Create app.py

app.py

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+from fastapi import FastAPI, HTTPException, Query
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain_mistralai import ChatMistralAI
+from langchain.prompts import PromptTemplate
+import os
+
+app = FastAPI()
+
+# ✅ Root Route to Prevent 404 Error
+@app.get("/")
+def home():
+    return {"message": "Welcome to the RAG API! Use /query to ask questions."}
+
+# ✅ Set environment variables for API keys (Replace with actual keys)
+os.environ['MISTRAL_API_KEY'] = "your-mistral-api-key"
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = "your-huggingface-api-token"
+
+# ✅ Load and process documents
+def load_docs(directory=r"C:\Users\San_D\Desktop\Project API\RAG\DATA"):
+    loader = PyPDFDirectoryLoader(directory)
+    return loader.load()
+
+documents = load_docs()
+
+def split_docs(documents, chunk_size=512, chunk_overlap=20):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap, separators=["\n\n", "\n", " ", ""])
+    return text_splitter.split_documents(documents)
+
+docs = split_docs(documents)
+
+# ✅ Initialize embeddings and vector store
+embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+if docs:
+    index = FAISS.from_documents(docs, embeddings)
+else:
+    index = None  # Prevents errors if no documents are found
+
+def get_similar_docs(query, k=4):
+    if index:
+        return index.similarity_search(query, k=k)
+    return []
+
+# ✅ Load LLM
+llm = ChatMistralAI(model="mistral-large-latest", temperature=0.7, max_tokens=150)
+
+# ✅ Define prompt
+med_prompt = PromptTemplate.from_template(
+    """
+    You are an expert medical assistant.
+    Based only on the summaries provided below, answer the given question.
+    Give all details as much as possible.
+    If you don't know the answer, respond with "I don't know".
+    
+    Source material: {context}
+    
+    Question: {question}
+    Answer:
+    """
+)
+
+chain = med_prompt | llm
+
+@app.post("/query")
+def get_answer(query: str = Query(..., description="The user's question")):
+    relevant_docs = get_similar_docs(query)
+    if not relevant_docs:
+        raise HTTPException(status_code=404, detail="No relevant documents found.")
+    
+    response = chain.invoke({"context": relevant_docs, "question": query})
+    return {"answer": response.content}
+
+# ✅ Run Uvicorn if executed directly (for local testing)
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)