feat: add embedding based table search support

containers/bundled_querybook_config.yaml

@@ -3,6 +3,23 @@ FLASK_SECRET_KEY: SOME_RANDOM_SECRET_KEY
 
 DATABASE_CONN: mysql+pymysql://test:passw0rd@mysql:3306/querybook2?charset=utf8mb4
 REDIS_URL: redis://redis:6379/0
-ELASTICSEARCH_HOST: elasticsearch:9200
+ELASTICSEARCH_HOST: http://elasticsearch:9200
+# ELASTICSEARCH_CONNECTION_TYPE: aws
 # Uncomment for email
 # EMAILER_CONN: dockerhostforward
+
+# Uncomment below to enable AI Assistant
+# AI_ASSISTANT_PROVIDER: openai
+# AI_ASSISTANT_CONFIG:
+#     model_name: gpt-3.5-turbo
+#     temperature: 0
+
+# Uncomment below to enable vector store to support embedding based table search.
+# Please check langchain doc for the configs of each provider.
+# EMBEDDINGS_PROVIDER: openai
+# EMBEDDINGS_CONFIG: ~
+# VECTOR_STORE_PROVIDER: opensearch
+# VECTOR_STORE_CONFIG:
+#     embeddings_arg_name: 'embedding_function'
+#     opensearch_url: http://elasticsearch:9200
+#     index_name: 'vector_index_v1'

docker-compose.yml

@@ -99,12 +99,13 @@ services:
         command: ['--character-set-server=utf8mb4', '--collation-server=utf8mb4_unicode_ci']
     elasticsearch:
         container_name: querybook_elasticsearch
-        image: docker.elastic.co/elasticsearch/elasticsearch:7.16.2
+        image: opensearchproject/opensearch:2.9.0
         environment:
             cluster.name: docker-cluster
             bootstrap.memory_lock: 'true'
             discovery.type: single-node
-            ES_JAVA_OPTS: -Xms750m -Xmx750m
+            plugins.security.disabled: true
+            OPENSEARCH_JAVA_OPTS: -Xms750m -Xmx750m
         ulimits:
             memlock:
                 soft: -1
@@ -113,7 +114,7 @@ services:
                 soft: 65536
                 hard: 65536
         volumes:
-            - esdata1:/usr/share/elasticsearch/data
+            - osdata1:/usr/share/opensearch/data
         ports:
             - 9200:9200
         healthcheck:
@@ -161,7 +162,7 @@ services:
 
 volumes:
     my-db:
-    esdata1:
+    osdata1:
         driver: local
     # file:
     #     driver: local

plugins/vector_store_plugin/__init__.py

@@ -0,0 +1,2 @@
+ALL_PLUGIN_VECTOR_STORES = {}
+ALL_PLUGIN_EMBEDDINGS = {}

querybook/config/querybook_default_config.yaml

@@ -86,11 +86,13 @@ EVENT_LOGGER_NAME: ~
 STATS_LOGGER_NAME: ~
 
 # --------------- AI Assistant ---------------
-# Example config for OpenAI
-# AI_ASSISTANT_PROVIDER: openai
-# AI_ASSISTANT_CONFIG:
-#     model_name: gpt-3.5-turbo
-#     temperature: 0
 AI_ASSISTANT_PROVIDER: ~
 AI_ASSISTANT_CONFIG:
     model_name: ~
+
+EMBEDDINGS_PROVIDER: ~
+EMBEDDINGS_CONFIG: ~
+VECTOR_STORE_PROVIDER: ~
+VECTOR_STORE_CONFIG:
+    embeddings_arg_name: 'embedding_function'
+    index_name: 'vector_index_v1'

querybook/server/const/ai_assistant.py

@@ -3,12 +3,9 @@
 
 # KEEP IT CONSISTENT AS webapp/const/aiAssistant.ts
 class AICommandType(Enum):
-    SQL_FIX = "SQL_FIX"
-    SQL_TITLE = "SQL_TITLE"
-    TEXT_TO_SQL = "TEXT_TO_SQL"
-    RESET_MEMORY = "RESET_MEMORY"
+    SQL_FIX = "sql_fix"
+    SQL_TITLE = "sql_title"
+    TEXT_TO_SQL = "text_to_sql"
 
 
 AI_ASSISTANT_NAMESPACE = "/ai_assistant"
-AI_ASSISTANT_REQUEST_EVENT = "ai_assistant_request"
-AI_ASSISTANT_RESPONSE_EVENT = "ai_assistant_response"

querybook/server/datasources_socketio/ai_assistant.py

@@ -1,13 +1,32 @@
-from const.ai_assistant import (
-    AI_ASSISTANT_NAMESPACE,
-    AI_ASSISTANT_REQUEST_EVENT,
-)
+from const.ai_assistant import AI_ASSISTANT_NAMESPACE, AICommandType
+from lib.ai_assistant import ai_assistant
 
 from .helper import register_socket
 
 
-@register_socket(AI_ASSISTANT_REQUEST_EVENT, namespace=AI_ASSISTANT_NAMESPACE)
-def ai_assistant_request(command_type: str, payload={}):
-    from lib.ai_assistant import ai_assistant
+@register_socket(AICommandType.TEXT_TO_SQL.value, namespace=AI_ASSISTANT_NAMESPACE)
+def text_to_sql(payload={}):
+    original_query = payload["original_query"]
+    query_engine_id = payload["query_engine_id"]
+    tables = payload.get("tables", [])
+    question = payload["question"]
+    ai_assistant.generate_sql_query(
+        query_engine_id=query_engine_id,
+        tables=tables,
+        question=question,
+        original_query=original_query,
+    )
 
-    ai_assistant.handle_ai_command(command_type, payload)
+
+@register_socket(AICommandType.SQL_TITLE.value, namespace=AI_ASSISTANT_NAMESPACE)
+def sql_title(payload={}):
+    query = payload["query"]
+    ai_assistant.generate_title_from_query(query=query)
+
+
+@register_socket(AICommandType.SQL_FIX.value, namespace=AI_ASSISTANT_NAMESPACE)
+def sql_fix(payload={}):
+    query_execution_id = payload["query_execution_id"]
+    ai_assistant.query_auto_fix(
+        query_execution_id=query_execution_id,
+    )

querybook/server/env.py

@@ -134,3 +134,8 @@ class QuerybookSettings(object):
     # AI Assistant
     AI_ASSISTANT_PROVIDER = get_env_config("AI_ASSISTANT_PROVIDER")
     AI_ASSISTANT_CONFIG = get_env_config("AI_ASSISTANT_CONFIG") or {}
+
+    VECTOR_STORE_PROVIDER = get_env_config("VECTOR_STORE_PROVIDER")
+    VECTOR_STORE_CONFIG = get_env_config("VECTOR_STORE_CONFIG") or {}
+    EMBEDDINGS_PROVIDER = get_env_config("EMBEDDINGS_PROVIDER")
+    EMBEDDINGS_CONFIG = get_env_config("EMBEDDINGS_CONFIG") or {}

querybook/server/lib/ai_assistant/ai_socket.py

@@ -0,0 +1,59 @@
+import functools
+
+from flask import request
+
+from app.flask_app import socketio
+from const.ai_assistant import AI_ASSISTANT_NAMESPACE, AICommandType
+
+
+class AIWebSocket:
+    def __init__(self, socketio, command_type: AICommandType):
+        self.socketio = socketio
+        self.command_type = command_type
+        self.room = request.sid
+
+    def _send(self, event_type, payload: dict = None):
+        self.socketio.emit(
+            self.command_type.value,
+            (
+                event_type,
+                payload,
+            ),
+            namespace=AI_ASSISTANT_NAMESPACE,
+            room=self.room,
+        )
+
+    def send_data(self, data: dict):
+        self._send("data", data)
+
+    def send_delta_data(self, data: str):
+        self._send("delta_data", data)
+
+    def send_delta_end(self):
+        self._send("delta_end")
+
+    def send_tables_for_sql_gen(self, data: list[str]):
+        self._send("tables", data)
+
+    def send_error(self, error: str):
+        self._send("error", error)
+        self.close()
+
+    def close(self):
+        self._send("close")
+
+
+def with_ai_socket(command_type: AICommandType):
+    def decorator_fn(fn):
+        @functools.wraps(fn)
+        def func(*args, **kwargs):
+            if not kwargs.get("socket"):
+                kwargs["socket"] = AIWebSocket(socketio, command_type)
+
+            result = fn(*args, **kwargs)
+
+            return result
+
+        return func
+
+    return decorator_fn

querybook/server/lib/ai_assistant/assistants/openai_assistant.py

@@ -1,10 +1,9 @@
-from lib.ai_assistant.base_ai_assistant import BaseAIAssistant
-from lib.logger import get_logger
-
-from langchain.chat_models import ChatOpenAI
-from langchain.callbacks.manager import CallbackManager
 import openai
+from langchain.callbacks.manager import CallbackManager
+from langchain.chat_models import ChatOpenAI
 
+from lib.ai_assistant.base_ai_assistant import BaseAIAssistant
+from lib.logger import get_logger
 
 LOG = get_logger(__file__)
 
@@ -24,9 +23,13 @@ def _get_error_msg(self, error) -> str:
 
         return super()._get_error_msg(error)
 
-    def _get_llm(self, callback_handler):
+    def _get_llm(self, callback_handler=None):
+        if not callback_handler:
+            # non-streaming
+            return ChatOpenAI(**self._config)
+
         return ChatOpenAI(
             **self._config,
             streaming=True,
-            callback_manager=CallbackManager([callback_handler]),
+            callback_manager=CallbackManager([callback_handler])
         )