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feat: Migrate streaming to StreamCallbacks.jl
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- Add StreamCallbacks.jl as a dependency
- Update request_body_live to use StreamCallbacks.jl
- Add support for custom stream parsing and sinks
- Update documentation with comprehensive streaming examples
- Maintain backward compatibility with existing streamcallback usage

Resolves JuliaML#65
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@devin-ai-integration
devin-ai-integration[bot] committed Nov 17, 2024
1 parent f70f44d commit 013c1f2
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2 changes: 2 additions & 0 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -7,11 +7,13 @@ version = "0.10.1"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
StreamCallbacks = "c7e14b3c-d701-4e11-a899-b3d7637fd44d"

[compat]
Dates = "1"
HTTP = "1"
JSON3 = "1"
StreamCallbacks = "0.1"
julia = "1"

[extras]
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188 changes: 105 additions & 83 deletions src/OpenAI.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -88,48 +88,38 @@ function request_body(url, method; input, headers, query, kwargs...)
end

function request_body_live(url; method, input, headers, streamcallback, kwargs...)
resp = nothing

body = sprint() do output
resp = HTTP.open("POST", url, headers) do stream
body = String(take!(input))
write(stream, body)

HTTP.closewrite(stream) # indicate we're done writing to the request

r = HTTP.startread(stream) # start reading the response
isdone = false

while !eof(stream) || !isdone
# Extract all available messages
masterchunk = String(readavailable(stream))

# Split into subchunks on newlines.
# Occasionally, the streaming will append multiple messages together,
# and iterating through each line in turn will make sure that
# streamingcallback is called on each message in turn.
chunks = String.(filter(!isempty, split(masterchunk, "\n")))
# Create a StreamCallback based on the provided streamcallback
callback = if streamcallback isa Function
# If it's a function, wrap it in a StreamCallback with OpenAIStream flavor
StreamCallback(
out = chunk -> streamcallback(String(chunk.data)),
flavor = OpenAIStream()
)
elseif streamcallback isa IO
# If it's an IO, create a StreamCallback that writes to it
StreamCallback(
out = chunk -> write(streamcallback, String(chunk.data)),
flavor = OpenAIStream()
)
elseif streamcallback isa StreamCallback
# If it's already a StreamCallback, use it as is
streamcallback
else
# Default case, create a basic StreamCallback
StreamCallback(
out = chunk -> nothing,
flavor = OpenAIStream()
)
end

# Iterate through each chunk in turn.
for chunk in chunks
if occursin(chunk, "data: [DONE]") # TODO - maybe don't strip, but instead us a regex in the endswith call
isdone = true
end
# Use StreamCallbacks.jl's streamed_request!
body = String(take!(input))
resp = streamed_request!(callback, url, headers, body; kwargs...)

# call the callback (if present) on the latest chunk
if !isnothing(streamcallback)
streamcallback(chunk)
end
# Build the response body from the accumulated chunks
response_body = build_response_body(callback)

# append the latest chunk to the body
print(output, chunk)
end
end
HTTP.closeread(stream)
end
end

return resp, body
return resp, response_body
end

function status_error(resp, log = nothing)
Expand Down Expand Up @@ -180,14 +170,23 @@ function _request(api::AbstractString,
return if isnothing(streamcallback)
OpenAIResponse(resp.status, JSON3.read(body))
else
# assemble the streaming response body into a proper JSON object
lines = split(body, "\n") # split body into lines

# throw out empty lines, skip "data: [DONE] bits
lines = filter(x -> !isempty(x) && !occursin("[DONE]", x), lines)
# Handle both StreamCallbacks.jl and legacy streaming responses
lines = if streamcallback isa StreamCallback
# StreamCallbacks.jl response is already properly formatted
String.(filter(!isempty, split(body, "\n")))
else
# Legacy streaming response handling
filter(x -> !isempty(x) && !occursin("[DONE]", x), split(body, "\n"))
end

# read each line, which looks like "data: {<json elements>}"
parsed = map(line -> JSON3.read(line[6:end]), lines)
# Parse each line as JSON, handling both formats
parsed = map(lines) do line
if startswith(line, "data: ")
JSON3.read(line[6:end])
else
JSON3.read(line)
end
end

OpenAIResponse(resp.status, parsed)
end
Expand Down Expand Up @@ -295,68 +294,91 @@ Create chat
- `api_key::String`: OpenAI API key
- `model_id::String`: Model id
- `messages::Vector`: The chat history so far.
- `streamcallback=nothing`: Function to call on each chunk (delta) of the chat response in streaming mode.
- `streamcallback=nothing`: Callback for streaming responses. Can be:
- A function that takes a String (basic streaming)
- An IO object to write chunks to
- A StreamCallback object for advanced streaming control (see StreamCallbacks.jl)
# Keyword Arguments (check the OpenAI docs for the exhaustive list):
- `temperature::Float64=1.0`: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
- `top_p::Float64=1.0`: An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
!!! note
Do not use `stream=true` option here, instead use the `streamcallback` keyword argument (see the relevant section below).
Do not use `stream=true` option here, instead use the `streamcallback` keyword argument (see the Streaming section below).
For more details about the endpoint and additional arguments, visit <https://platform.openai.com/docs/api-reference/chat>
# HTTP.request keyword arguments:
- `http_kwargs::NamedTuple=NamedTuple()`: Keyword arguments to pass to HTTP.request (e. g., `http_kwargs=(connection_timeout=2,)` to set a connection timeout of 2 seconds).
## Example:
## Basic Example:
```julia
julia> CC = create_chat("..........", "gpt-4o-mini",
julia> CC = create_chat("..........", "gpt-4-turbo-preview",
[Dict("role" => "user", "content"=> "What is the OpenAI mission?")]
);
julia> CC.response.choices[1][:message][:content]
"\n\nThe OpenAI mission is to create safe and beneficial artificial intelligence (AI) that can help humanity achieve its full potential. The organization aims to discover and develop technical approaches to AI that are safe and aligned with human values. OpenAI believes that AI can help to solve some of the world's most pressing problems, such as climate change, disease, inequality, and poverty. The organization is committed to advancing research and development in AI while ensuring that it is used ethically and responsibly."
"OpenAI's mission is to ensure artificial general intelligence benefits all of humanity."
```
### Streaming
## Streaming
The package supports three ways to handle streaming responses:
When a function that takes a single `String` as an argument is passed in the `streamcallback` argument, a request will be made in
in streaming mode. The `streamcallback` callback will be called on every line of the streamed response. Here we use a callback
that prints out the current time to demonstrate how different parts of the response are received at different times.
### 1. Basic Streaming (Function Callback)
The response body will reflect the chunked nature of the response, so some reassembly will be required to recover the full
message returned by the API.
Pass a function that takes a String argument to process each chunk:
```julia
julia> CC = create_chat(key, "gpt-4o-mini",
[Dict("role" => "user", "content"=> "What continent is New York in? Two word answer.")],
streamcallback = x->println(Dates.now()));
2023-03-27T12:34:50.428
2023-03-27T12:34:50.524
2023-03-27T12:34:50.524
2023-03-27T12:34:50.524
2023-03-27T12:34:50.545
2023-03-27T12:34:50.556
2023-03-27T12:34:50.556
julia> map(r->r["choices"][1]["delta"], CC.response)
5-element Vector{JSON3.Object{Base.CodeUnits{UInt8, SubString{String}}, SubArray{UInt64, 1, Vector{UInt64}, Tuple{UnitRange{Int64}}, true}}}:
{
"role": "assistant"
}
{
"content": "North"
}
{
"content": " America"
}
{
"content": "."
}
{}
julia> CC = create_chat(key, "gpt-4-turbo-preview",
[Dict("role" => "user", "content"=> "Count to 5 slowly")],
streamcallback = chunk -> println("Received: ", chunk)
);
Received: One
Received: , two
Received: , three
Received: , four
Received: , five
```
### 2. IO Streaming
Stream directly to an IO object:
```julia
julia> output_buffer = IOBuffer()
julia> CC = create_chat(key, "gpt-4-turbo-preview",
[Dict("role" => "user", "content"=> "Say hello")],
streamcallback = output_buffer
);
julia> String(take!(output_buffer))
"Hello! How can I help you today?"
```
### 3. Advanced Streaming with StreamCallbacks.jl
For advanced streaming control, use StreamCallbacks.jl's StreamCallback:
```julia
using StreamCallbacks
# Custom chunk processing
callback = StreamCallback(
# Process each chunk
out = chunk -> println("Token: ", chunk.content),
# Use OpenAI-specific stream parsing
flavor = OpenAIStream()
)
CC = create_chat(key, "gpt-4-turbo-preview",
[Dict("role" => "user", "content"=> "Count to 3")],
streamcallback = callback
)
```
For advanced streaming features like custom stream parsing, specialized sinks, or detailed chunk inspection,
refer to the [StreamCallbacks.jl](https://github.com/svilupp/StreamCallbacks.jl) package documentation.
"""
function create_chat(api_key::String,
model_id::String,
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