mirror of
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partner: Add groq partner integration and chat model (#17856)
Description: Add a Groq chat model issue: TODO Dependencies: groq Twitter handle: N/A
This commit is contained in:
Graden Rea
GitHub
1
libs/partners/groq/.gitignore
vendored
Normal file
1
libs/partners/groq/.gitignore
vendored
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@@ -0,0 +1 @@
|
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__pycache__
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21
libs/partners/groq/LICENSE
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21
libs/partners/groq/LICENSE
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@@ -0,0 +1,21 @@
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MIT License
|
||||
|
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Copyright (c) 2023 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
|
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SOFTWARE.
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57
libs/partners/groq/Makefile
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57
libs/partners/groq/Makefile
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@@ -0,0 +1,57 @@
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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 integration_tests: TEST_FILE=tests/integration_tests/
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test tests 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/groq --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=langchain_groq
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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 $(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_groq -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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70
libs/partners/groq/README.md
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70
libs/partners/groq/README.md
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@@ -0,0 +1,70 @@
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# langchain-groq
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## Welcome to Groq! 🚀
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At Groq, we've developed the world's first Language Processing Unit™, or LPU. The Groq LPU has a deterministic, single core streaming architecture that sets the standard for GenAI inference speed with predictable and repeatable performance for any given workload.
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Beyond the architecture, our software is designed to empower developers like you with the tools you need to create innovative, powerful AI applications. With Groq as your engine, you can:
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* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥
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* Know the exact performance and compute time for any given workload 🔮
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* Take advantage of our cutting-edge technology to stay ahead of the competition 💪
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Want more Groq? Check out our [website](https://groq.com) for more resources and join our [Discord community](https://discord.gg/JvNsBDKeCG) to connect with our developers!
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## Installation and Setup
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Install the integration package:
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```bash
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pip install langchain-groq
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```
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Request an [API key](https://wow.groq.com) and set it as an environment variable
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```bash
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export GROQ_API_KEY=gsk_...
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```
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## Chat Model
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See a [usage example](https://python.langchain.com/docs/integrations/chat/groq).
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## Development
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To develop the `langchain-groq` package, you'll need to follow these instructions:
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### Install dev dependencies
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```bash
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poetry install --with test,test_integration,lint,codespell
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```
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### Build the package
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```bash
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poetry build
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```
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### Run unit tests
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Unit tests live in `tests/unit_tests` and SHOULD NOT require an internet connection or a valid API KEY. Run unit tests with
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```bash
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make tests
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```
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### Run integration tests
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Integration tests live in `tests/integration_tests` and require a connection to the Groq API and a valid API KEY.
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```bash
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make integration_tests
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```
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### Lint & Format
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Run additional tests and linters to ensure your code is up to standard.
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```bash
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make lint spell_check check_imports
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```
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3
libs/partners/groq/langchain_groq/__init__.py
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3
libs/partners/groq/langchain_groq/__init__.py
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@@ -0,0 +1,3 @@
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from langchain_groq.chat_models import ChatGroq
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__all__ = ["ChatGroq"]
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505
libs/partners/groq/langchain_groq/chat_models.py
Normal file
505
libs/partners/groq/langchain_groq/chat_models.py
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"""Groq Chat wrapper."""
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from __future__ import annotations
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import os
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import warnings
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from typing import (
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Any,
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AsyncIterator,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Tuple,
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Type,
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Union,
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cast,
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)
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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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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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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ToolMessage,
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ToolMessageChunk,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
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from langchain_core.utils import (
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convert_to_secret_str,
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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class ChatGroq(BaseChatModel):
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"""`Groq` Chat large language models API.
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|
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To use, you should have the
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environment variable ``GROQ_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the groq.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatGroq
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groq = ChatGroq(model_name="mixtral-8x7b-32768")
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"""
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client: Any = Field(default=None, exclude=True) #: :meta private:
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model_name: str = Field(default="mixtral-8x7b-32768", alias="model")
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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groq_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `groq_API_KEY` if not provided."""
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groq_api_base: Optional[str] = Field(default=None, alias="base_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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# to support explicit proxy for Groq
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groq_proxy: Optional[str] = None
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request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
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default=None, alias="timeout"
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)
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"""Timeout for requests to Groq completion API. Can be float, httpx.Timeout or
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None."""
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max_retries: int = 2
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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default_headers: Union[Mapping[str, str], None] = None
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default_query: Union[Mapping[str, object], None] = None
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# Configure a custom httpx client. See the
|
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# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
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http_client: Union[Any, None] = None
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"""Optional httpx.Client."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
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"""Build extra kwargs from additional params that were passed in."""
|
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
|
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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warnings.warn(
|
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f"""WARNING! {field_name} is not default parameter.
|
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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|
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
|
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
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f"Instead they were passed in as part of `model_kwargs` parameter."
|
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)
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|
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values["model_kwargs"] = extra
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return values
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|
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@root_validator()
|
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def validate_environment(cls, values: Dict) -> Dict:
|
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"""Validate that api key and python package exists in environment."""
|
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if values["n"] < 1:
|
||||
raise ValueError("n must be at least 1.")
|
||||
if values["n"] > 1 and values["streaming"]:
|
||||
raise ValueError("n must be 1 when streaming.")
|
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|
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if values["temperature"] == 0:
|
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values["temperature"] = 1e-8
|
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|
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values["groq_api_key"] = convert_to_secret_str(
|
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get_from_dict_or_env(values, "groq_api_key", "GROQ_API_KEY")
|
||||
)
|
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values["groq_api_base"] = values["groq_api_base"] or os.getenv("GROQ_API_BASE")
|
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values["groq_proxy"] = values["groq_proxy"] = os.getenv("GROQ_PROXY")
|
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|
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client_params = {
|
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"api_key": values["groq_api_key"].get_secret_value(),
|
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"base_url": values["groq_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
|
||||
try:
|
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import groq
|
||||
|
||||
if not values.get("client"):
|
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values["client"] = groq.Groq(**client_params).chat.completions
|
||||
if not values.get("async_client"):
|
||||
values["async_client"] = groq.AsyncGroq(
|
||||
**client_params
|
||||
).chat.completions
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import groq python package. "
|
||||
"Please install it with `pip install groq`."
|
||||
)
|
||||
return values
|
||||
|
||||
#
|
||||
# Serializable class method overrides
|
||||
#
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"groq_api_key": "GROQ_API_KEY"}
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
"""Return whether this model can be serialized by Langchain."""
|
||||
return True
|
||||
|
||||
#
|
||||
# BaseChatModel method overrides
|
||||
#
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of model."""
|
||||
return "groq-chat"
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._stream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return generate_from_stream(stream_iter)
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {
|
||||
**params,
|
||||
**({"stream": stream} if stream is not None else {}),
|
||||
**kwargs,
|
||||
}
|
||||
response = self.client.create(messages=message_dicts, **params)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._astream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return await agenerate_from_stream(stream_iter)
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {
|
||||
**params,
|
||||
**({"stream": stream} if stream is not None else {}),
|
||||
**kwargs,
|
||||
}
|
||||
response = await self.async_client.create(messages=message_dicts, **params)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
for chunk in self.client.create(messages=message_dicts, **params):
|
||||
if not isinstance(chunk, dict):
|
||||
chunk = chunk.dict()
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
choice = chunk["choices"][0]
|
||||
chunk = _convert_delta_to_message_chunk(
|
||||
choice["delta"], default_chunk_class
|
||||
)
|
||||
generation_info = {}
|
||||
if finish_reason := choice.get("finish_reason"):
|
||||
generation_info["finish_reason"] = finish_reason
|
||||
logprobs = choice.get("logprobs")
|
||||
if logprobs:
|
||||
generation_info["logprobs"] = logprobs
|
||||
default_chunk_class = chunk.__class__
|
||||
chunk = ChatGenerationChunk(
|
||||
message=chunk, generation_info=generation_info or None
|
||||
)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
async for chunk in await self.async_client.create(
|
||||
messages=message_dicts, **params
|
||||
):
|
||||
if not isinstance(chunk, dict):
|
||||
chunk = chunk.dict()
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
choice = chunk["choices"][0]
|
||||
chunk = _convert_delta_to_message_chunk(
|
||||
choice["delta"], default_chunk_class
|
||||
)
|
||||
generation_info = {}
|
||||
if finish_reason := choice.get("finish_reason"):
|
||||
generation_info["finish_reason"] = finish_reason
|
||||
logprobs = choice.get("logprobs")
|
||||
if logprobs:
|
||||
generation_info["logprobs"] = logprobs
|
||||
default_chunk_class = chunk.__class__
|
||||
chunk = ChatGenerationChunk(
|
||||
message=chunk, generation_info=generation_info or None
|
||||
)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
token=chunk.text, chunk=chunk, logprobs=logprobs
|
||||
)
|
||||
|
||||
#
|
||||
# Internal methods
|
||||
#
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Groq API."""
|
||||
params = {
|
||||
"model": self.model_name,
|
||||
"stream": self.streaming,
|
||||
"n": self.n,
|
||||
"temperature": self.temperature,
|
||||
**self.model_kwargs,
|
||||
}
|
||||
if self.max_tokens is not None:
|
||||
params["max_tokens"] = self.max_tokens
|
||||
return params
|
||||
|
||||
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
|
||||
generations = []
|
||||
if not isinstance(response, dict):
|
||||
response = response.dict()
|
||||
for res in response["choices"]:
|
||||
message = _convert_dict_to_message(res["message"])
|
||||
generation_info = dict(finish_reason=res.get("finish_reason"))
|
||||
if "logprobs" in res:
|
||||
generation_info["logprobs"] = res["logprobs"]
|
||||
gen = ChatGeneration(
|
||||
message=message,
|
||||
generation_info=generation_info,
|
||||
)
|
||||
generations.append(gen)
|
||||
token_usage = response.get("usage", {})
|
||||
llm_output = {
|
||||
"token_usage": token_usage,
|
||||
"model_name": self.model_name,
|
||||
"system_fingerprint": response.get("system_fingerprint", ""),
|
||||
}
|
||||
return ChatResult(generations=generations, llm_output=llm_output)
|
||||
|
||||
def _create_message_dicts(
|
||||
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
||||
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
||||
params = self._default_params
|
||||
if stop is not None:
|
||||
if "stop" in params:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
params["stop"] = stop
|
||||
message_dicts = [_convert_message_to_dict(m) for m in messages]
|
||||
return message_dicts, params
|
||||
|
||||
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
||||
overall_token_usage: dict = {}
|
||||
system_fingerprint = None
|
||||
for output in llm_outputs:
|
||||
if output is None:
|
||||
# Happens in streaming
|
||||
continue
|
||||
token_usage = output["token_usage"]
|
||||
if token_usage is not None:
|
||||
for k, v in token_usage.items():
|
||||
if k in overall_token_usage:
|
||||
overall_token_usage[k] += v
|
||||
else:
|
||||
overall_token_usage[k] = v
|
||||
if system_fingerprint is None:
|
||||
system_fingerprint = output.get("system_fingerprint")
|
||||
combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
|
||||
if system_fingerprint:
|
||||
combined["system_fingerprint"] = system_fingerprint
|
||||
return combined
|
||||
|
||||
|
||||
#
|
||||
# Type conversion helpers
|
||||
#
|
||||
def _convert_message_to_dict(message: BaseMessage) -> dict:
|
||||
"""Convert a LangChain message to a dictionary.
|
||||
|
||||
Args:
|
||||
message: The LangChain message.
|
||||
|
||||
Returns:
|
||||
The dictionary.
|
||||
"""
|
||||
message_dict: Dict[str, Any]
|
||||
if isinstance(message, ChatMessage):
|
||||
message_dict = {"role": message.role, "content": message.content}
|
||||
elif isinstance(message, HumanMessage):
|
||||
message_dict = {"role": "user", "content": message.content}
|
||||
elif isinstance(message, AIMessage):
|
||||
message_dict = {"role": "assistant", "content": message.content}
|
||||
if "function_call" in message.additional_kwargs:
|
||||
message_dict["function_call"] = message.additional_kwargs["function_call"]
|
||||
# If function call only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
if "tool_calls" in message.additional_kwargs:
|
||||
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
|
||||
# If tool calls only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
elif isinstance(message, SystemMessage):
|
||||
message_dict = {"role": "system", "content": message.content}
|
||||
elif isinstance(message, FunctionMessage):
|
||||
message_dict = {
|
||||
"role": "function",
|
||||
"content": message.content,
|
||||
"name": message.name,
|
||||
}
|
||||
elif isinstance(message, ToolMessage):
|
||||
message_dict = {
|
||||
"role": "tool",
|
||||
"content": message.content,
|
||||
"tool_call_id": message.tool_call_id,
|
||||
}
|
||||
else:
|
||||
raise TypeError(f"Got unknown type {message}")
|
||||
if "name" in message.additional_kwargs:
|
||||
message_dict["name"] = message.additional_kwargs["name"]
|
||||
return message_dict
|
||||
|
||||
|
||||
def _convert_delta_to_message_chunk(
|
||||
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
|
||||
) -> BaseMessageChunk:
|
||||
role = cast(str, _dict.get("role"))
|
||||
content = cast(str, _dict.get("content") or "")
|
||||
additional_kwargs: Dict = {}
|
||||
if _dict.get("function_call"):
|
||||
function_call = dict(_dict["function_call"])
|
||||
if "name" in function_call and function_call["name"] is None:
|
||||
function_call["name"] = ""
|
||||
additional_kwargs["function_call"] = function_call
|
||||
if _dict.get("tool_calls"):
|
||||
additional_kwargs["tool_calls"] = _dict["tool_calls"]
|
||||
|
||||
if role == "user" or default_class == HumanMessageChunk:
|
||||
return HumanMessageChunk(content=content)
|
||||
elif role == "assistant" or default_class == AIMessageChunk:
|
||||
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
|
||||
elif role == "system" or default_class == SystemMessageChunk:
|
||||
return SystemMessageChunk(content=content)
|
||||
elif role == "function" or default_class == FunctionMessageChunk:
|
||||
return FunctionMessageChunk(content=content, name=_dict["name"])
|
||||
elif role == "tool" or default_class == ToolMessageChunk:
|
||||
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
|
||||
elif role or default_class == ChatMessageChunk:
|
||||
return ChatMessageChunk(content=content, role=role)
|
||||
else:
|
||||
return default_class(content=content) # type: ignore
|
||||
|
||||
|
||||
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
|
||||
"""Convert a dictionary to a LangChain message.
|
||||
|
||||
Args:
|
||||
_dict: The dictionary.
|
||||
|
||||
Returns:
|
||||
The LangChain message.
|
||||
"""
|
||||
role = _dict.get("role")
|
||||
if role == "user":
|
||||
return HumanMessage(content=_dict.get("content", ""))
|
||||
elif role == "assistant":
|
||||
content = _dict.get("content", "")
|
||||
additional_kwargs: Dict = {}
|
||||
if function_call := _dict.get("function_call"):
|
||||
additional_kwargs["function_call"] = dict(function_call)
|
||||
if tool_calls := _dict.get("tool_calls"):
|
||||
additional_kwargs["tool_calls"] = tool_calls
|
||||
return AIMessage(content=content, additional_kwargs=additional_kwargs)
|
||||
elif role == "system":
|
||||
return SystemMessage(content=_dict.get("content", ""))
|
||||
elif role == "function":
|
||||
return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name"))
|
||||
elif role == "tool":
|
||||
additional_kwargs = {}
|
||||
if "name" in _dict:
|
||||
additional_kwargs["name"] = _dict["name"]
|
||||
return ToolMessage(
|
||||
content=_dict.get("content", ""),
|
||||
tool_call_id=_dict.get("tool_call_id"),
|
||||
additional_kwargs=additional_kwargs,
|
||||
)
|
||||
else:
|
||||
return ChatMessage(content=_dict.get("content", ""), role=role)
|
||||
0
libs/partners/groq/langchain_groq/py.typed
Normal file
0
libs/partners/groq/langchain_groq/py.typed
Normal file
871
libs/partners/groq/poetry.lock
generated
Normal file
871
libs/partners/groq/poetry.lock
generated
Normal file
@@ -0,0 +1,871 @@
|
||||
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.6.0"
|
||||
description = "Reusable constraint types to use with typing.Annotated"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
|
||||
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.2.0"
|
||||
description = "High level compatibility layer for multiple asynchronous event loop implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
|
||||
{file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
|
||||
]
|
||||
|
||||
[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 = "2023.11.17"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
|
||||
{file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
|
||||
]
|
||||
|
||||
[[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"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"},
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||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"},
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|
||||
{file = "ruff-0.1.8-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b6a21ab023124eafb7cef6d038f835cb1155cd5ea798edd8d9eb2f8b84be07d9"},
|
||||
{file = "ruff-0.1.8-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:ce697c463458555027dfb194cb96d26608abab920fa85213deb5edf26e026664"},
|
||||
{file = "ruff-0.1.8-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:db6cedd9ffed55548ab313ad718bc34582d394e27a7875b4b952c2d29c001b26"},
|
||||
{file = "ruff-0.1.8-py3-none-musllinux_1_2_i686.whl", hash = "sha256:05ffe9dbd278965271252704eddb97b4384bf58b971054d517decfbf8c523f05"},
|
||||
{file = "ruff-0.1.8-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:5daaeaf00ae3c1efec9742ff294b06c3a2a9db8d3db51ee4851c12ad385cda30"},
|
||||
{file = "ruff-0.1.8-py3-none-win32.whl", hash = "sha256:e49fbdfe257fa41e5c9e13c79b9e79a23a79bd0e40b9314bc53840f520c2c0b3"},
|
||||
{file = "ruff-0.1.8-py3-none-win_amd64.whl", hash = "sha256:f41f692f1691ad87f51708b823af4bb2c5c87c9248ddd3191c8f088e66ce590a"},
|
||||
{file = "ruff-0.1.8-py3-none-win_arm64.whl", hash = "sha256:aa8ee4f8440023b0a6c3707f76cadce8657553655dcbb5fc9b2f9bb9bee389f6"},
|
||||
{file = "ruff-0.1.8.tar.gz", hash = "sha256:f7ee467677467526cfe135eab86a40a0e8db43117936ac4f9b469ce9cdb3fb62"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sniffio"
|
||||
version = "1.3.0"
|
||||
description = "Sniff out which async library your code is running under"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
|
||||
{file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.2.3"
|
||||
description = "Retry code until it succeeds"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"},
|
||||
{file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
doc = ["reno", "sphinx", "tornado (>=4.5)"]
|
||||
|
||||
[[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 = "4.9.0"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
|
||||
{file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.1.0"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
|
||||
{file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
|
||||
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
|
||||
zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "watchdog"
|
||||
version = "3.0.0"
|
||||
description = "Filesystem events monitoring"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:336adfc6f5cc4e037d52db31194f7581ff744b67382eb6021c868322e32eef41"},
|
||||
{file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a70a8dcde91be523c35b2bf96196edc5730edb347e374c7de7cd20c43ed95397"},
|
||||
{file = "watchdog-3.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:adfdeab2da79ea2f76f87eb42a3ab1966a5313e5a69a0213a3cc06ef692b0e96"},
|
||||
{file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b57a1e730af3156d13b7fdddfc23dea6487fceca29fc75c5a868beed29177ae"},
|
||||
{file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7ade88d0d778b1b222adebcc0927428f883db07017618a5e684fd03b83342bd9"},
|
||||
{file = "watchdog-3.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7e447d172af52ad204d19982739aa2346245cc5ba6f579d16dac4bfec226d2e7"},
|
||||
{file = "watchdog-3.0.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9fac43a7466eb73e64a9940ac9ed6369baa39b3bf221ae23493a9ec4d0022674"},
|
||||
{file = "watchdog-3.0.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:8ae9cda41fa114e28faf86cb137d751a17ffd0316d1c34ccf2235e8a84365c7f"},
|
||||
{file = "watchdog-3.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:25f70b4aa53bd743729c7475d7ec41093a580528b100e9a8c5b5efe8899592fc"},
|
||||
{file = "watchdog-3.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4f94069eb16657d2c6faada4624c39464f65c05606af50bb7902e036e3219be3"},
|
||||
{file = "watchdog-3.0.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7c5f84b5194c24dd573fa6472685b2a27cc5a17fe5f7b6fd40345378ca6812e3"},
|
||||
{file = "watchdog-3.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3aa7f6a12e831ddfe78cdd4f8996af9cf334fd6346531b16cec61c3b3c0d8da0"},
|
||||
{file = "watchdog-3.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:233b5817932685d39a7896b1090353fc8efc1ef99c9c054e46c8002561252fb8"},
|
||||
{file = "watchdog-3.0.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:13bbbb462ee42ec3c5723e1205be8ced776f05b100e4737518c67c8325cf6100"},
|
||||
{file = "watchdog-3.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8f3ceecd20d71067c7fd4c9e832d4e22584318983cabc013dbf3f70ea95de346"},
|
||||
{file = "watchdog-3.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:c9d8c8ec7efb887333cf71e328e39cffbf771d8f8f95d308ea4125bf5f90ba64"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0e06ab8858a76e1219e68c7573dfeba9dd1c0219476c5a44d5333b01d7e1743a"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:d00e6be486affb5781468457b21a6cbe848c33ef43f9ea4a73b4882e5f188a44"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:c07253088265c363d1ddf4b3cdb808d59a0468ecd017770ed716991620b8f77a"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:5113334cf8cf0ac8cd45e1f8309a603291b614191c9add34d33075727a967709"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:51f90f73b4697bac9c9a78394c3acbbd331ccd3655c11be1a15ae6fe289a8c83"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:ba07e92756c97e3aca0912b5cbc4e5ad802f4557212788e72a72a47ff376950d"},
|
||||
{file = "watchdog-3.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:d429c2430c93b7903914e4db9a966c7f2b068dd2ebdd2fa9b9ce094c7d459f33"},
|
||||
{file = "watchdog-3.0.0-py3-none-win32.whl", hash = "sha256:3ed7c71a9dccfe838c2f0b6314ed0d9b22e77d268c67e015450a29036a81f60f"},
|
||||
{file = "watchdog-3.0.0-py3-none-win_amd64.whl", hash = "sha256:4c9956d27be0bb08fc5f30d9d0179a855436e655f046d288e2bcc11adfae893c"},
|
||||
{file = "watchdog-3.0.0-py3-none-win_ia64.whl", hash = "sha256:5d9f3a10e02d7371cd929b5d8f11e87d4bad890212ed3901f9b4d68767bee759"},
|
||||
{file = "watchdog-3.0.0.tar.gz", hash = "sha256:4d98a320595da7a7c5a18fc48cb633c2e73cda78f93cac2ef42d42bf609a33f9"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
watchmedo = ["PyYAML (>=3.10)"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "672d4fc5b14a6506a6c37ebe445fbd88bb3ee5f0c2276b183e2b93c21d04c641"
|
||||
95
libs/partners/groq/pyproject.toml
Normal file
95
libs/partners/groq/pyproject.toml
Normal file
@@ -0,0 +1,95 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-groq"
|
||||
version = "0.0.1"
|
||||
description = "An integration package connecting Groq 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/groq"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.8.1,<4.0"
|
||||
langchain-core = "^0.1"
|
||||
groq = ">=0.4.1,<1"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.3.0"
|
||||
pytest-mock = "^3.10.0"
|
||||
pytest-watcher = "^0.3.4"
|
||||
pytest-asyncio = "^0.21.1"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.codespell]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.codespell.dependencies]
|
||||
codespell = "^2.2.0"
|
||||
|
||||
[tool.poetry.group.lint]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.1.5"
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^0.991"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.test_integration]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.ruff]
|
||||
select = [
|
||||
"E", # pycodestyle
|
||||
"F", # pyflakes
|
||||
"I", # isort
|
||||
"W", # Warnings
|
||||
]
|
||||
|
||||
[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.
|
||||
addopts = "--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",
|
||||
"scheduled: mark tests to run in scheduled testing",
|
||||
]
|
||||
filterwarnings = [
|
||||
"error",
|
||||
# Maintain support for pydantic 1.X
|
||||
'default:The `dict` method is deprecated; use `model_dump` instead.*:DeprecationWarning',
|
||||
]
|
||||
asyncio_mode = "auto"
|
||||
17
libs/partners/groq/scripts/check_imports.py
Normal file
17
libs/partners/groq/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)
|
||||
traceback.print_exc()
|
||||
print()
|
||||
|
||||
sys.exit(1 if has_failure else 0)
|
||||
27
libs/partners/groq/scripts/check_pydantic.sh
Executable file
27
libs/partners/groq/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 "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
17
libs/partners/groq/scripts/lint_imports.sh
Executable file
17
libs/partners/groq/scripts/lint_imports.sh
Executable file
@@ -0,0 +1,17 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
# Initialize a variable to keep track of errors
|
||||
errors=0
|
||||
|
||||
# make sure not importing from langchain or langchain_experimental
|
||||
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_experimental\.' . && 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/groq/tests/__init__.py
Normal file
0
libs/partners/groq/tests/__init__.py
Normal file
233
libs/partners/groq/tests/integration_tests/test_chat_models.py
Normal file
233
libs/partners/groq/tests/integration_tests/test_chat_models.py
Normal file
@@ -0,0 +1,233 @@
|
||||
"""Test ChatGroq chat model."""
|
||||
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from langchain_core.messages import (
|
||||
BaseMessage,
|
||||
BaseMessageChunk,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.outputs import ChatGeneration, LLMResult
|
||||
|
||||
from langchain_groq import ChatGroq
|
||||
from tests.unit_tests.fake.callbacks import (
|
||||
FakeCallbackHandler,
|
||||
FakeCallbackHandlerWithChatStart,
|
||||
)
|
||||
|
||||
|
||||
#
|
||||
# Smoke test Runnable interface
|
||||
#
|
||||
@pytest.mark.scheduled
|
||||
def test_invoke() -> None:
|
||||
"""Test Chat wrapper."""
|
||||
chat = ChatGroq(
|
||||
temperature=0.7,
|
||||
base_url=None,
|
||||
groq_proxy=None,
|
||||
timeout=10.0,
|
||||
max_retries=3,
|
||||
http_client=None,
|
||||
n=1,
|
||||
max_tokens=10,
|
||||
default_headers=None,
|
||||
default_query=None,
|
||||
)
|
||||
message = HumanMessage(content="Welcome to the Groqetship")
|
||||
response = chat.invoke([message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_ainvoke() -> None:
|
||||
"""Test ainvoke tokens from ChatGroq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
result = await llm.ainvoke("Welcome to the Groqetship!", config={"tags": ["foo"]})
|
||||
assert isinstance(result, BaseMessage)
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
def test_batch() -> None:
|
||||
"""Test batch tokens from ChatGroq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
result = llm.batch(["Hello!", "Welcome to the Groqetship!"])
|
||||
for token in result:
|
||||
assert isinstance(token, BaseMessage)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_abatch() -> None:
|
||||
"""Test abatch tokens from ChatGroq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
result = await llm.abatch(["Hello!", "Welcome to the Groqetship!"])
|
||||
for token in result:
|
||||
assert isinstance(token, BaseMessage)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_stream() -> None:
|
||||
"""Test streaming tokens from Groq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
for token in llm.stream("Welcome to the Groqetship!"):
|
||||
assert isinstance(token, BaseMessageChunk)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_astream() -> None:
|
||||
"""Test streaming tokens from Groq."""
|
||||
llm = ChatGroq(max_tokens=10)
|
||||
|
||||
async for token in llm.astream("Welcome to the Groqetship!"):
|
||||
assert isinstance(token, BaseMessageChunk)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
#
|
||||
# Test Legacy generate methods
|
||||
#
|
||||
@pytest.mark.scheduled
|
||||
def test_generate() -> None:
|
||||
"""Test sync generate."""
|
||||
n = 1
|
||||
chat = ChatGroq(max_tokens=10)
|
||||
message = HumanMessage(content="Hello", n=1)
|
||||
response = chat.generate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output
|
||||
assert response.llm_output["model_name"] == chat.model_name
|
||||
for generations in response.generations:
|
||||
assert len(generations) == n
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_agenerate() -> None:
|
||||
"""Test async generation."""
|
||||
n = 1
|
||||
chat = ChatGroq(max_tokens=10, n=1)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output
|
||||
assert response.llm_output["model_name"] == chat.model_name
|
||||
for generations in response.generations:
|
||||
assert len(generations) == n
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
#
|
||||
# Test streaming flags in invoke and generate
|
||||
#
|
||||
@pytest.mark.scheduled
|
||||
def test_invoke_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
chat = ChatGroq(
|
||||
max_tokens=2,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callbacks=[callback_handler],
|
||||
)
|
||||
message = HumanMessage(content="Welcome to the Groqetship")
|
||||
response = chat.invoke([message])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, BaseMessage)
|
||||
|
||||
|
||||
@pytest.mark.scheduled
|
||||
async def test_agenerate_streaming() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandlerWithChatStart()
|
||||
chat = ChatGroq(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
callbacks=[callback_handler],
|
||||
)
|
||||
message = HumanMessage(content="Welcome to the Groqetship")
|
||||
response = await chat.agenerate([[message], [message]])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 2
|
||||
assert response.llm_output is not None
|
||||
assert response.llm_output["model_name"] == chat.model_name
|
||||
for generations in response.generations:
|
||||
assert len(generations) == 1
|
||||
for generation in generations:
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
#
|
||||
# Misc tests
|
||||
#
|
||||
def test_streaming_generation_info() -> None:
|
||||
"""Test that generation info is preserved when streaming."""
|
||||
|
||||
class _FakeCallback(FakeCallbackHandler):
|
||||
saved_things: dict = {}
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
# Save the generation
|
||||
self.saved_things["generation"] = args[0]
|
||||
|
||||
callback = _FakeCallback()
|
||||
chat = ChatGroq(
|
||||
max_tokens=2,
|
||||
temperature=0,
|
||||
callbacks=[callback],
|
||||
)
|
||||
list(chat.stream("Respond with the single word Hello"))
|
||||
generation = callback.saved_things["generation"]
|
||||
# `Hello!` is two tokens, assert that that is what is returned
|
||||
assert isinstance(generation, LLMResult)
|
||||
assert generation.generations[0][0].text == "Hello"
|
||||
|
||||
|
||||
def test_system_message() -> None:
|
||||
"""Test ChatGroq wrapper with system message."""
|
||||
chat = ChatGroq(max_tokens=10)
|
||||
system_message = SystemMessage(content="You are to chat with the user.")
|
||||
human_message = HumanMessage(content="Hello")
|
||||
response = chat.invoke([system_message, human_message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
# Groq does not currently support N > 1
|
||||
# @pytest.mark.scheduled
|
||||
# def test_chat_multiple_completions() -> None:
|
||||
# """Test ChatGroq wrapper with multiple completions."""
|
||||
# chat = ChatGroq(max_tokens=10, n=5)
|
||||
# message = HumanMessage(content="Hello")
|
||||
# response = chat._generate([message])
|
||||
# assert isinstance(response, ChatResult)
|
||||
# assert len(response.generations) == 5
|
||||
# for generation in response.generations:
|
||||
# assert isinstance(generation.message, BaseMessage)
|
||||
# assert isinstance(generation.message.content, str)
|
||||
@@ -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
libs/partners/groq/tests/unit_tests/__init__.py
Normal file
0
libs/partners/groq/tests/unit_tests/__init__.py
Normal file
393
libs/partners/groq/tests/unit_tests/fake/callbacks.py
Normal file
393
libs/partners/groq/tests/unit_tests/fake/callbacks.py
Normal file
@@ -0,0 +1,393 @@
|
||||
"""A fake callback handler for testing purposes."""
|
||||
from itertools import chain
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
from uuid import UUID
|
||||
|
||||
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
|
||||
class BaseFakeCallbackHandler(BaseModel):
|
||||
"""Base fake callback handler for testing."""
|
||||
|
||||
starts: int = 0
|
||||
ends: int = 0
|
||||
errors: int = 0
|
||||
errors_args: List[Any] = []
|
||||
text: int = 0
|
||||
ignore_llm_: bool = False
|
||||
ignore_chain_: bool = False
|
||||
ignore_agent_: bool = False
|
||||
ignore_retriever_: bool = False
|
||||
ignore_chat_model_: bool = False
|
||||
|
||||
# to allow for similar callback handlers that are not technically equal
|
||||
fake_id: Union[str, None] = None
|
||||
|
||||
# add finer-grained counters for easier debugging of failing tests
|
||||
chain_starts: int = 0
|
||||
chain_ends: int = 0
|
||||
llm_starts: int = 0
|
||||
llm_ends: int = 0
|
||||
llm_streams: int = 0
|
||||
tool_starts: int = 0
|
||||
tool_ends: int = 0
|
||||
agent_actions: int = 0
|
||||
agent_ends: int = 0
|
||||
chat_model_starts: int = 0
|
||||
retriever_starts: int = 0
|
||||
retriever_ends: int = 0
|
||||
retriever_errors: int = 0
|
||||
retries: int = 0
|
||||
|
||||
|
||||
class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
|
||||
"""Base fake callback handler mixin for testing."""
|
||||
|
||||
def on_llm_start_common(self) -> None:
|
||||
self.llm_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_llm_end_common(self) -> None:
|
||||
self.llm_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_llm_error_common(self, *args: Any, **kwargs: Any) -> None:
|
||||
self.errors += 1
|
||||
self.errors_args.append({"args": args, "kwargs": kwargs})
|
||||
|
||||
def on_llm_new_token_common(self) -> None:
|
||||
self.llm_streams += 1
|
||||
|
||||
def on_retry_common(self) -> None:
|
||||
self.retries += 1
|
||||
|
||||
def on_chain_start_common(self) -> None:
|
||||
self.chain_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_chain_end_common(self) -> None:
|
||||
self.chain_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chain_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_tool_start_common(self) -> None:
|
||||
self.tool_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_tool_end_common(self) -> None:
|
||||
self.tool_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_tool_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_agent_action_common(self) -> None:
|
||||
self.agent_actions += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_agent_finish_common(self) -> None:
|
||||
self.agent_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chat_model_start_common(self) -> None:
|
||||
self.chat_model_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_text_common(self) -> None:
|
||||
self.text += 1
|
||||
|
||||
def on_retriever_start_common(self) -> None:
|
||||
self.starts += 1
|
||||
self.retriever_starts += 1
|
||||
|
||||
def on_retriever_end_common(self) -> None:
|
||||
self.ends += 1
|
||||
self.retriever_ends += 1
|
||||
|
||||
def on_retriever_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
self.retriever_errors += 1
|
||||
|
||||
|
||||
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
@property
|
||||
def ignore_retriever(self) -> bool:
|
||||
"""Whether to ignore retriever callbacks."""
|
||||
return self.ignore_retriever_
|
||||
|
||||
def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_start_common()
|
||||
|
||||
def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_end_common()
|
||||
|
||||
def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_error_common(*args, **kwargs)
|
||||
|
||||
def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_start_common()
|
||||
|
||||
def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_end_common()
|
||||
|
||||
def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_error_common()
|
||||
|
||||
def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_start_common()
|
||||
|
||||
def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_end_common()
|
||||
|
||||
def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_error_common()
|
||||
|
||||
def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_action_common()
|
||||
|
||||
def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_text_common()
|
||||
|
||||
def on_retriever_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_start_common()
|
||||
|
||||
def on_retriever_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_end_common()
|
||||
|
||||
def on_retriever_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_error_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":
|
||||
return self
|
||||
|
||||
|
||||
class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
|
||||
def on_chat_model_start(
|
||||
self,
|
||||
serialized: Dict[str, Any],
|
||||
messages: List[List[BaseMessage]],
|
||||
*,
|
||||
run_id: UUID,
|
||||
parent_run_id: Optional[UUID] = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
|
||||
self.on_chat_model_start_common()
|
||||
|
||||
|
||||
class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake async callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
async def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
async def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_start_common()
|
||||
|
||||
async def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
async def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_end_common()
|
||||
|
||||
async def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_llm_error_common(*args, **kwargs)
|
||||
|
||||
async def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_start_common()
|
||||
|
||||
async def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_end_common()
|
||||
|
||||
async def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_chain_error_common()
|
||||
|
||||
async def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_start_common()
|
||||
|
||||
async def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_end_common()
|
||||
|
||||
async def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_tool_error_common()
|
||||
|
||||
async def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_agent_action_common()
|
||||
|
||||
async def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
async def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
self.on_text_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler":
|
||||
return self
|
||||
207
libs/partners/groq/tests/unit_tests/test_chat_models.py
Normal file
207
libs/partners/groq/tests/unit_tests/test_chat_models.py
Normal file
@@ -0,0 +1,207 @@
|
||||
"""Test Groq Chat API wrapper."""
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Any
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import langchain_core.load as lc_load
|
||||
import pytest
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
FunctionMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
|
||||
from langchain_groq.chat_models import ChatGroq, _convert_dict_to_message
|
||||
|
||||
os.environ["GROQ_API_KEY"] = "fake-key"
|
||||
|
||||
|
||||
def test_groq_model_param() -> None:
|
||||
llm = ChatGroq(model="foo")
|
||||
assert llm.model_name == "foo"
|
||||
llm = ChatGroq(model_name="foo")
|
||||
assert llm.model_name == "foo"
|
||||
|
||||
|
||||
def test_function_message_dict_to_function_message() -> None:
|
||||
content = json.dumps({"result": "Example #1"})
|
||||
name = "test_function"
|
||||
result = _convert_dict_to_message(
|
||||
{
|
||||
"role": "function",
|
||||
"name": name,
|
||||
"content": content,
|
||||
}
|
||||
)
|
||||
assert isinstance(result, FunctionMessage)
|
||||
assert result.name == name
|
||||
assert result.content == content
|
||||
|
||||
|
||||
def test__convert_dict_to_message_human() -> None:
|
||||
message = {"role": "user", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = HumanMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_ai() -> None:
|
||||
message = {"role": "assistant", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = AIMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
def test__convert_dict_to_message_system() -> None:
|
||||
message = {"role": "system", "content": "foo"}
|
||||
result = _convert_dict_to_message(message)
|
||||
expected_output = SystemMessage(content="foo")
|
||||
assert result == expected_output
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_completion() -> dict:
|
||||
return {
|
||||
"id": "chatcmpl-7fcZavknQda3SQ",
|
||||
"object": "chat.completion",
|
||||
"created": 1689989000,
|
||||
"model": "test-model",
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "Bar Baz",
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def test_groq_invoke(mock_completion: dict) -> None:
|
||||
llm = ChatGroq()
|
||||
mock_client = MagicMock()
|
||||
completed = False
|
||||
|
||||
def mock_create(*args: Any, **kwargs: Any) -> Any:
|
||||
nonlocal completed
|
||||
completed = True
|
||||
return mock_completion
|
||||
|
||||
mock_client.create = mock_create
|
||||
with patch.object(
|
||||
llm,
|
||||
"client",
|
||||
mock_client,
|
||||
):
|
||||
res = llm.invoke("bar")
|
||||
assert res.content == "Bar Baz"
|
||||
assert type(res) == AIMessage
|
||||
assert completed
|
||||
|
||||
|
||||
async def test_groq_ainvoke(mock_completion: dict) -> None:
|
||||
llm = ChatGroq()
|
||||
mock_client = AsyncMock()
|
||||
completed = False
|
||||
|
||||
async def mock_create(*args: Any, **kwargs: Any) -> Any:
|
||||
nonlocal completed
|
||||
completed = True
|
||||
return mock_completion
|
||||
|
||||
mock_client.create = mock_create
|
||||
with patch.object(
|
||||
llm,
|
||||
"async_client",
|
||||
mock_client,
|
||||
):
|
||||
res = await llm.ainvoke("bar")
|
||||
assert res.content == "Bar Baz"
|
||||
assert type(res) == AIMessage
|
||||
assert completed
|
||||
|
||||
|
||||
def test_chat_groq_extra_kwargs() -> None:
|
||||
"""Test extra kwargs to chat groq."""
|
||||
# Check that foo is saved in extra_kwargs.
|
||||
with pytest.warns(UserWarning) as record:
|
||||
llm = ChatGroq(foo=3, max_tokens=10)
|
||||
assert llm.max_tokens == 10
|
||||
assert llm.model_kwargs == {"foo": 3}
|
||||
assert len(record) == 1
|
||||
assert type(record[0].message) is UserWarning
|
||||
assert "foo is not default parameter" in record[0].message.args[0]
|
||||
|
||||
# Test that if extra_kwargs are provided, they are added to it.
|
||||
with pytest.warns(UserWarning) as record:
|
||||
llm = ChatGroq(foo=3, model_kwargs={"bar": 2})
|
||||
assert llm.model_kwargs == {"foo": 3, "bar": 2}
|
||||
assert len(record) == 1
|
||||
assert type(record[0].message) is UserWarning
|
||||
assert "foo is not default parameter" in record[0].message.args[0]
|
||||
|
||||
# Test that if provided twice it errors
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(foo=3, model_kwargs={"foo": 2})
|
||||
|
||||
# Test that if explicit param is specified in kwargs it errors
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(model_kwargs={"temperature": 0.2})
|
||||
|
||||
# Test that "model" cannot be specified in kwargs
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(model_kwargs={"model": "test-model"})
|
||||
|
||||
|
||||
def test_chat_groq_invalid_streaming_params() -> None:
|
||||
"""Test that an error is raised if streaming is invoked with n>1."""
|
||||
with pytest.raises(ValueError):
|
||||
ChatGroq(
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
temperature=0,
|
||||
n=5,
|
||||
)
|
||||
|
||||
|
||||
def test_chat_groq_secret() -> None:
|
||||
"""Test that secret is not printed"""
|
||||
secret = "secretKey"
|
||||
not_secret = "safe"
|
||||
llm = ChatGroq(api_key=secret, model_kwargs={"not_secret": not_secret})
|
||||
stringified = str(llm)
|
||||
assert not_secret in stringified
|
||||
assert secret not in stringified
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:The function `loads` is in beta")
|
||||
def test_groq_serialization() -> None:
|
||||
"""Test that ChatGroq can be successfully serialized and deserialized"""
|
||||
api_key1 = "top secret"
|
||||
api_key2 = "topest secret"
|
||||
llm = ChatGroq(api_key=api_key1, temperature=0.5)
|
||||
dump = lc_load.dumps(llm)
|
||||
llm2 = lc_load.loads(
|
||||
dump,
|
||||
valid_namespaces=["langchain_groq"],
|
||||
secrets_map={"GROQ_API_KEY": api_key2},
|
||||
)
|
||||
|
||||
assert type(llm2) is ChatGroq
|
||||
|
||||
# Ensure api key wasn't dumped and instead was read from secret map.
|
||||
assert llm.groq_api_key is not None
|
||||
assert llm.groq_api_key.get_secret_value() not in dump
|
||||
assert llm2.groq_api_key is not None
|
||||
assert llm2.groq_api_key.get_secret_value() == api_key2
|
||||
|
||||
# Ensure a non-secret field was preserved
|
||||
assert llm.temperature == llm2.temperature
|
||||
|
||||
# Ensure a None was preserved
|
||||
assert llm.groq_api_base == llm2.groq_api_base
|
||||
7
libs/partners/groq/tests/unit_tests/test_imports.py
Normal file
7
libs/partners/groq/tests/unit_tests/test_imports.py
Normal file
@@ -0,0 +1,7 @@
|
||||
from langchain_groq import __all__
|
||||
|
||||
EXPECTED_ALL = ["ChatGroq"]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
Reference in New Issue
Block a user