add intent code and change text input

application/pages/1_🌍_Natural_Language_Querying.py

@@ -10,8 +10,10 @@
 from nlq.business.nlq_chain import NLQChain
 from nlq.business.profile import ProfileManagement
 from utils.database import get_db_url_dialect
+from nlq.business.vector_store import VectorStore
 from utils.llm import claude3_to_sql, create_vector_embedding_with_bedrock, retrieve_results_from_opensearch, \
-    upload_results_to_opensearch
+    upload_results_to_opensearch, get_query_intent
+
 
 def sample_question_clicked(sample):
     """Update the selected_sample variable with the text of the clicked button"""
@@ -20,19 +22,9 @@ def sample_question_clicked(sample):
 
 def upvote_clicked(question, sql, env_vars):
     # HACK: configurable opensearch endpoint
-    target_profile = 'shopping_guide'
-    aos_config = env_vars['data_sources'][target_profile]['opensearch']
-    upload_results_to_opensearch(
-        region_name=['region_name'],
-        domain=aos_config['domain'],
-        opensearch_user=aos_config['opensearch_user'],
-        opensearch_password=aos_config['opensearch_password'],
-        index_name=aos_config['index_name'],
-        query=question,
-        sql=sql,
-        host=aos_config['opensearch_host'],
-        port=aos_config['opensearch_port']
-    )
+
+    current_profile = st.session_state.current_profile
+    VectorStore.add_sample(current_profile, question, sql)
     logger.info(f'up voted "{question}" with sql "{sql}"')
 
 
@@ -128,6 +120,9 @@ def main():
     if 'nlq_chain' not in st.session_state:
         st.session_state['nlq_chain'] = None
 
+    if "messages" not in st.session_state:
+        st.session_state.messages = {}
+
     bedrock_model_ids = ['anthropic.claude-3-sonnet-20240229-v1:0', 'anthropic.claude-3-haiku-20240307-v1:0',
                          'anthropic.claude-v2:1']
 
@@ -139,7 +134,8 @@ def main():
             # clear session state
             st.session_state.selected_sample = ''
             st.session_state.current_profile = selected_profile
-
+            if selected_profile not in st.session_state.messages:
+                st.session_state.messages[selected_profile] = []
             st.session_state.nlq_chain = NLQChain(selected_profile)
 
         st.session_state['option'] = st.selectbox("Choose your option", ["Text2SQL"])
@@ -174,7 +170,8 @@ def main():
 
     # Display the predefined search samples as buttons within columns
     for i, sample in enumerate(search_samples[0:question_column_number]):
-        search_sample_columns[i].button(sample, use_container_width=True, on_click=sample_question_clicked, args=[sample])
+        search_sample_columns[i].button(sample, use_container_width=True, on_click=sample_question_clicked,
+                                        args=[sample])
 
     # Display more predefined search samples as buttons within columns, if there are more samples than columns
     if len(search_samples) > question_column_number:
@@ -189,19 +186,32 @@ def main():
                 else:
                     col_num += 1
 
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
+    # Display chat messages from history
+    if selected_profile in st.session_state.messages:
+        for message in st.session_state.messages[selected_profile]:
+            with st.chat_message(message["role"]):
+                if "SQL:" in message["content"]:
+                    st.code(message["content"].replace("SQL:", ""), language="sql")
+                elif isinstance(message["content"], pd.DataFrame):
+                    st.table(message["content"])
+                else:
+                    st.markdown(message["content"])
+
+    text_placeholder = "Type your query here..."
 
-    search_box = st.text_input('Search Box', value=st.session_state['selected_sample'],
-                               placeholder='Type your query here...', max_chars=1000, key='search_box',
-                               label_visibility='collapsed')
+    search_box = st.chat_input(placeholder=text_placeholder)
+    if st.session_state['selected_sample'] != "":
+        search_box = st.session_state['selected_sample']
+        st.session_state['selected_sample'] = ""
 
     current_nlq_chain = st.session_state.nlq_chain
 
+    search_intent_flag = True
+
     # add select box for which model to use
-    if st.button('Run', type='primary', use_container_width=True) or \
+    if search_box != "Type your query here..." or \
             current_nlq_chain.is_visualization_config_changed():
-        if len(search_box) > 0:
+        if search_box is not None and len(search_box) > 0:
             with st.chat_message("user"):
                 current_nlq_chain.set_question(search_box)
                 st.markdown(current_nlq_chain.get_question())
@@ -266,52 +276,61 @@ def main():
                             conn_name = database_profile['conn_name']
                             db_url = ConnectionManagement.get_db_url_by_name(conn_name)
                             database_profile['db_url'] = db_url
-                        response = claude3_to_sql(database_profile['tables_info'],
-                                                  database_profile['hints'],
-                                                  search_box,
-                                                  model_id=model_type,
-                                                  examples=retrieve_result,
-                                                  dialect=get_db_url_dialect(database_profile['db_url']),
-                                                  model_provider=model_provider)
-
-                        logger.info(f'got llm response: {response}')
-                        current_nlq_chain.set_generated_sql_response(response)
+
+                        intent_response = get_query_intent(model_type, search_box)
+
+                        intent = intent_response.get("intent", "normal_search")
+                        if intent == "reject_search":
+                            search_intent_flag = False
+
+                        if search_intent_flag:
+                            response = claude3_to_sql(database_profile['tables_info'],
+                                                      database_profile['hints'],
+                                                      search_box,
+                                                      model_id=model_type,
+                                                      examples=retrieve_result,
+                                                      dialect=get_db_url_dialect(database_profile['db_url']),
+                                                      model_provider=model_provider)
+
+                            logger.info(f'got llm response: {response}')
+                            current_nlq_chain.set_generated_sql_response(response)
                 else:
                     logger.info('get generated sql from memory')
 
-                st.session_state.messages = []
-
-                # Add user message to chat history
-                st.session_state.messages.append({"role": "user", "content": st.session_state['selected_sample']})
+                if search_intent_flag:
+                    # Add user message to chat history
+                    st.session_state.messages[selected_profile].append({"role": "user", "content": search_box})
 
-                # Add assistant response to chat history
-                st.session_state.messages.append({"role": "assistant", "content":
-                    current_nlq_chain.get_generated_sql()})
-                st.session_state.messages.append({"role": "assistant", "content":
-                    current_nlq_chain.get_generated_sql_explain()})
+                    # Add assistant response to chat history
+                    st.session_state.messages[selected_profile].append(
+                        {"role": "assistant", "content": "SQL:" + current_nlq_chain.get_generated_sql()})
+                    st.session_state.messages[selected_profile].append(
+                        {"role": "assistant", "content": current_nlq_chain.get_generated_sql_explain()})
 
-                st.markdown('The generated SQL statement is:')
-                st.code(current_nlq_chain.get_generated_sql(), language="sql")
+                    st.markdown('The generated SQL statement is:')
+                    st.code(current_nlq_chain.get_generated_sql(), language="sql")
 
-                st.markdown('Generation process explanations:')
-                st.markdown(current_nlq_chain.get_generated_sql_explain())
+                    st.markdown('Generation process explanations:')
+                    st.markdown(current_nlq_chain.get_generated_sql_explain())
 
-                st.markdown('You can provide feedback:')
+                    st.markdown('You can provide feedback:')
 
-                # add a upvote(green)/downvote button with logo
-                feedback = st.columns(2)
-                feedback[0].button('👍 Upvote (save as embedding for retrieval)', type='secondary',
-                                   use_container_width=True,
-                                   on_click=upvote_clicked,
-                                   args=[current_nlq_chain.get_question(),
-                                         current_nlq_chain.get_generated_sql(),
-                                         env_vars])
+                    # add a upvote(green)/downvote button with logo
+                    feedback = st.columns(2)
+                    feedback[0].button('👍 Upvote (save as embedding for retrieval)', type='secondary',
+                                       use_container_width=True,
+                                       on_click=upvote_clicked,
+                                       args=[current_nlq_chain.get_question(),
+                                             current_nlq_chain.get_generated_sql(),
+                                             env_vars])
 
-                if feedback[1].button('👎 Downvote', type='secondary', use_container_width=True):
-                    # do something here
-                    pass
+                    if feedback[1].button('👎 Downvote', type='secondary', use_container_width=True):
+                        # do something here
+                        pass
+                else:
+                    st.markdown('Your query statement is currently not supported by the system')
 
-            if visualize_results:
+            if visualize_results and search_intent_flag:
                 do_visualize_results(current_nlq_chain)
         else:
             st.error("Please enter a valid query.")

application/requirements.txt

@@ -8,3 +8,5 @@ PyMySQL==1.1.0
 python-dotenv~=1.0.0
 plotly~=5.18.0
 cryptography==42.0.4
+langchain~=0.1.11
+langchain-core~=0.1.30

application/utils/llm.py

@@ -6,9 +6,10 @@
 from opensearchpy import OpenSearch
 from utils import opensearch
 from utils.prompt import POSTGRES_DIALECT_PROMPT_CLAUDE3, MYSQL_DIALECT_PROMPT_CLAUDE3, \
-    DEFAULT_DIALECT_PROMPT
+    DEFAULT_DIALECT_PROMPT, SEARCH_INTENT_PROMPT_CLAUDE3
 import os
 from loguru import logger
+from langchain_core.output_parsers import JsonOutputParser
 
 BEDROCK_AWS_REGION = os.environ.get('BEDROCK_REGION', 'us-west-2')
 
@@ -24,6 +25,7 @@
 # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-claude.html
 
 bedrock = None
+json_parse = JsonOutputParser()
 
 
 @logger.catch
@@ -123,6 +125,22 @@ def claude3_to_sql(ddl, hints, search_box, examples=None, model_id=None, dialect
     return final_response
 
 
+def get_query_intent(model_id, search_box):
+    default_intent = {"intent": "normal_search"}
+    try:
+        system_prompt = SEARCH_INTENT_PROMPT_CLAUDE3
+        max_tokens = 2048
+        user_message = {"role": "user", "content": search_box}
+        messages = [user_message]
+        response = invoke_model_claude3(model_id, system_prompt, messages, max_tokens)
+        final_response = response.get("content")[0].get("text")
+        logger.info(f'{final_response=}')
+        intent_result_dict = json_parse.parse(final_response)
+        return intent_result_dict
+    except Exception as e:
+        return default_intent
+
+
 def create_vector_embedding_with_bedrock(text, index_name):
     payload = {"inputText": f"{text}"}
     body = json.dumps(payload)

application/utils/prompt.py

@@ -15,3 +15,41 @@
 Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
 Pay attention to use CURDATE() function to get the current date, if the question involves "today". Aside from giving the SQL answer, concisely explain yourself after giving the answer
 in the same language as the question.""".format(top_k=TOP_K)
+
+SEARCH_INTENT_PROMPT_CLAUDE3 = """You are an intent classifier and entity extractor, and you need to perform intent classification and entity extraction on search queries.
+Background: I want to query data in the database, and you need to help me determine the user's relevant intent and extract the keywords from the query statement. Finally, return a JSON structure.
+
+There are 2 main intents:
+<intent>
+- normal_search: Query relevant data from the data table
+- reject_search: Delete data from the table, add data to the table, modify data in the table, display usernames and passwords in the table, and other topics unrelated to data query
+</intent>
+
+When the intent is normal_search, you need to extract the keywords from the query statement.
+
+Here are some examples:
+
+<example>
+question : 希尔顿在欧洲上线了多少酒店数
+answer :
+{
+    "intent" : "normal_search",
+    "slot" : ["希尔顿", "欧洲", "上线", "酒店数"]
+}
+
+question : 苹果手机3月份在京东有多少订单
+answer :
+{
+    "intent" : "normal_search",
+    "slot" : ["苹果手机", "3月", "京东", "订单"]
+}
+
+question : 修改订单表中的第一行数据
+answer :
+{
+    "intent" : "reject_search"
+}
+</example>
+
+Please perform intent recognition and entity extraction. Return only the JSON structure, without any other annotations.
+"""