Adding rerank as a retriever

[jpfcabral]

This PR resolves #298

Added:

• BedrockRerank based on BaseDocumentCompressor so we can use ContextualCompressionRetriever
• Import from root import i.e: from langchain_aws import BedrockRerank
• Unit tests

Some snippets:

• Example 1 (from documents):

from langchain_core.documents import Document
from langchain_aws import BedrockRerank

# Initialize the class
reranker = BedrockRerank(top_n=5, aws_region="us-west-2")

# List of documents to rerank
documents = [
    Document(page_content="LangChain is a powerful library for LLMs."),
    Document(page_content="AWS Bedrock enables access to AI models."),
    Document(page_content="Artificial intelligence is transforming the world."),
]

# Query for reranking
query = "What is AWS Bedrock?"

# Call the rerank method
results = reranker.compress_documents(documents, query)

# Display the most relevant documents
for doc in results:
    print(f"Content: {doc.page_content}")
    print(f"Score: {doc.metadata['relevance_score']}")

• Example 2 (with contextual compression retriever):

from langchain_aws import BedrockEmbeddings
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_aws import BedrockRerank

# Create a vector store using FAISS with Bedrock embeddings
documents = [
    Document(page_content="LangChain integrates LLM models."),
    Document(page_content="AWS Bedrock provides cloud-based AI models."),
    Document(page_content="Machine learning can be used for predictions."),
]
embeddings = BedrockEmbeddings()
vectorstore = FAISS.from_documents(documents, embeddings)

# Create the document compressor using BedrockRerank
reranker = BedrockRerank(top_n=2)

# Create the retriever with contextual compression
retriever = ContextualCompressionRetriever(
    base_compressor=reranker,
    base_retriever=vectorstore.as_retriever(),
)

# Execute a query
query = "How does AWS Bedrock work?"
retrieved_docs = retriever.get_relevant_documents(query)

# Display the most relevant documents
for doc in retrieved_docs:
    print(f"Content: {doc.page_content}")
    print(f"Score: {doc.metadata.get('relevance_score', 'N/A')}")

• Example 3 (from list):

from langchain_aws import BedrockRerank

# Initialize BedrockRerank
reranker = BedrockRerank(top_n=3, aws_region="us-west-2")

# Unstructured documents
documents = [
    "LangChain is used to integrate LLM models.",
    "AWS Bedrock provides access to cloud-based models.",
    "Machine learning is revolutionizing the world.",
]

# Query
query = "What is the role of AWS Bedrock?"

# Rerank the documents
results = reranker.rerank(query=query, documents=documents)

# Display the results
for res in results:
    print(f"Index: {res['index']}, Score: {res['relevance_score']}")
    print(f"Document: {documents[res['index']]}")