mirror of https://github.com/hwchase17/langchain
community[minor]: Added VLite as VectorStore (#20245)
Support [VLite](https://github.com/sdan/vlite) as a new VectorStore type. **Description**: vlite is a simple and blazing fast vector database(vdb) made with numpy. It abstracts a lot of the functionality around using a vdb in the retrieval augmented generation(RAG) pipeline such as embeddings generation, chunking, and file processing while still giving developers the functionality to change how they're made/stored. **Before submitting**: Added tests [here](pull/18775/head^2c09c2ebd5c/libs/community/tests/integration_tests/vectorstores/test_vlite.py) Added ipython notebook [here](c09c2ebd5c/docs/docs/integrations/vectorstores/vlite.ipynb) Added simple docs on how to use [here](c09c2ebd5c/docs/docs/integrations/providers/vlite.mdx) **Profiles** Maintainers: @sdan Twitter handles: [@sdand](https://x.com/sdand) --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
sdan
3 months ago
committed by
GitHub
parent
7824291252
commit
a7c5e41443
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# vlite
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This page covers how to use [vlite](https://github.com/sdan/vlite) within LangChain. vlite is a simple and fast vector database for storing and retrieving embeddings.
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## Installation and Setup
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To install vlite, run the following command:
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```bash
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pip install vlite
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```
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For PDF OCR support, install the `vlite[ocr]` extra:
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```bash
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pip install vlite[ocr]
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```
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## VectorStore
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vlite provides a wrapper around its vector database, allowing you to use it as a vectorstore for semantic search and example selection.
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To import the vlite vectorstore:
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```python
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from langchain_community.vectorstores import vlite
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```
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### Usage
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For a more detailed walkthrough of the vlite wrapper, see [this notebook](/docs/integrations/vectorstores/vlite).
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@ -0,0 +1,186 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"# vlite\n",
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"\n",
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"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",
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"\n",
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"## Installation\n",
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"\n",
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"To use the VLite in LangChain, you need to install the `vlite` package:\n",
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"\n",
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"```bash\n",
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"!pip install vlite\n",
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"```\n",
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"\n",
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"## Importing VLite\n",
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"\n",
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"```python\n",
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"from langchain.vectorstores import VLite\n",
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"```\n",
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"\n",
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"## Basic Example\n",
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"\n",
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"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",
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"\n",
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"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",
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"\n",
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"```python\n",
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"from langchain.document_loaders import TextLoader\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"\n",
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"# Load the document and split it into chunks\n",
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"loader = TextLoader(\"path/to/document.txt\")\n",
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"documents = loader.load()\n",
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"\n",
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"# Create a VLite instance\n",
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"vlite = VLite(collection=\"my_collection\")\n",
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"\n",
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"# Add documents to the VLite vector database\n",
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"vlite.add_documents(documents)\n",
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"\n",
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"# Perform a similarity search\n",
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"query = \"What is the main topic of the document?\"\n",
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"docs = vlite.similarity_search(query)\n",
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"\n",
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"# Print the most relevant document\n",
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"print(docs[0].page_content)\n",
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"```\n",
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"\n",
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"## Adding Texts and Documents\n",
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"\n",
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"You can add texts or documents to the VLite vector database using the `add_texts` and `add_documents` methods, respectively.\n",
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"\n",
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"```python\n",
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"# Add texts to the VLite vector database\n",
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"texts = [\"This is the first text.\", \"This is the second text.\"]\n",
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"vlite.add_texts(texts)\n",
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"\n",
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"# Add documents to the VLite vector database\n",
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"documents = [Document(page_content=\"This is a document.\", metadata={\"source\": \"example.txt\"})]\n",
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"vlite.add_documents(documents)\n",
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"```\n",
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"\n",
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"## Similarity Search\n",
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"\n",
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"VLite provides methods for performing similarity search on the stored documents.\n",
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"\n",
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"```python\n",
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"# Perform a similarity search\n",
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"query = \"What is the main topic of the document?\"\n",
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"docs = vlite.similarity_search(query, k=3)\n",
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"\n",
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"# Perform a similarity search with scores\n",
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"docs_with_scores = vlite.similarity_search_with_score(query, k=3)\n",
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"```\n",
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"\n",
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"## Max Marginal Relevance Search\n",
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"\n",
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"VLite also supports Max Marginal Relevance (MMR) search, which optimizes for both similarity to the query and diversity among the retrieved documents.\n",
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"\n",
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"```python\n",
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"# Perform an MMR search\n",
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"docs = vlite.max_marginal_relevance_search(query, k=3)\n",
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"```\n",
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"\n",
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"## Updating and Deleting Documents\n",
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"\n",
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"You can update or delete documents in the VLite vector database using the `update_document` and `delete` methods.\n",
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"\n",
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"```python\n",
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"# Update a document\n",
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"document_id = \"doc_id_1\"\n",
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"updated_document = Document(page_content=\"Updated content\", metadata={\"source\": \"updated.txt\"})\n",
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"vlite.update_document(document_id, updated_document)\n",
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"\n",
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"# Delete documents\n",
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"document_ids = [\"doc_id_1\", \"doc_id_2\"]\n",
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"vlite.delete(document_ids)\n",
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"```\n",
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"\n",
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"## Retrieving Documents\n",
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"\n",
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"You can retrieve documents from the VLite vector database based on their IDs or metadata using the `get` method.\n",
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"\n",
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"```python\n",
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"# Retrieve documents by IDs\n",
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"document_ids = [\"doc_id_1\", \"doc_id_2\"]\n",
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"docs = vlite.get(ids=document_ids)\n",
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"\n",
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"# Retrieve documents by metadata\n",
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"metadata_filter = {\"source\": \"example.txt\"}\n",
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"docs = vlite.get(where=metadata_filter)\n",
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"```\n",
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"\n",
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"## Creating VLite Instances\n",
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"\n",
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"You can create VLite instances using various methods:\n",
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"\n",
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"```python\n",
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"# Create a VLite instance from texts\n",
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"vlite = VLite.from_texts(texts)\n",
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"\n",
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"# Create a VLite instance from documents\n",
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"vlite = VLite.from_documents(documents)\n",
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"\n",
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"# Create a VLite instance from an existing index\n",
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"vlite = VLite.from_existing_index(collection=\"existing_collection\")\n",
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"```\n",
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"\n",
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"## Additional Features\n",
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"\n",
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"VLite provides additional features for managing the vector database:\n",
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"\n",
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"```python\n",
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"from langchain.vectorstores import VLite\n",
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"vlite = VLite(collection=\"my_collection\")\n",
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"\n",
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"# Get the number of items in the collection\n",
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"count = vlite.count()\n",
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"\n",
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"# Save the collection\n",
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"vlite.save()\n",
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"\n",
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"# Clear the collection\n",
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"vlite.clear()\n",
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"\n",
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"# Get collection information\n",
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"vlite.info()\n",
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"\n",
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"# Dump the collection data\n",
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"data = vlite.dump()\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -0,0 +1,247 @@
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from __future__ import annotations
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# Standard library imports
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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from uuid import uuid4
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# LangChain imports
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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class VLite(VectorStore):
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"""VLite is a simple and fast vector database for semantic search."""
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def __init__(
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self,
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embedding_function: Embeddings,
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collection: Optional[str] = None,
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**kwargs: Any,
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):
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super().__init__()
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self.embedding_function = embedding_function
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self.collection = collection or f"vlite_{uuid4().hex}"
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# Third-party imports
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try:
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from vlite import VLite
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except ImportError:
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raise ImportError(
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"Could not import vlite python package. "
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"Please install it with `pip install vlite`."
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)
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self.vlite = VLite(collection=self.collection, **kwargs)
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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kwargs: vectorstore specific parameters
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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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texts = list(texts)
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ids = kwargs.pop("ids", [str(uuid4()) for _ in texts])
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embeddings = self.embedding_function.embed_documents(texts)
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if not metadatas:
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metadatas = [{} for _ in texts]
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data_points = [
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{"text": text, "metadata": metadata, "id": id, "embedding": embedding}
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for text, metadata, id, embedding in zip(texts, metadatas, ids, embeddings)
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]
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results = self.vlite.add(data_points)
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return [result[0] for result in results]
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def add_documents(
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self,
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documents: List[Document],
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**kwargs: Any,
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) -> List[str]:
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"""Add a list of documents to the vectorstore.
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Args:
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documents: List of documents to add to the vectorstore.
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kwargs: vectorstore specific parameters such as "file_path" for processing
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directly with vlite.
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Returns:
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List of ids from adding the documents into the vectorstore.
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"""
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ids = kwargs.pop("ids", [str(uuid4()) for _ in documents])
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texts = []
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metadatas = []
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for doc, id in zip(documents, ids):
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if "file_path" in kwargs:
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# Third-party imports
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try:
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from vlite.utils import process_file
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except ImportError:
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raise ImportError(
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"Could not import vlite python package. "
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"Please install it with `pip install vlite`."
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)
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processed_data = process_file(kwargs["file_path"])
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texts.extend(processed_data)
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metadatas.extend([doc.metadata] * len(processed_data))
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ids.extend([f"{id}_{i}" for i in range(len(processed_data))])
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else:
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texts.append(doc.page_content)
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metadatas.append(doc.metadata)
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return self.add_texts(texts, metadatas, ids=ids)
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query.
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"""
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docs_and_scores = self.similarity_search_with_score(query, k=k)
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return [doc for doc, _ in docs_and_scores]
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def similarity_search_with_score(
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self,
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query: str,
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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filter: Filter by metadata. Defaults to None.
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Returns:
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List of Tuples of (doc, score), where score is the similarity score.
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"""
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metadata = filter or {}
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embedding = self.embedding_function.embed_query(query)
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results = self.vlite.retrieve(
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text=query,
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top_k=k,
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metadata=metadata,
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return_scores=True,
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embedding=embedding,
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)
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documents_with_scores = [
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(Document(page_content=text, metadata=metadata), score)
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for text, score, metadata in results
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]
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return documents_with_scores
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def update_document(self, document_id: str, document: Document) -> None:
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"""Update an existing document in the vectorstore."""
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self.vlite.update(
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document_id, text=document.page_content, metadata=document.metadata
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)
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def get(self, ids: List[str]) -> List[Document]:
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"""Get documents by their IDs."""
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results = self.vlite.get(ids)
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documents = [
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Document(page_content=text, metadata=metadata) for text, metadata in results
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]
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return documents
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def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
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"""Delete by ids."""
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if ids is not None:
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self.vlite.delete(ids, **kwargs)
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return True
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return None
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@classmethod
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def from_existing_index(
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cls,
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embedding: Embeddings,
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collection: str,
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**kwargs: Any,
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) -> VLite:
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"""Load an existing VLite index.
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Args:
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embedding: Embedding function
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collection: Name of the collection to load.
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Returns:
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VLite vector store.
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"""
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vlite = cls(embedding_function=embedding, collection=collection, **kwargs)
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return vlite
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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collection: Optional[str] = None,
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**kwargs: Any,
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) -> VLite:
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"""Construct VLite wrapper from raw documents.
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This is a user-friendly interface that:
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1. Embeds documents.
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2. Adds the documents to the vectorstore.
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This is intended to be a quick way to get started.
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Example:
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.. code-block:: python
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from langchain import VLite
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from langchain.embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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vlite = VLite.from_texts(texts, embeddings)
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"""
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vlite = cls(embedding_function=embedding, collection=collection, **kwargs)
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vlite.add_texts(texts, metadatas, **kwargs)
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return vlite
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@classmethod
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def from_documents(
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cls,
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documents: List[Document],
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embedding: Embeddings,
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collection: Optional[str] = None,
|
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**kwargs: Any,
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) -> VLite:
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"""Construct VLite wrapper from a list of documents.
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||||
|
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This is a user-friendly interface that:
|
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1. Embeds documents.
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2. Adds the documents to the vectorstore.
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|
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This is intended to be a quick way to get started.
|
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|
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Example:
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.. code-block:: python
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from langchain import VLite
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from langchain.embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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vlite = VLite.from_documents(documents, embeddings)
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"""
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vlite = cls(embedding_function=embedding, collection=collection, **kwargs)
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vlite.add_documents(documents, **kwargs)
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return vlite
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@ -0,0 +1,88 @@
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"""Test VLite functionality."""
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from langchain_core.documents import Document
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from langchain_community.embeddings import FakeEmbeddings
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from langchain_community.vectorstores import VLite
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|
||||
def test_vlite() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = VLite.from_texts(texts=texts, embedding=FakeEmbeddings())
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_vlite_with_metadatas() -> None:
|
||||
"""Test end to end construction and search with metadata."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = VLite.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
||||
|
||||
|
||||
def test_vlite_with_metadatas_with_scores() -> None:
|
||||
"""Test end to end construction and search with metadata and scores."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = VLite.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
|
||||
|
||||
|
||||
def test_vlite_update_document() -> None:
|
||||
"""Test updating a document."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = VLite.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), ids=["1", "2", "3"]
|
||||
)
|
||||
docsearch.update_document("1", Document(page_content="updated_foo"))
|
||||
output = docsearch.similarity_search("updated_foo", k=1)
|
||||
assert output == [Document(page_content="updated_foo")]
|
||||
|
||||
|
||||
def test_vlite_delete_document() -> None:
|
||||
"""Test deleting a document."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = VLite.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), ids=["1", "2", "3"]
|
||||
)
|
||||
docsearch.delete(["1"])
|
||||
output = docsearch.similarity_search("foo", k=3)
|
||||
assert Document(page_content="foo") not in output
|
||||
|
||||
|
||||
def test_vlite_get_documents() -> None:
|
||||
"""Test getting documents by IDs."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = VLite.from_texts(
|
||||
texts=texts,
|
||||
embedding=FakeEmbeddings(),
|
||||
metadatas=metadatas,
|
||||
ids=["1", "2", "3"],
|
||||
)
|
||||
output = docsearch.get(ids=["1", "3"])
|
||||
assert output == [
|
||||
Document(page_content="foo", metadata={"page": "0"}),
|
||||
Document(page_content="baz", metadata={"page": "2"}),
|
||||
]
|
||||
|
||||
|
||||
def test_vlite_from_existing_index() -> None:
|
||||
"""Test loading from an existing index."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
VLite.from_texts(
|
||||
texts=texts, embedding=FakeEmbeddings(), collection="test_collection"
|
||||
)
|
||||
new_docsearch = VLite.from_existing_index(
|
||||
collection="test_collection", embedding=FakeEmbeddings()
|
||||
)
|
||||
output = new_docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
Loading…
Reference in New Issue