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text message process works

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This commit is contained in:
narawat
2026-08-16 17:24:29 +07:00
parent a29a82b74c
commit 25f8468696
3 changed files with 41 additions and 17 deletions
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src/agentCore.jl
+23 -6
View File
@@ -227,8 +227,11 @@ function _agentLoop(agent::yiemAgent)
else # _processMessage() done and no followUp message.
agent.agentEventSink("_agentLoop 4 agent._state.messages length $(length(agent._state.messages))")
result = agent._state.messages[end]
filtered_content = [c for c in result.content if !(c isa reasoningContent)]
put!(agent.outputChannel, result)
agent.agentEventSink(result.content[1].text)
if !isempty(filtered_content) && filtered_content[1] isa textContent
agent.agentEventSink(filtered_content[1].text)
end
processingTask = nothing # reset
newUserMsg = nothing # reset
result = nothing # reset
@@ -381,7 +384,7 @@ function _processMessage(
# Extract tool calls from LLM response content blocks
hasToolCalls, toolCallList, assistant_msg = _extractToolCalls(response)
agentEventSink("hasToolCalls: $hasToolCalls\ntoolCallList: $toolCallList _state.messages length $(length(agentMsgHistory))")
agentEventSink(string(assistant_msg))
agentEventSink("assistant_msg " * string(assistant_msg))
agentEventSink("_processMessage 11-1")
# Add assistant message (tool calls or text) to history for next LLM turn
@@ -406,6 +409,7 @@ function _processMessage(
# save toolResults to messages
for toolResult in toolResultBatch.messages
agentEventSink("toolResult " * string(toolResult))
push!(agentMsgHistory, toolResult)
end
agentEventSink("_processMessage 14 _state.messages length $(length(agentMsgHistory))")
@@ -554,7 +558,9 @@ The message is constructed from:
`(hasToolCalls, toolCallList, message)` where message is `assistantMessageToolCall`
when tool calls exist, `assistantMessage` otherwise
# Example response = JSON.Object{String, Any}("finish_reason" => "tool_calls", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "", "reasoning_content" => "Here's a thinking process:\n\n1. **Identify User Request**: The user is asking for the weather in Bangkok.\n2. **Locate Relevant Tool**: I have a `getWeather` function available.\n3. **Check Function Parameters**:\n - `city` (required): City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\n - `units` (optional, default \"celsius\"): Temperature scale (\"celsius\" or \"fahrenheit\")\n4. **Prepare Parameters**:\n - `city`: \"Bangkok, Thailand\" (adding country for clarity, though just \"Bangkok\" might work, following the example format is safer)\n - `units`: Not specified, so I'll use the default (\"celsius\")\n5. **Execute Tool Call**: Call `getWeather` with `city: \"Bangkok, Thailand\"`\n6. **Anticipate Response**: The function will return current weather and forecast data for Bangkok. I'll then format it nicely for the user.\n - *Self-Correction/Verification during thought*: The prompt says \"city: City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\". I'll use \"Bangkok, Thailand\". The `units` parameter is optional, so I'll omit it to use the default.\n - Proceed with tool call.✅\n", "tool_calls" => Any[JSON.Object{String, Any}("type" => "function", "function" => JSON.Object{String, Any}("name" => "getWeather", "arguments" => "{\"city\":\"Bangkok, Thailand\"}"), "id" => "6fOilR5QPcdppbAAHluhkRyUDu3oWMAL")]))
# Example
1) llm_useTool = JSON.Object{String, Any}("finish_reason" => "tool_calls", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "", "reasoning_content" => "Here's a thinking process:\n\n1. **Identify User Request**: The user is asking for the weather in Bangkok.\n2. **Locate Relevant Tool**: I have a `getWeather` function available.\n3. **Check Function Parameters**:\n - `city` (required): City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\n - `units` (optional, default \"celsius\"): Temperature scale (\"celsius\" or \"fahrenheit\")\n4. **Prepare Parameters**:\n - `city`: \"Bangkok, Thailand\" (adding country for clarity, though just \"Bangkok\" might work, following the example format is safer)\n - `units`: Not specified, so I'll use the default (\"celsius\")\n5. **Execute Tool Call**: Call `getWeather` with `city: \"Bangkok, Thailand\"`\n6. **Anticipate Response**: The function will return current weather and forecast data for Bangkok. I'll then format it nicely for the user.\n - *Self-Correction/Verification during thought*: The prompt says \"city: City and country, e.g., 'San Francisco, CA' or 'Tokyo, Japan'\". I'll use \"Bangkok, Thailand\". The `units` parameter is optional, so I'll omit it to use the default.\n - Proceed with tool call.✅\n", "tool_calls" => Any[JSON.Object{String, Any}("type" => "function", "function" => JSON.Object{String, Any}("name" => "getWeather", "arguments" => "{\"city\":\"Bangkok, Thailand\"}"), "id" => "6fOilR5QPcdppbAAHluhkRyUDu3oWMAL")]))
2) llm_notUseTool = JSON.Object{String, Any}("finish_reason" => "stop", "index" => 0, "message" => JSON.Object{String, Any}("role" => "assistant", "content" => "The weather in London, UK is currently Sunny with a temperature of 22°C.", "reasoning_content" => "The user asked for the weather in London.\nI called the `getWeather` tool for London, UK.\nThe response indicates it's Sunny and 22°C.\nI will convey this information to the user.\n"))
"""
function _extractToolCalls(response)
hasToolCalls = false
@@ -642,14 +648,15 @@ function _extractToolCalls(response)
end
if reasoning_text isa String
reasoning_text = reasoning_text
elseif reasoning_text isa textContent
elseif reasoning_text isa reasoningContent
reasoning_text = reasoning_text.text
else
reasoning_text = ""
end
reasoning_content = !isempty(reasoning_text) ? [textContent(reasoning_text)] : textContent[]
reasoning_content = !isempty(reasoning_text) ? [reasoningContent(reasoning_text)] : reasoningContent[]
# Collect content blocks from response.content array (Format 2: OpenAI-style)
# or as a plain string (Format 1: LMStudio.jl non-tool-calls response)
content_from_response = get(response, "content", nothing)
content_blocks = Vector{messageContent}()
if content_from_response isa Vector
@@ -662,12 +669,22 @@ function _extractToolCalls(response)
elseif get(block, "type", "") == "tool_calls"
# tool_calls blocks — don't add text content for these
elseif get(block, "type", "") == "reasoning"
push!(content_blocks, textContent(get(block, "text", "")))
push!(content_blocks, reasoningContent(get(block, "text", "")))
else
push!(content_blocks, textContent(get(block, "text", "")))
end
end
end
elseif content_from_response isa AbstractString && !isempty(content_from_response)
push!(content_blocks, textContent(content_from_response))
end
# Format 1 fallback: response["message"]["content"] as plain string
if isempty(content_blocks) && msg !== nothing && msg isa AbstractDict
msg_content = get(msg, "content", nothing)
if msg_content isa AbstractString && !isempty(msg_content)
push!(content_blocks, textContent(msg_content))
end
end
# Combine reasoning_content field + content array blocks, deduplicating reasoning
src/type.jl
+13 -10
View File
@@ -4,8 +4,8 @@
messageContent, agentMessage, agent,
# Model types
modelCost, llmModel, llmUsage,
# Message content types
textContent, imageContent,
# Message content types
textContent, imageContent, reasoningContent,
# Message types
userMessage, assistantMessageToolCall, assistantMessage, toolResultMessage,
# Tool types
@@ -31,6 +31,13 @@ using GeneralUtils
const Timestamp = DateTime
struct agentToolCall # A tool invocation from the LLM
type::String # Always "function"
id::String # Unique tool call identifier
name::String # Tool name
arguments::Dict{String, Any} # Parsed tool arguments
end
# ------------------------------------------------------------------------------------------------ #
# LLM model info #
# ------------------------------------------------------------------------------------------------ #
@@ -75,6 +82,10 @@ struct imageContent <: messageContent # Image message content
mimeType::String # MIME type (e.g., "image/png")
end
struct reasoningContent <: messageContent # LLM reasoning/thinking content
text::String # The reasoning text
end
# ------------------------------------------------------------------------------------------------ #
# Message types #
@@ -404,14 +415,6 @@ function agentState(
end
struct agentToolCall # A tool invocation from the LLM
type::String # Always "function"
id::String # Unique tool call identifier
name::String # Tool name
arguments::Dict{String, Any} # Parsed tool arguments
end
"""
Context for preparing the next conversation turn.
test/_extractToolCalls.jl
+5 -1
View File
@@ -44,7 +44,7 @@ import YiemAgent.agentCore: _extractToolCalls
@test assistant_msg.role == "assistant"
@test assistant_msg.stopReason == "tool_calls"
@test length(assistant_msg.content) == 1
@test assistant_msg.content[1] isa textContent
@test assistant_msg.content[1] isa reasoningContent
@test assistant_msg.content[1].text == "Let me check the weather."
end
@@ -241,7 +241,9 @@ import YiemAgent.agentCore: _extractToolCalls
@test has_toolcalls == false
@test length(tc_list) == 0
@test length(assistant_msg.content) == 2
@test assistant_msg.content[1] isa reasoningContent
@test assistant_msg.content[1].text == "Thinking..."
@test assistant_msg.content[2] isa textContent
@test assistant_msg.content[2].text == "Here's the answer."
end
@@ -307,7 +309,9 @@ import YiemAgent.agentCore: _extractToolCalls
)
has_toolcalls, tc_list, assistant_msg = _extractToolCalls(response)
@test length(assistant_msg.content) == 2
@test assistant_msg.content[1] isa reasoningContent
@test assistant_msg.content[1].text == "internal thought"
@test assistant_msg.content[2] isa textContent
@test assistant_msg.content[2].text == "output"
end