# Deep Lake Vector Store API ## Search Options for Deep Lake Vector Stores in the Deep Lake API This tutorial requires installation of: ```bash !pip3 install "deeplake[enterprise]" langchain openai tiktoken ``` ### Vector Search Using Python Logic Let's load the same vector store used in the Quickstart and run embeddings search based on a user prompt using the Deep Lake Vector Store module. ```python from deeplake.core.vectorstore.deeplake_vectorstore import DeepLakeVectorStore import openai import os os.environ['OPENAI_API_KEY'] = vector_store_path = 'hub://activeloop/paul_graham_essay' vector_store = DeepLakeVectorStore( path = vector_store_path, read_only = True ) ``` Next, let's define an embedding function using OpenAI. It must work for a single string and a list of strings, so that it can both be used to embed a prompt and a batch of texts. ```python def embedding_function(texts, model="text-embedding-ada-002"): if isinstance(texts, str): texts = [texts] texts = [t.replace("\n", " ") for t in texts] return [data['embedding']for data in openai.Embedding.create(input = texts, model=model)['data']] ``` #### Simple Vector Search Lets run a simple vector search using default options, which performs simple cosine similarity search in Python on the client. ```python prompt = "What are the first programs he tried writing?" search_results = vector_store.search(embedding_data=prompt, embedding_function=embedding_function) ``` The `search_results` is a dictionary with keys for the `text`, `score`, `id`, and `metadata`, with data ordered by score. By default, it returns 4 samples ordered by similarity score, and if we examine the first returned text, it appears to contain the text about trust and safety models that is relevant to the prompt. ```python search_results['text'][0] ``` Returns: ``` What I Worked On February 2021 Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights. ``` #### Advanced Vector Search Vector search can be combined with other search logic for performing more advanced queries. Let's define a function compatible with [deeplake.filter](/v3.6.0/getting-started/deep-learning/dataset-filtering.md) for filtering data prior to the vector search. The function below will filter samples that contain the word `"program"` in the `text` tensor. ```python def filter_fn(x): # x is a single row in Deep Lake, 'text' is the tensor name, .data()['value'] is the method for fetching the data return "program" in x['text'].data()['value'].lower() ``` Let's run the vector search with the filter above, return more samples (`k = 10`), and perform similarity search using L2 metric (`distance_metric = "l2"`): ```python prompt = "What are the first programs he tried writing?" search_results_filter = vector_store.search(embedding_data=prompt, embedding_function=embedding_function, filter=filter_fn, k=10, distance_metric='l2') ``` We can verity that the word `"program"` is present in all of the results: ```python all(["program" in result for result in search_results_filter["text"]]) # Returns True ``` ### Vector Search Using Compute Engine Deep Lake offers advanced search features using [Compute Engine](/v3.6.0/enterprise-features/compute-engine.md), which executes queries with higher performance in C++, and offers querying using Deep Lake's [Tensor Query Language (TQL)](/v3.6.0/enterprise-features/compute-engine/querying-datasets.md). {% hint style="warning" %} In order to use Compute Engine, Deep Lake data must be stored in Deep Lake Storage, or in the user's cloud while being connected to Deep Lake using [Managed Credentials](/v3.6.0/storage-and-credentials/managed-credentials.md). {% endhint %} Let's load a larger Vector Store for running more interesting queries: ```python vector_store_path = "hub://activeloop/twitter-algorithm" vector_store = DeepLakeVectorStore( path = vector_store_path, read_only = True ) ``` {% hint style="warning" %} NOTE: this Vector Store is stored in `us-east`, and query performance may vary significantly depending on your location. In real-world use-cases, users would store their vector stores in regions optimized for their use case. {% endhint %} #### Simple Vector Search Lets run a simple vector search and specify `exec_option = "compute_engine"`, which will performs cosine similarity search using Compute Engine on the client. ```python prompt = "What do the trust and safety models do?" search_results = vector_store.search(embedding_data = prompt, embedding_function = embedding_function, exec_option = "compute_engine") ``` If we examine the first returned text, it appears to contain the text about trust and safety models that is relevant to the prompt. ```python search_results['text'][0] ``` Returns: ``` Trust and Safety Models ======================= We decided to open source the training code of the following models: - pNSFWMedia: Model to detect tweets with NSFW images. This includes adult and porn content. - pNSFWText: Model to detect tweets with NSFW text, adult/sexual topics. - pToxicity: Model to detect toxic tweets. Toxicity includes marginal content like insults and certain types of harassment. Toxic content does not violate Twitter's terms of service. - pAbuse: Model to detect abusive content. This includes violations of Twitter's terms of service, including hate speech, targeted harassment and abusive behavior. We have several more models and rules that we are not going to open source at this time because of the adversarial nature of this area. The team is considering open sourcing more models going forward and will keep the community posted accordingly. ``` #### Coming Soon (Advanced Vector Search) ### Vector Search Using Managed Tensor Database #### Tutorial Coming Soon --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.