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import faiss
from sentence_transformers import SentenceTransformer
import time
import json
from langchain_core.documents import Document
def init_cache():
index = faiss.IndexFlatL2(1024)
if index.is_trained:
print("Index trained")
# Initialize Sentence Transformer model
encoder = SentenceTransformer("multilingual-e5-large")
return index, encoder
def retrieve_cache(json_file):
try:
with open(json_file, "r") as file:
cache = json.load(file)
except FileNotFoundError:
cache = {"query": [], "embeddings": [], "answers": [], "response_text": []}
return cache
def store_cache(json_file, cache):
with open(json_file, "w") as file:
json.dump(cache, file)
class SemanticCache:
def __init__(self, retriever, json_file="cache_file.json", thresold=0.35):
# Initialize Faiss index with Euclidean distance
self.retriever = retriever
self.index, self.encoder = init_cache()
# Set Euclidean distance threshold
# a distance of 0 means identicals sentences
# We only return from cache sentences under this thresold
self.euclidean_threshold = thresold
self.json_file = json_file
self.cache = retrieve_cache(self.json_file)
def query_database(self, query_text):
results = self.retriever.get_relevant_documents(query_text)
return results
def get_relevant_documents(self, query: str, use_cache=True) -> str:
# Method to retrieve an answer from the cache or generate a new one
start_time = time.time()
try:
# First we obtain the embeddings corresponding to the user query
embedding = self.encoder.encode([query])
# Search for the nearest neighbor in the index
self.index.nprobe = 8
D, I = self.index.search(embedding, 1)
if use_cache:
if D[0] >= 0:
if I[0][0] >= 0 and D[0][0] <= self.euclidean_threshold:
row_id = int(I[0][0])
print("Answer recovered from Cache. ")
print(f"{D[0][0]:.3f} smaller than {self.euclidean_threshold}")
print(f"Found cache in row: {row_id} with score {D[0][0]:.3f}")
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Time taken: {elapsed_time:.3f} seconds")
return [Document(**doc) for doc in self.cache["answers"][row_id]]
# Handle the case when there are not enough results
# or Euclidean distance is not met, asking to chromaDB.
answer = self.query_database(query)
# response_text = answer["documents"][0][0]
self.cache["query"].append(query)
self.cache["embeddings"].append(embedding[0].tolist())
self.cache["answers"].append([doc.__dict__ for doc in answer])
self.index.add(embedding)
store_cache(self.json_file, self.cache)
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Time taken: {elapsed_time:.3f} seconds")
return answer
except Exception as e:
raise RuntimeError(f"Error during 'get_relevant_documents' method: {e}") |