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| import openai, os | |
| from langchain.chains import LLMChain, RetrievalQA | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.document_loaders import PyPDFLoader, WebBaseLoader | |
| from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader | |
| from langchain.document_loaders.generic import GenericLoader | |
| from langchain.document_loaders.parsers import OpenAIWhisperParser | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.prompts import PromptTemplate | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from langchain.vectorstores import MongoDBAtlasVectorSearch | |
| from pymongo import MongoClient | |
| PDF_URL = "https://arxiv.org/pdf/2303.08774.pdf" | |
| WEB_URL = "https://openai.com/research/gpt-4" | |
| YOUTUBE_URL_1 = "https://www.youtube.com/watch?v=--khbXchTeE" | |
| YOUTUBE_URL_2 = "https://www.youtube.com/watch?v=hdhZwyf24mE" | |
| YOUTUBE_URL_3 = "https://www.youtube.com/watch?v=vw-KWfKwvTQ" | |
| YOUTUBE_DIR = "/data/youtube" | |
| CHROMA_DIR = "/data/chroma" | |
| MONGODB_ATLAS_CLUSTER_URI = os.environ["MONGODB_ATLAS_CLUSTER_URI"] | |
| MONGODB_DB_NAME = "langchain_db" | |
| MONGODB_COLLECTION_NAME = "gpt-4" | |
| MONGODB_INDEX_NAME = "default" | |
| LLM_CHAIN_PROMPT = PromptTemplate(input_variables = ["question"], template = os.environ["LLM_TEMPLATE"]) | |
| RAG_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = os.environ["RAG_TEMPLATE"]) | |
| client = MongoClient(MONGODB_ATLAS_CLUSTER_URI) | |
| collection = client[MONGODB_DB_NAME][MONGODB_COLLECTION_NAME] | |
| config = { | |
| "chunk_overlap": 150, | |
| "chunk_size": 1500, | |
| "k": 3, | |
| "model_name": "gpt-4-0613", | |
| "temperature": 0, | |
| } | |
| def document_loading_splitting(): | |
| # Document loading | |
| docs = [] | |
| # Load PDF | |
| loader = PyPDFLoader(PDF_URL) | |
| docs.extend(loader.load()) | |
| # Load Web | |
| loader = WebBaseLoader(WEB_URL) | |
| docs.extend(loader.load()) | |
| # Load YouTube | |
| loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1, | |
| YOUTUBE_URL_2, | |
| YOUTUBE_URL_3], YOUTUBE_DIR), | |
| OpenAIWhisperParser()) | |
| docs.extend(loader.load()) | |
| # Document splitting | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = config["chunk_overlap"], | |
| chunk_size = config["chunk_size"]) | |
| split_documents = text_splitter.split_documents(docs) | |
| return split_documents | |
| def document_storage_chroma(documents): | |
| Chroma.from_documents(documents = documents, | |
| embedding = OpenAIEmbeddings(disallowed_special = ()), | |
| persist_directory = CHROMA_DIR) | |
| def document_storage_mongodb(documents): | |
| MongoDBAtlasVectorSearch.from_documents(documents = documents, | |
| embedding = OpenAIEmbeddings(disallowed_special = ()), | |
| collection = collection, | |
| index_name = MONGODB_INDEX_NAME) | |
| def document_retrieval_chroma(llm, prompt): | |
| return Chroma(embedding_function = OpenAIEmbeddings(), | |
| persist_directory = CHROMA_DIR) | |
| def document_retrieval_mongodb(llm, prompt): | |
| return MongoDBAtlasVectorSearch.from_connection_string(MONGODB_ATLAS_CLUSTER_URI, | |
| MONGODB_DB_NAME + "." + MONGODB_COLLECTION_NAME, | |
| OpenAIEmbeddings(disallowed_special = ()), | |
| index_name = MONGODB_INDEX_NAME) | |
| def get_llm(openai_api_key): | |
| return ChatOpenAI(model_name = config["model_name"], | |
| openai_api_key = openai_api_key, | |
| temperature = config["temperature"]) | |
| def llm_chain(openai_api_key, prompt): | |
| llm_chain = LLMChain(llm = get_llm(openai_api_key), | |
| prompt = LLM_CHAIN_PROMPT, | |
| verbose = False) | |
| completion = llm_chain.generate([{"question": prompt}]) | |
| return completion, llm_chain | |
| def rag_chain(openai_api_key, prompt): | |
| llm = get_llm(openai_api_key) | |
| db = document_retrieval_chroma(llm, prompt) | |
| rag_chain = RetrievalQA.from_chain_type(llm, | |
| chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, | |
| retriever = db.as_retriever(search_kwargs = {"k": config["k"]}), | |
| return_source_documents = True, | |
| verbose = False) | |
| completion = rag_chain({"query": prompt}) | |
| return completion, rag_chain |