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| 1 | +Sure, here's an example implementation using Python and Langchain to handle document references in a RAG architecture: |
| 2 | + |
| 3 | +```python |
| 4 | +from langchain.document_loaders import TextLoader |
| 5 | +from langchain.embeddings import HuggingFaceEmbeddings |
| 6 | +from langchain.vectorstores import Chroma |
| 7 | +from langchain.chains import RetrievalQA |
| 8 | +from langchain.llms import HuggingFaceHub |
| 9 | + |
| 10 | +class DocumentReferenceRAG: |
| 11 | + def __init__(self, documents): |
| 12 | + self.documents = documents |
| 13 | + self.embeddings = HuggingFaceEmbeddings() |
| 14 | + self.vectorstore = Chroma.from_documents(self.documents, self.embeddings) |
| 15 | + self.llm = HuggingFaceHub(repo_id="google/flan-t5-xl") |
| 16 | + self.qa = RetrievalQA.from_chain_type(llm=self.llm, chain_type="stuff", retriever=self.vectorstore.as_retriever()) |
| 17 | + |
| 18 | + def answer_question(self, question, max_recursion_depth=3): |
| 19 | + return self._recursive_answer(question, max_recursion_depth) |
| 20 | + |
| 21 | + def _recursive_answer(self, question, max_recursion_depth, processed_docs=None): |
| 22 | + if processed_docs is None: |
| 23 | + processed_docs = set() |
| 24 | + |
| 25 | + result = self.qa.run(question) |
| 26 | + processed_docs.add(result.source_documents[0].metadata['source']) |
| 27 | + |
| 28 | + for doc in result.source_documents: |
| 29 | + if 'referenced_docs' in doc.metadata: |
| 30 | + for ref_doc_link in doc.metadata['referenced_docs']: |
| 31 | + if ref_doc_link not in processed_docs and max_recursion_depth > 0: |
| 32 | + ref_doc = self._retrieve_document(ref_doc_link) |
| 33 | + if ref_doc: |
| 34 | + self.documents.append(ref_doc) |
| 35 | + self.vectorstore = Chroma.from_documents(self.documents, self.embeddings) |
| 36 | + self.qa = RetrievalQA.from_chain_type(llm=self.llm, chain_type="stuff", retriever=self.vectorstore.as_retriever()) |
| 37 | + result = self._recursive_answer(question, max_recursion_depth - 1, processed_docs) |
| 38 | + break |
| 39 | + |
| 40 | + return result |
| 41 | + |
| 42 | + def _retrieve_document(self, doc_link): |
| 43 | + # Implement document retrieval logic based on the provided link |
| 44 | + # For example, load the document from a file or database |
| 45 | + loader = TextLoader(doc_link) |
| 46 | + return loader.load()[0] |
| 47 | + |
| 48 | +# Example usage |
| 49 | +doc1 = TextLoader('doc1.txt').load()[0] |
| 50 | +doc2 = TextLoader('doc2.txt').load()[0] |
| 51 | +doc3 = TextLoader('doc3.txt').load()[0] |
| 52 | +doc4 = TextLoader('doc4.txt').load()[0] |
| 53 | +doc5 = TextLoader('doc5.txt').load()[0] |
| 54 | + |
| 55 | +rag = DocumentReferenceRAG([doc1, doc2, doc3, doc4, doc5]) |
| 56 | +question = "What is the relationship between document 1 and document 3?" |
| 57 | +answer = rag.answer_question(question) |
| 58 | +print(answer) |
| 59 | +``` |
| 60 | + |
| 61 | +In this example: |
| 62 | + |
| 63 | +1. The `DocumentReferenceRAG` class is defined to handle the recursive retrieval and processing of documents. |
| 64 | + |
| 65 | +2. The `__init__` method initializes the necessary components: |
| 66 | + - Loads the initial set of documents |
| 67 | + - Creates document embeddings using HuggingFaceEmbeddings |
| 68 | + - Stores the documents in a Chroma vector store |
| 69 | + - Sets up the LLM (HuggingFaceHub) and RetrievalQA chain |
| 70 | + |
| 71 | +3. The `answer_question` method takes a question and an optional maximum recursion depth. It calls the `_recursive_answer` method to generate the answer. |
| 72 | + |
| 73 | +4. The `_recursive_answer` method implements the recursive retrieval process: |
| 74 | + - Generates an initial answer using the RetrievalQA chain |
| 75 | + - Checks if the generated answer references any other documents |
| 76 | + - If referenced documents are found, recursively retrieves them using the `_retrieve_document` method |
| 77 | + - Appends the retrieved documents to the document collection and updates the vector store and RetrievalQA chain |
| 78 | + - Repeats the process until no new referenced documents are found or the maximum recursion depth is reached |
| 79 | + |
| 80 | +5. The `_retrieve_document` method is a placeholder for the actual document retrieval logic. In this example, it loads the document from a file using the `TextLoader` from Langchain. |
| 81 | + |
| 82 | +6. In the example usage, five documents are loaded, and the `DocumentReferenceRAG` class is instantiated with these documents. |
| 83 | + |
| 84 | +7. A question is asked, and the `answer_question` method is called to generate the final answer, considering the referenced documents. |
| 85 | + |
| 86 | +This implementation demonstrates how to extend a RAG architecture to handle document references using Langchain. The recursive retrieval process ensures that all relevant documents are considered when answering questions, even if they are referenced within other documents. |
| 87 | + |
| 88 | +Remember to customize the `_retrieve_document` method to match your specific document storage and retrieval mechanism. Additionally, you may want to add more error handling and optimization techniques based on your requirements. |
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