update

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

Author: eric-epsilla

examples/Question_Answering_Pipeline_with_LangChain_and_Epsilla.py

@@ -0,0 +1,65 @@
+#!/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-community
+pip install -U langchain-openai
+"""
+
+
+# Step2. Configure the OpenAI API Key
+import os
+
+os.environ["OPENAI_API_KEY"] = "*****"
+
+
+# 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 vectordb
+
+client = vectordb.Client(protocol="https", host="demo.epsilla.com", port="443")
+
+status_code, response = client.load_db("MyDB", "/data/MyDB")
+print(status_code, response)
+
+vector_store = Epsilla.from_documents(
+    documents,
+    embeddings,
+    client,
+    db_path="/data/MyDB",
+    db_name="MyDB",
+    collection_name="MyCollection",
+)
+
+# 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)