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from flask import Flask, request, jsonify
from langchain_community.vectorstores import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_ollama import OllamaLLM
from langchain.chains.question_answering import load_qa_chain
from langchain import PromptTemplate
app = Flask(__name__)
# Initialize the language models
llm = OllamaLLM(model="llama3.2")
# Initialize HuggingFaceEmbeddings and Chroma
model_name = "intfloat/multilingual-e5-large"
load_from_dir = "Hadith_Chroma_db"
embedding_llm = HuggingFaceEmbeddings(model_name=model_name)
loaded_vector_db = Chroma(
persist_directory=load_from_dir,
embedding_function=embedding_llm
)
def get_similar_docs(query):
"""Retrieve similar documents based on the query."""
similar_docs = loaded_vector_db.similarity_search(query, k=2)
return similar_docs
def ask_llms(query_text):
"""Ask the LLM to provide an answer based on similar documents."""
similar_docs = get_similar_docs(query_text)
qna_template = '\n'.join([
"Answer the following question using the context provided.",
"If the answer is not included in the context, say 'No answer available'.",
"### Context:",
"{context}",
"### Question:",
"{question}",
"### Answer:"
])
qna_prompt = PromptTemplate(
template=qna_template,
input_variables=['context', 'question'],
verbose=True
)
stuff_chain = load_qa_chain(llm, chain_type="stuff", prompt=qna_prompt)
final_answer = stuff_chain.invoke({
"input_documents": similar_docs,
"question": query_text
})
return final_answer['output_text']
@app.route('/ai', methods=['POST'])
def aiPost():
"""Handle POST requests to the /ai endpoint."""
try:
json_content = request.json
if not json_content or 'query' not in json_content:
return jsonify({"error": "Invalid input, 'query' field is required"}), 400
query = json_content.get('query')
# Get the response from the LLM based on the query
response = ask_llms(query)
return jsonify({"response": response})
except Exception as e:
return jsonify({"error": str(e)}), 500
def start_app():
"""Start the Flask app."""
app.run(host="0.0.0.0", port=8080, debug=True)
if __name__ == '__main__':
start_app()
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