15191185377

Committed 22 May 2025 03:52PM UTC coverage: 90.342% (-0.07%) from 90.411%

Build # 15191185377

Build Type

Pull #9424

github

Committed by

web-flow

Commit Message

Merge 010c037c7 into 4a5e4d3e6

Pull Request Pull Request #9424: feat: Update streaming chunk

Run Details

11169 of 12363 relevant lines covered (90.34%)

0.9 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

10.53

haystack/components/generators/utils.py

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0

from haystack.dataclasses import StreamingChunk


def print_streaming_chunk(chunk: StreamingChunk) -> None:
    """
    Callback function to handle and display streaming output chunks.

    This function processes a `StreamingChunk` object by:
    - Printing tool call metadata (if any), including function names and arguments, as they arrive.
    - Printing tool call results when available.
    - Printing the main content (e.g., text tokens) of the chunk as it is received.

    The function outputs data directly to stdout and flushes output buffers to ensure immediate display during
    streaming.

    :param chunk: A chunk of streaming data containing content and optional metadata, such as tool calls and
        tool results.
    """
    if chunk.start and chunk.index and chunk.index > 0:
        # If this is not the first content block of the message, add two new lines
        print("\n\n", flush=True, end="")

    ## Tool Call streaming
    if chunk.tool_call:
        # Presence of tool_name indicates beginning of a tool call
        # or chunk.tool_call.name: would be equivalent here
        if chunk.start:
            print("[TOOL CALL]\n", flush=True, end="")
            print(f"Tool: {chunk.tool_call.tool_name} ", flush=True, end="")
            print("\nArguments: ", flush=True, end="")

        # print the tool arguments
        if chunk.tool_call.arguments:
            print(chunk.tool_call.arguments, flush=True, end="")

    ## Tool Call Result streaming
    # Print tool call results if available (from ToolInvoker)
    if chunk.tool_call_result:
        # Tool Call Result is fully formed so delta accumulation is not needed
        print(f"[TOOL RESULT]\n{chunk.tool_call_result.result}", flush=True, end="")

    ## Normal content streaming
    # Print the main content of the chunk (from ChatGenerator)
    if chunk.content:
        if chunk.start:
            print("[ASSISTANT]\n", flush=True, end="")
        print(chunk.content, flush=True, end="")

    # End of LLM assistant message so we add two new lines
    # This ensures spacing between multiple LLM messages (e.g. Agent) or Tool Call Result
    if chunk.meta.get("finish_reason") is not None:
        print("\n\n", flush=True, end="")

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4
5	from haystack.dataclasses import StreamingChunk	1✔
6
7
8	def print_streaming_chunk(chunk: StreamingChunk) -> None:	1✔
9	"""
10	Callback function to handle and display streaming output chunks.
11
12	This function processes a `StreamingChunk` object by:
13	- Printing tool call metadata (if any), including function names and arguments, as they arrive.
14	- Printing tool call results when available.
15	- Printing the main content (e.g., text tokens) of the chunk as it is received.
16
17	The function outputs data directly to stdout and flushes output buffers to ensure immediate display during
18	streaming.
19
20	:param chunk: A chunk of streaming data containing content and optional metadata, such as tool calls and
21	tool results.
22	"""
23	if chunk.start and chunk.index and chunk.index > 0:	×
24	# If this is not the first content block of the message, add two new lines
25	print("\n\n", flush=True, end="")	×
26
27	## Tool Call streaming
28	if chunk.tool_call:	×
29	# Presence of tool_name indicates beginning of a tool call
30	# or chunk.tool_call.name: would be equivalent here
31	if chunk.start:	×
32	print("[TOOL CALL]\n", flush=True, end="")	×
33	print(f"Tool: {chunk.tool_call.tool_name} ", flush=True, end="")	×
34	print("\nArguments: ", flush=True, end="")	×
35
36	# print the tool arguments
37	if chunk.tool_call.arguments:	×
38	print(chunk.tool_call.arguments, flush=True, end="")	×
39
40	## Tool Call Result streaming
41	# Print tool call results if available (from ToolInvoker)
42	if chunk.tool_call_result:	×
43	# Tool Call Result is fully formed so delta accumulation is not needed
44	print(f"[TOOL RESULT]\n{chunk.tool_call_result.result}", flush=True, end="")	×
45
46	## Normal content streaming
47	# Print the main content of the chunk (from ChatGenerator)
48	if chunk.content:	×
49	if chunk.start:	×
50	print("[ASSISTANT]\n", flush=True, end="")	×
51	print(chunk.content, flush=True, end="")	×
52
53	# End of LLM assistant message so we add two new lines
54	# This ensures spacing between multiple LLM messages (e.g. Agent) or Tool Call Result
55	if chunk.meta.get("finish_reason") is not None:	×
56	print("\n\n", flush=True, end="")	×

deepset-ai / haystack / 15191185377

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous