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cg = CypherGenerator(self.llm_config)
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cypher = cg.get_cypher(query)
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if not cypher:
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return None
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cypher_res = self.nh.execute_cypher(cypher, self.nh.space_name)
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logger.info(f'cypher execution result={cypher_res}')
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if not cypher_res.is_succeeded():
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return {
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'cypher': '',
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'cypher_res': ''
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}
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res = {
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'cypher': cypher,
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'cypher_res': cypher_res
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}
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return res
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if __name__ == '__main__':
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# from configs.server_config import NEBULA_HOST, NEBULA_PORT, NEBULA_USER, NEBULA_PASSWORD, NEBULA_STORAGED_PORT
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# from configs.server_config import CHROMA_PERSISTENT_PATH
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from coagent.base_configs.env_config import (
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NEBULA_HOST, NEBULA_PORT, NEBULA_USER, NEBULA_PASSWORD, NEBULA_STORAGED_PORT,
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CHROMA_PERSISTENT_PATH
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)
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codebase_name = 'testing'
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nh = NebulaHandler(host=NEBULA_HOST, port=NEBULA_PORT, username=NEBULA_USER,
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password=NEBULA_PASSWORD, space_name=codebase_name)
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nh.add_host(NEBULA_HOST, NEBULA_STORAGED_PORT)
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time.sleep(0.5)
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ch = ChromaHandler(path=CHROMA_PERSISTENT_PATH, collection_name=codebase_name)
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cs = CodeSearch(nh, ch)
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# res = cs.search_by_tag(tag_list=['createFineTuneCompletion', 'OpenAiApi'])
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# logger.debug(res)
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# res = cs.search_by_cypher('代码中一共有多少个类', 'openai')
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# logger.debug(res)
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res = cs.search_by_desciption('使用不同的HTTP请求类型（GET、POST、DELETE等）来执行不同的操作', 'openai')
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logger.debug(res)
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