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Gradient - update page for new secrets mgmt #177

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Feb 15, 2024
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Gradient - update page for new secrets mgmt #177

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15 changes: 9 additions & 6 deletions integrations/gradient.md
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Expand Up @@ -47,8 +47,8 @@ from haystack.components.writers import DocumentWriter

from haystack_integrations.components.embedders.gradient import GradientDocumentEmbedder

os.environ["GRADIENT_ACCESS_TOKEN"] = "Your Gradient Access Token"
os.environ["GRADIENT_WORKSPACE_ID"] = "Your Gradient Workspace id: "
os.environ["GRADIENT_ACCESS_TOKEN"] = "YOUR-GRADIENT-ACCESS-TOKEN"
os.environ["GRADIENT_WORKSPACE_ID"] = "YOUR-GRADIENT-WORKSPACE-ID"

documents = [
Document(content="My name is Jean and I live in Paris."),
Expand All @@ -67,6 +67,8 @@ indexing_pipeline.run({"document_embedder": {"documents": documents}})
You can use embedding models with `GradientTextEmbedder` and generative models with `GradientGenerator`. These two are commonly used together in a query pipeline such as a retrievel-augmented generative (RAG) pipeline such as the one below.

```python
import os

from haystack.components.builders.answer_builder import AnswerBuilder
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
Expand All @@ -85,12 +87,13 @@ Given these documents, answer the question.\nDocuments:
\nAnswer:
"""

gradient_access_token = os.environ.get("GRADIENT_ACCESS_TOKEN")
os.environ["GRADIENT_ACCESS_TOKEN"] = "YOUR-GRADIENT-ACCESS-TOKEN"
os.environ["GRADIENT_WORKSPACE_ID"] = "YOUR-GRADIENT-WORKSPACE-ID"

retriever = InMemoryEmbeddingRetriever(document_store)
prompt_builder = PromptBuilder(template=prompt_template)
embedder = GradientTextEmbedder(access_token=gradient_access_token)
generator = GradientGenerator(access_token=gradient_access_token, base_model_slug="llama2-7b-chat")
embedder = GradientTextEmbedder()
generator = GradientGenerator(base_model_slug="llama2-7b-chat")

rag_pipeline = Pipeline()
rag_pipeline.add_component(instance=embedder, name="text_embedder")
Expand All @@ -117,7 +120,7 @@ rag_pipeline.run(
```

## Examples
You can find a full code example showing how to use the integration in [this Colab](https://colab.research.google.com/drive/1kE_NAKKgZztQJMbgm2esyTVkAxlrpGtd#scrollTo=coE-fMtTJ-Pp).
You can find a full code example showing how to use the integration in [this Colab](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/gradient-embeders-and-generators-for-notion-rag.ipynb).

## License

Expand Down