Cohere Rerank on LangChain (Integration Guide)

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from langchain.retrievers import ContextualCompressionRetriever
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from langchain_cohere import CohereEmbeddings
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from langchain_cohere import ChatCohere
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from langchain_cohere import CohereRerank, CohereRagRetriever
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from langchain.text_splitter import CharacterTextSplitter
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from langchain_community.document_loaders import TextLoader
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from langchain_community.vectorstores import Chroma
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from langchain_community.document_loaders import WebBaseLoader
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user_query = "what is Cohere Toolkit?"
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-a-03-2025"
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)
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# Define the Cohere embedding model
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embeddings = CohereEmbeddings(
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    cohere_api_key="COHERE_API_KEY", model="embed-english-light-v3.0"
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)
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# Load text files and split into chunks, you can also use data gathered elsewhere in your application
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raw_documents = WebBaseLoader(
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    "https://docs.cohere.com/docs/cohere-toolkit"
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).load()
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text_splitter = CharacterTextSplitter(
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    chunk_size=1000, chunk_overlap=0
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)
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documents = text_splitter.split_documents(raw_documents)
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# Create a vector store from the documents
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db = Chroma.from_documents(documents, embeddings)
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# Create Cohere's reranker with the vector DB using Cohere's embeddings as the base retriever
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reranker = CohereRerank(
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    cohere_api_key="COHERE_API_KEY", model="rerank-english-v3.0"
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)
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compression_retriever = ContextualCompressionRetriever(
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    base_compressor=reranker, base_retriever=db.as_retriever()
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)
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compressed_docs = compression_retriever.get_relevant_documents(
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    user_query
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)
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# Print the relevant documents from using the embeddings and reranker
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print(compressed_docs)
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# Create the cohere rag retriever using the chat model
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rag = CohereRagRetriever(llm=llm, connectors=[])
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docs = rag.get_relevant_documents(
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    user_query,
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    documents=compressed_docs,
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)
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# Print the documents
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print("Documents:")
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for doc in docs[:-1]:
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    print(doc.metadata)
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    print("\n\n" + doc.page_content)
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    print("\n\n" + "-" * 30 + "\n\n")
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# Print the final generation
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answer = docs[-1].page_content
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print("Answer:")
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print(answer)
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# Print the final citations
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citations = docs[-1].metadata["citations"]
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print("Citations:")
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print(citations)

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llm = CohereRerank(
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    base_url="<YOUR_DEPLOYMENT_URL>",
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    cohere_api_key="COHERE_API_KEY",
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    model="MODEL_NAME",
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)