Draft: Use Anthropic & Vertex AI proxy endpoints

What does this MR do and why?

This MR demonstrates how to use Anthropic & Vertex AI proxy endpoints introduced in AI Gateway ADR 002: Exposing proxy endpoints to AI providers. It also contains documentation update about how to test with OIDC.

This MR requires feat: use OR expression for the required scope ... (gitlab-org/modelops/applied-ml/code-suggestions/ai-assist!814 - merged) in AI Gateway.

Ref: Refactor current Anthropic and Vertex client (#458207 - closed)

MR acceptance checklist

Anthropic Example:

[1] pry(main)> Gitlab::Llm::Anthropic::Client.new(User.first, unit_primitive: 'explain_vulnerability').complete(prompt: "\n\nHuman: What is a security vulnerability?\n\nAssistant:")
=> {"type"=>"completion",
 "id"=>"compl_01STe9ZxKyVAkieJfrNiQZsC",
 "completion"=>
  " A security vulnerability is a weakness in a system that allows an attacker to violate the system's intended security policy. Some examples of security vulnerabilities include:\n\n- Buffer overflows - This allows attackers to overwrite memory areas, allowing them to execute malicious code or crash the system.\n\n- SQL injection - This allows attackers to insert malicious SQL code into input fields for execution, allowing them to access unauthorized data.\n\n- Cross-site scripting (XSS) - This allows attackers to inject client-side scripts into web pages viewed by other users, letting them bypass access controls.\n\n- Missing access controls - This allows attackers access to resources or functionality that should be restricted.\n\n- Weak passwords - Easy to guess passwords allow attackers access to systems with a valid username and password. \n\n- Unpatched software - Running unpatched or outdated software that has publicly known vulnerabilities provides an opening for attackers.\n\nThe common factor is that vulnerabilities allow attackers to compromise confidentiality, integrity, or availability in a way that undermines security policies - whether to gain unauthorized access, escalate user privileges, deny service, or expose private information. Identifying and remediating vulnerabilities is crucial for good security practices.",
 "stop_reason"=>"stop_sequence",
 "model"=>"claude-2.1",
 "stop"=>"\n\nHuman:",
 "log_id"=>"compl_01STe9ZxKyVAkieJfrNiQZsC"}

Vertex AI Example:

[1] pry(main)> Gitlab::Llm::VertexAi::Client.new(User.first, unit_primitive: 'explain_vulnerability').chat(content: "Hi, how are you?")
=> {"predictions"=>
  [{"candidates"=>[{"content"=>" I'm doing great, thanks for asking! How can I help you today?", "author"=>"1"}],
    "groundingMetadata"=>[{}],
    "safetyAttributes"=>[{"scores"=>[], "blocked"=>false, "categories"=>[]}],
    "citationMetadata"=>[{"citations"=>[]}]}],
 "metadata"=>{"tokenMetadata"=>{"inputTokenCount"=>{"totalBillableCharacters"=>13, "totalTokens"=>6}, "outputTokenCount"=>{"totalBillableCharacters"=>50, "totalTokens"=>17}}}}

[3] pry(main)> Gitlab::Llm::VertexAi::Client.new(User.first, unit_primitive: 'explain_vulnerability').messages_chat(content: [{"author": "user", "content": "Hi, how are you?"}])
 User Load (0.5ms)  SELECT "users".* FROM "users" ORDER BY "users"."id" ASC LIMIT 1 /*application:console,correlation_id:88f166e90c33d936629b8082e2d3681e,db_config_name:main,console_hostname:shinya-XPS-15-9530,console_username:shinya,line:/app/models/concerns/use_sql_function_for_primary_key_lookups.rb:8:in `_query_by_sql'*/
=> {"predictions"=>
 [{"safetyAttributes"=>
    [{"blocked"=>false,
      "scores"=>[0.1, 0.3],
      "safetyRatings"=>
       [{"probabilityScore"=>0.2, "severityScore"=>0.1, "category"=>"Dangerous Content", "severity"=>"NEGLIGIBLE"},
        {"probabilityScore"=>0.1, "category"=>"Harassment", "severity"=>"NEGLIGIBLE", "severityScore"=>0},
        {"severityScore"=>0, "probabilityScore"=>0, "severity"=>"NEGLIGIBLE", "category"=>"Hate Speech"},
        {"category"=>"Sexually Explicit", "severityScore"=>0.1, "severity"=>"NEGLIGIBLE", "probabilityScore"=>0.3}],
      "categories"=>["Insult", "Sexual"]}],
   "groundingMetadata"=>[{}],
   "citationMetadata"=>[{"citations"=>[]}],
   "candidates"=>[{"content"=>" I'm doing great, thanks for asking! How about you?", "author"=>"1"}]}],
"metadata"=>{"tokenMetadata"=>{"outputTokenCount"=>{"totalBillableCharacters"=>42, "totalTokens"=>14}, "inputTokenCount"=>{"totalBillableCharacters"=>13, "totalTokens"=>6}}}}


[4] pry(main)> Gitlab::Llm::VertexAi::Client.new(User.first, unit_primitive: 'explain_vulnerability').text(content: "Hi, how are you?")
=> {"predictions"=>
  [{"content"=>" As an AI language model, I don't have personal feelings or emotions, but I'm here to assist you with any questions or information you may need. How can I help you today?",
    "citationMetadata"=>{"citations"=>[]},
    "safetyAttributes"=>
     {"scores"=>[0.1, 0.2, 0.2, 0.1],
      "categories"=>["Finance", "Health", "Religion & Belief", "Sexual"],
      "blocked"=>false,
      "safetyRatings"=>
       [{"severityScore"=>0, "probabilityScore"=>0, "category"=>"Dangerous Content", "severity"=>"NEGLIGIBLE"},
        {"severityScore"=>0, "category"=>"Harassment", "severity"=>"NEGLIGIBLE", "probabilityScore"=>0},
        {"severity"=>"NEGLIGIBLE", "probabilityScore"=>0, "category"=>"Hate Speech", "severityScore"=>0},
        {"severityScore"=>0.1, "probabilityScore"=>0.1, "category"=>"Sexually Explicit", "severity"=>"NEGLIGIBLE"}]}}],
 "metadata"=>{"tokenMetadata"=>{"outputTokenCount"=>{"totalBillableCharacters"=>138, "totalTokens"=>40}, "inputTokenCount"=>{"totalBillableCharacters"=>13, "totalTokens"=>6}}}}

 [5] pry(main)> Gitlab::Llm::VertexAi::Client.new(User.first, unit_primitive: 'explain_vulnerability').code(content: "print('Hello")
=> {"predictions"=>[{"citationMetadata"=>{"citations"=>[]}, "safetyAttributes"=>{"categories"=>[], "scores"=>[], "blocked"=>false}, "content"=>" world!')", "score"=>-2.214948892593384}],
"metadata"=>{"tokenMetadata"=>{"outputTokenCount"=>{"totalBillableCharacters"=>8, "totalTokens"=>2}, "inputTokenCount"=>{"totalBillableCharacters"=>12, "totalTokens"=>3}}}}

[6] pry(main)> Gitlab::Llm::VertexAi::Client.new(User.first, unit_primitive: 'explain_vulnerability').code_completion(content: {"prefix": "print('Hello", "suffix": ""})
=> {"predictions"=>[{"safetyAttributes"=>{"blocked"=>false, "scores"=>[], "categories"=>[]}, "content"=>" World !')", "score"=>-3.305152654647827, "citationMetadata"=>{"citations"=>[]}}],
 "metadata"=>{"tokenMetadata"=>{"outputTokenCount"=>{"totalBillableCharacters"=>8, "totalTokens"=>6}, "inputTokenCount"=>{"totalBillableCharacters"=>12, "totalTokens"=>3}}}}

Screenshots or screen recordings

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Before	After

How to set up and validate locally

Numbered steps to set up and validate the change are strongly suggested.