langchain/docs/versioned_docs/version-0.2.x/integrations/vectorstores/vlite.ipynb

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    "\n",
    "# vlite\n",
    "\n",
    "VLite is a simple and blazing fast vector database that allows you to store and retrieve data semantically using embeddings. Made with numpy, vlite is a lightweight batteries-included database to implement RAG, similarity search, and embeddings into your projects.\n",
    "\n",
    "## Installation\n",
    "\n",
    "To use the VLite in LangChain, you need to install the `vlite` package:\n",
    "\n",
    "```bash\n",
    "!pip install vlite\n",
    "```\n",
    "\n",
    "## Importing VLite\n",
    "\n",
    "```python\n",
    "from langchain.vectorstores import VLite\n",
    "```\n",
    "\n",
    "## Basic Example\n",
    "\n",
    "In this basic example, we load a text document, and store them in the VLite vector database. Then, we perform a similarity search to retrieve relevant documents based on a query.\n",
    "\n",
    "VLite handles chunking and embedding of the text for you, and you can change these parameters by pre-chunking the text and/or embeddings those chunks into the VLite database.\n",
    "\n",
    "```python\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
    "# Load the document and split it into chunks\n",
    "loader = TextLoader(\"path/to/document.txt\")\n",
    "documents = loader.load()\n",
    "\n",
    "# Create a VLite instance\n",
    "vlite = VLite(collection=\"my_collection\")\n",
    "\n",
    "# Add documents to the VLite vector database\n",
    "vlite.add_documents(documents)\n",
    "\n",
    "# Perform a similarity search\n",
    "query = \"What is the main topic of the document?\"\n",
    "docs = vlite.similarity_search(query)\n",
    "\n",
    "# Print the most relevant document\n",
    "print(docs[0].page_content)\n",
    "```\n",
    "\n",
    "## Adding Texts and Documents\n",
    "\n",
    "You can add texts or documents to the VLite vector database using the `add_texts` and `add_documents` methods, respectively.\n",
    "\n",
    "```python\n",
    "# Add texts to the VLite vector database\n",
    "texts = [\"This is the first text.\", \"This is the second text.\"]\n",
    "vlite.add_texts(texts)\n",
    "\n",
    "# Add documents to the VLite vector database\n",
    "documents = [Document(page_content=\"This is a document.\", metadata={\"source\": \"example.txt\"})]\n",
    "vlite.add_documents(documents)\n",
    "```\n",
    "\n",
    "## Similarity Search\n",
    "\n",
    "VLite provides methods for performing similarity search on the stored documents.\n",
    "\n",
    "```python\n",
    "# Perform a similarity search\n",
    "query = \"What is the main topic of the document?\"\n",
    "docs = vlite.similarity_search(query, k=3)\n",
    "\n",
    "# Perform a similarity search with scores\n",
    "docs_with_scores = vlite.similarity_search_with_score(query, k=3)\n",
    "```\n",
    "\n",
    "## Max Marginal Relevance Search\n",
    "\n",
    "VLite also supports Max Marginal Relevance (MMR) search, which optimizes for both similarity to the query and diversity among the retrieved documents.\n",
    "\n",
    "```python\n",
    "# Perform an MMR search\n",
    "docs = vlite.max_marginal_relevance_search(query, k=3)\n",
    "```\n",
    "\n",
    "## Updating and Deleting Documents\n",
    "\n",
    "You can update or delete documents in the VLite vector database using the `update_document` and `delete` methods.\n",
    "\n",
    "```python\n",
    "# Update a document\n",
    "document_id = \"doc_id_1\"\n",
    "updated_document = Document(page_content=\"Updated content\", metadata={\"source\": \"updated.txt\"})\n",
    "vlite.update_document(document_id, updated_document)\n",
    "\n",
    "# Delete documents\n",
    "document_ids = [\"doc_id_1\", \"doc_id_2\"]\n",
    "vlite.delete(document_ids)\n",
    "```\n",
    "\n",
    "## Retrieving Documents\n",
    "\n",
    "You can retrieve documents from the VLite vector database based on their IDs or metadata using the `get` method.\n",
    "\n",
    "```python\n",
    "# Retrieve documents by IDs\n",
    "document_ids = [\"doc_id_1\", \"doc_id_2\"]\n",
    "docs = vlite.get(ids=document_ids)\n",
    "\n",
    "# Retrieve documents by metadata\n",
    "metadata_filter = {\"source\": \"example.txt\"}\n",
    "docs = vlite.get(where=metadata_filter)\n",
    "```\n",
    "\n",
    "## Creating VLite Instances\n",
    "\n",
    "You can create VLite instances using various methods:\n",
    "\n",
    "```python\n",
    "# Create a VLite instance from texts\n",
    "vlite = VLite.from_texts(texts)\n",
    "\n",
    "# Create a VLite instance from documents\n",
    "vlite = VLite.from_documents(documents)\n",
    "\n",
    "# Create a VLite instance from an existing index\n",
    "vlite = VLite.from_existing_index(collection=\"existing_collection\")\n",
    "```\n",
    "\n",
    "## Additional Features\n",
    "\n",
    "VLite provides additional features for managing the vector database:\n",
    "\n",
    "```python\n",
    "from langchain.vectorstores import VLite\n",
    "vlite = VLite(collection=\"my_collection\")\n",
    "\n",
    "# Get the number of items in the collection\n",
    "count = vlite.count()\n",
    "\n",
    "# Save the collection\n",
    "vlite.save()\n",
    "\n",
    "# Clear the collection\n",
    "vlite.clear()\n",
    "\n",
    "# Get collection information\n",
    "vlite.info()\n",
    "\n",
    "# Dump the collection data\n",
    "data = vlite.dump()\n",
    "```"
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