add Question_Answering_Pipeline_with_LangChain_and_EpsillaCloud

Signed-off-by: eric-epsilla <eric@epsilla.com>

Author: eric-epsilla

examples/Question_Answering_Pipeline_with_LangChain_and_EpsillaCloud.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+
+
+# Question Answering Pipeline with LangChain and Epsilla
+# Step1. Install the required packages
+"""
+pip install langchain
+pip install openai
+pip install tiktoken
+pip install pyepsilla
+pip install -U langchain-openai
+pip uninstall -y langchain-community
+git clone https://github.com/epsilla-cloud/langchain.git
+cd ./langchain/libs/community
+pip install .
+"""
+
+
+
+# Step2. Configure the OpenAI API Key
+import os
+
+os.environ["OPENAI_API_KEY"] = ""
+epsilla_api_key = os.getenv("EPSILLA_API_KEY", "")
+project_id = os.getenv("EPSILLA_PROJECT_ID", "")
+db_id = os.getenv("EPSILLA_DB_ID", "")
+db_sharding_id = os.getenv("EPSILLA_DB_SHARDING_ID", 0)
+
+# Step3. Load the documents
+from langchain.document_loaders import WebBaseLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+
+loader = WebBaseLoader(
+    "https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt"
+)
+documents = loader.load()
+documents = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0).split_documents(
+    documents
+)
+embeddings = OpenAIEmbeddings()
+
+
+# Step4. Load the vector store
+from langchain.vectorstores import Epsilla
+from pyepsilla import cloud, vectordb
+
+
+db_name = f"db_{db_id.replace('-', '_')}"
+db_path = f"/data/{project_id}/{db_name}/s{db_sharding_id}"
+table_name = "MyCollection"
+
+# Connect to Epsilla Cloud
+cloud_client = cloud.Client(
+    project_id=project_id,
+    api_key=epsilla_api_key,
+)
+
+# Connect to Vectordb
+db_client = cloud_client.vectordb(db_id)
+
+vector_store = Epsilla.from_documents(
+    documents,
+    embeddings,
+    db_client,
+    db_path=db_path,
+    db_name=db_name,
+    collection_name=table_name,
+)
+
+# Step4. Create the QA for Retrieval
+from langchain.chains import RetrievalQA
+from langchain_openai import OpenAI
+
+qa = RetrievalQA.from_chain_type(
+    llm=OpenAI(), chain_type="stuff", retriever=vector_store.as_retriever()
+)
+query = "What did the president say about Ketanji Brown Jackson"
+resp = qa.invoke(query)
+print("resp:", resp)