mirror of
https://github.com/deepset-ai/haystack.git
synced 2026-01-13 07:37:25 +00:00
121 lines
5.1 KiB
Plaintext
121 lines
5.1 KiB
Plaintext
---
|
||
title: "CohereDocumentEmbedder"
|
||
id: coheredocumentembedder
|
||
slug: "/coheredocumentembedder"
|
||
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 Cohere embedding models."
|
||
---
|
||
|
||
# CohereDocumentEmbedder
|
||
|
||
This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Cohere embedding models.
|
||
|
||
The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector that represents the query is compared with those of the documents to find the most similar or relevant documents.
|
||
|
||
| | |
|
||
| --- | --- |
|
||
| **Most common position in a pipeline** | Before a [`DocumentWriter`](/docs/pipeline-components/writers/documentwriter.mdx) in an indexing pipeline |
|
||
| **Mandatory init variables** | "api_key": The Cohere API key. Can be set with `COHERE_API_KEY` or `CO_API_KEY` env var. |
|
||
| **Mandatory run variables** | “documents”: A list of documents to be embedded |
|
||
| **Output variables** | “documents”: A list of documents (enriched with embeddings) <br /> <br />“meta”: A dictionary of metadata strings |
|
||
| **API reference** | [Cohere](/reference/integrations-cohere) |
|
||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere |
|
||
|
||
## Overview
|
||
|
||
`CohereDocumentEmbedder` enriches the metadata of documents with an embedding of their content. To embed a string, you should use the [`CohereTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/coheretextembedder).
|
||
|
||
The component supports the following Cohere models:
|
||
`"embed-english-v3.0"`, `"embed-english-light-v3.0"`, `"embed-multilingual-v3.0"`,
|
||
`"embed-multilingual-light-v3.0"`, `"embed-english-v2.0"`, `"embed-english-light-v2.0"`,
|
||
`"embed-multilingual-v2.0"`. The default model is `embed-english-v2.0`. This list of all supported models can be found in Cohere’s [model documentation](https://docs.cohere.com/docs/models#representation).
|
||
|
||
To start using this integration with Haystack, install it with:
|
||
|
||
```shell
|
||
pip install cohere-haystack
|
||
```
|
||
|
||
The component uses a `COHERE_API_KEY` or `CO_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:
|
||
|
||
```python
|
||
embedder = CohereDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))
|
||
```
|
||
|
||
To get a Cohere API key, head over to https://cohere.com/.
|
||
|
||
### Embedding Metadata
|
||
|
||
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.
|
||
|
||
You can do this by using the Document Embedder:
|
||
|
||
```python
|
||
from haystack import Document
|
||
from cohere_haystack.embedders.document_embedder import CohereDocumentEmbedder
|
||
|
||
doc = Document(content="some text", meta={"title": "relevant title", "page number": 18})
|
||
|
||
embedder = CohereDocumentEmbedder(api_key=Secret.from_token("<your-api-key>", meta_fields_to_embed=["title"])
|
||
|
||
docs_w_embeddings = embedder.run(documents=[doc])["documents"]
|
||
```
|
||
|
||
## Usage
|
||
|
||
### On its own
|
||
|
||
Remember to set `COHERE_API_KEY` as an environment variable first, or pass it in directly.
|
||
|
||
Here is how you can use the component on its own:
|
||
|
||
```python
|
||
from haystack import Document
|
||
from haystack_integrations.components.embedders.cohere.document_embedder import CohereDocumentEmbedder
|
||
|
||
doc = Document(content="I love pizza!")
|
||
|
||
embedder = CohereDocumentEmbedder()
|
||
|
||
result = embedder.run([doc])
|
||
print(result['documents'][0].embedding)
|
||
## [-0.453125, 1.2236328, 2.0058594, 0.67871094...]
|
||
```
|
||
|
||
### In a pipeline
|
||
|
||
```python
|
||
from haystack import Pipeline
|
||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||
from haystack.components.writers import DocumentWriter
|
||
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
|
||
|
||
from haystack_integrations.components.embedders.cohere.document_embedder import CohereDocumentEmbedder
|
||
from haystack_integrations.components.embedders.cohere.text_embedder import CohereTextEmbedder
|
||
|
||
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")]
|
||
|
||
indexing_pipeline = Pipeline()
|
||
indexing_pipeline.add_component("embedder", CohereDocumentEmbedder())
|
||
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||
indexing_pipeline.connect("embedder", "writer")
|
||
|
||
indexing_pipeline.run({"embedder": {"documents": documents}})
|
||
|
||
query_pipeline = Pipeline()
|
||
query_pipeline.add_component("text_embedder", CohereTextEmbedder())
|
||
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=..., text: 'My name is Wolfgang and I live in Berlin')
|
||
```
|