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ollama: init package (#23615)
Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Isaac Francisco
GitHub
1
libs/partners/ollama/.gitignore
vendored
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1
libs/partners/ollama/.gitignore
vendored
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@@ -0,0 +1 @@
|
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__pycache__
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21
libs/partners/ollama/LICENSE
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21
libs/partners/ollama/LICENSE
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@@ -0,0 +1,21 @@
|
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MIT License
|
||||
|
||||
Copyright (c) 2024 LangChain, Inc.
|
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|
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Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
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64
libs/partners/ollama/Makefile
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64
libs/partners/ollama/Makefile
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@@ -0,0 +1,64 @@
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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
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# Default target executed when no arguments are given to make.
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all: help
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# Define a variable for the test file path.
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TEST_FILE ?= tests/unit_tests/
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integration_test: TEST_FILE = tests/integration_tests/
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# note: leaving out integration_tests (with s) command to skip release testing for now
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# TODO(erick) configure ollama server to run in CI, in separate repo
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# unit tests are run with the --disable-socket flag to prevent network calls
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test tests:
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poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
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# integration tests are run without the --disable-socket flag to allow network calls
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integration_test integration_tests:
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poetry run pytest $(TEST_FILE)
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######################
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# LINTING AND FORMATTING
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######################
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# Define a variable for Python and notebook files.
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PYTHON_FILES=.
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MYPY_CACHE=.mypy_cache
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lint format: PYTHON_FILES=.
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lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/ollama --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=langchain_ollama
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lint_tests: PYTHON_FILES=tests
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lint_tests: MYPY_CACHE=.mypy_cache_test
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lint lint_diff lint_package lint_tests:
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poetry run ruff .
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poetry run ruff format $(PYTHON_FILES) --diff
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poetry run ruff --select I $(PYTHON_FILES)
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mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
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format format_diff:
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poetry run ruff format $(PYTHON_FILES)
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poetry run ruff --select I --fix $(PYTHON_FILES)
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spell_check:
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poetry run codespell --toml pyproject.toml
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spell_fix:
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poetry run codespell --toml pyproject.toml -w
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check_imports: $(shell find langchain_ollama -name '*.py')
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poetry run python ./scripts/check_imports.py $^
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######################
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# HELP
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######################
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help:
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@echo '----'
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@echo 'check_imports - check imports'
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@echo 'format - run code formatters'
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@echo 'lint - run linters'
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@echo 'test - run unit tests'
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@echo 'tests - run unit tests'
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@echo 'test TEST_FILE=<test_file> - run all tests in file'
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44
libs/partners/ollama/README.md
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44
libs/partners/ollama/README.md
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# langchain-ollama
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This package contains the LangChain integration with Ollama
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## Installation
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```bash
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pip install -U langchain-ollama
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```
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You will also need to run the Ollama server locally.
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You can download it [here](https://ollama.com/download).
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## Chat Models
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`ChatOllama` class exposes chat models from Ollama.
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```python
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from langchain_ollama import ChatOllama
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llm = ChatOllama(model="llama3-groq-tool-use")
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llm.invoke("Sing a ballad of LangChain.")
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```
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## Embeddings
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`OllamaEmbeddings` class exposes embeddings from Ollama.
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```python
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from langchain_ollama import OllamaEmbeddings
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embeddings = OllamaEmbeddings(model="llama3")
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embeddings.embed_query("What is the meaning of life?")
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```
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## LLMs
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`OllamaLLM` class exposes LLMs from Ollama.
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```python
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from langchain_ollama import OllamaLLM
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llm = OllamaLLM(model="llama3")
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llm.invoke("The meaning of life is")
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```
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19
libs/partners/ollama/langchain_ollama/__init__.py
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19
libs/partners/ollama/langchain_ollama/__init__.py
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from importlib import metadata
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from langchain_ollama.chat_models import ChatOllama
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from langchain_ollama.embeddings import OllamaEmbeddings
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from langchain_ollama.llms import OllamaLLM
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try:
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__version__ = metadata.version(__package__)
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except metadata.PackageNotFoundError:
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# Case where package metadata is not available.
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__version__ = ""
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del metadata # optional, avoids polluting the results of dir(__package__)
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__all__ = [
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"ChatOllama",
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"OllamaLLM",
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"OllamaEmbeddings",
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"__version__",
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]
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719
libs/partners/ollama/langchain_ollama/chat_models.py
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719
libs/partners/ollama/langchain_ollama/chat_models.py
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"""Ollama chat models."""
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Literal,
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Mapping,
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Optional,
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Sequence,
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Type,
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Union,
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cast,
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)
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from uuid import uuid4
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import ollama
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from langchain_core.callbacks import (
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CallbackManagerForLLMRun,
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)
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from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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HumanMessage,
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SystemMessage,
|
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ToolCall,
|
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ToolMessage,
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)
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from langchain_core.messages.ai import UsageMetadata
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from langchain_core.messages.tool import tool_call
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import Runnable
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from langchain_core.tools import BaseTool
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from ollama import AsyncClient, Message, Options
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def _get_usage_metadata_from_generation_info(
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generation_info: Optional[Mapping[str, Any]],
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) -> Optional[UsageMetadata]:
|
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"""Get usage metadata from ollama generation info mapping."""
|
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if generation_info is None:
|
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return None
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input_tokens: Optional[int] = generation_info.get("prompt_eval_count")
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output_tokens: Optional[int] = generation_info.get("eval_count")
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if input_tokens is not None and output_tokens is not None:
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return UsageMetadata(
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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total_tokens=input_tokens + output_tokens,
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)
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return None
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|
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|
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def _get_tool_calls_from_response(
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response: Mapping[str, Any],
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) -> List[ToolCall]:
|
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"""Get tool calls from ollama response."""
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tool_calls = []
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if "message" in response:
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if "tool_calls" in response["message"]:
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for tc in response["message"]["tool_calls"]:
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tool_calls.append(
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tool_call(
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id=str(uuid4()),
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name=tc["function"]["name"],
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args=tc["function"]["arguments"],
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)
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)
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return tool_calls
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def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
|
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return {
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"type": "function",
|
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"id": tool_call["id"],
|
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"function": {
|
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"name": tool_call["name"],
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"arguments": tool_call["args"],
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},
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}
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class ChatOllama(BaseChatModel):
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"""Ollama chat model integration.
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Setup:
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Install ``langchain-ollama`` and download any models you want to use from ollama.
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.. code-block:: bash
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ollama pull mistral:v0.3
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pip install -U langchain-ollama
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Key init args — completion params:
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model: str
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Name of Ollama model to use.
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temperature: float
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Sampling temperature. Ranges from 0.0 to 1.0.
|
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num_predict: Optional[int]
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Max number of tokens to generate.
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|
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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.. code-block:: python
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from langchain_ollama import ChatOllama
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|
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llm = ChatOllama(
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model = "llama3",
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temperature = 0.8,
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num_predict = 256,
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# other params ...
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)
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Invoke:
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.. code-block:: python
|
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|
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messages = [
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("system", "You are a helpful translator. Translate the user sentence to French."),
|
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("human", "I love programming."),
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]
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llm.invoke(messages)
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|
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.. code-block:: python
|
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|
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AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn\'t specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0')
|
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|
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Stream:
|
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.. code-block:: python
|
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|
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messages = [
|
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("human", "Return the words Hello World!"),
|
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]
|
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for chunk in llm.stream(messages):
|
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print(chunk)
|
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|
||||
|
||||
.. code-block:: python
|
||||
|
||||
content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
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content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
||||
content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
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content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
stream = llm.stream(messages)
|
||||
full = next(stream)
|
||||
for chunk in stream:
|
||||
full += chunk
|
||||
full
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890')
|
||||
|
||||
Async:
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("human", "Hello how are you!"),
|
||||
]
|
||||
await llm.ainvoke(messages)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0')
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("human", "Say hello world!"),
|
||||
]
|
||||
async for chunk in llm.astream(messages):
|
||||
print(chunk.content)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
HEL
|
||||
LO
|
||||
WORLD
|
||||
!
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
messages = [
|
||||
("human", "Say hello world!"),
|
||||
("human","Say goodbye world!")
|
||||
]
|
||||
await llm.abatch(messages)
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
[AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'),
|
||||
AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')]
|
||||
|
||||
JSON mode:
|
||||
.. code-block:: python
|
||||
|
||||
|
||||
json_llm = ChatOllama(format="json")
|
||||
messages = [
|
||||
("human", "Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only."),
|
||||
]
|
||||
llm.invoke(messages).content
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
'{"location": "Pune, India", "time_of_day": "morning"}'
|
||||
|
||||
Tool Calling:
|
||||
.. warning::
|
||||
Ollama currently does not support streaming for tools
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import ChatOllama
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field
|
||||
|
||||
class Multiply(BaseModel):
|
||||
a: int = Field(..., description="First integer")
|
||||
b: int = Field(..., description="Second integer")
|
||||
|
||||
ans = await chat.invoke("What is 45*67")
|
||||
ans.tool_calls
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
[{'name': 'Multiply',
|
||||
'args': {'a': 45, 'b': 67},
|
||||
'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
|
||||
'type': 'tool_call'}]
|
||||
""" # noqa: E501
|
||||
|
||||
model: str
|
||||
"""Model name to use."""
|
||||
|
||||
mirostat: Optional[int] = None
|
||||
"""Enable Mirostat sampling for controlling perplexity.
|
||||
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
|
||||
|
||||
mirostat_eta: Optional[float] = None
|
||||
"""Influences how quickly the algorithm responds to feedback
|
||||
from the generated text. A lower learning rate will result in
|
||||
slower adjustments, while a higher learning rate will make
|
||||
the algorithm more responsive. (Default: 0.1)"""
|
||||
|
||||
mirostat_tau: Optional[float] = None
|
||||
"""Controls the balance between coherence and diversity
|
||||
of the output. A lower value will result in more focused and
|
||||
coherent text. (Default: 5.0)"""
|
||||
|
||||
num_ctx: Optional[int] = None
|
||||
"""Sets the size of the context window used to generate the
|
||||
next token. (Default: 2048) """
|
||||
|
||||
num_gpu: Optional[int] = None
|
||||
"""The number of GPUs to use. On macOS it defaults to 1 to
|
||||
enable metal support, 0 to disable."""
|
||||
|
||||
num_thread: Optional[int] = None
|
||||
"""Sets the number of threads to use during computation.
|
||||
By default, Ollama will detect this for optimal performance.
|
||||
It is recommended to set this value to the number of physical
|
||||
CPU cores your system has (as opposed to the logical number of cores)."""
|
||||
|
||||
num_predict: Optional[int] = None
|
||||
"""Maximum number of tokens to predict when generating text.
|
||||
(Default: 128, -1 = infinite generation, -2 = fill context)"""
|
||||
|
||||
repeat_last_n: Optional[int] = None
|
||||
"""Sets how far back for the model to look back to prevent
|
||||
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
|
||||
|
||||
repeat_penalty: Optional[float] = None
|
||||
"""Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
|
||||
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
|
||||
will be more lenient. (Default: 1.1)"""
|
||||
|
||||
temperature: Optional[float] = None
|
||||
"""The temperature of the model. Increasing the temperature will
|
||||
make the model answer more creatively. (Default: 0.8)"""
|
||||
|
||||
stop: Optional[List[str]] = None
|
||||
"""Sets the stop tokens to use."""
|
||||
|
||||
tfs_z: Optional[float] = None
|
||||
"""Tail free sampling is used to reduce the impact of less probable
|
||||
tokens from the output. A higher value (e.g., 2.0) will reduce the
|
||||
impact more, while a value of 1.0 disables this setting. (default: 1)"""
|
||||
|
||||
top_k: Optional[int] = None
|
||||
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
|
||||
will give more diverse answers, while a lower value (e.g. 10)
|
||||
will be more conservative. (Default: 40)"""
|
||||
|
||||
top_p: Optional[float] = None
|
||||
"""Works together with top-k. A higher value (e.g., 0.95) will lead
|
||||
to more diverse text, while a lower value (e.g., 0.5) will
|
||||
generate more focused and conservative text. (Default: 0.9)"""
|
||||
|
||||
format: Literal["", "json"] = ""
|
||||
"""Specify the format of the output (options: json)"""
|
||||
|
||||
keep_alive: Optional[Union[int, str]] = None
|
||||
"""How long the model will stay loaded into memory."""
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Ollama."""
|
||||
return {
|
||||
"model": self.model,
|
||||
"format": self.format,
|
||||
"options": {
|
||||
"mirostat": self.mirostat,
|
||||
"mirostat_eta": self.mirostat_eta,
|
||||
"mirostat_tau": self.mirostat_tau,
|
||||
"num_ctx": self.num_ctx,
|
||||
"num_gpu": self.num_gpu,
|
||||
"num_thread": self.num_thread,
|
||||
"num_predict": self.num_predict,
|
||||
"repeat_last_n": self.repeat_last_n,
|
||||
"repeat_penalty": self.repeat_penalty,
|
||||
"temperature": self.temperature,
|
||||
"stop": self.stop,
|
||||
"tfs_z": self.tfs_z,
|
||||
"top_k": self.top_k,
|
||||
"top_p": self.top_p,
|
||||
},
|
||||
"keep_alive": self.keep_alive,
|
||||
}
|
||||
|
||||
def _convert_messages_to_ollama_messages(
|
||||
self, messages: List[BaseMessage]
|
||||
) -> Sequence[Message]:
|
||||
ollama_messages: List = []
|
||||
for message in messages:
|
||||
role = ""
|
||||
tool_call_id: Optional[str] = None
|
||||
tool_calls: Optional[List[Dict[str, Any]]] = None
|
||||
if isinstance(message, HumanMessage):
|
||||
role = "user"
|
||||
elif isinstance(message, AIMessage):
|
||||
role = "assistant"
|
||||
tool_calls = (
|
||||
[
|
||||
_lc_tool_call_to_openai_tool_call(tool_call)
|
||||
for tool_call in message.tool_calls
|
||||
]
|
||||
if message.tool_calls
|
||||
else None
|
||||
)
|
||||
elif isinstance(message, SystemMessage):
|
||||
role = "system"
|
||||
elif isinstance(message, ToolMessage):
|
||||
role = "tool"
|
||||
tool_call_id = message.tool_call_id
|
||||
else:
|
||||
raise ValueError("Received unsupported message type for Ollama.")
|
||||
|
||||
content = ""
|
||||
images = []
|
||||
if isinstance(message.content, str):
|
||||
content = message.content
|
||||
else:
|
||||
for content_part in cast(List[Dict], message.content):
|
||||
if content_part.get("type") == "text":
|
||||
content += f"\n{content_part['text']}"
|
||||
elif content_part.get("type") == "tool_use":
|
||||
continue
|
||||
elif content_part.get("type") == "image_url":
|
||||
image_url = None
|
||||
temp_image_url = content_part.get("image_url")
|
||||
if isinstance(temp_image_url, str):
|
||||
image_url = content_part["image_url"]
|
||||
elif (
|
||||
isinstance(temp_image_url, dict) and "url" in temp_image_url
|
||||
):
|
||||
image_url = temp_image_url
|
||||
else:
|
||||
raise ValueError(
|
||||
"Only string image_url or dict with string 'url' "
|
||||
"inside content parts are supported."
|
||||
)
|
||||
|
||||
image_url_components = image_url.split(",")
|
||||
# Support data:image/jpeg;base64,<image> format
|
||||
# and base64 strings
|
||||
if len(image_url_components) > 1:
|
||||
images.append(image_url_components[1])
|
||||
else:
|
||||
images.append(image_url_components[0])
|
||||
|
||||
else:
|
||||
raise ValueError(
|
||||
"Unsupported message content type. "
|
||||
"Must either have type 'text' or type 'image_url' "
|
||||
"with a string 'image_url' field."
|
||||
)
|
||||
msg = {
|
||||
"role": role,
|
||||
"content": content,
|
||||
"images": images,
|
||||
}
|
||||
if tool_call_id:
|
||||
msg["tool_call_id"] = tool_call_id
|
||||
if tool_calls:
|
||||
msg["tool_calls"] = tool_calls
|
||||
ollama_messages.append(msg)
|
||||
|
||||
return ollama_messages
|
||||
|
||||
async def _acreate_chat_stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
|
||||
ollama_messages = self._convert_messages_to_ollama_messages(messages)
|
||||
|
||||
stop = stop if stop is not None else self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
if "tools" in kwargs:
|
||||
yield await AsyncClient().chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=False,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
tools=kwargs["tools"],
|
||||
) # type:ignore
|
||||
else:
|
||||
async for part in await AsyncClient().chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
): # type:ignore
|
||||
yield part
|
||||
|
||||
def _create_chat_stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[Union[Mapping[str, Any], str]]:
|
||||
ollama_messages = self._convert_messages_to_ollama_messages(messages)
|
||||
|
||||
stop = stop if stop is not None else self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
if "tools" in kwargs:
|
||||
yield ollama.chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=False,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
tools=kwargs["tools"],
|
||||
)
|
||||
else:
|
||||
yield from ollama.chat(
|
||||
model=params["model"],
|
||||
messages=ollama_messages,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
)
|
||||
|
||||
def _chat_stream_with_aggregation(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> ChatGenerationChunk:
|
||||
final_chunk = None
|
||||
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
async def _achat_stream_with_aggregation(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> ChatGenerationChunk:
|
||||
final_chunk = None
|
||||
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
def _get_ls_params(
|
||||
self, stop: Optional[List[str]] = None, **kwargs: Any
|
||||
) -> LangSmithParams:
|
||||
"""Get standard params for tracing."""
|
||||
params = self._get_invocation_params(stop=stop, **kwargs)
|
||||
ls_params = LangSmithParams(
|
||||
ls_provider="ollama",
|
||||
ls_model_name=self.model,
|
||||
ls_model_type="chat",
|
||||
ls_temperature=params.get("temperature", self.temperature),
|
||||
)
|
||||
if ls_stop := stop or params.get("stop", None) or self.stop:
|
||||
ls_params["ls_stop"] = ls_stop
|
||||
return ls_params
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
final_chunk = self._chat_stream_with_aggregation(
|
||||
messages, stop, run_manager, verbose=self.verbose, **kwargs
|
||||
)
|
||||
generation_info = final_chunk.generation_info
|
||||
chat_generation = ChatGeneration(
|
||||
message=AIMessage(
|
||||
content=final_chunk.text,
|
||||
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
|
||||
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
|
||||
),
|
||||
generation_info=generation_info,
|
||||
)
|
||||
return ChatResult(generations=[chat_generation])
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(
|
||||
content=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
and "content" in stream_resp["message"]
|
||||
else ""
|
||||
),
|
||||
usage_metadata=_get_usage_metadata_from_generation_info(
|
||||
stream_resp
|
||||
),
|
||||
tool_calls=_get_tool_calls_from_response(stream_resp),
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
final_chunk = await self._achat_stream_with_aggregation(
|
||||
messages, stop, run_manager, verbose=self.verbose, **kwargs
|
||||
)
|
||||
generation_info = final_chunk.generation_info
|
||||
chat_generation = ChatGeneration(
|
||||
message=AIMessage(
|
||||
content=final_chunk.text,
|
||||
usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
|
||||
tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
|
||||
),
|
||||
generation_info=generation_info,
|
||||
)
|
||||
return ChatResult(generations=[chat_generation])
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of chat model."""
|
||||
return "chat-ollama"
|
||||
|
||||
def bind_tools(
|
||||
self,
|
||||
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
||||
**kwargs: Any,
|
||||
) -> Runnable[LanguageModelInput, BaseMessage]:
|
||||
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
||||
return super().bind(tools=formatted_tools, **kwargs)
|
||||
51
libs/partners/ollama/langchain_ollama/embeddings.py
Normal file
51
libs/partners/ollama/langchain_ollama/embeddings.py
Normal file
@@ -0,0 +1,51 @@
|
||||
from typing import List
|
||||
|
||||
import ollama
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.pydantic_v1 import BaseModel, Extra
|
||||
from ollama import AsyncClient
|
||||
|
||||
|
||||
class OllamaEmbeddings(BaseModel, Embeddings):
|
||||
"""OllamaEmbeddings embedding model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
|
||||
model = OllamaEmbeddings(model="llama3")
|
||||
embedder.embed_query("what is the place that jonathan worked at?")
|
||||
"""
|
||||
|
||||
model: str
|
||||
"""Model name to use."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Embed search docs."""
|
||||
embedded_docs = []
|
||||
for doc in texts:
|
||||
embedded_docs.append(list(ollama.embeddings(self.model, doc)["embedding"]))
|
||||
return embedded_docs
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Embed query text."""
|
||||
return self.embed_documents([text])[0]
|
||||
|
||||
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Embed search docs."""
|
||||
embedded_docs = []
|
||||
for doc in texts:
|
||||
embedded_docs.append(
|
||||
list((await AsyncClient().embeddings(self.model, doc))["embedding"])
|
||||
)
|
||||
return embedded_docs
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
"""Embed query text."""
|
||||
return (await self.aembed_documents([text]))[0]
|
||||
347
libs/partners/ollama/langchain_ollama/llms.py
Normal file
347
libs/partners/ollama/langchain_ollama/llms.py
Normal file
@@ -0,0 +1,347 @@
|
||||
"""Ollama large language models."""
|
||||
|
||||
from typing import (
|
||||
Any,
|
||||
AsyncIterator,
|
||||
Dict,
|
||||
Iterator,
|
||||
List,
|
||||
Literal,
|
||||
Mapping,
|
||||
Optional,
|
||||
Union,
|
||||
)
|
||||
|
||||
import ollama
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models import BaseLLM
|
||||
from langchain_core.outputs import GenerationChunk, LLMResult
|
||||
from ollama import AsyncClient, Options
|
||||
|
||||
|
||||
class OllamaLLM(BaseLLM):
|
||||
"""OllamaLLM large language models.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import OllamaLLM
|
||||
|
||||
model = OllamaLLM(model="llama3")
|
||||
model.invoke("Come up with 10 names for a song about parrots")
|
||||
"""
|
||||
|
||||
model: str
|
||||
"""Model name to use."""
|
||||
|
||||
mirostat: Optional[int] = None
|
||||
"""Enable Mirostat sampling for controlling perplexity.
|
||||
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
|
||||
|
||||
mirostat_eta: Optional[float] = None
|
||||
"""Influences how quickly the algorithm responds to feedback
|
||||
from the generated text. A lower learning rate will result in
|
||||
slower adjustments, while a higher learning rate will make
|
||||
the algorithm more responsive. (Default: 0.1)"""
|
||||
|
||||
mirostat_tau: Optional[float] = None
|
||||
"""Controls the balance between coherence and diversity
|
||||
of the output. A lower value will result in more focused and
|
||||
coherent text. (Default: 5.0)"""
|
||||
|
||||
num_ctx: Optional[int] = None
|
||||
"""Sets the size of the context window used to generate the
|
||||
next token. (Default: 2048) """
|
||||
|
||||
num_gpu: Optional[int] = None
|
||||
"""The number of GPUs to use. On macOS it defaults to 1 to
|
||||
enable metal support, 0 to disable."""
|
||||
|
||||
num_thread: Optional[int] = None
|
||||
"""Sets the number of threads to use during computation.
|
||||
By default, Ollama will detect this for optimal performance.
|
||||
It is recommended to set this value to the number of physical
|
||||
CPU cores your system has (as opposed to the logical number of cores)."""
|
||||
|
||||
num_predict: Optional[int] = None
|
||||
"""Maximum number of tokens to predict when generating text.
|
||||
(Default: 128, -1 = infinite generation, -2 = fill context)"""
|
||||
|
||||
repeat_last_n: Optional[int] = None
|
||||
"""Sets how far back for the model to look back to prevent
|
||||
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
|
||||
|
||||
repeat_penalty: Optional[float] = None
|
||||
"""Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
|
||||
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
|
||||
will be more lenient. (Default: 1.1)"""
|
||||
|
||||
temperature: Optional[float] = None
|
||||
"""The temperature of the model. Increasing the temperature will
|
||||
make the model answer more creatively. (Default: 0.8)"""
|
||||
|
||||
stop: Optional[List[str]] = None
|
||||
"""Sets the stop tokens to use."""
|
||||
|
||||
tfs_z: Optional[float] = None
|
||||
"""Tail free sampling is used to reduce the impact of less probable
|
||||
tokens from the output. A higher value (e.g., 2.0) will reduce the
|
||||
impact more, while a value of 1.0 disables this setting. (default: 1)"""
|
||||
|
||||
top_k: Optional[int] = None
|
||||
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
|
||||
will give more diverse answers, while a lower value (e.g. 10)
|
||||
will be more conservative. (Default: 40)"""
|
||||
|
||||
top_p: Optional[float] = None
|
||||
"""Works together with top-k. A higher value (e.g., 0.95) will lead
|
||||
to more diverse text, while a lower value (e.g., 0.5) will
|
||||
generate more focused and conservative text. (Default: 0.9)"""
|
||||
|
||||
format: Literal["", "json"] = ""
|
||||
"""Specify the format of the output (options: json)"""
|
||||
|
||||
keep_alive: Optional[Union[int, str]] = None
|
||||
"""How long the model will stay loaded into memory."""
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Ollama."""
|
||||
return {
|
||||
"model": self.model,
|
||||
"format": self.format,
|
||||
"options": {
|
||||
"mirostat": self.mirostat,
|
||||
"mirostat_eta": self.mirostat_eta,
|
||||
"mirostat_tau": self.mirostat_tau,
|
||||
"num_ctx": self.num_ctx,
|
||||
"num_gpu": self.num_gpu,
|
||||
"num_thread": self.num_thread,
|
||||
"num_predict": self.num_predict,
|
||||
"repeat_last_n": self.repeat_last_n,
|
||||
"repeat_penalty": self.repeat_penalty,
|
||||
"temperature": self.temperature,
|
||||
"stop": self.stop,
|
||||
"tfs_z": self.tfs_z,
|
||||
"top_k": self.top_k,
|
||||
"top_p": self.top_p,
|
||||
},
|
||||
"keep_alive": self.keep_alive,
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of LLM."""
|
||||
return "ollama-llm"
|
||||
|
||||
async def _acreate_generate_stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[Union[Mapping[str, Any], str]]:
|
||||
if self.stop is not None and stop is not None:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
elif self.stop is not None:
|
||||
stop = self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
async for part in await AsyncClient().generate(
|
||||
model=params["model"],
|
||||
prompt=prompt,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
): # type: ignore
|
||||
yield part
|
||||
|
||||
def _create_generate_stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[Union[Mapping[str, Any], str]]:
|
||||
if self.stop is not None and stop is not None:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
elif self.stop is not None:
|
||||
stop = self.stop
|
||||
|
||||
params = self._default_params
|
||||
|
||||
for key in self._default_params:
|
||||
if key in kwargs:
|
||||
params[key] = kwargs[key]
|
||||
|
||||
params["options"]["stop"] = stop
|
||||
yield from ollama.generate(
|
||||
model=params["model"],
|
||||
prompt=prompt,
|
||||
stream=True,
|
||||
options=Options(**params["options"]),
|
||||
keep_alive=params["keep_alive"],
|
||||
format=params["format"],
|
||||
)
|
||||
|
||||
async def _astream_with_aggregation(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> GenerationChunk:
|
||||
final_chunk = None
|
||||
async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=stream_resp["response"] if "response" in stream_resp else "",
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
def _stream_with_aggregation(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
verbose: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> GenerationChunk:
|
||||
final_chunk = None
|
||||
for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=stream_resp["response"] if "response" in stream_resp else "",
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if final_chunk is None:
|
||||
final_chunk = chunk
|
||||
else:
|
||||
final_chunk += chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
chunk=chunk,
|
||||
verbose=verbose,
|
||||
)
|
||||
if final_chunk is None:
|
||||
raise ValueError("No data received from Ollama stream.")
|
||||
|
||||
return final_chunk
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
generations = []
|
||||
for prompt in prompts:
|
||||
final_chunk = self._stream_with_aggregation(
|
||||
prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
verbose=self.verbose,
|
||||
**kwargs,
|
||||
)
|
||||
generations.append([final_chunk])
|
||||
return LLMResult(generations=generations) # type: ignore[arg-type]
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
prompts: List[str],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> LLMResult:
|
||||
generations = []
|
||||
for prompt in prompts:
|
||||
final_chunk = await self._astream_with_aggregation(
|
||||
prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
verbose=self.verbose,
|
||||
**kwargs,
|
||||
)
|
||||
generations.append([final_chunk])
|
||||
return LLMResult(generations=generations) # type: ignore[arg-type]
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
else ""
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[GenerationChunk]:
|
||||
async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
|
||||
if not isinstance(stream_resp, str):
|
||||
chunk = GenerationChunk(
|
||||
text=(
|
||||
stream_resp["message"]["content"]
|
||||
if "message" in stream_resp
|
||||
else ""
|
||||
),
|
||||
generation_info=(
|
||||
dict(stream_resp) if stream_resp.get("done") is True else None
|
||||
),
|
||||
)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
yield chunk
|
||||
0
libs/partners/ollama/langchain_ollama/py.typed
Normal file
0
libs/partners/ollama/langchain_ollama/py.typed
Normal file
879
libs/partners/ollama/poetry.lock
generated
Normal file
879
libs/partners/ollama/poetry.lock
generated
Normal file
@@ -0,0 +1,879 @@
|
||||
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
description = "Reusable constraint types to use with typing.Annotated"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
|
||||
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.4.0"
|
||||
description = "High level compatibility layer for multiple asynchronous event loop implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
|
||||
{file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
|
||||
idna = ">=2.8"
|
||||
sniffio = ">=1.1"
|
||||
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
|
||||
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
|
||||
trio = ["trio (>=0.23)"]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2024.7.4"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
|
||||
{file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.3.2"
|
||||
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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{file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
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||||
{file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
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||||
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afd7e57eddb1a54f0f1a974bc4391af8bcce0b444685d936840f125cf046d5bd"},
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||||
{file = "PyYAML-6.0.1-cp36-cp36m-win32.whl", hash = "sha256:fca0e3a251908a499833aa292323f32437106001d436eca0e6e7833256674585"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-win_amd64.whl", hash = "sha256:f22ac1c3cac4dbc50079e965eba2c1058622631e526bd9afd45fedd49ba781fa"},
|
||||
{file = "PyYAML-6.0.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b1275ad35a5d18c62a7220633c913e1b42d44b46ee12554e5fd39c70a243d6a3"},
|
||||
{file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18aeb1bf9a78867dc38b259769503436b7c72f7a1f1f4c93ff9a17de54319b27"},
|
||||
{file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:596106435fa6ad000c2991a98fa58eeb8656ef2325d7e158344fb33864ed87e3"},
|
||||
{file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baa90d3f661d43131ca170712d903e6295d1f7a0f595074f151c0aed377c9b9c"},
|
||||
{file = "PyYAML-6.0.1-cp37-cp37m-win32.whl", hash = "sha256:9046c58c4395dff28dd494285c82ba00b546adfc7ef001486fbf0324bc174fba"},
|
||||
{file = "PyYAML-6.0.1-cp37-cp37m-win_amd64.whl", hash = "sha256:4fb147e7a67ef577a588a0e2c17b6db51dda102c71de36f8549b6816a96e1867"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1d4c7e777c441b20e32f52bd377e0c409713e8bb1386e1099c2415f26e479595"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c8098ddcc2a85b61647b2590f825f3db38891662cfc2fc776415143f599bb859"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
|
||||
{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "requests"
|
||||
version = "2.32.3"
|
||||
description = "Python HTTP for Humans."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
|
||||
{file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
certifi = ">=2017.4.17"
|
||||
charset-normalizer = ">=2,<4"
|
||||
idna = ">=2.5,<4"
|
||||
urllib3 = ">=1.21.1,<3"
|
||||
|
||||
[package.extras]
|
||||
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
|
||||
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.1.15"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:5fe8d54df166ecc24106db7dd6a68d44852d14eb0729ea4672bb4d96c320b7df"},
|
||||
{file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6f0bfbb53c4b4de117ac4d6ddfd33aa5fc31beeaa21d23c45c6dd249faf9126f"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e0d432aec35bfc0d800d4f70eba26e23a352386be3a6cf157083d18f6f5881c8"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9405fa9ac0e97f35aaddf185a1be194a589424b8713e3b97b762336ec79ff807"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c66ec24fe36841636e814b8f90f572a8c0cb0e54d8b5c2d0e300d28a0d7bffec"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:6f8ad828f01e8dd32cc58bc28375150171d198491fc901f6f98d2a39ba8e3ff5"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86811954eec63e9ea162af0ffa9f8d09088bab51b7438e8b6488b9401863c25e"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fd4025ac5e87d9b80e1f300207eb2fd099ff8200fa2320d7dc066a3f4622dc6b"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b17b93c02cdb6aeb696effecea1095ac93f3884a49a554a9afa76bb125c114c1"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:ddb87643be40f034e97e97f5bc2ef7ce39de20e34608f3f829db727a93fb82c5"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:abf4822129ed3a5ce54383d5f0e964e7fef74a41e48eb1dfad404151efc130a2"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6c629cf64bacfd136c07c78ac10a54578ec9d1bd2a9d395efbee0935868bf852"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:1bab866aafb53da39c2cadfb8e1c4550ac5340bb40300083eb8967ba25481447"},
|
||||
{file = "ruff-0.1.15-py3-none-win32.whl", hash = "sha256:2417e1cb6e2068389b07e6fa74c306b2810fe3ee3476d5b8a96616633f40d14f"},
|
||||
{file = "ruff-0.1.15-py3-none-win_amd64.whl", hash = "sha256:3837ac73d869efc4182d9036b1405ef4c73d9b1f88da2413875e34e0d6919587"},
|
||||
{file = "ruff-0.1.15-py3-none-win_arm64.whl", hash = "sha256:9a933dfb1c14ec7a33cceb1e49ec4a16b51ce3c20fd42663198746efc0427360"},
|
||||
{file = "ruff-0.1.15.tar.gz", hash = "sha256:f6dfa8c1b21c913c326919056c390966648b680966febcb796cc9d1aaab8564e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sniffio"
|
||||
version = "1.3.1"
|
||||
description = "Sniff out which async library your code is running under"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
|
||||
{file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "syrupy"
|
||||
version = "4.6.1"
|
||||
description = "Pytest Snapshot Test Utility"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4"
|
||||
files = [
|
||||
{file = "syrupy-4.6.1-py3-none-any.whl", hash = "sha256:203e52f9cb9fa749cf683f29bd68f02c16c3bc7e7e5fe8f2fc59bdfe488ce133"},
|
||||
{file = "syrupy-4.6.1.tar.gz", hash = "sha256:37a835c9ce7857eeef86d62145885e10b3cb9615bc6abeb4ce404b3f18e1bb36"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pytest = ">=7.0.0,<9.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.5.0"
|
||||
description = "Retry code until it succeeds"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
|
||||
{file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
doc = ["reno", "sphinx"]
|
||||
test = ["pytest", "tornado (>=4.5)", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
description = "A lil' TOML parser"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
|
||||
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.12.2"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.2"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
|
||||
{file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
|
||||
h2 = ["h2 (>=4,<5)"]
|
||||
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
|
||||
zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "832fbfff0734b5ca736b8175526b0f548100c280954b0323720e2e3da8430488"
|
||||
90
libs/partners/ollama/pyproject.toml
Normal file
90
libs/partners/ollama/pyproject.toml
Normal file
@@ -0,0 +1,90 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-ollama"
|
||||
version = "0.1.0rc0"
|
||||
description = "An integration package connecting Ollama and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
license = "MIT"
|
||||
|
||||
[tool.poetry.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/ollama"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.8.1,<4.0"
|
||||
ollama = ">=0.3.0,<1"
|
||||
langchain-core = "^0.2.20"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.4.3"
|
||||
pytest-asyncio = "^0.23.2"
|
||||
syrupy = "^4.0.2"
|
||||
pytest-socket = "^0.7.0"
|
||||
langchain-core = { path = "../../core", develop = true }
|
||||
langchain-standard-tests = { path = "../../standard-tests", develop = true }
|
||||
|
||||
[tool.poetry.group.codespell]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.codespell.dependencies]
|
||||
codespell = "^2.2.6"
|
||||
|
||||
[tool.poetry.group.test_integration]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
|
||||
[tool.poetry.group.lint]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.1.8"
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^1.7.1"
|
||||
langchain-core = { path = "../../core", develop = true }
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
langchain-core = { path = "../../core", develop = true }
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = [
|
||||
"E", # pycodestyle
|
||||
"F", # pyflakes
|
||||
"I", # isort
|
||||
"T201", # print
|
||||
]
|
||||
|
||||
[tool.mypy]
|
||||
disallow_untyped_defs = "True"
|
||||
|
||||
[tool.coverage.run]
|
||||
omit = ["tests/*"]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
# --strict-markers will raise errors on unknown marks.
|
||||
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
|
||||
#
|
||||
# https://docs.pytest.org/en/7.1.x/reference/reference.html
|
||||
# --strict-config any warnings encountered while parsing the `pytest`
|
||||
# section of the configuration file raise errors.
|
||||
#
|
||||
# https://github.com/tophat/syrupy
|
||||
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
|
||||
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
|
||||
# Registering custom markers.
|
||||
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
|
||||
markers = [
|
||||
"compile: mark placeholder test used to compile integration tests without running them",
|
||||
]
|
||||
asyncio_mode = "auto"
|
||||
17
libs/partners/ollama/scripts/check_imports.py
Normal file
17
libs/partners/ollama/scripts/check_imports.py
Normal file
@@ -0,0 +1,17 @@
|
||||
import sys
|
||||
import traceback
|
||||
from importlib.machinery import SourceFileLoader
|
||||
|
||||
if __name__ == "__main__":
|
||||
files = sys.argv[1:]
|
||||
has_failure = False
|
||||
for file in files:
|
||||
try:
|
||||
SourceFileLoader("x", file).load_module()
|
||||
except Exception:
|
||||
has_faillure = True
|
||||
print(file) # noqa: T201
|
||||
traceback.print_exc()
|
||||
print() # noqa: T201
|
||||
|
||||
sys.exit(1 if has_failure else 0)
|
||||
27
libs/partners/ollama/scripts/check_pydantic.sh
Executable file
27
libs/partners/ollama/scripts/check_pydantic.sh
Executable file
@@ -0,0 +1,27 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# This script searches for lines starting with "import pydantic" or "from pydantic"
|
||||
# in tracked files within a Git repository.
|
||||
#
|
||||
# Usage: ./scripts/check_pydantic.sh /path/to/repository
|
||||
|
||||
# Check if a path argument is provided
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: $0 /path/to/repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
repository_path="$1"
|
||||
|
||||
# Search for lines matching the pattern within the specified repository
|
||||
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
|
||||
|
||||
# Check if any matching lines were found
|
||||
if [ -n "$result" ]; then
|
||||
echo "ERROR: The following lines need to be updated:"
|
||||
echo "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
18
libs/partners/ollama/scripts/lint_imports.sh
Executable file
18
libs/partners/ollama/scripts/lint_imports.sh
Executable file
@@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
# Initialize a variable to keep track of errors
|
||||
errors=0
|
||||
|
||||
# make sure not importing from langchain, langchain_experimental, or langchain_community
|
||||
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1))
|
||||
|
||||
# Decide on an exit status based on the errors
|
||||
if [ "$errors" -gt 0 ]; then
|
||||
exit 1
|
||||
else
|
||||
exit 0
|
||||
fi
|
||||
0
libs/partners/ollama/tests/__init__.py
Normal file
0
libs/partners/ollama/tests/__init__.py
Normal file
@@ -0,0 +1,17 @@
|
||||
"""Test chat model integration."""
|
||||
|
||||
from typing import Type
|
||||
|
||||
from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
|
||||
|
||||
from langchain_ollama.chat_models import ChatOllama
|
||||
|
||||
|
||||
class TestChatOllama(ChatModelIntegrationTests):
|
||||
@property
|
||||
def chat_model_class(self) -> Type[ChatOllama]:
|
||||
return ChatOllama
|
||||
|
||||
@property
|
||||
def chat_model_params(self) -> dict:
|
||||
return {"model": "llama3-groq-tool-use"}
|
||||
@@ -0,0 +1,7 @@
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.compile
|
||||
def test_placeholder() -> None:
|
||||
"""Used for compiling integration tests without running any real tests."""
|
||||
pass
|
||||
@@ -0,0 +1,20 @@
|
||||
"""Test Ollama embeddings."""
|
||||
|
||||
from langchain_ollama.embeddings import OllamaEmbeddings
|
||||
|
||||
|
||||
def test_langchain_ollama_embedding_documents() -> None:
|
||||
"""Test cohere embeddings."""
|
||||
documents = ["foo bar"]
|
||||
embedding = OllamaEmbeddings(model="llama3")
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 1
|
||||
assert len(output[0]) > 0
|
||||
|
||||
|
||||
def test_langchain_ollama_embedding_query() -> None:
|
||||
"""Test cohere embeddings."""
|
||||
document = "foo bar"
|
||||
embedding = OllamaEmbeddings(model="llama3")
|
||||
output = embedding.embed_query(document)
|
||||
assert len(output) > 0
|
||||
66
libs/partners/ollama/tests/integration_tests/test_llms.py
Normal file
66
libs/partners/ollama/tests/integration_tests/test_llms.py
Normal file
@@ -0,0 +1,66 @@
|
||||
"""Test OllamaLLM llm."""
|
||||
|
||||
from langchain_ollama.llms import OllamaLLM
|
||||
|
||||
MODEL_NAME = "llama3"
|
||||
|
||||
|
||||
def test_stream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
for token in llm.stream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_astream() -> None:
|
||||
"""Test streaming tokens from OpenAI."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
async for token in llm.astream("I'm Pickle Rick"):
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_abatch() -> None:
|
||||
"""Test streaming tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_abatch_tags() -> None:
|
||||
"""Test batch tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = await llm.abatch(
|
||||
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
|
||||
)
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
def test_batch() -> None:
|
||||
"""Test batch tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
|
||||
for token in result:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
async def test_ainvoke() -> None:
|
||||
"""Test invoke tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
def test_invoke() -> None:
|
||||
"""Test invoke tokens from OllamaLLM."""
|
||||
llm = OllamaLLM(model=MODEL_NAME)
|
||||
|
||||
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
|
||||
assert isinstance(result, str)
|
||||
0
libs/partners/ollama/tests/unit_tests/__init__.py
Normal file
0
libs/partners/ollama/tests/unit_tests/__init__.py
Normal file
17
libs/partners/ollama/tests/unit_tests/test_chat_models.py
Normal file
17
libs/partners/ollama/tests/unit_tests/test_chat_models.py
Normal file
@@ -0,0 +1,17 @@
|
||||
"""Test chat model integration."""
|
||||
|
||||
from typing import Dict, Type
|
||||
|
||||
from langchain_standard_tests.unit_tests import ChatModelUnitTests
|
||||
|
||||
from langchain_ollama.chat_models import ChatOllama
|
||||
|
||||
|
||||
class TestChatOllama(ChatModelUnitTests):
|
||||
@property
|
||||
def chat_model_class(self) -> Type[ChatOllama]:
|
||||
return ChatOllama
|
||||
|
||||
@property
|
||||
def chat_model_params(self) -> Dict:
|
||||
return {"model": "llama3-groq-tool-use"}
|
||||
8
libs/partners/ollama/tests/unit_tests/test_embeddings.py
Normal file
8
libs/partners/ollama/tests/unit_tests/test_embeddings.py
Normal file
@@ -0,0 +1,8 @@
|
||||
"""Test embedding model integration."""
|
||||
|
||||
from langchain_ollama.embeddings import OllamaEmbeddings
|
||||
|
||||
|
||||
def test_initialization() -> None:
|
||||
"""Test embedding model initialization."""
|
||||
OllamaEmbeddings(model="llama3")
|
||||
12
libs/partners/ollama/tests/unit_tests/test_imports.py
Normal file
12
libs/partners/ollama/tests/unit_tests/test_imports.py
Normal file
@@ -0,0 +1,12 @@
|
||||
from langchain_ollama import __all__
|
||||
|
||||
EXPECTED_ALL = [
|
||||
"OllamaLLM",
|
||||
"ChatOllama",
|
||||
"OllamaEmbeddings",
|
||||
"__version__",
|
||||
]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
8
libs/partners/ollama/tests/unit_tests/test_llms.py
Normal file
8
libs/partners/ollama/tests/unit_tests/test_llms.py
Normal file
@@ -0,0 +1,8 @@
|
||||
"""Test Ollama Chat API wrapper."""
|
||||
|
||||
from langchain_ollama import OllamaLLM
|
||||
|
||||
|
||||
def test_initialization() -> None:
|
||||
"""Test integration initialization."""
|
||||
OllamaLLM(model="llama3")
|
||||
Reference in New Issue
Block a user