Cohere Rerank

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

python

%pip install llama-index > /dev/null
%pip install llama-index-postprocessor-cohere-rerank > /dev/null

python

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.response.pprint_utils import pprint_response

Download Data

python

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

python

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

# build index
index = VectorStoreIndex.from_documents(documents=documents)

Retrieve top 10 most relevant nodes, then filter with Cohere Rerank

python

import os
from llama_index.postprocessor.cohere_rerank import CohereRerank


api_key = os.environ["COHERE_API_KEY"]
cohere_rerank = CohereRerank(api_key=api_key, top_n=2)

python

query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[cohere_rerank],
)
response = query_engine.query(
    "What did Sam Altman do in this essay?",
)

python

pprint_response(response, show_source=True)

Directly retrieve top 2 most similar nodes

python

query_engine = index.as_query_engine(
    similarity_top_k=2,
)
response = query_engine.query(
    "What did Sam Altman do in this essay?",
)

Retrieved context is irrelevant and response is hallucinated.

python

pprint_response(response, show_source=True)