Rerank-english-v3.0 doesn't return re-ranked documents

!pip show cohere

Name: cohere
Version: 4.57
Summary: Python SDK for the Cohere API
Home-page: 
Author: Cohere
Author-email: 
License: 
Location: /Users/xxxxx/miniconda3/envs/rag/lib/python3.10/site-packages
Requires: aiohttp, backoff, fastavro, importlib_metadata, requests, urllib3

Enter API key

cohere_api_key = os.getenv("COHERE_API_KEY") or getpass("Enter Cohere API key: ")
co = cohere.Client(api_key=cohere_api_key)
print(co.datasets.list())

Enter Cohere API key: ········ DatasetsListResponse(datasets=None)

List of docs matched from Vector Store

query = "What are action groups in Amazon Bedrock"
print(matched_docs)

docs = ['Components in agents for Amazon BedrockBehind the scenes, agents for Amazon Bedrock automate the prompt engineering and orchestration of user-requested tasks.They can securely augment the prompts with company-specific information to provide responses back to the user in natural language.The agent breaks the user-requested task into multiple steps and orchestrates subtasks with the help of FMs.Action groups are tasks that the agent can perform autonomously....', 'Select a foundation model:In the Select model screen, you select a model.Provide clear and precise instructions to the agent about what tasks to perform and how to interact with the users.Add action groups:An action is a task the agent can perform by making API calls.A set of actions comprise an action group.You provide an API schema that defines all the APIs in the action group.You must provide an API schema in the OpenAPI schema JSON format.The Lambda function contains the business logic needed to perform API calls.You must associate a Lambda function to each action group.Give the action group a name and a description for the action.Select the Lambda function, provide an API schema file and selectNext.In the final step, review the agent configuration and selectCreate Agent....', 'General availability of Agents for Amazon Bedrock to help execute multistep tasks using systems, data sources, and company knowledge.LLMs are great at having conversations and generating content, but customers want their applications to be able to do even morelike take actions, solve problems, and interact with a range of systems to complete multi-step tasks like booking travel, filing insurance claims, or ordering replacement parts...' ]

Rerank the docs

RERANK_MODEL_NAME = "rerank-english-v3.0" # another option is rerank-multilingual-02
reranked_results = co.rerank(
    model=RERANK_MODEL_NAME, query=query, documents=matched_docs, top_n=3
)
print(reranked_results)

RerankResponse( id='64dc0d1d-7b67-49ef-8f62-c6c1bc6a07a6', results=[ RerankResponseResultsItem(document=None, index=0, relevance_score=0.99911696), RerankResponseResultsItem(document=None, index=1, relevance_score=0.99895966), RerankResponseResultsItem(document=None, index=2, relevance_score=0.67362124) ], meta=ApiMeta( api_version=ApiMetaApiVersion(version='1', is_deprecated=None, is_experimental=None), billed_units=ApiMetaBilledUnits( input_tokens=None, output_tokens=None, search_units=1, classifications=None ), tokens=None, warnings=None ) )

Print reranked results

for idx, r in enumerate(reranked_results):
  print(f"Document Rank: {idx + 1}, Document Index: {r.index}")
  print(f"Document: {r.document['text']}")
  print(f"Relevance Score: {r.relevance_score:.2f}")
  print("\n")

Document Rank: 1, Document Index: <built-in method index of tuple object at 0x11a9b9f40> ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ in :3 │ │ │ │ 1 for idx, r in enumerate(reranked_results): │ │ 2 print(f"Document Rank: {idx + 1}, Document Index: {r.index}") │ │ ❱ 3 print(f"Document: {r.document['text']}") │ │ 4 print(f"Relevance Score: {r.relevance_score:.2f}") │ │ 5 print("\n") │ │ 6 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ AttributeError: 'tuple' object has no attribute 'document'

cohere-ai / notebooks