
pipeline 中的最常见位置	在查询/RAG 管道中的嵌入检索器之前
必需的初始化变量	"api_key": An IBM Cloud API key. Can be set with环境变量 `WATSONX_API_KEY`。 "project_id": An IBM Cloud project ID. Can be set with环境变量 `WATSONX_PROJECT_ID`。
强制运行变量	"text": 字符串
输出变量	"embedding": 浮点数列表 "meta": 元数据字典
API 参考	Watsonx
GitHub 链接	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/watsonx

概述

To see the list of compatible IBM watsonx.ai embedding models, head over to IBM documentation. The default model forWatsonxTextEmbedder isibm/slate-30m-english-rtrvr. You can specify another model with themodel 参数指定其他模型。

使用WatsonxTextEmbedder to embed a simple string (such as a query) into a vector. For embedding lists of documents, use the WatsonxDocumentEmbedder, which enriches the document with the computed embedding, also known as vector.

该组件使用默认情况下，使用 WATSONX_API_KEY 和WATSONX_PROJECT_ID environment variables by default. Otherwise, you can pass API credentials at initialization withapi_key 和project_id:

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

用法

安装使用 watsonx-haystack 包来使用WatsonxTextEmbedder:

pip install watsonx-haystack

单独使用

以下是独立使用该组件的方法：

from haystack_integrations.components.embedders.watsonx.text_embedder import WatsonxTextEmbedder
from haystack.utils import Secret

text_to_embed = "I love pizza!"

text_embedder = WatsonxTextEmbedder(
    api_key=Secret.from_env_var("WATSONX_API_KEY"),
    project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
    model="ibm/slate-30m-english-rtrvr"
)

print(text_embedder.run(text_to_embed))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
# 'meta': {'model': 'ibm/slate-30m-english-rtrvr',
#              'truncated_input_tokens': 3}}

📘
We recommend setting WATSONX_API_KEY and WATSONX_PROJECT_ID as environment variables instead of setting them as parameters.

在 pipeline 中

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.watsonx.text_embedder import WatsonxTextEmbedder
from haystack_integrations.components.embedders.watsonx.document_embedder import WatsonxDocumentEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

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")]

document_embedder = WatsonxDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)['documents']
document_store.write_documents(documents_with_embeddings)

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=..., mimetype: 'text/plain', 
#  text: 'My name is Wolfgang and I live in Berlin')