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DOC-5537 Python vector set examples
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‎content/develop/clients/redis-py/vecsets.md‎

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@@ -47,12 +47,8 @@ pip install sentence-transformers
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In a new Python file, import the required classes:
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```python
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from sentence_transformers import SentenceTransformer
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import redis
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import numpy as np
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```
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{{< clients-example set="home_vecsets" step="import" >}}
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{{< /clients-example >}}
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The first of these imports is the
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`SentenceTransformer` class, which generates an embedding from a section of text.
@@ -65,77 +61,16 @@ tokens (see
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at the [Hugging Face](https://huggingface.co/) docs to learn more about the way tokens
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are related to the original text).
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```python
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model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
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```
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{{< clients-example set="home_vecsets" step="model" >}}
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{{< /clients-example >}}
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## Create the data
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The example data is contained a dictionary with some brief
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descriptions of famous people:
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```python
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peopleData = {
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"Marie Curie": {
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"born": 1867, "died": 1934,
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"description": """
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Polish-French chemist and physicist. The only person ever to win
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two Nobel prizes for two different sciences.
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"""
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},
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"Linus Pauling": {
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"born": 1901, "died": 1994,
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"description": """
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American chemist and peace activist. One of only two people to win two
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Nobel prizes in different fields (chemistry and peace).
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"""
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},
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"Freddie Mercury": {
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"born": 1946, "died": 1991,
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"description": """
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British musician, best known as the lead singer of the rock band
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Queen.
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"""
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},
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"Marie Fredriksson": {
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"born": 1958, "died": 2019,
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"description": """
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Swedish multi-instrumentalist, mainly known as the lead singer and
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keyboardist of the band Roxette.
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"""
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},
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"Paul Erdos": {
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"born": 1913, "died": 1996,
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"description": """
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Hungarian mathematician, known for his eccentric personality almost
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as much as his contributions to many different fields of mathematics.
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"""
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},
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"Maryam Mirzakhani": {
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"born": 1977, "died": 2017,
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"description": """
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Iranian mathematician. The first woman ever to win the Fields medal
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for her contributions to mathematics.
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"""
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},
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"Masako Natsume": {
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"born": 1957, "died": 1985,
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"description": """
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Japanese actress. She was very famous in Japan but was primarily
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known elsewhere in the world for her portrayal of Tripitaka in the
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TV series Monkey.
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"""
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},
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"Chaim Topol": {
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"born": 1935, "died": 2023,
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"description": """
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Israeli actor and singer, usually credited simply as 'Topol'. He was
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best known for his many appearances as Tevye in the musical Fiddler
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on the Roof.
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"""
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}
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}
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```
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{{< clients-example set="home_vecsets" step="data" >}}
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{{< /clients-example >}}
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## Add the data to a vector set
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@@ -164,22 +99,8 @@ The call to `vadd()` also adds the `born` and `died` values from the
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original dictionary as attribute data. You can access this during a query
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or by using the [`vgetattr()`]({{< relref "/commands/vgetattr" >}}) method.
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```py
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r = redis.Redis(decode_responses=True)
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for name, details in peopleData.items():
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emb = model.encode(details["description"]).astype(np.float32).tobytes()
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r.vset().vadd(
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"famousPeople",
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emb,
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name,
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attributes={
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"born": details["born"],
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"died": details["died"]
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}
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)
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```
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{{< clients-example set="home_vecsets" step="add_data" >}}
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{{< /clients-example >}}
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## Query the vector set
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Start with a simple query for "actors":
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```py
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query_value = "actors"
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actors_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes(),
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)
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print(f"'actors': {actors_results}")
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```
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{{< clients-example set="home_vecsets" step="basic_query" >}}
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{{< /clients-example >}}
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This returns the following list of elements (formatted slightly for clarity):
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You can use the `count` parameter of `vsim()` to limit the list of elements
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to just the most relevant few items:
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```py
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query_value = "actors"
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two_actors_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes(),
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count=2
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)
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print(f"'actors (2)': {two_actors_results}")
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# >>> 'actors (2)': ['Masako Natsume', 'Chaim Topol']
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```
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{{< clients-example set="home_vecsets" step="limited_query" >}}
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{{< /clients-example >}}
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The reason for using text embeddings rather than simple text search
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is that the embeddings represent semantic information. This allows a query
@@ -238,19 +141,8 @@ different. For example, the word "entertainer" doesn't appear in any of the
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descriptions but if you use it as a query, the actors and musicians are ranked
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highest in the results list:
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```py
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query_value = "entertainer"
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entertainer_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes()
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)
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print(f"'entertainer': {entertainer_results}")
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# >>> 'entertainer': ['Chaim Topol', 'Freddie Mercury',
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# >>> 'Marie Fredriksson', 'Masako Natsume', 'Linus Pauling',
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# 'Paul Erdos', 'Maryam Mirzakhani', 'Marie Curie']
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```
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{{< clients-example set="home_vecsets" step="entertainer_query" >}}
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{{< /clients-example >}}
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Similarly, if you use "science" as a query, you get the following results:
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repeat the "science" query, but this time limit the results to people
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who died before the year 2000:
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```py
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query_value = "science"
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science2000_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes(),
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filter=".died < 2000"
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)
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print(f"'science2000': {science2000_results}")
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# >>> 'science2000': ['Marie Curie', 'Linus Pauling',
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# 'Paul Erdos', 'Freddie Mercury', 'Masako Natsume']
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```
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{{< clients-example set="home_vecsets" step="filtered_query" >}}
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{{< /clients-example >}}
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Note that the boolean filter expression is applied to items in the list
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before the vector distance calculation is performed. Items that don't

‎local_examples/client-specific/home_vecsets.py‎

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# EXAMPLE: home_vecsets
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# STEP_START import
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from sentence_transformers import SentenceTransformer
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import redis
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import numpy as np
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# STEP_END
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# STEP_START model
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model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
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# STEP_END
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# STEP_START data
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peopleData = {
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"Marie Curie": {
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"born": 1867, "died": 1934,
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"description": """
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Polish-French chemist and physicist. The only person ever to win
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two Nobel prizes for two different sciences.
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"""
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},
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"Linus Pauling": {
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"born": 1901, "died": 1994,
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"description": """
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American chemist and peace activist. One of only two people to win two
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Nobel prizes in different fields (chemistry and peace).
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"""
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},
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"Freddie Mercury": {
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"born": 1946, "died": 1991,
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"description": """
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British musician, best known as the lead singer of the rock band
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Queen.
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"""
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},
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"Marie Fredriksson": {
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"born": 1958, "died": 2019,
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"description": """
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Swedish multi-instrumentalist, mainly known as the lead singer and
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keyboardist of the band Roxette.
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"""
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},
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"Paul Erdos": {
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"born": 1913, "died": 1996,
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"description": """
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Hungarian mathematician, known for his eccentric personality almost
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as much as his contributions to many different fields of mathematics.
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"""
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},
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"Maryam Mirzakhani": {
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"born": 1977, "died": 2017,
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"description": """
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Iranian mathematician. The first woman ever to win the Fields medal
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for her contributions to mathematics.
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"""
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},
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"Masako Natsume": {
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"born": 1957, "died": 1985,
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"description": """
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Japanese actress. She was very famous in Japan but was primarily
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known elsewhere in the world for her portrayal of Tripitaka in the
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TV series Monkey.
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"""
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},
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"Chaim Topol": {
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"born": 1935, "died": 2023,
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"description": """
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Israeli actor and singer, usually credited simply as 'Topol'. He was
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best known for his many appearances as Tevye in the musical Fiddler
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on the Roof.
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"""
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}
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}
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# STEP_END
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# STEP_START add_data
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r = redis.Redis(decode_responses=True)
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for name, details in peopleData.items():
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emb = model.encode(details["description"]).astype(np.float32).tobytes()
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r.vset().vadd(
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"famousPeople",
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emb,
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name,
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attributes={
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"born": details["born"],
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"died": details["died"]
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}
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)
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# STEP_END
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# STEP_START basic_query
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query_value = "actors"
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actors_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes(),
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)
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print(f"'actors': {actors_results}")
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# STEP_END
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# STEP_START limited_query
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query_value = "actors"
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two_actors_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes(),
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count=2
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)
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print(f"'actors (2)': {two_actors_results}")
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# >>> 'actors (2)': ['Masako Natsume', 'Chaim Topol']
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# STEP_END
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# STEP_START entertainer_query
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query_value = "entertainer"
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entertainer_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes()
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)
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print(f"'entertainer': {entertainer_results}")
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# >>> 'entertainer': ['Chaim Topol', 'Freddie Mercury',
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# 'Marie Fredriksson', 'Masako Natsume', 'Linus Pauling',
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# 'Paul Erdos', 'Maryam Mirzakhani', 'Marie Curie']
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# STEP_END
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query_value = "science"
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science_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes()
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)
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print(f"'science': {science_results}")
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# >>> 'science': ['Marie Curie', 'Linus Pauling',
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# 'Maryam Mirzakhani', 'Paul Erdos', 'Marie Fredriksson',
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# 'Freddie Mercury', 'Masako Natsume', 'Chaim Topol']
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# STEP_START filtered_query
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query_value = "science"
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science2000_results = r.vset().vsim(
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"famousPeople",
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model.encode(query_value).astype(np.float32).tobytes(),
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filter=".died < 2000"
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)
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print(f"'science2000': {science2000_results}")
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# >>> 'science2000': ['Marie Curie', 'Linus Pauling',
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# 'Paul Erdos', 'Freddie Mercury', 'Masako Natsume']
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# STEP_END

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