
Most common position in a pipeline	Before a `DocumentWriter` in an indexing pipeline
Mandatory init variables	"api_key": The IBM Cloud API key. Can be set with `WATSONX_API_KEY` env var. "project_id": The IBM Cloud project ID. Can be set with `WATSONX_PROJECT_ID` env var.
Mandatory run variables	"documents": A list of documents to be embedded
Output variables	"documents": A list of documents (enriched with embeddings) "meta": A dictionary of metadata strings
API reference	Watsonx
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/watsonx

Overview

WatsonxDocumentEmbedder enriches the metadata of documents with an embedding of their content. To embed a string, you should use the WatsonxTextEmbedder.

The component supports IBM watsonx.ai embedding models such as ibm/slate-30m-english-rtrvr and similar. The default model is ibm/slate-30m-english-rtrvr. This list of all supported models can be found in IBM's model documentation.

To start using this integration with Haystack, install it with:

pip install watsonx-haystack

The component uses WATSONX_API_KEY and WATSONX_PROJECT_ID environment variables by default. Otherwise, you can pass API credentials at initialization with api_key and project_id:

embedder = WatsonxDocumentEmbedder(
    api_key=Secret.from_token("<your-api-key>"),
    project_id=Secret.from_token("<your-project-id>")
)

To get IBM Cloud credentials, head over to https://cloud.ibm.com/.

Embedding Metadata

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

You can do this by using the Document Embedder:

from haystack import Document
from haystack_integrations.components.embedders.watsonx.document_embedder import WatsonxDocumentEmbedder
from haystack.utils import Secret

doc = Document(content="some text", meta={"title": "relevant title", "page number": 18})

embedder = WatsonxDocumentEmbedder(
    api_key=Secret.from_env_var("WATSONX_API_KEY"),
    project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
    meta_fields_to_embed=["title"]
)

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

Usage

Install the watsonx-haystack package to use the WatsonxDocumentEmbedder:

pip install watsonx-haystack

On its own

Remember to set WATSONX_API_KEY and WATSONX_PROJECT_ID as environment variables first, or pass them in directly.

Here is how you can use the component on its own:

from haystack import Document
from haystack_integrations.components.embedders.watsonx.document_embedder import WatsonxDocumentEmbedder

doc = Document(content="I love pizza!")

embedder = WatsonxDocumentEmbedder()

result = embedder.run([doc])
print(result['documents'][0].embedding)
# [-0.453125, 1.2236328, 2.0058594, 0.67871094...]

In a pipeline

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

from haystack_integrations.components.embedders.watsonx.document_embedder import WatsonxDocumentEmbedder
from haystack_integrations.components.embedders.watsonx.text_embedder import WatsonxTextEmbedder

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", WatsonxDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", WatsonxTextEmbedder())
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])

# Document(id=..., text: 'My name is Wolfgang and I live in Berlin')