OctoAI Generative AI with Weaviate
The model provider integration pages are new and still undergoing improvements. We appreciate any feedback on this forum thread.
v1.25.0
Weaviate's integration with OctoAI's APIs allows you to access open source and their models' capabilities directly from Weaviate.
Configure a Weaviate collection to use an OctoAI generative AI model, and Weaviate will perform retrieval augmented generation (RAG) using the specified model and your OctoAI API key.
More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the OctoAI generative model to generate outputs.
Requirements
Weaviate configuration
Your Weaviate instance must be configured with the OctoAI generative AI integration (generative-octoai
) module.
For Weaviate Cloud (WCD) users
This integration is enabled by default on Weaviate Cloud (WCD) serverless instances.
For self-hosted users
- Check the cluster metadata to verify if the module is enabled.
- Follow the how-to configure modules guide to enable the module in Weaviate.
API credentials
You must provide a valid OctoAI API key to Weaviate for this integration. Go to OctoAI to sign up and obtain an API key.
Provide the API key to Weaviate using one of the following methods:
- Set the
OCTOAI_APIKEY
environment variable that is available to Weaviate. - Provide the API key at runtime, as shown in the examples below.
- Python API v4
- JS/TS API v3
import weaviate
from weaviate.classes.init import Auth
import os
# Recommended: save sensitive data as environment variables
octoai_key = os.getenv("OCTOAI_APIKEY")
headers = {
"X-OctoAI-Api-Key": octoai_key,
}
client = weaviate.connect_to_weaviate_cloud(
cluster_url=weaviate_url, # `weaviate_url`: your Weaviate URL
auth_credentials=Auth.api_key(weaviate_key), # `weaviate_key`: your Weaviate API key
headers=headers
)
# Work with Weaviate
client.close()
import weaviate from 'weaviate-client'
const octoaiApiKey = process.env.OCTOAI_APIKEY || ''; // Replace with your inference API key
const client = await weaviate.connectToWeaviateCloud(
'WEAVIATE_INSTANCE_URL', // Replace with your instance URL
{
authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_APIKEY'),
headers: {
'X-OctoAI-Api-Key': octoaiApiKey,
}
}
)
// Work with Weaviate
client.close()
Configure collection
Configure a Weaviate collection to use an OctoAI generative AI model as follows:
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
client.collections.create(
"DemoCollection",
generative_config=Configure.Generative.octoai()
# Additional parameters not shown
)
await client.collections.create({
name: 'DemoCollection',
generative: weaviate.configure.generative.octoai(),
// Additional parameters not shown
});
Select a model
You can specify one of the available models for Weaviate to use, as shown in the following configuration example:
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
client.collections.create(
"DemoCollection",
generative_config=Configure.Generative.octoai(
model="meta-llama-3-70b-instruct"
)
# Additional parameters not shown
)
await client.collections.create({
name: 'DemoCollection',
generative: weaviate.configure.generative.octoai({
model: 'meta-llama-3-70b-instruct'
}),
// Additional parameters not shown
});
The default model is used if no model is specified.
Retrieval augmented generation
After configuring the generative AI integration, perform RAG operations, either with the single prompt or grouped task method.
Single prompt
To generate text for each object in the search results, use the single prompt method.
The example below generates outputs for each of the n
search results, where n
is specified by the limit
parameter.
When creating a single prompt query, use braces {}
to interpolate the object properties you want Weaviate to pass on to the language model. For example, to pass on the object's title
property, include {title}
in the query.
- Python API v4
- JS/TS API v3
collection = client.collections.get("DemoCollection")
response = collection.generate.near_text(
query="A holiday film", # The model provider integration will automatically vectorize the query
single_prompt="Translate this into French: {title}",
limit=2
)
for obj in response.objects:
print(obj.properties["title"])
print(f"Generated output: {obj.generated}") # Note that the generated output is per object
let myCollection = client.collections.get('DemoCollection');
const singlePromptResults = await myCollection.generate.nearText(
['A holiday film'],
{
singlePrompt: `Translate this into French: {title}`,
},
{
limit: 2,
}
);
for (const obj of singlePromptResults.objects) {
console.log(obj.properties['title']);
console.log(`Generated output: ${obj.generated}`); // Note that the generated output is per object
}
Grouped task
To generate one text for the entire set of search results, use the grouped task method.
In other words, when you have n
search results, the generative model generates one output for the entire group.
- Python API v4
- JS/TS API v3
collection = client.collections.get("DemoCollection")
response = collection.generate.near_text(
query="A holiday film", # The model provider integration will automatically vectorize the query
grouped_task="Write a fun tweet to promote readers to check out these films.",
limit=2
)
print(f"Generated output: {response.generated}") # Note that the generated output is per query
for obj in response.objects:
print(obj.properties["title"])
let myCollection = client.collections.get('DemoCollection');
const groupedTaskResults = await myCollection.generate.nearText(
['A holiday film'],
{
groupedTask: `Write a fun tweet to promote readers to check out these films.`,
},
{
limit: 2,
}
);
console.log(`Generated output: ${groupedTaskResults.generated}`); // Note that the generated output is per query
for (const obj of groupedTaskResults.objects) {
console.log(obj.properties['title']);
}
References
Generative parameters
Configure the following generative parameters to customize the model behavior.
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
client.collections.create(
"DemoCollection",
generative_config=Configure.Generative.octoai(
# # These parameters are optional
model = "meta-llama-3-70b-instruct",
max_tokens = 500,
temperature = 0.7,
base_url = "https://text.octoai.run"
)
)
await client.collections.create({
name: 'DemoCollection',
generative: weaviate.configure.generative.octoai({
model: 'meta-llama-3-70b-instruct',
maxTokens: 500,
temperature: 0.7,
baseURL: 'https://text.octoai.run'
}),
// Additional parameters not shown
});
For further details on model parameters, see the OctoAI API documentation.
Available models
qwen1.5-32b-chat
meta-llama-3-8b-instruct
meta-llama-3-70b-instruct
mixtral-8x22b-instruct
nous-hermes-2-mixtral-8x7b-dpo
mixtral-8x7b-instruct
mixtral-8x22b-finetuned
hermes-2-pro-mistral-7b
mistral-7b-instruct
(default)codellama-7b-instruct
codellama-13b-instruct
codellama-34b-instruct
llama-2-13b-chat
llama-2-70b-chat
Further resources
Other integrations
Code examples
Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:
- The how-to: manage data guides show how to perform data operations (i.e. create, update, delete).
- The how-to: search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.
References
- OctoAI API documentation
If you have any questions or feedback, let us know in the user forum.