Integration: Cohere
Use Cohere with Haystack
You can use Cohere Models in your Haystack pipelines with the EmbeddingRetriever, PromptNode, and CohereRanker.
Installation
pip install farm-haystack
Usage
You can use Cohere models in various ways:
Embedding Models
To use /embed models from Cohere, initialize an EmbeddingRetriever with the model name and Cohere API key. You can then use this EmbeddingRetriever in an indexing pipeline to create Cohere embeddings for documents and index them to a document store.
Below is the example indexing pipeline with PreProcessor, InMemoryDocumentStore and EmbeddingRetriever:
from haystack.nodes import EmbeddingRetriever
from haystack.document_stores import InMemoryDocumentStore
from haystack.pipelines import Pipeline
from haystack.schema import Document
document_store = InMemoryDocumentStore(embedding_dim=768)
preprocessor = PreProcessor()
retriever = EmbeddingRetriever(
embedding_model="embed-multilingual-v2.0", document_store=document_store, api_key=COHERE_API_KEY
)
indexing_pipeline = Pipeline()
indexing_pipeline.add_node(component=preprocessor, name="Preprocessor", inputs=["File"])
indexing_pipeline.add_node(component=retriever, name="Retriever", inputs=["Preprocessor"])
indexing_pipeline.add_node(component=document_store, name="document_store", inputs=["Retriever"])
indexing_pipeline.run(documents=[Document("This is my document")])
Generative Models (LLMs)
To use /generate models from Cohere, initialize a PromptNode with the model name, Cohere API key and the prompt template. You can then use this PromptNode in a question answering pipeline to generate answers based on the given context.
Below is the example of generative questions answering pipeline using RAG with EmbeddingRetriever and PromptNode:
from haystack.nodes import PromptNode, EmbeddingRetriever
from haystack.pipelines import Pipeline
retriever = EmbeddingRetriever(
embedding_model="embed-english-v2.0", document_store=document_store, api_key=COHERE_API_KEY
)
prompt_node = PromptNode(model_name_or_path="command", api_key=COHERE_API_KEY, default_prompt_template="deepset/question-answering")
query_pipeline = Pipeline()
query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])
query_pipeline.run("YOUR_QUERY")
Ranker Models
To use /rerank models from Cohere, initialize a CohereRanker with the model name, and Cohere API key. You can then use this CohereRanker to sort documents based on their relevancy to the query.
Below is the example of document retrieval pipeline with BM25Retriever and CohereRanker:
from haystack.nodes import CohereRanker, BM25Retriever
from haystack.pipelines import Pipeline
retriever = BM25Retriever(document_store=document_store)
ranker = CohereRanker(api_key=COHERE_API_KEY, model_name_or_path="rerank-english-v2.0")
document_retrieval_pipeline = Pipeline()
document_retrieval_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
document_retrieval_pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"])
document_retrieval_pipeline.run("YOUR_QUERY")