[RedisVL](https://www.redisvl.com/) provides a powerful, dedicated Python client library for using Redis as a Vector Database. This walks through how to integrate [Cohere embeddings](/guides/embeddings-vectors-search-retrieval-v2-text-embeddings-embeddings) with Redis using a dataset of Wikipedia articles to set up a pipeline for semantic search. It will cover:
- Setting up a Redis index
- Embedding passages and storing them in the database
- Embedding the user’s search query and searching against your Redis index
- Exploring different filtering options for your query
To see the full code sample, refer to this [notebook](https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/Cohere_Redis_Guide.ipynb). You can also consult [this guide](https://www.redisvl.com/user_guide/vectorizers_04.html#cohere) for more information on using Cohere with Redis.
## Prerequisites
The code samples on this page assume the following:
- You have docker running locally
```shell SHELL
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
```
- You have Redis installed (refer to the [Redis documentation](https://www.redisvl.com/overview/installation.html#redis-stack-local-development) for instructions).
- You have a Cohere API Key (available from the [dashboard](https://dashboard.cohere.com/api-keys)).
## Install Packages
Install and import the required Python Packages:
- `jsonlines`: for this example, the sample passages live in a `jsonl` file, and we will use jsonlines to load this data into our environment.
- `redisvl`: ensure you are on version `0.1.0` or later
- `cohere`: ensure you are on version `4.45` or later
To install the packages, use the following code
```shell SHELL
!pip install redisvl==0.1.0
!pip install cohere==4.45
!pip install jsonlines
```
### Import the required packages
```python PYTHON
from redis import Redis
from redisvl.index import SearchIndex
from redisvl.schema import IndexSchema
from redisvl.utils.vectorize import CohereTextVectorizer
from redisvl.query import VectorQuery
from redisvl.query.filter import Tag, Text, Num
import jsonlines
```
# Building a Retrieval Pipeline with Cohere and Redis
## Setting up the Schema.yaml
To configure a Redis index you can either specify a `yaml` file or import a dictionary. In this tutorial we will be using a `yaml` file with the following schema. Either use the [`yaml` file in this repo](https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/configs/redis_guide_schema.yaml), or create a `.yaml` file locally with the following configuration.
```yaml YAML
version: "0.1.0"
index:
name: semantic_search_demo
prefix: rvl
storage_type: hash
fields:
- name: url
type: text
- name: title
type: tag
- name: text
type: text
- name: wiki_id
type: numeric
- name: paragraph_id
type: numeric
- name: id
type: numeric
- name: views
type: numeric
- name: langs
type: numeric
- name: embedding
type: vector
attrs:
algorithm: flat
dims: 1024
distance_metric: cosine
datatype: float32
```
This index has a name of `semantic_search_demo` and uses `storage_type: hash` which means we must set `as_buffer=True` whenever we call the vectorizer. Hash data structures are serialized as a string and thus we store the embeddings in hashes as a byte string.
For this guide, we will be using the Cohere `embed-english-v3.0 model` which has a vector dimension size of `1024`.
## Initializing the Cohere Text Vectorizer
```python PYTHON
# create a vectorizer
api_key = "{Insert your cohere API Key}"
cohere_vectorizer = CohereTextVectorizer(
model="embed-english-v3.0",
api_config={"api_key": api_key},
)
```
Create a `CohereTextVectorizer` by specifying the embedding model and your api key.
Refer to [Cohere’s Embed Models](/guides/models-cohere-embed) for details around the available embedding models from Cohere and their respective dimensions.
## Initializing the Redis Index
```python PYTHON
# construct a search index from the schema - this schema is called "semantic_search_demo"
schema = IndexSchema.from_yaml("./schema.yaml")
client = Redis.from_url("redis://localhost:6379")
index = SearchIndex(schema, client)
# create the index (no data yet)
index.create(overwrite=True)
```
Note that we are using `SearchIndex.from_yaml` because we are choosing to import the schema from a yaml file, we could also do `SearchIndex.from_dict` as well.
```curl CURL
!rvl index listall
```
The above code checks to see if an index has been created. If it has, you should see something like this below:
```text TEXT
15:39:22 [RedisVL] INFO Indices:
15:39:22 [RedisVL] INFO 1. semantic_search_demo
```
Look inside the index to make sure it matches the schema you want
```curl CURL
!rvl index info -i semantic_search_demo
```
You should see something like this:
```
Look inside the index to make sure it matches the schema you want:
╭──────────────────────┬────────────────┬────────────┬─────────────────┬────────────╮
│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │
├──────────────────────┼────────────────┼────────────┼─────────────────┼────────────┤
│ semantic_search_demo │ HASH │ ['rvl'] │ [] │ 0 │
╰──────────────────────┴────────────────┴────────────┴─────────────────┴────────────╯
Index Fields:
╭──────────────┬──────────────┬─────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮
│ Name │ Attribute │ Type │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │
├──────────────┼──────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤
│ url │ url │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │
│ title │ title │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │
│ text │ text │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │
│ wiki_id │ wiki_id │ NUMERIC │ │ │ │ │ │ │ │ │
│ paragraph_id │ paragraph_id │ NUMERIC │ │ │ │ │ │ │ │ │
│ id │ id │ NUMERIC │ │ │ │ │ │ │ │ │
│ views │ views │ NUMERIC │ │ │ │ │ │ │ │ │
│ langs │ langs │ NUMERIC │ │ │ │ │ │ │ │ │
│ embedding │ embedding │ VECTOR │ algorithm │ FLAT │ data_type │ FLOAT32 │ dim │ 1024 │ distance_metric │ COSINE │
╰──────────────┴──────────────┴─────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯
```
You can also visit: [Localhost Redis GUI](http://localhost:8001/redis-stack/browser). The Redis GUI will show you the index in realtime.
:::frame{caption="Redis GUI"}
:::
## Loading your Documents and Embedding them into Redis
```python PYTHON
# read in your documents
jsonl_file_path = "data/redis_guide_data.jsonl"
corpus = []
text_to_embed = []
with jsonlines.open(jsonl_file_path, mode="r") as reader:
for line in reader:
corpus.append(line)
# we want to store the embeddings of the field called `text`
text_to_embed.append(line["text"])
# call embed_many which returns an array
# hash data structures get serialized as a string and thus we store the embeddings in hashes as a byte string (handled by numpy)
res = cohere_vectorizer.embed_many(
text_to_embed, input_type="search_document", as_buffer=True
)
```
We will be loading a subset of data which contains paragraphs from wikipedia - the data lives in a `jsonl` and we will need to parse it to get the text field which is what we are embedding. To do this, we load the file and read it line-by-line, creating a corpus object and a text\_to\_embed object. We then pass the text\_to\_embed object into `co.embed_many` which takes in an list of strings.
## Prepare your Data to be inserted into the Index
```python PYTHON
# contruct the data payload to be uploaded to your index
data = [
{
"url": row["url"],
"title": row["title"],
"text": row["text"],
"wiki_id": row["wiki_id"],
"paragraph_id": row["paragraph_id"],
"id": row["id"],
"views": row["views"],
"langs": row["langs"],
"embedding": v,
}
for row, v in zip(corpus, res)
]
# load the data into your index
index.load(data)
```
We want to preserve all the meta-data for each paragraph into our table and create a list of dictionaries which is inserted into the index
At this point, your Redis DB is ready for semantic search!
## Query your Redis DB
```python PYTHON
# use the Cohere vectorizer again to create a query embedding
query_embedding = cohere_vectorizer.embed(
"What did Microsoft release in 2015?",
input_type="search_query",
as_buffer=True,
)
query = VectorQuery(
vector=query_embedding,
vector_field_name="embedding",
return_fields=[
"url",
"wiki_id",
"paragraph_id",
"id",
"views",
"langs",
"title",
"text",
],
num_results=5,
)
results = index.query(query)
for doc in results:
print(
f"Title:{doc['title']}\nText:{doc['text']}\nDistance {doc['vector_distance']}\n\n"
)
```
Use the `VectorQuery` class to construct a query object - here you can specify the fields you’d like Redis to return as well as the number of results (i.e. for this example we set it to `5`).
# Redis Filters
## Adding Tag Filters
```python PYTHON
# Initialize a tag filter
tag_filter = Tag("title") == "Microsoft Office"
# set the tag filter on our existing query
query.set_filter(tag_filter)
results = index.query(query)
for doc in results:
print(
f"Title:{doc['title']}\nText:{doc['text']}\nDistance {doc['vector_distance']}\n"
)
```
One feature of Redis is the ability to add [filtering](https://www.redisvl.com/api/query.html) to your queries on the fly. Here we are constructing a `tag filter` on the column `title` which was initialized in our schema with `type=tag`.
## Using Filter Expressions
```python PYTHON
# define a tag match on the title, text match on the text field, and numeric filter on the views field
filter_data = (
(Tag("title") == "Elizabeth II")
& (Text("text") % "born")
& (Num("views") > 4500)
)
query_embedding = co.embed(
"When was she born?", input_type="search_query", as_buffer=True
)
# reinitialize the query with the filter expression
query = VectorQuery(
vector=query_embedding,
vector_field_name="embedding",
return_fields=[
"url",
"wiki_id",
"paragraph_id",
"id",
"views",
"langs",
"title",
"text",
],
num_results=5,
filter_expression=filter_data,
)
results = index.query(query)
print(results)
for doc in results:
print(
f"Title:{doc['title']}\nText:{doc['text']}\nDistance {doc['vector_distance']}\nView {doc['views']}"
)
```
Another feature of Redis is the ability to initialize a query with a set of filters called a [filter expression](https://www.redisvl.com/user_guide/hybrid_queries_02.html). A filter expression allows for the you to combine a set of filters over an arbitrary set of fields at query time.
## Related pages
- [Elasticsearch and Cohere (Integration Guide)](./integrations-elasticsearch-and-cohere.md)
- [MongoDB and Cohere (Integration Guide)](./integrations-mongodb-and-cohere.md)
- [Haystack and Cohere (Integration Guide)](./integrations-haystack-and-cohere.md)
- [Pinecone and Cohere (Integration Guide)](./integrations-pinecone-and-cohere.md)
- [Weaviate and Cohere (Integration Guide)](./integrations-weaviate-and-cohere.md)
- [Open Search and Cohere (Integration Guide)](./integrations-opensearch-and-cohere.md)
- [Vespa and Cohere (Integration Guide)](./integrations-vespa-and-cohere.md)
- [Qdrant and Cohere (Integration Guide)](./integrations-qdrant-and-cohere.md)
- [Milvus and Cohere (Integration Guide)](./integrations-milvus-and-cohere.md)
- [Zilliz and Cohere (Integration Guide)](./integrations-zilliz-and-cohere.md)
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