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119 lines
5.1 KiB
Plaintext
---
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title: "AzureOpenAIDocumentEmbedder"
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id: azureopenaidocumentembedder
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slug: "/azureopenaidocumentembedder"
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description: "This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Azure cognitive services for text and document embedding with models deployed on Azure."
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---
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# AzureOpenAIDocumentEmbedder
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This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Azure cognitive services for text and document embedding with models deployed on Azure.
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<div className="key-value-table">
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| | |
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| --- | --- |
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| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx) |
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| **Mandatory init variables** | `api_key`: The Azure OpenAI API key. Can be set with `AZURE_OPENAI_API_KEY` env var. <br />`azure_endpoint`: The endpoint of the model deployed on Azure. |
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| **Mandatory run variables** | `documents`: A list of documents |
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| **Output variables** | `documents`: A list of documents (enriched with embeddings) <br /> <br />`meta`: A dictionary of metadata |
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| **API reference** | [Embedders](/reference/embedders-api) |
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| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/azure_document_embedder.py |
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</div>
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## Overview
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The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents.
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To see the list of compatible embedding models, head over to Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?source=recommendations). The default model for `AzureOpenAITextEmbedder` is `text-embedding-ada-002`.
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This component should be used to embed a list of documents. To embed a string, you should use the [`AzureOpenAITextEmbedder`](azureopenaitextembedder.mdx).
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To work with Azure components, you will need an Azure OpenAI API key, as well as an Azure OpenAI Endpoint. You can learn more about them in Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference).
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The component uses `AZURE_OPENAI_API_KEY` or `AZURE_OPENAI_AD_TOKEN` environment variables by default. Otherwise, you can pass `api_key` or `azure_ad_token` at initialization:
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```python
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client = AzureOpenAIDocumentEmbedder(azure_endpoint="<Your Azure endpoint e.g. `https://your-company.azure.openai.com/>",
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api_key=Secret.from_token("<your-api-key>"),
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azure_deployment="<a model name>")
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```
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:::info
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We recommend using environment variables instead of initialization parameters.
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:::
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### Embedding Metadata
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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.
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You can do this easily by using the Document Embedder:
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```python
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from haystack import Document
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from haystack.components.embedders import AzureOpenAIDocumentEmbedder
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doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})
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embedder = AzureOpenAIDocumentEmbedder(meta_fields_to_embed=["title"])
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docs_w_embeddings = embedder.run(documents=[doc])["documents"]
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```
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## Usage
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### On its own
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```python
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from haystack import Document
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from haystack.components.embedders import AzureOpenAIDocumentEmbedder
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doc = Document(content="I love pizza!")
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document_embedder = AzureOpenAIDocumentEmbedder()
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result = document_embedder.run([doc])
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print(result['documents'][0].embedding)
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## [0.017020374536514282, -0.023255806416273117, ...]
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```
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### In a pipeline
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```python
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from haystack import Pipeline
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack.components.embedders import AzureOpenAITextEmbedder, AzureOpenAIDocumentEmbedder
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from haystack.components.writers import DocumentWriter
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from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
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document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
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documents = [Document(content="My name is Wolfgang and I live in Berlin"),
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Document(content="I saw a black horse running"),
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Document(content="Germany has many big cities")]
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indexing_pipeline = Pipeline()
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indexing_pipeline.add_component("embedder", AzureOpenAIDocumentEmbedder())
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indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
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indexing_pipeline.connect("embedder", "writer")
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indexing_pipeline.run({"embedder": {"documents": documents}})
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query_pipeline = Pipeline()
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query_pipeline.add_component("text_embedder", AzureOpenAITextEmbedder())
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query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
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query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
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query = "Who lives in Berlin?"
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result = query_pipeline.run({"text_embedder":{"text": query}})
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print(result['retriever']['documents'][0])
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## Document(id=..., mimetype: 'text/plain',
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## text: 'My name is Wolfgang and I live in Berlin')
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```
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