github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-08-15 15:46:47 +00:00
Code Issues Packages Projects Releases Wiki Activity

updates

Browse Source
This commit is contained in:
Mason Daugherty 2025-07-29 14:57:52 -04:00
parent 80971b69d0
commit 589ee059f2
No known key found for this signature in database
5 changed files with 46 additions and 44 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
libs/partners/ollama/langchain_ollama/__init__.py
View File

@ -10,6 +10,7 @@ service.
exist locally. This is useful for ensuring that the model is available before
attempting to use it, especially in environments where models may not be
pre-downloaded.
"""
from importlib import metadata
@ -20,6 +21,8 @@ from langchain_ollama.embeddings import OllamaEmbeddings
from langchain_ollama.llms import OllamaLLM
try:
if __package__ is None:
raise metadata.PackageNotFoundError
__version__ = metadata.version(__package__)
except metadata.PackageNotFoundError:
# Case where package metadata is not available.

4
libs/partners/ollama/langchain_ollama/_compat.py
View File

@ -53,8 +53,8 @@ def _convert_content_blocks_to_ollama_format(
text_content += text_block["text"]
elif block_type == "image":
image_block = cast(ImageContentBlock, block)
if image_block.get("source_type") == "base64":
images.append(image_block.get("data", ""))
if image_block.get("base64"):
images.append(image_block.get("base64", ""))
else:
msg = "Only base64 image data is supported by Ollama"
raise ValueError(msg)

2
libs/partners/ollama/langchain_ollama/chat_models_v1.py
View File

@ -2,6 +2,8 @@
This implementation provides native support for v1 messages with structured
content blocks and always returns AIMessageV1 format responses.
.. versionadded:: 1.0.0
"""
from __future__ import annotations

44
libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_reasoning.py
View File

@ -35,12 +35,10 @@ def test_stream_no_reasoning(model: str) -> None:
result += chunk
assert isinstance(result, AIMessageChunk)
assert result.content
assert "reasoning_content" not in result.additional_kwargs
assert "<think>" not in result.content and "</think>" not in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
if hasattr(result, "additional_kwargs"):
# v0 format
assert "reasoning_content" not in result.additional_kwargs
@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")])
@ -62,12 +60,10 @@ async def test_astream_no_reasoning(model: str) -> None:
result += chunk
assert isinstance(result, AIMessageChunk)
assert result.content
assert "reasoning_content" not in result.additional_kwargs
assert "<think>" not in result.content and "</think>" not in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
if hasattr(result, "additional_kwargs"):
# v0 format
assert "reasoning_content" not in result.additional_kwargs
@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")])
@ -91,8 +87,8 @@ def test_stream_reasoning_none(model: str) -> None:
assert result.content
assert "reasoning_content" not in result.additional_kwargs
assert "<think>" in result.content and "</think>" in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
# v0 format (content as string)
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
@ -118,8 +114,8 @@ async def test_astream_reasoning_none(model: str) -> None:
assert result.content
assert "reasoning_content" not in result.additional_kwargs
assert "<think>" in result.content and "</think>" in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
# v0 format (content as string)
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
@ -146,8 +142,8 @@ def test_reasoning_stream(model: str) -> None:
assert "reasoning_content" in result.additional_kwargs
assert len(result.additional_kwargs["reasoning_content"]) > 0
assert "<think>" not in result.content and "</think>" not in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
# v0 format (content as string)
assert "<think>" not in result.additional_kwargs["reasoning_content"]
assert "</think>" not in result.additional_kwargs["reasoning_content"]
@ -174,8 +170,8 @@ async def test_reasoning_astream(model: str) -> None:
assert "reasoning_content" in result.additional_kwargs
assert len(result.additional_kwargs["reasoning_content"]) > 0
assert "<think>" not in result.content and "</think>" not in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
# v0 format (content as string)
assert "<think>" not in result.additional_kwargs["reasoning_content"]
assert "</think>" not in result.additional_kwargs["reasoning_content"]
@ -188,10 +184,9 @@ def test_invoke_no_reasoning(model: str) -> None:
result = llm.invoke([message])
assert result.content
assert "<think>" not in result.content and "</think>" not in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
if hasattr(result, "additional_kwargs"):
# v0 format
assert "reasoning_content" not in result.additional_kwargs
@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")])
@ -202,10 +197,9 @@ async def test_ainvoke_no_reasoning(model: str) -> None:
result = await llm.ainvoke([message])
assert result.content
assert "<think>" not in result.content and "</think>" not in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
if hasattr(result, "additional_kwargs"):
# v0 format
assert "reasoning_content" not in result.additional_kwargs
@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")])
@ -217,8 +211,8 @@ def test_invoke_reasoning_none(model: str) -> None:
assert result.content
assert "reasoning_content" not in result.additional_kwargs
assert "<think>" in result.content and "</think>" in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
# v0 format (content as string)
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
@ -232,8 +226,8 @@ async def test_ainvoke_reasoning_none(model: str) -> None:
assert result.content
assert "reasoning_content" not in result.additional_kwargs
assert "<think>" in result.content and "</think>" in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
# v0 format (content as string)
assert "<think>" not in result.additional_kwargs.get("reasoning_content", "")
assert "</think>" not in result.additional_kwargs.get("reasoning_content", "")
@ -248,8 +242,8 @@ def test_reasoning_invoke(model: str) -> None:
assert "reasoning_content" in result.additional_kwargs
assert len(result.additional_kwargs["reasoning_content"]) > 0
assert "<think>" not in result.content and "</think>" not in result.content
# Only check additional_kwargs for v0 format (content as string)
if not isinstance(result.content, list):
# v0 format (content as string)
assert "<think>" not in result.additional_kwargs["reasoning_content"]
assert "</think>" not in result.additional_kwargs["reasoning_content"]

37
libs/partners/ollama/tests/unit_tests/test_base_v1.py
View File

@ -13,6 +13,8 @@ from langchain_ollama._compat import (
)
from langchain_ollama.chat_models_v1 import ChatOllamaV1
MODEL_NAME = "llama3.1"
class TestMessageConversion:
"""Test v1 message conversion utilities."""
@ -34,11 +36,10 @@ class TestMessageConversion:
message = HumanMessageV1(
content=[
TextContentBlock(type="text", text="Describe this image:"),
ImageContentBlock( # type: ignore[typeddict-unknown-key]
ImageContentBlock(
type="image",
mime_type="image/jpeg",
data="base64imagedata",
source_type="base64",
base64="base64imagedata",
),
]
)
@ -74,7 +75,7 @@ class TestMessageConversion:
def test_convert_from_ollama_format(self) -> None:
"""Test converting Ollama response to AIMessageV1."""
ollama_response = {
"model": "llama3",
"model": MODEL_NAME,
"created_at": "2024-01-01T00:00:00Z",
"message": {
"role": "assistant",
@ -93,13 +94,13 @@ class TestMessageConversion:
assert len(result.content) == 1
assert result.content[0]["type"] == "text"
assert result.content[0]["text"] == "Hello! How can I help you today?"
assert result.response_metadata["model_name"] == "llama3"
assert result.response_metadata.get("done") is True # type: ignore[typeddict-item]
assert result.response_metadata["model_name"] == MODEL_NAME # type: ignore[typeddict-not-required-key]
assert result.response_metadata.get("done") is True
def test_convert_chunk_to_v1(self) -> None:
"""Test converting Ollama streaming chunk to AIMessageChunkV1."""
chunk = {
"model": "llama3",
"model": MODEL_NAME,
"created_at": "2024-01-01T00:00:00Z",
"message": {"role": "assistant", "content": "Hello"},
"done": False,
@ -127,14 +128,14 @@ class TestChatOllamaV1:
def test_initialization(self) -> None:
"""Test ChatOllamaV1 initialization."""
llm = ChatOllamaV1(model="llama3")
llm = ChatOllamaV1(model=MODEL_NAME)
assert llm.model == "llama3"
assert llm.model == MODEL_NAME
assert llm._llm_type == "chat-ollama-v1"
def test_chat_params(self) -> None:
"""Test _chat_params method."""
llm = ChatOllamaV1(model="llama3", temperature=0.7)
llm = ChatOllamaV1(model=MODEL_NAME, temperature=0.7)
messages: list[MessageV1] = [
HumanMessageV1(content=[TextContentBlock(type="text", text="Hello")])
@ -142,26 +143,28 @@ class TestChatOllamaV1:
params = llm._chat_params(messages)
assert params["model"] == "llama3"
assert params["model"] == MODEL_NAME
assert len(params["messages"]) == 1
assert params["messages"][0]["role"] == "user"
assert params["messages"][0]["content"] == "Hello"
# Ensure options carry thru
assert params["options"].temperature == 0.7
def test_ls_params(self) -> None:
"""Test LangSmith parameters."""
llm = ChatOllamaV1(model="llama3", temperature=0.5)
llm = ChatOllamaV1(model=MODEL_NAME, temperature=0.5)
ls_params = llm._get_ls_params()
assert ls_params["ls_provider"] == "ollama"
assert ls_params["ls_model_name"] == "llama3"
assert ls_params["ls_model_type"] == "chat"
assert ls_params["ls_temperature"] == 0.5
assert ls_params["ls_provider"] == "ollama" # type: ignore[typeddict-not-required-key]
assert ls_params["ls_model_name"] == MODEL_NAME # type: ignore[typeddict-not-required-key]
assert ls_params["ls_model_type"] == "chat" # type: ignore[typeddict-not-required-key]
assert ls_params["ls_temperature"] == 0.5 # type: ignore[typeddict-not-required-key]
def test_bind_tools_basic(self) -> None:
"""Test basic tool binding functionality."""
llm = ChatOllamaV1(model="llama3")
llm = ChatOllamaV1(model=MODEL_NAME)
def test_tool(query: str) -> str:
"""A test tool."""