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98 lines
3.5 KiB
Plaintext
98 lines
3.5 KiB
Plaintext
---
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title: "PerplexityTextEmbedder"
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id: perplexitytextembedder
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slug: "/perplexitytextembedder"
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description: "`PerplexityTextEmbedder` transforms a string into a vector using a Perplexity embedding model."
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---
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# PerplexityTextEmbedder
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`PerplexityTextEmbedder` transforms a string into a vector that captures its semantics using a Perplexity embedding model.
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When you perform embedding retrieval, use this component to transform your query into a vector. Then, the embedding Retriever looks for 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 an embedding [Retriever](../retrievers.mdx) in a query/RAG pipeline |
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| **Mandatory init variables** | `api_key`: A Perplexity API key. Can be set with `PERPLEXITY_API_KEY` env var. |
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| **Mandatory run variables** | `text`: A string |
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| **Output variables** | `embedding`: A list of float numbers <br /> <br />`meta`: A dictionary of metadata |
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| **API reference** | [Integrations](/reference/integrations-perplexity) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/perplexity/src/haystack_integrations/components/embedders/perplexity/text_embedder.py |
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| **Package name** | `perplexity-haystack` |
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</div>
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## Overview
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`PerplexityTextEmbedder` supports the following embedding models:
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- `pplx-embed-v1-0.6b` (default)
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- `pplx-embed-v1-4b`
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Use `PerplexityTextEmbedder` to embed a single string, such as a query. For embedding lists of documents, use [PerplexityDocumentEmbedder](perplexitydocumentembedder.mdx).
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The component uses a `PERPLEXITY_API_KEY` environment variable by default. You can also pass an API key directly at initialization:
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```python
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from haystack_integrations.components.embedders.perplexity import PerplexityTextEmbedder
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from haystack.utils import Secret
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embedder = PerplexityTextEmbedder(api_key=Secret.from_token("<your-api-key>"))
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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_integrations.components.embedders.perplexity import PerplexityTextEmbedder
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text_embedder = PerplexityTextEmbedder()
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result = text_embedder.run("I love pizza!")
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print(result["embedding"])
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# [0.017020374536514282, -0.023255806416273117, ...]
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```
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:::info
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We recommend setting `PERPLEXITY_API_KEY` as an environment variable instead of passing 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 Document, Pipeline
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
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from haystack_integrations.components.embedders.perplexity import (
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PerplexityTextEmbedder,
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PerplexityDocumentEmbedder,
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)
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document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
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documents = [
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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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]
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document_embedder = PerplexityDocumentEmbedder()
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documents_with_embeddings = document_embedder.run(documents)["documents"]
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document_store.write_documents(documents_with_embeddings)
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query_pipeline = Pipeline()
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query_pipeline.add_component("text_embedder", PerplexityTextEmbedder())
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query_pipeline.add_component(
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"retriever",
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InMemoryEmbeddingRetriever(document_store=document_store),
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)
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query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
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result = query_pipeline.run({"text_embedder": {"text": "Who lives in Berlin?"}})
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print(result["retriever"]["documents"][0])
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```
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