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import qdrant_client
from llama_index.core import VectorStoreIndex, ServiceContext, SimpleDirectoryReader
from llama_index.core import load_index_from_storage
from llama_index.llms.ollama import Ollama
from llama_index.core import StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
from llama_index.core import set_global_service_context
import gradio as gr
DOC_PATH = '/esg_chatbot/data/pdf_esg'
INDEX_PATH = '/storage'
Settings.llm = Ollama(model="mistral")
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
service_context = ServiceContext.from_defaults(llm=Ollama(model="mistral"),embed_model = embed_model)
set_global_service_context(service_context)
def construct_index(doc_path=DOC_PATH, index_store=INDEX_PATH, use_cache=False):
client = qdrant_client.QdrantClient(path="./qdrant_data")
vector_store = QdrantVectorStore(client=client, collection_name="esg")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
if use_cache:
# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir=index_store)
index = load_index_from_storage(storage_context) # load index
else:
reader = SimpleDirectoryReader(input_dir='/esg_chatbot/data/pdf_esg')
documents = reader.load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist(index_store)
return None
def qabot(input_text, index_store = INDEX_PATH):
storage_context = StorageContext.from_defaults(persist_dir=index_store)
# Load the data
index = load_index_from_storage(storage_context)
query_engine = index.as_query_engine()
response = query_engine.query(input_text)
return response.response
if __name__ == "__main__":
construct_index(DOC_PATH, use_cache=False)
# create_index_retriever_query_engine()
iface = gr.Interface(fn=qabot, inputs=gr.Textbox(lines=7, label='Enter your query'),
outputs="text",
title="ESG Chatbot")
iface.launch(inline=False) |