9514bc4d67
core[minor], langchain[patch], openai[minor], anthropic[minor], fireworks[minor], groq[minor], mistralai[minor]
```python
class ToolCall(TypedDict):
name: str
args: Dict[str, Any]
id: Optional[str]
class InvalidToolCall(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
error: Optional[str]
class ToolCallChunk(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
index: Optional[int]
class AIMessage(BaseMessage):
...
tool_calls: List[ToolCall] = []
invalid_tool_calls: List[InvalidToolCall] = []
...
class AIMessageChunk(AIMessage, BaseMessageChunk):
...
tool_call_chunks: Optional[List[ToolCallChunk]] = None
...
```
Important considerations:
- Parsing logic occurs within different providers;
- ~Changing output type is a breaking change for anyone doing explicit
type checking;~
- ~Langsmith rendering will need to be updated:
https://github.com/langchain-ai/langchainplus/pull/3561~
- ~Langserve will need to be updated~
- Adding chunks:
- ~AIMessage + ToolCallsMessage = ToolCallsMessage if either has
non-null .tool_calls.~
- Tool call chunks are appended, merging when having equal values of
`index`.
- additional_kwargs accumulate the normal way.
- During streaming:
- ~Messages can change types (e.g., from AIMessageChunk to
AIToolCallsMessageChunk)~
- Output parsers parse additional_kwargs (during .invoke they read off
tool calls).
Packages outside of `partners/`:
- https://github.com/langchain-ai/langchain-cohere/pull/7
- https://github.com/langchain-ai/langchain-google/pull/123/files
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
|
||
|---|---|---|
| .. | ||
| langchain_groq | ||
| scripts | ||
| tests | ||
| .gitignore | ||
| LICENSE | ||
| Makefile | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
langchain-groq
Welcome to Groq! 🚀
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.
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:
- Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥
- Know the exact performance and compute time for any given workload 🔮
- Take advantage of our cutting-edge technology to stay ahead of the competition 💪
Want more Groq? Check out our website for more resources and join our Discord community to connect with our developers!
Installation and Setup
Install the integration package:
pip install langchain-groq
Request an API key and set it as an environment variable
export GROQ_API_KEY=gsk_...
Chat Model
See a usage example.
Development
To develop the langchain-groq package, you'll need to follow these instructions:
Install dev dependencies
poetry install --with test,test_integration,lint,codespell
Build the package
poetry build
Run unit tests
Unit tests live in tests/unit_tests and SHOULD NOT require an internet connection or a valid API KEY. Run unit tests with
make tests
Run integration tests
Integration tests live in tests/integration_tests and require a connection to the Groq API and a valid API KEY.
make integration_tests
Lint & Format
Run additional tests and linters to ensure your code is up to standard.
make lint spell_check check_imports