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import logging, os, sys
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
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"
CHROMA_DIR = "/data/db"
YOUTUBE_DIR = "/data/yt"
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"])
logging.basicConfig(stream = sys.stdout, level = logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream = sys.stdout))
def load_documents():
docs = []
# PDF
loader = PyPDFLoader(PDF_URL)
docs.extend(loader.load())
#print("docs = " + str(len(docs)))
# Web
loader = WebBaseLoader(WEB_URL)
docs.extend(loader.load())
#print("docs = " + str(len(docs)))
# YouTube
loader = GenericLoader(
YoutubeAudioLoader(
[YOUTUBE_URL_1, YOUTUBE_URL_2],
YOUTUBE_DIR),
OpenAIWhisperParser())
docs.extend(loader.load())
#print("docs = " + str(len(docs)))
return docs
def split_documents(config, docs):
text_splitter = RecursiveCharacterTextSplitter()
return text_splitter.split_documents(docs)
def store_documents_chroma(chunks):
Chroma.from_documents(
documents = chunks,
embedding = OpenAIEmbeddings(disallowed_special = ()),
persist_directory = CHROMA_DIR)
def store_documents_mongodb(chunks):
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
collection = client[MONGODB_DB_NAME][MONGODB_COLLECTION_NAME]
MongoDBAtlasVectorSearch.from_documents(
documents = chunks,
embedding = OpenAIEmbeddings(disallowed_special = ()),
collection = collection,
index_name = MONGODB_INDEX_NAME)
def rag_ingestion_langchain(config):
docs = load_documents()
chunks = split_documents(config, docs)
#store_documents_chroma(chunks)
store_documents_mongodb(chunks)
def get_vector_store_chroma():
return Chroma(
embedding_function = OpenAIEmbeddings(disallowed_special = ()),
persist_directory = CHROMA_DIR)
def get_vector_store_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 callback:
completion = llm_chain.generate([{"question": prompt}])
return completion, llm_chain, callback
def rag_chain(config, prompt):
#vector_store = get_vector_store_chroma()
vector_store = get_vector_store_mongodb()
rag_chain = RetrievalQA.from_chain_type(
get_llm(config),
chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT,
"verbose": True},
retriever = vector_store.as_retriever(search_kwargs = {"k": config["k"]}),
return_source_documents = True)
with get_openai_callback() as callback:
completion = rag_chain({"query": prompt})
return completion, rag_chain, callback |