github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-08-15 23:57:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

address some ollama edge cases

Browse Source
This commit is contained in:
Mason Daugherty 2025-08-05 21:39:40 -04:00
parent 652884eeed
commit 4dc3a39522
No known key found for this signature in database
1 changed files with 31 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
libs/partners/ollama/langchain_ollama/_compat.py
View File

@ -12,6 +12,7 @@ from langchain_core.messages.content_blocks import (
ReasoningContentBlock, ReasoningContentBlock,
TextContentBlock, TextContentBlock,
ToolCall, ToolCall,
ToolCallChunk,
) )
from langchain_core.v1.messages import ( from langchain_core.v1.messages import (
AIMessage, AIMessage,
@ -104,6 +105,12 @@ def _convert_content_blocks_to_ollama_format(
}, },
} }
) )
elif block_type == "invalid_tool_call":
# InvalidToolCall blocks are handled by converting to text
# May revisit this in the future
name = block.get("name", "unknown")
error = block.get("error", "unknown error")
text_content += f"[Invalid tool call: {name} - {error}]"
else: else:
# Skip other content block types that aren't supported # Skip other content block types that aren't supported
msg = f"Unsupported content block type: {block_type}" msg = f"Unsupported content block type: {block_type}"
@ -246,6 +253,7 @@ def _convert_to_v1_from_ollama_format(response: dict[str, Any]) -> AIMessage:
def _convert_chunk_to_v1(chunk: dict[str, Any]) -> AIMessageChunk: def _convert_chunk_to_v1(chunk: dict[str, Any]) -> AIMessageChunk:
"""Convert Ollama streaming chunk to AIMessageChunk.""" """Convert Ollama streaming chunk to AIMessageChunk."""
content: list[types.ContentBlock] = [] content: list[types.ContentBlock] = []
tool_call_chunks: list[ToolCallChunk] = []
# Handle reasoning content first in chunks # Handle reasoning content first in chunks
if "message" in chunk and "thinking" in chunk["message"]: if "message" in chunk and "thinking" in chunk["message"]:
@ -267,17 +275,31 @@ def _convert_chunk_to_v1(chunk: dict[str, Any]) -> AIMessageChunk:
# Handle streaming tool calls # Handle streaming tool calls
if "message" in chunk and "tool_calls" in chunk["message"]: if "message" in chunk and "tool_calls" in chunk["message"]:
tool_calls = chunk["message"]["tool_calls"] tool_calls = chunk["message"]["tool_calls"]
content.extend( for i, tool_call in enumerate(tool_calls):
[ tool_call_id = tool_call.get("id", f"lc_{uuid4()}")
tool_name = tool_call.get("function", {}).get("name", "")
tool_args = tool_call.get("function", {}).get("arguments", {})
# Add to content blocks for final representation
content.append(
ToolCall( ToolCall(
type="tool_call", type="tool_call",
id=tool_call.get("id", str(uuid4())), id=tool_call_id,
name=tool_call.get("function", {}).get("name", ""), name=tool_name,
args=tool_call.get("function", {}).get("arguments", {}), args=tool_args,
) )
for tool_call in tool_calls )
]
) # Add to tool call chunks for streaming
tool_call_chunks.append(
ToolCallChunk(
type="tool_call_chunk",
id=tool_call_id,
name=tool_name,
args=tool_args,
index=i,
)
)
# Build response metadata for final chunks # Build response metadata for final chunks
response_metadata = None response_metadata = None
@ -312,4 +334,5 @@ def _convert_chunk_to_v1(chunk: dict[str, Any]) -> AIMessageChunk:
content=content, content=content,
response_metadata=response_metadata or ResponseMetadata(), response_metadata=response_metadata or ResponseMetadata(),
usage_metadata=usage_metadata, usage_metadata=usage_metadata,
tool_call_chunks=tool_call_chunks,
) )