Migrate langchain rag cpu example to gpu (#10450)

* add langchain rag on gpu

* add rag example in readme

* add trust_remote_code in TransformersEmbeddings.from_model_id

* add trust_remote_code in TransformersEmbeddings.from_model_id in cpu

python/llm/example/CPU/LangChain/transformers_int4/rag.py

@@ -61,7 +61,10 @@ def main(args):
     texts = text_splitter.split_text(input_doc)
 
     # create embeddings and store into vectordb
-    embeddings = TransformersEmbeddings.from_model_id(model_id=model_path)
+    embeddings = TransformersEmbeddings.from_model_id(
+        model_id=model_path, 
+        model_kwargs={"trust_remote_code": True}
+        )
     docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
 
     #get relavant texts

python/llm/example/GPU/LangChain/transformer_int4_gpu/README.md

@@ -91,3 +91,13 @@ python chat.py -m MODEL_PATH -q QUESTION
 arguments info:
 - `-m MODEL_PATH`: **required**, path to the model
 - `-q QUESTION`: question to ask. Default is `What is AI?`.
+
+#### 5.1. RAG (Retrival Augmented Generation)
+
+```bash
+python rag.py -m <path_to_model> [-q QUESTION] [-i INPUT_PATH]
+```
+arguments info:
+- `-m MODEL_PATH`: **required**, path to the model.
+- `-q QUESTION`: question to ask. Default is `What is BigDL?`.
+- `-i INPUT_PATH`: path to the input doc.

python/llm/example/GPU/LangChain/transformer_int4_gpu/rag.py

@@ -0,0 +1,99 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This would makes sure Python is aware there is more than one sub-package within bigdl,
+# physically located elsewhere.
+# Otherwise there would be module not found error in non-pip's setting as Python would
+# only search the first bigdl package and end up finding only one sub-package.
+
+# Code is adapted from https://python.langchain.com/docs/modules/chains/additional/question_answering.html
+
+import torch
+import argparse
+
+from langchain.vectorstores import Chroma
+from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
+                                                     QA_PROMPT)
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.question_answering import load_qa_chain
+from langchain.callbacks.manager import CallbackManager
+
+from bigdl.llm.langchain.llms import TransformersLLM
+from bigdl.llm.langchain.embeddings import TransformersEmbeddings
+
+text_doc = '''
+BigDL seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:
+LLM: Low-bit (INT3/INT4/INT5/INT8) large language model library for Intel CPU/GPU
+Orca: Distributed Big Data & AI (TF & PyTorch) Pipeline on Spark and Ray
+Nano: Transparent Acceleration of Tensorflow & PyTorch Programs on Intel CPU/GPU
+DLlib: "Equivalent of Spark MLlib" for Deep Learning
+Chronos: Scalable Time Series Analysis using AutoML
+Friesian: End-to-End Recommendation Systems
+PPML: Secure Big Data and AI (with SGX Hardware Security)
+'''
+
+def main(args):
+
+    input_path = args.input_path 
+    model_path = args.model_path
+    query = args.question
+
+    # split texts of input doc
+    if input_path is None:
+        input_doc = text_doc
+    else:
+        with open(input_path) as f:
+            input_doc = f.read()
+            
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_text(input_doc)
+
+    # create embeddings and store into vectordb
+    embeddings = TransformersEmbeddings.from_model_id(
+        model_id=model_path, 
+        model_kwargs={"trust_remote_code": True},
+        device_map='xpu'
+        )
+    docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
+
+    #get relavant texts
+    docs = docsearch.get_relevant_documents(query)
+
+    bigdl_llm = TransformersLLM.from_model_id(
+        model_id=model_path,
+        model_kwargs={"temperature": 0, "max_length": 1024, "trust_remote_code": True},
+        device_map='xpu'
+    )
+
+    doc_chain = load_qa_chain(
+        bigdl_llm, chain_type="stuff", prompt=QA_PROMPT
+    )
+
+    output = doc_chain.run(input_documents=docs, question=query)
+    print(output)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='TransformersLLM Langchain QA over Docs Example')
+    parser.add_argument('-m','--model-path', type=str, required=True,
+                        help='the path to transformers model')
+    parser.add_argument('-i', '--input-path', type=str,
+                        help='the path to the input doc.')
+    parser.add_argument('-q', '--question', type=str, default='What is BigDL?',
+                        help='qustion you want to ask.')
+    args = parser.parse_args()
+    
+    main(args)