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narawat
2026-08-03 11:23:45 +07:00
parent c626d2cec5
commit 92eee07168
7 changed files with 624 additions and 1480 deletions
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src/OLD_interface.jl
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src/OLD_type.jl
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module type
export agent, sommelier, companion, virtualcustomer, agentcontext
using Dates, UUIDs, DataStructures, JSON, NATS
using GeneralUtils
# ---------------------------------------------- 100 --------------------------------------------- #
mutable struct agentcontext
text2textInstructLLM::Function
getTextEmbedding::Function
executeSQL::Function
similarSQLVectorDB::Function
insertSQLVectorDB::Function
similarSommelierDecision::Function
insertSommelierDecision::Function
find_related_tables_for_user_question::Function
pg_conn_str::String
agentconfig::AbstractDict
end
abstract type agent end
mutable struct sommelier <: agent
name::String # agent name
id::String # agent id
retailername::String
retailerid::String
tools::Dict
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
chathistory::Vector{Dict{String, Any}}
memory::Dict{String, Any}
context::agentcontext
llmFormatName::String
end
""" A sommelier agent.
# Arguments
- `context::agentcontext`
Application context containing shared functions for LLM, SQL, and vector database operations.
# Keyword Arguments
- `name::String`
Agent's name. Default: `"Assistant"`
- `id::String`
Agent's ID. Default: generated UUID string.
- `retailername::String`
Retailer name associated with the sommelier. Default: `"retailer_name"`
- `maxHistoryMsg::Integer`
Maximum history messages. Default: `20`
- `chathistory::Vector{Dict{String, String}}`
Chat history. Default: empty vector.
- `llmFormatName::String`
LLM format name. Default: `"granite3"`
# Return
- `sommelier`: An instantiated sommelier agent.
# Example
```julia
julia> using YiemAgent
julia> context = agentcontext(
text2textInstructLLM,
getTextEmbedding,
executeSQL,
similarSQLVectorDB,
insertSQLVectorDB,
similarSommelierDecision,
insertSommelierDecision
)
julia> agent = sommelier(context, name="WineExpert", id="123", retailername="MyWineShop")
```
"""
function sommelier(
context::agentcontext, # agent functions, db connect and other context
;
name::String= "Assistant",
id::String= string(uuid4()),
retailername::String= "not specified",
retailerid::String= "not specified",
maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{String, Any}} = Vector{Dict{String, Any}}(),
llmFormatName::String= "granite3"
)
tools = Dict( # update input format
"chatbox"=> Dict(
"description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
"input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
"output" => "" ,
),
"winestock"=> Dict(
"description" => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
"input" => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
"output" => """<output>Output are wines that match the search query in JSON format.""",
),
)
""" Memory
Chat history use openai format as follow:
image1_path = "test/large_image.png" ---
image1_bytes = read(image1_path) | this part must be done
image1_base64_string = base64encode(image1_bytes) | in frontend
mime_type = "image/png" | not in agent code
data1_uri = "data:<mime_type>;base64,<image1_base64_string>" ---
chathistory= [
Dict(
"role" => "system",
"content" => [
Dict("type" => "text", "text" => "You are a helpful assistant"),
]
),
Dict(
"role" => "user",
"content" => [
Dict("type" => "text", "text" => "<internal_context_for_assistant>
LLM context here...
</internal_context_for_assistant>
Do you know this wine? Just give me brief intro."
),
Dict(
"type" => "image_url",
"image_url" => Dict("url" => data1_uri)
),
]
),
]
shortmem = Dict(
"1"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
"2"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
...
)
"""
memory = Dict{String, Any}(
"shortmem"=> OrderedDict{String, Any}(),
"scratchpad"=> "",
"recap"=> OrderedDict{String, Any}(),
)
newAgent = sommelier(
name,
id,
retailername,
retailerid,
tools,
maxHistoryMsg,
chathistory,
memory,
context,
llmFormatName
)
systemmsg =
"""
# store_policy
- Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
- If you found wines in the store's database, they are in stock.
- You can only recommend wines that are currently in our inventory
- Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
- Ask the user one question at a time.
- Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
- Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
- Spicy foods should be paired only with light red wines.
- We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
- Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
# store_guidelines
- Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
- Customer may provide images for you to look up.
- Encourage the customer to explore different options and try new things.
- If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
- Your store carries only wine.
- Vintage 0 means non-vintage.
- Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
- User usually ask for something similar. This means you should use the search term based on the profile they like.
# situation
You are having conversation with a customer.
# your role
Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
# objective
- Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
- Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
# your responsibility includes
- According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
- Keep the conversation with the customer going smoothly
# your responsibility does NOT includes
- Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
- Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
- Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
# you should then respond to the user with interleaving plan, action_name, action_input in JSON format
1) "plan", Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
2) "action_name", (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
3) "action_input", The input to the action you are about to perform according to your plan.
After the action is executed you gets "action_result". It is the output from the action you selected.
# available actions
"CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
"SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is strictly supported search term including: retailer_name, wine price, winery, name, vintage, region, country, type of wine, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
Example query 1: "Dry, full-bodied red wine from Burgundy, France. Grape varietal could be Merlot or Syrah. price 100 to 1000 USD."
Example query 2: "Red or white wine, medium tannin, price under 700 USD"
Example query 3: "white wine from Tuscany, Italy or Bordeaux, France
"WINE_PRESENTATION_GUIDELINE", which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
"END_CONVER_GUIDELINE", which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
"""
system_msg = Dict(
"role" => "system",
"content" => [
Dict("type" => "text", "text" => systemmsg),
]
)
push!(newAgent.chathistory, system_msg)
return newAgent
end
mutable struct virtualcustomer <: agent
name::String # agent name
id::String # agent id
systemmsg::String # system message
tools::Dict
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
chathistory::Vector{Dict{String, Any}}
memory::Dict{String, Any}
context # NamedTuple of functions
llmFormatName::String
end
function virtualcustomer(
context, # NamedTuple of functions
;
name::String= "Assistant",
id::String= string(uuid4()),
maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{String, String}} = Vector{Dict{String, String}}(),
llmFormatName::String= "granite3",
systemmsg::String=
"""
Your name: $name
Your sex: Female
Your role: You are a helpful assistant.
You should follow the following guidelines:
- Focus on the latest conversation.
- Your like to be short and concise.
Let's begin!
""",
)
tools = Dict( # update input format
"chatbox"=> Dict(
"description" => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
"input" => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
"output" => "" ,
),
)
""" Memory
Ref: Chat prompt format is openai
chathistory = [
Dict(
"role" => "system",
"content" => [
Dict("type" => "text", "text" => system_msg),
]
),
Dict(
"role" => "user",
"content" => [
Dict("type" => "text", "text" => "Do you know this wine? Just give me brief intro."),
Dict(
"type" => "image_url",
"image_url" => Dict("url" => data1_uri)
)
]
)
]
"""
memory = Dict{String, Any}(
"shortmem"=> OrderedDict{String, Any}(
),
"scratchpad"=> "",
"events"=> Vector{Dict{String, Any}}(),
"state"=> Dict{String, Any}(
),
"recap"=> OrderedDict{String, Any}(),
)
newAgent = virtualcustomer(
name,
id,
systemmsg,
tools,
maxHistoryMsg,
chathistory,
memory,
context,
llmFormatName
)
return newAgent
end
end # module type
src/YiemAgent.jl
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@@ -16,8 +16,11 @@ module YiemAgent
include("llmfunction.jl")
using .llmfunction
include("interface.jl")
using .interface
include("core.jl")
using .core
include("api.jl")
using .api
# ---------------------------------------------- 100 --------------------------------------------- #
src/api.jl
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@@ -0,0 +1,329 @@
module api
export prompt
using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
DataFrames, Serde
using GeneralUtils
using ..type, ..util, ..llmfunction
# ---------------------------------------------- 100 --------------------------------------------- #
"""
Send a message to the agent's input channel.
Blocks if the input channel buffer is full (capacity 16 by default).
"""
function run_agent(agent::yiemAgent, msg)
put!(agent.input_ch, msg)
return agent
end
"""
Take a response from the agent's output channel.
Blocks until the agent sends a response.
"""
function take_response(agent::yiemAgent)
return take!(agent.output_ch)
end
"""
Send a follow-up message while the agent is still processing.
Follow-up messages are processed after all input_ch messages
and before any tool call results are sent.
"""
function follow_up(agent::yiemAgent, msg)
put!(agent.followUpQueue, msg)
return agent
end
"""
Gracefully stop the agent.
Sends a :shutdown signal, waits for the task to finish, then closes all channels.
"""
function stop_agent(agent::yiemAgent)
put!(agent.input_ch, :shutdown)
try
fetch(agent._task)
catch e
if e isa TaskFailedException
rethrow(e)
end
end
close(agent.input_ch)
close(agent.output_ch)
close(agent.followUpQueue)
return nothing
end
""" Recursively convert dictionary-like variable (e.g. JSON.Object) into an OrderedDict.
The function walks any nested structure composed of `AbstractDict` (e.g., `JSON.Object`,
`Dict`, `OrderedDict`) and `AbstractArray` and produces a new tree where
every dictionary-like node is an `OrderedDict` and every array-like node is a `Vector{Any}`.
Scalar values (numbers, strings, booleans, `nothing`, etc.) are returned unchanged.
Does **not** mutate the input; it always allocates new containers.
# Arguments
- `x`
Any Julia value. If `x` is an `AbstractDict` it will be converted to an `OrderedDict`;
if it is an `AbstractArray` its elements will be processed recursively.
# Keyword Arguments
- `keytype::Type=Any`
The key type for the output OrderedDict. Use `String` for `OrderedDict{String,Any}`,
`Symbol` for `OrderedDict{Symbol,Any}`, or `Any` to preserve original key types.
- `sort_order::Union{Nothing, Vector}=nothing`
Vector of keys specifying the desired order. Keys are arranged in the specified order
first, followed by any remaining keys.
# Return
- A newly allocated nested structure composed of `OrderedDict{keytype,Any}` and `Vector{Any}`
that mirrors the input shape but uses ordered Julia containers.
# Notes
- The function treats any `AbstractDict` as a mapping source, so it works with
`JSON.Object`, `Dict`, `OrderedDict`, etc.
- Arrays are returned as `Vector{Any}` with their elements processed recursively.
# Examples
```jldoctest
julia> using JSON, DataStructures
julia> d = Dict(
"a" => 4,
"b" => 6,
"c" => Dict(
"d"=>7,
:e=>Dict(
"f"=>"hey",
"g"=>Dict(
"world"=>[1, "2", 3, Dict(:dd=>4.7)]
)
)
)
)
julia> jsonstring = JSON.json(d)
julia> A1 = JSON.parse(jsonstring) # A1 type is JSON.Object
julia> A2 = dictify(A1; keytype=String)
OrderedDict{String,Any} with 3 entries:
"a" => 4
"b" => 6
"c" => OrderedDict("d"=>7, "e"=>Dict("f"=>"hey", "g"=>Dict("world"=>[1, "2", 3, 4.7])))
julia> A3 = dictify(A1; keytype=Symbol)
OrderedDict{Symbol,Any} with 3 entries:
:a => 4
:b => 6
:c => OrderedDict(:d=>7, :e=>Dict("f"=>"hey", "g"=>Dict("world"=>[1, "2", 3, 4.7])))
julia> B1 = dictify(d; keytype=String)
OrderedDict{String, Any} with 3 entries:
```
**With sort_order:**
```jldoctest
julia> d = Dict("a"=>1, "b"=>2, "c"=>3)
julia> dictify(d; sort_order=["c", "a"])
OrderedDict{String,Int} with 3 entries:
"c" => 3
"a" => 1
"b" => 2
```
"""
function dictify(x::T; keytype::Type=Any, sort_order::Union{Nothing, Vector}=nothing
)::OrderedDict where {T<:AbstractDict}
# this function is example
end
end # module interface
src/core.jl
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@@ -0,0 +1,147 @@
module core
# export prompt
using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
DataFrames, Serde
using GeneralUtils
using ..type, ..util, ..llmfunction
# ---------------------------------------------- 100 --------------------------------------------- #
"""
Private agent loop. Runs in a background @task.
Waits on input_ch and followUpQueue, processing whichever has a message first.
"""
function _agent_loop(agent::yiemAgent)
try
while true
# Wait on either channel — the one with a message fires first
# Wait on either channel — the one with a message is taken first
msg = nothing
while msg === nothing
if isready(agent.input_ch)
msg = take!(agent.input_ch)
elseif isready(agent.followUpQueue)
msg = take!(agent.followUpQueue)
else
yield()
end
end
# Check for shutdown signal
if msg === :shutdown
break
end
# Dispatch message through the processing pipeline
result = _process_message(agent, msg)
# Send response to user
put!(agent.output_ch, result)
end
catch e
# On any error, send error response and exit the loop
@error "Agent loop failed" error=e
end
end
"""
Process a single message through the agent pipeline.
This is where you add your LLM call, tool execution, etc.
"""
function _process_message(agent::yiemAgent, msg)
# WORKING Replace with actual processing logic
#
# 1. Add msg to agent._state.messages
# 2. Call agent.formatMsgForLLM(agent._state) to format for LLM
# 3. If preprocessMessages is set, call agent.preprocessMessages(...)
# 4. Call the LLM (blocking — the task waits here)
# 5. If agent has tools, handle tool calls in a loop
# 6. Build assistantMessage and return it
# Placeholder: echo back the message as a simple response
@warn "TODO: implement _process_message"
return assistantMessage(
role="assistant",
content=[textContent("Received: $(msg)")],
api="", model="", usage=nothing,
stopReason="end_turn",
errorMessage=nothing,
timestamp=now(),
)
end
end # end of module
src/interface.jl
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src/type.jl
+143 -115
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@@ -31,6 +31,23 @@ struct userMessage <: agentMessage # Message from the user
timestamp::Timestamp # When the message was sent
end
"""
Create a new user message.
# Arguments
- `role::String`: Always "user"
- `content::Vector{messageContent}`: Text and/or image content
- `timestamp::Timestamp`: When the message was sent
# Returns
- A new `userMessage` instance
# Examples
```julia
julia> msg = userMessage(content=[textContent("Hello")])
userMessage("user", [textContent("Hello")], DateTime(...))
```
"""
function userMessage(; role="user", content=Vector{messageContent}(), timestamp=now())
return userMessage(role, content, timestamp)
end
@@ -47,6 +64,29 @@ struct assistantMessage <: agentMessage # Message from the AI assistant
timestamp::Timestamp # When the message was received
end
"""
Create a new assistant message.
# Arguments
- `role::String`: Always "assistant"
- `content::Vector{messageContent}`: Text and/or image content
- `api::String`: API name used (e.g., "openai")
- `provider::String`: Provider name (e.g., "anthropic")
- `model::String`: Model identifier
- `usage::Usage`: Token usage for this message
- `stopReason::String`: Why generation stopped (e.g., "end_turn")
- `errorMessage::Union{String, Nothing}`: Error if generation failed
- `timestamp::Timestamp`: When the message was received
# Returns
- A new `assistantMessage` instance
# Examples
```julia
julia> msg = assistantMessage(content=[textContent("Hello!")], model="gpt-4")
assistantMessage("assistant", [textContent("Hello!")], "", "", "gpt-4", ..., "end_turn", nothing, DateTime(...))
```
"""
function assistantMessage(; role="assistant", content=Vector{messageContent}(),
api="", provider="", model="", usage=Usage(0, 0), stopReason="end_turn",
errorMessage=nothing, timestamp=now())
@@ -65,6 +105,29 @@ struct toolResultMessage <: agentMessage # Result returned from a tool execut
timestamp::Timestamp # When the result was recorded
end
"""
Create a new tool result message.
# Arguments
- `role::String`: Always "tool"
- `toolCallId::String`: ID matching the tool call
- `toolName::String`: Name of the executed tool
- `content::Vector{messageContent}`: Tool output content
- `details::Any`: Additional tool-specific details
- `usage::Union{Usage, Nothing}`: Token usage if applicable
- `addedToolNames::Union{Vector{String}, Nothing}`: Tools added during execution
- `isError::Bool`: Whether the tool call resulted in an error
- `timestamp::Timestamp`: When the result was recorded
# Returns
- A new `toolResultMessage` instance
# Examples
```julia
julia> msg = toolResultMessage(toolCallId="call_123", toolName="search", content=[textContent("results")])
toolResultMessage("tool", "call_123", "search", [textContent("results")], nothing, nothing, nothing, false, DateTime(...))
```
"""
function toolResultMessage(; role="tool", toolCallId="", toolName="",
content=Vector{messageContent}(), details=nothing, usage=nothing,
addedToolNames=nothing, isError=false, timestamp=now())
@@ -82,6 +145,21 @@ struct textContent <: messageContent # Plain text message content
text::String # The text content
end
"""
Create plain text message content.
# Arguments
- `text::String`: The text content
# Returns
- A new `textContent` instance
# Examples
```julia
julia> content = textContent("Hello, world!")
textContent("Hello, world!")
```
"""
function textContent(; text="")
return textContent(text)
end
@@ -91,6 +169,22 @@ struct imageContent <: messageContent # Image message content
mimeType::String # MIME type (e.g., "image/png")
end
"""
Create image message content.
# Arguments
- `data::String`: Base64-encoded image data
- `mimeType::String`: MIME type (e.g., "image/png")
# Returns
- A new `imageContent` instance
# Examples
```julia
julia> img = imageContent(data="base64data...", mimeType="image/png")
imageContent("base64data...", "image/png")
```
"""
function imageContent(; data="", mimeType="")
return imageContent(data, mimeType)
end
@@ -135,6 +229,27 @@ mutable struct agentState # Mutable runtime state of an agen
errorMessage::Union{String, Nothing} # Last error message
end
"""
Create a new mutable agent state.
Creates a deep copy of the provided tools and messages to isolate the
new state from external references.
# Arguments
- `systemPrompt::String`: System prompt text
- `model::llmModel`: LLM model to use (defaults to an unknown model)
- `tools::Vector{agentTool}`: Available tools (deep copied)
- `messages::Vector{agentMessage}`: Conversation messages (deep copied)
# Returns
- A new `agentState` instance with an empty pending tool calls list and no error
# Examples
```julia
julia> state = agentState(systemPrompt="You are a helpful assistant")
agentState("You are a helpful assistant", ..., agentTool[], agentMessage[], String[], nothing)
```
"""
function agentState(
systemPrompt::String="",
model::llmModel=llmModel{String}("", "", "unknown", "unknown", "", false, String[], modelCost(0.0, 0.0, 0.0, 0.0), 0, 0),
@@ -237,7 +352,34 @@ mutable struct yiemAgent <: agent # High-level agent wrapper
end
"""
docstring
Create a new yiemAgent instance with a background loop task.
Spawns a background `@spawn` task that runs the agent loop, listening
on `input_ch` and `followUpQueue` channels concurrently.
# Keyword Arguments
- `systemPrompt::String`: System prompt for the agent
- `model`: LLM model to use
- `tools::Vector{agentTool}`: Available tools (default: empty)
- `messages::Vector{agentMessage}`: Initial conversation messages (default: empty)
- `formatMsgForLLM::Function`: Convert agent messages to LLM message format (default: `defaultformatMsgForLLM`)
- `preprocessMessages::Union{Function, Nothing}`: Preprocess/transform messages before sending to LLM (default: `nothing`)
- `beforeToolCall::Union{Function, Nothing}`: Callback invoked before executing a tool call (default: `nothing`)
- `afterToolCall::Union{Function, Nothing}`: Callback invoked after executing a tool call (default: `nothing`)
- `prepareNextTurn::Union{Function, Nothing}`: Callback to prepare the next conversation turn (default: `nothing`)
- `prepareNextTurnWithContext::Union{Function, Nothing}`: Same but receives context (default: `nothing`)
- `sessionId::Union{String, Nothing}`: Optional session identifier (default: `nothing`)
- `maxRetryDelayMs::Union{Int64, Nothing}`: Maximum delay between retries in milliseconds (default: `nothing`)
- `toolExecution`: Default tool execution mode (sequential or parallel) (default: `nothing`)
# Returns
- A new `yiemAgent` instance with an active background task
# Examples
```julia
julia> agent = yiemAgent(systemPrompt="You are a helpful assistant", model=my_model)
yiemAgent(agentState(...), Channel(...), Channel(...), Channel(...), ..., ...)
```
"""
function yiemAgent(
; systemPrompt::String="",
@@ -283,120 +425,6 @@ function yiemAgent(
return agent
end
# ============================================================================
# Agent loop — runs in background, processes messages from input_ch / followUp
# ============================================================================
"""
Private agent loop. Runs in a background @task.
Waits on input_ch and followUpQueue concurrently via select().
"""
function _agent_loop(agent::yiemAgent) #WORKING
try
while true
# Wait on either channel — the one with a message fires first
msg = select(agent.input_ch, agent.followUpQueue).val
# Check for shutdown signal
if msg === :shutdown
break
end
# Dispatch message through the processing pipeline
result = _process_message(agent, msg)
# Send response to user
put!(agent.output_ch, result)
end
catch e
# On any error, send error response and exit the loop
@error "Agent loop failed" error=e
end
end
"""
Process a single message through the agent pipeline.
This is where you add your LLM call, tool execution, etc.
"""
function _process_message(agent::yiemAgent, msg)
# PENDING Replace with actual processing logic
#
# 1. Add msg to agent._state.messages
# 2. Call agent.formatMsgForLLM(agent._state) to format for LLM
# 3. If preprocessMessages is set, call agent.preprocessMessages(...)
# 4. Call the LLM (blocking — the task waits here)
# 5. If agent has tools, handle tool calls in a loop
# 6. Build assistantMessage and return it
# Placeholder: echo back the message as a simple response
@warn "TODO: implement _process_message"
return assistantMessage(
role="assistant",
content=[textContent("Received: $(msg)")],
api="", model="", usage=nothing,
stopReason="end_turn",
errorMessage=nothing,
timestamp=now(),
)
end
# ============================================================================
# Public API — interaction helpers
# ============================================================================
"""
Send a message to the agent's input channel.
Blocks if the input channel buffer is full (capacity 16 by default).
"""
function run_agent(agent::yiemAgent, msg)
put!(agent.input_ch, msg)
return agent
end
"""
Take a response from the agent's output channel.
Blocks until the agent sends a response.
"""
function take_response(agent::yiemAgent)
return take!(agent.output_ch)
end
"""
Send a follow-up message while the agent is still processing.
Follow-up messages are processed after all input_ch messages
and before any tool call results are sent.
"""
function follow_up(agent::yiemAgent, msg)
put!(agent.followUpQueue, msg)
return agent
end
"""
Gracefully stop the agent.
Sends a :shutdown signal, waits for the task to finish, then closes all channels.
"""
function stop_agent(agent::yiemAgent)
put!(agent.input_ch, :shutdown)
try
fetch(agent._task)
catch e
if e isa TaskFailedException
rethrow(e)
end
end
close(agent.input_ch)
close(agent.output_ch)
close(agent.followUpQueue)
return nothing
end