langchain/docs/modules/indexes/retrievers/examples/time_weighted_vectorstore.ipynb

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    "# Time Weighted VectorStore Retriever\n",
    "\n",
    "This retriever uses a combination of semantic similarity and recency.\n",
    "\n",
    "The algorithm for scoring them is:\n",
    "\n",
    "```\n",
    "semantic_similarity + (1.0 - decay_rate) ** hours_passed\n",
    "```\n",
    "\n",
    "Notably, hours_passed refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain \"fresh.\""
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    "import faiss\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "from langchain.docstore import InMemoryDocstore\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
    "from langchain.schema import Document\n",
    "from langchain.vectorstores import FAISS\n"
   ]
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    "## Low Decay Rate\n",
    "\n",
    "A low decay rate (in this, to be extreme, we will set close to 0) means memories will be \"remembered\" for longer. A decay rate of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup."
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    "# Define your embedding model\n",
    "embeddings_model = OpenAIEmbeddings()\n",
    "# Initialize the vectorstore as empty\n",
    "embedding_size = 1536\n",
    "index = faiss.IndexFlatL2(embedding_size)\n",
    "vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})\n",
    "retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.0000000000000000000000001, k=1) "
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    "yesterday = datetime.now() - timedelta(days=1)\n",
    "retriever.add_documents([Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})])\n",
    "retriever.add_documents([Document(page_content=\"hello foo\")])"
   ]
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       "[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 15, 46, 43, 860748), 'created_at': datetime.datetime(2023, 4, 16, 15, 46, 14, 469670), 'buffer_idx': 1})]"
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   "source": [
    "# \"Hello World\" is returned first because it is most salient, and the decay rate is close to 0., meaning it's still recent enough\n",
    "retriever.get_relevant_documents(\"hello world\")"
   ]
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    "## High Decay Rate\n",
    "\n",
    "With a high decay factor (e.g., several 9's), the recency score quickly goes to 0! If you set this all the way to 1, recency is 0 for all objects, once again making this equivalent to a vector lookup.\n"
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   "source": [
    "# Define your embedding model\n",
    "embeddings_model = OpenAIEmbeddings()\n",
    "# Initialize the vectorstore as empty\n",
    "embedding_size = 1536\n",
    "index = faiss.IndexFlatL2(embedding_size)\n",
    "vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})\n",
    "retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.999, k=1) "
   ]
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    "yesterday = datetime.now() - timedelta(days=1)\n",
    "retriever.add_documents([Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})])\n",
    "retriever.add_documents([Document(page_content=\"hello foo\")])"
   ]
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       "[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 15, 46, 17, 646927), 'created_at': datetime.datetime(2023, 4, 16, 15, 46, 14, 469670), 'buffer_idx': 1})]"
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   "source": [
    "# \"Hello Foo\" is returned first because \"hello world\" is mostly forgotten\n",
    "retriever.get_relevant_documents(\"hello world\")"
   ]
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