mirror of
https://github.com/deepset-ai/haystack.git
synced 2026-01-31 20:13:05 +00:00
99 lines
4.3 KiB
Plaintext
99 lines
4.3 KiB
Plaintext
|
---
|
|||
|
|
title: "STACKITTextEmbedder"
|
|||
|
|
id: stackittextembedder
|
|||
|
|
slug: "/stackittextembedder"
|
|||
|
|
description: "This component enables text embedding using the STACKIT API."
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
# STACKITTextEmbedder
|
|||
|
|
|
|||
|
|
This component enables text embedding using the STACKIT API.
|
|||
|
|
|
|||
|
|
<div className="key-value-table">
|
|||
|
|
|
|||
|
|
| | |
|
|||
|
|
| --- | --- |
|
|||
|
|
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx) in a query/RAG pipeline |
|
|||
|
|
| **Mandatory init variables** | `model`: The model used through the STACKIT API |
|
|||
|
|
| **Mandatory run variables** | `text`: A string |
|
|||
|
|
| **Output variables** | `embedding`: A list of float numbers |
|
|||
|
|
| **API reference** | [STACKIT](/reference/integrations-stackit) |
|
|||
|
|
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/stackit |
|
|||
|
|
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
## Overview
|
|||
|
|
|
|||
|
|
`STACKITTextEmbedder` enables text embedding models served by STACKIT through their API.
|
|||
|
|
|
|||
|
|
### Parameters
|
|||
|
|
|
|||
|
|
To use the `STACKITTextEmbedder`, ensure you have set a `STACKIT_API_KEY` as an environment variable. Alternatively, provide the API key as an environment variable with a different name or a token by setting `api_key` and using Haystack’s [secret management](../../concepts/secret-management.mdx).
|
|||
|
|
|
|||
|
|
Set your preferred supported model with the `model` parameter when initializing the component. See the full list of all supported models on the [STACKIT website](https://docs.stackit.cloud/stackit/en/models-licenses-319914532.html).
|
|||
|
|
|
|||
|
|
Optionally, you can change the default `api_base_url`, which is `"https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1"`.
|
|||
|
|
|
|||
|
|
You can pass any text generation parameters valid for the STACKIT Chat Completion API directly to this component with the `generation_kwargs` parameter in the init or run methods.
|
|||
|
|
|
|||
|
|
The component needs a text input to operate.
|
|||
|
|
|
|||
|
|
## Usage
|
|||
|
|
|
|||
|
|
Install the `stackit-haystack` package to use the `STACKITTextEmbedder` and set an environment variable called `STACKIT_API_KEY` to your API key.
|
|||
|
|
|
|||
|
|
```shell
|
|||
|
|
pip install stackit-haystack
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
### On its own
|
|||
|
|
|
|||
|
|
```python
|
|||
|
|
from haystack_integrations.components.embedders.stackit import STACKITTextEmbedder
|
|||
|
|
|
|||
|
|
text_embedder = STACKITTextEmbedder(model="intfloat/e5-mistral-7b-instruct")
|
|||
|
|
|
|||
|
|
print(text_embedder.run("I love pizza!"))
|
|||
|
|
|
|||
|
|
## {'embedding': [0.0215301513671875, 0.01499176025390625, ...]}
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
### In a pipeline
|
|||
|
|
|
|||
|
|
You can also use `STACKITTextEmbedder` in your pipeline.
|
|||
|
|
|
|||
|
|
```python
|
|||
|
|
from haystack import Document
|
|||
|
|
from haystack import Pipeline
|
|||
|
|
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
|||
|
|
from haystack_integrations.components.embedders.stackit import STACKITTextEmbedder, STACKITDocumentEmbedder
|
|||
|
|
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
|
|||
|
|
|
|||
|
|
document_store = InMemoryDocumentStore()
|
|||
|
|
|
|||
|
|
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 = STACKITDocumentEmbedder(model="intfloat/e5-mistral-7b-instruct")
|
|||
|
|
documents_with_embeddings = document_embedder.run(documents)['documents']
|
|||
|
|
document_store.write_documents(documents_with_embeddings)
|
|||
|
|
|
|||
|
|
text_embedder = STACKITTextEmbedder(model="intfloat/e5-mistral-7b-instruct")
|
|||
|
|
|
|||
|
|
query_pipeline = Pipeline()
|
|||
|
|
query_pipeline.add_component("text_embedder", text_embedder)
|
|||
|
|
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
|
|||
|
|
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
|
|||
|
|
|
|||
|
|
query = "Where does Wolfgang live?"
|
|||
|
|
|
|||
|
|
result = query_pipeline.run({"text_embedder":{"text": query}})
|
|||
|
|
|
|||
|
|
print(result['retriever']['documents'][0])
|
|||
|
|
|
|||
|
|
## Document(id=..., content: 'My name is Wolfgang and I live in Berlin', score: ...)
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
You can find more usage examples in the STACKIT integration [repository](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/stackit/examples) and its [integration page](https://haystack.deepset.ai/integrations/stackit).
|