Spaces:
Sleeping
Sleeping
File size: 1,942 Bytes
a98b8cc f0381b3 a98b8cc f0381b3 a98b8cc 4f708f6 a98b8cc 4f708f6 a98b8cc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import re
from sentence_transformers import SentenceTransformer
import chromadb
import google.generativeai as genai
import re
def load_documents(file_path, chunk_size=30):
with open(file_path, "r", encoding="utf-8") as f:
text = f.read()
words = re.findall(r"\w+|\S", text)
chunks = [
" ".join(words[i : i + chunk_size]) for i in range(0, len(words), chunk_size)
]
return [chunk.strip() for chunk in chunks if chunk.strip()]
def embed_documents(docs, model):
return model.encode(docs).tolist()
def build_chroma_db(docs, embeddings):
client = chromadb.Client()
collection = client.get_or_create_collection("rag_docs")
for i, (doc, emb) in enumerate(zip(docs, embeddings)):
collection.add(documents=[doc], embeddings=[emb], ids=[str(i)])
return collection
def retrieve(query, collection, model, top_k=3):
query_emb = model.encode([query]).tolist()[0]
results = collection.query(
query_embeddings=[query_emb], n_results=top_k, include=["documents"]
)
return results["documents"][0]
def call_gemini(query, context, api_key):
genai.configure(api_key=api_key)
model = genai.GenerativeModel("gemini-2.0-flash")
prompt = (
"Answer the following question based only on the provided context.\n\n"
f"Context:\n{context}\n\n"
f"Question: {query}\n"
"Answer:"
)
response = model.generate_content(prompt)
return response.text.strip()
def build_rag_chain(api_key):
docs = load_documents("data/info.txt")
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = embed_documents(docs, model)
collection = build_chroma_db(docs, embeddings)
def rag_qa(query):
retrieved_docs = retrieve(query, collection, model)
context = "\n\n".join(retrieved_docs)
answer = call_gemini(query, context, api_key)
return answer, retrieved_docs
return rag_qa
|