From d62775d4084f84873e8ecf2bd7436cf4fe53c61c Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Thu, 18 Jan 2024 16:33:30 -0800 Subject: [PATCH 01/17] Update to use langgraph and langchain 0.1 - for now still using pregel api, that will change in a future pr --- backend/app/storage.py | 17 +- .../agent_executor/checkpoint.py | 17 +- .../agent-executor/agent_executor/dnd.py | 11 +- .../agent-executor/agent_executor/ingest.py | 8 +- .../agent_executor/permchain.py | 12 +- .../agent-executor/agent_executor/upload.py | 6 +- .../packages/agent-executor/pyproject.toml | 2 +- .../gizmo_agent/agent_types/openai.py | 2 +- .../gizmo_agent/agent_types/xml/agent.py | 2 +- .../gizmo-agent/gizmo_agent/ingest.py | 6 +- .../packages/gizmo-agent/gizmo_agent/main.py | 7 +- .../packages/gizmo-agent/gizmo_agent/tools.py | 18 +- backend/packages/gizmo-agent/poetry.lock | 126 +++++++++-- backend/packages/gizmo-agent/pyproject.toml | 4 +- backend/poetry.lock | 206 +++++++++++------- backend/pyproject.toml | 4 +- backend/requirements.txt | 4 +- 17 files changed, 304 insertions(+), 148 deletions(-) diff --git a/backend/app/storage.py b/backend/app/storage.py index 10b0b559..ca8e5784 100644 --- a/backend/app/storage.py +++ b/backend/app/storage.py @@ -6,8 +6,9 @@ from agent_executor.checkpoint import RedisCheckpoint from langchain.schema.messages import AnyMessage from langchain.utilities.redis import get_client -from permchain.channels import Topic -from permchain.channels.base import ChannelsManager, create_checkpoint +from langgraph.channels import Topic +from langgraph.channels.base import ChannelsManager +from langgraph.checkpoint.base import empty_checkpoint from redis.client import Redis as RedisType from app.schema import Assistant, AssistantWithoutUserId, Thread, ThreadWithoutUserId @@ -148,9 +149,8 @@ def get_thread(user_id: str, thread_id: str) -> Thread | None: def get_thread_messages(user_id: str, thread_id: str): """Get all messages for a thread.""" client = RedisCheckpoint() - checkpoint = client.get( - {"configurable": {"user_id": user_id, "thread_id": thread_id}} - ) + config = {"configurable": {"user_id": user_id, "thread_id": thread_id}} + checkpoint = client.get(config) or empty_checkpoint() # TODO replace hardcoded messages channel with # channel extracted from agent with ChannelsManager( @@ -163,16 +163,15 @@ def post_thread_messages(user_id: str, thread_id: str, messages: Sequence[AnyMes """Add messages to a thread.""" client = RedisCheckpoint() config = {"configurable": {"user_id": user_id, "thread_id": thread_id}} - checkpoint = client.get(config) + checkpoint = client.get(config) or empty_checkpoint() # TODO replace hardcoded messages channel with # channel extracted from agent with ChannelsManager( {"messages": Topic(AnyMessage, accumulate=True)}, checkpoint ) as channels: channels["messages"].update(messages) - checkpoint = { - k: v for k, v in create_checkpoint(channels).items() if k == "messages" - } + checkpoint["channel_versions"]["messages"] += 1 + checkpoint["channel_values"]["messages"] = channels["messages"].checkpoint() client.put(config, checkpoint) diff --git a/backend/packages/agent-executor/agent_executor/checkpoint.py b/backend/packages/agent-executor/agent_executor/checkpoint.py index a2c5ddca..ca07cca2 100644 --- a/backend/packages/agent-executor/agent_executor/checkpoint.py +++ b/backend/packages/agent-executor/agent_executor/checkpoint.py @@ -1,13 +1,14 @@ import os import pickle from functools import partial -from typing import Any, Mapping +from typing import Any from langchain.pydantic_v1 import Field from langchain.schema.runnable import RunnableConfig from langchain.schema.runnable.utils import ConfigurableFieldSpec from langchain.utilities.redis import get_client -from permchain.checkpoint.base import BaseCheckpointAdapter +from langgraph.checkpoint import BaseCheckpointSaver +from langgraph.checkpoint.base import Checkpoint from redis.client import Redis as RedisType @@ -26,7 +27,7 @@ def _load(mapping: dict[bytes, bytes]) -> dict: } -class RedisCheckpoint(BaseCheckpointAdapter): +class RedisCheckpoint(BaseCheckpointSaver): client: RedisType = Field( default_factory=partial(get_client, os.environ.get("REDIS_URL")) ) @@ -60,8 +61,12 @@ def _hash_key(self, config: RunnableConfig) -> str: config["configurable"]["user_id"], config["configurable"]["thread_id"] ) - def get(self, config: RunnableConfig) -> Mapping[str, Any] | None: - return _load(self.client.hgetall(self._hash_key(config))) + def get(self, config: RunnableConfig) -> Checkpoint | None: + value = _load(self.client.hgetall(self._hash_key(config))) + if value.get("v") == 1: + return value + else: + return None - def put(self, config: RunnableConfig, checkpoint: Mapping[str, Any]) -> None: + def put(self, config: RunnableConfig, checkpoint: Checkpoint) -> None: return self.client.hmset(self._hash_key(config), _dump(checkpoint)) diff --git a/backend/packages/agent-executor/agent_executor/dnd.py b/backend/packages/agent-executor/agent_executor/dnd.py index 4fd39502..31b7c8cf 100644 --- a/backend/packages/agent-executor/agent_executor/dnd.py +++ b/backend/packages/agent-executor/agent_executor/dnd.py @@ -5,8 +5,9 @@ from langchain.utils.openai_functions import convert_pydantic_to_openai_function from langchain_core.language_models import BaseChatModel from langchain_core.messages import AIMessage, AnyMessage, HumanMessage -from permchain import BaseCheckpointAdapter, Channel, Pregel -from permchain.channels import LastValue, Topic +from langgraph.checkpoint import BaseCheckpointSaver +from langgraph.channels import LastValue, Topic +from langgraph.pregel import Channel, Pregel character_system_msg = """You are a dungeon master for a game of dungeons and dragons. @@ -86,7 +87,7 @@ def _maybe_update_character(message: AnyMessage): ) -def create_dnd_bot(llm: BaseChatModel, checkpoint: BaseCheckpointAdapter): +def create_dnd_bot(llm: BaseChatModel, checkpoint: BaseCheckpointSaver): character_model = llm.bind( functions=[convert_pydantic_to_openai_function(CharacterNotebook)], ) @@ -122,7 +123,7 @@ def _route_to_chain(_input): | _route_to_chain ) dnd = Pregel( - chains={"executor": executor, "update_state": state_chain}, + nodes={"executor": executor, "update_state": state_chain}, channels={ "messages": Topic(AnyMessage, accumulate=True), "character": LastValue(str), @@ -131,6 +132,6 @@ def _route_to_chain(_input): }, input=["messages"], output=["messages"], - checkpoint=checkpoint, + checkpointer=checkpoint, ) return dnd diff --git a/backend/packages/agent-executor/agent_executor/ingest.py b/backend/packages/agent-executor/agent_executor/ingest.py index 9570fd09..5c750892 100644 --- a/backend/packages/agent-executor/agent_executor/ingest.py +++ b/backend/packages/agent-executor/agent_executor/ingest.py @@ -8,10 +8,10 @@ """ from typing import List -from langchain.document_loaders import Blob -from langchain.document_loaders.base import BaseBlobParser -from langchain.schema import Document -from langchain.schema.vectorstore import VectorStore +from langchain_community.document_loaders import Blob +from langchain_community.document_loaders.base import BaseBlobParser +from langchain_core.documents import Document +from langchain_core.vectorstores import VectorStore from langchain.text_splitter import TextSplitter diff --git a/backend/packages/agent-executor/agent_executor/permchain.py b/backend/packages/agent-executor/agent_executor/permchain.py index bbf7d8e9..4c37152d 100644 --- a/backend/packages/agent-executor/agent_executor/permchain.py +++ b/backend/packages/agent-executor/agent_executor/permchain.py @@ -11,9 +11,9 @@ RunnablePassthrough, ) from langchain.tools import BaseTool -from permchain import Channel, Pregel, ReservedChannels -from permchain.channels import Topic -from permchain.checkpoint.base import BaseCheckpointAdapter +from langgraph.channels import Topic +from langgraph.checkpoint import BaseCheckpointSaver +from langgraph.pregel import Channel, Pregel, ReservedChannels def _create_agent_message( @@ -74,7 +74,7 @@ async def _arun_tool( def get_agent_executor( tools: list[BaseTool], agent: Runnable[dict[str, list[AnyMessage]], AgentAction | AgentFinish], - checkpoint: BaseCheckpointAdapter, + checkpoint: BaseCheckpointSaver, ) -> Pregel: tool_map = {tool.name: tool for tool in tools} tool_lambda = RunnableLambda(_run_tool, _arun_tool).bind(tools=tool_map) @@ -118,9 +118,9 @@ def route_last_message(input: dict[str, bool | Sequence[AnyMessage]]) -> Runnabl ) return Pregel( - chains={"executor": executor}, + nodes={"executor": executor}, channels={"messages": Topic(AnyMessage, accumulate=True)}, input=["messages"], output=["messages"], - checkpoint=checkpoint, + checkpointer=checkpoint, ) diff --git a/backend/packages/agent-executor/agent_executor/upload.py b/backend/packages/agent-executor/agent_executor/upload.py index 515548bd..e627c90d 100644 --- a/backend/packages/agent-executor/agent_executor/upload.py +++ b/backend/packages/agent-executor/agent_executor/upload.py @@ -10,9 +10,9 @@ from typing import Any, BinaryIO, List, Optional -from langchain.document_loaders.blob_loaders.schema import Blob -from langchain.schema.runnable import RunnableConfig, RunnableSerializable -from langchain.schema.vectorstore import VectorStore +from langchain_community.document_loaders.blob_loaders import Blob +from langchain_core.runnables import RunnableConfig, RunnableSerializable +from langchain_core.vectorstores import VectorStore from langchain.text_splitter import TextSplitter from agent_executor.ingest import ingest_blob diff --git a/backend/packages/agent-executor/pyproject.toml b/backend/packages/agent-executor/pyproject.toml index e37fd516..3be8ca52 100644 --- a/backend/packages/agent-executor/pyproject.toml +++ b/backend/packages/agent-executor/pyproject.toml @@ -8,7 +8,7 @@ authors = [ readme = "README.md" [tool.poetry.dependencies] -python = "^3.8.1" +python = "^3.9.0" langchain = ">=0.0.333" python-magic = "^0.4.27" diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py index 0e6c2a98..6dc0e9ac 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py +++ b/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py @@ -1,7 +1,7 @@ import os from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser -from langchain.chat_models import AzureChatOpenAI, ChatOpenAI +from langchain_openai import AzureChatOpenAI, ChatOpenAI from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain.tools.render import format_tool_to_openai_function diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py index 8ddea31d..18f17eac 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py +++ b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py @@ -1,7 +1,7 @@ import os import boto3 -from langchain.chat_models import BedrockChat, ChatAnthropic +from langchain_community.chat_models import BedrockChat, ChatAnthropic from langchain.schema.messages import AIMessage, HumanMessage from langchain.tools.render import render_text_description diff --git a/backend/packages/gizmo-agent/gizmo_agent/ingest.py b/backend/packages/gizmo-agent/gizmo_agent/ingest.py index e1e41c01..f8ce3210 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/ingest.py +++ b/backend/packages/gizmo-agent/gizmo_agent/ingest.py @@ -1,10 +1,10 @@ import os from agent_executor.upload import IngestRunnable -from langchain.embeddings import OpenAIEmbeddings -from langchain.schema.runnable import ConfigurableField from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain.vectorstores.redis import Redis +from langchain_openai import OpenAIEmbeddings +from langchain_community.vectorstores.redis import Redis +from langchain_core.runnables import ConfigurableField index_schema = { "tag": [{"name": "namespace"}], diff --git a/backend/packages/gizmo-agent/gizmo_agent/main.py b/backend/packages/gizmo-agent/gizmo_agent/main.py index 647e34ce..7df3add3 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/main.py +++ b/backend/packages/gizmo-agent/gizmo_agent/main.py @@ -3,15 +3,14 @@ from agent_executor.checkpoint import RedisCheckpoint from agent_executor.dnd import create_dnd_bot from agent_executor.permchain import get_agent_executor -from langchain.chat_models import ChatOpenAI +from langchain_openai import ChatOpenAI from langchain.pydantic_v1 import BaseModel, Field -from langchain.schema.messages import AnyMessage -from langchain.schema.runnable import ( +from langchain_core.messages import AnyMessage +from langchain_core.runnables import ( ConfigurableField, ConfigurableFieldMultiOption, RunnableBinding, ) - from gizmo_agent.agent_types import ( GizmoAgentType, get_openai_function_agent, diff --git a/backend/packages/gizmo-agent/gizmo_agent/tools.py b/backend/packages/gizmo-agent/gizmo_agent/tools.py index 29525dce..f7aa0e03 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/tools.py +++ b/backend/packages/gizmo-agent/gizmo_agent/tools.py @@ -1,14 +1,18 @@ from enum import Enum from langchain.pydantic_v1 import BaseModel, Field -from langchain.retrievers import KayAiRetriever, PubMedRetriever, WikipediaRetriever -from langchain.retrievers.you import YouRetriever -from langchain.tools import ArxivQueryRun, DuckDuckGoSearchRun +from langchain_community.retrievers import ( + KayAiRetriever, + PubMedRetriever, + WikipediaRetriever, +) +from langchain_community.retrievers.you import YouRetriever +from langchain_community.tools import ArxivQueryRun, DuckDuckGoSearchRun from langchain.tools.retriever import create_retriever_tool -from langchain.tools.tavily_search import TavilyAnswer, TavilySearchResults -from langchain.utilities import ArxivAPIWrapper -from langchain.utilities.tavily_search import TavilySearchAPIWrapper -from langchain.vectorstores.redis import RedisFilter +from langchain_community.tools.tavily_search import TavilyAnswer, TavilySearchResults +from langchain_community.utilities.arxiv import ArxivAPIWrapper +from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper +from langchain_community.vectorstores.redis import RedisFilter from gizmo_agent.ingest import vstore diff --git a/backend/packages/gizmo-agent/poetry.lock b/backend/packages/gizmo-agent/poetry.lock index b32d12b8..ad8d6a4a 100644 --- a/backend/packages/gizmo-agent/poetry.lock +++ b/backend/packages/gizmo-agent/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "aiofiles" @@ -144,9 +144,6 @@ files = [ {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 = "anthropic" version = "0.3.11" @@ -1311,6 +1308,30 @@ tomlkit = ">=0.12.2,<0.13.0" typer = {version = ">=0.9.0,<0.10.0", extras = ["all"]} uvicorn = ">=0.23.2,<0.24.0" +[[package]] +name = "langchain-core" +version = "0.1.12" +description = "Building applications with LLMs through composability" +optional = false +python-versions = ">=3.8.1,<4.0" +files = [ + {file = "langchain_core-0.1.12-py3-none-any.whl", hash = "sha256:d11c6262f7a9deff7de8fdf14498b8a951020dfed3a80f2358ab731ad04abef0"}, + {file = "langchain_core-0.1.12.tar.gz", hash = "sha256:f18e9300e9a07589b3e280e51befbc5a4513f535949406e55eb7a2dc40c3ce66"}, +] + +[package.dependencies] +anyio = ">=3,<5" +jsonpatch = ">=1.33,<2.0" +langsmith = ">=0.0.63,<0.1.0" +packaging = ">=23.2,<24.0" +pydantic = ">=1,<3" +PyYAML = ">=5.3" +requests = ">=2,<3" +tenacity = ">=8.1.0,<9.0.0" + +[package.extras] +extended-testing = ["jinja2 (>=3,<4)"] + [[package]] name = "langchain-experimental" version = "0.0.37" @@ -1328,6 +1349,23 @@ langchain = ">=0.0.308" [package.extras] extended-testing = ["faker (>=19.3.1,<20.0.0)", "presidio-analyzer (>=2.2.33,<3.0.0)", "presidio-anonymizer (>=2.2.33,<3.0.0)", "sentence-transformers (>=2,<3)", "vowpal-wabbit-next (==0.6.0)"] +[[package]] +name = "langchain-openai" +version = "0.0.2.post1" +description = "An integration package connecting OpenAI and LangChain" +optional = false +python-versions = ">=3.8.1,<4.0" +files = [ + {file = "langchain_openai-0.0.2.post1-py3-none-any.whl", hash = "sha256:ba468b94c23da9d8ccefe5d5a3c1c65b4b9702292523e53acc689a9110022e26"}, + {file = "langchain_openai-0.0.2.post1.tar.gz", hash = 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b/backend/requirements.txt index 2e3e3e08..9c832356 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -1,7 +1,7 @@ langchain-cli -e packages/gizmo-agent -e packages/agent-executor -langchain>=0.0.331 +langchain>=0.1.0 langserve>=0.0.23 -permchain +langgraph python-multipart From 43d61e619b8ca3dfd46ff6f4a000f57470e74443 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Thu, 18 Jan 2024 17:22:43 -0800 Subject: [PATCH 02/17] handle old checkpoint versions --- .../agent-executor/agent_executor/checkpoint.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/backend/packages/agent-executor/agent_executor/checkpoint.py b/backend/packages/agent-executor/agent_executor/checkpoint.py index ca07cca2..5e03c513 100644 --- a/backend/packages/agent-executor/agent_executor/checkpoint.py +++ b/backend/packages/agent-executor/agent_executor/checkpoint.py @@ -8,7 +8,7 @@ from langchain.schema.runnable.utils import ConfigurableFieldSpec from langchain.utilities.redis import get_client from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.checkpoint.base import Checkpoint +from langgraph.checkpoint.base import Checkpoint, empty_checkpoint from redis.client import Redis as RedisType @@ -64,8 +64,17 @@ def _hash_key(self, config: RunnableConfig) -> str: def get(self, config: RunnableConfig) -> Checkpoint | None: value = _load(self.client.hgetall(self._hash_key(config))) if value.get("v") == 1: + # langgraph version 1 return value + elif value.get("__pregel_version") == 1: + # permchain version 1 + value.pop("__pregel_version") + value.pop("__pregel_ts") + checkpoint = empty_checkpoint() + checkpoint["channel_values"] = value + return checkpoint else: + # unknown version return None def put(self, config: RunnableConfig, checkpoint: Checkpoint) -> None: From eb88e7daeed706e17316f49ffccf2a37fa0e4493 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Thu, 18 Jan 2024 17:45:41 -0800 Subject: [PATCH 03/17] Add versions --- backend/packages/agent-executor/agent_executor/checkpoint.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/backend/packages/agent-executor/agent_executor/checkpoint.py b/backend/packages/agent-executor/agent_executor/checkpoint.py index 5e03c513..5013b3b6 100644 --- a/backend/packages/agent-executor/agent_executor/checkpoint.py +++ b/backend/packages/agent-executor/agent_executor/checkpoint.py @@ -72,6 +72,8 @@ def get(self, config: RunnableConfig) -> Checkpoint | None: value.pop("__pregel_ts") checkpoint = empty_checkpoint() checkpoint["channel_values"] = value + for key in value: + checkpoint["channel_versions"][key] = 1 return checkpoint else: # unknown version From bd59e7f066dc4ef46fe7d57643b2f81639952081 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Thu, 25 Jan 2024 11:52:50 -0800 Subject: [PATCH 04/17] cr --- .../agent_executor/permchain.py | 125 +++++++++++------- .../gizmo_agent/agent_types/openai.py | 13 +- .../packages/gizmo-agent/gizmo_agent/main.py | 6 +- 3 files changed, 85 insertions(+), 59 deletions(-) diff --git a/backend/packages/agent-executor/agent_executor/permchain.py b/backend/packages/agent-executor/agent_executor/permchain.py index 4c37152d..3b997157 100644 --- a/backend/packages/agent-executor/agent_executor/permchain.py +++ b/backend/packages/agent-executor/agent_executor/permchain.py @@ -4,6 +4,7 @@ from langchain.schema.agent import AgentAction, AgentActionMessageLog, AgentFinish from langchain.schema.messages import AIMessage, AnyMessage, FunctionMessage +from langchain_core.language_models.base import LanguageModelLike from langchain.schema.runnable import ( Runnable, RunnableConfig, @@ -13,7 +14,12 @@ from langchain.tools import BaseTool from langgraph.channels import Topic from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.pregel import Channel, Pregel, ReservedChannels +from langgraph.graph.message import MessageGraph +from langgraph.graph import END +from langgraph.prebuilt import ToolExecutor +from langgraph.prebuilt import ToolInvocation + + def _create_agent_message( @@ -73,54 +79,75 @@ async def _arun_tool( def get_agent_executor( tools: list[BaseTool], - agent: Runnable[dict[str, list[AnyMessage]], AgentAction | AgentFinish], + llm: LanguageModelLike, checkpoint: BaseCheckpointSaver, -) -> Pregel: - tool_map = {tool.name: tool for tool in tools} - tool_lambda = RunnableLambda(_run_tool, _arun_tool).bind(tools=tool_map) - - tool_chain = itemgetter("messages") | tool_lambda | Channel.write_to("messages") - agent_chain = agent | _create_agent_message | Channel.write_to("messages") - - def route_last_message(input: dict[str, bool | Sequence[AnyMessage]]) -> Runnable: - if not input["messages"]: - # no messages, do nothing - return agent_chain - - message: AnyMessage = input["messages"][-1] - if isinstance(message.additional_kwargs.get("agent"), AgentFinish): - # finished, do nothing - return RunnablePassthrough() - - if input[ReservedChannels.is_last_step]: - # exhausted iterations without finishing, return stop message - return Channel.write_to( - messages=_create_agent_message( - AgentFinish( - { - "output": "Agent stopped due to iteration limit or time limit." - }, - "", - ) - ) - ) - - if isinstance(message.additional_kwargs.get("agent"), AgentAction): - # agent action, run it - return tool_chain - - # otherwise, run the agent - return agent_chain - - executor = ( - Channel.subscribe_to(["messages"]).join([ReservedChannels.is_last_step]) - | route_last_message +): + tool_executor = ToolExecutor(tools) + + # Define the function that determines whether to continue or not + def should_continue(messages): + last_message = messages[-1] + # If there is no function call, then we finish + if "function_call" not in last_message.additional_kwargs: + return "end" + # Otherwise if there is, we continue + else: + return "continue" + + # Define the function to execute tools + async def call_tool(messages): + # Based on the continue condition + # we know the last message involves a function call + last_message = messages[-1] + # We construct an ToolInvocation from the function_call + action = ToolInvocation( + tool=last_message.additional_kwargs["function_call"]["name"], + tool_input=json.loads(last_message.additional_kwargs["function_call"]["arguments"]), + ) + # We call the tool_executor and get back a response + response = await tool_executor.ainvoke(action) + # We use the response to create a FunctionMessage + function_message = FunctionMessage(content=str(response), name=action.tool) + # We return a list, because this will get added to the existing list + return function_message + + workflow = MessageGraph() + + # Define the two nodes we will cycle between + workflow.add_node("agent", llm) + workflow.add_node("action", call_tool) + + # Set the entrypoint as `agent` + # This means that this node is the first one called + workflow.set_entry_point("agent") + + # We now add a conditional edge + workflow.add_conditional_edges( + # First, we define the start node. We use `agent`. + # This means these are the edges taken after the `agent` node is called. + "agent", + # Next, we pass in the function that will determine which node is called next. + should_continue, + # Finally we pass in a mapping. + # The keys are strings, and the values are other nodes. + # END is a special node marking that the graph should finish. + # What will happen is we will call `should_continue`, and then the output of that + # will be matched against the keys in this mapping. + # Based on which one it matches, that node will then be called. + { + # If `tools`, then we call the tool node. + "continue": "action", + # Otherwise we finish. + "end": END + } ) - return Pregel( - nodes={"executor": executor}, - channels={"messages": Topic(AnyMessage, accumulate=True)}, - input=["messages"], - output=["messages"], - checkpointer=checkpoint, - ) + # We now add a normal edge from `tools` to `agent`. + # This means that after `tools` is called, `agent` node is called next. + workflow.add_edge('action', 'agent') + + # Finally, we compile it! + # This compiles it into a LangChain Runnable, + # meaning you can use it as you would any other runnable + app = workflow.compile(checkpointer=checkpoint) + return app diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py index 6dc0e9ac..9923d320 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py +++ b/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py @@ -4,6 +4,7 @@ from langchain_openai import AzureChatOpenAI, ChatOpenAI from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain.tools.render import format_tool_to_openai_function +from langchain_core.messages import SystemMessage def get_openai_function_agent( @@ -23,17 +24,15 @@ def get_openai_function_agent( openai_api_key=os.environ["AZURE_OPENAI_API_KEY"], streaming=True, ) - prompt = ChatPromptTemplate.from_messages( - [ - ("system", system_message), - MessagesPlaceholder(variable_name="messages"), - ] - ) + + def _get_messages(messages): + return [SystemMessage(content=system_message)] + messages + if tools: llm_with_tools = llm.bind( functions=[format_tool_to_openai_function(t) for t in tools] ) else: llm_with_tools = llm - agent = prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser() + agent = _get_messages | llm_with_tools return agent diff --git a/backend/packages/gizmo-agent/gizmo_agent/main.py b/backend/packages/gizmo-agent/gizmo_agent/main.py index 7df3add3..89480c29 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/main.py +++ b/backend/packages/gizmo-agent/gizmo_agent/main.py @@ -71,7 +71,7 @@ def __init__( else: raise ValueError("Unexpected agent type") agent_executor = get_agent_executor( - tools=_tools, agent=_agent, checkpoint=RedisCheckpoint() + tools=_tools, llm=_agent, checkpoint=RedisCheckpoint() ).with_config({"recursion_limit": 10}) super().__init__( tools=tools, @@ -152,9 +152,9 @@ class AgentOutput(BaseModel): from langchain.schema.messages import HumanMessage async def run(): - async for m in agent.astream_log( - {"messages": HumanMessage(content="whats your name")}, + async for m in agent.astream_events(HumanMessage(content="whats your name"), config={"configurable": {"user_id": "1", "thread_id": "test1"}}, + version="v1", ): print(m) From d5bb47be0f9dc030dfc1da640cba2bd43ae90f74 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Thu, 25 Jan 2024 18:05:20 -0800 Subject: [PATCH 05/17] cr --- .../gizmo_agent/main.py => app/agent.py} | 87 +- .../agent_types}/__init__.py | 0 .../agent_types/openai_agent.py} | 89 +- backend/app/agent_types/prompts.py | 27 + backend/app/agent_types/xml_agent.py | 144 + backend/app/api/runs.py | 2 +- .../agent_executor => app}/checkpoint.py | 0 .../agent_executor => app}/ingest.py | 0 .../agent_types/openai.py => app/llms.py} | 33 +- .../agent_executor => app}/parsing.py | 0 backend/app/server.py | 2 +- backend/app/storage.py | 2 +- .../gizmo-agent/gizmo_agent => app}/tools.py | 2 +- .../agent_executor => app}/upload.py | 34 +- backend/packages/README.md | 0 backend/packages/agent-executor/README.md | 1 - .../agent-executor/agent_executor/dnd.py | 137 - backend/packages/agent-executor/poetry.lock | 1615 --------- .../packages/agent-executor/pyproject.toml | 22 - backend/packages/gizmo-agent/README.md | 1 - .../gizmo-agent/gizmo_agent/__init__.py | 4 - .../gizmo_agent/agent_types/__init__.py | 19 - .../gizmo_agent/agent_types/xml/__init__.py | 0 .../gizmo_agent/agent_types/xml/agent.py | 73 - .../gizmo_agent/agent_types/xml/prompts.py | 50 - .../gizmo-agent/gizmo_agent/ingest.py | 29 - backend/packages/gizmo-agent/poetry.lock | 3119 ----------------- backend/packages/gizmo-agent/pyproject.toml | 39 - .../packages/gizmo-agent/tests/__init__.py | 0 .../unit_tests/agent_executor/test_parsing.py | 2 +- .../unit_tests/agent_executor/test_upload.py | 2 +- 31 files changed, 277 insertions(+), 5258 deletions(-) rename backend/{packages/gizmo-agent/gizmo_agent/main.py => app/agent.py} (60%) rename backend/{packages/agent-executor/agent_executor => app/agent_types}/__init__.py (100%) rename backend/{packages/agent-executor/agent_executor/permchain.py => app/agent_types/openai_agent.py} (59%) create mode 100644 backend/app/agent_types/prompts.py create mode 100644 backend/app/agent_types/xml_agent.py rename backend/{packages/agent-executor/agent_executor => app}/checkpoint.py (100%) rename backend/{packages/agent-executor/agent_executor => app}/ingest.py (100%) rename backend/{packages/gizmo-agent/gizmo_agent/agent_types/openai.py => app/llms.py} (50%) rename backend/{packages/agent-executor/agent_executor => app}/parsing.py (100%) rename backend/{packages/gizmo-agent/gizmo_agent => app}/tools.py (99%) rename backend/{packages/agent-executor/agent_executor => app}/upload.py (77%) delete mode 100644 backend/packages/README.md delete mode 100644 backend/packages/agent-executor/README.md delete mode 100644 backend/packages/agent-executor/agent_executor/dnd.py delete mode 100644 backend/packages/agent-executor/poetry.lock delete mode 100644 backend/packages/agent-executor/pyproject.toml delete mode 100644 backend/packages/gizmo-agent/README.md delete mode 100644 backend/packages/gizmo-agent/gizmo_agent/__init__.py delete mode 100644 backend/packages/gizmo-agent/gizmo_agent/agent_types/__init__.py delete mode 100644 backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/__init__.py delete mode 100644 backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py delete mode 100644 backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/prompts.py delete mode 100644 backend/packages/gizmo-agent/gizmo_agent/ingest.py delete mode 100644 backend/packages/gizmo-agent/poetry.lock delete mode 100644 backend/packages/gizmo-agent/pyproject.toml delete mode 100644 backend/packages/gizmo-agent/tests/__init__.py diff --git a/backend/packages/gizmo-agent/gizmo_agent/main.py b/backend/app/agent.py similarity index 60% rename from backend/packages/gizmo-agent/gizmo_agent/main.py rename to backend/app/agent.py index 89480c29..79889a25 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/main.py +++ b/backend/app/agent.py @@ -1,9 +1,9 @@ from typing import Any, Mapping, Optional, Sequence -from agent_executor.checkpoint import RedisCheckpoint -from agent_executor.dnd import create_dnd_bot -from agent_executor.permchain import get_agent_executor -from langchain_openai import ChatOpenAI +from app.checkpoint import RedisCheckpoint +from app.agent_types.openai_agent import get_openai_agent_executor +from app.agent_types.xml_agent import get_xml_agent_executor +from app.llms import get_openai_llm, get_anthropic_llm from langchain.pydantic_v1 import BaseModel, Field from langchain_core.messages import AnyMessage from langchain_core.runnables import ( @@ -11,12 +11,7 @@ ConfigurableFieldMultiOption, RunnableBinding, ) -from gizmo_agent.agent_types import ( - GizmoAgentType, - get_openai_function_agent, - # get_xml_agent, -) -from gizmo_agent.tools import ( +from app.tools import ( RETRIEVAL_DESCRIPTION, TOOL_OPTIONS, TOOLS, @@ -24,12 +19,22 @@ get_retrieval_tool, ) +from enum import Enum + + +class AgentType(str, Enum): + GPT_35_TURBO = "GPT 3.5 Turbo" + GPT_4 = "GPT 4" + AZURE_OPENAI = "GPT 4 (Azure OpenAI)" + CLAUDE2 = "Claude 2" + BEDROCK_CLAUDE2 = "Claude 2 (Amazon Bedrock)" + DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant." class ConfigurableAgent(RunnableBinding): tools: Sequence[str] - agent: GizmoAgentType + agent: AgentType system_message: str = DEFAULT_SYSTEM_MESSAGE retrieval_description: str = RETRIEVAL_DESCRIPTION assistant_id: Optional[str] = None @@ -39,7 +44,7 @@ def __init__( self, *, tools: Sequence[str], - agent: GizmoAgentType = GizmoAgentType.GPT_35_TURBO, + agent: AgentType = AgentType.GPT_35_TURBO, system_message: str = DEFAULT_SYSTEM_MESSAGE, assistant_id: Optional[str] = None, retrieval_description: str = RETRIEVAL_DESCRIPTION, @@ -58,21 +63,24 @@ def __init__( _tools.append(get_retrieval_tool(assistant_id, retrieval_description)) else: _tools.append(TOOLS[_tool]()) - if agent == GizmoAgentType.GPT_35_TURBO: - _agent = get_openai_function_agent(_tools, system_message) - # elif agent == GizmoAgentType.GPT_4: - # _agent = get_openai_function_agent(_tools, system_message, gpt_4=True) - # elif agent == GizmoAgentType.AZURE_OPENAI: - # _agent = get_openai_function_agent(_tools, system_message, azure=True) - # elif agent == GizmoAgentType.CLAUDE2: - # _agent = get_xml_agent(_tools, system_message) - # elif agent == GizmoAgentType.BEDROCK_CLAUDE2: - # _agent = get_xml_agent(_tools, system_message, bedrock=True) + if agent == AgentType.GPT_35_TURBO: + llm = get_openai_llm() + _agent = get_openai_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + elif agent == AgentType.GPT_4: + llm = get_openai_llm(gpt_4=True) + _agent = get_openai_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + elif agent == AgentType.AZURE_OPENAI: + llm = get_openai_llm(azure=True) + _agent = get_openai_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + elif agent == AgentType.CLAUDE2: + llm = get_anthropic_llm() + _agent = get_xml_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + elif agent == AgentType.BEDROCK_CLAUDE2: + llm = get_anthropic_llm(bedrock=True) + _agent = get_xml_agent_executor(_tools, llm, system_message, RedisCheckpoint()) else: raise ValueError("Unexpected agent type") - agent_executor = get_agent_executor( - tools=_tools, llm=_agent, checkpoint=RedisCheckpoint() - ).with_config({"recursion_limit": 10}) + agent_executor = _agent.with_config({"recursion_limit": 10}) super().__init__( tools=tools, agent=agent, @@ -92,30 +100,9 @@ class AgentOutput(BaseModel): messages: Sequence[AnyMessage] = Field(..., extra={"widget": {"type": "chat"}}) -dnd_llm = ChatOpenAI( - model="gpt-3.5-turbo-1106", temperature=0, streaming=True -).configurable_alternatives( - ConfigurableField(id="llm", name="LLM"), - default_key="gpt-35-turbo", - # azure_openai=AzureChatOpenAI( - # temperature=0, - # deployment_name=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], - # openai_api_base=os.environ["AZURE_OPENAI_API_BASE"], - # openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"], - # openai_api_key=os.environ["AZURE_OPENAI_API_KEY"], - # streaming=True, - # ), -) - - -dnd_bot = create_dnd_bot(dnd_llm, checkpoint=RedisCheckpoint()).with_types( - input_type=AgentInput, output_type=AgentOutput -) - - agent = ( ConfigurableAgent( - agent=GizmoAgentType.GPT_35_TURBO, + agent=AgentType.GPT_35_TURBO, tools=[], system_message=DEFAULT_SYSTEM_MESSAGE, retrieval_description=RETRIEVAL_DESCRIPTION, @@ -137,12 +124,6 @@ class AgentOutput(BaseModel): id="retrieval_description", name="Retrieval Description" ), ) - .configurable_alternatives( - ConfigurableField(id="type", name="Bot Type"), - default_key="agent", - prefix_keys=True, - dungeons_and_dragons=dnd_bot, - ) .with_types(input_type=AgentInput, output_type=AgentOutput) ) diff --git a/backend/packages/agent-executor/agent_executor/__init__.py b/backend/app/agent_types/__init__.py similarity index 100% rename from backend/packages/agent-executor/agent_executor/__init__.py rename to backend/app/agent_types/__init__.py diff --git a/backend/packages/agent-executor/agent_executor/permchain.py b/backend/app/agent_types/openai_agent.py similarity index 59% rename from backend/packages/agent-executor/agent_executor/permchain.py rename to backend/app/agent_types/openai_agent.py index 3b997157..017e39bc 100644 --- a/backend/packages/agent-executor/agent_executor/permchain.py +++ b/backend/app/agent_types/openai_agent.py @@ -1,18 +1,11 @@ import json -from operator import itemgetter -from typing import Sequence - -from langchain.schema.agent import AgentAction, AgentActionMessageLog, AgentFinish -from langchain.schema.messages import AIMessage, AnyMessage, FunctionMessage +from langchain.schema.messages import FunctionMessage from langchain_core.language_models.base import LanguageModelLike -from langchain.schema.runnable import ( - Runnable, - RunnableConfig, - RunnableLambda, - RunnablePassthrough, -) + +from langchain.tools.render import format_tool_to_openai_function +from langchain_core.messages import SystemMessage + from langchain.tools import BaseTool -from langgraph.channels import Topic from langgraph.checkpoint import BaseCheckpointSaver from langgraph.graph.message import MessageGraph from langgraph.graph import END @@ -20,68 +13,22 @@ from langgraph.prebuilt import ToolInvocation - - -def _create_agent_message( - output: AgentAction | AgentFinish -) -> list[AnyMessage] | AnyMessage: - if isinstance(output, AgentAction): - if isinstance(output, AgentActionMessageLog): - output.message_log[-1].additional_kwargs["agent"] = output - messages = output.message_log - output.message_log = [] # avoid circular reference for json dumps - return messages - else: - return AIMessage( - content=output.log, - additional_kwargs={"agent": output}, - ) - else: - return AIMessage( - content=output.return_values["output"], - additional_kwargs={"agent": output}, - ) - - -def _create_function_message( - agent_action: AgentAction, observation: str -) -> FunctionMessage: - if not isinstance(observation, str): - try: - content = json.dumps(observation, ensure_ascii=False) - except Exception: - content = str(observation) - else: - content = observation - return FunctionMessage( - name=agent_action.tool, - content=content, - ) - - -def _run_tool( - messages: list[AnyMessage], config: RunnableConfig, *, tools: dict[str, BaseTool] -) -> FunctionMessage: - action: AgentAction = messages[-1].additional_kwargs["agent"] - tool = tools[action.tool] - result = tool.invoke(action.tool_input, config) - return _create_function_message(action, result) - - -async def _arun_tool( - messages: list[AnyMessage], config: RunnableConfig, *, tools: dict[str, BaseTool] -) -> FunctionMessage: - action: AgentAction = messages[-1].additional_kwargs["agent"] - tool = tools[action.tool] - result = await tool.ainvoke(action.tool_input, config) - return _create_function_message(action, result) - - -def get_agent_executor( +def get_openai_agent_executor( tools: list[BaseTool], llm: LanguageModelLike, + system_message: str, checkpoint: BaseCheckpointSaver, ): + def _get_messages(messages): + return [SystemMessage(content=system_message)] + messages + + if tools: + llm_with_tools = llm.bind( + functions=[format_tool_to_openai_function(t) for t in tools] + ) + else: + llm_with_tools = llm + agent = _get_messages | llm_with_tools tool_executor = ToolExecutor(tools) # Define the function that determines whether to continue or not @@ -114,7 +61,7 @@ async def call_tool(messages): workflow = MessageGraph() # Define the two nodes we will cycle between - workflow.add_node("agent", llm) + workflow.add_node("agent", agent) workflow.add_node("action", call_tool) # Set the entrypoint as `agent` diff --git a/backend/app/agent_types/prompts.py b/backend/app/agent_types/prompts.py new file mode 100644 index 00000000..91fbcd10 --- /dev/null +++ b/backend/app/agent_types/prompts.py @@ -0,0 +1,27 @@ +xml_template = """{system_message} + +You have access to the following tools: + +{tools} + +In order to use a tool, you can use and tags. You will then get back a response in the form +For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: + +searchweather in SF +64 degrees + +When you are done, you can respond as normal to the user. + +Example 1: + +Human: Hi! + +Assistant: Hi! How are you? + +Human: What is the weather in SF? +Assistant: searchweather in SF +64 degrees +It is 64 degrees in SF + + +Begin!""" \ No newline at end of file diff --git a/backend/app/agent_types/xml_agent.py b/backend/app/agent_types/xml_agent.py new file mode 100644 index 00000000..c21e1f58 --- /dev/null +++ b/backend/app/agent_types/xml_agent.py @@ -0,0 +1,144 @@ +import json +from langchain.schema.messages import FunctionMessage +from langchain_core.language_models.base import LanguageModelLike + +from langchain_core.messages import SystemMessage, AIMessage, HumanMessage + +from langchain.tools import BaseTool +from langgraph.checkpoint import BaseCheckpointSaver +from langgraph.graph.message import MessageGraph +from langgraph.graph import END +from langgraph.prebuilt import ToolExecutor +from langgraph.prebuilt import ToolInvocation + +from langchain.tools.render import render_text_description + +from app.agent_types.prompts import xml_template + + +def _collapse_messages(messages): + log = "" + if isinstance(messages[-1], AIMessage): + scratchpad = messages[:-1] + final = messages[-1] + else: + scratchpad = messages + final = None + if len(scratchpad) % 2 != 0: + raise ValueError("Unexpected") + for i in range(0, len(scratchpad), 2): + action = messages[i] + observation = messages[i + 1] + log += f"{action.content}{observation.content}" + if final is not None: + log += final.content + return AIMessage(content=log) + + +def construct_chat_history(messages): + collapsed_messages = [] + temp_messages = [] + for message in messages: + if isinstance(message, HumanMessage): + if temp_messages: + collapsed_messages.append(_collapse_messages(temp_messages)) + temp_messages = [] + collapsed_messages.append(message) + else: + temp_messages.append(message) + + # Don't forget to add the last non-human message if it exists + if temp_messages: + collapsed_messages.append(_collapse_messages(temp_messages)) + + return collapsed_messages + + + +def get_xml_agent_executor( + tools: list[BaseTool], + llm: LanguageModelLike, + system_message: str, + checkpoint: BaseCheckpointSaver, +): + + formatted_system_message = xml_template.format(system_message=system_message, tools=render_text_description(tools), + tool_names=", ".join([t.name for t in tools])) + + llm_with_stop = llm.bind(stop=[""]) + def _get_messages(messages): + return [SystemMessage(content=formatted_system_message)] + construct_chat_history(messages) + + agent = _get_messages | llm_with_stop + tool_executor = ToolExecutor(tools) + + # Define the function that determines whether to continue or not + def should_continue(messages): + last_message = messages[-1] + if "" in last_message.content: + return "continue" + else: + return "end" + + # Define the function to execute tools + async def call_tool(messages): + # Based on the continue condition + # we know the last message involves a function call + last_message = messages[-1] + # We construct an ToolInvocation from the function_call + tool, tool_input = last_message.content.split("") + _tool = tool.split("")[1] + _tool_input = tool_input.split("")[1] + if "" in _tool_input: + _tool_input = _tool_input.split("")[0] + action = ToolInvocation( + tool=_tool, + tool_input=_tool_input, + ) + # We call the tool_executor and get back a response + response = await tool_executor.ainvoke(action) + # We use the response to create a FunctionMessage + function_message = FunctionMessage(content=str(response), name=action.tool) + # We return a list, because this will get added to the existing list + return function_message + + workflow = MessageGraph() + + # Define the two nodes we will cycle between + workflow.add_node("agent", agent) + workflow.add_node("action", call_tool) + + # Set the entrypoint as `agent` + # This means that this node is the first one called + workflow.set_entry_point("agent") + + # We now add a conditional edge + workflow.add_conditional_edges( + # First, we define the start node. We use `agent`. + # This means these are the edges taken after the `agent` node is called. + "agent", + # Next, we pass in the function that will determine which node is called next. + should_continue, + # Finally we pass in a mapping. + # The keys are strings, and the values are other nodes. + # END is a special node marking that the graph should finish. + # What will happen is we will call `should_continue`, and then the output of that + # will be matched against the keys in this mapping. + # Based on which one it matches, that node will then be called. + { + # If `tools`, then we call the tool node. + "continue": "action", + # Otherwise we finish. + "end": END + } + ) + + # We now add a normal edge from `tools` to `agent`. + # This means that after `tools` is called, `agent` node is called next. + workflow.add_edge('action', 'agent') + + # Finally, we compile it! + # This compiles it into a LangChain Runnable, + # meaning you can use it as you would any other runnable + app = workflow.compile(checkpointer=checkpoint) + return app diff --git a/backend/app/api/runs.py b/backend/app/api/runs.py index abeada0c..0dba41bd 100644 --- a/backend/app/api/runs.py +++ b/backend/app/api/runs.py @@ -7,7 +7,7 @@ import orjson from fastapi import APIRouter, BackgroundTasks, HTTPException, Request from fastapi.exceptions import RequestValidationError -from gizmo_agent import agent +from app.agent import agent from langchain.pydantic_v1 import ValidationError from langchain.schema.messages import AnyMessage, FunctionMessage from langchain.schema.output import ChatGeneration diff --git a/backend/packages/agent-executor/agent_executor/checkpoint.py b/backend/app/checkpoint.py similarity index 100% rename from backend/packages/agent-executor/agent_executor/checkpoint.py rename to backend/app/checkpoint.py diff --git a/backend/packages/agent-executor/agent_executor/ingest.py b/backend/app/ingest.py similarity index 100% rename from backend/packages/agent-executor/agent_executor/ingest.py rename to backend/app/ingest.py diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py b/backend/app/llms.py similarity index 50% rename from backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py rename to backend/app/llms.py index 9923d320..e1071099 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py +++ b/backend/app/llms.py @@ -1,14 +1,10 @@ -import os - -from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser from langchain_openai import AzureChatOpenAI, ChatOpenAI -from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain.tools.render import format_tool_to_openai_function -from langchain_core.messages import SystemMessage - +import os +from langchain_community.chat_models import BedrockChat, ChatAnthropic +import boto3 -def get_openai_function_agent( - tools, system_message, gpt_4: bool = False, azure: bool = False +def get_openai_llm( + gpt_4: bool = False, azure: bool = False ): if not azure: if gpt_4: @@ -24,15 +20,18 @@ def get_openai_function_agent( openai_api_key=os.environ["AZURE_OPENAI_API_KEY"], streaming=True, ) + return llm - def _get_messages(messages): - return [SystemMessage(content=system_message)] + messages - if tools: - llm_with_tools = llm.bind( - functions=[format_tool_to_openai_function(t) for t in tools] +def get_anthropic_llm(bedrock: bool = False): + if bedrock: + client = boto3.client( + "bedrock-runtime", + region_name="us-west-2", + aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"), + aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY"), ) + model = BedrockChat(model_id="anthropic.claude-v2", client=client) else: - llm_with_tools = llm - agent = _get_messages | llm_with_tools - return agent + model = ChatAnthropic(temperature=0, max_tokens_to_sample=2000) + return model diff --git a/backend/packages/agent-executor/agent_executor/parsing.py b/backend/app/parsing.py similarity index 100% rename from backend/packages/agent-executor/agent_executor/parsing.py rename to backend/app/parsing.py diff --git a/backend/app/server.py b/backend/app/server.py index 2fa5e436..7523097c 100644 --- a/backend/app/server.py +++ b/backend/app/server.py @@ -3,7 +3,7 @@ import orjson from fastapi import FastAPI, Form, UploadFile from fastapi.staticfiles import StaticFiles -from gizmo_agent import ingest_runnable +from app.upload import ingest_runnable from app.api import router as api_router diff --git a/backend/app/storage.py b/backend/app/storage.py index ca8e5784..3916f810 100644 --- a/backend/app/storage.py +++ b/backend/app/storage.py @@ -3,7 +3,7 @@ from typing import List, Sequence import orjson -from agent_executor.checkpoint import RedisCheckpoint +from app.checkpoint import RedisCheckpoint from langchain.schema.messages import AnyMessage from langchain.utilities.redis import get_client from langgraph.channels import Topic diff --git a/backend/packages/gizmo-agent/gizmo_agent/tools.py b/backend/app/tools.py similarity index 99% rename from backend/packages/gizmo-agent/gizmo_agent/tools.py rename to backend/app/tools.py index f7aa0e03..11015da6 100644 --- a/backend/packages/gizmo-agent/gizmo_agent/tools.py +++ b/backend/app/tools.py @@ -14,7 +14,7 @@ from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper from langchain_community.vectorstores.redis import RedisFilter -from gizmo_agent.ingest import vstore +from app.upload import vstore class DDGInput(BaseModel): diff --git a/backend/packages/agent-executor/agent_executor/upload.py b/backend/app/upload.py similarity index 77% rename from backend/packages/agent-executor/agent_executor/upload.py rename to backend/app/upload.py index e627c90d..acba476a 100644 --- a/backend/packages/agent-executor/agent_executor/upload.py +++ b/backend/app/upload.py @@ -14,9 +14,15 @@ from langchain_core.runnables import RunnableConfig, RunnableSerializable from langchain_core.vectorstores import VectorStore from langchain.text_splitter import TextSplitter +import os -from agent_executor.ingest import ingest_blob -from agent_executor.parsing import MIMETYPE_BASED_PARSER +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_openai import OpenAIEmbeddings +from langchain_community.vectorstores.redis import Redis +from langchain_core.runnables import ConfigurableField + +from app.ingest import ingest_blob +from app.parsing import MIMETYPE_BASED_PARSER def _guess_mimetype(file_bytes: bytes) -> str: @@ -94,3 +100,27 @@ def batch( ) ) return ids + + + +index_schema = { + "tag": [{"name": "namespace"}], +} +vstore = Redis( + redis_url=os.environ["REDIS_URL"], + index_name="opengpts", + embedding=OpenAIEmbeddings(), + index_schema=index_schema, +) + + +ingest_runnable = IngestRunnable( + text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200), + vectorstore=vstore, +).configurable_fields( + assistant_id=ConfigurableField( + id="assistant_id", + annotation=str, + name="Assistant ID", + ), +) \ No newline at end of file diff --git a/backend/packages/README.md b/backend/packages/README.md deleted file mode 100644 index e69de29b..00000000 diff --git a/backend/packages/agent-executor/README.md b/backend/packages/agent-executor/README.md deleted file mode 100644 index 3db94e99..00000000 --- a/backend/packages/agent-executor/README.md +++ /dev/null @@ -1 +0,0 @@ -# agent-executor diff --git a/backend/packages/agent-executor/agent_executor/dnd.py b/backend/packages/agent-executor/agent_executor/dnd.py deleted file mode 100644 index 31b7c8cf..00000000 --- a/backend/packages/agent-executor/agent_executor/dnd.py +++ /dev/null @@ -1,137 +0,0 @@ -import json - -from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain.pydantic_v1 import BaseModel, Field -from langchain.utils.openai_functions import convert_pydantic_to_openai_function -from langchain_core.language_models import BaseChatModel -from langchain_core.messages import AIMessage, AnyMessage, HumanMessage -from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.channels import LastValue, Topic -from langgraph.pregel import Channel, Pregel - -character_system_msg = """You are a dungeon master for a game of dungeons and dragons. - -You are interacting with the first (and only) player in the game. \ -Your job is to collect all needed information about their character. This will be used in the quest. \ -Feel free to ask them as many questions as needed to get to the relevant information. -The relevant information is: -- Character's name -- Character's race (or species) -- Character's class -- Character's alignment - -Once you have gathered enough information, write that info to `notebook`.""" - - -class CharacterNotebook(BaseModel): - """Notebook to write information to""" - - player_info: str = Field( - description="Information about a player that you will remember over time" - ) - - -character_prompt = ChatPromptTemplate.from_messages( - [("system", character_system_msg), MessagesPlaceholder(variable_name="messages")] -) - -gameplay_system_msg = """You are a dungeon master for a game of dungeons and dragons. - -You are leading a quest of one person. Their character description is here: - -{character} - -A summary of the game state is here: - -{state}""" - -game_prompt = ChatPromptTemplate.from_messages( - [("system", gameplay_system_msg), MessagesPlaceholder(variable_name="messages")] -) - - -class StateNotebook(BaseModel): - """Notebook to write information to""" - - state: str = Field(description="Information about the current game state") - - -state_prompt = ChatPromptTemplate.from_messages( - [ - ("system", gameplay_system_msg), - MessagesPlaceholder(variable_name="messages"), - ( - "human", - "If any updates to the game state are neccessary, please update the state notebook. If none are, just say no.", - ), - ] -) - - -def _maybe_update_state(message: AnyMessage): - if "function_call" in message.additional_kwargs: - return Channel.write_to( - "messages", - state=json.loads(message.additional_kwargs["function_call"]["arguments"])[ - "state" - ], - ) - - -def _maybe_update_character(message: AnyMessage): - if "function_call" in message.additional_kwargs: - args = json.loads(message.additional_kwargs["function_call"]["arguments"]) - return Channel.write_to( - messages=AIMessage(content="Ready for the quest?"), - character=args["player_info"], - ) - - -def create_dnd_bot(llm: BaseChatModel, checkpoint: BaseCheckpointSaver): - character_model = llm.bind( - functions=[convert_pydantic_to_openai_function(CharacterNotebook)], - ) - game_chain = game_prompt | llm | Channel.write_to("messages", check_update=True) - state_model = llm.bind( - functions=[convert_pydantic_to_openai_function(StateNotebook)], - stream=False, - ) - state_chain = ( - Channel.subscribe_to(["check_update"]).join(["messages", "character", "state"]) - | state_prompt - | state_model - | _maybe_update_state - ) - character_chain = ( - character_prompt - | character_model - | Channel.write_to("messages") - | _maybe_update_character - ) - - def _route_to_chain(_input): - messages = _input["messages"] - if not messages: - return - if not _input["character"] and isinstance(messages[-1], HumanMessage): - return character_chain - elif isinstance(messages[-1], HumanMessage): - return game_chain - - executor = ( - Channel.subscribe_to(["messages"]).join(["character", "state"]) - | _route_to_chain - ) - dnd = Pregel( - nodes={"executor": executor, "update_state": state_chain}, - channels={ - "messages": Topic(AnyMessage, accumulate=True), - "character": LastValue(str), - "state": LastValue(str), - "check_update": LastValue(bool), - }, - input=["messages"], - output=["messages"], - checkpointer=checkpoint, - ) - return dnd diff --git a/backend/packages/agent-executor/poetry.lock b/backend/packages/agent-executor/poetry.lock deleted file mode 100644 index cd0b272c..00000000 --- a/backend/packages/agent-executor/poetry.lock +++ /dev/null @@ -1,1615 +0,0 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. - 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"Lance Martin ", -] -readme = "README.md" - -[tool.poetry.dependencies] -python = "^3.9.0" -langchain = ">=0.0.333" -python-magic = "^0.4.27" - -[tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" - -[build-system] -requires = [ - "poetry-core", -] -build-backend = "poetry.core.masonry.api" diff --git a/backend/packages/gizmo-agent/README.md b/backend/packages/gizmo-agent/README.md deleted file mode 100644 index 1e5b0d15..00000000 --- a/backend/packages/gizmo-agent/README.md +++ /dev/null @@ -1 +0,0 @@ -# gizmo-agent diff --git a/backend/packages/gizmo-agent/gizmo_agent/__init__.py b/backend/packages/gizmo-agent/gizmo_agent/__init__.py deleted file mode 100644 index ad49400b..00000000 --- a/backend/packages/gizmo-agent/gizmo_agent/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from gizmo_agent.ingest import ingest_runnable -from gizmo_agent.main import agent - -__all__ = ["agent", "ingest_runnable"] diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/__init__.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/__init__.py deleted file mode 100644 index e9b139f6..00000000 --- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -from enum import Enum - -from .openai import get_openai_function_agent -from .xml.agent import get_xml_agent - - -class GizmoAgentType(str, Enum): - GPT_35_TURBO = "GPT 3.5 Turbo" - # GPT_4 = "GPT 4" - # AZURE_OPENAI = "GPT 4 (Azure OpenAI)" - # CLAUDE2 = "Claude 2" - # BEDROCK_CLAUDE2 = "Claude 2 (Amazon Bedrock)" - - -__all__ = [ - "get_openai_function_agent", - "get_xml_agent", - "GizmoAgentType", -] diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/__init__.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py deleted file mode 100644 index 18f17eac..00000000 --- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py +++ /dev/null @@ -1,73 +0,0 @@ -import os - -import boto3 -from langchain_community.chat_models import BedrockChat, ChatAnthropic -from langchain.schema.messages import AIMessage, HumanMessage -from langchain.tools.render import render_text_description - -from .prompts import conversational_prompt, parse_output - - -def _collapse_messages(messages): - log = "" - if isinstance(messages[-1], AIMessage): - scratchpad = messages[:-1] - final = messages[-1] - else: - scratchpad = messages - final = None - if len(scratchpad) % 2 != 0: - raise ValueError("Unexpected") - for i in range(0, len(scratchpad), 2): - action = messages[i] - observation = messages[i + 1] - log += f"{action.content}{observation.content}" - if final is not None: - log += final.content - return AIMessage(content=log) - - -def construct_chat_history(messages): - collapsed_messages = [] - temp_messages = [] - for message in messages: - if isinstance(message, HumanMessage): - if temp_messages: - collapsed_messages.append(_collapse_messages(temp_messages)) - temp_messages = [] - collapsed_messages.append(message) - else: - temp_messages.append(message) - - # Don't forget to add the last non-human message if it exists - if temp_messages: - collapsed_messages.append(_collapse_messages(temp_messages)) - - return collapsed_messages - - -def get_xml_agent(tools, system_message, bedrock=False): - if bedrock: - client = boto3.client( - "bedrock-runtime", - region_name="us-west-2", - aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"), - aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY"), - ) - model = BedrockChat(model_id="anthropic.claude-v2", client=client) - else: - model = ChatAnthropic(temperature=0, max_tokens_to_sample=2000) - prompt = conversational_prompt.partial( - tools=render_text_description(tools), - tool_names=", ".join([t.name for t in tools]), - system_message=system_message, - ) - llm_with_stop = model.bind(stop=[""]) - - agent = ( - {"messages": lambda x: construct_chat_history(x["messages"])} - | prompt - | llm_with_stop - | parse_output - ) - return agent diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/prompts.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/prompts.py deleted file mode 100644 index b16dcbff..00000000 --- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/prompts.py +++ /dev/null @@ -1,50 +0,0 @@ -from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain.schema import AgentAction, AgentFinish - -template = """{system_message} - -You have access to the following tools: - -{tools} - -In order to use a tool, you can use and tags. You will then get back a response in the form -For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: - -searchweather in SF -64 degrees - -When you are done, you can respond as normal to the user. - -Example 1: - -Human: Hi! - -Assistant: Hi! How are you? - -Human: What is the weather in SF? -Assistant: searchweather in SF -64 degrees -It is 64 degrees in SF - - -Begin!""" # noqa: E501 - -conversational_prompt = ChatPromptTemplate.from_messages( - [ - ("system", template), - MessagesPlaceholder(variable_name="messages"), - ] -) - - -def parse_output(message): - text = message.content - if "" in text: - tool, tool_input = text.split("") - _tool = tool.split("")[1] - _tool_input = tool_input.split("")[1] - if "" in _tool_input: - _tool_input = _tool_input.split("")[0] - return AgentAction(tool=_tool, tool_input=_tool_input, log=text) - else: - return AgentFinish(return_values={"output": text}, log=text) diff --git a/backend/packages/gizmo-agent/gizmo_agent/ingest.py b/backend/packages/gizmo-agent/gizmo_agent/ingest.py deleted file mode 100644 index f8ce3210..00000000 --- a/backend/packages/gizmo-agent/gizmo_agent/ingest.py +++ /dev/null @@ -1,29 +0,0 @@ -import os - -from agent_executor.upload import IngestRunnable -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain_openai import OpenAIEmbeddings -from langchain_community.vectorstores.redis import Redis -from langchain_core.runnables import ConfigurableField - -index_schema = { - "tag": [{"name": "namespace"}], -} -vstore = Redis( - redis_url=os.environ["REDIS_URL"], - index_name="opengpts", - embedding=OpenAIEmbeddings(), - index_schema=index_schema, -) - - -ingest_runnable = IngestRunnable( - text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200), - vectorstore=vstore, -).configurable_fields( - assistant_id=ConfigurableField( - id="assistant_id", - annotation=str, - name="Assistant ID", - ), -) diff --git a/backend/packages/gizmo-agent/poetry.lock b/backend/packages/gizmo-agent/poetry.lock deleted file mode 100644 index ad8d6a4a..00000000 --- a/backend/packages/gizmo-agent/poetry.lock +++ /dev/null @@ -1,3119 +0,0 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. - -[[package]] -name = "aiofiles" -version = "23.2.1" -description = "File support for asyncio." -optional = false -python-versions = ">=3.7" -files = [ - {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"}, - 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-[metadata] -lock-version = "2.0" -python-versions = "^3.9.0" -content-hash = "0c79e83b7d1d8adb2c49a73a994481f48fcc44f63266a86a664b6dde6028c676" diff --git a/backend/packages/gizmo-agent/pyproject.toml b/backend/packages/gizmo-agent/pyproject.toml deleted file mode 100644 index 80a53e31..00000000 --- a/backend/packages/gizmo-agent/pyproject.toml +++ /dev/null @@ -1,39 +0,0 @@ -[tool.poetry] -name = "gizmo-agent" -version = "0.1.0" -description = "" -authors = [ - "Lance Martin ", -] -readme = "README.md" - -[tool.poetry.dependencies] -python = "^3.9.0" -langchain = ">=0.0.333" -anthropic = "^0.3.11" -langchain-experimental = "^0.0.37" -duckduckgo-search = "^3.9.4" -python-magic = "^0.4.27" -bs4 = "^0.0.1" -unstructured = {extras = ["doc", "docx"], version = "^0.10.29"} -pdfminer-six = "^20221105" -wikipedia = "^1.4.0" -boto3 = "^1.28.84" -redis = "^5.0.1" -arxiv = "^2.0.0" -kay = "^0.1.2" -xmltodict = "^0.13.0" -langchain-openai = "^0.0.2.post1" - -[tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" - -[tool.langserve] -export_module = "gizmo_agent" -export_attr = "agent_executor" - -[build-system] -requires = [ - "poetry-core", -] -build-backend = "poetry.core.masonry.api" diff --git a/backend/packages/gizmo-agent/tests/__init__.py b/backend/packages/gizmo-agent/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/backend/tests/unit_tests/agent_executor/test_parsing.py b/backend/tests/unit_tests/agent_executor/test_parsing.py index b3d1cc46..0d2f9559 100644 --- a/backend/tests/unit_tests/agent_executor/test_parsing.py +++ b/backend/tests/unit_tests/agent_executor/test_parsing.py @@ -1,7 +1,7 @@ """Test parsing logic.""" import mimetypes -from agent_executor.parsing import MIMETYPE_BASED_PARSER, SUPPORTED_MIMETYPES +from app.parsing import MIMETYPE_BASED_PARSER, SUPPORTED_MIMETYPES from langchain.document_loaders import Blob from tests.unit_tests.fixtures import get_sample_paths diff --git a/backend/tests/unit_tests/agent_executor/test_upload.py b/backend/tests/unit_tests/agent_executor/test_upload.py index 5b1f67fb..ec66944d 100644 --- a/backend/tests/unit_tests/agent_executor/test_upload.py +++ b/backend/tests/unit_tests/agent_executor/test_upload.py @@ -1,6 +1,6 @@ from io import BytesIO -from agent_executor.upload import IngestRunnable, _guess_mimetype +from app.upload import IngestRunnable, _guess_mimetype from langchain.text_splitter import RecursiveCharacterTextSplitter from tests.unit_tests.fixtures import get_sample_paths From 08fb4156a2c7c1b5f9e592210e82fb46e05f84a4 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Thu, 25 Jan 2024 18:06:42 -0800 Subject: [PATCH 06/17] cr --- backend/app/agent.py | 24 ++++++++++++++++++------ backend/app/agent_types/openai_agent.py | 10 ++++++---- backend/app/agent_types/prompts.py | 2 +- backend/app/agent_types/xml_agent.py | 20 ++++++++++++-------- backend/app/llms.py | 5 ++--- backend/app/upload.py | 3 +-- 6 files changed, 40 insertions(+), 24 deletions(-) diff --git a/backend/app/agent.py b/backend/app/agent.py index 79889a25..01f7f86f 100644 --- a/backend/app/agent.py +++ b/backend/app/agent.py @@ -29,6 +29,7 @@ class AgentType(str, Enum): CLAUDE2 = "Claude 2" BEDROCK_CLAUDE2 = "Claude 2 (Amazon Bedrock)" + DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant." @@ -65,19 +66,29 @@ def __init__( _tools.append(TOOLS[_tool]()) if agent == AgentType.GPT_35_TURBO: llm = get_openai_llm() - _agent = get_openai_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + _agent = get_openai_agent_executor( + _tools, llm, system_message, RedisCheckpoint() + ) elif agent == AgentType.GPT_4: llm = get_openai_llm(gpt_4=True) - _agent = get_openai_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + _agent = get_openai_agent_executor( + _tools, llm, system_message, RedisCheckpoint() + ) elif agent == AgentType.AZURE_OPENAI: llm = get_openai_llm(azure=True) - _agent = get_openai_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + _agent = get_openai_agent_executor( + _tools, llm, system_message, RedisCheckpoint() + ) elif agent == AgentType.CLAUDE2: llm = get_anthropic_llm() - _agent = get_xml_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + _agent = get_xml_agent_executor( + _tools, llm, system_message, RedisCheckpoint() + ) elif agent == AgentType.BEDROCK_CLAUDE2: llm = get_anthropic_llm(bedrock=True) - _agent = get_xml_agent_executor(_tools, llm, system_message, RedisCheckpoint()) + _agent = get_xml_agent_executor( + _tools, llm, system_message, RedisCheckpoint() + ) else: raise ValueError("Unexpected agent type") agent_executor = _agent.with_config({"recursion_limit": 10}) @@ -133,7 +144,8 @@ class AgentOutput(BaseModel): from langchain.schema.messages import HumanMessage async def run(): - async for m in agent.astream_events(HumanMessage(content="whats your name"), + async for m in agent.astream_events( + HumanMessage(content="whats your name"), config={"configurable": {"user_id": "1", "thread_id": "test1"}}, version="v1", ): diff --git a/backend/app/agent_types/openai_agent.py b/backend/app/agent_types/openai_agent.py index 017e39bc..bc61b427 100644 --- a/backend/app/agent_types/openai_agent.py +++ b/backend/app/agent_types/openai_agent.py @@ -49,7 +49,9 @@ async def call_tool(messages): # We construct an ToolInvocation from the function_call action = ToolInvocation( tool=last_message.additional_kwargs["function_call"]["name"], - tool_input=json.loads(last_message.additional_kwargs["function_call"]["arguments"]), + tool_input=json.loads( + last_message.additional_kwargs["function_call"]["arguments"] + ), ) # We call the tool_executor and get back a response response = await tool_executor.ainvoke(action) @@ -85,13 +87,13 @@ async def call_tool(messages): # If `tools`, then we call the tool node. "continue": "action", # Otherwise we finish. - "end": END - } + "end": END, + }, ) # We now add a normal edge from `tools` to `agent`. # This means that after `tools` is called, `agent` node is called next. - workflow.add_edge('action', 'agent') + workflow.add_edge("action", "agent") # Finally, we compile it! # This compiles it into a LangChain Runnable, diff --git a/backend/app/agent_types/prompts.py b/backend/app/agent_types/prompts.py index 91fbcd10..125e6a80 100644 --- a/backend/app/agent_types/prompts.py +++ b/backend/app/agent_types/prompts.py @@ -24,4 +24,4 @@ It is 64 degrees in SF -Begin!""" \ No newline at end of file +Begin!""" diff --git a/backend/app/agent_types/xml_agent.py b/backend/app/agent_types/xml_agent.py index c21e1f58..5652da2e 100644 --- a/backend/app/agent_types/xml_agent.py +++ b/backend/app/agent_types/xml_agent.py @@ -54,20 +54,24 @@ def construct_chat_history(messages): return collapsed_messages - def get_xml_agent_executor( tools: list[BaseTool], llm: LanguageModelLike, system_message: str, checkpoint: BaseCheckpointSaver, ): - - formatted_system_message = xml_template.format(system_message=system_message, tools=render_text_description(tools), - tool_names=", ".join([t.name for t in tools])) + formatted_system_message = xml_template.format( + system_message=system_message, + tools=render_text_description(tools), + tool_names=", ".join([t.name for t in tools]), + ) llm_with_stop = llm.bind(stop=[""]) + def _get_messages(messages): - return [SystemMessage(content=formatted_system_message)] + construct_chat_history(messages) + return [ + SystemMessage(content=formatted_system_message) + ] + construct_chat_history(messages) agent = _get_messages | llm_with_stop tool_executor = ToolExecutor(tools) @@ -129,13 +133,13 @@ async def call_tool(messages): # If `tools`, then we call the tool node. "continue": "action", # Otherwise we finish. - "end": END - } + "end": END, + }, ) # We now add a normal edge from `tools` to `agent`. # This means that after `tools` is called, `agent` node is called next. - workflow.add_edge('action', 'agent') + workflow.add_edge("action", "agent") # Finally, we compile it! # This compiles it into a LangChain Runnable, diff --git a/backend/app/llms.py b/backend/app/llms.py index e1071099..4e69ecd1 100644 --- a/backend/app/llms.py +++ b/backend/app/llms.py @@ -3,9 +3,8 @@ from langchain_community.chat_models import BedrockChat, ChatAnthropic import boto3 -def get_openai_llm( - gpt_4: bool = False, azure: bool = False -): + +def get_openai_llm(gpt_4: bool = False, azure: bool = False): if not azure: if gpt_4: llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True) diff --git a/backend/app/upload.py b/backend/app/upload.py index acba476a..306a8690 100644 --- a/backend/app/upload.py +++ b/backend/app/upload.py @@ -102,7 +102,6 @@ def batch( return ids - index_schema = { "tag": [{"name": "namespace"}], } @@ -123,4 +122,4 @@ def batch( annotation=str, name="Assistant ID", ), -) \ No newline at end of file +) From 42efab1b773f5ce3470dd0815c62e9d1ed670662 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 10:35:18 -0800 Subject: [PATCH 07/17] Add missing deps --- backend/poetry.lock | 1129 ++++++---------------------------------- backend/pyproject.toml | 12 +- 2 files changed, 164 insertions(+), 977 deletions(-) diff --git a/backend/poetry.lock b/backend/poetry.lock index d421648c..70175e49 100644 --- a/backend/poetry.lock +++ b/backend/poetry.lock @@ -1,33 +1,5 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. -[[package]] -name = "agent-executor" -version = "0.1.0" -description = "" -optional = false -python-versions = "^3.9.0" -files = [] -develop = true - -[package.dependencies] -langchain = ">=0.0.333" -python-magic = "^0.4.27" - -[package.source] -type = "directory" -url = "packages/agent-executor" - 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\"docx\""} -python-iso639 = "*" -python-magic = "*" -rapidfuzz = "*" -requests = "*" -tabulate = "*" -typing-extensions = "*" - -[package.extras] -airtable = ["pyairtable"] -all-docs = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pypandoc", "python-docx (>=1.1.0)", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.11)", "unstructured.pytesseract (>=0.3.12)", "xlrd"] -azure = ["adlfs", "fsspec (==2023.9.1)"] -azure-cognitive-search = ["azure-search-documents"] -bedrock = ["boto3", "langchain"] -biomed = ["bs4"] -box = ["boxfs", "fsspec (==2023.9.1)"] -confluence = ["atlassian-python-api"] -csv = ["pandas"] -delta-table = ["deltalake", "fsspec (==2023.9.1)"] -discord = ["discord-py"] -doc = ["python-docx (>=1.1.0)"] -docx = ["python-docx (>=1.1.0)"] -dropbox = ["dropboxdrivefs", "fsspec (==2023.9.1)"] -elasticsearch = ["elasticsearch", "jq"] -embed-huggingface = ["huggingface", "langchain", "sentence-transformers"] -epub = 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-paddleocr = ["unstructured.paddleocr (==2.6.1.3)"] -pdf = ["onnx", "pdf2image", "pdfminer.six", "unstructured-inference (==0.7.11)", "unstructured.pytesseract (>=0.3.12)"] -ppt = ["python-pptx (<=0.6.23)"] -pptx = ["python-pptx (<=0.6.23)"] -reddit = ["praw"] -rst = ["pypandoc"] -rtf = ["pypandoc"] -s3 = ["fsspec (==2023.9.1)", "s3fs"] -salesforce = ["simple-salesforce"] -sharepoint = ["Office365-REST-Python-Client (<2.4.3)", "msal"] -slack = ["slack-sdk"] -tsv = ["pandas"] -wikipedia = ["wikipedia"] -xlsx = ["networkx", "openpyxl", "pandas", "xlrd"] - [[package]] name = "urllib3" version = "1.26.18" @@ -3430,4 +2611,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.9.0" -content-hash = "c272913224d22500bd8d61bcc12f1c38cc391842bf12a6998af96ac6393bbafc" +content-hash = "aabbc7d99cc4ee0d0eab2e605c68a822f4bf5364a1161fcb2462267e58833b6c" diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 5252047e..810010d7 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -16,16 +16,22 @@ langserve = "0.0.32" # Uncomment if you need to work from a development branch # This will only work for local development though! # langchain = { git = "git@github.com:langchain-ai/langchain.git/", branch = "nc/subclass-runnable-binding" , subdirectory = "libs/langchain"} -gizmo-agent = {path = "packages/gizmo-agent", develop = true} -agent-executor = {path = "packages/agent-executor", develop = true} orjson = "^3.9.10" redis = "^5.0.1" python-multipart = "^0.0.6" tiktoken = "^0.5.1" langchain = ">=0.0.338" -langgraph = "^0.0.15" +langgraph = "^0.0.19" pydantic = "<2.0" python-magic = "^0.4.27" +langchain-openai = "^0.0.5" +beautifulsoup4 = "^4.12.3" +boto3 = "^1.34.28" +duckduckgo-search = "^4.2" +arxiv = "^2.1.0" +kay = "^0.1.2" +xmltodict = "^0.13.0" +wikipedia = "^1.4.0" [tool.poetry.group.dev.dependencies] uvicorn = "^0.23.2" From cef13fff16a44cfa7fc46e4141534ca70d21bd4d Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Fri, 26 Jan 2024 10:51:14 -0800 Subject: [PATCH 08/17] cr --- backend/app/agent.py | 13 ++- backend/app/agent_types/google_agent.py | 100 ++++++++++++++++++++++++ backend/app/llms.py | 7 ++ 3 files changed, 117 insertions(+), 3 deletions(-) create mode 100644 backend/app/agent_types/google_agent.py diff --git a/backend/app/agent.py b/backend/app/agent.py index 01f7f86f..b2fbf9d5 100644 --- a/backend/app/agent.py +++ b/backend/app/agent.py @@ -3,7 +3,8 @@ from app.checkpoint import RedisCheckpoint from app.agent_types.openai_agent import get_openai_agent_executor from app.agent_types.xml_agent import get_xml_agent_executor -from app.llms import get_openai_llm, get_anthropic_llm +from app.agent_types.google_agent import get_google_agent_executor +from app.llms import get_openai_llm, get_anthropic_llm, get_google_llm from langchain.pydantic_v1 import BaseModel, Field from langchain_core.messages import AnyMessage from langchain_core.runnables import ( @@ -28,6 +29,7 @@ class AgentType(str, Enum): AZURE_OPENAI = "GPT 4 (Azure OpenAI)" CLAUDE2 = "Claude 2" BEDROCK_CLAUDE2 = "Claude 2 (Amazon Bedrock)" + GEMINI = "GEMINI" DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant." @@ -89,6 +91,11 @@ def __init__( _agent = get_xml_agent_executor( _tools, llm, system_message, RedisCheckpoint() ) + elif agent == AgentType.GEMINI: + llm = get_google_llm() + _agent = get_google_agent_executor( + _tools, llm, system_message, RedisCheckpoint() + ) else: raise ValueError("Unexpected agent type") agent_executor = _agent.with_config({"recursion_limit": 10}) @@ -113,7 +120,7 @@ class AgentOutput(BaseModel): agent = ( ConfigurableAgent( - agent=AgentType.GPT_35_TURBO, + agent=AgentType.GEMINI, tools=[], system_message=DEFAULT_SYSTEM_MESSAGE, retrieval_description=RETRIEVAL_DESCRIPTION, @@ -146,7 +153,7 @@ class AgentOutput(BaseModel): async def run(): async for m in agent.astream_events( HumanMessage(content="whats your name"), - config={"configurable": {"user_id": "1", "thread_id": "test1"}}, + config={"configurable": {"user_id": "2", "thread_id": "test1"}}, version="v1", ): print(m) diff --git a/backend/app/agent_types/google_agent.py b/backend/app/agent_types/google_agent.py new file mode 100644 index 00000000..46c275cc --- /dev/null +++ b/backend/app/agent_types/google_agent.py @@ -0,0 +1,100 @@ +import json +from langchain.schema.messages import FunctionMessage +from langchain_core.language_models.base import LanguageModelLike + +from langchain.tools.render import format_tool_to_openai_function +from langchain_core.messages import SystemMessage + +from langchain.tools import BaseTool +from langgraph.checkpoint import BaseCheckpointSaver +from langgraph.graph.message import MessageGraph +from langgraph.graph import END +from langgraph.prebuilt import ToolExecutor +from langgraph.prebuilt import ToolInvocation + + +def get_google_agent_executor( + tools: list[BaseTool], + llm: LanguageModelLike, + system_message: str, + checkpoint: BaseCheckpointSaver, +): + def _get_messages(messages): + return [SystemMessage(content=system_message)] + messages + + if tools: + llm_with_tools = llm.bind(functions=tools) + else: + llm_with_tools = llm + agent = _get_messages | llm_with_tools + tool_executor = ToolExecutor(tools) + + # Define the function that determines whether to continue or not + def should_continue(messages): + last_message = messages[-1] + # If there is no function call, then we finish + if "function_call" not in last_message.additional_kwargs: + return "end" + # Otherwise if there is, we continue + else: + return "continue" + + # Define the function to execute tools + async def call_tool(messages): + # Based on the continue condition + # we know the last message involves a function call + last_message = messages[-1] + # We construct an ToolInvocation from the function_call + action = ToolInvocation( + tool=last_message.additional_kwargs["function_call"]["name"], + tool_input=json.loads( + last_message.additional_kwargs["function_call"]["arguments"] + ), + ) + # We call the tool_executor and get back a response + response = await tool_executor.ainvoke(action) + # We use the response to create a FunctionMessage + function_message = FunctionMessage(content=str(response), name=action.tool) + # We return a list, because this will get added to the existing list + return function_message + + workflow = MessageGraph() + + # Define the two nodes we will cycle between + workflow.add_node("agent", agent) + workflow.add_node("action", call_tool) + + # Set the entrypoint as `agent` + # This means that this node is the first one called + workflow.set_entry_point("agent") + + # We now add a conditional edge + workflow.add_conditional_edges( + # First, we define the start node. We use `agent`. + # This means these are the edges taken after the `agent` node is called. + "agent", + # Next, we pass in the function that will determine which node is called next. + should_continue, + # Finally we pass in a mapping. + # The keys are strings, and the values are other nodes. + # END is a special node marking that the graph should finish. + # What will happen is we will call `should_continue`, and then the output of that + # will be matched against the keys in this mapping. + # Based on which one it matches, that node will then be called. + { + # If `tools`, then we call the tool node. + "continue": "action", + # Otherwise we finish. + "end": END, + }, + ) + + # We now add a normal edge from `tools` to `agent`. + # This means that after `tools` is called, `agent` node is called next. + workflow.add_edge("action", "agent") + + # Finally, we compile it! + # This compiles it into a LangChain Runnable, + # meaning you can use it as you would any other runnable + app = workflow.compile(checkpointer=checkpoint) + return app diff --git a/backend/app/llms.py b/backend/app/llms.py index 4e69ecd1..c5b4b669 100644 --- a/backend/app/llms.py +++ b/backend/app/llms.py @@ -2,6 +2,7 @@ import os from langchain_community.chat_models import BedrockChat, ChatAnthropic import boto3 +from langchain_google_vertexai import ChatVertexAI def get_openai_llm(gpt_4: bool = False, azure: bool = False): @@ -34,3 +35,9 @@ def get_anthropic_llm(bedrock: bool = False): else: model = ChatAnthropic(temperature=0, max_tokens_to_sample=2000) return model + + +def get_google_llm(): + return ChatVertexAI( + model_name="gemini-pro", convert_system_message_to_human=True, streaming=True + ) From 844792515b73a3f506a6c4f0fac0151566b2c7aa Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 11:21:49 -0800 Subject: [PATCH 09/17] Bring back config --- frontend/src/components/Config.tsx | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/frontend/src/components/Config.tsx b/frontend/src/components/Config.tsx index 9cf55bd4..570fa6c2 100644 --- a/frontend/src/components/Config.tsx +++ b/frontend/src/components/Config.tsx @@ -305,7 +305,7 @@ export function Config(props: { readonly={readonly} /> ); - } else if (key === "type==agent/system_message") { + } else if (key === "system_message") { return ( ); } else if ( - key === "type==agent/retrieval_description" && + key === "retrieval_description" && ( - values?.configurable?.["type==agent/tools"] as - | string[] - | undefined + values?.configurable?.["tools"] as string[] | undefined )?.includes("Retrieval") ) { return ( @@ -346,7 +344,7 @@ export function Config(props: { readonly={readonly} /> ); - } else if (key === "type==agent/tools") { + } else if (key === "tools") { return ( Date: Fri, 26 Jan 2024 12:51:24 -0800 Subject: [PATCH 10/17] Fix reading and writing thread messages --- backend/app/agent.py | 74 +++++++++++---------------- backend/app/api/runs.py | 18 +++---- backend/app/storage.py | 38 ++++++-------- backend/app/stream.py | 13 ++--- frontend/src/App.tsx | 18 +++---- frontend/src/hooks/useStreamState.tsx | 13 ++--- 6 files changed, 72 insertions(+), 102 deletions(-) diff --git a/backend/app/agent.py b/backend/app/agent.py index b2fbf9d5..8163591b 100644 --- a/backend/app/agent.py +++ b/backend/app/agent.py @@ -5,7 +5,6 @@ from app.agent_types.xml_agent import get_xml_agent_executor from app.agent_types.google_agent import get_google_agent_executor from app.llms import get_openai_llm, get_anthropic_llm, get_google_llm -from langchain.pydantic_v1 import BaseModel, Field from langchain_core.messages import AnyMessage from langchain_core.runnables import ( ConfigurableField, @@ -35,6 +34,34 @@ class AgentType(str, Enum): DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant." +def get_agent_executor( + tools: list, + agent: AgentType, + system_message: str, +): + checkpointer = RedisCheckpoint() + if agent == AgentType.GPT_35_TURBO: + llm = get_openai_llm() + return get_openai_agent_executor(tools, llm, system_message, checkpointer) + elif agent == AgentType.GPT_4: + llm = get_openai_llm(gpt_4=True) + return get_openai_agent_executor(tools, llm, system_message, checkpointer) + elif agent == AgentType.AZURE_OPENAI: + llm = get_openai_llm(azure=True) + return get_openai_agent_executor(tools, llm, system_message, checkpointer) + elif agent == AgentType.CLAUDE2: + llm = get_anthropic_llm() + return get_xml_agent_executor(tools, llm, system_message, checkpointer) + elif agent == AgentType.BEDROCK_CLAUDE2: + llm = get_anthropic_llm(bedrock=True) + return get_xml_agent_executor(tools, llm, system_message, checkpointer) + elif agent == AgentType.GEMINI: + llm = get_google_llm() + return get_google_agent_executor(tools, llm, system_message, checkpointer) + else: + raise ValueError("Unexpected agent type") + + class ConfigurableAgent(RunnableBinding): tools: Sequence[str] agent: AgentType @@ -66,39 +93,8 @@ def __init__( _tools.append(get_retrieval_tool(assistant_id, retrieval_description)) else: _tools.append(TOOLS[_tool]()) - if agent == AgentType.GPT_35_TURBO: - llm = get_openai_llm() - _agent = get_openai_agent_executor( - _tools, llm, system_message, RedisCheckpoint() - ) - elif agent == AgentType.GPT_4: - llm = get_openai_llm(gpt_4=True) - _agent = get_openai_agent_executor( - _tools, llm, system_message, RedisCheckpoint() - ) - elif agent == AgentType.AZURE_OPENAI: - llm = get_openai_llm(azure=True) - _agent = get_openai_agent_executor( - _tools, llm, system_message, RedisCheckpoint() - ) - elif agent == AgentType.CLAUDE2: - llm = get_anthropic_llm() - _agent = get_xml_agent_executor( - _tools, llm, system_message, RedisCheckpoint() - ) - elif agent == AgentType.BEDROCK_CLAUDE2: - llm = get_anthropic_llm(bedrock=True) - _agent = get_xml_agent_executor( - _tools, llm, system_message, RedisCheckpoint() - ) - elif agent == AgentType.GEMINI: - llm = get_google_llm() - _agent = get_google_agent_executor( - _tools, llm, system_message, RedisCheckpoint() - ) - else: - raise ValueError("Unexpected agent type") - agent_executor = _agent.with_config({"recursion_limit": 10}) + _agent = get_agent_executor(_tools, agent, system_message) + agent_executor = _agent.with_config({"recursion_limit": 50}) super().__init__( tools=tools, agent=agent, @@ -110,14 +106,6 @@ def __init__( ) -class AgentInput(BaseModel): - messages: Sequence[AnyMessage] = Field(default_factory=list) - - -class AgentOutput(BaseModel): - messages: Sequence[AnyMessage] = Field(..., extra={"widget": {"type": "chat"}}) - - agent = ( ConfigurableAgent( agent=AgentType.GEMINI, @@ -142,7 +130,7 @@ class AgentOutput(BaseModel): id="retrieval_description", name="Retrieval Description" ), ) - .with_types(input_type=AgentInput, output_type=AgentOutput) + .with_types(input_type=Sequence[AnyMessage], output_type=Sequence[AnyMessage]) ) if __name__ == "__main__": diff --git a/backend/app/api/runs.py b/backend/app/api/runs.py index 0dba41bd..1a8b1df2 100644 --- a/backend/app/api/runs.py +++ b/backend/app/api/runs.py @@ -30,18 +30,12 @@ _serializer = WellKnownLCSerializer() -class AgentInput(BaseModel): - """An input into an agent.""" - - messages: Sequence[AnyMessage] = Field(default_factory=list) - - class CreateRunPayload(BaseModel): """Payload for creating a run.""" assistant_id: str thread_id: str - input: AgentInput = Field(default_factory=AgentInput) + input: Sequence[AnyMessage] = Field(default_factory=list) async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUserId): @@ -73,6 +67,8 @@ async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUser }, } try: + print(body["input"]) + print(agent.get_input_schema(config)) input_ = _unpack_input(agent.get_input_schema(config).validate(body["input"])) except ValidationError as e: raise RequestValidationError(e.errors(), body=body) @@ -101,7 +97,7 @@ async def stream_run( ): """Create a run.""" input_, config, messages = await _run_input_and_config(request, opengpts_user_id) - streamer = StreamMessagesHandler(messages + input_["messages"]) + streamer = StreamMessagesHandler(messages + input_) event_aggregator = AsyncEventAggregatorCallback() config["callbacks"] = [streamer, event_aggregator] @@ -110,11 +106,11 @@ async def stream_run( async def consume_astream() -> None: try: async for chunk in agent.astream(input_, config): - await streamer.send_stream.send(chunk) + # await streamer.send_stream.send(chunk) # hack: function messages aren't generated by chat model # so the callback handler doesn't know about them - if chunk["messages"]: - message = chunk["messages"][-1] + if chunk.get("action"): + message = chunk["action"] if isinstance(message, FunctionMessage): streamer.output[uuid4()] = ChatGeneration(message=message) except Exception as e: diff --git a/backend/app/storage.py b/backend/app/storage.py index 3916f810..706fbe99 100644 --- a/backend/app/storage.py +++ b/backend/app/storage.py @@ -1,12 +1,11 @@ import os from datetime import datetime from typing import List, Sequence +from app.agent import AgentType, get_agent_executor import orjson -from app.checkpoint import RedisCheckpoint from langchain.schema.messages import AnyMessage from langchain.utilities.redis import get_client -from langgraph.channels import Topic from langgraph.channels.base import ChannelsManager from langgraph.checkpoint.base import empty_checkpoint from redis.client import Redis as RedisType @@ -146,33 +145,30 @@ def get_thread(user_id: str, thread_id: str) -> Thread | None: return load(thread_hash_keys, values) if any(values) else None +# TODO remove hardcoded channel name +MESSAGES_CHANNEL_NAME = "__root__" + + def get_thread_messages(user_id: str, thread_id: str): """Get all messages for a thread.""" - client = RedisCheckpoint() config = {"configurable": {"user_id": user_id, "thread_id": thread_id}} - checkpoint = client.get(config) or empty_checkpoint() - # TODO replace hardcoded messages channel with - # channel extracted from agent - with ChannelsManager( - {"messages": Topic(AnyMessage, accumulate=True)}, checkpoint - ) as channels: - return {k: v.get() for k, v in channels.items()} + app = get_agent_executor([], AgentType.GPT_35_TURBO, "") + checkpoint = app.checkpointer.get(config) or empty_checkpoint() + with ChannelsManager(app.channels, checkpoint) as channels: + return {"messages": channels[MESSAGES_CHANNEL_NAME].get()} def post_thread_messages(user_id: str, thread_id: str, messages: Sequence[AnyMessage]): """Add messages to a thread.""" - client = RedisCheckpoint() config = {"configurable": {"user_id": user_id, "thread_id": thread_id}} - checkpoint = client.get(config) or empty_checkpoint() - # TODO replace hardcoded messages channel with - # channel extracted from agent - with ChannelsManager( - {"messages": Topic(AnyMessage, accumulate=True)}, checkpoint - ) as channels: - channels["messages"].update(messages) - checkpoint["channel_versions"]["messages"] += 1 - checkpoint["channel_values"]["messages"] = channels["messages"].checkpoint() - client.put(config, checkpoint) + app = get_agent_executor([], AgentType.GPT_35_TURBO, "") + checkpoint = app.checkpointer.get(config) or empty_checkpoint() + with ChannelsManager(app.channels, checkpoint) as channels: + channel = channels[MESSAGES_CHANNEL_NAME] + channel.update([messages]) + checkpoint["channel_values"][MESSAGES_CHANNEL_NAME] = channel.checkpoint() + checkpoint["channel_versions"][MESSAGES_CHANNEL_NAME] += 1 + app.checkpointer.put(config, checkpoint) def put_thread(user_id: str, thread_id: str, *, assistant_id: str, name: str) -> Thread: diff --git a/backend/app/stream.py b/backend/app/stream.py index 98ed43aa..51581e20 100644 --- a/backend/app/stream.py +++ b/backend/app/stream.py @@ -54,15 +54,10 @@ def on_llm_new_token( self.output[run_id] += chunk # Send the messages to the stream self.send_stream.send_nowait( - { - "messages": ( - self.messages - + [ - map_chunk_to_msg(chunk.message) - for chunk in self.output.values() - ] - ) - } + ( + self.messages + + [map_chunk_to_msg(chunk.message) for chunk in self.output.values()] + ) ) diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index 06e3edf9..b6398bef 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -24,16 +24,14 @@ function App() { )?.config; if (!config) return; await startStream( - { - messages: [ - { - content: message, - additional_kwargs: {}, - type: "human", - example: false, - }, - ], - }, + [ + { + content: message, + additional_kwargs: {}, + type: "human", + example: false, + }, + ], chat.assistant_id, chat.thread_id ); diff --git a/frontend/src/hooks/useStreamState.tsx b/frontend/src/hooks/useStreamState.tsx index 24439cb8..70d1e7c0 100644 --- a/frontend/src/hooks/useStreamState.tsx +++ b/frontend/src/hooks/useStreamState.tsx @@ -12,7 +12,7 @@ export interface StreamState { export interface StreamStateProps { stream: StreamState | null; startStream: ( - input: { messages: Message[] }, + input: Message[], assistant_id: string, thread_id: string ) => Promise; @@ -24,14 +24,10 @@ export function useStreamState(): StreamStateProps { const [controller, setController] = useState(null); const startStream = useCallback( - async ( - input: { messages: Message[] }, - assistant_id: string, - thread_id: string - ) => { + async (input: Message[], assistant_id: string, thread_id: string) => { const controller = new AbortController(); setController(controller); - setCurrent({ status: "inflight", messages: input.messages, merge: true }); + setCurrent({ status: "inflight", messages: input, merge: true }); await fetchEventSource("/runs/stream", { signal: controller.signal, @@ -41,7 +37,8 @@ export function useStreamState(): StreamStateProps { openWhenHidden: true, onmessage(msg) { if (msg.event === "data") { - const { messages } = JSON.parse(msg.data); + console.log(msg.data); + const messages = JSON.parse(msg.data); setCurrent((current) => ({ status: "inflight", messages, From 890bd0e17857a5a7b821454c1513b39c27a67697 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 12:54:42 -0800 Subject: [PATCH 11/17] Add missing dep --- backend/poetry.lock | 624 ++++++++++++++++++++++++++++++++++++++++- backend/pyproject.toml | 2 + 2 files changed, 625 insertions(+), 1 deletion(-) diff --git a/backend/poetry.lock b/backend/poetry.lock index 70175e49..5dda15b9 100644 --- a/backend/poetry.lock +++ b/backend/poetry.lock @@ -237,6 +237,17 @@ urllib3 = [ [package.extras] crt = ["awscrt (==0.19.19)"] +[[package]] +name = "cachetools" +version = "5.3.2" +description = "Extensible memoizing collections and decorators" +optional = false +python-versions = ">=3.7" +files = [ + {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"}, + {file = "cachetools-5.3.2.tar.gz", hash = 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= "^0.0.3" +setuptools = "^69.0.3" [tool.poetry.group.dev.dependencies] uvicorn = "^0.23.2" From c701eba9183a49037b3ffe0a7f25e66e12da837e Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 12:56:58 -0800 Subject: [PATCH 12/17] Lint --- backend/app/agent_types/google_agent.py | 1 - backend/app/agent_types/xml_agent.py | 1 - 2 files changed, 2 deletions(-) diff --git a/backend/app/agent_types/google_agent.py b/backend/app/agent_types/google_agent.py index 46c275cc..a255bba8 100644 --- a/backend/app/agent_types/google_agent.py +++ b/backend/app/agent_types/google_agent.py @@ -2,7 +2,6 @@ from langchain.schema.messages import FunctionMessage from langchain_core.language_models.base import LanguageModelLike -from langchain.tools.render import format_tool_to_openai_function from langchain_core.messages import SystemMessage from langchain.tools import BaseTool diff --git a/backend/app/agent_types/xml_agent.py b/backend/app/agent_types/xml_agent.py index 5652da2e..ef4d8c66 100644 --- a/backend/app/agent_types/xml_agent.py +++ b/backend/app/agent_types/xml_agent.py @@ -1,4 +1,3 @@ -import json from langchain.schema.messages import FunctionMessage from langchain_core.language_models.base import LanguageModelLike From 4c2fe253ee2b4b436afd36f8335be243b0283db8 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 12:57:06 -0800 Subject: [PATCH 13/17] Format --- backend/app/agent.py | 14 +++++++------- backend/app/agent_types/google_agent.py | 10 ++++------ backend/app/agent_types/openai_agent.py | 12 +++++------- backend/app/agent_types/xml_agent.py | 14 +++++--------- backend/app/api/runs.py | 2 +- backend/app/ingest.py | 2 +- backend/app/llms.py | 5 +++-- backend/app/server.py | 2 +- backend/app/storage.py | 2 +- backend/app/tools.py | 2 +- backend/app/upload.py | 15 ++++++++------- .../unit_tests/agent_executor/test_parsing.py | 2 +- .../unit_tests/agent_executor/test_upload.py | 2 +- 13 files changed, 39 insertions(+), 45 deletions(-) diff --git a/backend/app/agent.py b/backend/app/agent.py index 8163591b..2c7709f6 100644 --- a/backend/app/agent.py +++ b/backend/app/agent.py @@ -1,16 +1,18 @@ +from enum import Enum from typing import Any, Mapping, Optional, Sequence -from app.checkpoint import RedisCheckpoint -from app.agent_types.openai_agent import get_openai_agent_executor -from app.agent_types.xml_agent import get_xml_agent_executor -from app.agent_types.google_agent import get_google_agent_executor -from app.llms import get_openai_llm, get_anthropic_llm, get_google_llm from langchain_core.messages import AnyMessage from langchain_core.runnables import ( ConfigurableField, ConfigurableFieldMultiOption, RunnableBinding, ) + +from app.agent_types.google_agent import get_google_agent_executor +from app.agent_types.openai_agent import get_openai_agent_executor +from app.agent_types.xml_agent import get_xml_agent_executor +from app.checkpoint import RedisCheckpoint +from app.llms import get_anthropic_llm, get_google_llm, get_openai_llm from app.tools import ( RETRIEVAL_DESCRIPTION, TOOL_OPTIONS, @@ -19,8 +21,6 @@ get_retrieval_tool, ) -from enum import Enum - class AgentType(str, Enum): GPT_35_TURBO = "GPT 3.5 Turbo" diff --git a/backend/app/agent_types/google_agent.py b/backend/app/agent_types/google_agent.py index a255bba8..daf8752e 100644 --- a/backend/app/agent_types/google_agent.py +++ b/backend/app/agent_types/google_agent.py @@ -1,15 +1,13 @@ import json + from langchain.schema.messages import FunctionMessage +from langchain.tools import BaseTool from langchain_core.language_models.base import LanguageModelLike - from langchain_core.messages import SystemMessage - -from langchain.tools import BaseTool from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.graph.message import MessageGraph from langgraph.graph import END -from langgraph.prebuilt import ToolExecutor -from langgraph.prebuilt import ToolInvocation +from langgraph.graph.message import MessageGraph +from langgraph.prebuilt import ToolExecutor, ToolInvocation def get_google_agent_executor( diff --git a/backend/app/agent_types/openai_agent.py b/backend/app/agent_types/openai_agent.py index bc61b427..8ee9a645 100644 --- a/backend/app/agent_types/openai_agent.py +++ b/backend/app/agent_types/openai_agent.py @@ -1,16 +1,14 @@ import json -from langchain.schema.messages import FunctionMessage -from langchain_core.language_models.base import LanguageModelLike +from langchain.schema.messages import FunctionMessage +from langchain.tools import BaseTool from langchain.tools.render import format_tool_to_openai_function +from langchain_core.language_models.base import LanguageModelLike from langchain_core.messages import SystemMessage - -from langchain.tools import BaseTool from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.graph.message import MessageGraph from langgraph.graph import END -from langgraph.prebuilt import ToolExecutor -from langgraph.prebuilt import ToolInvocation +from langgraph.graph.message import MessageGraph +from langgraph.prebuilt import ToolExecutor, ToolInvocation def get_openai_agent_executor( diff --git a/backend/app/agent_types/xml_agent.py b/backend/app/agent_types/xml_agent.py index ef4d8c66..2a1654cc 100644 --- a/backend/app/agent_types/xml_agent.py +++ b/backend/app/agent_types/xml_agent.py @@ -1,16 +1,12 @@ from langchain.schema.messages import FunctionMessage -from langchain_core.language_models.base import LanguageModelLike - -from langchain_core.messages import SystemMessage, AIMessage, HumanMessage - from langchain.tools import BaseTool +from langchain.tools.render import render_text_description +from langchain_core.language_models.base import LanguageModelLike +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.graph.message import MessageGraph from langgraph.graph import END -from langgraph.prebuilt import ToolExecutor -from langgraph.prebuilt import ToolInvocation - -from langchain.tools.render import render_text_description +from langgraph.graph.message import MessageGraph +from langgraph.prebuilt import ToolExecutor, ToolInvocation from app.agent_types.prompts import xml_template diff --git a/backend/app/api/runs.py b/backend/app/api/runs.py index 1a8b1df2..b8b42787 100644 --- a/backend/app/api/runs.py +++ b/backend/app/api/runs.py @@ -7,7 +7,6 @@ import orjson from fastapi import APIRouter, BackgroundTasks, HTTPException, Request from fastapi.exceptions import RequestValidationError -from app.agent import agent from langchain.pydantic_v1 import ValidationError from langchain.schema.messages import AnyMessage, FunctionMessage from langchain.schema.output import ChatGeneration @@ -20,6 +19,7 @@ from pydantic import BaseModel, Field from sse_starlette import EventSourceResponse +from app.agent import agent from app.schema import OpengptsUserId from app.storage import get_assistant, get_thread_messages, public_user_id from app.stream import StreamMessagesHandler diff --git a/backend/app/ingest.py b/backend/app/ingest.py index 5c750892..58dee61d 100644 --- a/backend/app/ingest.py +++ b/backend/app/ingest.py @@ -8,11 +8,11 @@ """ from typing import List +from langchain.text_splitter import TextSplitter from langchain_community.document_loaders import Blob from langchain_community.document_loaders.base import BaseBlobParser from langchain_core.documents import Document from langchain_core.vectorstores import VectorStore -from langchain.text_splitter import TextSplitter def _update_document_metadata(document: Document, namespace: str) -> None: diff --git a/backend/app/llms.py b/backend/app/llms.py index c5b4b669..f7de5da2 100644 --- a/backend/app/llms.py +++ b/backend/app/llms.py @@ -1,8 +1,9 @@ -from langchain_openai import AzureChatOpenAI, ChatOpenAI import os -from langchain_community.chat_models import BedrockChat, ChatAnthropic + import boto3 +from langchain_community.chat_models import BedrockChat, ChatAnthropic from langchain_google_vertexai import ChatVertexAI +from langchain_openai import AzureChatOpenAI, ChatOpenAI def get_openai_llm(gpt_4: bool = False, azure: bool = False): diff --git a/backend/app/server.py b/backend/app/server.py index 7523097c..82fdd920 100644 --- a/backend/app/server.py +++ b/backend/app/server.py @@ -3,9 +3,9 @@ import orjson from fastapi import FastAPI, Form, UploadFile from fastapi.staticfiles import StaticFiles -from app.upload import ingest_runnable from app.api import router as api_router +from app.upload import ingest_runnable app = FastAPI(title="OpenGPTs API") diff --git a/backend/app/storage.py b/backend/app/storage.py index 706fbe99..9f82a43b 100644 --- a/backend/app/storage.py +++ b/backend/app/storage.py @@ -1,7 +1,6 @@ import os from datetime import datetime from typing import List, Sequence -from app.agent import AgentType, get_agent_executor import orjson from langchain.schema.messages import AnyMessage @@ -10,6 +9,7 @@ from langgraph.checkpoint.base import empty_checkpoint from redis.client import Redis as RedisType +from app.agent import AgentType, get_agent_executor from app.schema import Assistant, AssistantWithoutUserId, Thread, ThreadWithoutUserId diff --git a/backend/app/tools.py b/backend/app/tools.py index 11015da6..74e0eecf 100644 --- a/backend/app/tools.py +++ b/backend/app/tools.py @@ -1,6 +1,7 @@ from enum import Enum from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools.retriever import create_retriever_tool from langchain_community.retrievers import ( KayAiRetriever, PubMedRetriever, @@ -8,7 +9,6 @@ ) from langchain_community.retrievers.you import YouRetriever from langchain_community.tools import ArxivQueryRun, DuckDuckGoSearchRun -from langchain.tools.retriever import create_retriever_tool from langchain_community.tools.tavily_search import TavilyAnswer, TavilySearchResults from langchain_community.utilities.arxiv import ArxivAPIWrapper from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper diff --git a/backend/app/upload.py b/backend/app/upload.py index 306a8690..8be0ff44 100644 --- a/backend/app/upload.py +++ b/backend/app/upload.py @@ -8,18 +8,19 @@ """ from __future__ import annotations +import os from typing import Any, BinaryIO, List, Optional +from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter from langchain_community.document_loaders.blob_loaders import Blob -from langchain_core.runnables import RunnableConfig, RunnableSerializable +from langchain_community.vectorstores.redis import Redis +from langchain_core.runnables import ( + ConfigurableField, + RunnableConfig, + RunnableSerializable, +) from langchain_core.vectorstores import VectorStore -from langchain.text_splitter import TextSplitter -import os - -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_openai import OpenAIEmbeddings -from langchain_community.vectorstores.redis import Redis -from langchain_core.runnables import ConfigurableField from app.ingest import ingest_blob from app.parsing import MIMETYPE_BASED_PARSER diff --git a/backend/tests/unit_tests/agent_executor/test_parsing.py b/backend/tests/unit_tests/agent_executor/test_parsing.py index 0d2f9559..ff75336a 100644 --- a/backend/tests/unit_tests/agent_executor/test_parsing.py +++ b/backend/tests/unit_tests/agent_executor/test_parsing.py @@ -1,9 +1,9 @@ """Test parsing logic.""" import mimetypes -from app.parsing import MIMETYPE_BASED_PARSER, SUPPORTED_MIMETYPES from langchain.document_loaders import Blob +from app.parsing import MIMETYPE_BASED_PARSER, SUPPORTED_MIMETYPES from tests.unit_tests.fixtures import get_sample_paths diff --git a/backend/tests/unit_tests/agent_executor/test_upload.py b/backend/tests/unit_tests/agent_executor/test_upload.py index ec66944d..7a4b43c4 100644 --- a/backend/tests/unit_tests/agent_executor/test_upload.py +++ b/backend/tests/unit_tests/agent_executor/test_upload.py @@ -1,8 +1,8 @@ from io import BytesIO -from app.upload import IngestRunnable, _guess_mimetype from langchain.text_splitter import RecursiveCharacterTextSplitter +from app.upload import IngestRunnable, _guess_mimetype from tests.unit_tests.fixtures import get_sample_paths from tests.unit_tests.utils import InMemoryVectorStore From b5e11f54cb0109da7ac53f9a64519bad49484893 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 13:01:16 -0800 Subject: [PATCH 14/17] Remove print --- backend/app/api/runs.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/backend/app/api/runs.py b/backend/app/api/runs.py index b8b42787..7d2cb4c6 100644 --- a/backend/app/api/runs.py +++ b/backend/app/api/runs.py @@ -67,8 +67,6 @@ async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUser }, } try: - print(body["input"]) - print(agent.get_input_schema(config)) input_ = _unpack_input(agent.get_input_schema(config).validate(body["input"])) except ValidationError as e: raise RequestValidationError(e.errors(), body=body) From 467c7addf3f88668876d768fe102774efe291016 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 13:18:11 -0800 Subject: [PATCH 15/17] Fix retrieval --- backend/app/upload.py | 4 ++-- backend/poetry.lock | 22 +++++++++++++++++++++- backend/pyproject.toml | 1 + frontend/src/components/Config.tsx | 2 +- 4 files changed, 25 insertions(+), 4 deletions(-) diff --git a/backend/app/upload.py b/backend/app/upload.py index 8be0ff44..87168003 100644 --- a/backend/app/upload.py +++ b/backend/app/upload.py @@ -30,10 +30,10 @@ def _guess_mimetype(file_bytes: bytes) -> str: """Guess the mime-type of a file.""" try: import magic - except ImportError: + except ImportError as e: raise ImportError( "magic package not found, please install it with `pip install python-magic`" - ) + ) from e mime = magic.Magic(mime=True) mime_type = mime.from_buffer(file_bytes) diff --git a/backend/poetry.lock b/backend/poetry.lock index 5dda15b9..ab13ff29 100644 --- a/backend/poetry.lock +++ b/backend/poetry.lock @@ -1893,6 +1893,26 @@ files = [ {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"}, ] +[[package]] +name = "pdfminer-six" +version = "20231228" +description = "PDF parser and analyzer" +optional = false +python-versions = ">=3.6" +files = [ + {file = "pdfminer.six-20231228-py3-none-any.whl", hash = "sha256:e8d3c3310e6fbc1fe414090123ab01351634b4ecb021232206c4c9a8ca3e3b8f"}, + {file = "pdfminer.six-20231228.tar.gz", hash = "sha256:6004da3ad1a7a4d45930cb950393df89b068e73be365a6ff64a838d37bcb08c4"}, +] + +[package.dependencies] +charset-normalizer = ">=2.0.0" +cryptography = ">=36.0.0" + +[package.extras] +dev = ["black", "mypy (==0.931)", "nox", "pytest"] +docs = ["sphinx", "sphinx-argparse"] +image = ["Pillow"] + [[package]] name = "pluggy" version = "1.3.0" @@ -3233,4 +3253,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.9.0" -content-hash = "f135b0d7ec439e852a6bb435b1cc4d9e59792a93fbbe222d04f6e3a0028394cf" +content-hash = "87914c875a6cfe86785734dd5a0cb33d14700ffb050e1186a935579def43c612" diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 419c85cc..b3806ae7 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -34,6 +34,7 @@ xmltodict = "^0.13.0" wikipedia = "^1.4.0" langchain-google-vertexai = "^0.0.3" setuptools = "^69.0.3" +pdfminer-six = "^20231228" [tool.poetry.group.dev.dependencies] uvicorn = "^0.23.2" diff --git a/frontend/src/components/Config.tsx b/frontend/src/components/Config.tsx index 570fa6c2..4b33ce14 100644 --- a/frontend/src/components/Config.tsx +++ b/frontend/src/components/Config.tsx @@ -364,7 +364,7 @@ export function Config(props: { ); } })} - {!props.config && values?.configurable?.type === "agent" && ( + {!props.config && ( Date: Fri, 26 Jan 2024 14:28:33 -0800 Subject: [PATCH 16/17] Update default --- backend/app/agent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/app/agent.py b/backend/app/agent.py index 2c7709f6..2bae07ad 100644 --- a/backend/app/agent.py +++ b/backend/app/agent.py @@ -108,7 +108,7 @@ def __init__( agent = ( ConfigurableAgent( - agent=AgentType.GEMINI, + agent=AgentType.GPT_35_TURBO, tools=[], system_message=DEFAULT_SYSTEM_MESSAGE, retrieval_description=RETRIEVAL_DESCRIPTION, From 82b85d92d7906f626352fac816e8c62d51052b65 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 26 Jan 2024 15:18:47 -0800 Subject: [PATCH 17/17] greed is good --- backend/app/api/runs.py | 101 +++++---------------------------- backend/app/storage.py | 7 ++- backend/app/stream.py | 121 +++++++++++++++++++++++++--------------- 3 files changed, 97 insertions(+), 132 deletions(-) diff --git a/backend/app/api/runs.py b/backend/app/api/runs.py index 7d2cb4c6..35988ae6 100644 --- a/backend/app/api/runs.py +++ b/backend/app/api/runs.py @@ -1,35 +1,27 @@ import asyncio import json -from typing import AsyncIterator, Sequence -from uuid import uuid4 +from typing import Sequence import langsmith.client -import orjson from fastapi import APIRouter, BackgroundTasks, HTTPException, Request from fastapi.exceptions import RequestValidationError from langchain.pydantic_v1 import ValidationError -from langchain.schema.messages import AnyMessage, FunctionMessage -from langchain.schema.output import ChatGeneration -from langchain.schema.runnable import RunnableConfig -from langserve.callbacks import AsyncEventAggregatorCallback +from langchain_core.messages import AnyMessage +from langchain_core.runnables import RunnableConfig from langserve.schema import FeedbackCreateRequest -from langserve.serialization import WellKnownLCSerializer -from langserve.server import _get_base_run_id_as_str, _unpack_input +from langserve.server import _unpack_input from langsmith.utils import tracing_is_enabled from pydantic import BaseModel, Field from sse_starlette import EventSourceResponse from app.agent import agent from app.schema import OpengptsUserId -from app.storage import get_assistant, get_thread_messages, public_user_id -from app.stream import StreamMessagesHandler +from app.storage import get_assistant, public_user_id +from app.stream import astream_messages, to_sse router = APIRouter() -_serializer = WellKnownLCSerializer() - - class CreateRunPayload(BaseModel): """Payload for creating a run.""" @@ -43,16 +35,13 @@ async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUser body = await request.json() except json.JSONDecodeError: raise RequestValidationError(errors=["Invalid JSON body"]) - assistant, public_assistant, state = await asyncio.gather( + assistant, public_assistant = await asyncio.gather( asyncio.get_running_loop().run_in_executor( None, get_assistant, opengpts_user_id, body["assistant_id"] ), asyncio.get_running_loop().run_in_executor( None, get_assistant, public_user_id, body["assistant_id"] ), - asyncio.get_running_loop().run_in_executor( - None, get_thread_messages, opengpts_user_id, body["thread_id"] - ), ) assistant = assistant or public_assistant if not assistant: @@ -71,94 +60,32 @@ async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUser except ValidationError as e: raise RequestValidationError(e.errors(), body=body) - return input_, config, state["messages"] + return input_, config @router.post("") async def create_run( - request: Request, payload: CreateRunPayload, # for openapi docs + request: Request, opengpts_user_id: OpengptsUserId, background_tasks: BackgroundTasks, ): """Create a run.""" - input_, config, messages = await _run_input_and_config(request, opengpts_user_id) + input_, config = await _run_input_and_config(request, opengpts_user_id) background_tasks.add_task(agent.ainvoke, input_, config) return {"status": "ok"} # TODO add a run id @router.post("/stream") async def stream_run( - request: Request, payload: CreateRunPayload, # for openapi docs + request: Request, opengpts_user_id: OpengptsUserId, ): """Create a run.""" - input_, config, messages = await _run_input_and_config(request, opengpts_user_id) - streamer = StreamMessagesHandler(messages + input_) - event_aggregator = AsyncEventAggregatorCallback() - config["callbacks"] = [streamer, event_aggregator] - - # Call the runnable in streaming mode, - # add each chunk to the output stream - async def consume_astream() -> None: - try: - async for chunk in agent.astream(input_, config): - # await streamer.send_stream.send(chunk) - # hack: function messages aren't generated by chat model - # so the callback handler doesn't know about them - if chunk.get("action"): - message = chunk["action"] - if isinstance(message, FunctionMessage): - streamer.output[uuid4()] = ChatGeneration(message=message) - except Exception as e: - await streamer.send_stream.send(e) - finally: - await streamer.send_stream.aclose() - - # Start the runnable in the background - task = asyncio.create_task(consume_astream()) - - # Consume the stream into an EventSourceResponse - async def _stream() -> AsyncIterator[dict]: - has_sent_metadata = False - - async for chunk in streamer.receive_stream: - if isinstance(chunk, BaseException): - yield { - "event": "error", - # Do not expose the error message to the client since - # the message may contain sensitive information. - # We'll add client side errors for validation as well. - "data": orjson.dumps( - {"status_code": 500, "message": "Internal Server Error"} - ).decode(), - } - raise chunk - else: - if not has_sent_metadata and event_aggregator.callback_events: - yield { - "event": "metadata", - "data": orjson.dumps( - {"run_id": _get_base_run_id_as_str(event_aggregator)} - ).decode(), - } - has_sent_metadata = True - - yield { - # EventSourceResponse expects a string for data - # so after serializing into bytes, we decode into utf-8 - # to get a string. - "data": _serializer.dumps(chunk).decode("utf-8"), - "event": "data", - } - - # Send an end event to signal the end of the stream - yield {"event": "end"} - # Wait for the runnable to finish - await task - - return EventSourceResponse(_stream()) + input_, config = await _run_input_and_config(request, opengpts_user_id) + + return EventSourceResponse(to_sse(astream_messages(agent, input_, config))) @router.get("/input_schema") diff --git a/backend/app/storage.py b/backend/app/storage.py index 9f82a43b..ad56a9f5 100644 --- a/backend/app/storage.py +++ b/backend/app/storage.py @@ -1,6 +1,7 @@ import os from datetime import datetime from typing import List, Sequence +from app.stream import map_chunk_to_msg import orjson from langchain.schema.messages import AnyMessage @@ -155,7 +156,11 @@ def get_thread_messages(user_id: str, thread_id: str): app = get_agent_executor([], AgentType.GPT_35_TURBO, "") checkpoint = app.checkpointer.get(config) or empty_checkpoint() with ChannelsManager(app.channels, checkpoint) as channels: - return {"messages": channels[MESSAGES_CHANNEL_NAME].get()} + return { + "messages": [ + map_chunk_to_msg(msg) for msg in channels[MESSAGES_CHANNEL_NAME].get() + ] + } def post_thread_messages(user_id: str, thread_id: str, messages: Sequence[AnyMessage]): diff --git a/backend/app/stream.py b/backend/app/stream.py index 51581e20..e6526037 100644 --- a/backend/app/stream.py +++ b/backend/app/stream.py @@ -1,10 +1,6 @@ -import math -from typing import Any, Dict, Optional, Sequence, Union -from uuid import UUID +from typing import AsyncIterator, Optional, Sequence, Union -from anyio import create_memory_object_stream -from langchain.callbacks.base import BaseCallbackHandler -from langchain.schema.messages import ( +from langchain_core.messages import ( AIMessage, AIMessageChunk, BaseMessage, @@ -15,50 +11,50 @@ FunctionMessageChunk, HumanMessage, HumanMessageChunk, + AnyMessage, ) -from langchain.schema.output import ChatGenerationChunk, GenerationChunk +from langchain_core.runnables import Runnable, RunnableConfig +from langserve.serialization import WellKnownLCSerializer +import orjson +MessagesStream = AsyncIterator[Union[list[AnyMessage], str]] -class StreamMessagesHandler(BaseCallbackHandler): - def __init__(self, messages: Sequence[BaseMessage]) -> None: - self.messages = messages - self.output: Dict[UUID, ChatGenerationChunk] = {} - send_stream, receive_stream = create_memory_object_stream(math.inf) - self.send_stream = send_stream - self.receive_stream = receive_stream - def on_llm_new_token( - self, - token: str, - *, - chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, - run_id: UUID, - **kwargs: Any, - ) -> Any: - # If this is being called for a non-Chat Model run, convert to AIMessage - if chunk is None: - chunk = ChatGenerationChunk(message=AIMessageChunk(content=token)) - # If we get something we don't know how to handle, ignore it - if not ( - isinstance(chunk, ChatGenerationChunk) - or isinstance(chunk, BaseMessageChunk) +async def astream_messages( + app: Runnable, input: Sequence[AnyMessage], config: RunnableConfig +) -> MessagesStream: + """Stream messages from the runnable.""" + root_run_id: Optional[str] = None + last_messages_list: Optional[list[AnyMessage]] = None + last_stream_run_id: Optional[str] = None + + async for event in app.astream_events( + input, config, version="v1", output_keys=["__root__"] + ): + if event["event"] == "on_chain_start" and not root_run_id: + root_run_id = event["run_id"] + + yield root_run_id + elif event["event"] == "on_chain_stream" and event["run_id"] == root_run_id: + last_messages_list = event["data"]["chunk"]["__root__"] + + yield last_messages_list + elif ( + event["event"] == "on_chat_model_stream" and last_messages_list is not None ): - return - # Convert messages to ChatGenerationChunks (workaround for old langchahin) - if isinstance(chunk, BaseMessageChunk): - chunk = ChatGenerationChunk(message=chunk) - # Accumulate the output (ChatGenerationChunk implements __add__) - if not self.output.get(run_id): - self.output[run_id] = chunk - else: - self.output[run_id] += chunk - # Send the messages to the stream - self.send_stream.send_nowait( - ( - self.messages - + [map_chunk_to_msg(chunk.message) for chunk in self.output.values()] + is_new_stream_run = ( + last_stream_run_id is None or last_stream_run_id != event["run_id"] ) - ) + is_diff_msg_type = last_messages_list and type( # noqa: E721 + last_messages_list[-1] + ) != type(event["data"]["chunk"]) + if is_new_stream_run or is_diff_msg_type: + last_stream_run_id = event["run_id"] + last_messages_list.append(event["data"]["chunk"]) + else: + last_messages_list[-1] = last_messages_list[-1] + event["data"]["chunk"] + + yield last_messages_list def map_chunk_to_msg(chunk: BaseMessageChunk) -> BaseMessage: @@ -75,3 +71,40 @@ def map_chunk_to_msg(chunk: BaseMessageChunk) -> BaseMessage: return ChatMessage(**args) else: raise ValueError(f"Unknown chunk type: {chunk}") + + +_serializer = WellKnownLCSerializer() + + +async def to_sse(messages_stream: MessagesStream) -> AsyncIterator[dict]: + """Consume the stream into an EventSourceResponse""" + try: + async for chunk in messages_stream: + # EventSourceResponse expects a string for data + # so after serializing into bytes, we decode into utf-8 + # to get a string. + if isinstance(chunk, str): + yield { + "event": "metadata", + "data": orjson.dumps({"run_id": chunk}).decode(), + } + else: + yield { + "event": "data", + "data": _serializer.dumps( + [map_chunk_to_msg(msg) for msg in chunk] + ).decode(), + } + except Exception: + yield { + "event": "error", + # Do not expose the error message to the client since + # the message may contain sensitive information. + # We'll add client side errors for validation as well. + "data": orjson.dumps( + {"status_code": 500, "message": "Internal Server Error"} + ).decode(), + } + + # Send an end event to signal the end of the stream + yield {"event": "end"}