Integration with Vector Databases

[SaiNivedh26]

I have added functions to integrate words into Vector bases. I have utilized chroma Database which is using all-MiniLM-L6-v2 model from the Sentence Transformers library.

In SimpleLLm/tools/vector_db.py , I have added code as follows :

import os
import chromadb
from chromadb.utils import embedding_functions

class VectorDB:
    def __init__(self):
        persistence_directory = "./chroma_db"
        self.client = chromadb.PersistentClient(path=persistence_directory)
        self.embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
        self.collection = self.client.get_or_create_collection(
            name="responses",
            embedding_function=self.embedding_function
        )

    def store_vectors(self, texts):
        self.collection.add(documents=texts, ids=[f"id_{i}" for i in range(len(texts))])

    def query_vectors(self, query_text):
        results = self.collection.query(query_texts=[query_text], n_results=5)
        return results['documents'][0]

    def store_response(self, text):
        self.collection.add(documents=[text], ids=[f"id_{self.collection.count()}"])

    def query_similar(self, query_text):
        return self.query_vectors(query_text)

Then in SimplerLLM/language/llm.py , the following modifications were added,in addition to existing code, In order to invoke the Execution of the Vector databases

Initialized instance of an class

    `self.vector_db = VectorDB()`

Then

def store_response_as_vector(self, texts):
        self.vector_db.store_vectors(texts)

    def find_similar_responses(self, text):
        return self.vector_db.query_similar(text)

The Below given Libraries are required to be Installed

pip install chromadb sentence-transformers

Finally in requirements.txt, gave the correct versions

sentence-transformers==3.0.1
chromadb==0.5.3

You can test this working by executing following Sample code

Note : This is sample, you can modify and test it, as per your wish

from SimplerLLM.language.llm import LLM, LLMProvider
from dotenv import load_dotenv
import os
import time

load_dotenv()

def test_vector_storage_and_retrieval():
    llm = LLM(provider=LLMProvider.OPENAI, model_name="gpt-3.5-turbo")

    prompts = [
        "What is artificial intelligence and how does it differ from human intelligence?",
        "Explain the process of machine learning and its key components.",
        "Describe the architecture of deep neural networks and their layers.",
        "What are the applications of natural language processing in everyday technology?",
        "How does computer vision work and what are its real-world applications?",
        "Explain the concept of reinforcement learning and its use in robotics.",
        "What are the ethical concerns surrounding AI development and deployment?",
        "How does transfer learning accelerate AI model development?",
        "Describe the differences between supervised, unsupervised, and semi-supervised learning.",
        "What is the role of big data in advancing AI capabilities?",
        "Explain the concept of explainable AI and why it's important.",
        "How do genetic algorithms work in optimization problems?",
        "What are the challenges in developing artificial general intelligence (AGI)?",
        "Describe the impact of AI on healthcare diagnostics and treatment.",
        "How does AI contribute to autonomous vehicle technology?"
    ]

    print("Storing responses as vectors...")
    start_time = time.time()
    llm.store_response_as_vector(prompts)
    end_time = time.time()
    print(f"Responses stored successfully. Time taken: {end_time - start_time:.2f} seconds")

    query_prompts = [
        "What are the fundamental principles of AI?",
        "How do machines learn from data?",
        "Explain the inner workings of neural networks.",
        "What are some practical applications of NLP?",
        "How is AI changing the automotive industry?",
        "What are the moral implications of using AI in decision-making?",
        "How is AI transforming the healthcare sector?",
        "What are the key differences between AI learning paradigms?",
        "How does AI handle complex optimization problems?",
        "What are the challenges in making AI systems more transparent?"
    ]

    print("\nQuerying for similar responses:")
    for query_prompt in query_prompts:
        print(f"\nQuery: {query_prompt}")
        start_time = time.time()
        similar_responses = llm.find_similar_responses(query_prompt)
        end_time = time.time()
        print(f"Time taken: {end_time - start_time:.2f} seconds")
        print("Similar responses:")
        for i, response in enumerate(similar_responses, 1):
            print(f"{i}. {response}")

def main():
    print("Starting vector storage and retrieval test...")
    test_vector_storage_and_retrieval()

if __name__ == "__main__":
    main()

Summary by CodeRabbit

• New Features

  • Introduced functionality for storing and retrieving responses as vectors using a language model.
  • Added methods for finding similar responses based on input prompts.

• Enhancements

  • Improved response storage and querying capabilities using a new vector database.

• Dependencies

  • Added sentence-transformers and chromadb to the project dependencies.

[coderabbitai[bot]]

Walkthrough

The recent changes introduce advanced vector handling and querying capabilities to SimplerLLM. Key updates include the integration of the VectorDB class for vector storage and retrieval, new methods in the OpenAILLM class for vector interactions, and additional dependencies in requirements.txt to support these functionalities. Moreover, the creation of a new file, new.py, demonstrates the practical application of these features with testing functions.

Changes

File/Path	Summary
SimplerLLM/language/llm.py	Added imports (os, dotenv, VectorDB), methods (store_response_as_vector, find_similar_responses), and VectorDB initialization in OpenAILLM class.
SimplerLLM/tools/vector_db.py	Introduced VectorDB class with methods for storing and querying vectors and responses.
new.py	Introduced functionality for storing and retrieving responses as vectors using a language model with test functions.
requirements.txt	Added sentence-transformers version 3.0.1 and chromadb version 0.5.3.

Poem

🐇 In code's embrace, vectors align,
Queries and storage, oh so fine!
With VectorDB, our paths entwine,
Responses like shadows, in rows they shine.
In new.py they find a way,
Whispering secrets, night and day.
🎶 SimplerLLM, a dance divine!

---

[!TIP]

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