Add support to specify custom table schema

[anupkg1985]

Current LangChain-Postgres framework, the database table name is hardcoded and defaults to the public schema due to the absence of a specified schema name. This limitation affects the flexibility of the PGVector connector, restricting its ability to dynamically configure during runtime. This poses significant challenges in multi-tenant architectures, where it's essential for different clients or projects to operate within separate schemas to ensure data isolation and management efficiency. Addressing this issue would involve enabling schema specification, thereby enhancing the connector's versatility and making it more suitable for diverse deployment scenarios.

Proposed solution -

• Introduced new argument/ parameter table_schema to `PGVector' class
• This new parameter is optional and defaulted to public schema

Discussions related to issue -
langchain-ai/langchain#20877
langchain-ai/langchain#18883

Example usage -

Default publicschema -

connection = "postgresql+psycopg://langchain:langchain@localhost:6024/langchain" 
collection_name = "my_docs"
embeddings = CohereEmbeddings()

vectorstore = PGVector(
    embeddings=embeddings,
    collection_name=collection_name,
    connection=connection,
    use_jsonb=True,
)

Specify custom table-schema -

connection = "postgresql+psycopg://langchain:langchain@localhost:6024/langchain" 
collection_name = "my_docs"
embeddings = CohereEmbeddings()

vectorstore = PGVector(
    embeddings=embeddings,
    collection_name=collection_name,
    connection=connection,
    use_jsonb=True,
    table_schema="custom"
)