15703861885

Committed 17 Jun 2025 09:44AM UTC coverage: 90.141% (-0.003%) from 90.144%

Build # 15703861885

Build Type

Pull #9525

github

Committed by

web-flow

Commit Message

Merge 40d810412 into 7dbac5b3c

Pull Request Pull Request #9525: refactor: Make `_convert_chat_completion_chunk_to_streaming_chunk` a member function of `OpenAIChatGenerator` and small fixes to setting `StreamingChunk.index`

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75.0

haystack/components/generators/utils.py

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

import json
from typing import Dict, List

from haystack import logging
from haystack.dataclasses import ChatMessage, StreamingChunk, ToolCall

logger = logging.getLogger(__name__)


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 the start of a new content block but not the first content block, print two new lines
        print("\n\n", flush=True, end="")

    ## Tool Call streaming
    if chunk.tool_calls:
        # Typically, if there are multiple tool calls in the chunk, typically this means that the tool calls are fully
        # formed and not just a delta.
        for tool_call in chunk.tool_calls:
            # If chunk.start is True indicates beginning of a tool call
            # Also presence of tool_call.tool_name indicates the start of a tool call too
            if chunk.start:
                print("[TOOL CALL]\n", flush=True, end="")
                print(f"Tool: {tool_call.tool_name} ", flush=True, end="")
                print("\nArguments: ", flush=True, end="")

            # print the tool arguments
            if tool_call.arguments:
                print(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 multiple Tool Call Results
    if chunk.meta.get("finish_reason") is not None:
        print("\n\n", flush=True, end="")


def _convert_streaming_chunks_to_chat_message(chunks: List[StreamingChunk]) -> ChatMessage:
    """
    Connects the streaming chunks into a single ChatMessage.

    :param chunks: The list of all `StreamingChunk` objects.

    :returns: The ChatMessage.
    """
    text = "".join([chunk.content for chunk in chunks])
    tool_calls = []

    # Process tool calls if present in any chunk
    tool_call_data: Dict[int, Dict[str, str]] = {}  # Track tool calls by index
    for chunk in chunks:
        if chunk.tool_calls:
            # We do this to make sure mypy is happy, but we enforce index is not None in the StreamingChunk dataclass if
            # tool_call is present
            assert chunk.index is not None

            for tool_call in chunk.tool_calls:
                # We use the index of the tool_call to track the tool call across chunks since the ID is not always
                # provided
                if tool_call.index not in tool_call_data:
                    tool_call_data[chunk.index] = {"id": "", "name": "", "arguments": ""}

                # Save the ID if present
                if tool_call.id is not None:
                    tool_call_data[chunk.index]["id"] = tool_call.id

                if tool_call.tool_name is not None:
                    tool_call_data[chunk.index]["name"] += tool_call.tool_name
                if tool_call.arguments is not None:
                    tool_call_data[chunk.index]["arguments"] += tool_call.arguments

    # Convert accumulated tool call data into ToolCall objects
    sorted_keys = sorted(tool_call_data.keys())
    for key in sorted_keys:
        tool_call = tool_call_data[key]
        try:
            arguments = json.loads(tool_call["arguments"])
            tool_calls.append(ToolCall(id=tool_call["id"], tool_name=tool_call["name"], arguments=arguments))
        except json.JSONDecodeError:
            logger.warning(
                "OpenAI returned a malformed JSON string for tool call arguments. This tool call "
                "will be skipped. To always generate a valid JSON, set `tools_strict` to `True`. "
                "Tool call ID: {_id}, Tool name: {_name}, Arguments: {_arguments}",
                _id=tool_call["id"],
                _name=tool_call["name"],
                _arguments=tool_call["arguments"],
            )

    # finish_reason can appear in different places so we look for the last one
    finish_reasons = [
        chunk.meta.get("finish_reason") for chunk in chunks if chunk.meta.get("finish_reason") is not None
    ]
    finish_reason = finish_reasons[-1] if finish_reasons else None

    meta = {
        "model": chunks[-1].meta.get("model"),
        "index": 0,
        "finish_reason": finish_reason,
        "completion_start_time": chunks[0].meta.get("received_at"),  # first chunk received
        "usage": chunks[-1].meta.get("usage"),  # last chunk has the final usage data if available
    }

    return ChatMessage.from_assistant(text=text or None, tool_calls=tool_calls, meta=meta)

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4
5	import json	1✔
6	from typing import Dict, List	1✔
7
8	from haystack import logging	1✔
9	from haystack.dataclasses import ChatMessage, StreamingChunk, ToolCall	1✔
10
11	logger = logging.getLogger(__name__)	1✔
12
13
14	def print_streaming_chunk(chunk: StreamingChunk) -> None:	1✔
15	"""
16	Callback function to handle and display streaming output chunks.
17
18	This function processes a `StreamingChunk` object by:
19	- Printing tool call metadata (if any), including function names and arguments, as they arrive.
20	- Printing tool call results when available.
21	- Printing the main content (e.g., text tokens) of the chunk as it is received.
22
23	The function outputs data directly to stdout and flushes output buffers to ensure immediate display during
24	streaming.
25
26	:param chunk: A chunk of streaming data containing content and optional metadata, such as tool calls and
27	tool results.
28	"""
29	if chunk.start and chunk.index and chunk.index > 0:	1✔
30	# If this is the start of a new content block but not the first content block, print two new lines
31	print("\n\n", flush=True, end="")	×
32
33	## Tool Call streaming
34	if chunk.tool_calls:	1✔
35	# Typically, if there are multiple tool calls in the chunk, typically this means that the tool calls are fully
36	# formed and not just a delta.
37	for tool_call in chunk.tool_calls:	×
38	# If chunk.start is True indicates beginning of a tool call
39	# Also presence of tool_call.tool_name indicates the start of a tool call too
40	if chunk.start:	×
41	print("[TOOL CALL]\n", flush=True, end="")	×
42	print(f"Tool: {tool_call.tool_name} ", flush=True, end="")	×
43	print("\nArguments: ", flush=True, end="")	×
44
45	# print the tool arguments
46	if tool_call.arguments:	×
47	print(tool_call.arguments, flush=True, end="")	×
48
49	## Tool Call Result streaming
50	# Print tool call results if available (from ToolInvoker)
51	if chunk.tool_call_result:	1✔
52	# Tool Call Result is fully formed so delta accumulation is not needed
53	print(f"[TOOL RESULT]\n{chunk.tool_call_result.result}", flush=True, end="")	1✔
54
55	## Normal content streaming
56	# Print the main content of the chunk (from ChatGenerator)
57	if chunk.content:	1✔
58	if chunk.start:	×
59	print("[ASSISTANT]\n", flush=True, end="")	×
60	print(chunk.content, flush=True, end="")	×
61
62	# End of LLM assistant message so we add two new lines
63	# This ensures spacing between multiple LLM messages (e.g. Agent) or multiple Tool Call Results
64	if chunk.meta.get("finish_reason") is not None:	1✔
65	print("\n\n", flush=True, end="")	1✔
66
67
68	def _convert_streaming_chunks_to_chat_message(chunks: List[StreamingChunk]) -> ChatMessage:	1✔
69	"""
70	Connects the streaming chunks into a single ChatMessage.
71
72	:param chunks: The list of all `StreamingChunk` objects.
73
74	:returns: The ChatMessage.
75	"""
76	text = "".join([chunk.content for chunk in chunks])	1✔
77	tool_calls = []	1✔
78
79	# Process tool calls if present in any chunk
80	tool_call_data: Dict[int, Dict[str, str]] = {} # Track tool calls by index	1✔
81	for chunk in chunks:	1✔
82	if chunk.tool_calls:	1✔
83	# We do this to make sure mypy is happy, but we enforce index is not None in the StreamingChunk dataclass if
84	# tool_call is present
85	assert chunk.index is not None	1✔
86
87	for tool_call in chunk.tool_calls:	1✔
88	# We use the index of the tool_call to track the tool call across chunks since the ID is not always
89	# provided
90	if tool_call.index not in tool_call_data:	1✔
91	tool_call_data[chunk.index] = {"id": "", "name": "", "arguments": ""}	1✔
92
93	# Save the ID if present
94	if tool_call.id is not None:	1✔
95	tool_call_data[chunk.index]["id"] = tool_call.id	1✔
96
97	if tool_call.tool_name is not None:	1✔
98	tool_call_data[chunk.index]["name"] += tool_call.tool_name	1✔
99	if tool_call.arguments is not None:	1✔
100	tool_call_data[chunk.index]["arguments"] += tool_call.arguments	1✔
101
102	# Convert accumulated tool call data into ToolCall objects
103	sorted_keys = sorted(tool_call_data.keys())	1✔
104	for key in sorted_keys:	1✔
105	tool_call = tool_call_data[key]	1✔
106	try:	1✔
107	arguments = json.loads(tool_call["arguments"])	1✔
108	tool_calls.append(ToolCall(id=tool_call["id"], tool_name=tool_call["name"], arguments=arguments))	1✔
109	except json.JSONDecodeError:	×
110	logger.warning(	×
111	"OpenAI returned a malformed JSON string for tool call arguments. This tool call "
112	"will be skipped. To always generate a valid JSON, set `tools_strict` to `True`. "
113	"Tool call ID: {_id}, Tool name: {_name}, Arguments: {_arguments}",
114	_id=tool_call["id"],
115	_name=tool_call["name"],
116	_arguments=tool_call["arguments"],
117	)
118
119	# finish_reason can appear in different places so we look for the last one
120	finish_reasons = [	1✔
121	chunk.meta.get("finish_reason") for chunk in chunks if chunk.meta.get("finish_reason") is not None
122	]
123	finish_reason = finish_reasons[-1] if finish_reasons else None	1✔
124
125	meta = {	1✔
126	"model": chunks[-1].meta.get("model"),
127	"index": 0,
128	"finish_reason": finish_reason,
129	"completion_start_time": chunks[0].meta.get("received_at"), # first chunk received
130	"usage": chunks[-1].meta.get("usage"), # last chunk has the final usage data if available
131	}
132
133	return ChatMessage.from_assistant(text=text or None, tool_calls=tool_calls, meta=meta)	1✔

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