Regarding RAG:
Python + the LangChain lib for the retriever + GPT for the answer generator is a common choice, but there exist many alternatives.
Code example for RAG-QA to answer questions about how to use Stack Exchange. The code uses Python 3.11 + LangChain lib + Faiss + GPT (via Azure):
import os
import pprint
import pandas
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
import yaml
with open('parameters.yaml') as f:
parameters = yaml.safe_load(f)
pprint.pprint(parameters)
model = "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"
embeddings = HuggingFaceEmbeddings(model_name = model)
def create_index():
# https://python.langchain.com/docs/integrations/vectorstores/faiss
# Uncomment the following line if you need to initialize FAISS with no AVX2 optimization
# os.environ['FAISS_NO_AVX2'] = '1'
langchain_documents = []
raw_documents_df = pandas.read_csv(os.path.join('data', 'help.csv'), header=0)
for raw_document_index, raw_document in raw_documents_df.iterrows():
print(raw_document)
new_doc = Document(page_content=raw_document['section_body'],
metadata={"document_id": str(raw_document['document_id']),
"url": str(raw_document['url']),
"header": str(raw_document['section_header']),
})
langchain_documents.append(new_doc)
db = FAISS.from_documents(langchain_documents, embeddings)
db.save_local("faiss_index")
def test_index():
query = "Where can I see my deleted questions?"
print('Query:', query)
new_db = FAISS.load_local("faiss_index", embeddings)
relevant_documents = new_db.similarity_search_with_score(query, k=3)
pprint.pprint(relevant_documents)
context = []
# Convert docs into a JSON context
for relevant_document in relevant_documents:
document_dictionary = {}
print('relevant_document', relevant_document)
document_dictionary['content'] = relevant_document[0].page_content
document_dictionary['score'] = relevant_document[1]
context.append(document_dictionary)
print('\n\n\nContext:')
pprint.pprint(context)
answer = generate_answer(context, query)
from openai import AzureOpenAI
def generate_answer(context, query):
pprint.pprint(context)
client = AzureOpenAI(
api_key=parameters['azure']['api_key'],
azure_endpoint = parameters['azure']['azure_endpoint'],
api_version = parameters['azure']['api_version']
)
llm_user_prompt = llm_user_prompt_template.format(question=query, context=context)
print('llm_user_prompt', llm_user_prompt)
messages = [{"role":"system",
"content": llm_system_prompt},
{"role": "user", "content": llm_user_prompt}]
chat_completion = client.chat.completions.create(messages=messages, model=parameters['azure']['model'], temperature=0)
print(chat_completion)
print('\n\n\nanswer', chat_completion.choices[0].message.content)
answer = chat_completion
return answer
llm_system_prompt = '''You are an assistant to help people use Stack Exchange based on the information given to you. When asked about anything that does not relate to Stack Exchange, only reply with 'Content not found'''
llm_user_prompt_template = """You are asked a question by the user and you must write an answer from only the data provided in variable 'Context'.\n""" \
"""You must use only the provided data in variable 'Context' to see if any of the text is relevant to answer the question.\n""" \
"""You must not use any other information from any other source or from previous knowledge beyond the provided 'Context'.\n""" \
"""Return 'content not found' if nothing relevant is found in provided Context or no Context was given\n""" \
""" Context is an array of JSONs in the following JSON format\n
# JSON format:
# {{
# "context": context text,
# "context title" : section title
# "score": score of the retrieved context.
# }}
Consider value in content key for context.
""" \
"""Question: "{question}" """ \
"""Context: "{context}" """ \
"""Constraints: \n""" \
"""1. User is already on Stack Exchange: don't suggest opening Stack Exchange.\n""" \
"""2. Users will be always using Stack Exchange application: don't assume or talk about any other website.\n""" \
"""3. Respond answers in numbered list. \n""" \
"""4. You must keep answers specific and list down all steps. \n""" \
"""5. If an answer is found, mention it in answer as list and bullet points\n"""
if __name__ == '__main__':
if not os.path.isdir('faiss_index'):
print('Creating index')
create_index()
test_index()
Assuming that help.csv contains a list of documents on how to use Stack Exchange, e.g.:
document_id,section_header,section_body,url
1,See one's deleted questions,"blah blah
",https://genai.stackexchange.com/help/on-topic
2,Account suspensions,"blah blah
",https://genai.stackexchange.com/help/dont-ask
3,Editing answers,"blah blah
",https://genai.stackexchange.com/help/closed-questions
and parameters.yaml:
azure:
api_key: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
azure_endpoint: https://xxxxxx.openai.azure.com/
api_version: 2023-07-01-preview
model: xxxxxxxxx
Tested with Python 3.11 with:
pip install langchain==0.1.1 langchain_openai==0.0.2.post1 sentence-transformers==2.2.2 langchain_community==0.0.13 faiss-cpu==1.7.4
Examples of common IDEs: PyCharm, Visual Studio.
