langchain

Commit Graph

Author	SHA1	Message	Date
Jacob Lee	181a61982f	anthropic[minor]: Adds streaming tool call support for Anthropic (#22687 ) Preserves string content chunks for non tool call requests for convenience. One thing - Anthropic events look like this: ``` RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start') RawContentBlockDeltaEvent(delta=TextDelta(text='<thinking>\nThe', type='text_delta'), index=0, type='content_block_delta') RawContentBlockDeltaEvent(delta=TextDelta(text=' provide', type='text_delta'), index=0, type='content_block_delta') ... RawContentBlockStartEvent(content_block=ToolUseBlock(id='toolu_01GJ6x2ddcMG3psDNNe4eDqb', input={}, name='get_weather', type='tool_use'), index=1, type='content_block_start') RawContentBlockDeltaEvent(delta=InputJsonDelta(partial_json='', type='input_json_delta'), index=1, type='content_block_delta') ``` Note that `delta` has a `type` field. With this implementation, I'm dropping it because `merge_list` behavior will concatenate strings. We currently have `index` as a special field when merging lists, would it be worth adding `type` too? If so, what do we set as a context block chunk? `text` vs. `text_delta`/`tool_use` vs `input_json_delta`? CC @ccurme @efriis @baskaryan	3 months ago
ccurme	f32d57f6f0	anthropic: refactor streaming to use events api; add streaming usage metadata (#22628 ) - Refactor streaming to use raw events; - Add `stream_usage` class attribute and kwarg to stream methods that, if True, will include separate chunks in the stream containing usage metadata. There are two ways to implement streaming with anthropic's python sdk. They have slight differences in how they surface usage metadata. 1. [Use helper functions](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-helpers). This is what we are doing now. ```python count = 1 with client.messages.stream(params) as stream: for text in stream.text_stream: snapshot = stream.current_message_snapshot print(f"{count}: {snapshot.usage} -- {text}") count = count + 1 final_snapshot = stream.get_final_message() print(f"{count}: {final_snapshot.usage}") ``` ``` 1: Usage(input_tokens=8, output_tokens=1) -- Hello 2: Usage(input_tokens=8, output_tokens=1) -- ! 3: Usage(input_tokens=8, output_tokens=1) -- How 4: Usage(input_tokens=8, output_tokens=1) -- can 5: Usage(input_tokens=8, output_tokens=1) -- I 6: Usage(input_tokens=8, output_tokens=1) -- assist 7: Usage(input_tokens=8, output_tokens=1) -- you 8: Usage(input_tokens=8, output_tokens=1) -- today 9: Usage(input_tokens=8, output_tokens=1) -- ? 10: Usage(input_tokens=8, output_tokens=12) ``` To do this correctly, we need to emit a new chunk at the end of the stream containing the usage metadata. 2. [Handle raw events](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-responses) ```python stream = client.messages.create(params, stream=True) count = 1 for event in stream: print(f"{count}: {event}") count = count + 1 ``` ``` 1: RawMessageStartEvent(message=Message(id='msg_01Vdyov2kADZTXqSKkfNJXcS', content=[], model='claude-3-haiku-20240307', role='assistant', stop_reason=None, stop_sequence=None, type='message', usage=Usage(input_tokens=8, output_tokens=1)), type='message_start') 2: RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start') 3: RawContentBlockDeltaEvent(delta=TextDelta(text='Hello', type='text_delta'), index=0, type='content_block_delta') 4: RawContentBlockDeltaEvent(delta=TextDelta(text='!', type='text_delta'), index=0, type='content_block_delta') 5: RawContentBlockDeltaEvent(delta=TextDelta(text=' How', type='text_delta'), index=0, type='content_block_delta') 6: RawContentBlockDeltaEvent(delta=TextDelta(text=' can', type='text_delta'), index=0, type='content_block_delta') 7: RawContentBlockDeltaEvent(delta=TextDelta(text=' I', type='text_delta'), index=0, type='content_block_delta') 8: RawContentBlockDeltaEvent(delta=TextDelta(text=' assist', type='text_delta'), index=0, type='content_block_delta') 9: RawContentBlockDeltaEvent(delta=TextDelta(text=' you', type='text_delta'), index=0, type='content_block_delta') 10: RawContentBlockDeltaEvent(delta=TextDelta(text=' today', type='text_delta'), index=0, type='content_block_delta') 11: RawContentBlockDeltaEvent(delta=TextDelta(text='?', type='text_delta'), index=0, type='content_block_delta') 12: RawContentBlockStopEvent(index=0, type='content_block_stop') 13: RawMessageDeltaEvent(delta=Delta(stop_reason='end_turn', stop_sequence=None), type='message_delta', usage=MessageDeltaUsage(output_tokens=12)) 14: RawMessageStopEvent(type='message_stop') ``` Here we implement the second option, in part because it should make things easier when implementing streaming tool calls in the near future. This would add two new chunks to the stream-- one at the beginning and one at the end-- with blank content and containing usage metadata. We add kwargs to the stream methods and a class attribute allowing for this behavior to be toggled. I enabled it by default. If we merge this we can add the same kwargs / attribute to OpenAI. Usage: ```python from langchain_anthropic import ChatAnthropic model = ChatAnthropic( model="claude-3-haiku-20240307", temperature=0 ) full = None for chunk in model.stream("hi"): full = chunk if full is None else full + chunk print(chunk) print(f"\nFull: {full}") ``` ``` content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 0, 'total_tokens': 8} content='Hello' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content='!' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content=' How' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content=' can' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content=' I' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content=' assist' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content=' you' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content=' today' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content='?' id='run-8a20843f-25c7-4025-ad72-9add395899e3' content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 0, 'output_tokens': 12, 'total_tokens': 12} Full: content='Hello! How can I assist you today?' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 12, 'total_tokens': 20} ```	4 months ago
ccurme	e08879147b	Revert "anthropic: stream token usage" (#22624 ) Reverts langchain-ai/langchain#20180	4 months ago
Bagatur	0d495f3f63	anthropic: stream token usage (#20180 ) open to other ideas <img width="1181" alt="Screenshot 2024-04-08 at 5 34 08 PM" src="https://github.com/langchain-ai/langchain/assets/22008038/03eb11c4-5eb5-43e3-9109-a13f76098fa4"> --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>	4 months ago
Bagatur	678a19a5f7	infra: bump anthropic mypy 1 (#22373 )	4 months ago
Bagatur	6416d16d39	anthropic[patch]: Release 0.1.13, tool_choice support (#21773 )	4 months ago
ccurme	481d3855dc	patch: remove usage of llm, chat model __call__ (#20788 ) - `llm(prompt)` -> `llm.invoke(prompt)` - `llm(prompt=prompt` -> `llm.invoke(prompt)` (same with `messages=`) - `llm(prompt, callbacks=callbacks)` -> `llm.invoke(prompt, config={"callbacks": callbacks})` - `llm(prompt, kwargs)` -> `llm.invoke(prompt, kwargs)`	5 months ago
Eugene Yurtsev	7a7851aa06	anthropic[patch]: Handle empty text block (#20566 ) Handle empty text block	5 months ago
Bagatur	9514bc4d67	core[minor], ...: add tool calls message (#18947 ) 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>	6 months ago
Bagatur	6860450e48	anthropic[patch]: use anthropic 0.23 (#20022 )	6 months ago
Bagatur	209de0a561	anthropic[minor]: tool use (#20016 )	6 months ago
Erick Friis	a5bcddc738	anthropic[patch]: streaming param (#18819 )	7 months ago
Erick Friis	25c7d52140	anthropic[patch]: multimodal (#18517 ) - anthropic[minor]: claude 3 - x - x --------- Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>	7 months ago
Erick Friis	24f9c700f2	anthropic[minor]: claude 3 (#18508 )	7 months ago
Erick Friis	3b5bdbfee8	anthropic[minor]: package move (#17974 )	7 months ago
chyroc	86d27fd684	Fix: fix partners name typo in tests (#15066 ) <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - Description: a description of the change, - Issue: the issue # it fixes if applicable, - Dependencies: any dependencies required for this change, - Twitter handle: we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Ran <rccalman@gmail.com>	9 months ago
Erick Friis	8a3360edf6	anthropic: beta messages integration (#14928 )	9 months ago

17 Commits (9b3a025f9c806a6f8a00030c7058c689536ae5a0)