betabageldb 0.1.1

BagelDB is a Python library for interacting with the BagelDB API.

Here's a step by step guide on how to use BagelDB python client.

1. Import the BagelDB client

   Start by importing the BagelDB client into your Python script.

   from betabageldb import BagelDB
   

2. Initialize the BagelDB client

   Initialize a new instance of the BagelDB client. You need to specify the index you want to work with at this stage. This index will be used for all subsequent insertions and searches. Make sure you have your OpenAI API key set in your environment variables as OPENAI_API_KEY.

   index = "myIndex"
   db = BagelDB(index)
   

3. Ping the BagelDB server

   To check the connection to the BagelDB server, you can use the ping() method. This method will return a response from the BagelDB API in JSON format.

   response = db.ping()
   print(response)
   

4. Get OpenAI embeddings

   Use the getOpenAIEmbedding(inputText, model='text-embedding-ada-002') method to get embeddings from OpenAI. The inputText is the text for which embeddings are required. model is optional and defaults to 'text-embedding-ada-002'.

   input_text = "Some text"
   embeddings = db.getOpenAIEmbedding(input_text)
   print(embeddings)
   

5. Insert vectors into BagelDB

   To insert vectors into BagelDB, use the insert(vectors) method. vectors is a list of vectors to be inserted. The index is already set during the initialization of the BagelDB client and is not required here.

   vectors = [{'id': 'vec1', 'values': [0.1, 0.2, 0.3], 'metadata': {'key': 'value'}}]
   insert_response = db.insert(vectors)
   print(insert_response)
   

6. Search for a vector in BagelDB

   To search for a vector in BagelDB, use the search(vector) method. vector is the vector for which the search is to be performed. The index is already set during the initialization of the BagelDB client and is not required here.

   vector = [0.1, 0.2, 0.3]
   search_response = db.search(vector)
   print(search_response)
   

Remember to handle exceptions in your application. The methods in the BagelDB client can raise exceptions if a network error occurs or if the response from the server indicates a failed HTTP status code.

7. Insert vectors from texts

   The insertFromTexts(texts, model='text-embedding-ada-002') method can be used to convert a list of texts to their respective embeddings and insert these as vectors into BagelDB. The texts parameter is a list of strings for which embeddings are required, and model is optional and defaults to 'text-embedding-ada-002'. Each vector will be assigned an id incrementally starting from 0 and metadata will contain original text.

   texts = ["Some text 1", "Some text 2"]
   insert_response = db.insertFromTexts(texts)
   print(insert_response)
   

8. Search for a vector from text

   To search for a vector derived from a given text in BagelDB, you can use the searchFromText(text, model='text-embedding-ada-002') method. text is the string for which the corresponding embedding is to be found and searched, and model is optional and defaults to 'text-embedding-ada-002'.

   text = "Some text"
   search_response = db.searchFromText(text)
   print(search_response)
   

These new methods, insertFromTexts and searchFromText, simplify the process of converting text to embeddings and performing operations on BagelDB, providing a higher level of abstraction for users to work with.

Remember to handle exceptions in your application. The methods in the BagelDB client can raise exceptions if a network error occurs or if the response from the server indicates a failed HTTP status code.