Create app.py

app.py

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+from fastapi import FastAPI
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import faiss
+import torch
+from sentence_transformers import SentenceTransformer
+
+app = FastAPI()
+
+# Load models
+embed_model = SentenceTransformer('all-MiniLM-L6-v2')
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
+
+# Sample documents
+documents = [
+    "Startup India provides funding and tax benefits for new startups in India.",
+    "Angel investors are individuals who invest in early-stage startups in exchange for equity.",
+    "A pitch deck is a presentation that startups use to attract investors.",
+    "The government offers startup grants through various schemes.",
+    "Networking events connect entrepreneurs with investors and mentors."
+]
+
+# Convert documents to embeddings and store in FAISS
+doc_vectors = embed_model.encode(documents, convert_to_numpy=True)
+dimension = doc_vectors.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(doc_vectors)
+
+def retrieve_docs(query, top_k=2):
+    query_vector = embed_model.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_vector, top_k)
+    return [documents[i] for i in indices[0]]
+
+def generate_response(query):
+    retrieved_docs = retrieve_docs(query)
+    context = " ".join(retrieved_docs)
+    prompt = f"Context: {context}\nQuestion: {query}\nAnswer:"
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+@app.get("/chat")
+def chat(query: str):
+    return {"response": generate_response(query)}