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authornavanchauhan <navanchauhan@gmail.com>2022-05-22 12:03:28 -0600
committernavanchauhan <navanchauhan@gmail.com>2022-05-22 12:03:28 -0600
commitd382b50c111f2f2867a4af0176285d0cea7b591a (patch)
treeafbf871b800e367d2639fef1802381e2676e29ef
parent885747d29973e3a1b04a68bc40a3e72ca0b711e7 (diff)
added new post movie recommender
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-rw-r--r--docs/feed.rss408
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diff --git a/Content/posts/2022-05-21-Similar-Movies-Recommender.md b/Content/posts/2022-05-21-Similar-Movies-Recommender.md
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@@ -0,0 +1,400 @@
+---
+date: 2022-05-21 17:56
+description: Building a Content Based Similar Movies Recommender System
+tags: Python, Transformers, Movies, Recommender-System
+---
+
+# Building a Simple Similar Movies Recommender System
+
+## Why?
+
+I recently came across a movie/tv-show recommender, [couchmoney.tv](https://couchmoney.tv/). I loved it. I decided that I wanted to build something similar, so I could tinker with it as much as I wanted.
+
+I also wanted a recommendation system I could use via a REST API. Although I have not included that part in this post, I did eventually create it.
+
+
+## How?
+
+By measuring the cosine of the angle between two vectors, you can get a value in the range [0,1] with 0 meaning no similarity. Now, if we find a way to represent information about movies as a vector, we can use cosine similarity as a metric to find similar movies.
+
+As we are recommending just based on the content of the movies, this is called a content based recommendation system.
+
+## Data Collection
+
+Trakt exposes a nice API to search for movies/tv-shows. To access the API, you first need to get an API key (the Trakt ID you get when you create a new application).
+
+I decided to use SQL-Alchemy with a SQLite backend just to make my life easier if I decided on switching to Postgres anytime I felt like.
+
+First, I needed to check the total number of records in Trakt’s database.
+
+```python
+import requests
+import os
+
+trakt_id = os.getenv("TRAKT_ID")
+
+api_base = "https://api.trakt.tv"
+
+headers = {
+ "Content-Type": "application/json",
+ "trakt-api-version": "2",
+ "trakt-api-key": trakt_id
+}
+
+params = {
+ "query": "",
+ "years": "1900-2021",
+ "page": "1",
+ "extended": "full",
+ "languages": "en"
+}
+
+res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
+total_items = res.headers["x-pagination-item-count"]
+print(f"There are {total_items} movies")
+```
+
+```
+There are 333946 movies
+```
+
+First, I needed to declare the database schema in (`database.py`):
+
+```python
+import sqlalchemy
+from sqlalchemy import create_engine
+from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey, PickleType
+from sqlalchemy import insert
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.exc import IntegrityError
+
+meta = MetaData()
+
+movies_table = Table(
+ "movies",
+ meta,
+ Column("trakt_id", Integer, primary_key=True, autoincrement=False),
+ Column("title", String),
+ Column("overview", String),
+ Column("genres", String),
+ Column("year", Integer),
+ Column("released", String),
+ Column("runtime", Integer),
+ Column("country", String),
+ Column("language", String),
+ Column("rating", Integer),
+ Column("votes", Integer),
+ Column("comment_count", Integer),
+ Column("tagline", String),
+ Column("embeddings", PickleType)
+
+)
+
+# Helper function to connect to the db
+def init_db_stuff(database_url: str):
+ engine = create_engine(database_url)
+ meta.create_all(engine)
+ Session = sessionmaker(bind=engine)
+ return engine, Session
+```
+
+In the end, I could have dropped the embeddings field from the table schema as I never got around to using it.
+
+### Scripting Time
+
+```python
+from database import *
+from tqdm import tqdm
+import requests
+import os
+
+trakt_id = os.getenv("TRAKT_ID")
+
+max_requests = 5000 # How many requests I wanted to wrap everything up in
+req_count = 0 # A counter for how many requests I have made
+
+years = "1900-2021"
+page = 1 # The initial page number for the search
+extended = "full" # Required to get additional information
+limit = "10" # No of entires per request -- This will be automatically picked based on max_requests
+languages = "en" # Limit to English
+
+api_base = "https://api.trakt.tv"
+database_url = "sqlite:///jlm.db"
+
+headers = {
+ "Content-Type": "application/json",
+ "trakt-api-version": "2",
+ "trakt-api-key": trakt_id
+}
+
+params = {
+ "query": "",
+ "years": years,
+ "page": page,
+ "extended": extended,
+ "limit": limit,
+ "languages": languages
+}
+
+# Helper function to get desirable values from the response
+def create_movie_dict(movie: dict):
+ m = movie["movie"]
+ movie_dict = {
+ "title": m["title"],
+ "overview": m["overview"],
+ "genres": m["genres"],
+ "language": m["language"],
+ "year": int(m["year"]),
+ "trakt_id": m["ids"]["trakt"],
+ "released": m["released"],
+ "runtime": int(m["runtime"]),
+ "country": m["country"],
+ "rating": int(m["rating"]),
+ "votes": int(m["votes"]),
+ "comment_count": int(m["comment_count"]),
+ "tagline": m["tagline"]
+ }
+ return movie_dict
+
+# Get total number of items
+params["limit"] = 1
+res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
+total_items = res.headers["x-pagination-item-count"]
+
+engine, Session = init_db_stuff(database_url)
+
+
+for page in tqdm(range(1,max_requests+1)):
+ params["page"] = page
+ params["limit"] = int(int(total_items)/max_requests)
+ movies = []
+ res = requests.get(f"{api_base}/search/movie",headers=headers,params=params)
+
+ if res.status_code == 500:
+ break
+ elif res.status_code == 200:
+ None
+ else:
+ print(f"OwO Code {res.status_code}")
+
+ for movie in res.json():
+ movies.append(create_movie_dict(movie))
+
+ with engine.connect() as conn:
+ for movie in movies:
+ with conn.begin() as trans:
+ stmt = insert(movies_table).values(
+ trakt_id=movie["trakt_id"], title=movie["title"], genres=" ".join(movie["genres"]),
+ language=movie["language"], year=movie["year"], released=movie["released"],
+ runtime=movie["runtime"], country=movie["country"], overview=movie["overview"],
+ rating=movie["rating"], votes=movie["votes"], comment_count=movie["comment_count"],
+ tagline=movie["tagline"])
+ try:
+ result = conn.execute(stmt)
+ trans.commit()
+ except IntegrityError:
+ trans.rollback()
+ req_count += 1
+```
+
+(Note: I was well within the rate-limit so I did not have to slow down or implement any other measures)
+
+Running this script took me approximately 3 hours, and resulted in an SQLite database of 141.5 MB
+
+## Embeddings!
+
+I did not want to put my poor Mac through the estimated 23 hours it would have taken to embed the sentences. I decided to use Google Colab instead.
+
+Because of the small size of the database file, I was able to just upload the file.
+
+For the encoding model, I decided to use the pretrained `paraphrase-multilingual-MiniLM-L12-v2` model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt.
+
+While deciding how I was going to process the embeddings, I came across multiple solutions:
+
+* [Milvus](https://milvus.io) - An open-source vector database with similar search functionality
+
+* [FAISS](https://faiss.ai) - A library for efficient similarity search
+
+* [Pinecone](https://pinecone.io) - A fully managed vector database with similar search functionality
+
+I did not want to waste time setting up the first two, so I decided to go with Pinecone which offers 1M 768-dim vectors for free with no credit card required (Our embeddings are 384-dim dense).
+
+Getting started with Pinecone was as easy as:
+
+* Signing up
+
+* Specifying the index name and vector dimensions along with the similarity search metric (Cosine Similarity for our use case)
+
+* Getting the API key
+
+* Installing the Python module (pinecone-client)
+
+```python
+import pandas as pd
+import pinecone
+from sentence_transformers import SentenceTransformer
+from tqdm import tqdm
+
+database_url = "sqlite:///jlm.db"
+PINECONE_KEY = "not-this-at-all"
+batch_size = 32
+
+pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")
+index = pinecone.Index("movies")
+
+model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2", device="cuda")
+engine, Session = init_db_stuff(database_url)
+
+df = pd.read_sql("Select * from movies", engine)
+df["combined_text"] = df["title"] + ": " + df["overview"].fillna('') + " - " + df["tagline"].fillna('') + " Genres:- " + df["genres"].fillna('')
+
+# Creating the embedding and inserting it into the database
+for x in tqdm(range(0,len(df),batch_size)):
+ to_send = []
+ trakt_ids = df["trakt_id"][x:x+batch_size].tolist()
+ sentences = df["combined_text"][x:x+batch_size].tolist()
+ embeddings = model.encode(sentences)
+ for idx, value in enumerate(trakt_ids):
+ to_send.append(
+ (
+ str(value), embeddings[idx].tolist()
+ ))
+ index.upsert(to_send)
+```
+
+That's it!
+
+## Interacting with Vectors
+
+We use the `trakt_id` for the movie as the ID for the vectors and upsert it into the index.
+
+To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.
+
+```python
+def get_trakt_id(df, title: str):
+ rec = df[df["title"].str.lower()==movie_name.lower()]
+ if len(rec.trakt_id.values.tolist()) > 1:
+ print(f"multiple values found... {len(rec.trakt_id.values)}")
+ for x in range(len(rec)):
+ print(f"[{x}] {rec['title'].tolist()[x]} ({rec['year'].tolist()[x]}) - {rec['overview'].tolist()}")
+ print("===")
+ z = int(input("Choose No: "))
+ return rec.trakt_id.values[z]
+ return rec.trakt_id.values[0]
+
+def get_vector_value(trakt_id: int):
+ fetch_response = index.fetch(ids=[str(trakt_id)])
+ return fetch_response["vectors"][str(trakt_id)]["values"]
+
+def query_vectors(vector: list, top_k: int = 20, include_values: bool = False, include_metada: bool = True):
+ query_response = index.query(
+ queries=[
+ (vector),
+ ],
+ top_k=top_k,
+ include_values=include_values,
+ include_metadata=include_metada
+ )
+ return query_response
+
+def query2ids(query_response):
+ trakt_ids = []
+ for match in query_response["results"][0]["matches"]:
+ trakt_ids.append(int(match["id"]))
+ return trakt_ids
+
+def get_deets_by_trakt_id(df, trakt_id: int):
+ df = df[df["trakt_id"]==trakt_id]
+ return {
+ "title": df.title.values[0],
+ "overview": df.overview.values[0],
+ "runtime": df.runtime.values[0],
+ "year": df.year.values[0]
+ }
+```
+
+### Testing it Out
+
+```python
+movie_name = "Now You See Me"
+
+movie_trakt_id = get_trakt_id(df, movie_name)
+print(movie_trakt_id)
+movie_vector = get_vector_value(movie_trakt_id)
+movie_queries = query_vectors(movie_vector)
+movie_ids = query2ids(movie_queries)
+print(movie_ids)
+
+for trakt_id in movie_ids:
+ deets = get_deets_by_trakt_id(df, trakt_id)
+ print(f"{deets['title']} ({deets['year']}): {deets['overview']}")
+```
+
+Output:
+
+```
+55786
+[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
+Now You See Me (2013): An FBI agent and an Interpol detective track a team of illusionists who pull off bank heists during their performances and reward their audiences with the money.
+Trapped (1949): U.S. Treasury Department agents go after a ring of counterfeiters.
+Brute Sanity (2018): An FBI-trained neuropsychologist teams up with a thief to find a reality-altering device while her insane ex-boss unleashes bizarre traps to stop her.
+The Chase (2017): Some FBI agents hunt down a criminal
+Surveillance (2008): An FBI agent tracks a serial killer with the help of three of his would-be victims - all of whom have wildly different stories to tell.
+Marauders (2016): An untraceable group of elite bank robbers is chased by a suicidal FBI agent who uncovers a deeper purpose behind the robbery-homicides.
+Miracles for Sale (1939): A maker of illusions for magicians protects an ingenue likely to be murdered.
+Deceptors (2005): A Ghostbusters knock-off where a group of con-artists create bogus monsters to scare up some cash. They run for their lives when real spooks attack.
+The Outfit (1993): A renegade FBI agent sparks an explosive mob war between gangster crime lords Legs Diamond and Dutch Schultz.
+Bank Alarm (1937): A federal agent learns the gangsters he's been investigating have kidnapped his sister.
+The Courier (2012): A shady FBI agent recruits a courier to deliver a mysterious package to a vengeful master criminal who has recently resurfaced with a diabolical plan.
+After the Sunset (2004): An FBI agent is suspicious of two master thieves, quietly enjoying their retirement near what may - or may not - be the biggest score of their careers.
+Down Three Dark Streets (1954): An FBI Agent takes on the three unrelated cases of a dead agent to track down his killer.
+The Executioner (1970): A British intelligence agent must track down a fellow spy suspected of being a double agent.
+Ace of Cactus Range (1924): A Secret Service agent goes undercover to unmask the leader of a gang of diamond thieves.
+Firepower (1979): A mercenary is hired by the FBI to track down a powerful recluse criminal, a woman is also trying to track him down for her own personal vendetta.
+Heroes & Villains (2018): an FBI agent chases a thug to great tunes
+Federal Fugitives (1941): A government agent goes undercover in order to apprehend a saboteur who caused a plane crash.
+Hell on Earth (2012): An FBI Agent on the trail of a group of drug traffickers learns that their corruption runs deeper than she ever imagined, and finds herself in a supernatural - and deadly - situation.
+Spies (2015): A secret agent must perform a heist without time on his side
+```
+
+For now, I am happy with the recommendations.
+
+## Simple UI
+
+The code for the flask app can be found on GitHub: [navanchauhan/FlixRec](https://github.com/navanchauhan/FlixRec) or on my [Gitea instance](https://pi4.navan.dev/gitea/navan/FlixRec)
+
+I quickly whipped up a simple Flask App to deal with problems of multiple movies sharing the title, and typos in the search query.
+
+### Home Page
+
+![Home Page](/assets/flixrec/home.png)
+
+### Handling Multiple Movies with Same Title
+
+![Multiple Movies with Same Title](/assets/flixrec/multiple.png)
+
+### Results Page
+
+![Results Page](/assets/flixrec/results.png)
+
+Includes additional filter options
+
+![Advance Filtering Options](/assets/flixrec/filter.png)
+
+Test it out at [https://flixrec.navan.dev](https://flixrec.navan.dev)
+
+## Current Limittations
+
+* Does not work well with popular franchises
+* No Genre Filter
+
+## Future Addons
+
+* Include Cast Data
+ * e.g. If it sees a movie with Tom Hanks and Meg Ryan, then it will boost similar movies including them
+ * e.g. If it sees the movie has been directed my McG, then it will boost similar movies directed by them
+* REST API
+* TV Shows
+* Multilingual database
+* Filter based on popularity: The data already exists in the indexed database \ No newline at end of file
diff --git a/Resources/assets/flixrec/filter.png b/Resources/assets/flixrec/filter.png
new file mode 100644
index 0000000..c1e4c52
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+++ b/Resources/assets/flixrec/filter.png
Binary files differ
diff --git a/Resources/assets/flixrec/home.png b/Resources/assets/flixrec/home.png
new file mode 100644
index 0000000..2d6fb51
--- /dev/null
+++ b/Resources/assets/flixrec/home.png
Binary files differ
diff --git a/Resources/assets/flixrec/multiple.png b/Resources/assets/flixrec/multiple.png
new file mode 100644
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--- /dev/null
+++ b/Resources/assets/flixrec/multiple.png
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diff --git a/Resources/assets/flixrec/results.png b/Resources/assets/flixrec/results.png
new file mode 100644
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diff --git a/docs/feed.rss b/docs/feed.rss
index 2b53f53..3f65a70 100644
--- a/docs/feed.rss
+++ b/docs/feed.rss
@@ -4,8 +4,8 @@
<title>Navan's Archive</title>
<description>Rare Tips, Tricks and Posts</description>
<link>https://web.navan.dev/</link><language>en</language>
- <lastBuildDate>Sun, 07 Nov 2021 17:42:49 -0000</lastBuildDate>
- <pubDate>Sun, 07 Nov 2021 17:42:49 -0000</pubDate>
+ <lastBuildDate>Sun, 22 May 2022 11:59:10 -0000</lastBuildDate>
+ <pubDate>Sun, 22 May 2022 11:59:10 -0000</pubDate>
<ttl>250</ttl>
<atom:link href="https://web.navan.dev/feed.rss" rel="self" type="application/rss+xml"/>
@@ -567,6 +567,410 @@ export BABEL_LIBDIR="/usr/lib/openbabel/3.1.0"
<item>
<guid isPermaLink="true">
+ https://web.navan.dev/posts/2022-05-21-Similar-Movies-Recommender.html
+ </guid>
+ <title>
+ Building a Simple Similar Movies Recommender System
+ </title>
+ <description>
+ Building a Content Based Similar Movies Recommender System
+ </description>
+ <link>https://web.navan.dev/posts/2022-05-21-Similar-Movies-Recommender.html</link>
+ <pubDate>Sat, 21 May 2022 17:56:00 -0000</pubDate>
+ <content:encoded><![CDATA[<h1>Building a Simple Similar Movies Recommender System</h1>
+
+<h2>Why?</h2>
+
+<p>I recently came across a movie/tv-show recommender, <a rel="noopener" target="_blank" href="https://couchmoney.tv/">couchmoney.tv</a>. I loved it. I decided that I wanted to build something similar, so I could tinker with it as much as I wanted.</p>
+
+<p>I also wanted a recommendation system I could use via a REST API. Although I have not included that part in this post, I did eventually create it.</p>
+
+<h2>How?</h2>
+
+<p>By measuring the cosine of the angle between two vectors, you can get a value in the range [0,1] with 0 meaning no similarity. Now, if we find a way to represent information about movies as a vector, we can use cosine similarity as a metric to find similar movies.</p>
+
+<p>As we are recommending just based on the content of the movies, this is called a content based recommendation system.</p>
+
+<h2>Data Collection</h2>
+
+<p>Trakt exposes a nice API to search for movies/tv-shows. To access the API, you first need to get an API key (the Trakt ID you get when you create a new application). </p>
+
+<p>I decided to use SQL-Alchemy with a SQLite backend just to make my life easier if I decided on switching to Postgres anytime I felt like. </p>
+
+<p>First, I needed to check the total number of records in Trakt’s database.</p>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">trakt_id</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;TRAKT_ID&quot;</span><span class="p">)</span>
+
+<span class="n">api_base</span> <span class="o">=</span> <span class="s2">&quot;https://api.trakt.tv&quot;</span>
+
+<span class="n">headers</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;Content-Type&quot;</span><span class="p">:</span> <span class="s2">&quot;application/json&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-version&quot;</span><span class="p">:</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-key&quot;</span><span class="p">:</span> <span class="n">trakt_id</span>
+<span class="p">}</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;query&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;years&quot;</span><span class="p">:</span> <span class="s2">&quot;1900-2021&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;page&quot;</span><span class="p">:</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;extended&quot;</span><span class="p">:</span> <span class="s2">&quot;full&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;languages&quot;</span><span class="p">:</span> <span class="s2">&quot;en&quot;</span>
+<span class="p">}</span>
+
+<span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">api_base</span><span class="si">}</span><span class="s2">/search/movie&quot;</span><span class="p">,</span><span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span><span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="n">total_items</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">headers</span><span class="p">[</span><span class="s2">&quot;x-pagination-item-count&quot;</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;There are </span><span class="si">{</span><span class="n">total_items</span><span class="si">}</span><span class="s2"> movies&quot;</span><span class="p">)</span>
+</code></pre></div>
+
+<pre><code>There are 333946 movies
+</code></pre>
+
+<p>First, I needed to declare the database schema in (<code>database.py</code>):</p>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">sqlalchemy</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">create_engine</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">Table</span><span class="p">,</span> <span class="n">Column</span><span class="p">,</span> <span class="n">Integer</span><span class="p">,</span> <span class="n">String</span><span class="p">,</span> <span class="n">MetaData</span><span class="p">,</span> <span class="n">ForeignKey</span><span class="p">,</span> <span class="n">PickleType</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">insert</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy.orm</span> <span class="kn">import</span> <span class="n">sessionmaker</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy.exc</span> <span class="kn">import</span> <span class="n">IntegrityError</span>
+
+<span class="n">meta</span> <span class="o">=</span> <span class="n">MetaData</span><span class="p">()</span>
+
+<span class="n">movies_table</span> <span class="o">=</span> <span class="n">Table</span><span class="p">(</span>
+ <span class="s2">&quot;movies&quot;</span><span class="p">,</span>
+ <span class="n">meta</span><span class="p">,</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">,</span> <span class="n">primary_key</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">autoincrement</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;title&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;overview&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;genres&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;year&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;released&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;runtime&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;country&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;language&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;rating&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;votes&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;comment_count&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;tagline&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;embeddings&quot;</span><span class="p">,</span> <span class="n">PickleType</span><span class="p">)</span>
+
+<span class="p">)</span>
+
+<span class="c1"># Helper function to connect to the db</span>
+<span class="k">def</span> <span class="nf">init_db_stuff</span><span class="p">(</span><span class="n">database_url</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+ <span class="n">engine</span> <span class="o">=</span> <span class="n">create_engine</span><span class="p">(</span><span class="n">database_url</span><span class="p">)</span>
+ <span class="n">meta</span><span class="o">.</span><span class="n">create_all</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
+ <span class="n">Session</span> <span class="o">=</span> <span class="n">sessionmaker</span><span class="p">(</span><span class="n">bind</span><span class="o">=</span><span class="n">engine</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">engine</span><span class="p">,</span> <span class="n">Session</span>
+</code></pre></div>
+
+<p>In the end, I could have dropped the embeddings field from the table schema as I never got around to using it.</p>
+
+<h3>Scripting Time</h3>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">from</span> <span class="nn">database</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">trakt_id</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;TRAKT_ID&quot;</span><span class="p">)</span>
+
+<span class="n">max_requests</span> <span class="o">=</span> <span class="mi">5000</span> <span class="c1"># How many requests I wanted to wrap everything up in</span>
+<span class="n">req_count</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># A counter for how many requests I have made</span>
+
+<span class="n">years</span> <span class="o">=</span> <span class="s2">&quot;1900-2021&quot;</span>
+<span class="n">page</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># The initial page number for the search</span>
+<span class="n">extended</span> <span class="o">=</span> <span class="s2">&quot;full&quot;</span> <span class="c1"># Required to get additional information </span>
+<span class="n">limit</span> <span class="o">=</span> <span class="s2">&quot;10&quot;</span> <span class="c1"># No of entires per request -- This will be automatically picked based on max_requests</span>
+<span class="n">languages</span> <span class="o">=</span> <span class="s2">&quot;en&quot;</span> <span class="c1"># Limit to English</span>
+
+<span class="n">api_base</span> <span class="o">=</span> <span class="s2">&quot;https://api.trakt.tv&quot;</span>
+<span class="n">database_url</span> <span class="o">=</span> <span class="s2">&quot;sqlite:///jlm.db&quot;</span>
+
+<span class="n">headers</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;Content-Type&quot;</span><span class="p">:</span> <span class="s2">&quot;application/json&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-version&quot;</span><span class="p">:</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-key&quot;</span><span class="p">:</span> <span class="n">trakt_id</span>
+<span class="p">}</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;query&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;years&quot;</span><span class="p">:</span> <span class="n">years</span><span class="p">,</span>
+ <span class="s2">&quot;page&quot;</span><span class="p">:</span> <span class="n">page</span><span class="p">,</span>
+ <span class="s2">&quot;extended&quot;</span><span class="p">:</span> <span class="n">extended</span><span class="p">,</span>
+ <span class="s2">&quot;limit&quot;</span><span class="p">:</span> <span class="n">limit</span><span class="p">,</span>
+ <span class="s2">&quot;languages&quot;</span><span class="p">:</span> <span class="n">languages</span>
+<span class="p">}</span>
+
+<span class="c1"># Helper function to get desirable values from the response</span>
+<span class="k">def</span> <span class="nf">create_movie_dict</span><span class="p">(</span><span class="n">movie</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+ <span class="n">m</span> <span class="o">=</span> <span class="n">movie</span><span class="p">[</span><span class="s2">&quot;movie&quot;</span><span class="p">]</span>
+ <span class="n">movie_dict</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;title&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;overview&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;overview&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;genres&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;genres&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;language&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;language&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;year&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;year&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;trakt_id&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;ids&quot;</span><span class="p">][</span><span class="s2">&quot;trakt&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;released&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;released&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;runtime&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;runtime&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;country&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;country&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;rating&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;rating&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;votes&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;votes&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;comment_count&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;comment_count&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;tagline&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;tagline&quot;</span><span class="p">]</span>
+ <span class="p">}</span>
+ <span class="k">return</span> <span class="n">movie_dict</span>
+
+<span class="c1"># Get total number of items</span>
+<span class="n">params</span><span class="p">[</span><span class="s2">&quot;limit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">api_base</span><span class="si">}</span><span class="s2">/search/movie&quot;</span><span class="p">,</span><span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span><span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="n">total_items</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">headers</span><span class="p">[</span><span class="s2">&quot;x-pagination-item-count&quot;</span><span class="p">]</span>
+
+<span class="n">engine</span><span class="p">,</span> <span class="n">Session</span> <span class="o">=</span> <span class="n">init_db_stuff</span><span class="p">(</span><span class="n">database_url</span><span class="p">)</span>
+
+
+<span class="k">for</span> <span class="n">page</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">max_requests</span><span class="o">+</span><span class="mi">1</span><span class="p">)):</span>
+ <span class="n">params</span><span class="p">[</span><span class="s2">&quot;page&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">page</span>
+ <span class="n">params</span><span class="p">[</span><span class="s2">&quot;limit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">total_items</span><span class="p">)</span><span class="o">/</span><span class="n">max_requests</span><span class="p">)</span>
+ <span class="n">movies</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">api_base</span><span class="si">}</span><span class="s2">/search/movie&quot;</span><span class="p">,</span><span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span><span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="n">res</span><span class="o">.</span><span class="n">status_code</span> <span class="o">==</span> <span class="mi">500</span><span class="p">:</span>
+ <span class="k">break</span>
+ <span class="k">elif</span> <span class="n">res</span><span class="o">.</span><span class="n">status_code</span> <span class="o">==</span> <span class="mi">200</span><span class="p">:</span>
+ <span class="kc">None</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;OwO Code </span><span class="si">{</span><span class="n">res</span><span class="o">.</span><span class="n">status_code</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+ <span class="k">for</span> <span class="n">movie</span> <span class="ow">in</span> <span class="n">res</span><span class="o">.</span><span class="n">json</span><span class="p">():</span>
+ <span class="n">movies</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_movie_dict</span><span class="p">(</span><span class="n">movie</span><span class="p">))</span>
+
+ <span class="k">with</span> <span class="n">engine</span><span class="o">.</span><span class="n">connect</span><span class="p">()</span> <span class="k">as</span> <span class="n">conn</span><span class="p">:</span>
+ <span class="k">for</span> <span class="n">movie</span> <span class="ow">in</span> <span class="n">movies</span><span class="p">:</span>
+ <span class="k">with</span> <span class="n">conn</span><span class="o">.</span><span class="n">begin</span><span class="p">()</span> <span class="k">as</span> <span class="n">trans</span><span class="p">:</span>
+ <span class="n">stmt</span> <span class="o">=</span> <span class="n">insert</span><span class="p">(</span><span class="n">movies_table</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">(</span>
+ <span class="n">trakt_id</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">],</span> <span class="n">genres</span><span class="o">=</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;genres&quot;</span><span class="p">]),</span>
+ <span class="n">language</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;language&quot;</span><span class="p">],</span> <span class="n">year</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;year&quot;</span><span class="p">],</span> <span class="n">released</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;released&quot;</span><span class="p">],</span>
+ <span class="n">runtime</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;runtime&quot;</span><span class="p">],</span> <span class="n">country</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;country&quot;</span><span class="p">],</span> <span class="n">overview</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;overview&quot;</span><span class="p">],</span>
+ <span class="n">rating</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;rating&quot;</span><span class="p">],</span> <span class="n">votes</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;votes&quot;</span><span class="p">],</span> <span class="n">comment_count</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;comment_count&quot;</span><span class="p">],</span>
+ <span class="n">tagline</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;tagline&quot;</span><span class="p">])</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">result</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="n">stmt</span><span class="p">)</span>
+ <span class="n">trans</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
+ <span class="k">except</span> <span class="n">IntegrityError</span><span class="p">:</span>
+ <span class="n">trans</span><span class="o">.</span><span class="n">rollback</span><span class="p">()</span>
+ <span class="n">req_count</span> <span class="o">+=</span> <span class="mi">1</span>
+</code></pre></div>
+
+<p>(Note: I was well within the rate-limit so I did not have to slow down or implement any other measures)</p>
+
+<p>Running this script took me approximately 3 hours, and resulted in an SQLite database of 141.5 MB</p>
+
+<h2>Embeddings!</h2>
+
+<p>I did not want to put my poor Mac through the estimated 23 hours it would have taken to embed the sentences. I decided to use Google Colab instead.</p>
+
+<p>Because of the small size of the database file, I was able to just upload the file.</p>
+
+<p>For the encoding model, I decided to use the pretrained <code>paraphrase-multilingual-MiniLM-L12-v2</code> model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt. </p>
+
+<p>While deciding how I was going to process the embeddings, I came across multiple solutions:</p>
+
+<ul>
+<li><p><a rel="noopener" target="_blank" href="https://milvus.io">Milvus</a> - An open-source vector database with similar search functionality</p></li>
+<li><p><a rel="noopener" target="_blank" href="https://faiss.ai">FAISS</a> - A library for efficient similarity search</p></li>
+<li><p><a rel="noopener" target="_blank" href="https://pinecone.io">Pinecone</a> - A fully managed vector database with similar search functionality</p></li>
+</ul>
+
+<p>I did not want to waste time setting up the first two, so I decided to go with Pinecone which offers 1M 768-dim vectors for free with no credit card required (Our embeddings are 384-dim dense).</p>
+
+<p>Getting started with Pinecone was as easy as:</p>
+
+<ul>
+<li><p>Signing up</p></li>
+<li><p>Specifying the index name and vector dimensions along with the similarity search metric (Cosine Similarity for our use case)</p></li>
+<li><p>Getting the API key</p></li>
+<li><p>Installing the Python module (pinecone-client)</p></li>
+</ul>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">pinecone</span>
+<span class="kn">from</span> <span class="nn">sentence_transformers</span> <span class="kn">import</span> <span class="n">SentenceTransformer</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="n">database_url</span> <span class="o">=</span> <span class="s2">&quot;sqlite:///jlm.db&quot;</span>
+<span class="n">PINECONE_KEY</span> <span class="o">=</span> <span class="s2">&quot;not-this-at-all&quot;</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
+
+<span class="n">pinecone</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">api_key</span><span class="o">=</span><span class="n">PINECONE_KEY</span><span class="p">,</span> <span class="n">environment</span><span class="o">=</span><span class="s2">&quot;us-west1-gcp&quot;</span><span class="p">)</span>
+<span class="n">index</span> <span class="o">=</span> <span class="n">pinecone</span><span class="o">.</span><span class="n">Index</span><span class="p">(</span><span class="s2">&quot;movies&quot;</span><span class="p">)</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">SentenceTransformer</span><span class="p">(</span><span class="s2">&quot;paraphrase-multilingual-MiniLM-L12-v2&quot;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">engine</span><span class="p">,</span> <span class="n">Session</span> <span class="o">=</span> <span class="n">init_db_stuff</span><span class="p">(</span><span class="n">database_url</span><span class="p">)</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">(</span><span class="s2">&quot;Select * from movies&quot;</span><span class="p">,</span> <span class="n">engine</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;combined_text&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;: &quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;overview&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; - &quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;tagline&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; Genres:- &quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;genres&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+
+<span class="c1"># Creating the embedding and inserting it into the database</span>
+<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span><span class="n">batch_size</span><span class="p">)):</span>
+ <span class="n">to_send</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="n">trakt_ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">][</span><span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">+</span><span class="n">batch_size</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+ <span class="n">sentences</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;combined_text&quot;</span><span class="p">][</span><span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">+</span><span class="n">batch_size</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+ <span class="n">embeddings</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">sentences</span><span class="p">)</span>
+ <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">trakt_ids</span><span class="p">):</span>
+ <span class="n">to_send</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+ <span class="p">(</span>
+ <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="n">embeddings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+ <span class="p">))</span>
+ <span class="n">index</span><span class="o">.</span><span class="n">upsert</span><span class="p">(</span><span class="n">to_send</span><span class="p">)</span>
+</code></pre></div>
+
+<p>That's it!</p>
+
+<h2>Interacting with Vectors</h2>
+
+<p>We use the <code>trakt_id</code> for the movie as the ID for the vectors and upsert it into the index. </p>
+
+<p>To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.</p>
+
+<div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">get_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+ <span class="n">rec</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">==</span><span class="n">movie_name</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;multiple values found... </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+ <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rec</span><span class="p">)):</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;[</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">] </span><span class="si">{</span><span class="n">rec</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[</span><span class="n">x</span><span class="p">]</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">rec</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[</span><span class="n">x</span><span class="p">]</span><span class="si">}</span><span class="s2">) - </span><span class="si">{</span><span class="n">rec</span><span class="p">[</span><span class="s1">&#39;overview&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;===&quot;</span><span class="p">)</span>
+ <span class="n">z</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">input</span><span class="p">(</span><span class="s2">&quot;Choose No: &quot;</span><span class="p">))</span>
+ <span class="k">return</span> <span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">z</span><span class="p">]</span>
+ <span class="k">return</span> <span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">get_vector_value</span><span class="p">(</span><span class="n">trakt_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
+ <span class="n">fetch_response</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">fetch</span><span class="p">(</span><span class="n">ids</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">trakt_id</span><span class="p">)])</span>
+ <span class="k">return</span> <span class="n">fetch_response</span><span class="p">[</span><span class="s2">&quot;vectors&quot;</span><span class="p">][</span><span class="nb">str</span><span class="p">(</span><span class="n">trakt_id</span><span class="p">)][</span><span class="s2">&quot;values&quot;</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">query_vectors</span><span class="p">(</span><span class="n">vector</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">top_k</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="n">include_values</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">include_metada</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+ <span class="n">query_response</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">query</span><span class="p">(</span>
+ <span class="n">queries</span><span class="o">=</span><span class="p">[</span>
+ <span class="p">(</span><span class="n">vector</span><span class="p">),</span>
+ <span class="p">],</span>
+ <span class="n">top_k</span><span class="o">=</span><span class="n">top_k</span><span class="p">,</span>
+ <span class="n">include_values</span><span class="o">=</span><span class="n">include_values</span><span class="p">,</span>
+ <span class="n">include_metadata</span><span class="o">=</span><span class="n">include_metada</span>
+ <span class="p">)</span>
+ <span class="k">return</span> <span class="n">query_response</span>
+
+<span class="k">def</span> <span class="nf">query2ids</span><span class="p">(</span><span class="n">query_response</span><span class="p">):</span>
+ <span class="n">trakt_ids</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="k">for</span> <span class="n">match</span> <span class="ow">in</span> <span class="n">query_response</span><span class="p">[</span><span class="s2">&quot;results&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;matches&quot;</span><span class="p">]:</span>
+ <span class="n">trakt_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">match</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]))</span>
+ <span class="k">return</span> <span class="n">trakt_ids</span>
+
+<span class="k">def</span> <span class="nf">get_deets_by_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">trakt_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
+ <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">]</span><span class="o">==</span><span class="n">trakt_id</span><span class="p">]</span>
+ <span class="k">return</span> <span class="p">{</span>
+ <span class="s2">&quot;title&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+ <span class="s2">&quot;overview&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">overview</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+ <span class="s2">&quot;runtime&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+ <span class="s2">&quot;year&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">year</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+ <span class="p">}</span>
+</code></pre></div>
+
+<h3>Testing it Out</h3>
+
+<div class="codehilite"><pre><span></span><code><span class="n">movie_name</span> <span class="o">=</span> <span class="s2">&quot;Now You See Me&quot;</span>
+
+<span class="n">movie_trakt_id</span> <span class="o">=</span> <span class="n">get_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">movie_name</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">movie_trakt_id</span><span class="p">)</span>
+<span class="n">movie_vector</span> <span class="o">=</span> <span class="n">get_vector_value</span><span class="p">(</span><span class="n">movie_trakt_id</span><span class="p">)</span>
+<span class="n">movie_queries</span> <span class="o">=</span> <span class="n">query_vectors</span><span class="p">(</span><span class="n">movie_vector</span><span class="p">)</span>
+<span class="n">movie_ids</span> <span class="o">=</span> <span class="n">query2ids</span><span class="p">(</span><span class="n">movie_queries</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">movie_ids</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">trakt_id</span> <span class="ow">in</span> <span class="n">movie_ids</span><span class="p">:</span>
+ <span class="n">deets</span> <span class="o">=</span> <span class="n">get_deets_by_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">trakt_id</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">deets</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">deets</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">): </span><span class="si">{</span><span class="n">deets</span><span class="p">[</span><span class="s1">&#39;overview&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</code></pre></div>
+
+<p>Output:</p>
+
+<pre><code>[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
+Now You See Me (2013): An FBI agent and an Interpol detective track a team of illusionists who pull off bank heists during their performances and reward their audiences with the money.
+Trapped (1949): U.S. Treasury Department agents go after a ring of counterfeiters.
+Brute Sanity (2018): An FBI-trained neuropsychologist teams up with a thief to find a reality-altering device while her insane ex-boss unleashes bizarre traps to stop her.
+The Chase (2017): Some FBI agents hunt down a criminal
+Surveillance (2008): An FBI agent tracks a serial killer with the help of three of his would-be victims - all of whom have wildly different stories to tell.
+Marauders (2016): An untraceable group of elite bank robbers is chased by a suicidal FBI agent who uncovers a deeper purpose behind the robbery-homicides.
+Miracles for Sale (1939): A maker of illusions for magicians protects an ingenue likely to be murdered.
+Deceptors (2005): A Ghostbusters knock-off where a group of con-artists create bogus monsters to scare up some cash. They run for their lives when real spooks attack.
+The Outfit (1993): A renegade FBI agent sparks an explosive mob war between gangster crime lords Legs Diamond and Dutch Schultz.
+Bank Alarm (1937): A federal agent learns the gangsters he's been investigating have kidnapped his sister.
+The Courier (2012): A shady FBI agent recruits a courier to deliver a mysterious package to a vengeful master criminal who has recently resurfaced with a diabolical plan.
+After the Sunset (2004): An FBI agent is suspicious of two master thieves, quietly enjoying their retirement near what may - or may not - be the biggest score of their careers.
+Down Three Dark Streets (1954): An FBI Agent takes on the three unrelated cases of a dead agent to track down his killer.
+The Executioner (1970): A British intelligence agent must track down a fellow spy suspected of being a double agent.
+Ace of Cactus Range (1924): A Secret Service agent goes undercover to unmask the leader of a gang of diamond thieves.
+Firepower (1979): A mercenary is hired by the FBI to track down a powerful recluse criminal, a woman is also trying to track him down for her own personal vendetta.
+Heroes &amp; Villains (2018): an FBI agent chases a thug to great tunes
+Federal Fugitives (1941): A government agent goes undercover in order to apprehend a saboteur who caused a plane crash.
+Hell on Earth (2012): An FBI Agent on the trail of a group of drug traffickers learns that their corruption runs deeper than she ever imagined, and finds herself in a supernatural - and deadly - situation.
+Spies (2015): A secret agent must perform a heist without time on his side
+</code></pre>
+
+<p>For now, I am happy with the recommendations.</p>
+
+<h2>Simple UI</h2>
+
+<p>The code for the flask app can be found on GitHub: <a rel="noopener" target="_blank" href="https://github.com/navanchauhan/FlixRec">navanchauhan/FlixRec</a> or on my <a rel="noopener" target="_blank" href="https://pi4.navan.dev/gitea/navan/FlixRec">Gitea instance</a></p>
+
+<p>I quickly whipped up a simple Flask App to deal with problems of multiple movies sharing the title, and typos in the search query.</p>
+
+<h3>Home Page</h3>
+
+<p><img src="/assets/flixrec/home.png" alt="Home Page" /></p>
+
+<h3>Handling Multiple Movies with Same Title</h3>
+
+<p><img src="/assets/flixrec/multiple.png" alt="Multiple Movies with Same Title" /></p>
+
+<h3>Results Page</h3>
+
+<p><img src="/assets/flixrec/results.png" alt="Results Page" /></p>
+
+<p>Includes additional filter options</p>
+
+<p><img src="/assets/flixrec/filter.png" alt="Advance Filtering Options" /></p>
+
+<p>Test it out at <a rel="noopener" target="_blank" href="https://flixrec.navan.dev">https://flixrec.navan.dev</a></p>
+
+<h2>Current Limittations</h2>
+
+<ul>
+<li>Does not work well with popular franchises</li>
+<li>No Genre Filter</li>
+</ul>
+
+<h2>Future Addons</h2>
+
+<ul>
+<li>Include Cast Data
+<ul>
+<li>e.g. If it sees a movie with Tom Hanks and Meg Ryan, then it will boost similar movies including them</li>
+<li>e.g. If it sees the movie has been directed my McG, then it will boost similar movies directed by them</li>
+</ul></li>
+<li>REST API</li>
+<li>TV Shows</li>
+<li>Multilingual database</li>
+<li>Filter based on popularity: The data already exists in the indexed database</li>
+</ul>
+]]></content:encoded>
+ </item>
+
+ <item>
+ <guid isPermaLink="true">
https://web.navan.dev/posts/2020-08-01-Natural-Feature-Tracking-ARJS.html
</guid>
<title>
diff --git a/docs/index.html b/docs/index.html
index 66eee2a..d55f8ee 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -45,17 +45,34 @@
<ul>
+ <li><a href="/posts/2022-05-21-Similar-Movies-Recommender.html">Building a Simple Similar Movies Recommender System</a></li>
+ <ul>
+ <li>Building a Content Based Similar Movies Recommender System</li>
+ <li>Published On: 2022-05-21 17:56</li>
+ <li>Tags:
+
+ Python,
+
+ Transformers,
+
+ Movies,
+
+ Recommender-System
+
+ </ul>
+
+
<li><a href="/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html">Making a Crude ML Powered Chatbot in Swift using CoreML</a></li>
<ul>
<li>Writing a simple Machine-Learning powered Chatbot (or, daresay virtual personal assistant ) in Swift using CoreML.</li>
<li>Published On: 2021-06-27 23:26</li>
<li>Tags:
- Swift,
+ Swift,
- CoreML,
+ CoreML,
- NLP,
+ NLP
</ul>
@@ -66,9 +83,9 @@
<li>Published On: 2021-06-26 13:04</li>
<li>Tags:
- Cheminformatics,
+ Cheminformatics,
- JavaScript,
+ JavaScript
</ul>
@@ -79,11 +96,11 @@
<li>Published On: 2021-06-25 16:20</li>
<li>Tags:
- iOS,
+ iOS,
- Shortcuts,
+ Shortcuts,
- Fun,
+ Fun
</ul>
@@ -94,11 +111,11 @@
<li>Published On: 2021-06-25 00:08</li>
<li>Tags:
- Python,
+ Python,
- Twitter,
+ Twitter,
- Eh,
+ Eh
</ul>
@@ -109,13 +126,13 @@
<li>Published On: 2020-12-01 20:52</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Code-Snippet,
+ Code-Snippet,
- HTML,
+ HTML,
- JavaScript,
+ JavaScript
</ul>
@@ -126,11 +143,11 @@
<li>Published On: 2020-11-17 15:04</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Code-Snippet,
+ Code-Snippet,
- Web-Development,
+ Web-Development
</ul>
@@ -141,11 +158,11 @@
<li>Published On: 2020-10-11 16:12</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Review,
+ Review,
- Webcam,
+ Webcam
</ul>
@@ -156,13 +173,13 @@
<li>Published On: 2020-08-01 15:43</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- AR.js,
+ AR.js,
- JavaScript,
+ JavaScript,
- Augmented-Reality,
+ Augmented-Reality
</ul>
@@ -173,11 +190,11 @@
<li>Published On: 2020-07-01 14:23</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Code-Snippet,
+ Code-Snippet,
- Colab,
+ Colab
</ul>
@@ -188,15 +205,15 @@
<li>Published On: 2020-06-02 23:23</li>
<li>Tags:
- iOS,
+ iOS,
- Jailbreak,
+ Jailbreak,
- Cheminformatics,
+ Cheminformatics,
- AutoDock Vina,
+ AutoDock Vina,
- Molecular-Docking,
+ Molecular-Docking
</ul>
@@ -207,15 +224,15 @@
<li>Published On: 2020-06-01 13:10</li>
<li>Tags:
- Code-Snippet,
+ Code-Snippet,
- Molecular-Docking,
+ Molecular-Docking,
- Cheminformatics,
+ Cheminformatics,
- Open-Babel,
+ Open-Babel,
- AutoDock Vina,
+ AutoDock Vina
</ul>
@@ -226,13 +243,13 @@
<li>Published On: 2020-05-31 23:30</li>
<li>Tags:
- iOS,
+ iOS,
- Jailbreak,
+ Jailbreak,
- Cheminformatics,
+ Cheminformatics,
- Open-Babel,
+ Open-Babel
</ul>
@@ -243,9 +260,9 @@
<li>Published On: 2020-04-13 11:41</li>
<li>Tags:
- Molecular-Dynamics,
+ Molecular-Dynamics,
- macOS,
+ macOS
</ul>
@@ -256,9 +273,9 @@
<li>Published On: 2020-03-17 17:40</li>
<li>Tags:
- publication,
+ publication,
- pre-print,
+ pre-print
</ul>
@@ -269,9 +286,9 @@
<li>Published On: 2020-03-14 22:23</li>
<li>Tags:
- publication,
+ publication,
- pre-print,
+ pre-print
</ul>
@@ -282,9 +299,9 @@
<li>Published On: 2020-03-08 23:17</li>
<li>Tags:
- Vaporwave,
+ Vaporwave,
- Music,
+ Music
</ul>
@@ -295,9 +312,9 @@
<li>Published On: 2020-03-03 18:37</li>
<li>Tags:
- Android-TV,
+ Android-TV,
- Android,
+ Android
</ul>
@@ -308,13 +325,13 @@
<li>Published On: 2020-01-19 15:27</li>
<li>Tags:
- Code-Snippet,
+ Code-Snippet,
- tutorial,
+ tutorial,
- Raspberry-Pi,
+ Raspberry-Pi,
- Linux,
+ Linux
</ul>
@@ -325,11 +342,11 @@
<li>Published On: 2020-01-16 10:36</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Colab,
+ Colab,
- Turicreate,
+ Turicreate
</ul>
@@ -340,13 +357,13 @@
<li>Published On: 2020-01-15 23:36</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Colab,
+ Colab,
- Turicreate,
+ Turicreate,
- Kaggle,
+ Kaggle
</ul>
@@ -357,9 +374,9 @@
<li>Published On: 2020-01-14 00:10</li>
<li>Tags:
- Code-Snippet,
+ Code-Snippet,
- Tutorial,
+ Tutorial
</ul>
@@ -370,13 +387,13 @@
<li>Published On: 2019-12-22 11:10</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Colab,
+ Colab,
- SwiftUI,
+ SwiftUI,
- Turicreate,
+ Turicreate
</ul>
@@ -387,11 +404,11 @@
<li>Published On: 2019-12-16 14:16</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Tensorflow,
+ Tensorflow,
- Colab,
+ Colab
</ul>
@@ -402,11 +419,11 @@
<li>Published On: 2019-12-10 11:10</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Tensorflow,
+ Tensorflow,
- Code-Snippet,
+ Code-Snippet
</ul>
@@ -417,11 +434,11 @@
<li>Published On: 2019-12-08 14:16</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Tensorflow,
+ Tensorflow,
- Colab,
+ Colab
</ul>
@@ -432,9 +449,9 @@
<li>Published On: 2019-12-08 13:27</li>
<li>Tags:
- Code-Snippet,
+ Code-Snippet,
- Tutorial,
+ Tutorial
</ul>
@@ -445,7 +462,7 @@
<li>Published On: 2019-12-04 18:23</li>
<li>Tags:
- Tutorial,
+ Tutorial
</ul>
@@ -456,7 +473,7 @@
<li>Published On: 2019-05-14 02:42</li>
<li>Tags:
- publication,
+ publication
</ul>
@@ -467,15 +484,15 @@
<li>Published On: 2019-05-05 12:34</li>
<li>Tags:
- Tutorial,
+ Tutorial,
- Jailbreak,
+ Jailbreak,
- Designing,
+ Designing,
- Snowboard,
+ Snowboard,
- Anemone,
+ Anemone
</ul>
@@ -486,7 +503,7 @@
<li>Published On: 2019-04-16 17:39</li>
<li>Tags:
- hello-world,
+ hello-world
</ul>
@@ -497,7 +514,7 @@
<li>Published On: 2010-01-24 23:43</li>
<li>Tags:
- Experiment,
+ Experiment
</ul>
diff --git a/docs/posts/2022-05-21-Similar-Movies-Recommender.html b/docs/posts/2022-05-21-Similar-Movies-Recommender.html
new file mode 100644
index 0000000..42b887a
--- /dev/null
+++ b/docs/posts/2022-05-21-Similar-Movies-Recommender.html
@@ -0,0 +1,438 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+
+ <link rel="stylesheet" href="/assets/main.css" />
+ <link rel="stylesheet" href="/assets/sakura.css" />
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+ <title>Hey - Post - Building a Simple Similar Movies Recommender System</title>
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+<body>
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+|
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+<main>
+ <h1>Building a Simple Similar Movies Recommender System</h1>
+
+<h2>Why?</h2>
+
+<p>I recently came across a movie/tv-show recommender, <a rel="noopener" target="_blank" href="https://couchmoney.tv/">couchmoney.tv</a>. I loved it. I decided that I wanted to build something similar, so I could tinker with it as much as I wanted.</p>
+
+<p>I also wanted a recommendation system I could use via a REST API. Although I have not included that part in this post, I did eventually create it.</p>
+
+<h2>How?</h2>
+
+<p>By measuring the cosine of the angle between two vectors, you can get a value in the range [0,1] with 0 meaning no similarity. Now, if we find a way to represent information about movies as a vector, we can use cosine similarity as a metric to find similar movies.</p>
+
+<p>As we are recommending just based on the content of the movies, this is called a content based recommendation system.</p>
+
+<h2>Data Collection</h2>
+
+<p>Trakt exposes a nice API to search for movies/tv-shows. To access the API, you first need to get an API key (the Trakt ID you get when you create a new application). </p>
+
+<p>I decided to use SQL-Alchemy with a SQLite backend just to make my life easier if I decided on switching to Postgres anytime I felt like. </p>
+
+<p>First, I needed to check the total number of records in Trakt’s database.</p>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">trakt_id</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;TRAKT_ID&quot;</span><span class="p">)</span>
+
+<span class="n">api_base</span> <span class="o">=</span> <span class="s2">&quot;https://api.trakt.tv&quot;</span>
+
+<span class="n">headers</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;Content-Type&quot;</span><span class="p">:</span> <span class="s2">&quot;application/json&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-version&quot;</span><span class="p">:</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-key&quot;</span><span class="p">:</span> <span class="n">trakt_id</span>
+<span class="p">}</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;query&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;years&quot;</span><span class="p">:</span> <span class="s2">&quot;1900-2021&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;page&quot;</span><span class="p">:</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;extended&quot;</span><span class="p">:</span> <span class="s2">&quot;full&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;languages&quot;</span><span class="p">:</span> <span class="s2">&quot;en&quot;</span>
+<span class="p">}</span>
+
+<span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">api_base</span><span class="si">}</span><span class="s2">/search/movie&quot;</span><span class="p">,</span><span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span><span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="n">total_items</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">headers</span><span class="p">[</span><span class="s2">&quot;x-pagination-item-count&quot;</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;There are </span><span class="si">{</span><span class="n">total_items</span><span class="si">}</span><span class="s2"> movies&quot;</span><span class="p">)</span>
+</code></pre></div>
+
+<pre><code>There are 333946 movies
+</code></pre>
+
+<p>First, I needed to declare the database schema in (<code>database.py</code>):</p>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">sqlalchemy</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">create_engine</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">Table</span><span class="p">,</span> <span class="n">Column</span><span class="p">,</span> <span class="n">Integer</span><span class="p">,</span> <span class="n">String</span><span class="p">,</span> <span class="n">MetaData</span><span class="p">,</span> <span class="n">ForeignKey</span><span class="p">,</span> <span class="n">PickleType</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">insert</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy.orm</span> <span class="kn">import</span> <span class="n">sessionmaker</span>
+<span class="kn">from</span> <span class="nn">sqlalchemy.exc</span> <span class="kn">import</span> <span class="n">IntegrityError</span>
+
+<span class="n">meta</span> <span class="o">=</span> <span class="n">MetaData</span><span class="p">()</span>
+
+<span class="n">movies_table</span> <span class="o">=</span> <span class="n">Table</span><span class="p">(</span>
+ <span class="s2">&quot;movies&quot;</span><span class="p">,</span>
+ <span class="n">meta</span><span class="p">,</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">,</span> <span class="n">primary_key</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">autoincrement</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;title&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;overview&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;genres&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;year&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;released&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;runtime&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;country&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;language&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;rating&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;votes&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;comment_count&quot;</span><span class="p">,</span> <span class="n">Integer</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;tagline&quot;</span><span class="p">,</span> <span class="n">String</span><span class="p">),</span>
+ <span class="n">Column</span><span class="p">(</span><span class="s2">&quot;embeddings&quot;</span><span class="p">,</span> <span class="n">PickleType</span><span class="p">)</span>
+
+<span class="p">)</span>
+
+<span class="c1"># Helper function to connect to the db</span>
+<span class="k">def</span> <span class="nf">init_db_stuff</span><span class="p">(</span><span class="n">database_url</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+ <span class="n">engine</span> <span class="o">=</span> <span class="n">create_engine</span><span class="p">(</span><span class="n">database_url</span><span class="p">)</span>
+ <span class="n">meta</span><span class="o">.</span><span class="n">create_all</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
+ <span class="n">Session</span> <span class="o">=</span> <span class="n">sessionmaker</span><span class="p">(</span><span class="n">bind</span><span class="o">=</span><span class="n">engine</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">engine</span><span class="p">,</span> <span class="n">Session</span>
+</code></pre></div>
+
+<p>In the end, I could have dropped the embeddings field from the table schema as I never got around to using it.</p>
+
+<h3>Scripting Time</h3>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">from</span> <span class="nn">database</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">trakt_id</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;TRAKT_ID&quot;</span><span class="p">)</span>
+
+<span class="n">max_requests</span> <span class="o">=</span> <span class="mi">5000</span> <span class="c1"># How many requests I wanted to wrap everything up in</span>
+<span class="n">req_count</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># A counter for how many requests I have made</span>
+
+<span class="n">years</span> <span class="o">=</span> <span class="s2">&quot;1900-2021&quot;</span>
+<span class="n">page</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># The initial page number for the search</span>
+<span class="n">extended</span> <span class="o">=</span> <span class="s2">&quot;full&quot;</span> <span class="c1"># Required to get additional information </span>
+<span class="n">limit</span> <span class="o">=</span> <span class="s2">&quot;10&quot;</span> <span class="c1"># No of entires per request -- This will be automatically picked based on max_requests</span>
+<span class="n">languages</span> <span class="o">=</span> <span class="s2">&quot;en&quot;</span> <span class="c1"># Limit to English</span>
+
+<span class="n">api_base</span> <span class="o">=</span> <span class="s2">&quot;https://api.trakt.tv&quot;</span>
+<span class="n">database_url</span> <span class="o">=</span> <span class="s2">&quot;sqlite:///jlm.db&quot;</span>
+
+<span class="n">headers</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;Content-Type&quot;</span><span class="p">:</span> <span class="s2">&quot;application/json&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-version&quot;</span><span class="p">:</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;trakt-api-key&quot;</span><span class="p">:</span> <span class="n">trakt_id</span>
+<span class="p">}</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;query&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+ <span class="s2">&quot;years&quot;</span><span class="p">:</span> <span class="n">years</span><span class="p">,</span>
+ <span class="s2">&quot;page&quot;</span><span class="p">:</span> <span class="n">page</span><span class="p">,</span>
+ <span class="s2">&quot;extended&quot;</span><span class="p">:</span> <span class="n">extended</span><span class="p">,</span>
+ <span class="s2">&quot;limit&quot;</span><span class="p">:</span> <span class="n">limit</span><span class="p">,</span>
+ <span class="s2">&quot;languages&quot;</span><span class="p">:</span> <span class="n">languages</span>
+<span class="p">}</span>
+
+<span class="c1"># Helper function to get desirable values from the response</span>
+<span class="k">def</span> <span class="nf">create_movie_dict</span><span class="p">(</span><span class="n">movie</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+ <span class="n">m</span> <span class="o">=</span> <span class="n">movie</span><span class="p">[</span><span class="s2">&quot;movie&quot;</span><span class="p">]</span>
+ <span class="n">movie_dict</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s2">&quot;title&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;overview&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;overview&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;genres&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;genres&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;language&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;language&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;year&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;year&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;trakt_id&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;ids&quot;</span><span class="p">][</span><span class="s2">&quot;trakt&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;released&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;released&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;runtime&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;runtime&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;country&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;country&quot;</span><span class="p">],</span>
+ <span class="s2">&quot;rating&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;rating&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;votes&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;votes&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;comment_count&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;comment_count&quot;</span><span class="p">]),</span>
+ <span class="s2">&quot;tagline&quot;</span><span class="p">:</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;tagline&quot;</span><span class="p">]</span>
+ <span class="p">}</span>
+ <span class="k">return</span> <span class="n">movie_dict</span>
+
+<span class="c1"># Get total number of items</span>
+<span class="n">params</span><span class="p">[</span><span class="s2">&quot;limit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">api_base</span><span class="si">}</span><span class="s2">/search/movie&quot;</span><span class="p">,</span><span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span><span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+<span class="n">total_items</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">headers</span><span class="p">[</span><span class="s2">&quot;x-pagination-item-count&quot;</span><span class="p">]</span>
+
+<span class="n">engine</span><span class="p">,</span> <span class="n">Session</span> <span class="o">=</span> <span class="n">init_db_stuff</span><span class="p">(</span><span class="n">database_url</span><span class="p">)</span>
+
+
+<span class="k">for</span> <span class="n">page</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">max_requests</span><span class="o">+</span><span class="mi">1</span><span class="p">)):</span>
+ <span class="n">params</span><span class="p">[</span><span class="s2">&quot;page&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">page</span>
+ <span class="n">params</span><span class="p">[</span><span class="s2">&quot;limit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">total_items</span><span class="p">)</span><span class="o">/</span><span class="n">max_requests</span><span class="p">)</span>
+ <span class="n">movies</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">api_base</span><span class="si">}</span><span class="s2">/search/movie&quot;</span><span class="p">,</span><span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span><span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="n">res</span><span class="o">.</span><span class="n">status_code</span> <span class="o">==</span> <span class="mi">500</span><span class="p">:</span>
+ <span class="k">break</span>
+ <span class="k">elif</span> <span class="n">res</span><span class="o">.</span><span class="n">status_code</span> <span class="o">==</span> <span class="mi">200</span><span class="p">:</span>
+ <span class="kc">None</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;OwO Code </span><span class="si">{</span><span class="n">res</span><span class="o">.</span><span class="n">status_code</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+ <span class="k">for</span> <span class="n">movie</span> <span class="ow">in</span> <span class="n">res</span><span class="o">.</span><span class="n">json</span><span class="p">():</span>
+ <span class="n">movies</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_movie_dict</span><span class="p">(</span><span class="n">movie</span><span class="p">))</span>
+
+ <span class="k">with</span> <span class="n">engine</span><span class="o">.</span><span class="n">connect</span><span class="p">()</span> <span class="k">as</span> <span class="n">conn</span><span class="p">:</span>
+ <span class="k">for</span> <span class="n">movie</span> <span class="ow">in</span> <span class="n">movies</span><span class="p">:</span>
+ <span class="k">with</span> <span class="n">conn</span><span class="o">.</span><span class="n">begin</span><span class="p">()</span> <span class="k">as</span> <span class="n">trans</span><span class="p">:</span>
+ <span class="n">stmt</span> <span class="o">=</span> <span class="n">insert</span><span class="p">(</span><span class="n">movies_table</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">(</span>
+ <span class="n">trakt_id</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">],</span> <span class="n">genres</span><span class="o">=</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;genres&quot;</span><span class="p">]),</span>
+ <span class="n">language</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;language&quot;</span><span class="p">],</span> <span class="n">year</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;year&quot;</span><span class="p">],</span> <span class="n">released</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;released&quot;</span><span class="p">],</span>
+ <span class="n">runtime</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;runtime&quot;</span><span class="p">],</span> <span class="n">country</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;country&quot;</span><span class="p">],</span> <span class="n">overview</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;overview&quot;</span><span class="p">],</span>
+ <span class="n">rating</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;rating&quot;</span><span class="p">],</span> <span class="n">votes</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;votes&quot;</span><span class="p">],</span> <span class="n">comment_count</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;comment_count&quot;</span><span class="p">],</span>
+ <span class="n">tagline</span><span class="o">=</span><span class="n">movie</span><span class="p">[</span><span class="s2">&quot;tagline&quot;</span><span class="p">])</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">result</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="n">stmt</span><span class="p">)</span>
+ <span class="n">trans</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
+ <span class="k">except</span> <span class="n">IntegrityError</span><span class="p">:</span>
+ <span class="n">trans</span><span class="o">.</span><span class="n">rollback</span><span class="p">()</span>
+ <span class="n">req_count</span> <span class="o">+=</span> <span class="mi">1</span>
+</code></pre></div>
+
+<p>(Note: I was well within the rate-limit so I did not have to slow down or implement any other measures)</p>
+
+<p>Running this script took me approximately 3 hours, and resulted in an SQLite database of 141.5 MB</p>
+
+<h2>Embeddings!</h2>
+
+<p>I did not want to put my poor Mac through the estimated 23 hours it would have taken to embed the sentences. I decided to use Google Colab instead.</p>
+
+<p>Because of the small size of the database file, I was able to just upload the file.</p>
+
+<p>For the encoding model, I decided to use the pretrained <code>paraphrase-multilingual-MiniLM-L12-v2</code> model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt. </p>
+
+<p>While deciding how I was going to process the embeddings, I came across multiple solutions:</p>
+
+<ul>
+<li><p><a rel="noopener" target="_blank" href="https://milvus.io">Milvus</a> - An open-source vector database with similar search functionality</p></li>
+<li><p><a rel="noopener" target="_blank" href="https://faiss.ai">FAISS</a> - A library for efficient similarity search</p></li>
+<li><p><a rel="noopener" target="_blank" href="https://pinecone.io">Pinecone</a> - A fully managed vector database with similar search functionality</p></li>
+</ul>
+
+<p>I did not want to waste time setting up the first two, so I decided to go with Pinecone which offers 1M 768-dim vectors for free with no credit card required (Our embeddings are 384-dim dense).</p>
+
+<p>Getting started with Pinecone was as easy as:</p>
+
+<ul>
+<li><p>Signing up</p></li>
+<li><p>Specifying the index name and vector dimensions along with the similarity search metric (Cosine Similarity for our use case)</p></li>
+<li><p>Getting the API key</p></li>
+<li><p>Installing the Python module (pinecone-client)</p></li>
+</ul>
+
+<div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">pinecone</span>
+<span class="kn">from</span> <span class="nn">sentence_transformers</span> <span class="kn">import</span> <span class="n">SentenceTransformer</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="n">database_url</span> <span class="o">=</span> <span class="s2">&quot;sqlite:///jlm.db&quot;</span>
+<span class="n">PINECONE_KEY</span> <span class="o">=</span> <span class="s2">&quot;not-this-at-all&quot;</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
+
+<span class="n">pinecone</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">api_key</span><span class="o">=</span><span class="n">PINECONE_KEY</span><span class="p">,</span> <span class="n">environment</span><span class="o">=</span><span class="s2">&quot;us-west1-gcp&quot;</span><span class="p">)</span>
+<span class="n">index</span> <span class="o">=</span> <span class="n">pinecone</span><span class="o">.</span><span class="n">Index</span><span class="p">(</span><span class="s2">&quot;movies&quot;</span><span class="p">)</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">SentenceTransformer</span><span class="p">(</span><span class="s2">&quot;paraphrase-multilingual-MiniLM-L12-v2&quot;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">engine</span><span class="p">,</span> <span class="n">Session</span> <span class="o">=</span> <span class="n">init_db_stuff</span><span class="p">(</span><span class="n">database_url</span><span class="p">)</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">(</span><span class="s2">&quot;Select * from movies&quot;</span><span class="p">,</span> <span class="n">engine</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;combined_text&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;: &quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;overview&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; - &quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;tagline&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; Genres:- &quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;genres&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+
+<span class="c1"># Creating the embedding and inserting it into the database</span>
+<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span><span class="n">batch_size</span><span class="p">)):</span>
+ <span class="n">to_send</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="n">trakt_ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">][</span><span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">+</span><span class="n">batch_size</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+ <span class="n">sentences</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;combined_text&quot;</span><span class="p">][</span><span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">+</span><span class="n">batch_size</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+ <span class="n">embeddings</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">sentences</span><span class="p">)</span>
+ <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">trakt_ids</span><span class="p">):</span>
+ <span class="n">to_send</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+ <span class="p">(</span>
+ <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="n">embeddings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+ <span class="p">))</span>
+ <span class="n">index</span><span class="o">.</span><span class="n">upsert</span><span class="p">(</span><span class="n">to_send</span><span class="p">)</span>
+</code></pre></div>
+
+<p>That's it!</p>
+
+<h2>Interacting with Vectors</h2>
+
+<p>We use the <code>trakt_id</code> for the movie as the ID for the vectors and upsert it into the index. </p>
+
+<p>To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.</p>
+
+<div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">get_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+ <span class="n">rec</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">==</span><span class="n">movie_name</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;multiple values found... </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+ <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rec</span><span class="p">)):</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;[</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s2">] </span><span class="si">{</span><span class="n">rec</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[</span><span class="n">x</span><span class="p">]</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">rec</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[</span><span class="n">x</span><span class="p">]</span><span class="si">}</span><span class="s2">) - </span><span class="si">{</span><span class="n">rec</span><span class="p">[</span><span class="s1">&#39;overview&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;===&quot;</span><span class="p">)</span>
+ <span class="n">z</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">input</span><span class="p">(</span><span class="s2">&quot;Choose No: &quot;</span><span class="p">))</span>
+ <span class="k">return</span> <span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">z</span><span class="p">]</span>
+ <span class="k">return</span> <span class="n">rec</span><span class="o">.</span><span class="n">trakt_id</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">get_vector_value</span><span class="p">(</span><span class="n">trakt_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
+ <span class="n">fetch_response</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">fetch</span><span class="p">(</span><span class="n">ids</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">trakt_id</span><span class="p">)])</span>
+ <span class="k">return</span> <span class="n">fetch_response</span><span class="p">[</span><span class="s2">&quot;vectors&quot;</span><span class="p">][</span><span class="nb">str</span><span class="p">(</span><span class="n">trakt_id</span><span class="p">)][</span><span class="s2">&quot;values&quot;</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">query_vectors</span><span class="p">(</span><span class="n">vector</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">top_k</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="n">include_values</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">include_metada</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+ <span class="n">query_response</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">query</span><span class="p">(</span>
+ <span class="n">queries</span><span class="o">=</span><span class="p">[</span>
+ <span class="p">(</span><span class="n">vector</span><span class="p">),</span>
+ <span class="p">],</span>
+ <span class="n">top_k</span><span class="o">=</span><span class="n">top_k</span><span class="p">,</span>
+ <span class="n">include_values</span><span class="o">=</span><span class="n">include_values</span><span class="p">,</span>
+ <span class="n">include_metadata</span><span class="o">=</span><span class="n">include_metada</span>
+ <span class="p">)</span>
+ <span class="k">return</span> <span class="n">query_response</span>
+
+<span class="k">def</span> <span class="nf">query2ids</span><span class="p">(</span><span class="n">query_response</span><span class="p">):</span>
+ <span class="n">trakt_ids</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="k">for</span> <span class="n">match</span> <span class="ow">in</span> <span class="n">query_response</span><span class="p">[</span><span class="s2">&quot;results&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;matches&quot;</span><span class="p">]:</span>
+ <span class="n">trakt_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">match</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]))</span>
+ <span class="k">return</span> <span class="n">trakt_ids</span>
+
+<span class="k">def</span> <span class="nf">get_deets_by_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">trakt_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
+ <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;trakt_id&quot;</span><span class="p">]</span><span class="o">==</span><span class="n">trakt_id</span><span class="p">]</span>
+ <span class="k">return</span> <span class="p">{</span>
+ <span class="s2">&quot;title&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+ <span class="s2">&quot;overview&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">overview</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+ <span class="s2">&quot;runtime&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+ <span class="s2">&quot;year&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">year</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+ <span class="p">}</span>
+</code></pre></div>
+
+<h3>Testing it Out</h3>
+
+<div class="codehilite"><pre><span></span><code><span class="n">movie_name</span> <span class="o">=</span> <span class="s2">&quot;Now You See Me&quot;</span>
+
+<span class="n">movie_trakt_id</span> <span class="o">=</span> <span class="n">get_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">movie_name</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">movie_trakt_id</span><span class="p">)</span>
+<span class="n">movie_vector</span> <span class="o">=</span> <span class="n">get_vector_value</span><span class="p">(</span><span class="n">movie_trakt_id</span><span class="p">)</span>
+<span class="n">movie_queries</span> <span class="o">=</span> <span class="n">query_vectors</span><span class="p">(</span><span class="n">movie_vector</span><span class="p">)</span>
+<span class="n">movie_ids</span> <span class="o">=</span> <span class="n">query2ids</span><span class="p">(</span><span class="n">movie_queries</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">movie_ids</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">trakt_id</span> <span class="ow">in</span> <span class="n">movie_ids</span><span class="p">:</span>
+ <span class="n">deets</span> <span class="o">=</span> <span class="n">get_deets_by_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">trakt_id</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">deets</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="n">deets</span><span class="p">[</span><span class="s1">&#39;year&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">): </span><span class="si">{</span><span class="n">deets</span><span class="p">[</span><span class="s1">&#39;overview&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</code></pre></div>
+
+<p>Output:</p>
+
+<pre><code>[55786, 18374, 299592, 662622, 6054, 227458, 139687, 303950, 70000, 129307, 70823, 5766, 23950, 137696, 655723, 32842, 413269, 145994, 197990, 373832]
+Now You See Me (2013): An FBI agent and an Interpol detective track a team of illusionists who pull off bank heists during their performances and reward their audiences with the money.
+Trapped (1949): U.S. Treasury Department agents go after a ring of counterfeiters.
+Brute Sanity (2018): An FBI-trained neuropsychologist teams up with a thief to find a reality-altering device while her insane ex-boss unleashes bizarre traps to stop her.
+The Chase (2017): Some FBI agents hunt down a criminal
+Surveillance (2008): An FBI agent tracks a serial killer with the help of three of his would-be victims - all of whom have wildly different stories to tell.
+Marauders (2016): An untraceable group of elite bank robbers is chased by a suicidal FBI agent who uncovers a deeper purpose behind the robbery-homicides.
+Miracles for Sale (1939): A maker of illusions for magicians protects an ingenue likely to be murdered.
+Deceptors (2005): A Ghostbusters knock-off where a group of con-artists create bogus monsters to scare up some cash. They run for their lives when real spooks attack.
+The Outfit (1993): A renegade FBI agent sparks an explosive mob war between gangster crime lords Legs Diamond and Dutch Schultz.
+Bank Alarm (1937): A federal agent learns the gangsters he's been investigating have kidnapped his sister.
+The Courier (2012): A shady FBI agent recruits a courier to deliver a mysterious package to a vengeful master criminal who has recently resurfaced with a diabolical plan.
+After the Sunset (2004): An FBI agent is suspicious of two master thieves, quietly enjoying their retirement near what may - or may not - be the biggest score of their careers.
+Down Three Dark Streets (1954): An FBI Agent takes on the three unrelated cases of a dead agent to track down his killer.
+The Executioner (1970): A British intelligence agent must track down a fellow spy suspected of being a double agent.
+Ace of Cactus Range (1924): A Secret Service agent goes undercover to unmask the leader of a gang of diamond thieves.
+Firepower (1979): A mercenary is hired by the FBI to track down a powerful recluse criminal, a woman is also trying to track him down for her own personal vendetta.
+Heroes &amp; Villains (2018): an FBI agent chases a thug to great tunes
+Federal Fugitives (1941): A government agent goes undercover in order to apprehend a saboteur who caused a plane crash.
+Hell on Earth (2012): An FBI Agent on the trail of a group of drug traffickers learns that their corruption runs deeper than she ever imagined, and finds herself in a supernatural - and deadly - situation.
+Spies (2015): A secret agent must perform a heist without time on his side
+</code></pre>
+
+<p>For now, I am happy with the recommendations.</p>
+
+<h2>Simple UI</h2>
+
+<p>The code for the flask app can be found on GitHub: <a rel="noopener" target="_blank" href="https://github.com/navanchauhan/FlixRec">navanchauhan/FlixRec</a> or on my <a rel="noopener" target="_blank" href="https://pi4.navan.dev/gitea/navan/FlixRec">Gitea instance</a></p>
+
+<p>I quickly whipped up a simple Flask App to deal with problems of multiple movies sharing the title, and typos in the search query.</p>
+
+<h3>Home Page</h3>
+
+<p><img src="/assets/flixrec/home.png" alt="Home Page" /></p>
+
+<h3>Handling Multiple Movies with Same Title</h3>
+
+<p><img src="/assets/flixrec/multiple.png" alt="Multiple Movies with Same Title" /></p>
+
+<h3>Results Page</h3>
+
+<p><img src="/assets/flixrec/results.png" alt="Results Page" /></p>
+
+<p>Includes additional filter options</p>
+
+<p><img src="/assets/flixrec/filter.png" alt="Advance Filtering Options" /></p>
+
+<p>Test it out at <a rel="noopener" target="_blank" href="https://flixrec.navan.dev">https://flixrec.navan.dev</a></p>
+
+<h2>Current Limittations</h2>
+
+<ul>
+<li>Does not work well with popular franchises</li>
+<li>No Genre Filter</li>
+</ul>
+
+<h2>Future Addons</h2>
+
+<ul>
+<li>Include Cast Data
+<ul>
+<li>e.g. If it sees a movie with Tom Hanks and Meg Ryan, then it will boost similar movies including them</li>
+<li>e.g. If it sees the movie has been directed my McG, then it will boost similar movies directed by them</li>
+</ul></li>
+<li>REST API</li>
+<li>TV Shows</li>
+<li>Multilingual database</li>
+<li>Filter based on popularity: The data already exists in the indexed database</li>
+</ul>
+
+</main>
+
+
+<script src="assets/manup.min.js"></script>
+<script src="/pwabuilder-sw-register.js"></script>
+</body>
+</html> \ No newline at end of file
diff --git a/docs/posts/index.html b/docs/posts/index.html
index bb704f8..d1e3bf4 100644
--- a/docs/posts/index.html
+++ b/docs/posts/index.html
@@ -48,6 +48,23 @@
<ul>
+ <li><a href="/posts/2022-05-21-Similar-Movies-Recommender.html">Building a Simple Similar Movies Recommender System</a></li>
+ <ul>
+ <li>Building a Content Based Similar Movies Recommender System</li>
+ <li>Published On: 2022-05-21 17:56</li>
+ <li>Tags:
+
+ Python,
+
+ Transformers,
+
+ Movies,
+
+ Recommender-System,
+
+ </ul>
+
+
<li><a href="/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html">Making a Crude ML Powered Chatbot in Swift using CoreML</a></li>
<ul>
<li>Writing a simple Machine-Learning powered Chatbot (or, daresay virtual personal assistant ) in Swift using CoreML.</li>
generated by cgit v1.2.3 (git 2.25.1) at 2024-09-19 22:00:20 +0000