File size: 4,735 Bytes
03ab966 3ede494 478d345 eceefb4 40e55f0 dcc2644 516ec1c 6514d80 516ec1c 3ede494 c8a9d42 3ede494 340e058 3ede494 340e058 3ede494 340e058 dcc2644 4d86a48 4115e3a 4a679bd 340e058 c8a9d42 3ede494 340e058 772eaa5 340e058 c8efcca 3ede494 772eaa5 340e058 c8efcca 3ede494 772eaa5 c8a9d42 9c86fb0 c8a9d42 9c86fb0 772eaa5 9c86fb0 772eaa5 ab0af2e 3ede494 772eaa5 ab0af2e 772eaa5 d07fa33 03ab966 d07fa33 704c818 f43960a 5edb564 b03208e f43960a 4e625ab 3ede494 d07fa33 9ec3206 4e80daf 772eaa5 40e55f0 772eaa5 96012de f43960a 056f437 704c818 f43960a 478d345 cde25a7 4e625ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
import openai, os
from langchain.callbacks import get_openai_callback
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
RAG_CHROMA = "Chroma"
RAG_MONGODB = "MongoDB"
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_DIR = "/data/yt"
CHROMA_DIR = "/data/db"
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]
def load_documents():
docs = []
# PDF
loader = PyPDFLoader(PDF_URL)
docs.extend(loader.load())
# Web
loader = WebBaseLoader(WEB_URL)
docs.extend(loader.load())
# YouTube
loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1, YOUTUBE_URL_2], YOUTUBE_DIR),
OpenAIWhisperParser())
docs.extend(loader.load())
return docs
def split_documents(config, docs):
text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = config["chunk_overlap"],
chunk_size = config["chunk_size"])
return text_splitter.split_documents(docs)
def store_chroma(chunks):
Chroma.from_documents(documents = chunks,
embedding = OpenAIEmbeddings(disallowed_special = ()),
persist_directory = CHROMA_DIR)
def store_mongodb(chunks):
MongoDBAtlasVectorSearch.from_documents(documents = chunks,
embedding = OpenAIEmbeddings(disallowed_special = ()),
collection = collection,
index_name = MONGODB_INDEX_NAME)
def rag_ingestion(config):
docs = load_documents()
chunks = split_documents(config, docs)
store_chroma(chunks)
store_mongodb(chunks)
def retrieve_chroma():
return Chroma(embedding_function = OpenAIEmbeddings(disallowed_special = ()),
persist_directory = CHROMA_DIR)
def retrieve_mongodb():
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(config):
return ChatOpenAI(model_name = config["model_name"],
temperature = config["temperature"])
def llm_chain(config, prompt):
llm_chain = LLMChain(llm = get_llm(config),
prompt = LLM_CHAIN_PROMPT)
with get_openai_callback() as cb:
completion = llm_chain.generate([{"question": prompt}])
return completion, llm_chain, cb
def rag_chain(config, rag_option, prompt):
llm = get_llm(config)
if (rag_option == RAG_CHROMA):
db = retrieve_chroma()
elif (rag_option == RAG_MONGODB):
db = retrieve_mongodb()
rag_chain = RetrievalQA.from_chain_type(llm,
chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT,
"verbose": True},
retriever = db.as_retriever(search_kwargs = {"k": config["k"]}),
return_source_documents = True)
with get_openai_callback() as cb:
completion = rag_chain({"query": prompt})
return completion, rag_chain, cb |