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116 lines
4.6 KiB
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116 lines
4.6 KiB
Plaintext
---
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title: "OpenAIDocumentEmbedder"
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id: openaidocumentembedder
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slug: "/openaidocumentembedder"
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description: "OpenAIDocumentEmbedder computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses OpenAI embedding models."
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---
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# OpenAIDocumentEmbedder
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OpenAIDocumentEmbedder computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses OpenAI embedding models.
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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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<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) in an indexing pipeline |
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| **Mandatory init variables** | `api_key`: An OpenAI API key. Can be set with `OPENAI_API_KEY` env var. |
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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/openai_document_embedder.py |
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</div>
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## Overview
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To see the list of compatible OpenAI embedding models, head over to OpenAI [documentation](https://platform.openai.com/docs/guides/embeddings/embedding-models). The default model for `OpenAIDocumentEmbedder` is `text-embedding-ada-002`. You can specify another model with the `model` parameter when initializing this component.
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This component should be used to embed a list of documents. To embed a string, use the [OpenAITextEmbedder](openaitextembedder.mdx).
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The component uses an `OPENAI_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:
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```
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embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))
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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 OpenAIDocumentEmbedder
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doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})
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embedder = OpenAIDocumentEmbedder(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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Here is how you can use the component on its own:
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```python
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from haystack.components.embedders import OpenAIDocumentEmbedder
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doc = Document(content="I love pizza!")
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document_embedder = OpenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))
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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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:::note
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We recommend setting OPENAI_API_KEY as an environment variable instead of setting it as a parameter.
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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 OpenAITextEmbedder, OpenAIDocumentEmbedder
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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", OpenAIDocumentEmbedder())
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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", OpenAITextEmbedder())
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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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