PGVector Support for Custom Connection Object

[Knucklessg1]

Why are these changes needed?

This PR contains adding support for custom psycopg connections.

A user can define the connection object.

This is important because a connection object may have to be very custom for certain environments. We should allow the end user to specify the connection object for their environment.

Fix included for .gitattributes to commit certain files with lf line endings instead of crlf. (This was breaking bash scripts in the repo)

Fix included for psycopg[binary] dependency being installed for Windows and Mac, Linux can use the pure python implementation psycopg.

conn = psycopg.connect(conninfo=connection_string_encoded, autocommit=True)

ragproxyagent = RetrieveUserProxyAgent(
    name="ragproxyagent",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=1,
    retrieve_config={
        "task": "code",
        "docs_path": [
            "https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Examples/Integrate%20-%20Spark.md",
            "https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Research.md",
            os.path.join(os.path.abspath(""), "..", "website", "docs"),
        ],
        "custom_text_types": ["non-existent-type"],
        "chunk_token_size": 2000,
        "model": config_list[0]["model"],
        "vector_db": "pgvector",  # PGVector database
        "db_config": {
            "conn": conn 
        },
        "get_or_create": True,  # set to False if you don't want to reuse an existing collection
        "overwrite": False,  # set to True if you want to overwrite an existing collection
    },
    code_execution_config=False,  # set to False if you don't want to execute the code
)

And pass that into the db_config for the retrieve agent.

This also contains a fix for the psycopg.connect() using the username field directly.

Related issue number

NA

Checks

• I've included any doc changes needed for https://microsoft.github.io/autogen/. See https://microsoft.github.io/autogen/docs/Contribute#documentation to build and test documentation locally.
• I've added tests (if relevant) corresponding to the changes introduced in this PR.
• I've made sure all auto checks have passed.
• I have tested all 3 forms of authentication using a new PGVector docker image

[gitguardian]

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GitGuardian id	GitGuardian status	Secret	Commit	Filename
10493810	Triggered	Generic Password	8d19f65	test/agentchat/contrib/vectordb/test_pgvectordb.py	View secret
10493810	Triggered	Generic Password	4b7ba2b	notebook/agentchat_pgvector_RetrieveChat.ipynb	View secret
10493810	Triggered	Generic Password	4b7ba2b	notebook/agentchat_pgvector_RetrieveChat.ipynb	View secret
10493810	Triggered	Generic Password	4b7ba2b	notebook/agentchat_pgvector_RetrieveChat.ipynb	View secret
10493810	Triggered	Generic Password	fdbc3d5	test/agentchat/contrib/vectordb/test_pgvectordb.py	View secret
10493810	Triggered	Generic Password	6e91d73	notebook/agentchat_pgvector_RetrieveChat.ipynb	View secret
10493810	Triggered	Generic Password	10e2c2e	notebook/agentchat_pgvector_RetrieveChat.ipynb	View secret
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[chenyanbiao]

Hi @Knucklessg1 thanks for this awesome added feature!

Not sure if this is the right place to ask this question but would appreciate any help on it. Is chunk token size being used to split docs while using pgvector as a vectordatabase. I don't quite see the code where it splits based on chunk token size (normal usage for local file) but max token of the model by default for each docs(link), which means that the full local docs/files will be added to the vectordb and be input into the context directy based on vector distance.

[Knucklessg1]

@chenyanbiao did you take a look at the retrieve_utils.py?

This is where the logic for the split is happening. It's split the same way regardless of vectordb backend.

[chenyanbiao]

@Knucklessg1 Thanks for the response. Yes, it is what I am looking at. I understand that both ways use the same logic of splitting. My confusion is that the non-vectordb solution parse the parameter of chunk_token_size (link) while the vectordb solution parse the parameter of max_token for the split function (link), which is not consistent.