| from langchain_ollama.llms import OllamaLLM |
| from langchain_community.document_loaders import PyPDFLoader |
| from langchain.text_splitter import RecursiveCharacterTextSplitter |
| from langchain.vectorstores import Chroma |
| from langchain_huggingface import HuggingFaceEmbeddings |
| from langchain_core.prompts import ChatPromptTemplate |
| from langchain.chains.combine_documents import create_stuff_documents_chain |
| from langchain.chains import create_retrieval_chain |
|
|
| def load_and_process_pdf(pdf_path): |
| loader = PyPDFLoader(pdf_path) |
| pages = loader.load_and_split() |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) |
| splits = text_splitter.split_documents(pages) |
| return splits |
|
|
| def create_vectorstore(splits): |
| embeddings = HuggingFaceEmbeddings() |
| vectorstore = Chroma( |
| collection_name="example_collection", |
| embedding_function=embeddings, |
| persist_directory="./chroma_langchain_db" |
| ) |
| vectorstore.add_documents(splits) |
| return vectorstore |
|
|
| def create_rag_chain(): |
| llm = OllamaLLM(model="gemma2:2b") |
| |
| system_prompt = ( |
| "You are an assistant for question-answering tasks. " |
| "Use the following pieces of retrieved context to answer " |
| "the question. If you don't know the answer, say that you " |
| "don't know. Provide a clear and concise answer, using " |
| "up to three sentences." |
| "\n\n" |
| "{context}" |
| ) |
| |
| prompt = ChatPromptTemplate.from_messages( |
| [ |
| ("system", system_prompt), |
| ("human", "{input}"), |
| ] |
| ) |
| |
| question_answer_chain = create_stuff_documents_chain(llm, prompt) |
| return question_answer_chain |
|
|
| def get_response(rag_chain, vectorstore, question): |
| retriever = vectorstore.as_retriever() |
| retrieval_chain = create_retrieval_chain(retriever, rag_chain) |
| response = retrieval_chain.invoke({"input": question}) |
| return response["answer"] |
