Feature: add pgvector retriever

[poppingtonic]

Implements a retriever that (as the name suggests) uses pgvector to retrieve passages,
using a raw SQL query and a postgresql connection managed by psycopg2.

It needs to register the pgvector extension with the psycopg2 connection

Returns a list of dspy.Example objects

Args:
    db_url (str): A PostgreSQL database URL in psycopg2's DSN format
    pg_table_name (Optional[str]): name of the table containing passages
    openai_client (openai.OpenAI): OpenAI client to use for computing query embeddings
    k (Optional[int]): Default number of top passages to retrieve. Defaults to 20
    embedding_field (str = "embedding"): Field containing passage embeddings. Defaults to "embedding"
    fields (List[str] = ['text']): Fields to retrieve from the table. Defaults to "text"

Examples:
    Below is a code snippet that shows how to use PgVector as the default retriever

    ```python
    import dspy
    import openai
    import psycopg2

    openai.api_key = os.environ.get("OPENAI_API_KEY", None)
    openai_client = openai.OpenAI()
    
    llm = dspy.OpenAI(model="gpt-3.5-turbo")
    
    DATABASE_URL should be in the format postgresql:https://user:password@host/database 
    db_url=os.getenv("DATABASE_URL")

    retriever_model = PgVectorRM(conn, openai_client=openai_client, "paragraphs", fields=["text", "document_id"], k=20)
    dspy.settings.configure(lm=llm, rm=retriever_model)
    ```

    Below is a code snippet that shows how to use PgVector in the forward() function of a module
    ```python
    self.retrieve = PgVectorRM(db_url, openai_client=openai_client, "paragraphs", fields=["text", "document_id"], k=20)

[insop]

It would be great if you could update this document.

https://github.com/stanfordnlp/dspy/blob/main/docs/retrieval_models_client.md

[poppingtonic]

Hi @insop, I'll write this. Thank you!