discourse-ai/spec/lib/completions/endpoints/anthropic_spec.rb

861 lines
27 KiB
Ruby

# frozen_string_literal: true
require_relative "endpoint_compliance"
RSpec.describe DiscourseAi::Completions::Endpoints::Anthropic do
let(:url) { "https://api.anthropic.com/v1/messages" }
fab!(:model) { Fabricate(:anthropic_model, name: "claude-3-opus", vision_enabled: true) }
let(:llm) { DiscourseAi::Completions::Llm.proxy("custom:#{model.id}") }
let(:image100x100) { plugin_file_from_fixtures("100x100.jpg") }
let(:upload100x100) do
UploadCreator.new(image100x100, "image.jpg").create_for(Discourse.system_user.id)
end
let(:prompt) do
DiscourseAi::Completions::Prompt.new(
"You are hello bot",
messages: [type: :user, id: "user1", content: "hello"],
)
end
let(:echo_tool) do
{
name: "echo",
description: "echo something",
parameters: [{ name: "text", type: "string", description: "text to echo", required: true }],
}
end
let(:google_tool) do
{
name: "google",
description: "google something",
parameters: [
{ name: "query", type: "string", description: "text to google", required: true },
],
}
end
let(:prompt_with_echo_tool) do
prompt_with_tools = prompt
prompt.tools = [echo_tool]
prompt_with_tools
end
let(:prompt_with_google_tool) do
prompt_with_tools = prompt
prompt.tools = [echo_tool]
prompt_with_tools
end
it "does not eat spaces with tool calls" do
body = <<~STRING
event: message_start
data: {"type":"message_start","message":{"id":"msg_01Ju4j2MiGQb9KV9EEQ522Y3","type":"message","role":"assistant","model":"claude-3-haiku-20240307","content":[],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1293,"output_tokens":1}} }
event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"tool_use","id":"toolu_01DjrShFRRHp9SnHYRFRc53F","name":"search","input":{}} }
event: ping
data: {"type": "ping"}
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":""} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"{\\"searc"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"h_qu"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"er"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"y\\": \\"s"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"<a>m"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":" "} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"sam\\""} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":", \\"cate"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"gory"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"\\": \\"gene"} }
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"ral\\"}"} }
event: content_block_stop
data: {"type":"content_block_stop","index":0 }
event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"tool_use","stop_sequence":null},"usage":{"output_tokens":70} }
event: message_stop
data: {"type":"message_stop"}
STRING
result = []
body = body.scan(/.*\n/)
EndpointMock.with_chunk_array_support do
stub_request(:post, url).to_return(status: 200, body: body)
llm.generate(
prompt_with_google_tool,
user: Discourse.system_user,
partial_tool_calls: true,
) { |partial| result << partial.dup }
end
tool_call =
DiscourseAi::Completions::ToolCall.new(
name: "search",
id: "toolu_01DjrShFRRHp9SnHYRFRc53F",
parameters: {
search_query: "s<a>m sam",
category: "general",
},
)
expect(result.last).to eq(tool_call)
search_queries = result.filter(&:partial).map { |r| r.parameters[:search_query] }
categories = result.filter(&:partial).map { |r| r.parameters[:category] }
expect(categories).to eq([nil, nil, nil, nil, "gene", "general"])
expect(search_queries).to eq(["s", "s<a>m", "s<a>m ", "s<a>m sam", "s<a>m sam", "s<a>m sam"])
end
it "can stream a response" do
body = (<<~STRING).strip
event: message_start
data: {"type": "message_start", "message": {"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", "type": "message", "role": "assistant", "content": [], "model": "claude-3-opus-20240229", "stop_reason": null, "stop_sequence": null, "usage": {"input_tokens": 25, "output_tokens": 1}}}
event: content_block_start
data: {"type": "content_block_start", "index":0, "content_block": {"type": "text", "text": ""}}
event: ping
data: {"type": "ping"}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "Hello"}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "!"}}
event: content_block_stop
data: {"type": "content_block_stop", "index": 0}
event: message_delta
data: {"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence":null, "usage":{"output_tokens": 15}}}
event: message_stop
data: {"type": "message_stop"}
STRING
parsed_body = nil
stub_request(:post, url).with(
body:
proc do |req_body|
parsed_body = JSON.parse(req_body, symbolize_names: true)
true
end,
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(status: 200, body: body)
result = +""
llm.generate(prompt, user: Discourse.system_user, feature_name: "testing") do |partial, cancel|
result << partial
end
expect(result).to eq("Hello!")
expected_body = {
model: "claude-3-opus-20240229",
max_tokens: 4096,
messages: [{ role: "user", content: "user1: hello" }],
system: "You are hello bot",
stream: true,
}
expect(parsed_body).to eq(expected_body)
log = AiApiAuditLog.order(:id).last
expect(log.provider_id).to eq(AiApiAuditLog::Provider::Anthropic)
expect(log.feature_name).to eq("testing")
expect(log.response_tokens).to eq(15)
expect(log.request_tokens).to eq(25)
expect(log.raw_request_payload).to eq(expected_body.to_json)
expect(log.raw_response_payload.strip).to eq(body.strip)
end
it "supports non streaming tool calls" do
tool = {
name: "calculate",
description: "calculate something",
parameters: [
{
name: "expression",
type: "string",
description: "expression to calculate",
required: true,
},
],
}
prompt =
DiscourseAi::Completions::Prompt.new(
"You a calculator",
messages: [{ type: :user, id: "user1", content: "calculate 2758975 + 21.11" }],
tools: [tool],
)
body = {
id: "msg_01RdJkxCbsEj9VFyFYAkfy2S",
type: "message",
role: "assistant",
model: "claude-3-haiku-20240307",
content: [
{ type: "text", text: "Here is the calculation:" },
{
type: "tool_use",
id: "toolu_012kBdhG4eHaV68W56p4N94h",
name: "calculate",
input: {
expression: "2758975 + 21.11",
},
},
],
stop_reason: "tool_use",
stop_sequence: nil,
usage: {
input_tokens: 345,
output_tokens: 65,
},
}.to_json
stub_request(:post, url).to_return(body: body)
result = llm.generate(prompt, user: Discourse.system_user)
tool_call =
DiscourseAi::Completions::ToolCall.new(
name: "calculate",
id: "toolu_012kBdhG4eHaV68W56p4N94h",
parameters: {
expression: "2758975 + 21.11",
},
)
expect(result).to eq(["Here is the calculation:", tool_call])
log = AiApiAuditLog.order(:id).last
expect(log.request_tokens).to eq(345)
expect(log.response_tokens).to eq(65)
end
it "can send images via a completion prompt" do
prompt =
DiscourseAi::Completions::Prompt.new(
"You are image bot",
messages: [type: :user, id: "user1", content: ["hello", { upload_id: upload100x100.id }]],
)
encoded = prompt.encoded_uploads(prompt.messages.last)
request_body = {
model: "claude-3-opus-20240229",
max_tokens: 4096,
messages: [
{
role: "user",
content: [
{ type: "text", text: "user1: hello" },
{
type: "image",
source: {
type: "base64",
media_type: "image/jpeg",
data: encoded[0][:base64],
},
},
],
},
],
system: "You are image bot",
}
response_body = <<~STRING
{
"content": [
{
"text": "What a cool image",
"type": "text"
}
],
"id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
"model": "claude-3-opus-20240229",
"role": "assistant",
"stop_reason": "end_turn",
"stop_sequence": null,
"type": "message",
"usage": {
"input_tokens": 10,
"output_tokens": 25
}
}
STRING
requested_body = nil
stub_request(:post, url).with(
body:
proc do |req_body|
requested_body = JSON.parse(req_body, symbolize_names: true)
true
end,
).to_return(status: 200, body: response_body)
result = llm.generate(prompt, user: Discourse.system_user)
expect(result).to eq("What a cool image")
expect(requested_body).to eq(request_body)
end
it "can support reasoning" do
body = <<~STRING
{
"content": [
{
"text": "Hello!",
"type": "text"
}
],
"id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
"model": "claude-3-opus-20240229",
"role": "assistant",
"stop_reason": "end_turn",
"stop_sequence": null,
"type": "message",
"usage": {
"input_tokens": 10,
"output_tokens": 25
}
}
STRING
parsed_body = nil
stub_request(:post, url).with(
body:
proc do |req_body|
parsed_body = JSON.parse(req_body, symbolize_names: true)
true
end,
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(status: 200, body: body)
model.provider_params["enable_reasoning"] = true
model.provider_params["reasoning_tokens"] = 10_000
model.save!
proxy = DiscourseAi::Completions::Llm.proxy("custom:#{model.id}")
result = proxy.generate(prompt, user: Discourse.system_user)
expect(result).to eq("Hello!")
expected_body = {
model: "claude-3-opus-20240229",
max_tokens: 40_000,
thinking: {
type: "enabled",
budget_tokens: 10_000,
},
messages: [{ role: "user", content: "user1: hello" }],
system: "You are hello bot",
}
expect(parsed_body).to eq(expected_body)
log = AiApiAuditLog.order(:id).last
expect(log.provider_id).to eq(AiApiAuditLog::Provider::Anthropic)
expect(log.request_tokens).to eq(10)
expect(log.response_tokens).to eq(25)
end
it "can operate in regular mode" do
body = <<~STRING
{
"content": [
{
"text": "Hello!",
"type": "text"
}
],
"id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
"model": "claude-3-opus-20240229",
"role": "assistant",
"stop_reason": "end_turn",
"stop_sequence": null,
"type": "message",
"usage": {
"input_tokens": 10,
"output_tokens": 25
}
}
STRING
parsed_body = nil
stub_request(:post, url).with(
body:
proc do |req_body|
parsed_body = JSON.parse(req_body, symbolize_names: true)
true
end,
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(status: 200, body: body)
proxy = DiscourseAi::Completions::Llm.proxy("custom:#{model.id}")
result = proxy.generate(prompt, user: Discourse.system_user)
expect(result).to eq("Hello!")
expected_body = {
model: "claude-3-opus-20240229",
max_tokens: 4096,
messages: [{ role: "user", content: "user1: hello" }],
system: "You are hello bot",
}
expect(parsed_body).to eq(expected_body)
log = AiApiAuditLog.order(:id).last
expect(log.provider_id).to eq(AiApiAuditLog::Provider::Anthropic)
expect(log.request_tokens).to eq(10)
expect(log.response_tokens).to eq(25)
end
it "can send through thinking tokens via a completion prompt" do
body = {
id: "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY",
type: "message",
role: "assistant",
content: [{ type: "text", text: "world" }],
model: "claude-3-7-sonnet-20250219",
stop_reason: "end_turn",
usage: {
input_tokens: 25,
output_tokens: 40,
},
}.to_json
parsed_body = nil
stub_request(:post, url).with(
body: ->(req_body) { parsed_body = JSON.parse(req_body) },
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(status: 200, body: body)
prompt = DiscourseAi::Completions::Prompt.new("system prompt")
prompt.push(type: :user, content: "hello")
prompt.push(
type: :model,
id: "user1",
content: "hello",
thinking: "I am thinking",
thinking_signature: "signature",
redacted_thinking_signature: "redacted_signature",
)
result = llm.generate(prompt, user: Discourse.system_user)
expect(result).to eq("world")
expected_body = {
"model" => "claude-3-opus-20240229",
"max_tokens" => 4096,
"messages" => [
{ "role" => "user", "content" => "hello" },
{
"role" => "assistant",
"content" => [
{ "type" => "thinking", "thinking" => "I am thinking", "signature" => "signature" },
{ "type" => "redacted_thinking", "data" => "redacted_signature" },
{ "type" => "text", "text" => "hello" },
],
},
],
"system" => "system prompt",
}
expect(parsed_body).to eq(expected_body)
end
it "can handle a response with thinking blocks in non-streaming mode" do
body = {
id: "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY",
type: "message",
role: "assistant",
content: [
{
type: "thinking",
thinking: "This is my thinking process about prime numbers...",
signature: "abc123signature",
},
{ type: "redacted_thinking", data: "abd456signature" },
{ type: "text", text: "Yes, there are infinitely many prime numbers where n mod 4 = 3." },
],
model: "claude-3-7-sonnet-20250219",
stop_reason: "end_turn",
usage: {
input_tokens: 25,
output_tokens: 40,
},
}.to_json
stub_request(:post, url).with(
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(status: 200, body: body)
result =
llm.generate(
"hello",
user: Discourse.system_user,
feature_name: "testing",
output_thinking: true,
)
# Result should be an array with both thinking and text content
expect(result).to be_an(Array)
expect(result.length).to eq(3)
# First item should be a Thinking object
expect(result[0]).to be_a(DiscourseAi::Completions::Thinking)
expect(result[0].message).to eq("This is my thinking process about prime numbers...")
expect(result[0].signature).to eq("abc123signature")
expect(result[1]).to be_a(DiscourseAi::Completions::Thinking)
expect(result[1].signature).to eq("abd456signature")
expect(result[1].redacted).to eq(true)
# Second item should be the text response
expect(result[2]).to eq("Yes, there are infinitely many prime numbers where n mod 4 = 3.")
# Verify audit log
log = AiApiAuditLog.order(:id).last
expect(log.provider_id).to eq(AiApiAuditLog::Provider::Anthropic)
expect(log.feature_name).to eq("testing")
expect(log.response_tokens).to eq(40)
end
it "can stream a response with thinking blocks" do
body = (<<~STRING).strip
event: message_start
data: {"type": "message_start", "message": {"id": "msg_01...", "type": "message", "role": "assistant", "content": [], "model": "claude-3-opus-20240229", "stop_reason": null, "stop_sequence": null, "usage": {"input_tokens": 25}}}
event: content_block_start
data: {"type": "content_block_start", "index": 0, "content_block": {"type": "thinking", "thinking": ""}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "Let me solve this step by step:\\n\\n1. First break down 27 * 453"}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\\n2. 453 = 400 + 50 + 3"}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}}
event: content_block_stop
data: {"type": "content_block_stop", "index": 0}
event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"redacted_thinking","data":"AAA=="} }
event: ping
data: {"type": "ping"}
event: content_block_stop
data: {"type":"content_block_stop","index":0 }
event: content_block_start
data: {"type": "content_block_start", "index": 1, "content_block": {"type": "text", "text": ""}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 1, "delta": {"type": "text_delta", "text": "27 * 453 = 12,231"}}
event: content_block_stop
data: {"type": "content_block_stop", "index": 1}
event: message_delta
data: {"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence": null, "usage": {"output_tokens": 30}}}
event: message_stop
data: {"type": "message_stop"}
STRING
parsed_body = nil
stub_request(:post, url).with(
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(status: 200, body: body)
thinking_chunks = []
text_chunks = []
llm.generate(
"hello there",
user: Discourse.system_user,
feature_name: "testing",
output_thinking: true,
) do |partial, cancel|
if partial.is_a?(DiscourseAi::Completions::Thinking)
thinking_chunks << partial
else
text_chunks << partial
end
end
expected_thinking = [
DiscourseAi::Completions::Thinking.new(message: "", signature: "", partial: true),
DiscourseAi::Completions::Thinking.new(
message: "Let me solve this step by step:\n\n1. First break down 27 * 453",
partial: true,
),
DiscourseAi::Completions::Thinking.new(message: "\n2. 453 = 400 + 50 + 3", partial: true),
DiscourseAi::Completions::Thinking.new(
message:
"Let me solve this step by step:\n\n1. First break down 27 * 453\n2. 453 = 400 + 50 + 3",
signature: "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds...",
partial: false,
),
DiscourseAi::Completions::Thinking.new(message: nil, signature: "AAA==", redacted: true),
]
expect(thinking_chunks).to eq(expected_thinking)
expect(text_chunks).to eq(["27 * 453 = 12,231"])
log = AiApiAuditLog.order(:id).last
expect(log.provider_id).to eq(AiApiAuditLog::Provider::Anthropic)
expect(log.feature_name).to eq("testing")
expect(log.response_tokens).to eq(30)
end
describe "parameter disabling" do
it "excludes disabled parameters from the request" do
model.update!(provider_params: { disable_top_p: true, disable_temperature: true })
parsed_body = nil
stub_request(:post, url).with(
body:
proc do |req_body|
parsed_body = JSON.parse(req_body, symbolize_names: true)
true
end,
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(
status: 200,
body: {
id: "msg_123",
type: "message",
role: "assistant",
content: [{ type: "text", text: "test response" }],
model: "claude-3-opus-20240229",
usage: {
input_tokens: 10,
output_tokens: 5,
},
}.to_json,
)
# Request with parameters that should be ignored
llm.generate(
prompt,
user: Discourse.system_user,
top_p: 0.9,
temperature: 0.8,
max_tokens: 500,
)
# Verify disabled parameters aren't included
expect(parsed_body).not_to have_key(:top_p)
expect(parsed_body).not_to have_key(:temperature)
# Verify other parameters still work
expect(parsed_body).to have_key(:max_tokens)
expect(parsed_body[:max_tokens]).to eq(500)
end
end
describe "disabled tool use" do
it "can properly disable tool use with :none" do
prompt =
DiscourseAi::Completions::Prompt.new(
"You are a bot",
messages: [type: :user, id: "user1", content: "don't use any tools please"],
tools: [echo_tool],
tool_choice: :none,
)
response_body = {
id: "msg_01RdJkxCbsEj9VFyFYAkfy2S",
type: "message",
role: "assistant",
model: "claude-3-haiku-20240307",
content: [
{ type: "text", text: "I won't use any tools. Here's a direct response instead." },
],
stop_reason: "end_turn",
stop_sequence: nil,
usage: {
input_tokens: 345,
output_tokens: 65,
},
}.to_json
parsed_body = nil
stub_request(:post, url).with(
body:
proc do |req_body|
parsed_body = JSON.parse(req_body, symbolize_names: true)
true
end,
).to_return(status: 200, body: response_body)
result = llm.generate(prompt, user: Discourse.system_user)
# Verify that tool_choice is set to { type: "none" }
expect(parsed_body[:tool_choice]).to eq({ type: "none" })
# Verify that an assistant message with no_more_tool_calls_text was added
messages = parsed_body[:messages]
expect(messages.length).to eq(2) # user message + added assistant message
last_message = messages.last
expect(last_message[:role]).to eq("assistant")
expect(last_message[:content]).to eq(
DiscourseAi::Completions::Dialects::Dialect.no_more_tool_calls_text,
)
expect(result).to eq("I won't use any tools. Here's a direct response instead.")
end
end
describe "structured output via prefilling" do
it "forces the response to be a JSON and using the given JSON schema" do
schema = {
type: "json_schema",
json_schema: {
name: "reply",
schema: {
type: "object",
properties: {
key: {
type: "string",
},
},
required: ["key"],
additionalProperties: false,
},
strict: true,
},
}
body = (<<~STRING).strip
event: message_start
data: {"type": "message_start", "message": {"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", "type": "message", "role": "assistant", "content": [], "model": "claude-3-opus-20240229", "stop_reason": null, "stop_sequence": null, "usage": {"input_tokens": 25, "output_tokens": 1}}}
event: content_block_start
data: {"type": "content_block_start", "index":0, "content_block": {"type": "text", "text": ""}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "\\""}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "key"}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "\\":\\""}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "Hello!"}}
event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "\\"}"}}
event: content_block_stop
data: {"type": "content_block_stop", "index": 0}
event: message_delta
data: {"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence":null, "usage":{"output_tokens": 15}}}
event: message_stop
data: {"type": "message_stop"}
STRING
parsed_body = nil
stub_request(:post, url).with(
body:
proc do |req_body|
parsed_body = JSON.parse(req_body, symbolize_names: true)
true
end,
headers: {
"Content-Type" => "application/json",
"X-Api-Key" => "123",
"Anthropic-Version" => "2023-06-01",
},
).to_return(status: 200, body: body)
structured_output = nil
llm.generate(
prompt,
user: Discourse.system_user,
feature_name: "testing",
response_format: schema,
) { |partial, cancel| structured_output = partial }
expect(structured_output.read_latest_buffered_chunk).to eq({ key: "Hello!" })
expected_body = {
model: "claude-3-opus-20240229",
max_tokens: 4096,
messages: [{ role: "user", content: "user1: hello" }, { role: "assistant", content: "{" }],
system: "You are hello bot",
stream: true,
}
expect(parsed_body).to eq(expected_body)
end
end
end