Artteiv tosanoob commited on
Commit
9235b6d
1 Parent(s): 75df934

Cuongtruong's request (#1)

Browse files

- SQLite3 fix db_instance on websocket creation (1ebe5e1fc9e08fa37c143808aaf4dc4ebd182440)


Co-authored-by: Trương Tấn Cường <tosanoob@users.noreply.huggingface.co>

Files changed (3) hide show
  1. .gitignore +5 -0
  2. chat/consumers.py +4 -1
  3. chat/model_manage.py +7 -7
.gitignore ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ arxivdb/
2
+ models/
3
+ apikey.txt
4
+ db.sqlite3
5
+ hotfix.ipynb
chat/consumers.py CHANGED
@@ -1,18 +1,21 @@
1
  import json
2
  from . import model_manage as md
 
3
  from channels.generic.websocket import WebsocketConsumer
4
 
5
 
6
  class ChatConsumer(WebsocketConsumer):
7
  def connect(self):
8
  self.accept()
 
 
9
  def disconnect(self, close_code):
10
  pass
11
  def receive(self, text_data):
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  text_data_json = json.loads(text_data)
13
  message = text_data_json["message"]
14
  print(message)
15
- record, messagee = md.full_chain_single_question(message)
16
  print("First answer: ",record)
17
  self.send(text_data=json.dumps({"message": messagee}))
18
 
 
1
  import json
2
  from . import model_manage as md
3
+ from chat.arxiv_bot.arxiv_bot_utils import ArxivSQL
4
  from channels.generic.websocket import WebsocketConsumer
5
 
6
 
7
  class ChatConsumer(WebsocketConsumer):
8
  def connect(self):
9
  self.accept()
10
+ self.db_instance = ArxivSQL()
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+
12
  def disconnect(self, close_code):
13
  pass
14
  def receive(self, text_data):
15
  text_data_json = json.loads(text_data)
16
  message = text_data_json["message"]
17
  print(message)
18
+ record, messagee = md.full_chain_single_question(message, self.db_instance)
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  print("First answer: ",record)
20
  self.send(text_data=json.dumps({"message": messagee}))
21
 
chat/model_manage.py CHANGED
@@ -104,12 +104,12 @@ def response(args):
104
  if type(new_records) == str:
105
  return "Error occured, information not found", new_records
106
  utils.db.add(new_records)
107
- utils.sqldb.add(new_records)
108
  results = utils.db.query_relevant(keywords=keywords, query_texts=query_texts)
109
  ids = results['metadatas'][0]
110
  print("Re-queried on chromadb, results: ",ids)
111
  paper_id = [id['paper_id'] for id in ids]
112
- paper_info = utils.sqldb.query_id(paper_id)
113
  print(paper_info)
114
  records = [] # get title (2), author (3), link (6)
115
  result_string = ""
@@ -125,7 +125,7 @@ def response(args):
125
  if "title" in keys:
126
  title = args['title']
127
  authors = utils.authors_str_to_list(args['author'])
128
- paper_info = utils.sqldb.query(title = title,author = authors)
129
  # if query not found then go crawl brh
130
  # print(paper_info)
131
 
@@ -136,8 +136,8 @@ def response(args):
136
  # print(new_records)
137
  return "Error occured, information not found", "Information not found"
138
  utils.db.add(new_records)
139
- utils.sqldb.add(new_records)
140
- paper_info = utils.sqldb.query(title = title,author = authors)
141
  print("Re-queried on chromadb, results: ",paper_info)
142
  # -------------------------------------
143
  records = [] # get title (2), author (3), link (6)
@@ -150,13 +150,13 @@ def response(args):
150
  return "Information not found", "Information not found"
151
  return result_string, records
152
  # invoke llm and return result
153
- def full_chain_single_question(input_prompt):
154
  try:
155
  first_prompt = extract_keyword_prompt(input_prompt)
156
  temp_answer = model.generate_content(first_prompt).text
157
 
158
  args = json.loads(utils.trimming(temp_answer))
159
- contexts, results = response(args)
160
  if not results:
161
  # print(contexts)
162
  return "Random question, direct return", contexts
 
104
  if type(new_records) == str:
105
  return "Error occured, information not found", new_records
106
  utils.db.add(new_records)
107
+ db_instance.add(new_records)
108
  results = utils.db.query_relevant(keywords=keywords, query_texts=query_texts)
109
  ids = results['metadatas'][0]
110
  print("Re-queried on chromadb, results: ",ids)
111
  paper_id = [id['paper_id'] for id in ids]
112
+ paper_info = db_instance.query_id(paper_id)
113
  print(paper_info)
114
  records = [] # get title (2), author (3), link (6)
115
  result_string = ""
 
125
  if "title" in keys:
126
  title = args['title']
127
  authors = utils.authors_str_to_list(args['author'])
128
+ paper_info = db_instance.query(title = title,author = authors)
129
  # if query not found then go crawl brh
130
  # print(paper_info)
131
 
 
136
  # print(new_records)
137
  return "Error occured, information not found", "Information not found"
138
  utils.db.add(new_records)
139
+ db_instance.add(new_records)
140
+ paper_info = db_instance.query(title = title,author = authors)
141
  print("Re-queried on chromadb, results: ",paper_info)
142
  # -------------------------------------
143
  records = [] # get title (2), author (3), link (6)
 
150
  return "Information not found", "Information not found"
151
  return result_string, records
152
  # invoke llm and return result
153
+ def full_chain_single_question(input_prompt, db_instance):
154
  try:
155
  first_prompt = extract_keyword_prompt(input_prompt)
156
  temp_answer = model.generate_content(first_prompt).text
157
 
158
  args = json.loads(utils.trimming(temp_answer))
159
+ contexts, results = response(args, db_instance)
160
  if not results:
161
  # print(contexts)
162
  return "Random question, direct return", contexts