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https://github.com/hwchase17/langchain.git
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community[patch]: Add maritalk streaming (sync and async) (#19203)
Co-authored-by: RosevalJr <rdmalajr@gmail.com> Co-authored-by: Roseval Donisete Malaquias Junior <roseval@maritaca.ai>
This commit is contained in:
Rodrigo Nogueira
GitHub
parent
cc6191cb90
commit
90f19028e5
@ -33,7 +33,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install langchain langchain-core langchain-community"
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"!pip install langchain langchain-core langchain-community httpx"
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]
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},
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{
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@ -89,6 +89,58 @@
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"print(response) # should answer something like \"1. Max\\n2. Bella\\n3. Charlie\\n4. Rocky\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Stream Generation\n",
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"\n",
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"For tasks involving the generation of long text, such as creating an extensive article or translating a large document, it can be advantageous to receive the response in parts, as the text is generated, instead of waiting for the complete text. This makes the application more responsive and efficient, especially when the generated text is extensive. We offer two approaches to meet this need: one synchronous and another asynchronous.\n",
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"\n",
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"#### Synchronous:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.messages import HumanMessage\n",
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"\n",
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"messages = [HumanMessage(content=\"Suggest 3 names for my dog\")]\n",
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"\n",
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"for chunk in llm.stream(messages):\n",
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" print(chunk.content, end=\"\", flush=True)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Asynchronous:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.messages import HumanMessage\n",
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"\n",
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"\n",
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"async def async_invoke_chain(animal: str):\n",
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" messages = [HumanMessage(content=f\"Suggest 3 names for my {animal}\")]\n",
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" async for chunk in llm._astream(messages):\n",
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" print(chunk.message.content, end=\"\", flush=True)\n",
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"\n",
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"\n",
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"await async_invoke_chain(\"dog\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -1,10 +1,21 @@
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import json
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from http import HTTPStatus
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from typing import Any, Dict, List, Optional, Union
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.chat_models import SimpleChatModel
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
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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 BaseChatModel
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import Field
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from requests import Response
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from requests.exceptions import HTTPError
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@ -34,7 +45,7 @@ class MaritalkHTTPError(HTTPError):
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return formatted_message
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class ChatMaritalk(SimpleChatModel):
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class ChatMaritalk(BaseChatModel):
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"""`MariTalk` Chat models API.
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This class allows interacting with the MariTalk chatbot API.
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@ -132,7 +143,6 @@ class ChatMaritalk(SimpleChatModel):
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If an error occurs (e.g., rate limiting), returns a string
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describing the error.
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"""
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try:
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url = "https://chat.maritaca.ai/api/chat/inference"
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headers = {"authorization": f"Key {self.api_key}"}
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stopping_tokens = stop if stop is not None else []
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@ -157,11 +167,195 @@ class ChatMaritalk(SimpleChatModel):
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else:
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raise MaritalkHTTPError(response)
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except requests.exceptions.RequestException as e:
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return f"An error occurred: {str(e)}"
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async def _acall(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""
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Asynchronously sends the parsed messages to the MariTalk API and returns
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the generated response or an error message.
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# Fallback return statement, in case of unexpected code paths
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return "An unexpected error occurred"
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This method makes an HTTP POST request to the MariTalk API with the
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provided messages and other parameters using async I/O.
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If the request is successful and the API returns a response,
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this method returns a string containing the answer.
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If the request is rate-limited or encounters another error,
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it returns a string with the error message.
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"""
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try:
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import httpx
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url = "https://chat.maritaca.ai/api/chat/inference"
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headers = {"authorization": f"Key {self.api_key}"}
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stopping_tokens = stop if stop is not None else []
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parsed_messages = self.parse_messages_for_model(messages)
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data = {
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"messages": parsed_messages,
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"model": self.model,
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"do_sample": self.do_sample,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"stopping_tokens": stopping_tokens,
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**kwargs,
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}
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async with httpx.AsyncClient() as client:
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response = await client.post(
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url, json=data, headers=headers, timeout=None
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)
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if response.status_code == 200:
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return response.json().get("answer", "No answer found")
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else:
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raise MaritalkHTTPError(response)
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except ImportError:
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raise ImportError(
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"Could not import httpx python package. "
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"Please install it with `pip install httpx`."
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)
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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headers = {"Authorization": f"Key {self.api_key}"}
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stopping_tokens = stop if stop is not None else []
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parsed_messages = self.parse_messages_for_model(messages)
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data = {
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"messages": parsed_messages,
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"model": self.model,
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"do_sample": self.do_sample,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"stopping_tokens": stopping_tokens,
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"stream": True,
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**kwargs,
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}
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response = requests.post(
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"https://chat.maritaca.ai/api/chat/inference",
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data=json.dumps(data),
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headers=headers,
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stream=True,
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)
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if response.ok:
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for line in response.iter_lines():
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if line.startswith(b"data: "):
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response_data = line.replace(b"data: ", b"").decode("utf-8")
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if response_data:
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parsed_data = json.loads(response_data)
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if "text" in parsed_data:
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delta = parsed_data["text"]
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chunk = ChatGenerationChunk(
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message=AIMessageChunk(content=delta)
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)
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if run_manager:
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run_manager.on_llm_new_token(delta, chunk=chunk)
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yield chunk
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else:
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raise MaritalkHTTPError(response)
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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try:
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import httpx
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headers = {"Authorization": f"Key {self.api_key}"}
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stopping_tokens = stop if stop is not None else []
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parsed_messages = self.parse_messages_for_model(messages)
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data = {
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"messages": parsed_messages,
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"model": self.model,
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"do_sample": self.do_sample,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"stopping_tokens": stopping_tokens,
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"stream": True,
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**kwargs,
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}
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async with httpx.AsyncClient() as client:
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async with client.stream(
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"POST",
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"https://chat.maritaca.ai/api/chat/inference",
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data=json.dumps(data),
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headers=headers,
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timeout=None,
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) as response:
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if response.status_code == 200:
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async for line in response.aiter_lines():
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if line.startswith("data: "):
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response_data = line.replace("data: ", "")
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if response_data:
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parsed_data = json.loads(response_data)
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if "text" in parsed_data:
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delta = parsed_data["text"]
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chunk = ChatGenerationChunk(
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message=AIMessageChunk(content=delta)
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)
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if run_manager:
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await run_manager.on_llm_new_token(
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delta, chunk=chunk
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)
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yield chunk
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else:
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raise MaritalkHTTPError(response)
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except ImportError:
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raise ImportError(
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"Could not import httpx python package. "
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"Please install it with `pip install httpx`."
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)
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
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message = AIMessage(content=output_str)
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generation = ChatGeneration(message=message)
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return ChatResult(generations=[generation])
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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output_str = await self._acall(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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message = AIMessage(content=output_str)
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generation = ChatGeneration(message=message)
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return ChatResult(generations=[generation])
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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