Integration with Weaviate enables performing Retrieval Augmented Generation (RAG) directly within the Weaviate database. This combines the power of Friendli Engine and Weaviate’s efficient storage and fast retrieval capabilities to generate personalized and context-aware responses.

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How to use

Before you start, ensure you’ve already obtained the FRIENDLI_TOKEN from the Friendli Suite. Also, set up your Weaviate instance following this guide. Your Weaviate instance must be configured with the FriendliAI generative AI integration (generative-friendliai) module. 
pip install -qU weaviate-client

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Instantiation

Now we can instantiate a Weaviate collection using our model. We provide usage examples for each type of endpoint. Choose the one that best suits your needs. You can specify one of the available models for the serverless endpoints. The default model (i.e. meta-llama-3.3-70b-instruct) will be used if no model is specified. 
import weaviate
import os
from weaviate.classes.init import Auth
from weaviate.classes.config import Configure

headers = {
    "X-Friendli-Api-Key": os.getenv("FRIENDLI_TOKEN"),
}

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
)

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.friendliai(
        model = "meta-llama-3.3-70b-instruct",
    )
    # Additional parameters not shown
)

client.close()

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Configurable parameters

Configure the following generative parameters to customize the model behavior. 
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.friendliai(
        # These parameters are optional
        model = "meta-llama-3.3-70b-instruct",
        max_tokens = 500,
        temperature = 0.7,
    )
)

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Retrieval Augmented Generation

After configuring Weaviate, perform RAG operations, either with the single prompt or grouped task method.

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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. 
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

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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. 
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"])

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Further resources

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: