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import os, requests
from llama_hub.youtube_transcript import YoutubeTranscriptReader
from llama_index import download_loader, PromptTemplate, ServiceContext
from llama_index.indices.vector_store.base import VectorStoreIndex
from llama_index.llms import OpenAI
from llama_index.storage.storage_context import StorageContext
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from pathlib import Path
from pymongo import MongoClient
from rag_base import BaseRAG
class LlamaIndexRAG(BaseRAG):
MONGODB_DB_NAME = "llamaindex_db"
def load_documents(self):
docs = []
# PDF
PDFReader = download_loader("PDFReader")
loader = PDFReader()
out_dir = Path("data")
if not out_dir.exists():
os.makedirs(out_dir)
out_path = out_dir / "gpt-4.pdf"
if not out_path.exists():
r = requests.get(self.PDF_URL)
with open(out_path, "wb") as f:
f.write(r.content)
docs.extend(loader.load_data(file = Path(out_path)))
#print("docs = " + str(len(docs)))
# Web
SimpleWebPageReader = download_loader("SimpleWebPageReader")
loader = SimpleWebPageReader()
docs.extend(loader.load_data(urls = [self.WEB_URL]))
#print("docs = " + str(len(docs)))
# YouTube
loader = YoutubeTranscriptReader()
docs.extend(loader.load_data(ytlinks = [self.YOUTUBE_URL_1,
self.YOUTUBE_URL_2]))
#print("docs = " + str(len(docs)))
return docs
def get_llm(self, config):
return OpenAI(
model = config["model_name"],
temperature = config["temperature"]
)
def get_vector_store(self):
return MongoDBAtlasVectorSearch(
MongoClient(self.MONGODB_ATLAS_CLUSTER_URI),
db_name = self.MONGODB_DB_NAME,
collection_name = self.MONGODB_COLLECTION_NAME,
index_name = self.MONGODB_INDEX_NAME
)
def get_service_context(config):
return ServiceContext.from_defaults(
chunk_overlap = config["chunk_overlap"],
chunk_size = config["chunk_size"],
llm = self.get_llm(config)
)
def get_storage_context():
return StorageContext.from_defaults(
vector_store = self.get_vector_store()
)
def store_documents(self, config, docs):
storage_context = StorageContext.from_defaults(
vector_store = self.get_vector_store()
)
VectorStoreIndex.from_documents(
docs,
service_context = self.get_service_context(config),
storage_context = self.get_storage_context()
)
def ingestion(self, config):
docs = self.load_documents()
self.store_documents(config, docs)
def retrieval(self, config, prompt):
index = VectorStoreIndex.from_vector_store(
vector_store = self.get_vector_store()
)
query_engine = index.as_query_engine(
service_context = self.get_service_context(config),
similarity_top_k = config["k"]
)
return query_engine.query(prompt) |