Code Companion: Dive Deep into Your Codebase with Your Own Intelligent AI Chatbot!

1. 
   Codebase Processor: Automatically clones your GitHub repository, vectorizes your code, and stores these embeddings in the PostgreSQL Vector Database.


2. 
   Intelligent Chatbot: Ready to answer all your code queries, it’s your code assistant, right at your fingertips!
   Behind the scenes, it harnesses RAG (Retrieval-Augmented Generation) to embed important objects into a vector store, allowing for super-accurate similarity searches to give you the answers you need.

Built with: langchain, psycopg2

Languages: Python 🐍

Demo

The github repo url for the same:

It has options in the left pane, to toggle between 'Process Codebase' and 'Chatbot', while the default selected option is 'Process Codebase'.

This screen provides a drop-down list to choose from the projects created, and get started on chatting with the assistant.

On asking queries, the response is delivered, and the screen looks something like this:

Tools Used

We have made use of:

1. 
   pgvector: We tapped into the open-source power of pgvector through the langchain_postgres library to store vectorized documents and handle similarity searches.
   

from langchain_postgres.vectorstores import PGVector

vectorstore = PGVector(

        embeddings=embeddings,

        collection_name=collection_name,

        connection=connection,

        use_jsonb=True,

        )

1. 
   pgai: Our custom integration of Timescale's pgai with the LangChain's PGVector Class gave us a streamlined approach for creating embeddings! Using pgAI, we crafted a custom PgAIEmbeddings class to harness the OpenAI model via pgAI.
   

from langchain_postgres.vectorstores import PGVector

vectorstore = PGVector(

       embeddings=**PgAIEmbeddings**(connection_string=connection),

        collection_name=collection_name,

        connection=connection,

        use_jsonb=True,

        )

Here’s a sneak peek at how we handled embeddings in our PgAIEmbeddings class:

def embed_documents(self, texts: List[str]) -> List[List[float]]:

    """Embed a list of documents using pgAI's OpenAI embedding function"""

    embeddings = []

    with psycopg2.connect(self.connection_string) as conn:

        with conn.cursor() as cur:

            for text in texts:

                cur.execute(

                    "SELECT ai.openai_embed('text-embedding-ada-002', %s) as embedding",

                    (text,)

                )

                result = cur.fetchone()

                # Parse the embedding string into a list of floats

                embedding = self._parse_embedding(result[0])

                embeddings.append(embedding)

    return embeddings

1. 
   Open AI: Acting as both the embedding creator and the brains behind the answers, OpenAI’s LLM brings this platform to life!

Final Thoughts

This platform is a complete game-changer for code exploration, making it fun, intuitive, and unbelievably insightful. Just imagine using it to create code summaries, suggest improvements, JIRA user story and test cases creation, modernize legacy code or even aid in debugging—possibilities are endless! With pgvector and pgai as our foundation, our app is set up for endless growth and flexibility.

Prize Categories:

We're in the running for the main prize category! This challenge asked us to build an AI application using open-source tools with PostgreSQL as a vector database, alongside at least two of the following: pgvector, pgvectorscale, pgai, and pgai Vectorizer. We did that—and more!