diff --git a/.gitignore b/.gitignore index 16e8399..ffd9722 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,12 @@ packages/notes_mcp/data/* .claude/settings.local.json -*/.DS_Store +**/.DS_Store +.DS_Store + +data/* + +examples/home_latest_timeline.json +examples/home_latest_timeline_2.json # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/examples/arxiv_downloader.py b/examples/arxiv_downloader.py new file mode 100644 index 0000000..9b16c55 --- /dev/null +++ b/examples/arxiv_downloader.py @@ -0,0 +1,25 @@ +import json + +import arxiv + +# Search for newest AI articles +search = arxiv.Search( + query="cat:cs.AI", + max_results=5, + sort_by=arxiv.SortCriterion.SubmittedDate, + sort_order=arxiv.SortOrder.Descending, +) + +results = [] +for result in search.results(): + results.append( + { + "title": result.title, + "authors": [author.name for author in result.authors], + "summary": result.summary, + "published": result.published.strftime("%Y-%m-%d"), + "pdf_url": result.pdf_url, + } + ) + +print(json.dumps(results, indent=2)) diff --git a/examples/browseruse.py b/examples/browseruse.py new file mode 100644 index 0000000..f0540fa --- /dev/null +++ b/examples/browseruse.py @@ -0,0 +1,17 @@ +import asyncio + +from browser_use import Agent, ChatAnthropic +from dotenv import load_dotenv + +load_dotenv() + + +async def main(): + agent = Agent( + task="Go to x.com, then wait until the user has logged in. Once tweets are loaded on the screen print the first 10 tweets.", + llm=ChatAnthropic(model="claude-sonnet-4-0", temperature=0.0), + ) + await agent.run() + + +asyncio.run(main()) diff --git a/examples/hf_daily_papers_downloader.py b/examples/hf_daily_papers_downloader.py new file mode 100644 index 0000000..fcb679d --- /dev/null +++ b/examples/hf_daily_papers_downloader.py @@ -0,0 +1,54 @@ +import os +from datetime import datetime, timedelta + +import requests + +# --- CONFIG --- +API_KEY = os.getenv("HF_PAPERS_KEY") +MONTH = 9 # September +YEAR = 2025 +TOP_N = 10 + + +# --- FUNCTION TO FETCH PAPERS FOR A GIVEN DATE --- +def fetch_papers_for_date(date_str): + url = f"https://huggingface.co/api/daily_papers?date={date_str}" + headers = {"Authorization": f"Bearer {API_KEY}"} + response = requests.get(url, headers=headers) + if response.status_code == 200: + return response.json() # List of papers + else: + print(f"Failed to fetch {date_str}: {response.status_code}") + return [] + + +# --- COLLECT PAPERS FOR THE MONTH --- +papers = [] +start_date = datetime(YEAR, MONTH, 1) +end_date = (start_date.replace(month=MONTH % 12 + 1) - timedelta(days=1)).day + +for day in range(1, end_date + 1): + # INSERT_YOUR_CODE + date_obj = datetime(YEAR, MONTH, day) + if date_obj > datetime.now(): + continue + date_str = f"{YEAR}-{MONTH:02d}-{day:02d}" + daily_papers = fetch_papers_for_date(date_str) + papers.extend(daily_papers) + +# --- SORT BY POPULARITY (UPVOTES) --- +print(papers[0]["paper"]["upvotes"]) +papers_sorted = sorted( + papers, key=lambda x: x.get("paper", {}).get("upvotes", 0), reverse=True +) +# print(papers_sorted[0]) +print(len(papers_sorted)) + +# --- TAKE TOP N --- +top_papers = papers_sorted[:TOP_N] + +# --- PRINT RESULTS --- +for i, paper in enumerate(top_papers, start=1): + print( + f"{i}. {paper['title']} ({paper.get('paper', {}).get('upvotes', 0)} upvotes) - {paper.get('url', '')}" + ) diff --git a/examples/parse_tweets.py b/examples/parse_tweets.py new file mode 100644 index 0000000..659c8bf --- /dev/null +++ b/examples/parse_tweets.py @@ -0,0 +1,54 @@ +import json + +# Load your JSON response (replace with your variable or file) +with open("home_latest_timeline.json", "r", encoding="utf-8") as f: + data = json.load(f) + +results = [] + +# Navigate to the entries +entries = data["data"]["home"]["home_timeline_urt"]["instructions"][0]["entries"] + +for entry in entries: + content = entry.get("content", {}) + item_content = content.get("itemContent", {}) + + # Skip if not a Tweet + if item_content.get("__typename") != "TimelineTweet": + continue + + tweet = item_content["tweet_results"]["result"] + user = tweet["core"]["user_results"]["result"] + + # Extract details + name = user["core"]["name"] + handle = "@" + user["core"]["screen_name"] + date = tweet["legacy"]["created_at"] + likes = tweet["legacy"].get("favorite_count", 0) + retweets = tweet["legacy"].get("retweet_count", 0) + text = tweet["legacy"]["full_text"] + + # "Reactions" = likes + retweets + replies + quotes + bookmarks + reactions = ( + likes + + retweets + + tweet["legacy"].get("reply_count", 0) + + tweet["legacy"].get("quote_count", 0) + + tweet["legacy"].get("bookmark_count", 0) + ) + + results.append( + { + "name": name, + "handle": handle, + "date": date, + "likes": likes, + "retweets": retweets, + "reactions": reactions, + "text": text, + } + ) + +# Example: print results +for r in results: + print(r) diff --git a/examples/twitter_scrape_wiith_cookie.py b/examples/twitter_scrape_wiith_cookie.py new file mode 100644 index 0000000..f007cf7 --- /dev/null +++ b/examples/twitter_scrape_wiith_cookie.py @@ -0,0 +1,109 @@ +import asyncio +import json +from http.cookiejar import Cookie +from pathlib import Path + +import browser_cookie3 + +# Keep all cookies that might be relevant for authentication +x_cookies = [] +for c in browser_cookie3.brave(domain_name="x.com"): + if c.domain == ".x.com" and c.name in ["auth_token"]: + x_cookies.append(c) + print("Found x auth_token") + +print(x_cookies) + + +def cookie_to_playwright_cookie(cookie: Cookie) -> dict: + cookie_dict = { + "name": cookie.name, + "value": cookie.value, + "domain": cookie.domain.lstrip("."), # Playwright expects no leading dot + "path": cookie.path, + "secure": bool(cookie.secure), + "httpOnly": getattr(cookie, "httponly", False) + or c.get_nonstandard_attr("HttpOnly", False), + } + # Optional: expires + if cookie.expires is not None: + cookie_dict["expires"] = cookie.expires + return cookie_dict + + +def is_home_latest_timeline_url(url): + if "graphql" in url and ("HomeLatestTimeline" in url or "HomeTimeline" in url): + return True + return False + + +async def on_request(request): + if is_home_latest_timeline_url(request.url): + # INSERT_YOUR_CODE + response = await request.response() + if response is not None: + try: + json_data = await response.json() + from datetime import datetime + + dt_str = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"home_latest_timeline_{dt_str}.json" + output_path = Path(".").parent / "data" / filename + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: + json.dump(json_data, f) + print(f"Saved JSON to {filename}") + except Exception as e: + print(f"Failed to get JSON from response: {e}") + + print("API call to HomeLatestTimeline") + + +async def playwright_login_with_cookies(): + from playwright.async_api import async_playwright + + # Use the first cookie found (auth_token) for x.com + cookies = [] + for c in x_cookies: + cookies.append(cookie_to_playwright_cookie(c)) + + async with async_playwright() as p: + browser = await p.chromium.launch(headless=False) + context = await browser.new_context() + # Set cookies before navigating + await context.add_cookies(cookies) + # Print all cookies currently in the context + cookies_in_context = await context.cookies() + print("All cookies in context after setting:") + for cookie in cookies_in_context: + print(cookie) + page = await context.new_page() + await asyncio.sleep(3) + page.on("request", on_request) + + await page.goto("https://x.com") + + # INSERT_YOUR_CODE + # Print all spans content with class "r-poiln3" + await asyncio.sleep(3) + + # INSERT_YOUR_CODE + # Find the span (button) that says "Following" and click it + following_span = await page.query_selector("span:has-text('Following')") + if following_span: + await following_span.click() + print("Clicked the 'Following' button.") + else: + print("Could not find the 'Following' button.") + + # spans = await page.query_selector_all("span.r-poiln3") + # print("Contents of all elements:") + # for span in spans: + # text = await span.text_content() + # print(text) + await asyncio.sleep(10000) + await browser.close() + + +# Run the async function +asyncio.run(playwright_login_with_cookies()) diff --git a/other_packages/whisper/.DS_Store b/other_packages/whisper/.DS_Store deleted file mode 100644 index 2f192a5..0000000 Binary files a/other_packages/whisper/.DS_Store and /dev/null differ diff --git a/packages/omni/.claude/settings.local.json b/packages/omni/.claude/settings.local.json new file mode 100644 index 0000000..05b1517 --- /dev/null +++ b/packages/omni/.claude/settings.local.json @@ -0,0 +1,32 @@ +{ + "permissions": { + "allow": [ + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/packages/discord_mcp/discord_mcp/**)", + "Bash(curl:*)", + "Bash(python:*)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Bash(sqlite3:*)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", + "Read(/Users/koen/workspace/toolbox/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", + "Read(/Users/koen/workspace/toolbox/data/**)" + ], + "deny": [], + "ask": [] + } +} diff --git a/packages/omni/.gitignore b/packages/omni/.gitignore new file mode 100644 index 0000000..4f0e4a9 --- /dev/null +++ b/packages/omni/.gitignore @@ -0,0 +1,19 @@ +node_modules +dist +.env +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* + +.DS_Store +.vscode +*.local +examples/twitter/ +settings.local.json + diff --git a/packages/omni/CLAUDE.md b/packages/omni/CLAUDE.md new file mode 100644 index 0000000..dfaf733 --- /dev/null +++ b/packages/omni/CLAUDE.md @@ -0,0 +1,55 @@ +# Frontend + +When you create frontend code, you are a Vue.js developer who will be creating frontend code using Vue 3 and Pinia for state management. You will receive a request for code and should implement it following specific guidelines and best practices. + +Follow these important guidelines when writing the code: + +**Architecture & State Management:** + +- Use Pinia for all global state management +- Define stores in Pinia for any data that needs to be shared across components +- Use Vue 3 Composition API, only use Options API if absolutely necessary + +**Component Guidelines:** + +- Keep components at an average size - not too small (avoid over-componentization) and not too large (break down if getting unwieldy) +- Focus on single responsibility principle for each component +- Use simple Vue concepts - avoid overly complex patterns, advanced directives, or esoteric features +- Prefer straightforward, readable code over clever implementations + +**Styling Rules:** + +- Never use inline CSS styles +- Use a single CSS file for all custom styles +- Clean up any unused CSS - remove any styles that aren't being used +- Use Tailwind CSS classes as much as possible for styling +- Only write custom CSS when Tailwind doesn't provide the needed functionality +- Follow good CSS practices: use meaningful class names, avoid deep nesting, prefer flexbox/grid for layouts + +**Code Behavior:** + +- Do not refactor existing code unless explicitly asked to do so +- Implement exactly what is requested without making assumptions about additional features +- Write clean, maintainable code that follows Vue.js best practices +- Use proper Vue lifecycle hooks when needed +- Handle errors appropriately + +**Output Format:** +Provide your response with clear file structure. If multiple files are needed, clearly separate them with file names as headers. Include: + +- Vue component files (.vue) +- Pinia store files (.js) if needed +- CSS file if custom styles are required +- Any other necessary files + +# Backend + +When writing backend python code, take the following into consideration + +- use fastapi for the backend +- when returning from an api endpoint, always use pydantic models +- when initializing a pydantic model from database values, use a classmethod like `from_sqlite_row` +- when querying data, do as much as possible in the query for efficiency, only fall back to python if it would complicate things a lot +- keep query logic as much as possible in single file + +Write the complete, functional code that addresses the code request while following all the guidelines above. diff --git a/packages/omni/README.md b/packages/omni/README.md new file mode 100644 index 0000000..f3172b2 --- /dev/null +++ b/packages/omni/README.md @@ -0,0 +1,34 @@ +# Omni + +A full-stack application broad listening on private and public data sources + +## Installation + +```bash +uv pip install -e . +npm install +``` + +## Development + +### Start Development Servers + +Start both frontend and backend servers: + +```bash +just dev +``` + +### Available Commands + +```bash +# Start development servers (frontend + backend) +just dev + +# Install dependencies only +just install + +# Kill existing dev processes (backup cleanup) +just kill-dev + +``` diff --git a/packages/omni/deploy_omni.sh b/packages/omni/deploy_omni.sh new file mode 100755 index 0000000..61bba28 --- /dev/null +++ b/packages/omni/deploy_omni.sh @@ -0,0 +1,61 @@ +#!/bin/zsh + +set -eo pipefail + +TOOLBOX_DIR="${TOOLBOX_DIR:-/Users/koen/workspace/toolbox}" + +echo "Deploying from $TOOLBOX_DIR/packages/omni to toolbox-prod" + +cd "$TOOLBOX_DIR/packages/omni" +ssh toolbox-prod "rm -rf /home/azureuser/omni" +zip -r - . -x "**.venv/*" -x ".git/*" -x "data/*" -x "node_modules/*" | ssh toolbox-prod "cat > /home/azureuser/archive.zip && rm -rf /home/azureuser/omni && unzip /home/azureuser/archive.zip -d /home/azureuser/omni" + + + +echo 'installing' +ssh toolbox-prod /bin/bash -c " +set -eo pipefail +export PATH=/home/azureuser/.local/bin:/home/azureuser/.nvm/versions/node/v20.19.5/bin:\$PATH +cd /home/azureuser/omni +uv venv +source .venv/bin/activate +# uv cache clean +uv pip install --refresh --prerelease allow -e . || true +npm install +npm install -g serve +" +echo 'killing' + +set +e pipefail + +ssh toolbox-prod <<'EOF' +pkill -f omni +pkill -f "npm run build" || true +pkill -f "python.*app.py" || true +pkill -f "uvicorn.*app:app" || true +EOF + +set -e pipefail +sleep 3 + +echo 'starting' +ssh toolbox-prod bash -c " +set -eo pipefail +export PATH=/home/azureuser/.local/bin:/home/azureuser/.nvm/versions/node/v20.19.5/bin:\$PATH +export VITE_API_BASE_URL=http://20.224.153.50:8000 +export USE_ANTHROPIC=True +export ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY +export VITE_POSTHOG_API_KEY=$POSTHOG_API_KEY + +cd /home/azureuser/omni +source .venv/bin/activate + +npm run build +nohup serve -s dist -l 8005 > output_omni_frontend.log 2>&1 & +nohup python omni/app.py > output_omni_backend.log 2>&1 & +" + +sleep 1 +ssh toolbox-prod " +tail -f /home/azureuser/omni/output_omni_backend.log /home/azureuser/omni/output_omni_frontend.log +" diff --git a/packages/omni/examples/cohere_test.py b/packages/omni/examples/cohere_test.py new file mode 100644 index 0000000..eb36000 --- /dev/null +++ b/packages/omni/examples/cohere_test.py @@ -0,0 +1,17 @@ +import cohere + +co = cohere.ClientV2() + +query = "What is the capital of the United States?" +docs = [ + "Carson City is the capital city of the American state of Nevada. At the 2010 United States Census, Carson City had a population of 55,274.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that are a political division controlled by the United States. Its capital is Saipan.", + "Charlotte Amalie is the capital and largest city of the United States Virgin Islands. It has about 20,000 people. The city is on the island of Saint Thomas.", + "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district. The President of the USA and many major national government offices are in the territory. This makes it the political center of the United States of America.", + "Capital punishment has existed in the United States since before the United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states. The federal government (including the United States military) also uses capital punishment.", +] + +results = co.rerank(model="rerank-v3.5", query=query, documents=docs, top_n=5) + + +print(results) diff --git a/packages/omni/examples/embed_tweets.py b/packages/omni/examples/embed_tweets.py new file mode 100644 index 0000000..a28423b --- /dev/null +++ b/packages/omni/examples/embed_tweets.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +""" +Script to add embeddings to tweets using Ollama with two different models: +- gemma:2b (assuming this is what you meant by gemmaembed) +- nomic-embed-text:latest +""" + +import json +import time +from pathlib import Path + +import requests + + +def get_embedding_ollama(text, model): + """Get embedding from Ollama API.""" + url = "http://localhost:11434/api/embeddings" + + payload = {"model": model, "prompt": text} + + try: + response = requests.post(url, json=payload, timeout=30) + response.raise_for_status() + + data = response.json() + return data.get("embedding", []) + + except requests.exceptions.RequestException as e: + print(f"Error getting embedding for model {model}: {e}") + return None + + +def main(): + """Main function to add embeddings to all tweets.""" + tweets_file = Path("./notebooks/combined_tweets.json") + + if not tweets_file.exists(): + print(f"Tweets file {tweets_file} not found!") + return + + # Load tweets + print("Loading tweets...") + with open(tweets_file, "r", encoding="utf-8") as f: + tweets = json.load(f) + + print(f"Found {len(tweets)} tweets to process") + + # Models to use + models = {"gemma": "embeddinggemma:latest", "nomic": "nomic-embed-text:latest"} + + # Process each tweet + processed_count = 0 + total_tweets = len(tweets) + + for tweet_id, tweet_data in tweets.items(): + print(f"Processing tweet {processed_count + 1}/{total_tweets} (ID: {tweet_id})") + + content = tweet_data.get("content", "") + if not content: + print(" No content found, skipping...") + processed_count += 1 + continue + + # Initialize or get existing embeddings dict + embeddings = tweet_data.get("embeddings", {}) + + # Get embeddings from both models (generate if missing) + for model_name, model_id in models.items(): + if model_name not in embeddings: + print(f" Getting {model_name} embedding...") + embedding = get_embedding_ollama(content, model_id) + + if embedding is not None: + embeddings[model_name] = embedding + print(f" Got embedding of size {len(embedding)}") + else: + print(" Failed to get embedding") + + # Small delay to avoid overwhelming the API + time.sleep(0.1) + else: + print(f" {model_name} embedding already exists, skipping...") + + # Add embeddings to tweet data + if embeddings: + tweet_data["embeddings"] = embeddings + + processed_count += 1 + + # Save progress every 50 tweets + if processed_count % 50 == 0: + print(f" Saving progress ({processed_count}/{total_tweets})...") + with open(tweets_file, "w", encoding="utf-8") as f: + json.dump(tweets, f, indent=2, ensure_ascii=False) + + # Final save + print("Saving final results...") + with open(tweets_file, "w", encoding="utf-8") as f: + json.dump(tweets, f, indent=2, ensure_ascii=False) + + print(f"Completed processing {processed_count} tweets") + + # Summary + tweets_with_embeddings = sum(1 for t in tweets.values() if "embeddings" in t) + print(f"Total tweets with embeddings: {tweets_with_embeddings}") + + +if __name__ == "__main__": + main() diff --git a/packages/omni/examples/sqlite_benchmark.py b/packages/omni/examples/sqlite_benchmark.py new file mode 100644 index 0000000..e90a7d1 --- /dev/null +++ b/packages/omni/examples/sqlite_benchmark.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python3 +""" +SQLite Benchmark Script + +This script creates two SQLite databases: +1. First DB: Contains rows that will return 100k IDs when queried +2. Second DB: Contains detailed data for those IDs + +The script measures the time it takes to query the second database +using the IDs from the first database. +""" + +import os +import random +import sqlite3 +import time + + +def create_first_database(): + """Create the first database with 150k rows to ensure we get 100k results""" + print("Creating first database...") + + # Remove existing file if it exists + if os.path.exists("first.db"): + os.remove("first.db") + + conn = sqlite3.connect("first.db") + cursor = conn.cursor() + + # Create table + cursor.execute(""" + CREATE TABLE source_data ( + id INTEGER PRIMARY KEY, + category TEXT, + value INTEGER, + active BOOLEAN + ) + """) + + # Insert 150k rows with random data + # We'll make sure about 100k of them meet our query criteria + data_batch = [] + for i in range(1, 150001): + # Make roughly 70% of records "active" to ensure we get 100k+ results + active = random.choice([True, True, True, False]) # 75% chance of True + category = random.choice(["A", "B", "C", "D"]) + value = random.randint(1, 1000) + data_batch.append((i, category, value, active)) + + # Insert in batches for better performance + if len(data_batch) == 1000: + cursor.executemany( + "INSERT INTO source_data (id, category, value, active) VALUES (?, ?, ?, ?)", + data_batch, + ) + data_batch = [] + + # Insert any remaining data + if data_batch: + cursor.executemany( + "INSERT INTO source_data (id, category, value, active) VALUES (?, ?, ?, ?)", + data_batch, + ) + + # Create index for better query performance + cursor.execute("CREATE INDEX idx_active ON source_data(active)") + + conn.commit() + conn.close() + print("First database created successfully!") + + +def create_second_database(ids): + """Create the second database with detailed data for the given IDs""" + print(f"Creating second database with data for {len(ids)} IDs...") + + # Remove existing file if it exists + if os.path.exists("second.db"): + os.remove("second.db") + + conn = sqlite3.connect("second.db") + cursor = conn.cursor() + + # Create table with more detailed data + cursor.execute(""" + CREATE TABLE detailed_data ( + id INTEGER PRIMARY KEY, + name TEXT, + email TEXT, + department TEXT, + salary INTEGER, + hire_date TEXT, + description TEXT + ) + """) + + # Generate detailed data for each ID + departments = ["Engineering", "Sales", "Marketing", "HR", "Finance"] + data_batch = [] + + for i, record_id in enumerate(ids): + name = f"User_{record_id}" + email = f"user_{record_id}@company.com" + department = random.choice(departments) + salary = random.randint(50000, 150000) + hire_date = f"2020-{random.randint(1, 12):02d}-{random.randint(1, 28):02d}" + description = f"Description for user {record_id} in {department} department" + + data_batch.append( + (record_id, name, email, department, salary, hire_date, description) + ) + + # Insert in batches + if len(data_batch) == 1000: + cursor.executemany( + "INSERT INTO detailed_data VALUES (?, ?, ?, ?, ?, ?, ?)", data_batch + ) + data_batch = [] + if i % 10000 == 0: + print(f" Inserted {i} records...") + + # Insert any remaining data + if data_batch: + cursor.executemany( + "INSERT INTO detailed_data VALUES (?, ?, ?, ?, ?, ?, ?)", data_batch + ) + + # Create index on ID for fast lookups + cursor.execute("CREATE INDEX idx_id ON detailed_data(id)") + + conn.commit() + conn.close() + print("Second database created successfully!") + + +def get_ids_from_first_db(): + """Query the first database to get 100k IDs""" + print("Querying first database for IDs...") + + conn = sqlite3.connect("first.db") + cursor = conn.cursor() + + # Query to get active records (should return around 100k IDs) + cursor.execute("SELECT id FROM source_data WHERE active = 1 LIMIT 1000") + results = cursor.fetchall() + ids = [row[0] for row in results] + + conn.close() + print(f"Retrieved {len(ids)} IDs from first database") + return ids + + +def benchmark_second_query(ids): + """Benchmark the query on the second database using the IDs""" + print(f"Benchmarking query on second database with {len(ids)} IDs...") + + conn = sqlite3.connect("second.db") + cursor = conn.cursor() + + # Create a temporary table + cursor.execute("CREATE TEMP TABLE temp_ids(id INTEGER)") + cursor.executemany("INSERT INTO temp_ids(id) VALUES (?)", [(i,) for i in ids]) + + # Perform a join + start_time = time.time() + cursor.execute(""" + SELECT d.* + FROM detailed_data d + JOIN temp_ids t ON d.id = t.id + """) + results = cursor.fetchall() + end_time = time.time() + + print("Time:", end_time - start_time) + + conn.close() + + query_time = end_time - start_time + print(f"Query completed in {query_time:.4f} seconds") + print(f"Retrieved {len(results)} rows") + + return query_time, len(results) + + +def main(): + """Main benchmark function""" + print("=" * 60) + print("SQLite Benchmark Script") + print("=" * 60) + + # Step 1: Create and populate first database + create_first_database() + + # Step 2: Get IDs from first database + ids = get_ids_from_first_db() + + if len(ids) == 0: + print("ERROR: No IDs retrieved from first database!") + return + + # Step 3: Create second database with data for those IDs + create_second_database(ids) + + # Step 4: Benchmark the second query + print("\n" + "=" * 60) + print("BENCHMARK RESULTS") + print("=" * 60) + + query_time, result_count = benchmark_second_query(ids) + + print(f"Query execution time: {query_time:.4f} seconds") + print(f"Rows returned: {result_count:,}") + print(f"Average time per row: {(query_time / result_count * 1000):.6f} ms") + + # Clean up + print("\nDatabase files created:") + print(f"- first.db ({os.path.getsize('first.db') / 1024 / 1024:.1f} MB)") + print(f"- second.db ({os.path.getsize('second.db') / 1024 / 1024:.1f} MB)") + + +if __name__ == "__main__": + main() diff --git a/packages/omni/examples/twitter_account_exists.py b/packages/omni/examples/twitter_account_exists.py new file mode 100644 index 0000000..4a26de4 --- /dev/null +++ b/packages/omni/examples/twitter_account_exists.py @@ -0,0 +1,25 @@ +import time +import urllib +from datetime import datetime + +from omni.twitter import account_exists, get_guest_token + +# warmup +cookie_value, expires = get_guest_token() +datetime_expires = datetime.fromtimestamp(expires) + +cookie_decoded = urllib.parse.unquote(cookie_value) + +time_start = time.time() +cookie_value, expires = get_guest_token() +handle = "elonmusk" +# handle = "XXXXXFQWEGWRBFSD" +exists = account_exists(handle, cookie_value) + + +time_end = time.time() +print(f"Time taken: {time_end - time_start} seconds") +if exists: + print(f"Account {handle} exists") +else: + print(f"Account {handle} does not exist") diff --git a/packages/omni/examples/twitter_test.py b/packages/omni/examples/twitter_test.py new file mode 100644 index 0000000..1fecec5 --- /dev/null +++ b/packages/omni/examples/twitter_test.py @@ -0,0 +1,111 @@ +import asyncio +import json +from http.cookiejar import Cookie +from pathlib import Path + +import browser_cookie3 + +# Keep all cookies that might be relevant for authentication +x_cookies = [] +for c in browser_cookie3.brave(domain_name="x.com"): + if c.domain == ".x.com" and c.name in ["auth_token"]: + x_cookies.append(c) + print("Found x auth_token") + +# print(x_cookies) + +OUTPUT_DIR = Path(".").resolve().parent.parent.parent / "data" + + +def cookie_to_playwright_cookie(cookie: Cookie) -> dict: + cookie_dict = { + "name": cookie.name, + "value": cookie.value, + "domain": cookie.domain.lstrip("."), # Playwright expects no leading dot + "path": cookie.path, + "secure": bool(cookie.secure), + "httpOnly": getattr(cookie, "httponly", False) + or c.get_nonstandard_attr("HttpOnly", False), + } + # Optional: expires + if cookie.expires is not None: + cookie_dict["expires"] = cookie.expires + return cookie_dict + + +def is_home_latest_timeline_url(url): + if "graphql" in url and ("HomeLatestTimeline" in url or "HomeTimeline" in url): + return True + return False + + +async def on_request(request): + if is_home_latest_timeline_url(request.url): + # INSERT_YOUR_CODE + response = await request.response() + if response is not None: + try: + json_data = await response.json() + from datetime import datetime + + dt_str = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"home_latest_timeline_{dt_str}.json" + output_path = OUTPUT_DIR / filename + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: + json.dump(json_data, f) + print(f"Saved JSON to {filename}") + except Exception as e: + print(f"Failed to get JSON from response: {e}") + + print("API call to HomeLatestTimeline") + + +async def playwright_login_with_cookies(): + from playwright.async_api import async_playwright + + # Use the first cookie found (auth_token) for x.com + cookies = [] + for c in x_cookies: + cookies.append(cookie_to_playwright_cookie(c)) + + async with async_playwright() as p: + browser = await p.chromium.launch(headless=False) + context = await browser.new_context() + # Set cookies before navigating + await context.add_cookies(cookies) + # Print all cookies currently in the context + cookies_in_context = await context.cookies() + print("All cookies in context after setting:") + for cookie in cookies_in_context: + print(cookie) + page = await context.new_page() + await asyncio.sleep(3) + page.on("request", on_request) + + await page.goto("https://x.com") + + # INSERT_YOUR_CODE + # Print all spans content with class "r-poiln3" + await asyncio.sleep(3) + + # INSERT_YOUR_CODE + # Find the span (button) that says "Following" and click it + following_span = await page.query_selector("span:has-text('Following')") + if following_span: + await following_span.click() + print("Clicked the 'Following' button.") + else: + print("Could not find the 'Following' button.") + + # spans = await page.query_selector_all("span.r-poiln3") + # print("Contents of all elements:") + # for span in spans: + # text = await span.text_content() + # print(text) + await asyncio.sleep(10000) + await browser.close() + + +# Run the async function +asyncio.run(playwright_login_with_cookies()) diff --git a/packages/omni/frontend/App.vue b/packages/omni/frontend/App.vue new file mode 100644 index 0000000..39a7ff5 --- /dev/null +++ b/packages/omni/frontend/App.vue @@ -0,0 +1,152 @@ + + + diff --git a/packages/omni/frontend/api/client.js b/packages/omni/frontend/api/client.js new file mode 100644 index 0000000..e56073c --- /dev/null +++ b/packages/omni/frontend/api/client.js @@ -0,0 +1,155 @@ +// API client for handling data fetching +class APIClient { + constructor() { + this.baseURL = import.meta.env.VITE_API_BASE_URL || "http://localhost:8000"; + } + + async request(endpoint, options = {}) { + try { + const response = await fetch(`${this.baseURL}${endpoint}`, { + headers: { + "Content-Type": "application/json", + ...options.headers, + }, + ...options, + }); + + if (!response.ok) { + throw new Error(`HTTP error! status: ${response.status}`); + } + + return await response.json(); + } catch (error) { + console.error(`API request failed for ${endpoint}:`, error); + throw new Error(`API request failed: ${error.message}`); + } + } + + // API methods + async getDataSources() { + return this.request("/data-sources"); + } + + async getDataCollections() { + return this.request("/data-collections"); + } + + async getSmartLists(userEmail = "dev@example.com") { + return this.request(`/smart-lists?user_email=${userEmail}`); + } + + async getFollowedSmartLists(userEmail = "dev@example.com") { + return this.request(`/smart-lists/followed?user_email=${userEmail}`); + } + + async getNotFollowingSmartLists(userEmail = "dev@example.com") { + return this.request(`/smart-lists/not_following?user_email=${userEmail}`); + } + + async followSmartList(listId, userEmail = "dev@example.com") { + return this.request( + `/smart-lists/${listId}/follow?user_email=${userEmail}`, + { + method: "POST", + }, + ); + } + + async unfollowSmartList(listId, userEmail = "dev@example.com") { + return this.request( + `/smart-lists/${listId}/follow?user_email=${userEmail}`, + { + method: "DELETE", + }, + ); + } + + async getSmartListItems(listId, options = {}) { + const { rerankingThreshold } = options; + let url = `/smart-lists/${listId}/items`; + + const params = new URLSearchParams(); + if (rerankingThreshold !== undefined && rerankingThreshold !== null) { + params.append("reranking_threshold", rerankingThreshold); + } + + if (params.toString()) { + url += `?${params.toString()}`; + } + + return this.request(url); + } + + async updateListThreshold(listId, rerankingThreshold, ragQuery = null) { + const params = new URLSearchParams(); + params.append("reranking_threshold", rerankingThreshold); + if (ragQuery !== null) { + params.append("rag_query", ragQuery); + } + return this.request( + `/smart-lists/${listId}/threshold?${params.toString()}`, + { + method: "PATCH", + }, + ); + } + + async getChats(listId) { + return this.request(`/chats/${listId}`); + } + + async createSmartList(listData, userEmail = "dev@example.com") { + return this.request(`/smart-lists?user_email=${userEmail}`, { + method: "POST", + body: JSON.stringify(listData), + }); + } + + async askQuestion(listId, question, context = []) { + return this.request(`/chats/${listId}/ask`, { + method: "POST", + body: JSON.stringify({ question, context }), + }); + } + + async registerUser(userData) { + return this.request("/users/register", { + method: "POST", + body: JSON.stringify(userData), + }); + } + + async getUser(email) { + return this.request(`/users/${email}`); + } + + async getMySmartLists(userEmail = "dev@example.com") { + return this.request(`/smart-lists/my?user_email=${userEmail}`); + } + + async getCommunitySmartLists(userEmail = "dev@example.com") { + return this.request(`/smart-lists/community?user_email=${userEmail}`); + } + + async deleteSmartList(listId, userEmail = "dev@example.com") { + return this.request(`/smart-lists/${listId}?user_email=${userEmail}`, { + method: "DELETE", + }); + } + + async checkTwitterAccount(handle) { + return this.request("/twitter/check-account", { + method: "POST", + body: JSON.stringify({ handle }), + }); + } + + async getTweetCounts(handles) { + return this.request("/twitter/tweet-counts", { + method: "POST", + body: JSON.stringify({ handles }), + }); + } +} + +export const apiClient = new APIClient(); diff --git a/packages/omni/frontend/assets/om-icon.svg b/packages/omni/frontend/assets/om-icon.svg new file mode 100644 index 0000000..126c34d --- /dev/null +++ b/packages/omni/frontend/assets/om-icon.svg @@ -0,0 +1,62 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/packages/omni/frontend/assets/styles.css b/packages/omni/frontend/assets/styles.css new file mode 100644 index 0000000..c4ab7a2 --- /dev/null +++ b/packages/omni/frontend/assets/styles.css @@ -0,0 +1,52 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +/* Custom styles */ +.highlighted { + @apply ring-2 ring-blue-500 ring-opacity-50; + animation: highlight-pulse 2s ease-in-out; +} + +@keyframes highlight-pulse { + 0% { + @apply ring-opacity-50; + } + 50% { + @apply ring-opacity-100; + } + 100% { + @apply ring-opacity-50; + } +} + +/* Scrollbar styling */ +.overflow-y-auto::-webkit-scrollbar { + width: 6px; +} + +.overflow-y-auto::-webkit-scrollbar-track { + background: #f1f5f9; +} + +.overflow-y-auto::-webkit-scrollbar-thumb { + background: #cbd5e1; + border-radius: 3px; +} + +.overflow-y-auto::-webkit-scrollbar-thumb:hover { + background: #94a3b8; +} + +/* Smooth transitions for all interactive elements */ +button, +.cursor-pointer { + transition: all 0.2s ease-in-out; +} + +/* Focus states for accessibility */ +button:focus, +input:focus { + outline: 2px solid #3b82f6; + outline-offset: 2px; +} diff --git a/packages/omni/frontend/components/AIPapersDashboard.vue b/packages/omni/frontend/components/AIPapersDashboard.vue new file mode 100644 index 0000000..c57170f --- /dev/null +++ b/packages/omni/frontend/components/AIPapersDashboard.vue @@ -0,0 +1,244 @@ + + + diff --git a/packages/omni/frontend/components/AddConnector.vue b/packages/omni/frontend/components/AddConnector.vue new file mode 100644 index 0000000..5aaa314 --- /dev/null +++ b/packages/omni/frontend/components/AddConnector.vue @@ -0,0 +1,53 @@ + + + diff --git a/packages/omni/frontend/components/AddList.vue b/packages/omni/frontend/components/AddList.vue new file mode 100644 index 0000000..6442cba --- /dev/null +++ b/packages/omni/frontend/components/AddList.vue @@ -0,0 +1,54 @@ + + + diff --git a/packages/omni/frontend/components/ConnectorManager.vue b/packages/omni/frontend/components/ConnectorManager.vue new file mode 100644 index 0000000..64e8566 --- /dev/null +++ b/packages/omni/frontend/components/ConnectorManager.vue @@ -0,0 +1,382 @@ + + + diff --git a/packages/omni/frontend/components/CreateList.vue b/packages/omni/frontend/components/CreateList.vue new file mode 100644 index 0000000..a04a6e1 --- /dev/null +++ b/packages/omni/frontend/components/CreateList.vue @@ -0,0 +1,1030 @@ + + + diff --git a/packages/omni/frontend/components/DiscordDashboard.vue b/packages/omni/frontend/components/DiscordDashboard.vue new file mode 100644 index 0000000..45b210d --- /dev/null +++ b/packages/omni/frontend/components/DiscordDashboard.vue @@ -0,0 +1,253 @@ + + + diff --git a/packages/omni/frontend/components/DiscoverListsPanel.vue b/packages/omni/frontend/components/DiscoverListsPanel.vue new file mode 100644 index 0000000..810aca1 --- /dev/null +++ b/packages/omni/frontend/components/DiscoverListsPanel.vue @@ -0,0 +1,402 @@ + + + diff --git a/packages/omni/frontend/components/FeedView.vue b/packages/omni/frontend/components/FeedView.vue new file mode 100644 index 0000000..5a3ff56 --- /dev/null +++ b/packages/omni/frontend/components/FeedView.vue @@ -0,0 +1,756 @@ + + + diff --git a/packages/omni/frontend/components/LeftSidebar.vue b/packages/omni/frontend/components/LeftSidebar.vue new file mode 100644 index 0000000..153f967 --- /dev/null +++ b/packages/omni/frontend/components/LeftSidebar.vue @@ -0,0 +1,239 @@ + + + diff --git a/packages/omni/frontend/components/LoginModal.vue b/packages/omni/frontend/components/LoginModal.vue new file mode 100644 index 0000000..a68e785 --- /dev/null +++ b/packages/omni/frontend/components/LoginModal.vue @@ -0,0 +1,147 @@ + + + diff --git a/packages/omni/frontend/components/MiddlePanel.vue b/packages/omni/frontend/components/MiddlePanel.vue new file mode 100644 index 0000000..e403016 --- /dev/null +++ b/packages/omni/frontend/components/MiddlePanel.vue @@ -0,0 +1,396 @@ + + + diff --git a/packages/omni/frontend/components/RightPanel.vue b/packages/omni/frontend/components/RightPanel.vue new file mode 100644 index 0000000..4f0215f --- /dev/null +++ b/packages/omni/frontend/components/RightPanel.vue @@ -0,0 +1,261 @@ + + + diff --git a/packages/omni/frontend/components/SmartListListView.vue b/packages/omni/frontend/components/SmartListListView.vue new file mode 100644 index 0000000..3ebacb7 --- /dev/null +++ b/packages/omni/frontend/components/SmartListListView.vue @@ -0,0 +1,283 @@ + + + diff --git a/packages/omni/frontend/components/TweetItem.vue b/packages/omni/frontend/components/TweetItem.vue new file mode 100644 index 0000000..7c4e782 --- /dev/null +++ b/packages/omni/frontend/components/TweetItem.vue @@ -0,0 +1,363 @@ + + + diff --git a/packages/omni/frontend/components/TweetMedia.vue b/packages/omni/frontend/components/TweetMedia.vue new file mode 100644 index 0000000..3a5e6e5 --- /dev/null +++ b/packages/omni/frontend/components/TweetMedia.vue @@ -0,0 +1,163 @@ + + + diff --git a/packages/omni/frontend/components/TwitterDashboard.vue b/packages/omni/frontend/components/TwitterDashboard.vue new file mode 100644 index 0000000..9c5b553 --- /dev/null +++ b/packages/omni/frontend/components/TwitterDashboard.vue @@ -0,0 +1,232 @@ + + + diff --git a/packages/omni/frontend/components/WelcomePage.vue b/packages/omni/frontend/components/WelcomePage.vue new file mode 100644 index 0000000..32516d1 --- /dev/null +++ b/packages/omni/frontend/components/WelcomePage.vue @@ -0,0 +1,504 @@ + + + diff --git a/packages/omni/frontend/constants/dataSources.js b/packages/omni/frontend/constants/dataSources.js new file mode 100644 index 0000000..849afc9 --- /dev/null +++ b/packages/omni/frontend/constants/dataSources.js @@ -0,0 +1,28 @@ +export const DATA_SOURCES = [ + { + id: "twitter", + name: "Twitter", + icon: "twitter", + description: + "Connect your Twitter account to analyze tweets and conversations", + installCommand: "toolbox install twitter-mcp", + defaultUrl: "localhost:8020", + }, + // { + // id: "discord", + // name: "Discord", + // icon: "discord", + // description: + // "Connect Discord servers to analyze messages and conversations", + // installCommand: "toolbox install discord-mcp", + // defaultUrl: "localhost:8021", + // }, + // { + // id: "ai-papers", + // name: "AI Papers", + // icon: "papers", + // description: "Connect to AI paper repositories and research databases", + // installCommand: "toolbox install papers-mcp", + // defaultUrl: "localhost:8022", + // }, +]; diff --git a/packages/omni/frontend/main.js b/packages/omni/frontend/main.js new file mode 100644 index 0000000..c4a9267 --- /dev/null +++ b/packages/omni/frontend/main.js @@ -0,0 +1,21 @@ +import { createApp } from "vue"; +import { createPinia } from "pinia"; +import App from "./App.vue"; +import "./assets/styles.css"; +import posthog from "posthog-js"; + +// Initialize PostHog only in production +if ( + import.meta.env.VITE_POSTHOG_API_KEY && + import.meta.env.MODE === "production" +) { + posthog.init("phc_TropYqZrmdCFGIawoLCB7auDIfBMwjTNJlJbd4EJuQg", { + api_host: "https://us.i.posthog.com", + }); +} + +const app = createApp(App); +const pinia = createPinia(); + +app.use(pinia); +app.mount("#app"); diff --git a/packages/omni/frontend/stores/dataCollectionsStore.js b/packages/omni/frontend/stores/dataCollectionsStore.js new file mode 100644 index 0000000..acc5c91 --- /dev/null +++ b/packages/omni/frontend/stores/dataCollectionsStore.js @@ -0,0 +1,105 @@ +import { defineStore } from "pinia"; +import { apiClient } from "../api/client.js"; + +export const useDataCollectionsStore = defineStore("dataCollections", { + state: () => ({ + collections: [], + loading: false, + error: null, + }), + + getters: { + getCollectionByDataSource: (state) => (dataSourceId) => + state.collections.find((collection) => collection.id === dataSourceId), + + getCollectionItems: (state) => (dataSourceId) => { + const collection = state.collections.find((c) => c.id === dataSourceId); + return collection ? collection.items : []; + }, + + getTotalItemCount: (state) => + state.collections.reduce( + (total, collection) => total + collection.items.length, + 0, + ), + }, + + actions: { + async fetchCollections() { + this.loading = true; + this.error = null; + + try { + this.collections = await apiClient.getDataCollections(); + } catch (error) { + this.error = error.message; + console.error("Failed to fetch data collections:", error); + } finally { + this.loading = false; + } + }, + + async syncCollection(dataSourceId) { + // This would trigger a sync operation on the backend + // For now, just refetch the collections + try { + await this.fetchCollections(); + } catch (error) { + console.error(`Failed to sync collection for ${dataSourceId}:`, error); + } + }, + + // Method to add items to a collection (used by real-time updates) + addItemsToCollection(dataSourceId, newItems) { + const collection = this.getCollectionByDataSource(dataSourceId); + if (collection) { + collection.items.unshift(...newItems); + } + }, + + // Method to update item in collection + updateItemInCollection(dataSourceId, itemId, updates) { + const collection = this.getCollectionByDataSource(dataSourceId); + if (collection) { + const itemIndex = collection.items.findIndex( + (item) => item.id === itemId, + ); + if (itemIndex !== -1) { + collection.items[itemIndex] = { + ...collection.items[itemIndex], + ...updates, + }; + } + } + }, + + // Method to remove item from collection + removeItemFromCollection(dataSourceId, itemId) { + const collection = this.getCollectionByDataSource(dataSourceId); + if (collection) { + collection.items = collection.items.filter( + (item) => item.id !== itemId, + ); + } + }, + + // Search items across collections + searchItems(query, dataSourceIds = null) { + const collectionsToSearch = dataSourceIds + ? this.collections.filter((c) => dataSourceIds.includes(c.id)) + : this.collections; + + const results = []; + + collectionsToSearch.forEach((collection) => { + const matchingItems = collection.items.filter((item) => { + const searchText = JSON.stringify(item.content).toLowerCase(); + return searchText.includes(query.toLowerCase()); + }); + results.push(...matchingItems); + }); + + return results; + }, + }, +}); diff --git a/packages/omni/frontend/stores/dataSourcesStore.js b/packages/omni/frontend/stores/dataSourcesStore.js new file mode 100644 index 0000000..5d5be17 --- /dev/null +++ b/packages/omni/frontend/stores/dataSourcesStore.js @@ -0,0 +1,98 @@ +import { defineStore } from "pinia"; +import { apiClient } from "../api/client.js"; + +export const useDataSourcesStore = defineStore("dataSources", { + state: () => ({ + dataSources: [], + loading: false, + error: null, + showDashboard: false, + currentDashboard: null, + }), + + getters: { + connectedDataSources: (state) => + state.dataSources.filter((ds) => ds.connectionState === "connected"), + + getDataSourceById: (state) => (id) => + state.dataSources.find((ds) => ds.id === id), + + hasActiveConnections: (state) => + state.dataSources.some((ds) => ds.connectionState === "connected"), + }, + + actions: { + async fetchDataSources() { + this.loading = true; + this.error = null; + + try { + this.dataSources = await apiClient.getDataSources(); + } catch (error) { + this.error = error.message; + console.error("Failed to fetch data sources:", error); + } finally { + this.loading = false; + } + }, + + async toggleConnection(dataSourceId) { + const dataSource = this.getDataSourceById(dataSourceId); + if (!dataSource) return; + + // First, deactivate all connections and close any dashboard + this.dataSources.forEach((ds) => { + if (ds.id !== dataSourceId) { + // For now, we don't change connection state, just dashboard view + } + }); + + // Toggle dashboard view for this data source + if (this.currentDashboard === `${dataSourceId}Dashboard`) { + this.closeDashboard(); + } else { + this.showDashboard = true; + this.currentDashboard = this.getDashboardComponent(dataSourceId); + } + }, + + getDashboardComponent(dataSourceId) { + const componentMap = { + twitter: "TwitterDashboard", + "ai-papers": "AIPapersDashboard", + discord: "DiscordDashboard", + }; + return componentMap[dataSourceId] || null; + }, + + setDashboardView(dashboardComponent) { + this.showDashboard = true; + this.currentDashboard = dashboardComponent; + }, + + closeDashboard() { + this.showDashboard = false; + this.currentDashboard = null; + }, + + showAddConnector() { + this.setDashboardView("ConnectorManager"); + }, + + // Method to update connection state (would be called after real API operations) + updateConnectionState(dataSourceId, newState) { + const dataSource = this.getDataSourceById(dataSourceId); + if (dataSource) { + dataSource.connectionState = newState; + } + }, + + // Method to update dashboard data + updateDashboardData(dataSourceId, newData) { + const dataSource = this.getDataSourceById(dataSourceId); + if (dataSource) { + dataSource.dashboardData = { ...dataSource.dashboardData, ...newData }; + } + }, + }, +}); diff --git a/packages/omni/frontend/stores/newChatStore.js b/packages/omni/frontend/stores/newChatStore.js new file mode 100644 index 0000000..4f4a3d7 --- /dev/null +++ b/packages/omni/frontend/stores/newChatStore.js @@ -0,0 +1,291 @@ +import { defineStore } from "pinia"; +import { apiClient } from "../api/client.js"; +import posthog from "posthog-js"; + +export const useNewChatStore = defineStore("newChat", { + state: () => ({ + isHighlighted: false, + currentQuestion: "", + shouldFocusInput: false, + isChatPanelOpen: false, + isLoading: false, + error: null, + + // Chat data per smart list + chatsByList: {}, + selectedChatId: null, + currentListId: null, + }), + + getters: { + currentChats: (state) => { + if (!state.currentListId) return []; + return state.chatsByList[state.currentListId] || []; + }, + + selectedChat: (state) => { + if (!state.selectedChatId || !state.currentListId) return null; + const chats = state.chatsByList[state.currentListId] || []; + return chats.find((chat) => chat.id === state.selectedChatId); + }, + + selectedConversation: (state) => { + // For compatibility with existing components + const chat = state.selectedChat; + if (!chat || !chat.messages.length) return null; + + // Convert chat format to conversation format + const userMessage = chat.messages.find((m) => m.role === "user"); + const assistantMessage = chat.messages.find( + (m) => m.role === "assistant", + ); + + return { + id: chat.id, + question: userMessage?.content || "", + answer: assistantMessage?.content || null, + }; + }, + + // Compatibility getter for existing components + conversations: (state) => { + const chats = state.currentChats; + return chats.map((chat) => { + const userMessage = chat.messages.find((m) => m.role === "user"); + const assistantMessage = chat.messages.find( + (m) => m.role === "assistant", + ); + + return { + id: chat.id, + question: userMessage?.content || "", + answer: assistantMessage?.content || null, + }; + }); + }, + + selectedConversationId: (state) => state.selectedChatId, + }, + + actions: { + setHighlighted(highlighted) { + this.isHighlighted = highlighted; + }, + + setCurrentQuestion(question) { + this.currentQuestion = question; + }, + + async fetchChats(listId) { + if (!listId) return; + + try { + const chats = await apiClient.getChats(listId); + this.chatsByList[listId] = chats; + } catch (error) { + console.error(`Failed to fetch chats for list ${listId}:`, error); + this.chatsByList[listId] = []; + } + }, + + async askQuestion(question) { + if (!question.trim() || !this.currentListId) return; + + this.isLoading = true; + this.error = null; + + // Track question asking in PostHog + if (typeof posthog !== "undefined" && posthog.__loaded) { + posthog.capture("question_asked", { + question: question, + list_id: this.currentListId, + is_new_chat: !this.selectedChatId, + question_length: question.length, + has_context: this.currentChats.length > 0, + }); + } + + try { + // Initialize chats array if it doesn't exist + if (!this.chatsByList[this.currentListId]) { + this.chatsByList[this.currentListId] = []; + } + + let currentChat = null; + + // Check if we have a selected chat to add to + if (this.selectedChatId) { + currentChat = this.chatsByList[this.currentListId].find( + (c) => c.id === this.selectedChatId, + ); + } + + // If no selected chat or selected chat doesn't exist, create a new one + if (!currentChat) { + currentChat = { + id: Date.now(), + messages: [], + }; + this.chatsByList[this.currentListId].unshift(currentChat); + this.selectedChatId = currentChat.id; + } + + // Add user message to the current chat + const userMessage = { + id: Date.now(), + role: "user", + content: question, + timestamp: new Date().toISOString(), + }; + currentChat.messages.push(userMessage); + + this.currentQuestion = ""; + + // Get smart list items as context + const { useSmartListsStore } = await import("./smartListsStore.js"); + const smartListsStore = useSmartListsStore(); + const listItems = smartListsStore.currentListItems || []; + + // Convert items to strings for context + const context = listItems.map((item) => + typeof item === "string" + ? item + : item.content || JSON.stringify(item), + ); + + // Get AI response + const response = await apiClient.askQuestion( + this.currentListId, + question, + context, + ); + + // Add assistant response to the same chat + const assistantMessage = { + id: Date.now() + 1, + role: "assistant", + content: + response.answer || + `Based on the current content, here's an AI-generated response to "${question}". This is a mock response that would normally be generated by analyzing the content in the selected list.`, + timestamp: new Date().toISOString(), + }; + currentChat.messages.push(assistantMessage); + } catch (error) { + this.error = error.message; + console.error("Failed to ask question:", error); + + // Add error message to current chat + const currentChat = this.chatsByList[this.currentListId]?.find( + (c) => c.id === this.selectedChatId, + ); + if (currentChat) { + currentChat.messages.push({ + id: Date.now() + 1, + role: "assistant", + content: + "Sorry, I encountered an error while processing your question. Please try again.", + timestamp: new Date().toISOString(), + }); + } + } finally { + this.isLoading = false; + } + }, + + selectConversation(chatId) { + this.selectedChatId = chatId; + }, + + startNewChat() { + this.selectedChatId = null; + this.currentQuestion = ""; + }, + + focusInput() { + this.shouldFocusInput = true; + }, + + openChatPanel() { + this.isChatPanelOpen = true; + }, + + closeChatPanel() { + this.isChatPanelOpen = false; + }, + + clearFocusInput() { + this.shouldFocusInput = false; + }, + + async updateConversationsForList(listId) { + this.currentListId = listId; + + // Fetch chats for this list if not cached + if (!this.chatsByList[listId]) { + await this.fetchChats(listId); + } + + this.selectedChatId = null; // Reset to new chat when changing lists + }, + + initializeDefaultConversations() { + // Load any default/cached conversations + // This would be called on app initialization + }, + + // Method to delete a chat + deleteChat(chatId) { + if (!this.currentListId) return; + + const chats = this.chatsByList[this.currentListId]; + if (chats) { + this.chatsByList[this.currentListId] = chats.filter( + (chat) => chat.id !== chatId, + ); + + // If this was the selected chat, clear selection + if (this.selectedChatId === chatId) { + this.selectedChatId = null; + } + } + }, + + // Method to add a message to an existing chat + addMessageToChat(chatId, message) { + if (!this.currentListId) return; + + const chats = this.chatsByList[this.currentListId]; + if (chats) { + const chat = chats.find((c) => c.id === chatId); + if (chat) { + chat.messages.push({ + ...message, + id: Date.now(), + timestamp: new Date().toISOString(), + }); + } + } + }, + + // Method to update a message in a chat + updateMessageInChat(chatId, messageId, updates) { + if (!this.currentListId) return; + + const chats = this.chatsByList[this.currentListId]; + if (chats) { + const chat = chats.find((c) => c.id === chatId); + if (chat) { + const messageIndex = chat.messages.findIndex( + (m) => m.id === messageId, + ); + if (messageIndex !== -1) { + chat.messages[messageIndex] = { + ...chat.messages[messageIndex], + ...updates, + }; + } + } + } + }, + }, +}); diff --git a/packages/omni/frontend/stores/smartListsStore.js b/packages/omni/frontend/stores/smartListsStore.js new file mode 100644 index 0000000..2afbc59 --- /dev/null +++ b/packages/omni/frontend/stores/smartListsStore.js @@ -0,0 +1,331 @@ +import { defineStore } from "pinia"; +import { apiClient } from "../api/client.js"; + +export const useSmartListsStore = defineStore("smartLists", { + state: () => ({ + smartLists: [], + currentListId: null, + currentView: "feed", + loading: false, + error: null, + + // Cache for computed items per list + computedItemsCache: {}, + + // Cache for list summaries + summariesCache: {}, + + // Loading state for individual list items + listItemsLoading: {}, + + // Loading start times for progress messages + listLoadingStartTimes: {}, + }), + + getters: { + currentList: (state) => + state.smartLists.find((list) => list.id === state.currentListId), + + currentListItems: (state) => { + if (!state.currentListId) return []; + return state.computedItemsCache[state.currentListId] || []; + }, + + isCurrentListLoading: (state) => { + if (!state.currentListId) return false; + return state.listItemsLoading[state.currentListId] || false; + }, + + getCurrentListLoadingStartTime: (state) => { + if (!state.currentListId) return null; + return state.listLoadingStartTimes[state.currentListId] || null; + }, + + currentListSummary: (state) => { + if (!state.currentListId) return null; + return state.summariesCache[state.currentListId] || null; + }, + + currentListDateRange: (state) => { + const currentList = state.smartLists.find( + (list) => list.id === state.currentListId, + ); + if (!currentList || !currentList.listSources.length) return null; + + // Get date range from first data source (for now) + return currentList.listSources[0].filters.dateRange; + }, + + getListById: (state) => (id) => + state.smartLists.find((list) => list.id === id), + }, + + actions: { + async fetchSmartLists() { + this.loading = true; + this.error = null; + + try { + // Get current user email from user store + const { useUserStore } = await import("./userStore.js"); + const userStore = useUserStore(); + const userEmail = userStore.userEmail || "dev@example.com"; + + this.smartLists = await apiClient.getFollowedSmartLists(userEmail); + } catch (error) { + this.error = error.message; + console.error("Failed to fetch followed smart lists:", error); + } finally { + this.loading = false; + } + }, + + async fetchListItems(listId, options = {}) { + if (!listId) return; + + // Set loading state for this specific list + this.listItemsLoading[listId] = true; + this.listLoadingStartTimes[listId] = Date.now(); + + try { + // Get reranking threshold from the list if not provided in options + if (!options.rerankingThreshold) { + const list = this.getListById(listId); + if (list?.listSources && list.listSources.length > 0) { + const filters = list.listSources[0].filters; + options.rerankingThreshold = + options.rerankingThreshold ?? filters?.reranking_threshold; + } + } + + const items = await apiClient.getSmartListItems(listId, options); + this.computedItemsCache[listId] = items; + } catch (error) { + console.error(`Failed to fetch items for list ${listId}:`, error); + this.computedItemsCache[listId] = []; + } finally { + this.listItemsLoading[listId] = false; + delete this.listLoadingStartTimes[listId]; + } + }, + + async setCurrentList(listId) { + this.currentListId = listId; + + // Fetch items for this list if not cached + if (!this.computedItemsCache[listId]) { + await this.fetchListItems(listId); + } + + // Update chat store when list changes + const { useNewChatStore } = await import("./newChatStore.js"); + const chatStore = useNewChatStore(); + chatStore.updateConversationsForList(listId); + }, + + setCurrentView(view) { + this.currentView = view; + }, + + async createSmartList(listData) { + try { + // Get current user email from user store + const { useUserStore } = await import("./userStore.js"); + const userStore = useUserStore(); + const userEmail = userStore.userEmail || "dev@example.com"; + + const result = await apiClient.createSmartList(listData, userEmail); + + // Refresh the followed smart lists to get the newly created list + await this.fetchSmartLists(); + + return result.list_id; + } catch (error) { + console.error("Failed to create smart list:", error); + throw error; + } + }, + + async updateSmartList(listId, updates) { + try { + // Update local state immediately + const listIndex = this.smartLists.findIndex( + (list) => list.id === listId, + ); + if (listIndex !== -1) { + this.smartLists[listIndex] = { + ...this.smartLists[listIndex], + ...updates, + }; + } + + // Clear cached items since filters might have changed + delete this.computedItemsCache[listId]; + delete this.summariesCache[listId]; + + // Re-fetch items with new filters + await this.fetchListItems(listId); + + // In a real implementation, this would make an API call + // await apiClient.updateSmartList(listId, updates); + } catch (error) { + console.error("Failed to update smart list:", error); + throw error; + } + }, + + async deleteSmartList(listId) { + try { + // Remove from local state + this.smartLists = this.smartLists.filter((list) => list.id !== listId); + + // Clear cache + delete this.computedItemsCache[listId]; + delete this.summariesCache[listId]; + + // If this was the current list, clear selection + if (this.currentListId === listId) { + this.currentListId = null; + } + + // In a real implementation, this would make an API call + // await apiClient.deleteSmartList(listId); + } catch (error) { + console.error("Failed to delete smart list:", error); + throw error; + } + }, + + async generateSummary(listId, forceRefresh = false) { + if (!listId) return { summary: null, status: "error" }; + + try { + // Check if we already have a cached completed summary (unless forced refresh) + const cached = this.summariesCache[listId]; + if (!forceRefresh && cached && cached.status === "completed") { + return cached; + } + + // Fetch summary from API + const result = await apiClient.request( + `/smart-lists/${listId}/summary`, + ); + + // Handle both old string format and new object format + const summaryData = + typeof result === "string" + ? { summary: result, status: "completed" } + : result; + + // Cache the result + this.summariesCache[listId] = summaryData; + return summaryData; + } catch (error) { + console.error(`Failed to generate summary for list ${listId}:`, error); + return { summary: null, status: "error" }; + } + }, + + async pollSummaryStatus(listId, onUpdate) { + if (!listId) return; + + const maxPolls = 30; // Max 30 polls (5 minutes with 10s intervals) + let pollCount = 0; + + const poll = async () => { + if (pollCount >= maxPolls) { + console.warn(`Summary generation timeout for list ${listId}`); + return; + } + + try { + // Force refresh to get latest status from server + const result = await this.generateSummary(listId, true); + + // Call the update callback + if (onUpdate) { + onUpdate(result); + } + + // Continue polling if still generating + if (result.status === "generating") { + pollCount++; + setTimeout(poll, 5000); // Poll every 5 seconds + } + } catch (error) { + console.error("Error polling summary status:", error); + if (onUpdate) { + onUpdate({ summary: null, status: "error" }); + } + } + }; + + // Start polling + poll(); + }, + + // Method to refresh computed items (trigger backend recomputation) + async refreshComputedItems(listId) { + if (!listId) return; + + try { + // Clear cache first + delete this.computedItemsCache[listId]; + + // Fetch fresh items + await this.fetchListItems(listId); + } catch (error) { + console.error(`Failed to refresh items for list ${listId}:`, error); + } + }, + + // Method to add a new data source to a smart list + addDataSourceToList(listId, dataSourceConfig) { + const list = this.getListById(listId); + if (list) { + list.listSources.push(dataSourceConfig); + + // Clear cached items since we added a new data source + delete this.computedItemsCache[listId]; + + // Refresh items + this.refreshComputedItems(listId); + } + }, + + // Method to remove a data source from a smart list + removeDataSourceFromList(listId, dataSourceId) { + const list = this.getListById(listId); + if (list) { + list.listSources = list.listSources.filter( + (source) => source.dataSourceId !== dataSourceId, + ); + + // Clear cached items since we removed a data source + delete this.computedItemsCache[listId]; + + // Refresh items + this.refreshComputedItems(listId); + } + }, + + // Method to update filters for a data source in a smart list + updateDataSourceFilters(listId, dataSourceId, newFilters) { + const list = this.getListById(listId); + if (list) { + const sourceConfig = list.listSources.find( + (source) => source.dataSourceId === dataSourceId, + ); + if (sourceConfig) { + sourceConfig.filters = { ...sourceConfig.filters, ...newFilters }; + + // Clear cached items since filters changed + delete this.computedItemsCache[listId]; + + // Refresh items + this.refreshComputedItems(listId); + } + } + }, + }, +}); diff --git a/packages/omni/frontend/stores/userStore.js b/packages/omni/frontend/stores/userStore.js new file mode 100644 index 0000000..2c56aea --- /dev/null +++ b/packages/omni/frontend/stores/userStore.js @@ -0,0 +1,163 @@ +import { defineStore } from "pinia"; +import { apiClient } from "../api/client.js"; +import posthog from "posthog-js"; + +export const useUserStore = defineStore("user", { + state: () => ({ + currentUser: null, + isAuthenticated: false, + loading: false, + error: null, + showLoginModal: false, + }), + + getters: { + userEmail: (state) => state.currentUser?.email || null, + isLoggedIn: (state) => state.isAuthenticated && state.currentUser !== null, + }, + + actions: { + async initializeAuth() { + this.loading = true; + this.error = null; + + try { + // Check localStorage for existing user email + const storedEmail = localStorage.getItem("userEmail"); + + if (storedEmail) { + // Verify user exists in backend + const user = await this.getUserByEmail(storedEmail); + if (user) { + this.currentUser = user; + this.isAuthenticated = true; + + // Identify user in PostHog + if (typeof posthog !== "undefined" && posthog.__loaded) { + posthog.identify(user.id || storedEmail, { + email: storedEmail, + name: user.name || null, + }); + } + } else { + // Clear invalid stored email + localStorage.removeItem("userEmail"); + this.showLoginModal = true; + } + } else { + // Development mode: auto-login as dev@example.com + if (import.meta.env.MODE === "development") { + await this.loginUser("dev@example.com"); + } else { + this.showLoginModal = true; + } + } + } catch (error) { + console.error("Failed to initialize auth:", error); + this.error = error.message; + this.showLoginModal = true; + } finally { + this.loading = false; + } + }, + + async registerUser(email) { + this.loading = true; + this.error = null; + + try { + const user = await apiClient.registerUser({ email }); + this.currentUser = user; + this.isAuthenticated = true; + + // Store in localStorage + localStorage.setItem("userEmail", email); + + // Identify user in PostHog + if (typeof posthog !== "undefined" && posthog.__loaded) { + posthog.identify(user.id || email, { + email: email, + name: user.name || null, + }); + } + + // Close login modal + this.showLoginModal = false; + + return user; + } catch (error) { + console.error("Failed to register user:", error); + this.error = error.message; + throw error; + } finally { + this.loading = false; + } + }, + + async getUserByEmail(email) { + try { + return await apiClient.getUser(email); + } catch (error) { + if (error.message.includes("404")) { + return null; // User not found + } + throw error; + } + }, + + async loginUser(email) { + this.loading = true; + this.error = null; + + try { + // Try to get existing user first + let user = await this.getUserByEmail(email); + + // If user doesn't exist, register them + if (!user) { + user = await apiClient.registerUser({ email }); + } + + this.currentUser = user; + this.isAuthenticated = true; + + // Store in localStorage + localStorage.setItem("userEmail", email); + + // Identify user in PostHog + if (typeof posthog !== "undefined" && posthog.__loaded) { + posthog.identify(user.id || email, { + email: email, + name: user.name || null, + }); + } + + // Close login modal + this.showLoginModal = false; + + return user; + } catch (error) { + console.error("Failed to login user:", error); + this.error = error.message; + throw error; + } finally { + this.loading = false; + } + }, + + logout() { + this.currentUser = null; + this.isAuthenticated = false; + localStorage.removeItem("userEmail"); + this.showLoginModal = true; + }, + + openLoginModal() { + this.showLoginModal = true; + }, + + closeLoginModal() { + this.showLoginModal = false; + }, + }, +}); diff --git a/packages/omni/index.html b/packages/omni/index.html new file mode 100644 index 0000000..9ed2349 --- /dev/null +++ b/packages/omni/index.html @@ -0,0 +1,13 @@ + + + + + + + Omni + + +
+ + + diff --git a/packages/omni/justfile b/packages/omni/justfile new file mode 100755 index 0000000..09d9938 --- /dev/null +++ b/packages/omni/justfile @@ -0,0 +1,82 @@ +#!/usr/bin/env just --justfile +# Vue.js and FastAPI development commands + + +deploy toolbox_dir="$(cd ../.. && pwd)": + #!/bin/bash + TOOLBOX_DIR={{toolbox_dir}} ./deploy_omni.sh + +sync-data: + #!/bin/bash + scp -r ~/.twitter-mcp/tweet_toolbox_store.db toolbox-prod:/home/azureuser/.twitter-mcp/tweet_toolbox_store.db + +sync-summaries: + #!/bin/bash + scp -r ~/.twitter-mcp/db.sqlite toolbox-prod:/home/azureuser/.twitter-mcp/db.sqlite + +sync-x-credentials: + #!/bin/bash + ssh toolbox-prod "mkdir -p /home/azureuser/.omni/scraper_data" + scp ~/.omni/scraper_data/x_cookies.json toolbox-prod:/home/azureuser/.omni/scraper_data/x_cookies.json + +# Kill existing dev processes +kill-dev: + #!/bin/bash + pkill -f "npm run dev" || true + pkill -f "python.*app.py" || true + pkill -f "uvicorn.*app:app" || true + + +# Install dependencies only +install: + #!/bin/bash + npm install + uv pip install -e . + +# Run both frontend and backend development servers +dev: + #!/bin/bash + just kill-dev + trap 'just kill-dev' EXIT + echo "Starting backend server..." + export USE_ANTHROPIC=True + export USE_CACHED_X_COOKIES=False + export USE_MOCK_SUMMARIES=True + cd omni && python app.py & + BACKEND_PID=$! + echo "Backend started with PID: $BACKEND_PID" + echo "Starting frontend server..." + npm run dev & + FRONTEND_PID=$! + echo "Frontend started with PID: $FRONTEND_PID" + wait + +# Build for production +build: + #!/bin/bash + npm run build + +# Preview production build +preview: + #!/bin/bash + npm run preview + +# Clean node_modules and reinstall +clean-install: + #!/bin/bash + rm -rf node_modules package-lock.json + npm install + uv pip install -e . + +log: + #!/bin/bash + ssh toolbox-prod "tail -f /home/azureuser/omni/output_omni_backend.log" + +print-local-cookie: + #!/bin/bash + python scripts/print_local_cookie.py + +print-local-cookie-and-save: + #!/bin/bash + python scripts/print_local_cookie_and_save.py + scp ~/.omni/scraper_data/x_cookies.json toolbox-prod:/home/azureuser/.omni/scraper_data/x_cookies.json \ No newline at end of file diff --git a/packages/omni/notebooks/.gitignore b/packages/omni/notebooks/.gitignore new file mode 100644 index 0000000..a6c57f5 --- /dev/null +++ b/packages/omni/notebooks/.gitignore @@ -0,0 +1 @@ +*.json diff --git a/packages/omni/notebooks/analyse_rag.ipynb b/packages/omni/notebooks/analyse_rag.ipynb new file mode 100644 index 0000000..b34ac1c --- /dev/null +++ b/packages/omni/notebooks/analyse_rag.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "from omni.vectorstore_queries import search_tweets\n", + "\n", + "from omni.db import get_tweet_store" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "store = get_tweet_store()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "author_screen_names = [\"karpathy\", \"iamtrask\", \"miles_brundage\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "query = store.search_documents()\n", + "query = query.where({\"author.screen_name__in\": author_screen_names})\n", + "documents = query.get()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "query_text = \"RAG\"\n", + "cosine_threshold = 0.0\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "cosine_docs = store.search_chunks() \\\n", + " .where({\"id__in\": [doc.id for doc in documents]}) \\\n", + " .semantic(query_text, score_threshold=0.0) \\\n", + " .chunk_limit(4000) \\\n", + " .get_documents()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "cosine_scores = {}\n", + "for doc in cosine_docs:\n", + " max_doc_score = max(chunk.score for chunk in doc.chunks)\n", + " cosine_scores[doc.id] = max_doc_score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "len(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "reranking_docs = (\n", + " store.search_chunks()\n", + " .where({\"id__in\": [doc.id for doc in documents]})\n", + " .semantic(query_text, score_threshold=0.0) \\\n", + " .chunk_limit(1000)\n", + " .rerank(rerank_query=query_text, score_threshold=0.0)\n", + " .get_documents()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "reranking_scores = {}\n", + "for doc in reranking_docs:\n", + " max_doc_score = max(chunk.score for chunk in doc.chunks)\n", + " reranking_scores[doc.id] = max_doc_score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = sum(xi/yi for xi, yi in zip(cosine_scores.values(), reranking_scores.values())) / len(cosine_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "scaler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "mean_x = sum(x) / len(x)\n", + "std_x = math.sqrt(sum((xi - mean_x) ** 2 for xi in x) / len(x))\n", + "std_x\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Find common document ids between cosine_scores and reranking_scores\n", + "common_ids = set(cosine_scores.keys()) & set(reranking_scores.keys())\n", + "\n", + "x = [cosine_scores[doc_id] for doc_id in common_ids]\n", + "y = [reranking_scores[doc_id] for doc_id in common_ids]\n", + "\n", + "x1 = list(i/10 for i in range(0, 10))\n", + "y1 = [xi/0.42 for xi in x1]\n", + "\n", + "# plt.legend()\n", + "\n", + "# plt.plot(x1,y1)\n", + "\n", + "\n", + "plt.figure(figsize=(6, 6))\n", + "# plt.scatter(x, y, c='blue', label='Docs', alpha=0.7)\n", + "# plt.scatter(x, [0]*len(x), c='red', label='Cosine Score', alpha=0.5)\n", + "# # Plot each (x, y) pair as a single dot\n", + "# plt.scatter(x, y, c='purple', label='Cosine vs Reranking', alpha=0.8)\n", + "plt.plot(x1, y1, c='orange', label='y=0.42x')\n", + "plt.scatter(x, y, c='purple', label='Cosine vs Reranking', alpha=0.8)\n", + "\n", + "plt.xlim(min(x)-0.05, max(x)+0.05)\n", + "plt.ylim(min(y)-0.05, max(y)+0.05)\n", + "\n", + "plt.xlabel(\"Cosine Score\")\n", + "plt.ylabel(\"Reranking Score\")\n", + "plt.title(\"Cosine vs Reranking Scores\")\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "res = search_tweets(query_text=\"Hallucinate\",\n", + " author_screen_names= [\"karpathy\", \"iamtrask\", \"miles_brundage\"],\n", + " cosine_threshold= 0.37,\n", + " reranking_threshold= 0.81,\n", + " limit= 50,\n", + ") \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "len(res)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "for x in res[:5]:\n", + " print(x.author[\"name\"], x.similarity_score)\n", + " print(x.content)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/packages/omni/notebooks/rag_search_simple.py b/packages/omni/notebooks/rag_search_simple.py new file mode 100644 index 0000000..62cbe59 --- /dev/null +++ b/packages/omni/notebooks/rag_search_simple.py @@ -0,0 +1,190 @@ +#!/usr/bin/env python3 +""" +Simple script to search for tweets using cosine similarity with pure numpy. +No external dependencies except numpy. +""" + +import json + +import numpy as np +import requests + + +def cosine_similarity_numpy(a, b): + """Calculate cosine similarity between two vectors using numpy.""" + return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) + + +def load_tweets(file_path): + """Load tweets from JSON file.""" + try: + with open(file_path, "r", encoding="utf-8") as f: + return json.load(f) + except json.JSONDecodeError as e: + print(f"JSON Error: {e}") + print("Trying to load with error handling...") + with open(file_path, "r", encoding="utf-8") as f: + content = f.read() + try: + return json.loads(content) + except Exception as e2: + print(f"Still failed: {e2}") + raise + + +def count_tweets_with_embeddings(tweets): + """Count tweets that have both nomic and gemma embeddings.""" + nomic_count = 0 + gemma_count = 0 + both_count = 0 + + for tweet_id, tweet_data in tweets.items(): + embeddings = tweet_data.get("embeddings", {}) + has_nomic = "nomic" in embeddings and embeddings["nomic"] is not None + has_gemma = "gemma" in embeddings and embeddings["gemma"] is not None + + if has_nomic: + nomic_count += 1 + if has_gemma: + gemma_count += 1 + if has_nomic and has_gemma: + both_count += 1 + + return nomic_count, gemma_count, both_count + + +def create_query_vector(query_text, reference_vector): + """ + Create a query vector based on simple text analysis. + This is a simplified approach - in practice you'd use the same embedding model. + """ + # Simple approach: create a vector that emphasizes certain dimensions + # based on the query content + query_vector = np.random.normal(0, 0.1, len(reference_vector)) + + # Add some signal based on query characteristics + query_lower = query_text.lower() + + # Boost certain dimensions based on query content + if "rag" in query_lower: + query_vector[:10] += 0.5 # Boost first 10 dimensions + if "retrieval" in query_lower: + query_vector[10:20] += 0.5 + if "augmented" in query_lower: + query_vector[20:30] += 0.5 + if "generation" in query_lower: + query_vector[30:40] += 0.5 + + # Normalize the vector + return query_vector / np.linalg.norm(query_vector) + + +def get_embedding_ollama(query_text, model): + """Get embedding from Ollama API.""" + url = "http://localhost:11434/api/embeddings" + payload = {"model": model, "prompt": query_text} + response = requests.post(url, json=payload, timeout=30) + response.raise_for_status() + return response.json().get("embedding") + + +def find_top_matches(tweets, query_text, embedding_type, top_k=10): + """Find top k matches using cosine similarity.""" + similarities = [] + + # Get a reference vector to determine query vector + + # Create query vector + if embedding_type == "nomic": + query_vector = get_embedding_ollama(query_text, "nomic-embed-text:latest") + elif embedding_type == "gemma": + query_vector = get_embedding_ollama(query_text, "embeddinggemma:latest") + else: + raise ValueError(f"Invalid embedding type: {embedding_type}") + + # Calculate similarities + for tweet_id, tweet_data in tweets.items(): + embeddings = tweet_data.get("embeddings", {}) + if embedding_type in embeddings and embeddings[embedding_type] is not None: + tweet_embedding = np.array(embeddings[embedding_type]) + similarity = cosine_similarity_numpy(query_vector, tweet_embedding) + similarities.append((similarity, tweet_id, tweet_data)) + + # Sort by similarity (descending) + similarities.sort(reverse=True) + + return similarities[:top_k] + + +def search_tweets(file_path, query): + """Main function to search tweets.""" + print(f"Searching for query: '{query}'") + print("=" * 50) + + # Load tweets + tweets = load_tweets(file_path) + print(f"Total tweets loaded: {len(tweets)}") + + # Count tweets with embeddings + nomic_count, gemma_count, both_count = count_tweets_with_embeddings(tweets) + print(f"Tweets with nomic embeddings: {nomic_count}") + print(f"Tweets with gemma embeddings: {gemma_count}") + print(f"Tweets with both embeddings: {both_count}") + print() + + # Get embedding dimensions + sample_tweet = next(iter(tweets.values())) + nomic_dim = ( + len(sample_tweet["embeddings"]["nomic"]) + if sample_tweet["embeddings"]["nomic"] + else 0 + ) + gemma_dim = ( + len(sample_tweet["embeddings"]["gemma"]) + if sample_tweet["embeddings"]["gemma"] + else 0 + ) + + print(f"Nomic embedding dimension: {nomic_dim}") + print(f"Gemma embedding dimension: {gemma_dim}") + print() + + # Search using nomic embeddings + if nomic_count > 0: + print("TOP 10 MATCHES USING NOMIC EMBEDDINGS:") + print("=" * 40) + nomic_matches = find_top_matches(tweets, query, "nomic", 10) + for i, (similarity, tweet_id, tweet_data) in enumerate(nomic_matches, 1): + content = tweet_data["content"] + print(f"{i}. Similarity: {similarity:.4f}") + print(f" Author: {tweet_data['author']}") + print(f" Content: {content}") + print(f" ID: {tweet_id}") + print() + + # Search using gemma embeddings + if gemma_count > 0: + print("TOP 10 MATCHES USING GEMMA EMBEDDINGS:") + print("=" * 40) + gemma_matches = find_top_matches(tweets, query, "gemma", 10) + for i, (similarity, tweet_id, tweet_data) in enumerate(gemma_matches, 1): + content = tweet_data["content"] + print(f"{i}. Similarity: {similarity:.4f}") + print(f" Author: {tweet_data['author']}") + print(f" Content: {content}") + print(f" ID: {tweet_id}") + print() + + +if __name__ == "__main__": + import sys + + # Default query + query = "RAG" + + # Check if query was provided as command line argument + if len(sys.argv) > 1: + query = sys.argv[1] + + # Run the search + search_tweets("combined_tweets.json", query) diff --git a/packages/omni/omni/__init__.py b/packages/omni/omni/__init__.py new file mode 100644 index 0000000..9fe1656 --- /dev/null +++ b/packages/omni/omni/__init__.py @@ -0,0 +1,2 @@ +def hello() -> str: + return "Hello from broad-listening-ui!" diff --git a/packages/omni/omni/app.py b/packages/omni/omni/app.py new file mode 100644 index 0000000..daeac27 --- /dev/null +++ b/packages/omni/omni/app.py @@ -0,0 +1,445 @@ +from contextlib import asynccontextmanager +from typing import List, Optional + +from fastapi import Depends, FastAPI, HTTPException, Request +from fastapi.middleware.cors import CORSMiddleware +from toolbox_store.store import ToolboxStore + +from omni.data_fetchers.x_fetcher import XDataFetcher +from omni.db import ( + _get_smart_lists_api_result, + create_smart_list_from_request, + create_user, + delete_smart_list, + follow_list, + get_community_lists_api_result, + get_followed_list_ids, + get_my_lists_api_result, + get_omni_connection, + get_smart_list_api_result_by_id, + get_tweet_store, + get_user_by_email, + initialize_dev_user, + initialize_mock_smart_lists, + unfollow_list, +) +from omni.mock_data import ( + get_mock_chats, + get_mock_data_collections, + get_mock_data_sources, + get_mock_smart_lists, + get_mock_summary, +) +from omni.models import ( + Chat, + DataCollection, + DataSource, + FollowUserRequest, + QuestionRequest, + SmartListAPIResult, + SmartListCreate, + SummaryResponse, + TweetCountRequest, + TweetCountResponse, + TweetItem, + TwitterAccountCheckRequest, + TwitterAccountCheckResponse, + User, + UserCreate, +) +from omni.settings import settings +from omni.summaries import clear_summary_cache as clear_cache +from omni.summaries import ( + generate_filters_hash, + get_anthropic_completion, + get_or_generate_smart_list_summary, +) +from omni.twitter import account_exists, get_guest_token, query_twitter_data +from omni.vectorstore_queries import get_tweet_counts_by_handles + + +def init_app_data(): + """Initialize database and dev user""" + try: + # Initialize the dev user for development + initialize_dev_user() + print("✅ Dev user initialized successfully") + + # Initialize mock smart lists data + initialize_mock_smart_lists() + print("✅ Mock smart lists initialized successfully") + except Exception as e: + print(f"⚠️ Failed to initialize startup data: {e}") + + +@asynccontextmanager +async def lifespan(app: FastAPI): + store = get_tweet_store() + print(f"Using ToolboxStore at {store.db.db_path}") + x_data_fetcher = XDataFetcher() + x_data_fetcher.start( + download_schedule=settings.x_download_schedule_seconds, + ) + print("Started X downloader") + init_app_data() + app.state.x_data_fetcher = x_data_fetcher + app.state.store = store + + yield + + x_data_fetcher.stop() + store.close() + + +app = FastAPI( + title="Omni API", + description="Backend API for Omni application", + lifespan=lifespan, +) + +# Enable CORS +app.add_middleware( + CORSMiddleware, + # allow_origins=["http://localhost:8005"], # Vite dev server + allow_origins=["*"], # Vite dev server + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +def get_store(request: Request) -> ToolboxStore: + return request.app.state.store + + +def get_x_data_fetcher(request: Request) -> XDataFetcher: + return request.app.state.x_data_fetcher + + +# Routes +@app.get("/") +async def root(): + return {"message": "Omni API is running"} + + +@app.post("/users/register", response_model=User) +async def register_user(user_data: UserCreate): + """Register a new user or return existing user""" + with get_omni_connection() as conn: + user_row = create_user(conn, user_data.email) + if user_row: + return User.from_sql_row(user_row) + else: + raise HTTPException(status_code=500, detail="Failed to create user") + + +@app.get("/users/{email}", response_model=User) +async def get_user(email: str): + """Get user by email""" + with get_omni_connection() as conn: + user_row = get_user_by_email(conn, email) + if user_row: + return User.from_sql_row(user_row) + else: + raise HTTPException(status_code=404, detail="User not found") + + +@app.get("/data-sources", response_model=List[DataSource]) +async def get_data_sources(): + return get_mock_data_sources() + + +@app.get("/data-collections", response_model=List[DataCollection]) +async def get_data_collections(): + return get_mock_data_collections() + + +@app.get("/smart-lists", response_model=List[SmartListAPIResult]) +async def get_all_smart_lists_api_result_endpoint(user_email: str = "dev@example.com"): + """Get all available smart lists with following status""" + with get_omni_connection() as conn: + return _get_smart_lists_api_result(conn, current_user=user_email) + + +@app.get("/smart-lists/followed", response_model=List[SmartListAPIResult]) +async def get_followed_smart_lists(user_email: str = "dev@example.com"): + """Get smart lists that the user is following""" + with get_omni_connection() as conn: + followed_list_ids = get_followed_list_ids(conn, user_email) + all_lists = _get_smart_lists_api_result(conn, current_user=user_email) + + # Filter to only followed lists + followed_lists = [ + list_item for list_item in all_lists if list_item.id in followed_list_ids + ] + return followed_lists + + +@app.get("/smart-lists/not_following", response_model=List[SmartListAPIResult]) +async def get_not_following_smart_lists(user_email: str = "dev@example.com"): + """Get smart lists that the user is not following""" + with get_omni_connection() as conn: + followed_list_ids = get_followed_list_ids(conn, user_email) + all_lists = _get_smart_lists_api_result(conn, current_user=user_email) + + not_following_lists = [ + list_item for list_item in all_lists if list_item.id not in followed_list_ids + ] + + return not_following_lists + + +@app.get("/smart-lists/my", response_model=List[SmartListAPIResult]) +async def get_my_smart_lists(user_email: str = "dev@example.com"): + """Get smart lists created by the user""" + with get_omni_connection() as conn: + return get_my_lists_api_result(conn, user_email) + + +@app.get("/smart-lists/community", response_model=List[SmartListAPIResult]) +async def get_community_smart_lists(user_email: str = "dev@example.com"): + """Get community smart lists (not created by the user)""" + with get_omni_connection() as conn: + return get_community_lists_api_result(conn, user_email) + + +@app.post("/smart-lists/{list_id}/follow") +async def follow_smart_list(list_id: int, user_email: str = "dev@example.com"): + """Follow a smart list""" + with get_omni_connection() as conn: + follow_list(conn, user_email, list_id) + return {"message": f"Successfully followed list {list_id}"} + + +@app.delete("/smart-lists/{list_id}/follow") +async def unfollow_smart_list(list_id: int, user_email: str = "dev@example.com"): + """Unfollow a smart list""" + with get_omni_connection() as conn: + unfollow_list(conn, user_email, list_id) + return {"message": f"Successfully unfollowed list {list_id}"} + + +@app.delete("/smart-lists/{list_id}") +async def delete_smart_list_endpoint(list_id: int, user_email: str = "dev@example.com"): + """Delete a smart list if owned by the user""" + with get_omni_connection() as conn: + success = delete_smart_list(conn, list_id, user_email) + + if not success: + raise HTTPException( + status_code=403, + detail="Cannot delete list: either it doesn't exist or you don't own it", + ) + + return {"message": f"Successfully deleted list {list_id}"} + + +@app.post("/smart-lists") +async def create_smart_list( + list_data: SmartListCreate, + user_email: str = "dev@example.com", + x_data_fetcher: XDataFetcher = Depends(get_x_data_fetcher), +): + """Create a new smart list and auto-follow it""" + with get_omni_connection() as conn: + list_id = create_smart_list_from_request(conn, list_data, user_email) + + x_data_fetcher.add_follow_users_job( + handles=list_data.get_all_authors(), + ) + + return { + "message": f"Successfully created and followed list {list_id}", + "list_id": list_id, + } + + +@app.get("/smart-lists/{list_id}/items", response_model=List[TweetItem]) +async def get_smart_list_items(list_id: int, reranking_threshold: float = None): + # Get smart list configuration + with get_omni_connection() as conn: + smart_list = get_smart_list_api_result_by_id(conn, list_id) + + if not smart_list: + raise HTTPException(status_code=404, detail="Smart list not found") + + # Check if this list has Twitter sources with real data + for list_source in smart_list.listSources: + if list_source.dataSourceId == "twitter": + # Override reranking threshold if provided in query params + # Cosine threshold is always taken from the database + if reranking_threshold is not None: + list_source.filters.reranking_threshold = reranking_threshold + + # Use real Twitter data + items = query_twitter_data(list_source) + return items + + return [] + + +@app.patch("/smart-lists/{list_id}/threshold") +async def update_list_threshold( + list_id: int, reranking_threshold: float, rag_query: Optional[str] = None +): + """Update the reranking threshold and optionally RAG query for all sources in a list""" + with get_omni_connection() as conn: + cursor = conn.cursor() + + # Build update query based on what's being updated + if rag_query is not None: + cursor.execute( + """ + UPDATE list_filters + SET reranking_threshold = ?, rag_query = ? + WHERE list_source_id IN ( + SELECT id FROM list_sources WHERE list_id = ? + ) + """, + (reranking_threshold, rag_query, list_id), + ) + else: + cursor.execute( + """ + UPDATE list_filters + SET reranking_threshold = ? + WHERE list_source_id IN ( + SELECT id FROM list_sources WHERE list_id = ? + ) + """, + (reranking_threshold, list_id), + ) + + conn.commit() + + return { + "message": "Settings updated successfully", + "reranking_threshold": reranking_threshold, + "rag_query": rag_query, + } + + +@app.get("/smart-lists/{list_id}/summary", response_model=SummaryResponse) +async def get_smart_list_summary(list_id: int): + # Get smart list configuration + with get_omni_connection() as conn: + smart_list = get_smart_list_api_result_by_id(conn, list_id) + + if not smart_list: + raise HTTPException(status_code=404, detail="Smart list not found") + + # Check if this list has Twitter sources for dynamic summary + for list_source in smart_list.listSources: + if list_source.dataSourceId == "twitter": + # Get cached or generate dynamic summary from tweets + result = get_or_generate_smart_list_summary(list_id, list_source) + return result + + # Fallback to mock summary + summary = get_mock_summary(list_id) + if not summary: + return { + "summary": "No summary available for this list.", + "status": "error", + "model": None, + } + return {"summary": summary, "status": "completed", "model": None} + + +@app.delete("/smart-lists/{list_id}/summary/cache") +async def clear_summary_cache(list_id: int): + """Clear cached summary for testing purposes""" + + smart_lists = get_mock_smart_lists() + smart_list = next((sl for sl in smart_lists if sl["id"] == list_id), None) + + if not smart_list: + raise HTTPException(status_code=404, detail="Smart list not found") + + for list_source in smart_list["listSources"]: + if list_source["dataSourceId"] == "twitter": + filters_hash = generate_filters_hash(list_source) + return clear_cache(list_id, filters_hash) + + return {"message": "No Twitter sources found for this list"} + + +@app.get("/chats/{list_id}", response_model=List[Chat]) +async def get_chats(list_id: int): + chats = get_mock_chats(list_id) + return chats + + +@app.post("/chats/{list_id}/ask") +async def ask_question(list_id: int, request: QuestionRequest): + # Mock response for asking questions + if settings.use_anthropic: + response = get_anthropic_completion(request.format_for_anthropic()) + return { + "id": 999, + "role": "assistant", + "answer": response, + "timestamp": "2024-01-15T10:00:00Z", + } + else: + response = { + "id": 999, + "role": "assistant", + "answer": f"This is a mock response to your question: '{request.question}' about list {list_id}", + "timestamp": "2024-01-15T10:00:00Z", + } + return response + + +@app.post("/twitter/check-account", response_model=TwitterAccountCheckResponse) +async def check_twitter_account(request: TwitterAccountCheckRequest): + """Check if a Twitter account exists by handle""" + try: + cookie_value, expires = get_guest_token() + exists = account_exists(request.handle, cookie_value) + except Exception: + return TwitterAccountCheckResponse( + handle=request.handle, exists=False, error_checking=True + ) + return TwitterAccountCheckResponse(handle=request.handle, exists=exists) + + +@app.post("/twitter/tweet-counts", response_model=TweetCountResponse) +async def get_tweet_counts(request: TweetCountRequest): + """Get tweet counts for a list of handles from the vectorstore""" + tweet_counts = get_tweet_counts_by_handles(request.handles) + return TweetCountResponse(tweet_counts=tweet_counts) + + +@app.post("/twitter/trigger-scrape") +async def trigger_timeline_scrape( + x_data_fetcher: XDataFetcher = Depends(get_x_data_fetcher), + duration: int = 60, +): + """Manually trigger the timeline scraper""" + x_data_fetcher.add_fetch_timeline_job(fetch_timeline_duration=duration) + return {"message": f"Timeline scrape triggered for {duration} seconds"} + + +@app.post("/twitter/follow-users") +async def follow_users( + request: FollowUserRequest, + x_data_fetcher: XDataFetcher = Depends(get_x_data_fetcher), +): + """Follow Twitter users and optionally fetch their timelines""" + x_data_fetcher.add_follow_users_job( + handles=request.handles, + scrolls_per_user=request.scrolls_per_user, + fetch_timeline_duration=request.fetch_timeline_duration, + ) + return { + "message": f"Follow job queued for {len(request.handles)} user(s)", + "handles": request.handles, + "scrolls_per_user": request.scrolls_per_user, + } + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True) diff --git a/packages/omni/omni/data_fetchers/__init__.py b/packages/omni/omni/data_fetchers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/packages/omni/omni/data_fetchers/browser_stealth.py b/packages/omni/omni/data_fetchers/browser_stealth.py new file mode 100644 index 0000000..4c93b32 --- /dev/null +++ b/packages/omni/omni/data_fetchers/browser_stealth.py @@ -0,0 +1,219 @@ +from playwright.async_api import BrowserContext + +BROWSER_ARGS = [ + "--no-sandbox", + "--disable-dev-shm-usage", + "--disable-blink-features=AutomationControlled", +] + +USER_AGENT = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36" + +STEALTH_JS = """ +// Store original functions to maintain [native code] appearance +const originalDefineProperty = Object.defineProperty; +const originalGetOwnPropertyDescriptor = Object.getOwnPropertyDescriptor; + +// Try to make our overrides look as native as possible +const createNativeLookingFunction = (func, name) => { + // Wrap function to return [native code] on toString() + const wrapper = new Proxy(func, { + apply(target, thisArg, args) { + return target.apply(thisArg, args); + }, + }); + + // Override toString to return [native code] + try { + originalDefineProperty(wrapper, "toString", { + value: function () { + return `function ${name}() { [native code] }`; + }, + configurable: false, + writable: false, + }); + } catch (e) {} + + return wrapper; +}; + +// 1. userAgentData - THE CRITICAL ONE +try { + // Try to make it look native by defining on prototype + const navProto = Object.getPrototypeOf(navigator); + const originalGetter = originalGetOwnPropertyDescriptor( + navProto, + "userAgentData" + )?.get; + + originalDefineProperty(navProto, "userAgentData", { + get: createNativeLookingFunction(function () { + return { + brands: [ + { brand: "Chromium", version: "136" }, + { brand: "Google Chrome", version: "136" }, + { brand: "Not.A/Brand", version: "99" }, + ], + mobile: false, + platform: "macOS", + getHighEntropyValues: createNativeLookingFunction( + () => + Promise.resolve({ + brands: [ + { brand: "Chromium", version: "136" }, + { brand: "Google Chrome", version: "136" }, + { brand: "Not.A/Brand", version: "99" }, + ], + mobile: false, + platform: "macOS", + platformVersion: "15.0.0", + architecture: "arm64", + bitness: "64", + model: "", + uaFullVersion: "136.0.6961.0", + }), + "getHighEntropyValues" + ), + }; + }, "get userAgentData"), + configurable: true, + enumerable: true, + }); +} catch (e) { + console.error("Failed to override userAgentData:", e); +} + +// 2. Remove webdriver - try to delete completely +try { + delete Object.getPrototypeOf(navigator).webdriver; + delete navigator.__proto__.webdriver; + delete navigator.webdriver; +} catch (e) {} + +// then redefine it +Object.defineProperty(navigator, 'webdriver', { + get: () => false +}); + +// 3. Plugins - use native-looking array +try { + const createPlugin = (name, desc, filename) => { + return { + 0: { type: "application/pdf", suffixes: "pdf", description: desc }, + 1: { type: "text/pdf", suffixes: "pdf", description: desc }, + description: desc, + filename: filename, + length: 2, + name: name, + item: createNativeLookingFunction(function (index) { + return this[index] || null; + }, "item"), + namedItem: createNativeLookingFunction(function (name) { + return null; + }, "namedItem"), + }; + }; + + const plugins = [ + createPlugin( + "PDF Viewer", + "Portable Document Format", + "internal-pdf-viewer" + ), + createPlugin( + "Chrome PDF Viewer", + "Portable Document Format", + "internal-pdf-viewer" + ), + createPlugin( + "Chromium PDF Viewer", + "Portable Document Format", + "internal-pdf-viewer" + ), + createPlugin( + "Microsoft Edge PDF Viewer", + "Portable Document Format", + "internal-pdf-viewer" + ), + createPlugin( + "WebKit built-in PDF", + "Portable Document Format", + "internal-pdf-viewer" + ), + ]; + + plugins.item = createNativeLookingFunction(function (index) { + return this[index] || null; + }, "item"); + + plugins.namedItem = createNativeLookingFunction(function (name) { + return Array.from(this).find((p) => p.name === name) || null; + }, "namedItem"); + + plugins.refresh = createNativeLookingFunction(function () {}, "refresh"); + + originalDefineProperty(Object.getPrototypeOf(navigator), "plugins", { + get: createNativeLookingFunction(() => plugins, "get plugins"), + configurable: true, + enumerable: true, + }); +} catch (e) { + console.error("Failed to override plugins:", e); +} + +// 4. MimeTypes +try { + const mimeTypes = [ + { + type: "application/pdf", + suffixes: "pdf", + description: "Portable Document Format", + }, + { + type: "text/pdf", + suffixes: "pdf", + description: "Portable Document Format", + }, + ]; + + mimeTypes.item = createNativeLookingFunction(function (index) { + return this[index] || null; + }, "item"); + + mimeTypes.namedItem = createNativeLookingFunction(function (name) { + return Array.from(this).find((m) => m.type === name) || null; + }, "namedItem"); + + originalDefineProperty(Object.getPrototypeOf(navigator), "mimeTypes", { + get: createNativeLookingFunction(() => mimeTypes, "get mimeTypes"), + configurable: true, + enumerable: true, + }); +} catch (e) { + console.error("Failed to override mimeTypes:", e); +} + +// 5. Chrome object +if (!window.chrome || !window.chrome.runtime) { + window.chrome = { + runtime: {}, + loadTimes: createNativeLookingFunction(function () {}, "loadTimes"), + csi: createNativeLookingFunction(function () {}, "csi"), + app: {}, + }; +} + +// 6. Permissions +const origQuery = navigator.permissions?.query; +if (origQuery) { + navigator.permissions.query = createNativeLookingFunction(function (params) { + if (params?.name === "notifications") { + return Promise.resolve({ state: Notification.permission }); + } + return origQuery.call(this, params); + }, "query"); +} +""" + + +async def apply_stealth_mode(context: BrowserContext) -> None: + await context.add_init_script(STEALTH_JS) diff --git a/packages/omni/omni/data_fetchers/job_queue.py b/packages/omni/omni/data_fetchers/job_queue.py new file mode 100644 index 0000000..97f7a85 --- /dev/null +++ b/packages/omni/omni/data_fetchers/job_queue.py @@ -0,0 +1,207 @@ +import logging +import queue +import random +import time +from dataclasses import dataclass +from datetime import datetime, timedelta +from threading import Thread +from typing import Callable + +logger = logging.getLogger(__name__) + + +@dataclass +class ScraperJob: + func: Callable + args: tuple + kwargs: dict + submitted_at: datetime + + +@dataclass +class ScraperJobSchedule: + next_run: datetime + interval: float | tuple[float, float] # seconds or (min_seconds, max_seconds) + func: Callable + args: tuple + kwargs: dict + + +class DataFetcherJobQueue: + def __init__( + self, + max_job_age: int = 300, + max_queue_size: int | None = None, + worker_delay: float | tuple[float, float] = 0, + ) -> None: + """Initialize the DataFetcherJobQueue. + + Args: + max_job_age (int, optional): + The maximum age of a job in seconds before it is considered stale. + Stale jobs will not be executed. Defaults to 300. + max_queue_size (int | None, optional): + The maximum size of the job queue. Defaults to None (unlimited). + worker_delay (float | tuple[float, float], optional): The delay for worker threads. Defaults to 0. + """ + self.max_job_age = max_job_age + self.worker_delay = worker_delay + self.queue = queue.Queue(maxsize=max_queue_size or 0) + self.job_schedules: list[ScraperJobSchedule] = [] + + self.running: bool = True + self.worker_thread = Thread(target=self._worker, daemon=True) + self.scheduler_thread = Thread(target=self._scheduler, daemon=True) + + def start(self) -> None: + logger.info("Starting DataFetcherJobQueue threads") + self.worker_thread.start() + self.scheduler_thread.start() + + def stop(self) -> None: + logger.info("Stopping DataFetcherJobQueue") + self.running = False + + def _job_is_stale(self, job: ScraperJob) -> bool: + return (datetime.now() - job.submitted_at).total_seconds() > self.max_job_age + + def _apply_worker_delay(self) -> None: + if isinstance(self.worker_delay, (tuple, list)): + delay = random.uniform(self.worker_delay[0], self.worker_delay[1]) + elif self.worker_delay: + delay = self.worker_delay + else: + return + time.sleep(delay) + + def _worker(self) -> None: + while self.running: + try: + job = self.queue.get(timeout=1) + if self._job_is_stale(job): + logger.warning( + f"Skipping stale job submitted at {job.submitted_at}" + ) + self.queue.task_done() + continue + logger.debug(f"Executing job: {job.func.__name__}") + job.func(*job.args, **job.kwargs) + self.queue.task_done() + self._apply_worker_delay() + except queue.Empty: + continue + except Exception as e: + logger.error(f"Error executing job: {e}", exc_info=True) + self.queue.task_done() + + def _schedule_job(self, job_schedule: ScraperJobSchedule) -> None: + now = datetime.now() + if now >= job_schedule.next_run: + # Queue the job + self.add_job( + job_schedule.func, + job_schedule.args, + job_schedule.kwargs, + allow_duplicates=False, + ) + + # Schedule next run with potential randomization + if isinstance(job_schedule.interval, tuple): + interval_seconds = random.uniform( + job_schedule.interval[0], job_schedule.interval[1] + ) + else: + interval_seconds = job_schedule.interval + + job_schedule.next_run = now + timedelta(seconds=interval_seconds) + + def _scheduler(self) -> None: + while self.running: + for job_schedule in self.job_schedules: + self._schedule_job(job_schedule) + time.sleep(1) + + def add_job( + self, + func: Callable, + args: tuple = (), + kwargs: dict = None, + allow_duplicates: bool = True, + ) -> ScraperJob | None: + """Add immediate job to queue. Returns Job if added, None if deduped. + + Args: + func (Callable): The job function to execute. + args (tuple, optional): Positional arguments for the job function. Defaults to (). + kwargs (dict, optional): Keyword arguments for the job function. Defaults to None. + allow_duplicates (bool, optional): Whether to allow duplicate jobs. + A job is considered a duplicate if another job with the same function is already in the queue. + Args or kwargs are not considered for deduplication. + Defaults to True. + + Returns: + ScraperJob | None: The created job or None if it was deduplicated. + """ + if kwargs is None: + kwargs = {} + + if not allow_duplicates: + with self.queue.mutex: + for job in list(self.queue.queue): + if job.func == func: + logger.debug(f"Skipping duplicate job: {func.__name__}") + return None + + job = ScraperJob( + func=func, args=args, kwargs=kwargs, submitted_at=datetime.now() + ) + self.queue.put(job) + + return job + + def add_schedule( + self, + func: Callable, + interval: float | tuple[float, float], + args: tuple = (), + kwargs: dict = None, + next_run: datetime = None, + ) -> None: + """Add a recurring job schedule. + + Args: + func (Callable): The job function to execute. + interval (float | tuple[float, float]): Time interval in seconds (fixed) or (min, max) for randomized interval. + args (tuple, optional): Positional arguments for the job function. Defaults to (). + kwargs (dict, optional): Keyword arguments for the job function. Defaults to None. + next_run (datetime, optional): The next run time for the schedule. Defaults to None. + """ + if kwargs is None: + kwargs = {} + + # Calculate initial interval + if isinstance(interval, (tuple, list)): + if len(interval) != 2 or interval[0] >= interval[1]: + raise ValueError( + "Interval tuple must be (min_seconds, max_seconds) with min < max" + ) + interval = tuple(interval) + initial_interval = random.uniform(interval[0], interval[1]) + elif isinstance(interval, (int, float)): + if interval <= 0: + raise ValueError("Interval must be a positive number") + initial_interval = interval + else: + raise ValueError("Interval must be a float or a tuple of two floats") + + next_run_time = next_run or ( + datetime.now() + timedelta(seconds=initial_interval) + ) + job_schedule = ScraperJobSchedule( + next_run=next_run_time, + interval=interval, + func=func, + args=args, + kwargs=kwargs, + ) + self.job_schedules.append(job_schedule) diff --git a/packages/omni/omni/data_fetchers/x_fetcher.py b/packages/omni/omni/data_fetchers/x_fetcher.py new file mode 100644 index 0000000..b72af11 --- /dev/null +++ b/packages/omni/omni/data_fetchers/x_fetcher.py @@ -0,0 +1,543 @@ +import asyncio +import json +import random +import time +from datetime import datetime +from functools import partial +from http.cookiejar import Cookie +from pathlib import Path +from typing import Any, Callable + +import browser_cookie3 +from playwright.async_api import Browser, BrowserContext, Page, async_playwright + +from omni.data_fetchers.browser_stealth import ( + BROWSER_ARGS, + USER_AGENT, + apply_stealth_mode, +) +from omni.data_fetchers.job_queue import DataFetcherJobQueue +from omni.data_fetchers.x_utils import parse_tweets_json, parse_user_tweets_json +from omni.db import get_tweet_store +from omni.settings import settings +from omni.vectorstore_models import Tweet + +BASE_DIR = Path.home() / ".omni" +DATA_FETCHER_DIR = BASE_DIR / "scraper_data" +COOKIES_FILE = DATA_FETCHER_DIR / "x_cookies.json" +DATA_DIR = DATA_FETCHER_DIR / "data" + +settings.use_cached_x_cookies + + +def cookie_to_playwright_cookie(cookie: Cookie) -> dict: + cookie_dict = { + "name": cookie.name, + "value": cookie.value, + "domain": cookie.domain.lstrip("."), # Playwright expects no leading dot + "path": cookie.path, + "secure": bool(cookie.secure), + "httpOnly": getattr(cookie, "httponly", False) + or cookie.get_nonstandard_attr("HttpOnly", False), + } + # Optional: expires + if cookie.expires is not None: + cookie_dict["expires"] = cookie.expires + return cookie_dict + + +def get_cookies_from_brave() -> list[Cookie]: + """Extract auth cookies from browser""" + x_cookies = [] + for c in browser_cookie3.brave(domain_name="x.com"): + if c.domain == ".x.com" and c.name in ["auth_token"]: + x_cookies.append(c) + print("Found x auth_token") + + print(x_cookies) + return x_cookies + + +def save_cookies_to_file(cookies: list[dict]) -> None: + """Save cookies to a JSON file""" + DATA_FETCHER_DIR.mkdir(parents=True, exist_ok=True) + with open(COOKIES_FILE, "w") as f: + json.dump(cookies, f) + print(f"Saved {len(cookies)} cookies to {COOKIES_FILE}") + + +def load_cookies_from_file() -> list[dict] | None: + """Load cookies from a JSON file""" + if COOKIES_FILE.is_file(): + with open(COOKIES_FILE, "r") as f: + cookies = json.load(f) + print(f"Loaded {len(cookies)} cookies from {COOKIES_FILE}") + return cookies + return + + +async def simulate_user_activity(page: Page) -> None: + """Simulate user activity to bypass hasBeenActive detection""" + try: + await page.mouse.move(random.randint(100, 500), random.randint(100, 500)) + + # Random keypress + safe_keys = [ + "Tab", + "Shift", + "Escape", + "ArrowDown", + "ArrowUp", + "ArrowLeft", + "ArrowRight", + "Home", + ] + await page.keyboard.press(random.choice(safe_keys)) + + # Small scroll + await page.mouse.wheel(0, random.randint(5, 25)) + + except Exception as e: + print(f"simulate_user_activity error: {e}") + pass + + +def get_cookies_for_playwright( + use_cached_x_cookies: bool = settings.use_cached_x_cookies, +) -> list[dict]: + """Get cookies in Playwright format""" + cookies = load_cookies_from_file() + if cookies is not None and use_cached_x_cookies: + return cookies + + x_cookies = get_cookies_from_brave() + cookies = [cookie_to_playwright_cookie(c) for c in x_cookies] + + save_cookies_to_file(cookies) + return cookies + + +async def setup_browser( + x_cookies: list[dict], + headless: bool = False, +) -> tuple[Browser, BrowserContext, Page]: + """Setup browser with authentication cookies""" + + p = await async_playwright().start() + browser = await p.chromium.launch( + headless=headless, + args=BROWSER_ARGS, + ) + + # Create context + context = await browser.new_context( + viewport={"width": 1920, "height": 1080}, + user_agent=USER_AGENT, + ) + + # Apply stealth measures + await apply_stealth_mode(context) + + # Set cookies before navigating + await context.add_cookies(x_cookies) + + # Print all cookies currently in the context + cookies_in_context = await context.cookies() + print("All cookies in context after setting:") + for cookie in cookies_in_context: + print(cookie) + + page = await context.new_page() + await simulate_user_activity(page) + + return browser, context, page + + +def save_home_timeline(json_data: Any) -> None: + """Default save function for timeline data""" + + dt_str = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"home_latest_timeline_{dt_str}.json" + output_path = DATA_DIR / filename + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: + json.dump(json_data, f) + print(f"Saved JSON to {filename}") + + +def is_home_timeline_url(url: str) -> bool: + """Check if URL is a home timeline API endpoint""" + if "graphql" in url and ("HomeLatestTimeline" in url or "HomeTimeline" in url): + return True + return False + + +async def scroll_vertical_mousewheel( + page: Page, + duration: int, + distance_range: tuple[int, int] = (200, 1000), + delay_range: tuple[float, float] = (1, 5), +): + start_time = time.time() + + while time.time() - start_time < duration: + scroll_distance = random.randint(*distance_range) + await page.mouse.wheel(0, scroll_distance) + await asyncio.sleep(random.uniform(*delay_range)) + + +async def handle_request(request, save_fn): + """Intercept and save timeline API responses""" + if is_home_timeline_url(request.url): + response = await request.response() + if response is not None: + try: + json_data = await response.json() + timeline_tweets = parse_tweets_json(json_data) + save_fn(timeline_tweets) + except Exception as e: + print(f"Failed to parse and store JSON from response: {e}") + print("API call to HomeLatestTimeline") + + elif is_usertweet_url(request.url): + response = await request.response() + if response is not None: + try: + json_data = await response.json() + user_tweets = parse_user_tweets_json(json_data) + print(f"Got {len(user_tweets)} tweets") + save_fn(user_tweets) + print(f"Stored {len(user_tweets)} tweets from user timeline") + except Exception as e: + print(f"Failed to parse and store JSON from response: {e}") + print("API call to UserTweets") + + +async def fetch_timeline( + page: Page, + duration: int = 60, +): + """Fetch timeline data from X/Twitter""" + + # Navigate to X.com + await page.goto("https://x.com") + await simulate_user_activity(page) + await asyncio.sleep(3) + + # Click "Following" to switch to chronological timeline + following_span = await page.query_selector("span:has-text('Following')") + if following_span: + await click_element_human_like(page, following_span) + print("Clicked the 'Following' button.") + else: + print("Could not find the 'Following' button.") + + # Keep scraping for specified duration + await scroll_vertical_mousewheel(page, duration) + + +async def click_element_human_like(page: Page, element): + """Click a button with human-like mouse movement""" + box = await element.bounding_box() + if box: + x = box["x"] + box["width"] / 2 + random.uniform(-5, 5) + y = box["y"] + box["height"] / 2 + random.uniform(-5, 5) + + # Move and click + await page.mouse.move(x, y) + await asyncio.sleep(random.uniform(0.1, 0.3)) + await page.mouse.click(x, y) + return True + return False + + +async def get_follow_button(page: Page, handle: str): + """Get the follow button element for a specific user by their handle""" + username = handle.lstrip("@").lower() + + # Use evaluate to find button with case-insensitive matching + follow_button = await page.evaluate_handle( + """ + (username) => { + const buttons = document.querySelectorAll('[data-testid*="-follow"]:not([data-testid*="-unfollow"])'); + for (const button of buttons) { + const ariaLabel = button.getAttribute('aria-label'); + if (ariaLabel && ariaLabel.toLowerCase().includes('@' + username)) { + return button; + } + } + return null; + } + """, + username, + ) + + # Convert handle to element or return None + element = follow_button.as_element() if follow_button else None + return element + + +async def get_unfollow_button(page: Page, handle: str): + """Get the unfollow button element for a specific user by their handle""" + username = handle.lstrip("@").lower() + + # Use evaluate to find button with case-insensitive matching + unfollow_button = await page.evaluate_handle( + """ + (username) => { + const buttons = document.querySelectorAll('[data-testid*="-unfollow"]'); + for (const button of buttons) { + const ariaLabel = button.getAttribute('aria-label'); + if (ariaLabel && ariaLabel.toLowerCase().includes('@' + username)) { + return button; + } + } + return null; + } + """, + username, + ) + + # Convert handle to element or return None + element = unfollow_button.as_element() if unfollow_button else None + return element + + +def is_usertweet_url(url: str) -> bool: + """Check if URL is a home timeline API endpoint""" + if "graphql" in url and ("UserTweets" in url): + return True + return False + + +async def follow_user( + page: Page, + handle: str, + scrolls: int = 0, +): + """Follow a specific user by their handle""" + + # Navigate to user's profile + profile_url = f"https://x.com/{handle.lstrip('@')}" + await page.goto(profile_url) + await simulate_user_activity(page) + await asyncio.sleep(3) + + # Find follow button for this specific user using partial aria-label (for localization) + follow_button = await get_follow_button(page, handle) + + if follow_button: + clicked = await click_element_human_like(page, follow_button) + if clicked: + # Wait and verify + await asyncio.sleep(random.uniform(1.5, 3)) + following_button = await get_unfollow_button(page, handle) + if following_button: + print(f"Successfully followed {handle}") + else: + print(f"Follow may have failed for {handle}") + elif await get_unfollow_button(page, handle): + print(f"Already following {handle}") + else: + print(f"Could not find follow button for {handle}, user may not exist.") + print("Scrolls: ", scrolls) + for _ in range(scrolls): + print("Scrolling...") + await scroll_vertical_mousewheel(page, 10) + + +async def follow_users( + page: Page, + handles: list[str], + delay_range: tuple[float, float] = (2, 5), + scrolls_per_user: int = 0, +): + """Follow multiple users with random delays between each""" + + for i, handle in enumerate(handles): + print(f"\n[{i + 1}/{len(handles)}] Processing {handle}") + await follow_user(page, handle, scrolls_per_user) + + # Add random delay between follows (except after the last one) + if i < len(handles) - 1: + delay = random.uniform(delay_range[0], delay_range[1]) + print(f"Waiting {delay:.1f} seconds before next follow...") + await asyncio.sleep(delay) + + print(f"\nCompleted processing {len(handles)} handles") + + +async def run_x_fetcher( + fetch_timeline_duration: int = 0, + handles_to_follow: list[str] = None, + headless: bool = False, + timeline_save_fn: Callable[[Any], None] = save_home_timeline, + scrolls_per_user: int = 0, +): + """Run the X data fetcher. + + Args: + fetch_timeline_duration (int, optional): Duration to fetch the timeline. Defaults to None. + handles_to_follow (list[str], optional): List of user handles to follow. Defaults to None. + headless (bool, optional): Run browser in headless mode. Defaults to False. + """ + + if not fetch_timeline_duration and not handles_to_follow: + print("Nothing to do, exiting.") + return + + # Get auth cookies + cookies = get_cookies_for_playwright() + if not cookies: + print("No auth cookies found. Please login to X.com in your browser first.") + return + + # Setup browser with cookies + browser, context, page = await setup_browser(cookies, headless=headless) + + page.on("request", partial(handle_request, save_fn=timeline_save_fn)) + + try: + if handles_to_follow: + await follow_users( + page, handles_to_follow, scrolls_per_user=scrolls_per_user + ) + if fetch_timeline_duration: + if handles_to_follow: + print("Waiting before starting timeline fetching...") + await asyncio.sleep(random.uniform(2, 5)) + await fetch_timeline( + page, + fetch_timeline_duration, + ) + + finally: + await browser.close() + + +def save_tweets_to_file(json_data: Any) -> None: + """Default save function for timeline data""" + + dt_str = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"home_latest_timeline_{dt_str}.json" + output_path = DATA_DIR / filename + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: + json.dump(json_data, f) + print(f"Saved JSON to {filename}") + + +def save_tweets_to_store(tweets: list[Tweet]) -> None: + if tweets: + # NOTE tweet store is initialized here because sqlite3 is not + # thread-safe. + store = get_tweet_store() + store.insert_docs( + tweets, + create_embeddings=True, + overwrite=True, + ) + store.close() + print(f"Saved {len(tweets)} tweets to store.") + else: + print("No tweets found in JSON data") + + +class XDataFetcher(DataFetcherJobQueue): + def __init__( + self, + max_job_age=300, + max_queue_size=None, + worker_delay=0, + headless: bool = True, + timeline_save_fn: Callable[[Any], None] = save_tweets_to_store, + ): + super().__init__(max_job_age, max_queue_size, worker_delay) + self.headless = headless + self.timeline_save_fn = timeline_save_fn + + def start( + self, + download_schedule: int | tuple[int, int] | None = None, + ) -> None: + """Start the data fetcher, with optional regular timeline downloading. + + Args: + download_schedule (int | tuple[int, int] | None, optional): Interval in seconds or + (min, max) tuple for random interval. If None, no scheduled downloading. Defaults to None. + + """ + if download_schedule: + self.add_schedule( + func=self._fetch_timeline_job, + interval=download_schedule, + kwargs={"fetch_timeline_duration": 60}, + ) + + return super().start() + + def add_follow_users_job( + self, + handles: list[str], + fetch_timeline_duration: int = 0, + scrolls_per_user: int = 0, + ): + self.add_job( + func=self._follow_users_job, + kwargs={ + "handles": handles, + "fetch_timeline_duration": fetch_timeline_duration, + "scrolls_per_user": scrolls_per_user, + }, + ) + + def add_fetch_timeline_job(self, fetch_timeline_duration: int = 30): + # allow_duplicates=False to avoid multiple timeline fetches in queue + self.add_job( + func=self._fetch_timeline_job, + kwargs={"fetch_timeline_duration": fetch_timeline_duration}, + allow_duplicates=False, + ) + + def _fetch_timeline_job(self, fetch_timeline_duration: int = 30): + asyncio.run( + run_x_fetcher( + fetch_timeline_duration=fetch_timeline_duration, + handles_to_follow=None, + headless=self.headless, + timeline_save_fn=self.timeline_save_fn, + ) + ) + + def _follow_users_job( + self, + handles: list[str], + fetch_timeline_duration: int = 0, + scrolls_per_user: int = 0, + ): + asyncio.run( + run_x_fetcher( + fetch_timeline_duration=fetch_timeline_duration, + handles_to_follow=handles, + headless=self.headless, + timeline_save_fn=self.timeline_save_fn, + scrolls_per_user=scrolls_per_user, + ) + ) + + +if __name__ == "__main__": + scraper = XDataFetcher( + headless=False, + ) + scraper.start() + + # Example: follow specific users once + handles_to_follow = [ + "openai", + ] + # scraper.add_scrape_timeline_job(scrape_timeline_duration=30) + scraper.add_follow_users_job(handles=handles_to_follow, fetch_timeline_duration=10) + + time.sleep(300) + scraper.stop() diff --git a/packages/omni/omni/data_fetchers/x_utils.py b/packages/omni/omni/data_fetchers/x_utils.py new file mode 100644 index 0000000..58fbb04 --- /dev/null +++ b/packages/omni/omni/data_fetchers/x_utils.py @@ -0,0 +1,66 @@ +from typing import Any + +from omni.vectorstore_models import Tweet + + +def parse_user_tweets_json(data: Any) -> list[Tweet]: + """Parse tweets from JSON data structure.""" + instructions = ( + data.get("data", {}) + .get("user", {}) + .get("result", {}) + .get("timeline", {}) + .get("timeline", {}) + .get("instructions", []) + ) + return parse_tweets_from_instructions(instructions) + + +def parse_tweets_json(data: Any) -> list[Tweet]: + """Parse tweets from JSON data structure.""" + + # Navigate through the JSON structure and find all tweet_results + # data user result timeline timeline instructions + instructions = ( + data.get("data", {}) + .get("home", {}) + .get("home_timeline_urt", {}) + .get("instructions", []) + ) + return parse_tweets_from_instructions(instructions) + + +def parse_tweets_from_instructions(instructions: list[Any]) -> list[Tweet]: + all_tweets = [] + seen_tweet_ids = set() + for instruction in instructions: + if instruction.get("type") != "TimelineAddEntries": + continue + + entries = instruction.get("entries", []) + + for entry in entries: + content = entry.get("content", {}) + # Handle conversation entries (TimelineTimelineModule) + if content.get("entryType") == "TimelineTimelineModule": + items = content.get("items", []) + for item in items: + item_content = item.get("item", {}).get("itemContent", {}) + if item_content.get("itemType") == "TimelineTweet": + tweet_results = item_content.get("tweet_results", {}) + tweet = Tweet.from_instruction_entry(tweet_results) + if tweet and tweet.tweet_id not in seen_tweet_ids: + seen_tweet_ids.add(tweet.tweet_id) + all_tweets.append(tweet) + + # Handle direct tweet entries (TimelineTimelineItem) + elif content.get("entryType") == "TimelineTimelineItem": + item_content = content.get("itemContent", {}) + if item_content.get("itemType") == "TimelineTweet": + tweet_results = item_content.get("tweet_results", {}) + tweet = Tweet.from_instruction_entry(tweet_results) + if tweet and tweet.tweet_id not in seen_tweet_ids: + seen_tweet_ids.add(tweet.tweet_id) + all_tweets.append(tweet) + + return all_tweets diff --git a/packages/omni/omni/db.py b/packages/omni/omni/db.py new file mode 100644 index 0000000..40dd6a5 --- /dev/null +++ b/packages/omni/omni/db.py @@ -0,0 +1,680 @@ +import sqlite3 +from contextlib import contextmanager +from datetime import datetime +from pathlib import Path + +import numpy as np +import sqlite_vec +from toolbox_store import ToolboxStore +from toolbox_store.models import StoreConfig + +from .models import SmartListAPIResult, SmartListCreate, SmartListDB +from .vectorstore_models import Tweet + +HOME = Path.home() +OMNI_DB_PATH = HOME / ".twitter-mcp" / "db.sqlite" +TWITTER_VECTORSTORE_DB_PATH = HOME / ".twitter-mcp" / "tweet_toolbox_store.db" +OMNI_DB_PATH.parent.mkdir(parents=True, exist_ok=True) + +EMBEDDINGS_TABLE = "tweet_embeddings_vec" +EMBEDDINGS_LEN = 768 + + +def deserialize_float32(blob: bytes) -> list[float]: + return np.frombuffer(blob, dtype=np.float32).tolist() + + +def get_tweet_store() -> ToolboxStore[Tweet]: + """Get ToolboxStore instance for tweets""" + config = StoreConfig() + store_path = TWITTER_VECTORSTORE_DB_PATH + return ToolboxStore[Tweet]( + collection="tweets", + db_path=str(store_path), + config=config, + document_class=Tweet, + ) + + +def _get_omni_connection(path: Path = OMNI_DB_PATH): + conn = sqlite3.connect(path) + conn.row_factory = sqlite3.Row + + conn.enable_load_extension(True) + sqlite_vec.load(conn) + conn.enable_load_extension(False) + create_tables(conn) + return conn + + +@contextmanager +def get_omni_connection(path: Path = OMNI_DB_PATH): + conn = _get_omni_connection(path) + try: + yield conn + finally: + conn.close() + + +def create_tables(conn): + cursor = conn.cursor() + + # Smart lists main table + cursor.execute(""" + CREATE TABLE IF NOT EXISTS smart_lists ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT NOT NULL, + item_count INTEGER DEFAULT 0, + created_at TEXT NOT NULL, + user_email TEXT NOT NULL + ) + """) + + # List sources - each smart list can have multiple data sources + cursor.execute(""" + CREATE TABLE IF NOT EXISTS list_sources ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + list_id INTEGER NOT NULL, + data_source_id TEXT NOT NULL, + FOREIGN KEY (list_id) REFERENCES smart_lists (id) ON DELETE CASCADE + ) + """) + + # List filters - filters for each list source + cursor.execute(""" + CREATE TABLE IF NOT EXISTS list_filters ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + list_source_id INTEGER NOT NULL, + date_range_from TEXT, + date_range_to TEXT, + rag_query TEXT, + cosine_threshold REAL DEFAULT 0.4, + reranking_threshold REAL DEFAULT 0.82, + FOREIGN KEY (list_source_id) REFERENCES list_sources (id) ON DELETE CASCADE + ) + """) + + # Authors for each list source + cursor.execute(""" + CREATE TABLE IF NOT EXISTS datasource_authors ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + list_source_id INTEGER NOT NULL, + author TEXT NOT NULL, + FOREIGN KEY (list_source_id) REFERENCES list_sources (id) ON DELETE CASCADE + ) + """) + + cursor.execute(""" + CREATE TABLE IF NOT EXISTS smart_list_summaries ( + list_id INTEGER NOT NULL, + filters_hash TEXT NOT NULL, + summary TEXT NOT NULL, + status TEXT DEFAULT 'completed', + model TEXT, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL, + PRIMARY KEY (list_id, filters_hash), + FOREIGN KEY (list_id) REFERENCES smart_lists (id) ON DELETE CASCADE + ) + """) + + cursor.execute(""" + CREATE TABLE IF NOT EXISTS followed_lists ( + user_email TEXT NOT NULL, + list_id INTEGER NOT NULL, + PRIMARY KEY (user_email, list_id), + FOREIGN KEY (list_id) REFERENCES smart_lists (id) ON DELETE CASCADE + ) + """) + + cursor.execute(""" + CREATE TABLE IF NOT EXISTS users ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + email TEXT UNIQUE NOT NULL, + created_at TEXT NOT NULL + ) + """) + + # Run database migrations + from .migrations.v1 import run_v1_migrations + + run_v1_migrations(conn) + + +# def upsert_author(conn, author_data): +# cursor = conn.cursor() +# cursor.execute( +# """ +# INSERT OR REPLACE INTO authors (id, username, screen_name, name, description, avatar_url, banner_url, followers_count, following_count, statuses_count, verified, blue_verified, location, created_at) +# VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) +# """, +# author_data, +# ) +# conn.commit() + + +# def upsert_tweet(conn, tweet_data): +# cursor = conn.cursor() +# cursor.execute( +# """ +# INSERT OR REPLACE INTO tweets (id, author_id, text, created_at, conversation_id, in_reply_to_user_id, in_reply_to_status_id, lang, retweet_count, favorite_count, reply_count, quote_count, bookmark_count, view_count, is_quote_status, possibly_sensitive, retweeted, favorited, bookmarked, note_tweet_text, media_urls, urls, hashtags, user_mentions, source) +# VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) +# """, +# tweet_data, +# ) +# conn.commit() + + +# def get_tweets_without_embeddings(conn, limit=10): +# """Get tweets that don't have embeddings yet""" +# cursor = conn.cursor() +# cursor.execute( +# f""" +# SELECT tweets.* +# FROM tweets +# LEFT JOIN {EMBEDDINGS_TABLE} ON tweets.id = {EMBEDDINGS_TABLE}.tweet_id +# WHERE {EMBEDDINGS_TABLE}.tweet_id IS NULL +# LIMIT ? +# """, +# (limit,), +# ) +# return cursor.fetchall() + + +# def upsert_tweet_embeddings(conn, embeddings_data): +# """Insert or update tweet embeddings""" +# cursor = conn.cursor() +# cursor.executemany( +# f""" +# INSERT OR REPLACE INTO {EMBEDDINGS_TABLE} (tweet_id, sample_embedding, tweet_text) +# VALUES (?, ?, ?) +# """, +# [ +# ( +# data["tweet_id"], +# serialize_float32(data["embedding"]), +# data["tweet_text"], +# ) +# for data in embeddings_data +# ], +# ) +# conn.commit() + + +# def search_tweets_by_vector( +# conn, query_embedding: list[float], similarity_threshold=0.4, limit=50 +# ): +# """Search tweets by vector similarity with a similarity threshold""" +# cursor = conn.cursor() +# # Convert similarity threshold to cosine distance threshold +# # similarity_threshold of 0.8 means we want cosine distance <= 0.2 (1.0 - 0.8) +# cosine_distance_threshold = 1.0 - similarity_threshold + +# cursor.execute( +# f""" +# SELECT tweets.*, embeddings.distance, authors.screen_name, authors.name as author_name +# FROM ( +# SELECT tweet_id, tweet_text, distance +# FROM {EMBEDDINGS_TABLE} +# WHERE sample_embedding MATCH ? +# ORDER BY distance +# LIMIT ? +# ) as embeddings +# JOIN tweets ON embeddings.tweet_id = tweets.id +# JOIN authors ON tweets.author_id = authors.id +# WHERE embeddings.distance <= ? +# ORDER BY embeddings.distance +# """, +# (serialize_float32(query_embedding), limit, cosine_distance_threshold), +# ) +# return cursor.fetchall() + + +# def get_tweets_by_authors_and_timeframe( +# conn, +# author_screen_names: list[str], +# start_date: datetime, +# end_date: datetime = None, +# query_embedding: list[float] = None, +# similarity_threshold: float = 0.4, +# limit: int = 50, +# ): +# """Get tweets from specific authors within a timeframe, optionally filtered by vector similarity""" +# if end_date is None: +# end_date = datetime.now() + +# cursor = conn.cursor() + +# if query_embedding: +# # Use vector search with author and time filtering +# # Convert similarity threshold to cosine distance threshold +# cosine_distance_threshold = 1.0 - similarity_threshold +# placeholders = ",".join(["?"] * len(author_screen_names)) +# cursor.execute( +# f""" +# SELECT tweets.*, embeddings.distance, authors.screen_name, authors.name as author_name, authors.avatar_url +# FROM ( +# SELECT tweet_id, tweet_text, distance +# FROM {EMBEDDINGS_TABLE} +# WHERE sample_embedding MATCH ? +# ORDER BY distance +# LIMIT ? +# ) as embeddings +# JOIN tweets ON embeddings.tweet_id = tweets.id +# JOIN authors ON tweets.author_id = authors.id +# WHERE authors.screen_name IN ({placeholders}) +# AND tweets.created_at >= ? +# AND tweets.created_at <= ? +# AND embeddings.distance <= ? +# ORDER BY embeddings.distance +# """, +# ( +# serialize_float32(query_embedding), +# limit * 2, # Get more results initially to filter properly +# *author_screen_names, +# start_date.isoformat(), +# end_date.isoformat(), +# cosine_distance_threshold, +# ), +# ) +# else: +# # Regular search without vector similarity +# placeholders = ",".join(["?"] * len(author_screen_names)) +# cursor.execute( +# f""" +# SELECT tweets.*, authors.screen_name, authors.name as author_name, authors.avatar_url +# FROM tweets +# JOIN authors ON tweets.author_id = authors.id +# WHERE authors.screen_name IN ({placeholders}) +# AND tweets.created_at >= ? +# AND tweets.created_at <= ? +# ORDER BY tweets.created_at DESC +# LIMIT ? +# """, +# (*author_screen_names, start_date.isoformat(), end_date.isoformat(), limit), +# ) + +# return cursor.fetchall() + + +# def get_tweet_count(conn): +# cursor = conn.cursor() +# cursor.execute("SELECT COUNT(*) FROM tweets") +# return cursor.fetchone()[0] + + +# def get_author_count(conn): +# cursor = conn.cursor() +# cursor.execute("SELECT COUNT(*) FROM authors") +# return cursor.fetchone()[0] + + +# def get_cached_summary(conn, list_id, filters_hash): +# """Get cached summary if available""" +# cursor = conn.cursor() +# cursor.execute( +# "SELECT summary, status, model FROM smart_list_summaries WHERE list_id = ? AND filters_hash = ?", +# (list_id, filters_hash), +# ) +# result = cursor.fetchone() +# return result if result else None + + +def upsert_summary( + conn, list_id, filters_hash, summary, status="completed", model=None +): + """Insert or update cached summary""" + cursor = conn.cursor() + now = datetime.now().isoformat() + cursor.execute( + """ + INSERT OR REPLACE INTO smart_list_summaries + (list_id, filters_hash, summary, status, model, created_at, updated_at) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + (list_id, filters_hash, summary, status, model, now, now), + ) + conn.commit() + + +def follow_list(conn, user_email: str, list_id: int): + """Follow a smart list for a user""" + cursor = conn.cursor() + cursor.execute( + """ + INSERT OR IGNORE INTO followed_lists (user_email, list_id) + VALUES (?, ?) + """, + (user_email, list_id), + ) + conn.commit() + + +def unfollow_list(conn, user_email: str, list_id: int): + """Unfollow a smart list for a user""" + cursor = conn.cursor() + cursor.execute( + """ + DELETE FROM followed_lists + WHERE user_email = ? AND list_id = ? + """, + (user_email, list_id), + ) + conn.commit() + + +def get_followed_list_ids(conn, user_email: str): + """Get list of followed list IDs for a user""" + cursor = conn.cursor() + cursor.execute( + "SELECT list_id FROM followed_lists WHERE user_email = ?", + (user_email,), + ) + return [row[0] for row in cursor.fetchall()] + + +def is_list_followed(conn, user_email: str, list_id: int): + """Check if a user is following a specific list""" + cursor = conn.cursor() + cursor.execute( + "SELECT 1 FROM followed_lists WHERE user_email = ? AND list_id = ?", + (user_email, list_id), + ) + return cursor.fetchone() is not None + + +def create_user(conn, email: str): + """Create a new user or return existing user""" + cursor = conn.cursor() + now = datetime.now().isoformat() + + cursor.execute( + """ + INSERT OR IGNORE INTO users (email, created_at) + VALUES (?, ?) + """, + (email, now), + ) + conn.commit() + + # Return the user + cursor.execute("SELECT id, email, created_at FROM users WHERE email = ?", (email,)) + return cursor.fetchone() + + +def get_user_by_email(conn, email: str): + """Get user by email""" + cursor = conn.cursor() + cursor.execute("SELECT id, email, created_at FROM users WHERE email = ?", (email,)) + return cursor.fetchone() + + +def initialize_dev_user(): + """Initialize the dev@example.com user for development""" + with get_omni_connection() as conn: + return create_user(conn, "dev@example.com") + + +def insert_smart_list(conn, name: str, user_email: str, item_count: int = 0): + """Insert a new smart list and return its ID""" + cursor = conn.cursor() + now = datetime.now().isoformat() + + cursor.execute( + """ + INSERT INTO smart_lists (name, item_count, created_at, user_email) + VALUES (?, ?, ?, ?) + """, + (name, item_count, now, user_email), + ) + + list_id = cursor.lastrowid + conn.commit() + return list_id + + +def insert_list_source(conn, list_id: int, data_source_id: str): + """Insert a list source and return its ID""" + cursor = conn.cursor() + + cursor.execute( + """ + INSERT INTO list_sources (list_id, data_source_id) + VALUES (?, ?) + """, + (list_id, data_source_id), + ) + + source_id = cursor.lastrowid + conn.commit() + return source_id + + +def insert_list_filters( + conn, + list_source_id: int, + date_range_from: str = None, + date_range_to: str = None, + rag_query: str = None, + cosine_threshold: float = 0.4, + reranking_threshold: float = 0.82, +): + """Insert filters for a list source""" + cursor = conn.cursor() + + cursor.execute( + """ + INSERT INTO list_filters (list_source_id, date_range_from, date_range_to, rag_query, cosine_threshold, reranking_threshold) + VALUES (?, ?, ?, ?, ?, ?) + """, + ( + list_source_id, + date_range_from, + date_range_to, + rag_query, + cosine_threshold, + reranking_threshold, + ), + ) + + conn.commit() + + +def insert_datasource_author(conn, list_source_id: int, author: str): + """Insert an author for a list source""" + cursor = conn.cursor() + + cursor.execute( + """ + INSERT INTO datasource_authors (list_source_id, author) + VALUES (?, ?) + """, + (list_source_id, author), + ) + + conn.commit() + + +def get_smart_list_api_result_by_id(conn, list_id: int) -> SmartListAPIResult: + """Get a smart list by its ID""" + smart_lists = get_all_smart_lists_api_result(conn) + return next((sl for sl in smart_lists if sl.id == list_id), None) + + +def _get_smart_lists_api_result( + conn, only_for_user: str = None, exclude_user: str = None, current_user: str = None +) -> list["SmartListAPIResult"]: + """Common function to get smart lists with filtering and following status""" + cursor = conn.cursor() + + base_query = """ + SELECT + sl.id, sl.name, sl.item_count, sl.created_at, sl.user_email, + ls.id as source_id, ls.data_source_id, + lf.date_range_from, lf.date_range_to, lf.rag_query, lf.cosine_threshold, lf.reranking_threshold, + da.author + FROM smart_lists sl + LEFT JOIN list_sources ls ON sl.id = ls.list_id + LEFT JOIN list_filters lf ON ls.id = lf.list_source_id + LEFT JOIN datasource_authors da ON ls.id = da.list_source_id + """ + + params = [] + where_conditions = [] + + if only_for_user: + where_conditions.append("sl.user_email = ?") + params.append(only_for_user) + + if exclude_user: + where_conditions.append("sl.user_email != ?") + params.append(exclude_user) + + if where_conditions: + query = f"{base_query} WHERE {' AND '.join(where_conditions)} ORDER BY sl.id, ls.id, da.id" + else: + query = f"{base_query} ORDER BY sl.id, ls.id, da.id" + + cursor.execute(query, params) + rows = cursor.fetchall() + + # Get followed list IDs if current_user is provided + followed_ids = [] + if current_user: + followed_ids = get_followed_list_ids(conn, current_user) + + # Group results by smart list + lists_data = {} + for row in rows: + list_id = row["id"] + if list_id not in lists_data: + smart_list = SmartListDB.from_sql_row(row) + lists_data[list_id] = {"smart_list": smart_list, "sources_data": []} + + if row["source_id"]: + lists_data[list_id]["sources_data"].append(row) + + # Convert to SmartListAPIResult models + result = [] + for data in lists_data.values(): + following = data["smart_list"].id in followed_ids if current_user else False + api_result = SmartListAPIResult.from_db_data( + data["smart_list"], data["sources_data"], following=following + ) + result.append(api_result) + + return result + + +def get_all_smart_lists_api_result(conn) -> list["SmartListAPIResult"]: + """Get all smart lists with their sources, filters, and authors using a single query""" + return _get_smart_lists_api_result(conn) + + +def get_my_lists_api_result(conn, user_email: str) -> list["SmartListAPIResult"]: + """Get smart lists created by the user, with following status""" + return _get_smart_lists_api_result( + conn, only_for_user=user_email, current_user=user_email + ) + + +def get_community_lists_api_result(conn, user_email: str) -> list["SmartListAPIResult"]: + """Get smart lists NOT created by the user (community lists), with following status""" + return _get_smart_lists_api_result( + conn, exclude_user=user_email, current_user=user_email + ) + + +def delete_smart_list(conn, list_id: int, user_email: str) -> bool: + """Delete a smart list if owned by the user. Returns True if deleted, False otherwise.""" + cursor = conn.cursor() + + # Check if the user owns this list + cursor.execute("SELECT user_email FROM smart_lists WHERE id = ?", (list_id,)) + row = cursor.fetchone() + + if not row or row["user_email"] != user_email: + return False # User doesn't own this list or list doesn't exist + + # Delete the list (CASCADE will handle related tables) + cursor.execute("DELETE FROM smart_lists WHERE id = ?", (list_id,)) + conn.commit() + + return cursor.rowcount > 0 + + +def create_smart_list_from_request( + conn, list_data: SmartListCreate, user_email: str +) -> int: + """Create a smart list from SmartListCreate request and auto-follow it. Returns the list ID.""" + + # Insert the smart list + list_id = insert_smart_list(conn, list_data.name, user_email, 0) + + # Insert sources and their filters/authors + for list_source in list_data.listSources: + source_id = insert_list_source(conn, list_id, list_source.dataSourceId) + + filters = list_source.filters + insert_list_filters( + conn, + source_id, + filters.dateRange.get("from"), + filters.dateRange.get("to"), + filters.ragQuery, + filters.cosine_threshold, + filters.reranking_threshold, + ) + + # Insert authors + for author in filters.authors: + insert_datasource_author(conn, source_id, author) + + # Auto-follow the created list + follow_list(conn, user_email, list_id) + + return list_id + + +def initialize_mock_smart_lists(): + """Insert mock smart lists data into the database""" + from .mock_data import get_mock_smart_lists + + with get_omni_connection() as conn: + # Check if we already have smart lists + cursor = conn.cursor() + cursor.execute("SELECT COUNT(*) FROM smart_lists") + count = cursor.fetchone()[0] + + if count > 0: + return # Already initialized + + mock_lists = get_mock_smart_lists() + + for mock_list in mock_lists: + # Insert the smart list + list_id = insert_smart_list( + conn, mock_list["name"], "example@list.org", mock_list["itemCount"] + ) + + # Insert sources and their filters/authors + for list_source in mock_list["listSources"]: + source_id = insert_list_source( + conn, list_id, list_source["dataSourceId"] + ) + + filters = list_source["filters"] + insert_list_filters( + conn, + source_id, + filters["dateRange"].get("from"), + filters["dateRange"].get("to"), + filters.get("ragQuery"), + filters.get("cosine_threshold", 0.4), + filters.get("reranking_threshold", 0.82), + ) + + # Insert authors if they exist + if "authors" in filters: + for author in filters["authors"]: + insert_datasource_author(conn, source_id, author) diff --git a/packages/omni/omni/migrations/__init__.py b/packages/omni/omni/migrations/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/packages/omni/omni/migrations/v1.py b/packages/omni/omni/migrations/v1.py new file mode 100644 index 0000000..5d5dc40 --- /dev/null +++ b/packages/omni/omni/migrations/v1.py @@ -0,0 +1,63 @@ +import sqlite3 + + +def run_v1_migrations(conn): + """Run version 1 database migrations""" + cursor = conn.cursor() + + # Handle migration for existing databases that don't have the model column + try: + cursor.execute("SELECT model FROM smart_list_summaries LIMIT 1") + except sqlite3.OperationalError: + # Column doesn't exist, add it + cursor.execute("ALTER TABLE smart_list_summaries ADD COLUMN model TEXT") + print("Added model column to smart_list_summaries table") + + # Handle migration for existing databases that don't have the user_email column + try: + cursor.execute("SELECT user_email FROM smart_lists LIMIT 1") + except sqlite3.OperationalError: + # Column doesn't exist, add it with default value + cursor.execute( + "ALTER TABLE smart_lists ADD COLUMN user_email TEXT DEFAULT 'example@list.org'" + ) + # Update existing rows to have the default value + cursor.execute( + "UPDATE smart_lists SET user_email = 'example@list.org' WHERE user_email IS NULL" + ) + print("Added user_email column to smart_lists table") + + # Handle migration for threshold -> cosine_threshold and add reranking_threshold + try: + cursor.execute("SELECT cosine_threshold FROM list_filters LIMIT 1") + except sqlite3.OperationalError: + # Column doesn't exist, need to migrate + # SQLite doesn't support renaming columns directly in older versions, + # so we'll add the new columns and copy data + cursor.execute( + "ALTER TABLE list_filters ADD COLUMN cosine_threshold REAL DEFAULT 0.4" + ) + cursor.execute( + "ALTER TABLE list_filters ADD COLUMN reranking_threshold REAL DEFAULT 0.82" + ) + # Copy data from threshold to cosine_threshold if threshold column exists + try: + cursor.execute("UPDATE list_filters SET cosine_threshold = threshold") + except sqlite3.OperationalError: + # threshold column doesn't exist, skip + pass + print( + "Added cosine_threshold and reranking_threshold columns to list_filters table" + ) + + # Drop the old threshold column if it exists + try: + cursor.execute("SELECT threshold FROM list_filters LIMIT 1") + # Column exists, drop it + cursor.execute("ALTER TABLE list_filters DROP COLUMN threshold") + print("Dropped old threshold column from list_filters table") + except sqlite3.OperationalError: + # Column doesn't exist, nothing to drop + pass + + conn.commit() diff --git a/packages/omni/omni/mock_data.py b/packages/omni/omni/mock_data.py new file mode 100644 index 0000000..adb9ef4 --- /dev/null +++ b/packages/omni/omni/mock_data.py @@ -0,0 +1,211 @@ +def get_mock_data_sources(): + return [ + { + "id": "twitter", + "name": "Twitter", + "icon": "twitter", + "description": "Connect your Twitter account to analyze tweets and conversations", + "installCommand": "toolbox install twitter-mcp", + "defaultUrl": "localhost:8020", + "connectionState": "connected", + "dashboardData": { + "handle": "@johndoe", + "totalTweets": 1247, + "latestTweets": [ + { + "id": 1, + "content": "Just discovered an amazing new AI breakthrough in transformer architectures! The potential applications are endless.", + "timestamp": "2h ago", + "likes": 42, + "retweets": 18, + }, + { + "id": 2, + "content": "Working on some exciting ML projects. The intersection of AI and creativity continues to amaze me every day.", + "timestamp": "5h ago", + "likes": 28, + "retweets": 7, + }, + ], + "embeddingCounts": { + "ollama/nomic-embed-text": 123, + "openai/text-embedding-3-small": 89, + }, + }, + }, + { + "id": "ai-papers", + "name": "AI Papers", + "icon": "papers", + "description": "Connect to AI paper repositories and research databases", + "installCommand": "toolbox install papers-mcp", + "defaultUrl": "localhost:8022", + "connectionState": "connected", + "dashboardData": { + "totalPapers": 847, + "trendingPapers": 156, + "syftboxPapers": 23, + "latestPaper": "Jan 15", + "latestPapers": [ + { + "id": 1, + "title": "Attention Is All You Need: Revisiting Transformer Architectures", + "summary": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms...", + "authors": "Vaswani et al.", + "arxivId": "2401.12345", + "timestamp": "2h ago", + }, + ], + "embeddingCounts": { + "ollama/nomic-embed-text": 234, + "openai/text-embedding-3-small": 178, + }, + }, + }, + { + "id": "discord", + "name": "Discord", + "icon": "discord", + "description": "Connect Discord servers to analyze messages and conversations", + "installCommand": "toolbox install discord-mcp", + "defaultUrl": "localhost:8021", + "connectionState": "disconnected", + "dashboardData": { + "totalServers": 0, + "totalChannels": 0, + "totalMessages": 0, + "embeddingCounts": {}, + }, + }, + ] + + +def get_mock_data_collections(): + return [ + {"id": "twitter", "name": "Twitter", "items": []}, + {"id": "ai-papers", "name": "AI Papers", "items": []}, + {"id": "discord", "name": "Discord", "items": []}, + ] + + +def get_mock_smart_lists(): + return [ + { + "id": 1, + "name": "Vibe coding", + "listSources": [ + { + "dataSourceId": "twitter", + "filters": { + "dateRange": {"from": "2025-07-01", "to": "2025-10-01"}, + "ragQuery": "vibe coding", + "threshold": 0.42, + "authors": [ + "@CrazyJvm", + "@seb_ruder", + "@Devendr06654102", + "@weaviate_io", + "@PSH_Lewis", + "@douwekiela", + "@steipete", + "@jeremyphoward", + "@bobvanluijt", + ], + }, + } + ], + "itemCount": 23, + }, + { + "id": 2, + "name": "LLM releases", + "listSources": [ + { + "dataSourceId": "twitter", + "filters": { + "dateRange": {"from": "2025-07-01", "to": "2025-10-01"}, + "ragQuery": "LLM model release", + "threshold": 0.43, + "authors": [ + "@CrazyJvm", + "@seb_ruder", + "@Devendr06654102", + "@weaviate_io", + "@PSH_Lewis", + "@douwekiela", + "@steipete", + "@jeremyphoward", + "@bobvanluijt", + ], + }, + } + ], + "computedItems": [], + "itemCount": 18, + }, + { + "id": 3, + "name": "RAG", + "listSources": [ + { + "dataSourceId": "twitter", + "filters": { + "dateRange": {"from": "2025-07-01", "to": "2025-10-01"}, + "ragQuery": "RAG retrieval augmented generation", + "threshold": 0.43, + "authors": [ + "@CrazyJvm", + "@seb_ruder", + "@Devendr06654102", + "@weaviate_io", + "@PSH_Lewis", + "@douwekiela", + "@steipete", + "@jeremyphoward", + "@bobvanluijt", + ], + }, + } + ], + "computedItems": [], + "itemCount": 15, + }, + ] + + +def get_mock_summary(list_id: int): + summaries = { + 1: "• Retro aesthetics dominating: terminal emulators, green-on-black themes, and vintage computing vibes [1,3]\n• Music-driven development: lo-fi beats, synthwave soundtracks integrated into coding workflows [2,4]\n• Mood-based tooling: editors that adapt themes to Spotify playlists and emotional states [4]\n• ASCII art and visual programming: neural style transfer turning code into art [2]", + 2: "• Massive context windows: GPT-4.5 Turbo (200K tokens), Claude 3.5 Sonnet (1M+ tokens) [5,6]\n• Multimodal breakthroughs: real-time conversation, video understanding, native training [6,8]\n• Coding excellence: Code Llama 3 70B beating GPT-4 on HumanEval benchmark [7]\n• Speed improvements: 40% faster inference across major model releases [5,8]", + 3: "• Hybrid search dominance: combining vector similarity with keyword matching for better retrieval [10]\n• Knowledge graph integration: 94% accuracy on complex multi-hop questions [9]\n• Simplified tooling: ChromaDB + LangChain enabling sub-100 line implementations [11]\n• Advanced techniques: query decomposition, document reranking, and answer synthesis [12]", + 4: "• Reasoning breakthroughs: OpenAI o1 showing emergent capabilities in mathematics and coding [13]\n• Human-level programming: AlphaCode 3 solving competitive programming at expert level [14]\n• Multi-agent systems: Claude, GPT-4, and Gemini collaborating on complex problems [16]\n• Safety + capability: Constitutional AI aligning powerful models without performance loss [15]", + } + return summaries.get( + list_id, + "Recent discussions focus on breakthrough developments and the importance of human-AI collaboration.", + ) + + +def get_mock_chats(list_id: int): + mock_chats = { + 1: [ + { + "id": 1, + "messages": [ + { + "id": 1, + "role": "user", + "content": "What is the best tool for vibe coding?", + "timestamp": "2024-01-15T10:00:00Z", + }, + { + "id": 2, + "role": "assistant", + "content": "Currently, Cursor/windsurf are popular as IDEs, and claude code and codex for CLIs.", + "timestamp": "2024-01-15T10:00:30Z", + }, + ], + } + ] + } + return mock_chats.get(list_id, []) diff --git a/packages/omni/omni/models.py b/packages/omni/omni/models.py new file mode 100644 index 0000000..6ff36ff --- /dev/null +++ b/packages/omni/omni/models.py @@ -0,0 +1,268 @@ +from typing import Dict, List, Optional, Union + +from pydantic import BaseModel + + +class DataSource(BaseModel): + id: str + name: str + icon: str + description: str + installCommand: str + defaultUrl: str + connectionState: str + dashboardData: Dict + + +class DataCollection(BaseModel): + id: str + name: str + items: List + + +# Database schema models (1:1 with database tables) +class SmartListDB(BaseModel): + id: int + name: str + item_count: int + created_at: str + user_email: str + + @classmethod + def from_sql_row(cls, row): + """Create SmartListDB instance from SQLite row""" + return cls( + id=row["id"], + name=row["name"], + item_count=row["item_count"], + created_at=row["created_at"], + user_email=row["user_email"], + ) + + +class ListSourceDB(BaseModel): + id: int + list_id: int + data_source_id: str + + @classmethod + def from_sql_row(cls, row): + """Create ListSourceDB instance from SQLite row""" + return cls( + id=row["id"], list_id=row["list_id"], data_source_id=row["data_source_id"] + ) + + +class ListFilterDB(BaseModel): + id: int + list_source_id: int + date_range_from: Optional[str] = None + date_range_to: Optional[str] = None + rag_query: Optional[str] = None + cosine_threshold: float = 0.4 + reranking_threshold: float = 0.82 + + @classmethod + def from_sql_row(cls, row): + """Create ListFilterDB instance from SQLite row""" + return cls( + id=row["id"], + list_source_id=row["list_source_id"], + date_range_from=row["date_range_from"], + date_range_to=row["date_range_to"], + rag_query=row["rag_query"], + cosine_threshold=row["cosine_threshold"] + if "cosine_threshold" in row.keys() + else 0.4, + reranking_threshold=row["reranking_threshold"] + if "reranking_threshold" in row.keys() + else 0.82, + ) + + +class DatasourceAuthorDB(BaseModel): + id: int + list_source_id: int + author: str + + @classmethod + def from_sql_row(cls, row): + """Create DatasourceAuthorDB instance from SQLite row""" + return cls( + id=row["id"], list_source_id=row["list_source_id"], author=row["author"] + ) + + +# API models (for backwards compatibility and API responses) +class SmartListFilter(BaseModel): + dateRange: Dict[str, str] + ragQuery: str + cosine_threshold: float = 0.4 + reranking_threshold: float = 0.82 + authors: List[str] = [] + + +class ListSource(BaseModel): + dataSourceId: str + filters: SmartListFilter + + +# class SmartList(BaseModel): +# id: int +# name: str +# listSources: List[ListSource] +# computedItems: List = [] +# itemCount: int + + +# Connected API result model +class SmartListAPIResult(BaseModel): + id: int + name: str + listSources: List[ListSource] + computedItems: List = [] + itemCount: int + owner_email: str + following: bool = False + + @classmethod + def from_db_data( + cls, smart_list: SmartListDB, sources_data: List[Dict], following: bool = False + ): + """Create SmartListAPIResult from database data""" + # Group sources by list_source_id + sources_by_id = {} + for row in sources_data: + source_id = row["source_id"] + if source_id not in sources_by_id: + sources_by_id[source_id] = { + "dataSourceId": row["data_source_id"], + "filters": { + "dateRange": { + "from": row["date_range_from"], + "to": row["date_range_to"], + }, + "ragQuery": row["rag_query"] or "", + "cosine_threshold": row["cosine_threshold"] + if "cosine_threshold" in row.keys() + else 0.4, + "reranking_threshold": row["reranking_threshold"] + if "reranking_threshold" in row.keys() + else 0.82, + "authors": [], + }, + } + + # Add author if present + if row["author"]: + sources_by_id[source_id]["filters"]["authors"].append(row["author"]) + + # Convert to ListSource models + list_sources = [] + for source_data in sources_by_id.values(): + filters = SmartListFilter(**source_data["filters"]) + list_source = ListSource( + dataSourceId=source_data["dataSourceId"], filters=filters + ) + list_sources.append(list_source) + + return cls( + id=smart_list.id, + name=smart_list.name, + listSources=list_sources, + itemCount=smart_list.item_count, + owner_email=smart_list.user_email, + following=following, + ) + + +class SmartListCreate(BaseModel): + name: str + listSources: List[ListSource] + + def get_all_authors(self) -> list[str]: + authors = set() + for source in self.listSources: + authors.update(source.filters.authors) + return list(authors) + + +class TweetItem(BaseModel): + id: Union[ + str, int + ] # Allow both string (for real tweet IDs) and int (for mock data) + type: str + content: str + author: Dict + likes: int + reactions: int + timestamp: str + similarity_score: Optional[float] = None + tweet_type: str = "original" # "original", "repost", "quote", "reply" + interaction_context: Dict = {} + media: Optional[str] = None # JSON string containing media data + + +class Message(BaseModel): + id: int + role: str + content: str + timestamp: str + + +class Chat(BaseModel): + id: int + messages: List[Message] + + +class QuestionRequest(BaseModel): + question: str + context: list[str] + + def format_for_anthropic(self): + return f"Question: {self.question}\nContext: {self.context}" + + +class SummaryResponse(BaseModel): + summary: str + status: str + model: Optional[str] = None + + +class UserCreate(BaseModel): + email: str + + +class User(BaseModel): + id: int + email: str + created_at: str + + @classmethod + def from_sql_row(cls, row): + """Create User instance from SQLite row""" + return cls(id=row[0], email=row[1], created_at=row[2]) + + +class TwitterAccountCheckRequest(BaseModel): + handle: str + + +class TwitterAccountCheckResponse(BaseModel): + handle: str + exists: bool + error_checking: bool = False + + +class TweetCountRequest(BaseModel): + handles: List[str] + + +class TweetCountResponse(BaseModel): + tweet_counts: Dict[str, int] + + +class FollowUserRequest(BaseModel): + handles: List[str] + scrolls_per_user: int = 0 + fetch_timeline_duration: int = 0 diff --git a/packages/omni/omni/py.typed b/packages/omni/omni/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/packages/omni/omni/settings.py b/packages/omni/omni/settings.py new file mode 100644 index 0000000..75becdc --- /dev/null +++ b/packages/omni/omni/settings.py @@ -0,0 +1,15 @@ +from pydantic_settings import BaseSettings + + +class Settings(BaseSettings): + use_anthropic: bool = False + use_mock_summaries: bool = False + + x_download_schedule_seconds: int | tuple[int, int] | None = [ + 7200 * 2, + 14400 * 2, + ] # every 4-8 hours + use_cached_x_cookies: bool = True + + +settings = Settings() diff --git a/packages/omni/omni/summaries.py b/packages/omni/omni/summaries.py new file mode 100644 index 0000000..cfce94f --- /dev/null +++ b/packages/omni/omni/summaries.py @@ -0,0 +1,359 @@ +import hashlib +import json +import os +import threading +from datetime import datetime +from typing import Any, Dict, Optional + +import requests + +from omni.db import get_omni_connection +from omni.models import SummaryResponse +from omni.settings import settings +from omni.vectorstore_queries import ( + search_tweets, +) + +# CLAUDE_MODEL = "claude-opus-4-1-20250805" +ANTRHOPIC_MODEL = "claude-sonnet-4-5-20250929" + + +def get_ollama_completion(prompt, model="llama3.2:1b"): + """Get completion from Ollama API""" + try: + response = requests.post( + "http://localhost:11434/api/generate", + json={ + "model": model, + "prompt": prompt, + "stream": False, + "options": { + "temperature": 0.7, + "top_p": 0.9, + "num_predict": 500, # Limit response length + }, + }, + timeout=60, # Reduced timeout + ) + response.raise_for_status() + data = response.json() + result = data.get("response", "").strip() + return result + except Exception as e: + print(f"Error getting Ollama completion: {e}") + return None + + +def get_anthropic_completion(prompt): + """Get completion from Anthropic API""" + api_key = os.getenv("ANTHROPIC_API_KEY") + if not api_key: + print("ANTHROPIC_API_KEY not found in environment") + return None + + try: + import anthropic + + client = anthropic.Anthropic(api_key=api_key) + response = client.messages.create( + model=ANTRHOPIC_MODEL, + max_tokens=1000, + messages=[{"role": "user", "content": prompt}], + ) + return response.content[0].text.strip() + except ImportError: + print("Anthropic package not installed") + return None + except Exception as e: + print(f"Error getting Anthropic completion: {e}") + return None + + +def generate_summary(query_text, tweets_text): + """Generate a summary from tweets text using Ollama or Anthropic""" + if not tweets_text.strip(): + return "No tweets available for summary.", None + + prompt = f""" +Please analyze the following tweets and create a concise bullet-point summary highlighting the key themes in these sources. +Only include sources in the summary if they are relevant to the query: {query_text}, if its not relevant, don't include it. +For each theme, include the source that it came from formatted as [@handle] +Do not create more than 6 main themes, only create a theme if multiple sources talk about the same theme. +Format as bullet points with brief(!) descriptions: + +Tweets: +{tweets_text} + +Summary (use bullet points starting with •): +remember to double check if each theme has multiple sources, otherwise exclude it.""" + + use_anthropic = settings.use_anthropic + + if use_anthropic: + print("Using Anthropic") + summary = get_anthropic_completion(prompt) + if summary: + return summary, ANTRHOPIC_MODEL + print("Anthropic failed, falling back to Ollama") + elif settings.use_mock_summaries: + print("Using mock summary") + return "Mock summary", "mock" + + # Default to Ollama + summary = get_ollama_completion(prompt) + if summary: + return summary, "llama3.2:1b" + + return "Unable to generate summary at this time.", None + + +def get_ollama_embedding(text, model="nomic-embed-text:latest"): + """Get embedding from Ollama API""" + try: + response = requests.post( + "http://localhost:11434/api/embeddings", + json={"model": model, "prompt": text}, + timeout=30, + ) + response.raise_for_status() + data = response.json() + return data.get("embedding") + except Exception as e: + print(f"Error getting embedding: {e}") + return None + + +def get_intended_model(): + """Get the model that will be used for summary generation""" + + if settings.use_anthropic: + return ANTRHOPIC_MODEL + elif settings.use_mock_summaries: + return "mock" + else: + return "llama3.2:1b" + + +def generate_filters_hash(list_source): + """Generate a hash for the list source filters for caching""" + # Convert Pydantic model to dict if necessary + if hasattr(list_source, "model_dump"): + list_source_dict = list_source.model_dump() + elif hasattr(list_source, "dict"): + list_source_dict = list_source.dict() + else: + list_source_dict = list_source + + filters_str = json.dumps(list_source_dict, sort_keys=True) + return hashlib.md5(filters_str.encode()).hexdigest() + + +def generate_summary_async(list_id, list_source, filters_hash): + """Generate summary in background thread""" + try: + # Mark as generating + upsert_summary(list_id, filters_hash, "", "generating") + + # Generate the actual summary + summary, model = _generate_smart_list_summary_internal(list_source) + + # Save completed summary + if model is not None: + upsert_summary(list_id, filters_hash, summary, "completed", model) + + except Exception as e: + print(f"Error in async summary generation: {e}") + upsert_summary(list_id, filters_hash, "Error generating summary", "error") + + +def _generate_smart_list_summary_internal(list_source): + """Internal function to generate summary (used by both sync and async)""" + + # Convert Pydantic model to dict if necessary + if hasattr(list_source, "model_dump"): + list_source_dict = list_source.model_dump() + elif hasattr(list_source, "dict"): + list_source_dict = list_source.dict() + else: + list_source_dict = list_source + + filters = list_source_dict["filters"] + authors = [ + author.lstrip("@") for author in filters.get("authors", []) + ] # Remove @ prefix + + if not authors: + return "No authors specified for summary.", None + + # Parse date range - handle empty dates + date_from = filters["dateRange"].get("from", "") + date_to = filters["dateRange"].get("to", "") + + start_date = datetime.fromisoformat(date_from) if date_from else None + end_date = datetime.fromisoformat(date_to) if date_to else None + + # Get RAG query text (only if query is provided and not empty) + query_text = None + if filters.get("ragQuery") and filters["ragQuery"].strip(): + query_text = filters["ragQuery"] + + try: + tweet_items = search_tweets( + query_text=query_text, + author_screen_names=authors, + start_date=start_date, + end_date=end_date, + cosine_threshold=filters.get("cosine_threshold", 0.4) + if query_text + else 0.0, + reranking_threshold=filters.get("reranking_threshold", 0.82) + if query_text + else 0.0, + limit=50, + ) + + if not tweet_items: + return "No tweets found matching the criteria.", None + + # Combine tweet texts for summary + tweets_text = "\n\n".join( + [ + f"{tweet_item.author['handle']}: {tweet_item.content}" + for tweet_item in tweet_items[:20] # Limit to first 20 for summary + if tweet_item.content + ] + ) + + return generate_summary(query_text, tweets_text) + + except Exception as e: + print(f"Error generating smart list summary: {e}") + return "Unable to generate summary due to an error.", None + + +def get_or_generate_smart_list_summary(list_id, list_source) -> SummaryResponse: + """Get cached summary or generate new one (async if needed)""" + + filters_hash = generate_filters_hash(list_source) + + # Get cached summary, excluding errors (will automatically get latest non-error) + cached = get_cached_summary(list_id, filters_hash, include_errors=False) + + # If we have a valid cached summary, return it + if cached: + print("Getting cached summary") + summary, status, model = cached + + # If we have a cached result but no model info, update it with intended model for generating status + if status == "generating" and model is None: + intended_model = get_intended_model() + return SummaryResponse(summary=summary, status=status, model=intended_model) + + return SummaryResponse(summary=summary, status=status, model=model) + else: + # No valid cached summary - regenerate + print("Generating new summary") + + # Start async generation + intended_model = get_intended_model() + thread = threading.Thread( + target=generate_summary_async, args=(list_id, list_source, filters_hash) + ) + thread.daemon = True + thread.start() + + return SummaryResponse(summary="", status="generating", model=intended_model) + + +# Summary cache management (using the same SQLite tables for now) +def get_cached_summary( + list_id: int, filters_hash: str, include_errors: bool = False +) -> Optional[tuple]: + """Get cached summary if available + + Args: + list_id: The list ID + filters_hash: The filters hash + include_errors: If True, returns summaries even if status is 'error'. + If False, only returns non-error summaries. + + Returns: + Tuple of (summary, status, model) or None + """ + + try: + with get_omni_connection() as conn: + cursor = conn.cursor() + + if include_errors: + # Return any cached summary, ordered by most recent + cursor.execute( + """SELECT summary, status, model + FROM smart_list_summaries + WHERE list_id = ? AND filters_hash = ? + ORDER BY updated_at DESC + LIMIT 1""", + (list_id, filters_hash), + ) + else: + # Only return non-error summaries, ordered by most recent + cursor.execute( + """SELECT summary, status, model + FROM smart_list_summaries + WHERE list_id = ? AND filters_hash = ? AND status != 'error' + ORDER BY updated_at DESC + LIMIT 1""", + (list_id, filters_hash), + ) + + result = cursor.fetchone() + return result if result else None + except Exception as e: + print(f"Error getting cached summary: {e}") + return None + + +def upsert_summary( + list_id: int, + filters_hash: str, + summary: str, + status: str = "completed", + model: Optional[str] = None, +): + """Insert or update cached summary""" + + try: + with get_omni_connection() as conn: + cursor = conn.cursor() + now = datetime.now().isoformat() + cursor.execute( + """ + INSERT OR REPLACE INTO smart_list_summaries + (list_id, filters_hash, summary, status, model, created_at, updated_at) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + (list_id, filters_hash, summary, status, model, now, now), + ) + conn.commit() + except Exception as e: + print(f"Error upserting summary: {e}") + + +def clear_summary_cache(list_id: int, filters_hash: str) -> Dict[str, Any]: + """Clear cached summary for a specific list and filter combination""" + + try: + with get_omni_connection() as conn: + cursor = conn.cursor() + cursor.execute( + "DELETE FROM smart_list_summaries WHERE list_id = ? AND filters_hash = ?", + (list_id, filters_hash), + ) + conn.commit() + deleted = cursor.rowcount + return { + "message": f"Cleared cache for list {list_id}", + "deleted_rows": deleted, + } + except Exception as e: + return {"message": f"Error clearing cache: {str(e)}"} diff --git a/packages/omni/omni/twitter.py b/packages/omni/omni/twitter.py new file mode 100644 index 0000000..4f546b8 --- /dev/null +++ b/packages/omni/omni/twitter.py @@ -0,0 +1,118 @@ +from datetime import datetime + +import httpx +import requests +from cachetools import TTLCache, cached + +from omni.vectorstore_queries import ( + search_tweets, +) + +TEN_DAYS_IN_SECONDS = 10 * 24 * 60 * 60 +twitter_guest_token_cache = TTLCache(maxsize=100, ttl=TEN_DAYS_IN_SECONDS) + + +def query_twitter_data(list_source) -> list[dict]: + """Query Twitter data based on list source filters""" + + filters = list_source.filters + authors = ( + [author.lstrip("@") for author in filters.authors] if filters.authors else None + ) # Remove @ prefix + + # Parse date range + from_date = filters.dateRange["from"] + to_date = filters.dateRange["to"] + if from_date: + start_date = datetime.fromisoformat(from_date + "T00:00:00Z") + else: + start_date = None + if to_date: + end_date = datetime.fromisoformat(to_date + "T23:59:59Z") + else: + end_date = None + + # Get RAG query text (only if query is provided and not empty) + query_text = None + if filters.ragQuery and filters.ragQuery.strip(): + query_text = filters.ragQuery + + # If no authors and no RAG query, return empty (nothing to search for) + if not authors and not query_text: + return [] + + try: + cosine_threshold = filters.reranking_threshold * 0.4 + # Use the unified search function + tweet_items = search_tweets( + query_text=query_text, + author_screen_names=authors, + start_date=start_date, + end_date=end_date, + cosine_threshold=cosine_threshold, + reranking_threshold=filters.reranking_threshold, + limit=20, # Limit to 20 for display + ) + + # Convert TweetItem objects to dictionaries for API response + return [tweet_item.model_dump() for tweet_item in tweet_items] + + except Exception: + import traceback + + traceback.print_exc() + # print(f"Error querying Twitter data: {e}") + return [] + + +@cached(twitter_guest_token_cache) +def get_guest_token(): + url = "https://twitter.com" + + with httpx.Client() as client: + response = client.get(url) + + for cookie in response.cookies.jar: + if cookie.name == "guest_id": + cookie_value = cookie.value + expires = cookie.expires + break + + return cookie_value, expires + + +def account_exists(handle: str, cookie_value: str) -> bool: + handle_url_encoded = handle.replace("@", "") + + url = f"https://api.x.com/graphql/96tVxbPqMZDoYB5pmzezKA/UserByScreenName?variables=%7B%22screen_name%22%3A%22{handle_url_encoded}%22%2C%22withGrokTranslatedBio%22%3Afalse%7D&features=%7B%22hidden_profile_subscriptions_enabled%22%3Atrue%2C%22payments_enabled%22%3Afalse%2C%22profile_label_improvements_pcf_label_in_post_enabled%22%3Atrue%2C%22rweb_tipjar_consumption_enabled%22%3Atrue%2C%22verified_phone_label_enabled%22%3Afalse%2C%22subscriptions_verification_info_is_identity_verified_enabled%22%3Atrue%2C%22subscriptions_verification_info_verified_since_enabled%22%3Atrue%2C%22highlights_tweets_tab_ui_enabled%22%3Atrue%2C%22responsive_web_twitter_article_notes_tab_enabled%22%3Atrue%2C%22subscriptions_feature_can_gift_premium%22%3Atrue%2C%22creator_subscriptions_tweet_preview_api_enabled%22%3Atrue%2C%22responsive_web_graphql_skip_user_profile_image_extensions_enabled%22%3Afalse%2C%22responsive_web_graphql_timeline_navigation_enabled%22%3Atrue%7D&fieldToggles=%7B%22withAuxiliaryUserLabels%22%3Atrue%7D" + + headers = { + "accept": "*/*", + "accept-language": "en-GB,en;q=0.9", + "authorization": "Bearer AAAAAAAAAAAAAAAAAAAAANRILgAAAAAAnNwIzUejRCOuH5E6I8xnZz4puTs%3D1Zv7ttfk8LF81IUq16cHjhLTvJu4FA33AGWWjCpTnA", + "cache-control": "no-cache", + "content-type": "application/json", + "origin": "https://x.com", + "pragma": "no-cache", + "priority": "u=1, i", + "referer": "https://x.com/", + "sec-ch-ua": '"Chromium";v="140", "Not=A?Brand";v="24", "Brave";v="140"', + "sec-ch-ua-mobile": "?0", + "sec-ch-ua-platform": '"macOS"', + "sec-fetch-dest": "empty", + "sec-fetch-mode": "cors", + "sec-fetch-site": "same-site", + "sec-gpc": "1", + "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36", + "x-client-transaction-id": "zcVrDRSIRWct0CXT5dceAT2N5xwliK+Nxkijb3TPCA16/oa44aGUkRBDP/wJm87H9A+kSckr/RdWjvO/n/ktx/La3vPEzg", + "x-guest-token": cookie_value, + "x-twitter-active-user": "yes", + "x-twitter-client-language": "en-GB", + # "x-xp-forwarded-for": "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", + } + + response = requests.get(url, headers=headers) + if response.json().get("data") == {}: + return False + else: + return True diff --git a/packages/omni/omni/vectorstore_models.py b/packages/omni/omni/vectorstore_models.py new file mode 100644 index 0000000..b7d8db4 --- /dev/null +++ b/packages/omni/omni/vectorstore_models.py @@ -0,0 +1,340 @@ +import json +import sqlite3 +from typing import Any + +from toolbox_store import TBDocument + + +class Tweet(TBDocument): + """Tweet document model for ToolboxStore""" + + # Core tweet fields + tweet_id: str + author: dict[str, Any] # JSON structure for author data + created_at_tweet: str # Tweet's created_at timestamp + conversation_id: str | None = None + in_reply_to_user_id: str | None = None + in_reply_to_status_id: str | None = None + lang: str | None = None + + # Engagement metrics + retweet_count: int = 0 + favorite_count: int = 0 + reply_count: int = 0 + quote_count: int = 0 + bookmark_count: int = 0 + view_count: int = 0 + + # Status flags + is_quote_status: bool = False + possibly_sensitive: bool = False + retweeted: bool = False + favorited: bool = False + bookmarked: bool = False + + # Additional content + note_tweet_text: str | None = None + media_urls: str | None = None + media: str | None = None # JSON field for complete media information + urls: str | None = None + hashtags: str | None = None + user_mentions: str | None = None + source_platform: str | None = None + + # Tweet type classification and context (Hybrid approach) + tweet_type: str = "original" # "original", "repost", "quote", "reply" + interaction_context: dict[str, Any] = { + "is_repost": False, + "original_tweet_id": None, + "original_author": None, + "quoted_tweet": None, # Full quoted tweet object + "reply_to": None, # Reply context + "repost_comment": None, # Comment added during repost + } + + @classmethod + def schema_extra_columns(cls) -> list[tuple[str, str]]: + return [ + ("tweet_id", "TEXT UNIQUE"), + ("author", "TEXT"), # JSON + ("created_at_tweet", "TEXT"), + ("conversation_id", "TEXT"), + ("in_reply_to_user_id", "TEXT"), + ("in_reply_to_status_id", "TEXT"), + ("lang", "TEXT"), + ("retweet_count", "INTEGER"), + ("favorite_count", "INTEGER"), + ("reply_count", "INTEGER"), + ("quote_count", "INTEGER"), + ("bookmark_count", "INTEGER"), + ("view_count", "INTEGER"), + ("is_quote_status", "BOOLEAN"), + ("possibly_sensitive", "BOOLEAN"), + ("retweeted", "BOOLEAN"), + ("favorited", "BOOLEAN"), + ("bookmarked", "BOOLEAN"), + ("note_tweet_text", "TEXT"), + ("media_urls", "TEXT"), + ("media", "TEXT"), # JSON + ("urls", "TEXT"), + ("hashtags", "TEXT"), + ("user_mentions", "TEXT"), + ("source_platform", "TEXT"), + ("tweet_type", "TEXT"), + ("interaction_context", "TEXT"), # JSON + ] + + @classmethod + def schema_extra( + cls, + documents_table: str, + chunks_table: str, + embeddings_table: str, + fts_table: str, + ) -> list[str]: + return [ + f"CREATE INDEX IF NOT EXISTS idx_{documents_table}_tweet_id ON {documents_table} (tweet_id)", + f"CREATE INDEX IF NOT EXISTS idx_{documents_table}_created_at_tweet ON {documents_table} (created_at_tweet)", + f"CREATE INDEX IF NOT EXISTS idx_{documents_table}_conversation_id ON {documents_table} (conversation_id)", + ] + + @classmethod + def from_instruction_entry(cls, tweet_result: dict[str, Any]) -> "Tweet | None": + """Parse a single tweet from a tweet_results JSON structure""" + result = tweet_result.get("result", {}) + tweet_id = result.get("rest_id") + if not tweet_id: + return None + + # Extract author - try multiple possible locations + author_data = {"screen_name": "Unknown", "name": "Unknown", "avatar_url": ""} + + # Try core.user_results first + if "core" in result and "user_results" in result["core"]: + user_result = result["core"]["user_results"].get("result", {}) + + # Get avatar from avatar.image_url + if "avatar" in user_result: + author_data["avatar_url"] = user_result["avatar"].get("image_url", "") + + if "core" in user_result: + author_data["screen_name"] = user_result["core"].get( + "screen_name", "Unknown" + ) + author_data["name"] = user_result["core"].get("name", "Unknown") + + # Also check legacy within user_result + if "legacy" in user_result: + user_legacy = user_result["legacy"] + author_data["screen_name"] = user_legacy.get( + "screen_name", author_data["screen_name"] + ) + author_data["name"] = user_legacy.get("name", author_data["name"]) + # Use legacy profile_image_url_https as fallback + if not author_data["avatar_url"]: + author_data["avatar_url"] = user_legacy.get( + "profile_image_url_https", "" + ) + + # Fallback: check if there's author info in the main legacy section + legacy = result.get("legacy", {}) + if legacy and "user" in legacy: + user_data = legacy["user"] + author_data["screen_name"] = user_data.get( + "screen_name", author_data["screen_name"] + ) + author_data["name"] = user_data.get("name", author_data["name"]) + author_data["avatar_url"] = user_data.get( + "profile_image_url_https", author_data["avatar_url"] + ) + + # Extract content and basic tweet info + legacy = result.get("legacy", {}) + if not legacy: + return None + + content = legacy.get("full_text", "") + if "note_tweet" in result: + note_results = result["note_tweet"].get("note_tweet_results", {}) + if "result" in note_results: + content = note_results["result"].get("text", "") + + if not content: + return None + + # Extract conversation and reply fields + conversation_id = legacy.get("conversation_id_str") + in_reply_to_status_id = legacy.get("in_reply_to_status_id_str") + in_reply_to_user_id = legacy.get("in_reply_to_user_id_str") + + # Determine tweet type and extract interaction context + tweet_type = "original" + interaction_context = { + "is_repost": False, + "original_tweet_id": None, + "original_author": None, + "quoted_tweet": None, + "reply_to": None, + "repost_comment": None, + } + + # Check for retweet + if legacy.get("retweeted_status_result"): + tweet_type = "repost" + interaction_context["is_repost"] = True + + # Extract original tweet data + rt_result = legacy["retweeted_status_result"]["result"] + rt_legacy = rt_result.get("legacy", {}) + + # Get original author + original_author = { + "screen_name": "Unknown", + "name": "Unknown", + "avatar_url": "", + } + if "core" in rt_result and "user_results" in rt_result["core"]: + rt_user_result = rt_result["core"]["user_results"].get("result", {}) + if "avatar" in rt_user_result: + original_author["avatar_url"] = rt_user_result["avatar"].get( + "image_url", "" + ) + if "core" in rt_user_result: + original_author["screen_name"] = rt_user_result["core"].get( + "screen_name", "Unknown" + ) + original_author["name"] = rt_user_result["core"].get( + "name", "Unknown" + ) + if "legacy" in rt_user_result: + rt_user_legacy = rt_user_result["legacy"] + original_author["screen_name"] = rt_user_legacy.get( + "screen_name", original_author["screen_name"] + ) + original_author["name"] = rt_user_legacy.get( + "name", original_author["name"] + ) + if not original_author["avatar_url"]: + original_author["avatar_url"] = rt_user_legacy.get( + "profile_image_url_https", "" + ) + + interaction_context["original_tweet_id"] = rt_result.get("rest_id") + interaction_context["original_author"] = original_author + interaction_context["repost_comment"] = content # The RT @user: ... text + + # For reposts, use the original content as the main content + content = rt_legacy.get("full_text", "") + + # Check for quote tweet + elif legacy.get("is_quote_status") and result.get("quoted_status_result"): + tweet_type = "quote" + + # Extract quoted tweet data + qt_result = result["quoted_status_result"]["result"] + qt_legacy = qt_result.get("legacy", {}) + + # Get quoted author + quoted_author = { + "screen_name": "Unknown", + "name": "Unknown", + "avatar_url": "", + } + if "core" in qt_result and "user_results" in qt_result["core"]: + qt_user_result = qt_result["core"]["user_results"].get("result", {}) + if "avatar" in qt_user_result: + quoted_author["avatar_url"] = qt_user_result["avatar"].get( + "image_url", "" + ) + if "core" in qt_user_result: + quoted_author["screen_name"] = qt_user_result["core"].get( + "screen_name", "Unknown" + ) + quoted_author["name"] = qt_user_result["core"].get( + "name", "Unknown" + ) + if "legacy" in qt_user_result: + qt_user_legacy = qt_user_result["legacy"] + quoted_author["screen_name"] = qt_user_legacy.get( + "screen_name", quoted_author["screen_name"] + ) + quoted_author["name"] = qt_user_legacy.get( + "name", quoted_author["name"] + ) + if not quoted_author["avatar_url"]: + quoted_author["avatar_url"] = qt_user_legacy.get( + "profile_image_url_https", "" + ) + + interaction_context["quoted_tweet"] = { + "id": qt_result.get("rest_id"), + "content": qt_legacy.get("full_text", ""), + "author": quoted_author, + "created_at": qt_legacy.get("created_at", ""), + "favorite_count": qt_legacy.get("favorite_count", 0), + "retweet_count": qt_legacy.get("retweet_count", 0), + } + + # Check for reply + elif in_reply_to_status_id: + tweet_type = "reply" + interaction_context["reply_to"] = { + "status_id": in_reply_to_status_id, + "user_id": in_reply_to_user_id, + } + + # Extract media information - keep it simple, just store the complete media array as JSON string + media_data = None + if ( + legacy + and "extended_entities" in legacy + and "media" in legacy["extended_entities"] + ): + media_data = json.dumps(legacy["extended_entities"]["media"]) + elif legacy and "entities" in legacy and "media" in legacy["entities"]: + media_data = json.dumps(legacy["entities"]["media"]) + + return cls( + id=tweet_id, + tweet_id=tweet_id, + content=content, + author=author_data, + source="twitter", + created_at_tweet=legacy.get("created_at", ""), + conversation_id=conversation_id, + in_reply_to_user_id=in_reply_to_user_id, + in_reply_to_status_id=in_reply_to_status_id, + lang=legacy.get("lang"), + retweet_count=legacy.get("retweet_count", 0), + favorite_count=legacy.get("favorite_count", 0), + media=media_data, + tweet_type=tweet_type, + interaction_context=interaction_context, + ) + + @classmethod + def from_sql_row(cls, row: dict[str, Any] | sqlite3.Row) -> "Tweet": + """Create a Tweet instance from a SQL row with proper JSON parsing.""" + if not isinstance(row, dict): + row_dict = dict(row) + else: + row_dict = row.copy() + + # Parse JSON fields + if "metadata" in row_dict and isinstance(row_dict["metadata"], str): + row_dict["metadata"] = json.loads(row_dict["metadata"]) + + if "author" in row_dict and isinstance(row_dict["author"], str): + row_dict["author"] = json.loads(row_dict["author"]) + + # Keep media as JSON string for frontend consumption + # Don't parse media field - leave it as JSON string + + if "interaction_context" in row_dict and isinstance( + row_dict["interaction_context"], str + ): + row_dict["interaction_context"] = json.loads( + row_dict["interaction_context"] + ) + + return cls.model_validate(row_dict) diff --git a/packages/omni/omni/vectorstore_queries.py b/packages/omni/omni/vectorstore_queries.py new file mode 100644 index 0000000..d76a498 --- /dev/null +++ b/packages/omni/omni/vectorstore_queries.py @@ -0,0 +1,164 @@ +""" +ToolboxStore-based implementations of tweet query functions. +These replace the old SQLite-based implementations in db.py. +""" + +from datetime import datetime +from typing import List, Optional + +from omni.db import get_tweet_store +from omni.models import TweetItem + + +def search_tweets( + query_text: Optional[str] = None, + author_screen_names: Optional[List[str]] = None, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + cosine_threshold: float = 0.4, + reranking_threshold: float = 0.82, + limit: int = 50, +) -> List[TweetItem]: + """Unified tweet search function that returns TweetItem objects""" + store = get_tweet_store() + + def convert_tweet_to_item(tweet, similarity_score=None): + """Convert a Tweet document to TweetItem""" + return TweetItem( + id=str(tweet.tweet_id), + type="tweet", + content=tweet.content, + author={ + "name": tweet.author.get("name", "Unknown") + if tweet.author + else "Unknown", + "handle": f"@{tweet.author.get('screen_name', 'unknown')}" + if tweet.author + else "@unknown", + "avatarUrl": tweet.author.get("avatar_url", "") if tweet.author else "", + }, + likes=tweet.favorite_count, + reactions=tweet.retweet_count, + timestamp=tweet.created_at_tweet[:10] if tweet.created_at_tweet else "", + similarity_score=similarity_score, + tweet_type=tweet.tweet_type, + interaction_context=tweet.interaction_context, + media=tweet.media, # Add media data from database + ) + + try: + print(author_screen_names) + print(start_date) + print(end_date) + print(f"cosine_threshold: {cosine_threshold}") + print(f"reranking_threshold: {reranking_threshold}") + print(limit) + query = store.search_documents() + if author_screen_names: + query = query.where({"author.screen_name__in": author_screen_names}) + if start_date: + query = query.where({"created_at__gte": start_date}) + if end_date: + query = query.where({"created_at__lt": end_date}) + documents = query.get() + print("INITIAL DOCUMENTS", len(documents)) + + if query_text and query_text.strip(): + res_documents = ( + store.search_chunks() + .where({"id__in": [doc.id for doc in documents]}) + .semantic(query_text, score_threshold=cosine_threshold) + .chunk_limit(limit * 10) + .rerank(score_threshold=reranking_threshold) + .get_documents() + ) + print("RES DOCUMENTS", len(res_documents)) + documents = [] + similarities = [] + for doc in res_documents: + max_doc_score = max(chunk.score for chunk in doc.chunks) + if max_doc_score >= reranking_threshold: + documents.append(doc) + similarities.append(max_doc_score) + + else: + similarities = [None for _ in documents] + + converted_documents = [ + convert_tweet_to_item(doc, similarity) + for doc, similarity in zip(documents, similarities) + ] + + return converted_documents + except Exception: + import traceback + + traceback.print_exc() + return [] + + +def get_tweet_count() -> int: + """Get total count of tweets in the store""" + store = get_tweet_store() + try: + stats = store.db.stats() + return stats.get("documents", 0) + except Exception as e: + print(f"Error getting tweet count: {e}") + return 0 + + +def get_author_count() -> int: + """Get count of unique authors in the store""" + store = get_tweet_store() + try: + # This is a bit tricky with ToolboxStore since authors are stored as JSON + # We'll need to use the database directly for this + cursor = store.db.conn.execute( + f"SELECT COUNT(DISTINCT json_extract(author, '$.screen_name')) FROM {store.db.documents_table}" + ) + return cursor.fetchone()[0] or 0 + except Exception as e: + print(f"Error getting author count: {e}") + return 0 + + +def get_tweet_counts_by_handles(handles: List[str]) -> dict[str, int]: + """Get tweet counts for a list of handles""" + store = get_tweet_store() + try: + # Clean handles by removing @ prefix if present + clean_handles = [handle.lstrip("@") for handle in handles] + + # Build query to count tweets for each handle + results = {} + + # Initialize all handles with 0 count + for handle in clean_handles: + results[handle] = 0 + + # Query the database directly for efficient counting + if clean_handles: + placeholders = ",".join("?" * len(clean_handles)) + # only include tweets that have a vector embedding, otherwise we shouldnt count them + query = f""" + SELECT + json_extract(d.author, '$.screen_name') AS screen_name, + COUNT(*) AS tweet_count + FROM {store.db.documents_table} d + INNER JOIN {store.db.chunks_table} c + ON d.id = c.document_id + WHERE json_extract(d.author, '$.screen_name') IN ({placeholders}) + GROUP BY json_extract(d.author, '$.screen_name') + """ + + cursor = store.db.conn.execute(query, clean_handles) + for screen_name, count in cursor.fetchall(): + if screen_name in results: + results[screen_name] = count + + return results + except Exception as e: + print(f"Error getting tweet counts by handles: {e}") + # Return zeros for all handles on error + return {handle.lstrip("@"): 0 for handle in handles} diff --git a/packages/omni/package-lock.json b/packages/omni/package-lock.json new file mode 100644 index 0000000..1363a88 --- /dev/null +++ b/packages/omni/package-lock.json @@ -0,0 +1,2785 @@ +{ + "name": "omni", + "version": "0.1.0", + 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b/packages/omni/package.json @@ -0,0 +1,24 @@ +{ + "name": "omni", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview" + }, + "dependencies": { + "@tailwindcss/typography": "^0.5.19", + "marked": "^16.3.0", + "pinia": "^2.1.0", + "posthog-js": "^1.268.8", + "vue": "^3.4.0" + }, + "devDependencies": { + "@vitejs/plugin-vue": "^5.0.0", + "autoprefixer": "^10.4.0", + "postcss": "^8.4.0", + "tailwindcss": "^3.4.0", + "vite": "^5.0.0" + } +} diff --git a/packages/omni/postcss.config.js b/packages/omni/postcss.config.js new file mode 100644 index 0000000..2aa7205 --- /dev/null +++ b/packages/omni/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/packages/omni/public/favicon.svg b/packages/omni/public/favicon.svg new file mode 100644 index 0000000..126c34d --- /dev/null +++ b/packages/omni/public/favicon.svg @@ -0,0 +1,62 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/packages/omni/pyproject.toml b/packages/omni/pyproject.toml new file mode 100644 index 0000000..9703ea4 --- /dev/null +++ b/packages/omni/pyproject.toml @@ -0,0 +1,34 @@ +[project] +name = "omni" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +authors = [ + { name = "Koen van der Veen", email = "koenlennartvanderveen@gmail.com" }, +] +requires-python = ">=3.12" +dependencies = [ + "fastapi>=0.104.1", + "uvicorn[standard]>=0.24.0", + "pydantic_settings", + "requests", + "numpy>=2.3.3", + "sqlite-vec>=0.1.6", + "toolbox_store>=0.1.1", + "tqdm>=4.67.1", + "apscheduler>=3.11.0", + "browser-cookie3>=0.20.1", + "playwright>=1.55.0", + "cachetools>=6.2.0", + "httpx==0.28.1", + "rerankers[transformers] @ git+https://github.com/eelcovdw/rerankers.git@fix-colbert-query-mask", + "anthropic>=0.68.1", +] + + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/packages/omni/pytest.ini b/packages/omni/pytest.ini new file mode 100644 index 0000000..d63bd73 --- /dev/null +++ b/packages/omni/pytest.ini @@ -0,0 +1,7 @@ +[tool:pytest] +testpaths = tests +python_files = test_*.py +python_classes = Test* +python_functions = test_* +asyncio_mode = auto +addopts = -v --tb=short \ No newline at end of file diff --git a/packages/omni/scripts/generate_embeddings.py b/packages/omni/scripts/generate_embeddings.py new file mode 100644 index 0000000..7220ca0 --- /dev/null +++ b/packages/omni/scripts/generate_embeddings.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +""" +Script to generate embeddings for tweets using ToolboxStore. +""" + +import os +import sys + +from tqdm import tqdm + +# Add the parent directory to the path so we can import from omni +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +from omni.db import get_tweet_store + + +def main(): + """Generate embeddings for all tweets without embeddings using ToolboxStore.""" + print("Generating embeddings for tweets using ToolboxStore...") + + store = get_tweet_store() + print(f"Connected to ToolboxStore at {store.db.db_path}") + + batch_size = 1000 + total_processed = 0 + + # First, get total count of docs without embeddings for progress bar + total_docs = len( + store.db.get_docs_without_embeddings(limit=10000) + ) # Get a large number to estimate total + + with tqdm(total=total_docs, desc="Creating embeddings") as pbar: + while True: + # Get documents without embeddings in batches + docs_without_embeddings = store.db.get_docs_without_embeddings( + limit=batch_size + ) + + if not docs_without_embeddings: + break + + # Create embeddings and chunks for these documents + chunks = store.embed_documents(docs_without_embeddings) + + # Insert the chunks (which includes embeddings) + store.insert_chunks(chunks) + + total_processed += len(docs_without_embeddings) + pbar.update(len(docs_without_embeddings)) + + # If we got fewer documents than batch_size, we're done + if len(docs_without_embeddings) < batch_size: + break + + if total_processed == 0: + print("✓ All tweets already have embeddings!") + else: + print( + f"✓ Embedding generation complete! Processed {total_processed} tweets total." + ) + + +if __name__ == "__main__": + main() diff --git a/packages/omni/scripts/parse_tweets.py b/packages/omni/scripts/parse_tweets.py new file mode 100644 index 0000000..e69c72a --- /dev/null +++ b/packages/omni/scripts/parse_tweets.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +""" +Script to parse all tweet JSON files from ../../data/ directory +and store them using ToolboxStore +""" + +import json +import os +import sys +from pathlib import Path + +# Add the parent directory to the path so we can import from omni +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +from omni.db import get_tweet_store +from omni.vectorstore_models import Tweet + + +def main(): + """Main function to process all JSON files and store tweets using ToolboxStore.""" + data_dir = Path("../../data/") + print(f"Looking for JSON files in {data_dir.resolve()}") + + if not data_dir.exists(): + print(f"Data directory {data_dir} does not exist!") + return + + json_files = list(data_dir.glob("*.json")) + if not json_files: + print(f"No JSON files found in {data_dir}") + return + + print(f"Found {len(json_files)} JSON files to process...") + + store = get_tweet_store() + print(f"Initialized ToolboxStore at {store.db.db_path}") + + # Drop tweets table to recreate with new schema + store.db.conn.execute("DROP TABLE IF EXISTS tweets_documents") + store.db.conn.commit() + print("Dropped existing tweets table") + + # Force schema creation + store.db.create_schema() + print("Recreated tables with new schema") + + all_tweets = [] + seen_tweet_ids = set() + + for json_file in json_files: + print(f"Processing {json_file.name}...") + + with open(json_file, "r", encoding="utf-8") as f: + data = json.load(f) + + file_tweet_count = 0 + + # Navigate through the JSON structure and find all tweet_results + instructions = ( + data.get("data", {}) + .get("home", {}) + .get("home_timeline_urt", {}) + .get("instructions", []) + ) + + for instruction in instructions: + if instruction.get("type") != "TimelineAddEntries": + continue + + entries = instruction.get("entries", []) + + for entry in entries: + content = entry.get("content", {}) + # Handle conversation entries (TimelineTimelineModule) + if content.get("entryType") == "TimelineTimelineModule": + items = content.get("items", []) + for item in items: + item_content = item.get("item", {}).get("itemContent", {}) + if item_content.get("itemType") == "TimelineTweet": + tweet_results = item_content.get("tweet_results", {}) + tweet = Tweet.from_instruction_entry(tweet_results) + if tweet and tweet.tweet_id not in seen_tweet_ids: + seen_tweet_ids.add(tweet.tweet_id) + all_tweets.append(tweet) + file_tweet_count += 1 + + # Handle direct tweet entries (TimelineTimelineItem) + elif content.get("entryType") == "TimelineTimelineItem": + item_content = content.get("itemContent", {}) + if item_content.get("itemType") == "TimelineTweet": + tweet_results = item_content.get("tweet_results", {}) + tweet = Tweet.from_instruction_entry(tweet_results) + if tweet and tweet.tweet_id not in seen_tweet_ids: + seen_tweet_ids.add(tweet.tweet_id) + all_tweets.append(tweet) + file_tweet_count += 1 + + print(f" Found {file_tweet_count} tweets") + + print(f"\nTotal unique tweets extracted: {len(all_tweets)}") + + if all_tweets: + print("Inserting tweets into ToolboxStore...") + store.insert_docs(all_tweets, create_embeddings=False) + print(f"Successfully inserted {len(all_tweets)} tweets into ToolboxStore") + + +if __name__ == "__main__": + main() diff --git a/packages/omni/scripts/print_local_cookie.py b/packages/omni/scripts/print_local_cookie.py new file mode 100644 index 0000000..fc0006f --- /dev/null +++ b/packages/omni/scripts/print_local_cookie.py @@ -0,0 +1,3 @@ +from omni.data_fetchers.x_fetcher import get_cookies_from_brave + +cookies = get_cookies_from_brave() diff --git a/packages/omni/scripts/print_local_cookie_and_save.py b/packages/omni/scripts/print_local_cookie_and_save.py new file mode 100644 index 0000000..c23579a --- /dev/null +++ b/packages/omni/scripts/print_local_cookie_and_save.py @@ -0,0 +1,9 @@ +from omni.data_fetchers.x_fetcher import ( + cookie_to_playwright_cookie, + get_cookies_from_brave, + save_cookies_to_file, +) + +cookies = get_cookies_from_brave() +cookies = [cookie_to_playwright_cookie(c) for c in cookies] +save_cookies_to_file(cookies) diff --git a/packages/omni/server_test.py b/packages/omni/server_test.py new file mode 100644 index 0000000..93123d3 --- /dev/null +++ b/packages/omni/server_test.py @@ -0,0 +1,28 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +app = FastAPI() + +# Allow CORS for your website or all origins for testing +origins = [ + "*", # For testing; replace with ["https://yourwebsite.com"] in production +] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, # who can access + allow_credentials=True, + allow_methods=["*"], # GET, POST, etc. + allow_headers=["*"], # headers your site can send +) + + +@app.get("/status") +async def status(): + return {"status": "ok", "message": "Hello from localhost!"} + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="127.0.0.1", port=12345) diff --git a/packages/omni/tailwind.config.js b/packages/omni/tailwind.config.js new file mode 100644 index 0000000..66367e1 --- /dev/null +++ b/packages/omni/tailwind.config.js @@ -0,0 +1,8 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: ["./index.html", "./frontend/**/*.{vue,js,ts,jsx,tsx}"], + theme: { + extend: {}, + }, + plugins: [require("@tailwindcss/typography")], +}; diff --git a/packages/omni/tests/__init__.py b/packages/omni/tests/__init__.py new file mode 100644 index 0000000..0ecdbdf --- /dev/null +++ b/packages/omni/tests/__init__.py @@ -0,0 +1 @@ +# Tests package for Omni API diff --git a/packages/omni/tests/test_twitter_items.py b/packages/omni/tests/test_twitter_items.py new file mode 100644 index 0000000..bdbaab3 --- /dev/null +++ b/packages/omni/tests/test_twitter_items.py @@ -0,0 +1,109 @@ +import os +import sys +import tempfile +from unittest.mock import patch + +from fastapi.testclient import TestClient + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +from omni.app import app +from omni.vectorstore_models import Tweet +from toolbox_store import ToolboxStore +from toolbox_store.models import StoreConfig + +client = TestClient(app) + + +def create_mock_store(): + config = StoreConfig(embedding_model="random", embedding_dim=32) + temp_file = tempfile.NamedTemporaryFile(suffix=".db", delete=False) + temp_file.close() + + store = ToolboxStore( + "tweets", db_path=temp_file.name, config=config, document_class=Tweet + ) + + mock_tweets = [ + Tweet( + id="1", + tweet_id="123", + content="coding aesthetics vibes music", + author={ + "screen_name": "test", + "name": "Test", + "avatar_url": "https://example.com/test.jpg", + }, + created_at_tweet="2025-08-01T10:30:00Z", + favorite_count=10, + retweet_count=5, + lang="en", + source="test", + ), + Tweet( + id="2", + tweet_id="456", + content="programming beautiful code vibes", + author={ + "screen_name": "dev", + "name": "Dev", + "avatar_url": "https://example.com/dev.jpg", + }, + created_at_tweet="2025-08-15T14:20:00Z", + favorite_count=20, + retweet_count=3, + lang="en", + source="test", + ), + ] + + store.insert_docs(mock_tweets, create_embeddings=True) + return store + + +def test_twitter_smart_list_items(): + with patch( + "omni.vectorstore_queries.get_tweet_store", side_effect=create_mock_store + ): + response = client.get("/smart-lists") + smart_lists = response.json() + + twitter_list = next( + ( + sl + for sl in smart_lists + if any( + s.get("dataSourceId") == "twitter" + for s in sl.get("listSources", []) + ) + ), + None, + ) + + assert twitter_list is not None + + response = client.get(f"/smart-lists/{twitter_list['id']}/items") + items = response.json() + + assert len(items) > 1 + assert all(item.get("similarity_score", 0) > 0 for item in items) + + # Verify frontend-expected JSON structure + for item in items: + assert "id" in item + assert "type" in item + assert item["type"] == "tweet" + assert "content" in item + assert "author" in item + assert "name" in item["author"] + assert "handle" in item["author"] + assert item["author"]["handle"].startswith("@") + assert "avatarUrl" in item["author"] + assert "likes" in item + assert "reactions" in item + assert "timestamp" in item + + +if __name__ == "__main__": + test_twitter_smart_list_items() + print("✅ Test passed!") diff --git a/packages/omni/vite.config.js b/packages/omni/vite.config.js new file mode 100644 index 0000000..870eca5 --- /dev/null +++ b/packages/omni/vite.config.js @@ -0,0 +1,14 @@ +import { defineConfig } from "vite"; +import vue from "@vitejs/plugin-vue"; + +export default defineConfig({ + plugins: [vue()], + resolve: { + alias: { + "@": "/frontend", + }, + }, + server: { + port: 8005, + }, +}); diff --git a/packages/slack_mcp/pyproject.toml b/packages/slack_mcp/pyproject.toml index 0cf2969..40e757a 100644 --- a/packages/slack_mcp/pyproject.toml +++ b/packages/slack_mcp/pyproject.toml @@ -9,12 +9,12 @@ dependencies = [ "pydantic-settings>=2.10.0", "slack_sdk==3.35.0", "sqlite-vec==0.1.7a2", - "numpy==2.3.2", + "numpy>=2.3.2", "tqdm==4.67.1", "loguru==0.7.3", "mcp>=1.9.2", "fastsyftbox", - "syft_core==0.2.8" + "syft_core==0.2.8", ] name = "slack_mcp" @@ -32,18 +32,16 @@ build-backend = "hatchling.build" fastsyftbox = { git = "https://github.com/OpenMined/fastsyftbox.git", rev = "fix-startup" } [tool.uv] -dev-dependencies = ["pre-commit>=4.1.0", "pytest-xdist>=3.6.1", "pytest>=8.3.4", "typer==0.16.0"] - -[dependency-groups] -dev = [ +dev-dependencies = [ + "pre-commit>=4.1.0", "pytest-xdist>=3.6.1", "pytest>=8.3.4", + "typer==0.16.0", ] -embeddings = [ - "torch==2.2.2", - "transformers==4.54.0", - "einops==0.8.1" -] + +[dependency-groups] +dev = ["pytest-xdist>=3.6.1", "pytest>=8.3.4"] +embeddings = ["torch==2.2.2", "transformers>=4.54.0", "einops==0.8.1"] [tool.mypy] python_version = "3.12" diff --git a/packages/toolbox_store/examples/minimal_example.py b/packages/toolbox_store/examples/minimal_example.py index f9964ef..e7accb3 100644 --- a/packages/toolbox_store/examples/minimal_example.py +++ b/packages/toolbox_store/examples/minimal_example.py @@ -23,12 +23,12 @@ # %% query = ( store.search_chunks() - .semantic("Airport security") + .semantic("Airport security", score_threshold=0.3) .where({"created_at__gte": datetime(2025, 9, 1)}) .where({"created_at__lt": datetime(2025, 10, 1)}) - # .keyword("history AI") # TODO sqlite fts index - # .hybrid(semantic_weight=0.8) # TODO linear combination of fts + semantic - # .rerank() # TODO reranker support + .keyword("history AI") + .hybrid(semantic_weight=0.8, keyword_weight=0.2) + .rerank() .chunk_limit(10) ) @@ -36,7 +36,7 @@ print(f"Retrieved {len(chunks)} chunks") for chunk in chunks: - print(f"- Chunk (doc {chunk.document_id}, dist {chunk.distance:.4f}):") + print(f"- Chunk (doc {chunk.document_id}, score {chunk.score:.4f}):") print(f" {chunk.content[:200]}...") print() # %% @@ -48,50 +48,7 @@ print(f"- Document {doc.id} (metadata: {doc.metadata}):") print(f" {doc.content[:200]}...") print(f" num retrieved chunks: {len(doc.chunks)}") - print(f" min distance: {doc.chunks[0].distance if doc.chunks else 'N/A'}") - print() - -# %% -query = ( - store.search_chunks() - .keyword("yates") - .where({"metadata.filename__contains": "001"}) - .chunk_limit(10) -) - -docs = query.get_documents() -print(f"Retrieved {len(docs)} documents") - -for doc in docs: - print(f"- Document {doc.id} (metadata: {doc.metadata}):") - print(f" {doc.content[:200]}...") - print(f" num retrieved chunks: {len(doc.chunks)}") - print(f" min distance: {doc.chunks[0].distance if doc.chunks else 'N/A'}") - print() - -# %% - -query = ( - store.search_chunks() - .semantic("airport security") # vector similarity - .keyword("yates") # bm25 - .hybrid(method="rrf") # fuse semantic + keyword scores - .where( - { - "metadata.filename__contains": "001", # django-style operators - "created_at__gte": datetime(2025, 9, 1), # datetimes are converted to str - } - ) - .chunk_limit(10) -) - -docs = query.get_documents() -print(f"Retrieved {len(docs)} documents") -for doc in docs: - print(f"- Document {doc.id} (metadata: {doc.metadata}):") - print(f" {doc.content[:500]}...") - print(f" num retrieved chunks: {len(doc.chunks)}") - print(f" min distance: {doc.chunks[0].distance if doc.chunks else 'N/A'}") + print(f" max chunk score: {doc.chunks[0].score if doc.chunks else 'N/A'}") print() # %% diff --git a/packages/toolbox_store/pyproject.toml b/packages/toolbox_store/pyproject.toml index 8780fc9..ec572d7 100644 --- a/packages/toolbox_store/pyproject.toml +++ b/packages/toolbox_store/pyproject.toml @@ -1,25 +1,31 @@ [project] -name = "toolbox-store" -version = "0.1.0" +name = "toolbox_store" +version = "0.1.1" description = "Add your description here" readme = "README.md" authors = [{ name = "eelcovdw", email = "eelcovdw@gmail.com" }] requires-python = ">=3.12" dependencies = [ - "httpx>=0.28.1", + "httpx>=0.28.1,<0.29", "importlib>=1.0.4", "numpy>=2.3.2", "packaging>=25.0", "pydantic>=2.11.7", "pydantic-settings>=2.10.1", + "rerankers[transformers]>=0.10.0", "semantic-text-splitter>=0.28.0", "sqlite-vec==0.1.7a2", + "torch>=2.2.2", + "transformers>=4.56", ] [build-system] requires = ["uv_build>=0.8.3,<0.9.0"] build-backend = "uv_build" +[tool.uv.sources] +rerankers = { git = "https://github.com/eelcovdw/rerankers.git", rev = "fix-colbert-query-mask" } + [dependency-groups] dev = [ "datasets>=4.0.0", diff --git a/packages/toolbox_store/src/toolbox_store/db.py b/packages/toolbox_store/src/toolbox_store/db.py index 45d9ba0..9c77fb4 100644 --- a/packages/toolbox_store/src/toolbox_store/db.py +++ b/packages/toolbox_store/src/toolbox_store/db.py @@ -55,13 +55,18 @@ def __init__( if reset and db_path != ":memory:": self.reset() self.collection = collection - self.conn = sqlite3.connect(str(db_path)) - self.conn.row_factory = sqlite3.Row - self.conn.enable_load_extension(True) - sqlite_vec.load(self.conn) + self.conn = self._create_connection() self.config = config or StoreConfig() self.document_class = document_class or TBDocument + def _create_connection(self) -> sqlite3.Connection: + conn = sqlite3.connect(str(self.db_path)) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL;") + conn.enable_load_extension(True) + sqlite_vec.load(conn) + return conn + def reset(self): self.db_path.unlink(missing_ok=True) @@ -157,9 +162,18 @@ def create_schema(self) -> None: self.conn.rollback() raise - def insert_documents(self, documents: list[T]): + def insert_documents( + self, documents: list[T], overwrite: bool = False + ) -> dict[str, list[str]]: + """ + Insert new documents. Fails if any document ID already exists. + For updating existing documents, use upsert_documents() instead. + """ + if overwrite: + return self.upsert_documents(documents) + if not documents: - return + return {"inserted": [], "updated": [], "unchanged": []} try: # Get fields from first document to build query @@ -167,8 +181,9 @@ def insert_documents(self, documents: list[T]): fields = list(first_doc.keys()) placeholders = [f":{field}" for field in fields] + # Use INSERT without OR REPLACE to fail on duplicates query = f""" - INSERT OR REPLACE INTO {self.documents_table} ({", ".join(fields)}) + INSERT INTO {self.documents_table} ({", ".join(fields)}) VALUES ({", ".join(placeholders)}) """ @@ -179,6 +194,104 @@ def insert_documents(self, documents: list[T]): self.conn.executemany(query, data) self.conn.commit() + return { + "inserted": [doc.id for doc in documents], + "updated": [], + "unchanged": [], + } + except Exception: + self.conn.rollback() + raise + + def _delete_chunks_by_doc_id(self, document_ids: list[str]) -> None: + """ + Delete chunks, embeddings, and FTS entries for given document IDs. + Internal method - does not commit the transaction. + + Args: + document_ids: List of document IDs whose chunks should be deleted + """ + if not document_ids: + return + + placeholders = ",".join("?" for _ in document_ids) + + # Delete from chunks table + self.conn.execute( + f"DELETE FROM {self.chunks_table} WHERE document_id IN ({placeholders})", + document_ids, + ) + + # Delete from embeddings table + self.conn.execute( + f"DELETE FROM {self.embeddings_table} WHERE document_id IN ({placeholders})", + document_ids, + ) + + # Delete from FTS table + self.conn.execute( + f"DELETE FROM {self.fts_table} WHERE document_id IN ({placeholders})", + document_ids, + ) + + def upsert_documents(self, documents: list[T]) -> dict[str, list[str]]: + """ + Upsert documents with proper chunk management. + If document content has changed (different content_hash), deletes old chunks. + + Returns: + Dictionary with 'inserted', 'updated', and 'unchanged' document IDs + """ + if not documents: + return {"inserted": [], "updated": [], "unchanged": []} + + try: + # Get existing documents and their content hashes + doc_ids = [doc.id for doc in documents] + placeholders = ",".join("?" for _ in doc_ids) + cursor = self.conn.execute( + f""" + SELECT id, content_hash + FROM {self.documents_table} + WHERE id IN ({placeholders}) + """, + doc_ids, + ) + existing = {row["id"]: row["content_hash"] for row in cursor} + + # Categorize documents + result = {"inserted": [], "updated": [], "unchanged": []} + changed_doc_ids = [] + + for doc in documents: + if doc.id not in existing: + result["inserted"].append(doc.id) + elif existing[doc.id] != doc.content_hash: + result["updated"].append(doc.id) + changed_doc_ids.append(doc.id) + else: + result["unchanged"].append(doc.id) + + # Delete chunks for changed documents + self._delete_chunks_by_doc_id(changed_doc_ids) + + # Insert/update all documents (including unchanged ones for simplicity) + if documents: + first_doc = documents[0].to_sql_dict() + fields = list(first_doc.keys()) + field_placeholders = [f":{field}" for field in fields] + + query = f""" + INSERT OR REPLACE INTO {self.documents_table} ({", ".join(fields)}) + VALUES ({", ".join(field_placeholders)}) + """ + + data = [doc.to_sql_dict() for doc in documents] + self.conn.executemany(query, data) + + self.conn.commit() + return result + except Exception: self.conn.rollback() raise @@ -314,23 +427,65 @@ def get_documents_by_id(self, ids: list[str]) -> list[T]: rows = cursor.fetchall() return [self.document_class.from_sql_row(row) for row in rows] + def delete_documents(self, ids: list[str]) -> None: + if not ids: + return + try: + placeholders = ",".join("?" for _ in ids) + + # Delete documents + self.conn.execute( + f"DELETE FROM {self.documents_table} WHERE id IN ({placeholders})", + ids, + ) + + # Delete associated chunks, embeddings, and FTS entries + self._delete_chunks_by_doc_id(ids) + + self.conn.commit() + except Exception: + self.conn.rollback() + raise + + def delete_chunks_by_doc_ids(self, document_ids: list[str]) -> None: + """ + Delete chunks, embeddings, and FTS entries for given document IDs. + Commits the transaction. + + Args: + document_ids: List of document IDs whose chunks should be deleted + """ + try: + self._delete_chunks_by_doc_id(document_ids) + self.conn.commit() + except Exception: + self.conn.rollback() + raise + def semantic_search( self, query_embedding: list[float], filters: dict[str, Any] | None = None, limit: int = 10, offset: int = 0, + score_threshold: float | None = None, ) -> list[RetrievedChunk]: """ Perform semantic search using a query embedding. Returns list of RetrievedEmbedding objects with distance scores. """ + if score_threshold is not None: + distance_threshold = 1 - score_threshold + else: + distance_threshold = None + params_dict = { "query_embedding": sqlite_vec.serialize_float32(query_embedding), "limit": limit, "offset": offset, "total_limit": limit + offset, + "distance_threshold": distance_threshold, } if filters: @@ -345,8 +500,13 @@ def semantic_search( else: where_clause, where_params = "", None + # Add distance threshold filter if provided (applied in outer query due to sqlite-vec constraints) + distance_filter = "" + if distance_threshold is not None: + distance_filter = "WHERE distance <= :distance_threshold" + # Single query joining embeddings with chunks to get all needed data - # Note: sqlite-vec requires LIMIT in the virtual table query, we apply OFFSET in outer query + # Note: sqlite-vec requires LIMIT in the virtual table query, we apply OFFSET and distance filter in outer query cursor = self.conn.execute( f""" SELECT @@ -362,6 +522,7 @@ def semantic_search( INNER JOIN {self.chunks_table} c ON e.document_id = c.document_id AND e.chunk_idx = c.chunk_idx + {distance_filter} ORDER BY distance LIMIT :limit OFFSET :offset """, @@ -377,6 +538,11 @@ def semantic_search( else: row_dict["embedding"] = None + # Convert distance to score + distance = row_dict.get("distance", None) + if distance is not None: + row_dict["score"] = 1 - distance + results.append(RetrievedChunk.from_sql_row(row_dict)) return results @@ -429,7 +595,19 @@ def keyword_search( params_dict, ) - rows = cursor.fetchall() + rows = [] + for row in cursor.fetchall(): + row_dict = dict(row) + embedding_blob = row_dict.get("embedding", None) + if isinstance(embedding_blob, bytes): + row_dict["embedding"] = deserialize_float32(embedding_blob) + else: + row_dict["embedding"] = None + + # Convert distance to score + distance = row_dict.get("distance", None) + if distance is not None: + row_dict["score"] = 1 - distance return [RetrievedChunk.from_sql_row(row) for row in rows] def get_docs_without_embeddings( diff --git a/packages/toolbox_store/src/toolbox_store/filters.py b/packages/toolbox_store/src/toolbox_store/filters.py index fab61be..9c0984b 100644 --- a/packages/toolbox_store/src/toolbox_store/filters.py +++ b/packages/toolbox_store/src/toolbox_store/filters.py @@ -90,7 +90,7 @@ def build_condition( placeholders = [f":{param_base}_{i}" for i in range(len(value))] for i, item in enumerate(value): params[f"{param_base}_{i}"] = prepare_value(item, "eq") - return f"{sql_field} IN ({','.join(placeholders)})" + return f"LOWER({sql_field}) IN ({','.join(placeholders)})" if op not in OPERATORS: raise ValueError(f"Unknown operator: {op}") diff --git a/packages/toolbox_store/src/toolbox_store/models.py b/packages/toolbox_store/src/toolbox_store/models.py index 56be4da..e43a74c 100644 --- a/packages/toolbox_store/src/toolbox_store/models.py +++ b/packages/toolbox_store/src/toolbox_store/models.py @@ -1,4 +1,3 @@ -import hashlib import json import re import sqlite3 @@ -10,7 +9,9 @@ from pydantic import BaseModel, Field, JsonValue, model_validator from pydantic_settings import BaseSettings -KEY_PATTERN = re.compile(r"^[a-zA-Z0-9_-]+$") +from toolbox_store.utils import _utcnow, hash_content + +VALID_FIELD_PATTERN = re.compile(r"^[a-zA-Z0-9_-]+$") TOOLBOX_DIR = Path.home() / ".toolbox" DEFAULT_CACHE_PATH = TOOLBOX_DIR / "vector_cache.db" @@ -22,15 +23,7 @@ def is_valid_field_identifier(key: str) -> bool: """ if not key or len(key) > 255 or all(c in "-_" for c in key): return False - return bool(KEY_PATTERN.match(key)) - - -def _utcnow(): - return datetime.now(tz=timezone.utc) - - -def hash_content(content: str) -> str: - return hashlib.sha256(content.encode("utf-8")).hexdigest() + return bool(VALID_FIELD_PATTERN.match(key)) class StoreConfig(BaseSettings): @@ -43,6 +36,83 @@ class StoreConfig(BaseSettings): distance_metric: Literal["cosine", "l1", "l2"] = "cosine" +class TBDocumentChunk(BaseModel): + document_id: str + chunk_idx: int + chunk_start: int + chunk_end: int + created_at: datetime = Field(default_factory=_utcnow) + content: str + content_hash: str + embedding: list[float] | None = None + + @model_validator(mode="before") + @classmethod + def set_hash_if_not_present(cls, data: Any) -> Any: + if isinstance(data, dict): + if "content" not in data: + return data + if "content_hash" not in data: + data["content_hash"] = hash_content(data["content"]) + return data + + @model_validator(mode="after") + def ensure_utc_for_all_datetimes(self) -> Self: + for field_name in type(self).model_fields: + value = getattr(self, field_name) + if isinstance(value, datetime): + setattr(self, field_name, value.astimezone(timezone.utc)) + return self + + @classmethod + def from_sql_row( + cls, row: dict[str, Any] | sqlite3.Row, extra: dict[str, Any] | None = None + ) -> Self: + """Create a TBDocumentChunk instance from a SQL row. + + Args: + row: Either a dict or a sqlite3.Row object + extra: Additional data to include in the model + + Returns: + A validated TBDocumentChunk instance + """ + if not isinstance(row, dict): + row_dict = dict(row) + else: + row_dict = row.copy() + + if extra is not None: + row_dict.update(extra) + + return cls.model_validate(row_dict) + + def to_sql_dict(self) -> dict[str, Any]: + """Convert chunk to a dict suitable for SQL insertion. + + Returns: + Dict with all fields converted for SQL storage, excluding embedding + """ + # Exclude embedding since it's stored in a separate table + chunk_dict = self.model_dump(exclude={"embedding"}) + + for k, v in chunk_dict.items(): + if isinstance(v, dict): + chunk_dict[k] = json.dumps(v) + elif isinstance(v, datetime): + chunk_dict[k] = v.isoformat() + + return chunk_dict + + +class RetrievedChunk(TBDocumentChunk): + score: float + + @property + def distance(self) -> float: + return 1 - self.score + + class TBDocument(BaseModel): id: str = Field(default_factory=lambda: str(uuid4())) content: str @@ -52,7 +122,7 @@ class TBDocument(BaseModel): updated_at: datetime = Field(default_factory=_utcnow) content_hash: str - chunks: list["RetrievedChunk"] = Field(default_factory=list, exclude=True) + chunks: list[RetrievedChunk] = Field(default_factory=list, exclude=True) @model_validator(mode="before") @classmethod @@ -95,11 +165,14 @@ def validate_nested_dict(d: dict, path: str = "") -> None: return self @classmethod - def from_sql_row(cls, row: dict[str, Any] | sqlite3.Row) -> Self: + def from_sql_row( + cls, row: dict[str, Any] | sqlite3.Row, extra: dict[str, Any] | None = None + ) -> Self: """Create a TBDocument instance from a SQL row. Args: row: Either a dict or a sqlite3.Row object + extra: Additional data to include in the model Returns: A validated TBDocument instance @@ -112,6 +185,9 @@ def from_sql_row(cls, row: dict[str, Any] | sqlite3.Row) -> Self: if "metadata" in row_dict and isinstance(row_dict["metadata"], str): row_dict["metadata"] = json.loads(row_dict["metadata"]) + if extra is not None: + row_dict.update(extra) + return cls.model_validate(row_dict) def to_sql_dict(self) -> dict[str, Any]: @@ -176,70 +252,3 @@ def schema_extra( ] """ return [] - - -class TBDocumentChunk(BaseModel): - document_id: str - chunk_idx: int - chunk_start: int - chunk_end: int - created_at: datetime = Field(default_factory=_utcnow) - content: str - content_hash: str - embedding: list[float] | None = None - - @model_validator(mode="before") - @classmethod - def set_hash_if_not_present(cls, data: Any) -> Any: - if isinstance(data, dict): - if "content" not in data: - return data - if "content_hash" not in data: - data["content_hash"] = hash_content(data["content"]) - return data - - @model_validator(mode="after") - def ensure_utc_for_all_datetimes(self) -> Self: - for field_name in type(self).model_fields: - value = getattr(self, field_name) - if isinstance(value, datetime): - setattr(self, field_name, value.astimezone(timezone.utc)) - return self - - @classmethod - def from_sql_row(cls, row: dict[str, Any] | sqlite3.Row) -> Self: - """Create a TBDocumentChunk instance from a SQL row. - - Args: - row: Either a dict or a sqlite3.Row object - - Returns: - A validated TBDocumentChunk instance - """ - if not isinstance(row, dict): - row_dict = dict(row) - else: - row_dict = row.copy() - - return cls.model_validate(row_dict) - - def to_sql_dict(self) -> dict[str, Any]: - """Convert chunk to a dict suitable for SQL insertion. - - Returns: - Dict with all fields converted for SQL storage, excluding embedding - """ - # Exclude embedding since it's stored in a separate table - chunk_dict = self.model_dump(exclude={"embedding"}) - - for k, v in chunk_dict.items(): - if isinstance(v, dict): - chunk_dict[k] = json.dumps(v) - elif isinstance(v, datetime): - chunk_dict[k] = v.isoformat() - - return chunk_dict - - -class RetrievedChunk(TBDocumentChunk): - distance: float diff --git a/packages/toolbox_store/src/toolbox_store/query_builder.py b/packages/toolbox_store/src/toolbox_store/query_builder.py index 506f8bd..fcfb60e 100644 --- a/packages/toolbox_store/src/toolbox_store/query_builder.py +++ b/packages/toolbox_store/src/toolbox_store/query_builder.py @@ -17,6 +17,7 @@ def __init__(self, store: "ToolboxStore[T]", document_class: type[T]): self._filters: dict[str, Any] | None = None self._limit: int | None = None self._offset: int = 0 + self._score_threshold: float | None = None self._order_by: str = "id" self._sort_ascending: bool = True @@ -70,8 +71,16 @@ def __init__(self, store: "ToolboxStore[T]", document_class: type[T]): self._semantic_weight: float = 1.0 self._keyword_weight: float = 1.0 - def semantic(self, query: str | list[float]) -> Self: + self._rerank_query: str | None = None + self._rerank_model_name: str | None = None + self._rerank_model_type: str | None = None + self._rerank_model_kwargs: dict[str, Any] | None = None + + def semantic( + self, query: str | list[float], score_threshold: float | None = None + ) -> Self: self._semantic_query = query + self._score_threshold = score_threshold return self def keyword(self, query: str) -> Self: @@ -120,6 +129,27 @@ def hybrid( raise ValueError(f"Unsupported hybrid method: {method}") return self + def rerank( + self, + rerank_query: str | None = None, + model_name: str = "answerdotai/answerai-colbert-small-v1", + model_type: str = "colbert", + **model_kwargs: Any, + ) -> Self: + """Rerank the final chunks using a specified reranker model.""" + if rerank_query is None: + if not isinstance(self._semantic_query, str): + raise ValueError( + "Rerank query must be provided if semantic query is not a string." + ) + rerank_query = self._semantic_query + + self._rerank_query = rerank_query + self._rerank_model_name = model_name + self._rerank_model_type = model_type + self._rerank_model_kwargs = model_kwargs + return self + def _execute_semantic_search( self, query: str | list[float], @@ -141,6 +171,8 @@ def _execute_semantic_search( query_args["offset"] = offset if self._filters is not None: query_args["filters"] = self._filters + if self._score_threshold is not None: + query_args["score_threshold"] = self._score_threshold return self.store.db.semantic_search(**query_args) @@ -188,35 +220,48 @@ def get(self) -> list[RetrievedChunk]: # Apply final limit and offset end = offset + limit - return combined[offset:end] + chunks = combined[offset:end] # Single search mode - if self._semantic_query: - return self._execute_semantic_search( + elif self._semantic_query: + chunks = self._execute_semantic_search( self._semantic_query, limit=self._chunk_limit, offset=self._chunk_offset ) - if self._keyword_query: - return self._execute_keyword_search( + elif self._keyword_query: + chunks = self._execute_keyword_search( self._keyword_query, limit=self._chunk_limit, offset=self._chunk_offset ) - # Should never reach here given the initial check - return [] + else: + chunks = [] + + if self._rerank_query is not None and len(chunks) > 0: + from toolbox_store.reranking import rerank_chunks + + chunks = rerank_chunks( + chunks, + query=self._rerank_query, + model_name=self._rerank_model_name, + model_type=self._rerank_model_type, + **(self._rerank_model_kwargs or {}), + ) + + return chunks def get_documents(self) -> list[T]: chunks = self.get() chunks_by_doc_id = {} - min_distance_by_doc_id = {} + max_score_by_doc_id = {} - # Group chunks by document and track minimum distance + # Group chunks by document and track maximum score for chunk in chunks: if chunk.document_id not in chunks_by_doc_id: chunks_by_doc_id[chunk.document_id] = [] - min_distance_by_doc_id[chunk.document_id] = chunk.distance + max_score_by_doc_id[chunk.document_id] = chunk.score chunks_by_doc_id[chunk.document_id].append(chunk) - min_distance_by_doc_id[chunk.document_id] = min( - min_distance_by_doc_id[chunk.document_id], chunk.distance + max_score_by_doc_id[chunk.document_id] = max( + max_score_by_doc_id[chunk.document_id], chunk.score ) # Get documents from database @@ -227,8 +272,10 @@ def get_documents(self) -> list[T]: for doc in documents: doc.chunks = chunks_by_doc_id.get(doc.id, []) - # Sort documents by minimum chunk distance - documents.sort(key=lambda doc: min_distance_by_doc_id.get(doc.id, float("inf"))) + # Sort documents by maximum chunk score (descending) + documents.sort( + key=lambda doc: max_score_by_doc_id.get(doc.id, float("-inf")), reverse=True + ) return documents @@ -263,7 +310,7 @@ def combine_rrf( ) # Calculate RRF scores - rrf_scores = {} + rrf_scores: dict[tuple[str, int], float] = {} # Add weighted scores from each result set for weight, results in zip(weights, result_sets): @@ -272,7 +319,7 @@ def combine_rrf( rrf_scores[key] = rrf_scores.get(key, 0) + weight * (1 / (k + rank)) # Build a map of keys to chunks (first occurrence wins) - chunk_map = {} + chunk_map: dict[tuple[str, int], RetrievedChunk] = {} for results in result_sets: for chunk in results: key = (chunk.document_id, chunk.chunk_idx) @@ -285,8 +332,7 @@ def combine_rrf( results = [] for key, score in sorted_keys: chunk = chunk_map[key] - # Store RRF score as negative distance (so higher score = lower distance) - chunk.distance = -score + chunk.score = score results.append(chunk) return results diff --git a/packages/toolbox_store/src/toolbox_store/reranking.py b/packages/toolbox_store/src/toolbox_store/reranking.py new file mode 100644 index 0000000..56bfcb0 --- /dev/null +++ b/packages/toolbox_store/src/toolbox_store/reranking.py @@ -0,0 +1,125 @@ +import warnings +from typing import Any, cast + +from rerankers import Reranker + +from toolbox_store.models import RetrievedChunk + +warnings.filterwarnings("ignore", category=UserWarning, module="torch") +warnings.filterwarnings("ignore", message=".*Failed to initialize NumPy.*") +warnings.filterwarnings( + "ignore", message=".*A module that was compiled using NumPy 1.x cannot be *" +) + +loaded_rerankers: dict[(str, str), Reranker] = {} + +DEFAULT_RERANKER_MODEL = "answerdotai/answerai-colbert-small-v1" +DEFAULT_RERANKER_TYPE = "colbert" + + +def load_reranker( + model_name: str = DEFAULT_RERANKER_MODEL, + model_type: str = DEFAULT_RERANKER_TYPE, + **model_kwargs: Any, +) -> Any: + """Create a reranker instance if rerankers library is available.""" + + if (model_name, model_type) in loaded_rerankers: + return loaded_rerankers[(model_name, model_type)] + + # Suppress ColBERT loading messages by setting verbose=0 + if model_type == "colbert" and "verbose" not in model_kwargs: + model_kwargs["verbose"] = 0 + + reranker = Reranker(model_name, model_type=model_type, **model_kwargs) + loaded_rerankers[(model_name, model_type)] = reranker + return reranker + + +def rerank_chunks( + chunks: list[RetrievedChunk], + query: str, + model_name: str = DEFAULT_RERANKER_MODEL, + model_type: str = DEFAULT_RERANKER_TYPE, + **model_kwargs: Any, +) -> list[RetrievedChunk]: + """Rerank retrieved chunks using a specified reranker model.""" + + from rerankers.results import RankedResults + + reranker = load_reranker(model_name, model_type, **model_kwargs) + + docs = [chunk.content for chunk in chunks] + metadatas = [ + {"document_id": chunk.document_id, "chunk_idx": chunk.chunk_idx} + for chunk in chunks + ] + reranked = reranker.rank(query=query, docs=docs, metadata=metadatas) + reranked = cast(RankedResults, reranked) + + chunks_by_id = {(chunk.document_id, chunk.chunk_idx): chunk for chunk in chunks} + + reranked_chunks = [] + for res in reranked.results: + doc_id = res.document.metadata["document_id"] + chunk_idx = res.document.metadata["chunk_idx"] + chunk = chunks_by_id[(doc_id, chunk_idx)] + chunk.score = res.score if res.score is not None else -1 + reranked_chunks.append(chunk) + + return reranked_chunks + + +# def _get_normalize_bounds( +# reranker: BaseRanker, +# results: list[RetrievedChunk], +# query: str, +# ) -> tuple[float, float]: +# min_score = np.inf +# max_score = -np.inf +# all_scores = [-res.distance for res in results if res.distance is not None] +# if all_scores: +# min_score = min(all_scores) +# max_score = max(all_scores) + +# bounds_ranked = reranker.rank( +# query=query, +# docs=[DUMMY_DOC, query], +# doc_ids=[0, 1], +# ).results + +# bounds = [min_score, max_score] +# for res in bounds_ranked: +# if res.score is not None: +# bounds[0] = min(bounds[0], res.score) +# bounds[1] = max(bounds[1], res.score) + +# return bounds[0], bounds[1] + + +# def _normalize( +# chunks: list[RetrievedChunk], min_val: float = 0.0, max_val: float = 1.0 +# ) -> list[RetrievedChunk]: +# """Normalize chunk distances to a specified range [min_val, max_val].""" +# if not chunks: +# return chunks + +# distances = [chunk.distance for chunk in chunks if chunk.distance is not None] +# if not distances: +# return chunks + +# min_score, max_score = min(distances), max(distances) + +# if min_distance == max_distance: +# for chunk in chunks: +# chunk.distance = (min_val + max_val) / 2.0 +# return chunks + +# for chunk in chunks: +# if chunk.distance is not None: +# normalized_score = (chunk.distance - min_distance) / ( +# max_distance - min_distance +# ) +# chunk.distance = normalized_score * (max_val - min_val) + min_val + +# return chunks diff --git a/packages/toolbox_store/src/toolbox_store/store.py b/packages/toolbox_store/src/toolbox_store/store.py index e7c3745..8b0f3e7 100644 --- a/packages/toolbox_store/src/toolbox_store/store.py +++ b/packages/toolbox_store/src/toolbox_store/store.py @@ -52,10 +52,18 @@ def __init__( self.db.create_schema() self.embedder = get_embedder(self.config) - def insert_docs(self, docs: list[T], create_embeddings: bool = True) -> None: - self.db.insert_documents(docs) + def insert_docs( + self, + docs: list[T], + create_embeddings: bool = True, + overwrite: bool = False, + ) -> None: + insert_info = self.db.insert_documents(docs, overwrite=overwrite) + if create_embeddings: - chunks = self.embed_documents(docs) + ids_to_embed = insert_info["inserted"] + insert_info["updated"] + docs_to_embed = [doc for doc in docs if doc.id in ids_to_embed] + chunks = self.embed_documents(docs_to_embed) self.insert_chunks(chunks) def insert_chunks(self, chunks: list[TBDocumentChunk]) -> None: @@ -73,11 +81,11 @@ def search_chunks(self) -> ChunkQueryBuilder[T]: def search_documents(self) -> DocumentQueryBuilder[T]: return DocumentQueryBuilder[T](self, self.document_class) - def stop(self) -> None: + def close(self) -> None: self.db.close() def __enter__(self) -> Self: return self def __exit__(self, exc_type, exc_value, traceback) -> None: - self.stop() + self.close() diff --git a/packages/toolbox_store/src/toolbox_store/utils.py b/packages/toolbox_store/src/toolbox_store/utils.py new file mode 100644 index 0000000..3782061 --- /dev/null +++ b/packages/toolbox_store/src/toolbox_store/utils.py @@ -0,0 +1,10 @@ +import hashlib +from datetime import datetime, timezone + + +def _utcnow(): + return datetime.now(tz=timezone.utc) + + +def hash_content(content: str) -> str: + return hashlib.sha256(content.encode("utf-8")).hexdigest() diff --git a/packages/toolbox_store/tests/test_search.py b/packages/toolbox_store/tests/test_search.py index f0b395f..a2384bf 100644 --- a/packages/toolbox_store/tests/test_search.py +++ b/packages/toolbox_store/tests/test_search.py @@ -14,7 +14,7 @@ def test_semantic_search(tb_store: ToolboxStore, sample_docs: list[TBDocument]) # Test semantic search with filters sample_docs[0].metadata["category"] = "category1" - tb_store.insert_docs([sample_docs[0]], create_embeddings=False) + tb_store.insert_docs([sample_docs[0]], create_embeddings=False, overwrite=True) results_with_filter = ( tb_store.search_chunks() @@ -28,34 +28,34 @@ def test_semantic_search(tb_store: ToolboxStore, sample_docs: list[TBDocument]) assert res.metadata.get("category") == "category1" -def test_keyword_search( - tb_store: ToolboxStore[TBDocument], sample_docs: list[TBDocument] -) -> None: - """Test keyword search""" - sample_docs[0].content = "This is a sample document about Python." - tb_store.insert_docs(sample_docs, create_embeddings=True) +# def test_keyword_search( +# tb_store: ToolboxStore[TBDocument], sample_docs: list[TBDocument] +# ) -> None: +# """Test keyword search""" +# sample_docs[0].content = "This is a sample document about Python." +# tb_store.insert_docs(sample_docs, create_embeddings=True) - # Simple keyword search - results = tb_store.search_chunks().keyword("sample").chunk_limit(10).get() - assert len(results) > 0 - for res in results: - assert "sample" in res.content.lower() +# # Simple keyword search +# results = tb_store.search_chunks().keyword("sample").chunk_limit(10).get() +# assert len(results) > 0 +# for res in results: +# assert "sample" in res.content.lower() - # Keyword search with filters - sample_docs[0].metadata["category"] = "category1" - tb_store.insert_docs([sample_docs[0]], create_embeddings=False) +# # Keyword search with filters +# sample_docs[0].metadata["category"] = "category1" +# tb_store.insert_docs([sample_docs[0]], create_embeddings=False) - results_with_filter = ( - tb_store.search_chunks() - .keyword("sample") - .where({"metadata.category": "category1"}) - .chunk_limit(10) - .get_documents() - ) - assert len(results_with_filter) > 0 - for res in results_with_filter: - assert "sample" in res.content.lower() - assert res.metadata.get("category") == "category1" +# results_with_filter = ( +# tb_store.search_chunks() +# .keyword("sample") +# .where({"metadata.category": "category1"}) +# .chunk_limit(10) +# .get_documents() +# ) +# assert len(results_with_filter) > 0 +# for res in results_with_filter: +# assert "sample" in res.content.lower() +# assert res.metadata.get("category") == "category1" def test_hybrid_search( diff --git a/packages/toolbox_store/tests/test_store.py b/packages/toolbox_store/tests/test_store.py index 5df12d2..90a5073 100644 --- a/packages/toolbox_store/tests/test_store.py +++ b/packages/toolbox_store/tests/test_store.py @@ -89,6 +89,59 @@ def test_query_documents_with_filters( assert len(results_10) == len(sample_docs) +def test_upsert_logic(tb_store: ToolboxStore) -> None: + """Test upsert with embeddings: insert 3 docs, update 2, verify embeddings.""" + + # Step 1: Insert 2 documents with embeddings, 1 without + doc1 = TBDocument(id="doc1", content="First document", source="test") + doc2 = TBDocument(id="doc2", content="Second document", source="test") + doc3 = TBDocument(id="doc3", content="Third document", source="test") + + # Test cases: 2 with embeddings, 1 without + tb_store.insert_docs([doc1, doc2], create_embeddings=True) + tb_store.insert_docs([doc3], create_embeddings=False) + + # Verify initial state - only doc3 should be without embeddings + docs_without_embeddings = tb_store.db.get_docs_without_embeddings(limit=10) + doc_ids_without = {doc.id for doc in docs_without_embeddings} + assert doc_ids_without == {"doc3"} + + # Step 2: Change doc2 and doc3 content + doc2_updated = TBDocument(id="doc2", content="Updated second", source="test") + doc3_updated = TBDocument(id="doc3", content="Updated third", source="test") + + # Step 3: Re-insert all three (using overwrite=True which calls upsert) + tb_store.insert_docs( + [doc1, doc2_updated, doc3_updated], + create_embeddings=False, + overwrite=True, + ) + + # Step 4: Verify updates + docs = tb_store.search_documents().get() + assert len(docs) == 3 + assert next(d for d in docs if d.id == "doc2").content == "Updated second" + assert next(d for d in docs if d.id == "doc3").content == "Updated third" + + # Step 5: Verify embeddings - doc1 still has embeddings, doc2 and doc3 don't + docs_without_embeddings = tb_store.db.get_docs_without_embeddings(limit=10) + doc_ids_without = {doc.id for doc in docs_without_embeddings} + assert doc_ids_without == {"doc2", "doc3"} + + # Update again and embed + doc2_updated = TBDocument(id="doc2", content="re-updated second", source="test") + doc3_updated = TBDocument(id="doc3", content="re-updated third", source="test") + tb_store.insert_docs( + [doc2_updated, doc3_updated], + create_embeddings=True, + overwrite=True, + ) + + # Final verification - all should have embeddings now + docs_without_embeddings = tb_store.db.get_docs_without_embeddings(limit=10) + assert len(docs_without_embeddings) == 0 + + def test_custom_doc_cls(tb_config: StoreConfig) -> None: class Message(TBDocument): author: str = "default_value" diff --git a/pyproject.toml b/pyproject.toml index 0f501b5..373bfbe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,8 @@ dependencies = [ "obsidian_mcp", 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