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. (Note: DeepLakeVectorStore class is deprecated, but you can still use it. The new API for calling Deep Lake's Vector Store is: VectorStore)
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 be used to embed a prompt and a batch of texts.
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
Let's run a simple vector search using default options, which performs a simple cosine similarity search in Python on the client.
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.
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.
Filter Search Using UDFs
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, and return more samples (k = 10), and perform similarity search using L2 metric (distance_metric = "l2"):
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',
exec_option = "python")
We can verity that the word "program" is present in all of the results:
all(["program" in result for result in search_results_filter["text"]])
# Returns True
UDFs are only supported with query execution using the Python engine, so in the search above, exec_option = "python" should be specified.
Filter Search Using Metadata Filters
Instead of using UDFs, a filter can be specified using dictionary syntax. For json tensors, the syntax is filter = {"tensor_name": {"key": "value"}}. For text tensors, it is filter = {"tensor": "value"}. In all cases, an exact match is performed.
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.
We are interested in answering a prompt based on the question:
prompt = "What does the python code do?"
Therefore, we apply a filter to only search for text that contains the word "python" and metadata where the source key contains ".py".
embedding = embedding_function(prompt)[0]
# Format the embedding array or list as a string, so it can be passed in the REST API request.
embedding_string = ",".join([str(item) for item in embedding])
tql_query = f"select * from (select text, metadata, cosine_similarity(embedding, ARRAY[{embedding_string}]) as score where contains(text, 'python') or contains(metadata['source'], '.py')) order by score desc limit 5"
Vector search can be combined with other search logic for performing more advanced queries. Let's define a function compatible with for filtering data before the vector search. The function below will filter samples that contain the word "program" in the text tensor.
Deep Lake offers advanced search that executes queries with higher performance in C++, and offers querying using Deep Lake's .
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 .
Now let's run a search that includes filtering of text, metadata, and embedding tensors. We do this using by combining embedding search syntax (cosine_similarity(embedding, ...)) and filtering syntax (where ....).
For Vector Stored in the , queries will automatically execute on the database (instead of the client). Vector Stores are created in the Managed Tensor Database by specifying vector_store_path = hub://org_id/dataset_name and runtime = {"tensor_db": True} during Vector Store creation.
If Vector Stores are not in the Managed Tensor Database, :
