+
+ `;
+
+ messageDiv.innerHTML = confirmationHTML;
+
+ messageDivContainer.appendChild(messageDiv);
+ chatMessages.appendChild(messageDivContainer);
+ chatMessages.scrollTop = chatMessages.scrollHeight;
+
+ // Disable the main input while waiting for confirmation
+ messageInput.disabled = true;
+ submitButton.disabled = true;
+}
+
+async function handleDestructiveConfirmation(confirmation, sqlQuery, confirmationId) {
+ // Find the specific confirmation dialog using the unique ID
+ const confirmationDialog = document.querySelector(`[data-confirmation-id="${confirmationId}"]`);
+ if (confirmationDialog) {
+ // Disable both confirmation buttons within this specific dialog
+ const confirmBtn = confirmationDialog.querySelector('.confirm-btn');
+ const cancelBtn = confirmationDialog.querySelector('.cancel-btn');
+ if (confirmBtn) confirmBtn.disabled = true;
+ if (cancelBtn) cancelBtn.disabled = true;
+ }
+
+ // Re-enable the input
+ messageInput.disabled = false;
+ submitButton.disabled = false;
+
+ // Add user's choice as a message
+ addMessage(`User choice: ${confirmation}`, true, false, false, false, window.currentUser);
+
+ if (confirmation === 'CANCEL') {
+ addMessage("Operation cancelled. The destructive SQL query was not executed.", false, true);
+ return;
+ }
+
+ // If confirmed, send confirmation to server
+ try {
+ const selectedValue = document.getElementById("graph-select").value;
+
+ const response = await fetch('/graphs/' + selectedValue + '/confirm', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ },
+ body: JSON.stringify({
+ confirmation: confirmation,
+ sql_query: sqlQuery,
+ chat: questions_history
+ })
+ });
- // Show suggestions again since we're ready for new input
- suggestionsContainer.style.display = 'flex';
+ if (!response.ok) {
+ throw new Error(`Server responded with ${response.status}`);
+ }
+
+ // Process the streaming response
+ const reader = response.body.getReader();
+ let decoder = new TextDecoder();
+ let buffer = '';
+
+ while (true) {
+ const { done, value } = await reader.read();
+
+ if (done) {
+ if (buffer.trim()) {
+ try {
+ const step = JSON.parse(buffer);
+ addMessage(step.message || JSON.stringify(step), false);
+ } catch (e) {
+ addMessage(buffer, false);
+ }
+ }
+ break;
+ }
+
+ const chunk = decoder.decode(value, { stream: true });
+ buffer += chunk;
+
+ let delimiterIndex;
+ while ((delimiterIndex = buffer.indexOf(MESSAGE_DELIMITER)) !== -1) {
+ const message = buffer.slice(0, delimiterIndex).trim();
+ buffer = buffer.slice(delimiterIndex + MESSAGE_DELIMITER.length);
+
+ if (!message) continue;
+
+ try {
+ const step = JSON.parse(message);
+
+ if (step.type === 'reasoning_step') {
+ addMessage(step.message, false);
+ } else if (step.type === 'query_result') {
+ if (step.data) {
+ addMessage(`Query Result: ${JSON.stringify(step.data)}`, false, false, true);
+ } else {
+ addMessage("No results found for the query.", false);
+ }
+ } else if (step.type === 'ai_response') {
+ addMessage(step.message, false, false, true);
+ } else if (step.type === 'error') {
+ addMessage(`Error: ${step.message}`, false, true);
+ } else {
+ addMessage(step.message || JSON.stringify(step), false);
+ }
+ } catch (e) {
+ addMessage("Failed: " + message, false);
+ }
+ }
+ }
+
+ } catch (error) {
+ console.error('Error:', error);
+ addMessage('Sorry, there was an error processing the confirmation: ' + error.message, false);
}
}
@@ -393,35 +561,47 @@ messageInput.addEventListener('keypress', (e) => {
}
});
-messageInput.addEventListener('input', (e) => {
- suggestionItems.forEach(item => {
- if (e.target.value && item.querySelector('p').textContent === e.target.value) {
- item.classList.add('active');
- } else {
- item.classList.remove('active');
- }
- });
-})
-
menuButton.addEventListener('click', toggleMenu);
sideMenuButton.addEventListener('click', toggleMenu);
-newChatButton.addEventListener('click', initChat);
-
-// Add event listener to each suggestion item
-suggestionItems.forEach(item => {
- item.addEventListener('click', () => {
- // Set the value of the message input to the text of the clicked suggestion item
- const text = item.querySelector('p').textContent;
- messageInput.value = text;
- // Remove 'active' from all suggestion items
- document.querySelectorAll('.suggestion-item.active').forEach(item => {
- item.classList.remove('active');
- });
- // Add 'active' to the clicked suggestion item
- item.classList.add('active');
- });
+// Reset confirmation modal elements
+const resetConfirmationModal = document.getElementById('reset-confirmation-modal');
+const resetConfirmBtn = document.getElementById('reset-confirm-btn');
+const resetCancelBtn = document.getElementById('reset-cancel-btn');
+
+// Show reset confirmation modal instead of directly resetting
+newChatButton.addEventListener('click', () => {
+ resetConfirmationModal.style.display = 'flex';
+ // Focus the Reset Session button when modal opens
+ setTimeout(() => {
+ resetConfirmBtn.focus();
+ }, 100); // Small delay to ensure modal is fully rendered
+});
+
+// Handle reset confirmation
+resetConfirmBtn.addEventListener('click', () => {
+ resetConfirmationModal.style.display = 'none';
+ initChat();
+});
+
+// Handle reset cancellation
+resetCancelBtn.addEventListener('click', () => {
+ resetConfirmationModal.style.display = 'none';
+});
+
+// Close modal when clicking outside of it
+resetConfirmationModal.addEventListener('click', (e) => {
+ if (e.target === resetConfirmationModal) {
+ resetConfirmationModal.style.display = 'none';
+ }
+});
+
+// Close modal with Escape key
+document.addEventListener('keydown', (e) => {
+ if (e.key === 'Escape' && resetConfirmationModal.style.display === 'flex') {
+ resetConfirmationModal.style.display = 'none';
+ }
});
document.addEventListener("DOMContentLoaded", function () {
@@ -429,10 +609,36 @@ document.addEventListener("DOMContentLoaded", function () {
const graphSelect = document.getElementById("graph-select");
// Fetch available graphs
- fetch("/graphs?token=" + TOKEN)
- .then(response => response.json())
+ fetch("/graphs")
+ .then(response => {
+ if (!response.ok) {
+ if (response.status === 401) {
+ throw new Error("Authentication required. Please log in to access graphs.");
+ }
+ throw new Error(`HTTP ${response.status}: ${response.statusText}`);
+ }
+ return response.json();
+ })
.then(data => {
graphSelect.innerHTML = "";
+
+ if (!data || data.length === 0) {
+ // No graphs available
+ const option = document.createElement("option");
+ option.value = "";
+ option.textContent = "No graphs available";
+ option.disabled = true;
+ graphSelect.appendChild(option);
+
+ // Disable chat input when no graphs are available
+ messageInput.disabled = true;
+ submitButton.disabled = true;
+ messageInput.placeholder = "Please upload a schema or connect a database to start chatting";
+
+ addMessage("No graphs are available. Please upload a schema file or connect to a database to get started.", false);
+ return;
+ }
+
data.forEach(graph => {
const option = document.createElement("option");
option.value = graph;
@@ -441,92 +647,36 @@ document.addEventListener("DOMContentLoaded", function () {
graphSelect.appendChild(option);
});
- // Fetch suggestions for the first graph (if any)
- if (data.length > 0) {
- fetchSuggestions();
- }
+ // Re-enable chat input when graphs are available
+ messageInput.disabled = false;
+ submitButton.disabled = false;
+ messageInput.placeholder = "Describe the SQL query you want...";
})
.catch(error => {
console.error("Error fetching graphs:", error);
- addMessage("Sorry, there was an error fetching the available graphs: " + error.message, false);
- });
-
- // Function to fetch suggestions based on selected graph
- function fetchSuggestions() {
- const graphSelect = document.getElementById("graph-select");
- const selectedGraph = graphSelect.value;
-
- if (!selectedGraph) {
- // Hide suggestions if no graph is selected
- suggestionItems.forEach(item => {
- item.style.display = 'none';
- });
- return;
- }
-
- suggestionItems.forEach(item => {
- item.style.display = 'flex';
- item.classList.remove('loaded');
- item.classList.add('loading');
- const button = item.querySelector('button');
- const p = item.querySelector('p');
- button.title = "Loading suggestion...";
- p.textContent = "";
+
+ // Show appropriate error message and disable input
+ if (error.message.includes("Authentication required")) {
+ addMessage("Authentication required. Please log in to access your graphs.", false);
+ // Don't disable input for auth errors as user needs to log in
+ } else {
+ addMessage("Sorry, there was an error fetching the available graphs: " + error.message, false);
+ messageInput.disabled = true;
+ submitButton.disabled = true;
+ messageInput.placeholder = "Cannot connect to server";
+ }
+
+ // Add a placeholder option to show the error state
+ graphSelect.innerHTML = "";
+ const option = document.createElement("option");
+ option.value = "";
+ option.textContent = error.message.includes("Authentication") ? "Please log in" : "Error loading graphs";
+ option.disabled = true;
+ graphSelect.appendChild(option);
});
- // Fetch suggestions for the selected graph
- fetch(`/suggestions?token=${TOKEN}&graph_id=${selectedGraph}`)
- .then(response => response.json())
- .then(suggestions => {
- // If no suggestions for this graph, hide the suggestions
- if (!suggestions || suggestions.length === 0) {
- suggestionItems.forEach(item => {
- item.style.display = 'none';
- });
- return;
- }
-
- // Hide unused suggestion slots
- for (let i = suggestions.length; i < suggestionItems.length; i++) {
- suggestionItems[i].style.display = 'none';
- }
-
- // Update each suggestion with fetched data and add loaded styling
- suggestions.forEach((suggestion, index) => {
- if (suggestionItems[index]) {
- const item = suggestionItems[index];
- const button = item.querySelector('button');
- const p = item.querySelector('p');
-
- // Add a slight delay for staggered animation
- setTimeout(() => {
- // Remove loading state and add content
- item.classList.remove('loading');
- item.classList.add('loaded');
-
- // Update content
- p.textContent = suggestion;
- button.title = suggestion;
-
- // Enable click functionality
- button.style.cursor = 'pointer';
- }, index * 300); // 300ms delay between each suggestion
- }
- });
- })
- .catch(error => {
- console.error("Error fetching suggestions:", error);
-
- // Hide suggestions on error
- suggestionItems.forEach(item => {
- item.style.display = 'none';
- });
- });
- }
-
graphSelect.addEventListener("change", function () {
initChat();
- fetchSuggestions(); // Fetch new suggestions when graph changes
});
});
@@ -540,7 +690,7 @@ fileUpload.addEventListener('change', function (e) {
const formData = new FormData();
formData.append('file', file);
- fetch("/graphs?token=" + TOKEN, {
+ fetch("/graphs", {
method: 'POST',
body: formData, // ✅ Correct, no need to set Content-Type manually
}).then(response => {
@@ -551,4 +701,216 @@ fileUpload.addEventListener('change', function (e) {
console.error('Error uploading file:', error);
addMessage('Sorry, there was an error uploading your file: ' + error.message, false);
});
+});
+
+document.addEventListener('DOMContentLoaded', function() {
+ // Authentication modal logic
+ var isAuthenticated = window.isAuthenticated !== undefined ? window.isAuthenticated : false;
+ var googleLoginModal = document.getElementById('google-login-modal');
+ var container = document.getElementById('container');
+ if (googleLoginModal && container) {
+ if (!isAuthenticated) {
+ googleLoginModal.style.display = 'flex';
+ container.style.filter = 'blur(2px)';
+ } else {
+ googleLoginModal.style.display = 'none';
+ container.style.filter = '';
+ }
+ }
+ // Postgres modal logic
+ var pgModal = document.getElementById('pg-modal');
+ var openPgModalBtn = document.getElementById('open-pg-modal');
+ var cancelPgModalBtn = document.getElementById('pg-modal-cancel');
+ var connectPgModalBtn = document.getElementById('pg-modal-connect');
+ var pgUrlInput = document.getElementById('pg-url-input');
+ if (openPgModalBtn && pgModal) {
+ openPgModalBtn.addEventListener('click', function() {
+ pgModal.style.display = 'flex';
+ // Focus the input field when modal opens
+ if (pgUrlInput) {
+ setTimeout(() => {
+ pgUrlInput.focus();
+ }, 100); // Small delay to ensure modal is fully rendered
+ }
+ });
+ }
+ if (cancelPgModalBtn && pgModal) {
+ cancelPgModalBtn.addEventListener('click', function() {
+ pgModal.style.display = 'none';
+ });
+ }
+ // Allow closing Postgres modal with Escape key
+ document.addEventListener('keydown', function(e) {
+ if (pgModal && pgModal.style.display === 'flex' && e.key === 'Escape') {
+ pgModal.style.display = 'none';
+ }
+ });
+ // Do NOT allow closing Google login modal with Escape or any other means except successful login
+
+ // Handle Connect button for Postgres modal
+ if (connectPgModalBtn && pgUrlInput && pgModal) {
+ // Add Enter key support for the input field
+ pgUrlInput.addEventListener('keypress', function(e) {
+ if (e.key === 'Enter') {
+ connectPgModalBtn.click();
+ }
+ });
+
+ connectPgModalBtn.addEventListener('click', function() {
+ const pgUrl = pgUrlInput.value.trim();
+ if (!pgUrl) {
+ alert('Please enter a Postgres URL.');
+ return;
+ }
+
+ // Show loading state
+ const connectText = connectPgModalBtn.querySelector('.pg-modal-connect-text');
+ const loadingSpinner = connectPgModalBtn.querySelector('.pg-modal-loading-spinner');
+ const cancelBtn = document.getElementById('pg-modal-cancel');
+
+ connectText.style.display = 'none';
+ loadingSpinner.style.display = 'flex';
+ connectPgModalBtn.disabled = true;
+ cancelBtn.disabled = true;
+ pgUrlInput.disabled = true;
+
+ fetch('/database', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json'
+ },
+ body: JSON.stringify({ url: pgUrl })
+ })
+ .then(response => response.json())
+ .then(data => {
+ // Reset loading state
+ connectText.style.display = 'inline';
+ loadingSpinner.style.display = 'none';
+ connectPgModalBtn.disabled = false;
+ cancelBtn.disabled = false;
+ pgUrlInput.disabled = false;
+
+ if (data.success) {
+ pgModal.style.display = 'none'; // Close modal on success
+ // Refresh the graph list to show the new database
+ location.reload();
+ } else {
+ alert('Failed to connect: ' + (data.error || 'Unknown error'));
+ }
+ })
+ .catch(error => {
+ // Reset loading state on error
+ connectText.style.display = 'inline';
+ loadingSpinner.style.display = 'none';
+ connectPgModalBtn.disabled = false;
+ cancelBtn.disabled = false;
+ pgUrlInput.disabled = false;
+
+ alert('Error connecting to database: ' + error.message);
+ });
+ });
+ }
+});
+
+// User Profile Dropdown Functionality
+document.addEventListener('DOMContentLoaded', function() {
+ const userProfileBtn = document.getElementById('user-profile-btn');
+ const userProfileDropdown = document.getElementById('user-profile-dropdown');
+
+ if (userProfileBtn && userProfileDropdown) {
+ // Toggle dropdown when profile button is clicked
+ userProfileBtn.addEventListener('click', function(e) {
+ e.stopPropagation();
+ userProfileDropdown.classList.toggle('show');
+ });
+
+ // Close dropdown when clicking outside
+ document.addEventListener('click', function(e) {
+ if (!userProfileBtn.contains(e.target) && !userProfileDropdown.contains(e.target)) {
+ userProfileDropdown.classList.remove('show');
+ }
+ });
+
+ // Close dropdown with Escape key
+ document.addEventListener('keydown', function(e) {
+ if (e.key === 'Escape' && userProfileDropdown.classList.contains('show')) {
+ userProfileDropdown.classList.remove('show');
+ }
+ });
+
+ // Prevent dropdown from closing when clicking inside it
+ userProfileDropdown.addEventListener('click', function(e) {
+ e.stopPropagation();
+ });
+ }
+});
+
+// Theme Toggle Functionality
+document.addEventListener('DOMContentLoaded', function() {
+ const themeToggleBtn = document.getElementById('theme-toggle-btn');
+
+ // Get theme from localStorage or default to 'system'
+ const currentTheme = localStorage.getItem('theme') || 'system';
+ document.documentElement.setAttribute('data-theme', currentTheme);
+
+ if (themeToggleBtn) {
+ themeToggleBtn.addEventListener('click', function() {
+ const currentTheme = document.documentElement.getAttribute('data-theme');
+ let newTheme;
+
+ // Cycle through themes: dark -> light -> system -> dark
+ switch (currentTheme) {
+ case 'dark':
+ newTheme = 'light';
+ break;
+ case 'light':
+ newTheme = 'system';
+ break;
+ case 'system':
+ default:
+ newTheme = 'dark';
+ break;
+ }
+
+ document.documentElement.setAttribute('data-theme', newTheme);
+ localStorage.setItem('theme', newTheme);
+
+ // Update button title
+ const titles = {
+ 'dark': 'Switch to Light Mode',
+ 'light': 'Switch to System Mode',
+ 'system': 'Switch to Dark Mode'
+ };
+ themeToggleBtn.title = titles[newTheme];
+ });
+
+ // Set initial title
+ const titles = {
+ 'dark': 'Switch to Light Mode',
+ 'light': 'Switch to System Mode',
+ 'system': 'Switch to Dark Mode'
+ };
+ themeToggleBtn.title = titles[currentTheme];
+ }
+});
+
+// Handle window resize to ensure proper menu behavior across breakpoints
+window.addEventListener('resize', function() {
+ const isMobile = window.innerWidth <= 768;
+
+ // If menu is open and we switch to mobile, remove any desktop padding
+ if (isMobile && menuContainer.classList.contains('open')) {
+ chatContainer.style.paddingRight = '';
+ chatContainer.style.paddingLeft = '';
+ }
+ // If menu is open and we switch to desktop, apply desktop padding
+ else if (!isMobile && menuContainer.classList.contains('open')) {
+ chatContainer.style.paddingRight = '10%';
+ chatContainer.style.paddingLeft = '10%';
+ }
+ // If menu is closed and we're on desktop, ensure default desktop padding
+ else if (!isMobile && !menuContainer.classList.contains('open')) {
+ chatContainer.style.paddingRight = '20%';
+ chatContainer.style.paddingLeft = '20%';
+ }
});
\ No newline at end of file
diff --git a/api/static/public/icons/github.svg b/api/static/public/icons/github.svg
new file mode 100644
index 00000000..97f58554
--- /dev/null
+++ b/api/static/public/icons/github.svg
@@ -0,0 +1,3 @@
+
\ No newline at end of file
diff --git a/api/static/public/icons/google.svg b/api/static/public/icons/google.svg
new file mode 100644
index 00000000..fb354fdb
--- /dev/null
+++ b/api/static/public/icons/google.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/api/static/public/icons/logo.svg b/api/static/public/icons/logo.svg
index 3ae7f5cd..60ebfd33 100644
--- a/api/static/public/icons/logo.svg
+++ b/api/static/public/icons/logo.svg
@@ -3,30 +3,25 @@
${step.message.replace(/\n/g, '
')}
+
+ ')}
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/api/templates/chat.j2 b/api/templates/chat.j2
new file mode 100644
index 00000000..6856dec5
--- /dev/null
+++ b/api/templates/chat.j2
@@ -0,0 +1,189 @@
+
+
+
+
+
+
+
-
-
-
-  }})
-
-
-
- Text-to-SQL(Natural Language to SQL Generator)
- -
-
-
-
- -
-
- - - -
- - - -
- - - -
-
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+ Text-to-SQL(Natural Language to SQL Generator)
+ +
+
+
+
+
+
+
+
+
+
+
+  }})
Sign in with Google
+
+
+  }})
Sign in with GitHub
+
+
+ Welcome to Text-to-SQL
+Please login to continue
+ +Sign in with Google
+
+
+ Sign in with GitHub
+
+
+
+
+
+
+
+ {# Set authentication state for JS before loading chat.js #}
+
+
+
+
+
\ No newline at end of file
diff --git a/api/utils.py b/api/utils.py
index 60d8c7b1..65932588 100644
--- a/api/utils.py
+++ b/api/utils.py
@@ -1,11 +1,20 @@
+"""Utility functions for the text2sql API."""
+
import json
from typing import List, Tuple
+
from litellm import completion
+
from api.config import Config
from api.constants import BENCHMARK
-def generate_db_description(db_name: str, table_names: List[str], temperature: float = 0.5,
- max_tokens: int = 150) -> str:
+
+def generate_db_description(
+ db_name: str,
+ table_names: List[str],
+ temperature: float = 0.5,
+ max_tokens: int = 150,
+) -> str:
"""
Generates a short and concise description of a database.
@@ -20,14 +29,14 @@ def generate_db_description(db_name: str, table_names: List[str], temperature: f
"""
if not isinstance(db_name, str):
raise TypeError("database_name must be a string.")
-
+
if not isinstance(table_names, list):
raise TypeError("table_names must be a list of strings.")
-
+
# Ensure all table names are strings
if not all(isinstance(table, str) for table in table_names):
raise ValueError("All items in table_names must be strings.")
-
+
if not table_names:
return f"{db_name} is a database with no tables."
@@ -40,25 +49,38 @@ def generate_db_description(db_name: str, table_names: List[str], temperature: f
tables_formatted = ", ".join(table_names[:-1]) + f", and {table_names[-1]}"
prompt = (
- f"You are a helpful assistant. Generate a concise description of the database named '{db_name}' "
- f"which contains the following tables: {tables_formatted}.\n\n"
- f"Description:"
+ f"You are a helpful assistant. Generate a concise description of "
+ f"the database named '{db_name}' which contains the following tables: "
+ f"{tables_formatted}.\n\nDescription:"
)
-
- response = completion(model=Config.COMPLETION_MODEL,
- messages=[
- {"role": "system", "content": "You are a helpful assistant."},
- {"role": "user", "content": prompt}
- ],
- temperature=temperature,
- max_tokens=max_tokens,
- n=1,
- stop=None,
- )
- description = response.choices[0].message['content']
+
+ response = completion(
+ model=Config.COMPLETION_MODEL,
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": prompt},
+ ],
+ temperature=temperature,
+ max_tokens=max_tokens,
+ n=1,
+ stop=None,
+ )
+ description = response.choices[0].message["content"]
return description
+
def llm_answer_validator(question: str, answer: str, expected_answer: str = None) -> str:
+ """
+ Validate an answer using LLM.
+
+ Args:
+ question: The original question
+ answer: The generated answer
+ expected_answer: The expected answer for comparison
+
+ Returns:
+ JSON string with validation results
+ """
prompt = """
You are evaluating an answer generated by a text-to-sql RAG-based system. Assess how well the Generated Answer (generated sql) addresses the Question
based on the Expected Answer.
@@ -76,18 +98,37 @@ def llm_answer_validator(question: str, answer: str, expected_answer: str = None
Output Json format:
{{"relevance_score": float, "explanation": "Your assessment here."}}
"""
- response = completion(model=Config.VALIDTOR_MODEL,
- messages=[
- {"role": "system", "content": "You are a Validator assistant."},
- {"role": "user", "content": prompt.format(question=question, expected_answer=expected_answer, generated_answer=answer)}
- ],
- response_format={"type": "json_object"},
-
- )
- validation_set = response.choices[0].message['content'].strip()
+ response = completion(
+ model=Config.VALIDATOR_MODEL,
+ messages=[
+ {"role": "system", "content": "You are a Validator assistant."},
+ {
+ "role": "user",
+ "content": prompt.format(
+ question=question,
+ expected_answer=expected_answer,
+ generated_answer=answer,
+ ),
+ },
+ ],
+ response_format={"type": "json_object"},
+ )
+ validation_set = response.choices[0].message["content"].strip()
return validation_set
+
def llm_table_validator(question: str, answer: str, tables: List[str]) -> Tuple[float, str]:
+ """
+ Validate table relevance using LLM.
+
+ Args:
+ question: The original question
+ answer: The generated answer
+ tables: List of available tables
+
+ Returns:
+ Tuple of relevance score and explanation
+ """
prompt = """
You are evaluating an answer generated by a text-to-sql RAG-based system. Assess how well the retrived Tables relevant to the question and supports the Generated Answer (generated sql).
- The tables are with the following structure:
@@ -106,19 +147,23 @@ def llm_table_validator(question: str, answer: str, tables: List[str]) -> Tuple[
Output Json format:
{{"relevance_score": float, "explanation": "Your assessment here."}}
"""
- response = completion(model=Config.VALIDTOR_MODEL,
- messages=[
- {"role": "system", "content": "You are a Validator assistant."},
- {"role": "user", "content": prompt.format(question=question, tables=tables, generated_answer=answer)}
- ],
- response_format={"type": "json_object"},
- )
- validation_set = response.choices[0].message['content'].strip()
+ response = completion(
+ model=Config.VALIDATOR_MODEL,
+ messages=[
+ {"role": "system", "content": "You are a Validator assistant."},
+ {
+ "role": "user",
+ "content": prompt.format(question=question, tables=tables, generated_answer=answer),
+ },
+ ],
+ response_format={"type": "json_object"},
+ )
+ validation_set = response.choices[0].message["content"].strip()
try:
val_res = json.loads(validation_set)
- score = val_res['relevance_score']
- explanation = val_res['explanation']
- except Exception as e:
+ score = val_res["relevance_score"]
+ explanation = val_res["explanation"]
+ except (json.JSONDecodeError, KeyError) as e:
print(f"Error: {e}")
score = 0.0
explanation = "Error: Unable to parse the response."
@@ -138,8 +183,7 @@ def run_benchmark():
for data in benchmark_data:
success, result = generate_db_description(
- db_name=data['database'],
- table_names=list(data['tables'].keys())
+ db_name=data["database"], table_names=list(data["tables"].keys())
)
if success:
@@ -147,4 +191,4 @@ def run_benchmark():
else:
results.append(f"Error: {result}")
- return results
\ No newline at end of file
+ return results
diff --git a/docs/postgres_loader.md b/docs/postgres_loader.md
new file mode 100644
index 00000000..d4024fd1
--- /dev/null
+++ b/docs/postgres_loader.md
@@ -0,0 +1,240 @@
+# PostgreSQL Schema Loader
+
+This loader connects to a PostgreSQL database and extracts the complete schema information, including tables, columns, relationships, and constraints. The extracted schema is then loaded into a graph database for further analysis and query generation.
+
+## Features
+
+- **Complete Schema Extraction**: Retrieves all tables, columns, data types, constraints, and relationships
+- **Foreign Key Relationships**: Automatically discovers and maps foreign key relationships between tables
+- **Column Metadata**: Extracts column comments, default values, nullability, and key types
+- **Batch Processing**: Efficiently processes large schemas with progress tracking
+- **Error Handling**: Robust error handling for connection issues and malformed schemas
+
+## Installation
+
+{% capture shell_0 %}
+poetry add psycopg2-binary
+{% endcapture %}
+
+{% capture shell_1 %}
+pip install psycopg2-binary
+{% endcapture %}
+
+{% include code_tabs.html id="install_tabs" shell=shell_0 shell2=shell_1 %}
+
+## Usage
+
+### Basic Usage
+
+{% capture python_0 %}
+from api.loaders.postgres_loader import PostgreSQLLoader
+
+# Connection URL format: postgresql://username:password@host:port/database
+connection_url = "postgresql://postgres:password@localhost:5432/mydatabase"
+graph_id = "my_schema_graph"
+
+success, message = PostgreSQLLoader.load(graph_id, connection_url)
+
+if success:
+ print(f"Schema loaded successfully: {message}")
+else:
+ print(f"Failed to load schema: {message}")
+{% endcapture %}
+
+{% capture javascript_0 %}
+import { PostgreSQLLoader } from 'your-pkg';
+
+const connectionUrl = "postgresql://postgres:password@localhost:5432/mydatabase";
+const graphId = "my_schema_graph";
+
+const [success, message] = await PostgreSQLLoader.load(graphId, connectionUrl);
+if (success) {
+ console.log(`Schema loaded successfully: ${message}`);
+} else {
+ console.log(`Failed to load schema: ${message}`);
+}
+{% endcapture %}
+
+{% capture java_0 %}
+String connectionUrl = "postgresql://postgres:password@localhost:5432/mydatabase";
+String graphId = "my_schema_graph";
+Pair