diff --git a/instant-distance-py/examples/translations/.gitignore b/instant-distance-py/examples/translations/.gitignore
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+data
diff --git a/instant-distance-py/examples/translations/README.md b/instant-distance-py/examples/translations/README.md
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+# Translation Example
+
+This example uses the pre-trained aligned word vectors made available by
+Facebook Research as part of
+[fastText](https://fasttext.cc/docs/en/aligned-vectors.html) to translate
+English words to French and Italian.
+
+## Trying it out
+
+Currently, macOS and Python 3.9 is required.
+
+First, install the requirements:
+
+```
+pip install -r requirements.txt
+```
+
+Run the following command to translate an english word:
+
+```
+python translate.py translate hello
+```
+
+```
+Loading indexes from filesystem...
+Language: fr, Translation: hello
+Language: it, Translation: hello
+Language: it, Translation: ciao
+Language: fr, Translation: bonjours
+Language: fr, Translation: bonjour
+Language: fr, Translation: bonsoir
+Language: fr, Translation: !
+Language: fr, Translation: salutations
+Language: it, Translation: buongiorno
+Language: it, Translation: hey
+```
+
+The translate command will download the vector data and build an index on the
+first run. Subsequent runs will be faster. 
+
+Downloading and building the index takes about **7 minutes** on a MacBook Pro (16-inch, 2019) with 2.4 GHz 8-Core Intel Core i9 and 64GB of RAM and a gigabit internet connection.
+
+If you like, you can rebuild the index at any time:
+
+```
+python translate.py build
+```
diff --git a/instant-distance-py/examples/translations/requirements.txt b/instant-distance-py/examples/translations/requirements.txt
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+aiohttp==3.7.4
+progress==1.5
+instant-distance===0.3.0
\ No newline at end of file
diff --git a/instant-distance-py/examples/translations/translate.py b/instant-distance-py/examples/translations/translate.py
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+++ b/instant-distance-py/examples/translations/translate.py
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+import asyncio
+import json
+import os
+import sys
+
+import aiohttp
+import instant_distance
+from progress.bar import IncrementalBar
+from progress.spinner import Spinner
+
+MAX_LINES = 100_000
+LANGS = ("en", "fr", "it")
+LANG_REPLACE = "$$lang"
+DL_TEMPLATE = f"https://dl.fbaipublicfiles.com/fasttext/vectors-aligned/wiki.{LANG_REPLACE}.align.vec"
+BUILT_IDX_PATH = f"./data/{'_'.join(LANGS)}.idx"
+WORD_MAP_PATH = f"./data/{'_'.join(LANGS)}.json"
+
+
+async def download_build_index():
+    """
+    This function downloads pre-trained word vectors trained on Wikipedia using fastText:
+    https://fasttext.cc/docs/en/aligned-vectors.html
+
+    The content is streamed and we take only the first 100,000 lines and drop the long tail
+    of less common words. We intercept each line and use this information to build
+    an instant-distance index file. We maintain a mapping of english word to embeddings
+    in order to convert the translation input to an embedding.
+    """
+    points = []
+    values = []
+    word_map = {}
+
+    print("Downloading vector files and building indexes...")
+    async with aiohttp.ClientSession() as session:
+        for lang in LANGS:
+            # Construct a url for each language
+            url = DL_TEMPLATE.replace(LANG_REPLACE, lang)
+
+            # Ensure the directory and files exist
+            os.makedirs(os.path.dirname(BUILT_IDX_PATH), exist_ok=True)
+
+            lineno = 0
+            with IncrementalBar(
+                f"Downloading {url.split('/')[-1]}", max=MAX_LINES
+            ) as bar:
+                async with session.get(url) as resp:
+                    while True:
+                        lineno += 1
+                        line = await resp.content.readline()
+                        if not line:
+                            # EOF
+                            break
+
+                        # We just use the top 100k embeddings to
+                        # save on space and time
+                        if lineno > MAX_LINES:
+                            break
+                        
+                        linestr = line.decode("utf-8")
+                        tokens = linestr.split(" ")
+
+                        # The first token is the word and the rest
+                        # are the embedding
+                        value = tokens[0]
+                        embedding = [float(p) for p in tokens[1:]]
+
+                        # We only go from english to the other two languages
+                        if lang == "en":
+                            word_map[value] = embedding
+                        else:
+                            # We track values here to build the instant-distance index
+                            # Every value is prepended with 2 character language code.
+                            # This allows us to determine language output later.
+                            values.append(lang + value)
+                            points.append(embedding)
+
+                        bar.next()
+
+    # Build the instant-distance index and dump it out to a file with .idx suffix
+    print("Building index... (this will take a while)")
+    hnsw = instant_distance.HnswMap.build(points, values, instant_distance.Config())
+    hnsw.dump(BUILT_IDX_PATH)
+
+    # Store the mapping from string to embedding in a .json file
+    with open(WORD_MAP_PATH, "w") as f:
+        json.dump(word_map, f)
+
+
+async def translate(word):
+    """
+    This function relies on the index built in the `download_build_index` function.
+    If the data does not yet exist, it will download and build the index.
+
+    The input is expected to be english. A word is first mapped onto an embedding
+    from the mapping stored as json. Then we use instant-distance to find the approximate
+    nearest neighbors to that point (embedding) in order to translate to other languages.
+    """
+    data_exists = os.path.isfile(BUILT_IDX_PATH) and os.path.isfile(WORD_MAP_PATH)
+    if not data_exists:
+        print("Instant Distance index not present. Building...")
+        await download_build_index()
+
+    print("Loading indexes from filesystem...")
+    with open(WORD_MAP_PATH, "r") as f:
+        word_map = json.load(f)
+
+    # Get an embedding for the given word
+    embedding = word_map.get(word)
+    if not embedding:
+        print(f"Word not recognized: {word}")
+        exit(1)
+
+    hnsw = instant_distance.HnswMap.load(BUILT_IDX_PATH)
+    search = instant_distance.Search()
+    hnsw.search(embedding, search)
+
+    # Print the results
+    for result in list(search)[:10]:
+        # We know that the first two characters of the value is the language code
+        # from when we built the index.
+        print(f"Language: {result.value[:2]}, Translation: {result.value[2:]}")
+
+
+async def main():
+    args = sys.argv[1:]
+    try:
+        if args[0] == "build":
+            await download_build_index()
+            exit(0)
+        elif args[0] == "translate":
+            await translate(args[1])
+            exit(0)
+    except IndexError:
+        pass
+
+    print(f"usage:\t{sys.argv[0]} prepare\n\t{sys.argv[0]} translate <english word>")
+    exit(1)
+
+
+loop = asyncio.get_event_loop()
+loop.run_until_complete(main())