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Generative Search - Cohere

caution
This section of the documentation is deprecated and will be removed in the future.
See the relevant model provider integration page for the most up-to-date information.

In short

Added in v1.19.0
  • The Generative Cohere (generative-cohere) module performs retrieval augmented generation, or RAG, using the data stored in your Weaviate instance.
  • The module can generate a response for each object returned from Weaviate, or a combined response for a group of objects.
  • The module enables generative search operations on the Weaviate instance.
  • The default model is command-r.

Introduction

generative-cohere performs retrieval augmented generation, or RAG, based on the data stored in your Weaviate instance.

The module works in two steps:

  1. (Weaviate) Run a search query in Weaviate to find relevant objects.
  2. (Cohere) Use a Cohere Large Language Model to generate a response based on the results (from the previous step) and the provided prompt or task.
note

You can use the Generative Cohere module with non-Cohere upstream modules. For example, you could use text2vec-openai or text2vec-huggingface to vectorize and query your data, but then rely on the generative-cohere module to generate a response.

The generative module can provide results for:

  • each returned object - singlePrompt
  • the group of all results together – groupedTask

You need to input both a query and a prompt (for individual responses) or a task (for all responses).

Cohere API key

generative-cohere requires an Cohere API key to perform the generation task.

Providing the key to Weaviate

You can provide your Cohere API key in two ways:

  1. During the configuration of your Docker instance, by adding COHERE_APIKEY under environment to your Docker Compose file, like this:

    environment:
    COHERE_APIKEY: 'your-key-goes-here'
    ...
  2. At run-time (recommended), by providing "X-Cohere-Api-Key" to the Weaviate client, like this:

import weaviate

client = weaviate.Client(
url = "https://WEAVIATE_INSTANCE_URL/", # Replace WEAVIATE_INSTANCE_URL with your instance URL
additional_headers = {
"X-Cohere-Api-Key": "YOUR-COHERE-API-KEY" # Replace with your API key
}
)

Module configuration

tip

If you use Weaviate Cloud (WCD), this module is already enabled and pre-configured. You cannot edit the configuration in WCD.

caution

Your Weaviate instance must be on 1.19.0 or newer.

If your instance is older than 1.19.0 then you need to migrate or upgrade it to a newer version.

Docker Compose file (Weaviate open source only)

You can enable the Generative Cohere module in your Docker Compose file (e.g. docker-compose.yml). Add the generative-cohere module (alongside any other module you may need) to the ENABLE_MODULES property, like this:

ENABLE_MODULES: 'text2vec-cohere,generative-cohere'

Here is a full example of a Docker configuration, which uses the generative-cohere module in combination with text2vec-cohere:

---
version: '3.4'
services:
weaviate:
command:
- --host
- 0.0.0.0
- --port
- '8080'
- --scheme
- http
image:
cr.weaviate.io/semitechnologies/weaviate:1.25.4
ports:
- 8080:8080
- 50051:50051
restart: on-failure:0
environment:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'text2vec-cohere'
ENABLE_MODULES: 'text2vec-cohere,generative-cohere'
COHERE_APIKEY: sk-foobar # this parameter is optional, as you can also provide it through the client
CLUSTER_HOSTNAME: 'node1'

Schema configuration

In your Weaviate schema, you can define settings for this module.

See this page for code examples on how to specify a generative module.

For example, the following schema configuration will set Weaviate to use the generative-cohere module with the Document class, with the command-xlarge-nightly model. You can also configure additional parameters for the Cohere endpoint through the parameters shown below. Other models you can use from Cohere are command-xlarge-beta and command-xlarge.

{
"classes": [
{
"class": "Document",
"description": "A class called document",
...,
"moduleConfig": {
"generative-cohere": {
"model": "command-xlarge-nightly", // Optional - Defaults to `command-xlarge-nightly`. Can also use`command-xlarge-beta` and `command-xlarge`
"temperatureProperty": <temperature>, // Optional
"maxTokensProperty": <maxTokens>, // Optional
"kProperty": <k>, // Optional
"stopSequencesProperty": <stopSequences>, // Optional
"returnLikelihoodsProperty": <returnLikelihoods>, // Optional
},
}
}
]
}
New to Weaviate Schemas?

If you are new to Weaviate, check out the Weaviate schema tutorial.

How to use

This module extends the _additional {...} property with a generate operator.

generate takes the following arguments:

FieldData TypeRequiredExampleDescription
singleResult {prompt}stringnoSummarize the following in a tweet: {summary}Generates a response for each individual search result. You need to include at least one result field in the prompt, between braces.
groupedResult {task}stringnoExplain why these results are similar to each otherGenerates a single response for all search results

Example of properties in the prompt

When piping the results to the prompt, at least one field returned by the query must be added to the prompt. If you don't add any fields, Weaviate will throw an error.

For example, assume your schema looks like this:

{
Article {
title
summary
}
}

You can add both title and summary to the prompt by enclosing them in curly brackets:

{
Get {
Article {
title
summary
_additional {
generate(
singleResult: {
prompt: """
Summarize the following in a tweet:

{title} - {summary}
"""
}
) {
singleResult
error
}
}
}
}
}

Example - single result

Here is an example of a query where:

  • we run a vector search (with nearText) to find articles about "Italian food"
  • then we ask the generator module to describe each result as a Facebook ad.
    • the query asks for the summary field, which it then includes in the prompt argument of the generate operator.
import weaviate
import os

client = weaviate.connect_to_local(
headers={
"X-Cohere-Api-Key": os.getenv("COHERE_APIKEY"),
}
)

try:
reviews = client.collections.get("WineReview")

# instruction for the generative module
generate_prompt = "Describe the following as a Facebook Ad: {review_body}"

response = reviews.generate.near_text(
query="fruity white wine",
single_prompt=generate_prompt,
limit=3
)

for o in response.objects:
print(o.generated) # "Single prompt" generations are attributes of each object
print(o.properties) # To inspect the retrieved object
finally:
client.close()

Example response - single result

{
"data": {
"Get": {
"Article": [
{
"_additional": {
"generate": {
"error": null,
"singleResult": "Italian food, as we know it today, might be a relatively modern concept. But it's hard to deny that there's something special about it. It could be the way the pasta tastes or the way the sauce smells. It could be the way the cheese stretches or the way the bread soaks up the sauce. Whatever it is, Italian food has a way of capturing our hearts and our stomachs. So if you're looking for a way to spice up your meal routine, why not try Italian? You might just find that it's your new favorite cuisine."
}
},
"summary": "Even the emoji for pasta isn't just pasta -- it's a steaming plate of spaghetti heaped with tomato sauce on top. But while today we think of tomatoes as inextricably linked to Italian food, that hasn't always been the case. \"People tend to think Italian food was always as it is now -- that Dante was eating pizza,\" says Dr Eva Del Soldato , associate professor of romance languages at the University of Pennsylvania, who leads courses on Italian food history. In fact, she says, Italy's complex history -- it wasn't unified until 1861 -- means that what we think of Italian food is, for the most part, a relatively modern concept. Diego Zancani, emeritus professor of medieval and modern languages at Oxford University and author of \"How We Fell in Love with Italian Food,\" agrees.",
"title": "How this fruit became the star of Italian cooking"
}
]
}
}
}

Example - grouped result

Here is an example of a query where:

  • we run a vector search (with nearText) to find publications about finance,
  • then we ask the generator module to explain why these articles are about finance.
import weaviate
import os

client = weaviate.connect_to_local(
headers={
"X-Cohere-Api-Key": os.getenv("COHERE_APIKEY"),
}
)

try:
reviews = client.collections.get("WineReview")

# instruction for the generative module
generate_prompt = "Explain what occasion these wines might be good for."

response = reviews.generate.near_text(
query="dry red wine",
grouped_task=generate_prompt,
limit=5
)

print(response.generated) # "Grouped task" generations are attributes of the entire response
for o in response.objects:
print(o.properties) # To inspect the retrieved object
finally:
client.close()

Example response - grouped result

{
"data": {
"Get": {
"Publication": [
{
"_additional": {
"generate": {
"error": null,
"groupedResult": "These magazines or newspapers are about finance because they cover topics related to finance, such as business news, financial markets, and economic trends. They also often feature articles about personal finance, such as investing, budgeting, and retirement planning."
}
},
"name": "Financial Times"
},
{
"_additional": {
"generate": null
},
"name": "Wall Street Journal"
},
{
"_additional": {
"generate": null
},
"name": "The New York Times Company"
}
]
}
}
}

Additional information

Supported models

You can use any of

  • command-r-plus (Added in v1.25.0)
  • command-r (default) (Added in v1.25.0)
  • command-xlarge
  • command-xlarge-beta
  • command-xlarge-nightly
  • command-medium
  • command-medium-nightly
  • command
  • command-nightly
  • command-light
  • command-light-nightly

Questions and feedback

If you have any questions or feedback, let us know in the user forum.