From 4252a97aeb605afed23dd1499ed37f33ca2026db Mon Sep 17 00:00:00 2001 From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com> Date: Wed, 28 Aug 2024 11:58:45 -0700 Subject: [PATCH] Rm poetry --- react_agent/__init__.py => .codespellignore | 0 .env.example | 1 + .github/workflows/integration-tests.yml | 41 + .github/workflows/unit-tests.yml | 57 + .gitignore | 1 + README.md | 83 +- langgraph.json | 2 +- poetry.lock | 2166 ------------------- pyproject.toml | 50 +- react_agent/graph.py | 91 - react_agent/utils/__init__.py | 3 - react_agent/utils/configuration.py | 28 - react_agent/utils/state.py | 52 - react_agent/utils/tools.py | 82 - react_agent/utils/utils.py | 13 - setup.py | 32 + src/enrichment_agent/__init__.py | 3 + src/enrichment_agent/configuration.py | 25 + src/enrichment_agent/graph.py | 191 ++ src/enrichment_agent/state.py | 70 + src/enrichment_agent/tools.py | 56 + src/enrichment_agent/utils.py | 25 + tests/integration_tests/test_graph.py | 23 + tests/unit_tests/test_configuration.py | 5 + 24 files changed, 629 insertions(+), 2471 deletions(-) rename react_agent/__init__.py => .codespellignore (100%) create mode 100644 .github/workflows/integration-tests.yml create mode 100644 .github/workflows/unit-tests.yml delete mode 100644 poetry.lock delete mode 100644 react_agent/graph.py delete mode 100644 react_agent/utils/__init__.py delete mode 100644 react_agent/utils/configuration.py delete mode 100644 react_agent/utils/state.py delete mode 100644 react_agent/utils/tools.py delete mode 100644 react_agent/utils/utils.py create mode 100644 setup.py create mode 100644 src/enrichment_agent/__init__.py create mode 100644 src/enrichment_agent/configuration.py create mode 100644 src/enrichment_agent/graph.py create mode 100644 src/enrichment_agent/state.py create mode 100644 src/enrichment_agent/tools.py create mode 100644 src/enrichment_agent/utils.py create mode 100644 tests/integration_tests/test_graph.py create mode 100644 tests/unit_tests/test_configuration.py diff --git a/react_agent/__init__.py b/.codespellignore similarity index 100% rename from react_agent/__init__.py rename to .codespellignore diff --git a/.env.example b/.env.example index c8712cf..9a3008a 100644 --- a/.env.example +++ b/.env.example @@ -1,3 +1,4 @@ ANTHROPIC_API_KEY=.... FIREWORKS_API_KEY=... OPENAI_API_KEY=... +TAVILY_API_KEY=... \ No newline at end of file diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml new file mode 100644 index 0000000..a4f82e9 --- /dev/null +++ b/.github/workflows/integration-tests.yml @@ -0,0 +1,41 @@ +# This workflow will run integration tests for the current project once per day + +name: Integration Tests + +on: + schedule: + - cron: "37 14 * * *" # Run at 7:37 AM Pacific Time (14:37 UTC) every day + workflow_dispatch: # Allows triggering the workflow manually in GitHub UI + +# If another scheduled run starts while this workflow is still running, +# cancel the earlier run in favor of the next run. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + integration-tests: + name: Integration Tests + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.11", "3.12"] + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + uv venv + uv pip install -r pyproject.toml + - name: Run integration tests + env: + ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }} + run: | + uv pip install pytest + uv run pytest tests/integration_tests diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml new file mode 100644 index 0000000..055407c --- /dev/null +++ b/.github/workflows/unit-tests.yml @@ -0,0 +1,57 @@ +# This workflow will run unit tests for the current project + +name: CI + +on: + push: + branches: ["main"] + pull_request: + workflow_dispatch: # Allows triggering the workflow manually in GitHub UI + +# If another push to the same PR or branch happens while this workflow is still running, +# cancel the earlier run in favor of the next run. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + unit-tests: + name: Unit Tests + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.11", "3.12"] + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + uv venv + uv pip install -r pyproject.toml + - name: Lint with ruff + run: | + uv pip install ruff + uv run ruff check . + - name: Lint with mypy + run: | + uv pip install mypy + uv run mypy --strict src/ + - name: Check README spelling + uses: codespell-project/actions-codespell@v2 + with: + ignore_words_file: .codespellignore + path: README.md + - name: Check code spelling + uses: codespell-project/actions-codespell@v2 + with: + ignore_words_file: .codespellignore + path: src/ + - name: Run tests with pytest + run: | + uv pip install pytest + uv run pytest tests/unit_tests diff --git a/.gitignore b/.gitignore index 82f9275..ab74e15 100644 --- a/.gitignore +++ b/.gitignore @@ -160,3 +160,4 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +.DS_Store diff --git a/README.md b/README.md index 5f2cc22..375d768 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,43 @@ -# LangGraph ReAct Agent Template +# LangGraph Data Enrichment Template -This LangGraph template implements a simple, extensible ReAct agent. +[![CI](https://github.com/langchain-ai/data-enrichment/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/langchain-ai/data-enrichment/actions/workflows/unit-tests.yml) +[![Integration Tests](https://github.com/langchain-ai/data-enrichment/actions/workflows/integration-tests.yml/badge.svg)](https://github.com/langchain-ai/data-enrichment/actions/workflows/integration-tests.yml) -![Graph view in LangGraph studio UI](./static/studio_ui.png) +This is a starter project to help you get started with developing a data enrichment agent using [LangGraph](https://github.com/langchain-ai/langgraph) in [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio). + +![](/static/studio.png) + +It contains an example graph exported from `src/enrichment_agent/graph.py` that implements a research assistant capable of automatically gathering information on various topics from the web. + +## What it does + +The enrichment agent: + +1. Takes a research topic as input +2. Searches the web for relevant information +3. Reads and extracts key details from websites +4. Organizes the findings into a structured format +5. Validates the gathered information for completeness and accuracy + +By default, it's set up to gather information based on the user-provided schema passed through the `template_schema` key in the state. + + +## Getting Started + +This template was designed for [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio). To use, clone this repo locally and open the folder in LangGraph Studio. + +Note that the `Deploy` button is currently not supported, but will be soon! + +You will need the latest versions of `langgraph` and `langchain`. See these instructions for help upgrading an [existing project](https://github.com/langchain-ai/langgraph#installation). + +You can also [click here](https://www.loom.com/share/81cafa32d57f4933bd5d9b08c70f460c?sid=4ebcb366-f27a-4c49-854d-169106b4f6fe) to see a (rough) video tour of Studio. + +To set up: + +1. Set up your API keys for the LLM (Claude) and search tool (Tavily) in the `.env` file. +2. Install the required dependencies (`poetry install`) +3. Customize whatever you'd like in the code. +4. Run the script with your research topic as input. ## Repo Structure @@ -10,21 +45,37 @@ This LangGraph template implements a simple, extensible ReAct agent. ├── LICENSE ├── README.md ├── langgraph.json -├── poetry.lock ├── pyproject.toml -├── react_agent -│   ├── __init__.py -│   ├── graph.py -│   └── utils -│   ├── __init__.py -│   ├── configuration.py # Define the configurable variables -│   ├── state.py # Define state variables and how they're updated -│   ├── tools.py # Define the tools your agent can access -│   └── utils.py # Other sundry utilities -└── tests # Add whatever tests you'd like here +├── src +│ └── enrichment_agent +│ ├── __init__.py +│ ├── configuration.py +│ ├── graph.py +│ ├── state.py +│ ├── tools.py +│ └── utils.py +├── static +│ └── studio_ui.png +└── tests ├── integration_tests - │   └── __init__.py + │ ├── __init__.py + │ └── test_graph.py └── unit_tests └── __init__.py ``` -r \ No newline at end of file + +## Development + +While iterating on your graph, you can edit past state and rerun your app from past states to debug specific nodes. Local changes will be automatically applied via hot reload. Try adding an interrupt before the agent calls tools, updating the default system message in `src/enrichment_agent/utils.py` to take on a persona, or adding additional nodes and edges! + +Follow up requests will be appended to the same thread. You can create an entirely new thread, clearing previous history, using the `+` button in the top right. + +You can find the latest (under construction) docs on [LangGraph](https://github.com/langchain-ai/langgraph) here, including examples and other references. Using those guides can help you pick the right patterns to adapt here for your use case. + +LangGraph Studio also integrates with [LangSmith](https://smith.langchain.com/) for more in-depth tracing and collaboration with teammates. + +## How to extend it + +1. **Customize research targets**: Modify the `InfoSchema` to gather different types of information. +2. **Enhance data sources and validation**: Add new tools, APIs, or implement more rigorous fact-checking. +3. **Improve output and interactivity**: Develop custom formatting and user feedback mechanisms. \ No newline at end of file diff --git a/langgraph.json b/langgraph.json index 4f31bba..4d77873 100644 --- a/langgraph.json +++ b/langgraph.json @@ -1,7 +1,7 @@ { "dependencies": ["."], "graphs": { - "agent": "./react_agent/graph.py:graph" + "agent": "./src/enrichment_agent/graph.py:graph" }, "env": ".env" } diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index f9a8fa0..0000000 --- a/poetry.lock +++ /dev/null @@ -1,2166 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - 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-[tool.poetry.group.dev.dependencies] -mypy = "^1.11.1" -ruff = "^0.6.1" +[project.optional-dependencies] +dev = ["mypy>=1.11.1", "ruff>=0.6.1", "pytest-asyncio"] [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +requires = ["setuptools>=73.0.0", "wheel"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] +include = ["*"] + +[tool.setuptools.package-data] +"*" = ["py.typed"] \ No newline at end of file diff --git a/react_agent/graph.py b/react_agent/graph.py deleted file mode 100644 index 46d6d19..0000000 --- a/react_agent/graph.py +++ /dev/null @@ -1,91 +0,0 @@ -"""Define a custom graph that implements a simple Reasoning and Action agent pattern. - -Works with a chat model that utilizes tool calling.""" - -from datetime import datetime, timezone - -from langchain.chat_models import init_chat_model -from langchain_core.messages import AIMessage -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.runnables import RunnableConfig -from langgraph.graph import StateGraph -from langgraph.prebuilt import ToolNode -from react_agent.utils.configuration import Configuration, ensure_configurable -from react_agent.utils.state import InputState, State -from react_agent.utils.tools import TOOLS - -# Define the function that calls the model - - -async def call_model(state: State, config: RunnableConfig): - """Call the LLM powering our "agent".""" - configuration = ensure_configurable(config) - # Feel free to customize the prompt, model, and other logic! - prompt = ChatPromptTemplate.from_messages( - [("system", configuration["system_prompt"]), ("placeholder", "{messages}")] - ) - model = init_chat_model(configuration["model_name"]).bind_tools(TOOLS) - - message_value = await prompt.ainvoke( - {**state, "system_time": datetime.now(tz=timezone.utc).isoformat()}, config - ) - response: AIMessage = await model.ainvoke(message_value, config) - if state["is_last_step"] and response.tool_calls: - return { - "messages": [ - AIMessage( - id=response.id, - content="Sorry, I could not find an answer to your question in the specified number of steps.", - ) - ] - } - # We return a list, because this will get added to the existing list - return {"messages": [response]} - - -# Define a new graph - -workflow = StateGraph(State, input=InputState, config_schema=Configuration) - -# Define the two nodes we will cycle between -workflow.add_node(call_model) -workflow.add_node("tools", ToolNode(TOOLS)) - - -# Set the entrypoint as `call_model` -# This means that this node is the first one called -workflow.add_edge("__start__", "call_model") - - -# Define the function that determines whether to continue or not -def route_model_output(state: State): - messages = state["messages"] - last_message = messages[-1] - # If there is no function call, then we finish - if not last_message.tool_calls: - return "__end__" - # Otherwise if there are tools called, we continue - else: - return "tools" - - -# We now add a conditional edge -workflow.add_conditional_edges( - # First, we define the edges' source node. We use `call_model`. - # This means these are the edges taken after the `call_model` node is called. - "call_model", - # Next, we pass in the function that will determine the sink node(s), which - # will be called after the source node is called. - route_model_output, -) - -# We now add a normal edge from `tools` to `call_model`. -# This means that after `tools` is called, `call_model` node is called next. -workflow.add_edge("tools", "call_model") - -# Finally, we compile it! -# This compiles it into a graph you can invoke and deploy. -graph = workflow.compile( - interrupt_before=[], # if you want to update the state before calling the tools - interrupt_after=[], -) diff --git a/react_agent/utils/__init__.py b/react_agent/utils/__init__.py deleted file mode 100644 index bf6bd6c..0000000 --- a/react_agent/utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from dotenv import load_dotenv - -load_dotenv() diff --git a/react_agent/utils/configuration.py b/react_agent/utils/configuration.py deleted file mode 100644 index 1648a63..0000000 --- a/react_agent/utils/configuration.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Define the configurable parameters for the agent.""" - -from typing_extensions import TypedDict -from langchain_core.runnables import RunnableConfig - - -class Configuration(TypedDict): - """The configuration for the agent.""" - - system_prompt: str - model_name: str - scraper_tool_model_name: str - - -def ensure_configurable(config: RunnableConfig) -> Configuration: - """Ensure the defaults are populated.""" - configurable = config.get("configurable") or {} - return Configuration( - system_prompt=configurable.get( - "system_prompt", - "You are a helpful AI assistant.\nSystem time: {system_time}", - ), - model_name=configurable.get("model_name", "claude-3-5-sonnet-20240620"), - scraper_tool_model_name=configurable.get( - "scraper_tool_model_name", - "accounts/fireworks/models/firefunction-v2", - ), - ) diff --git a/react_agent/utils/state.py b/react_agent/utils/state.py deleted file mode 100644 index 158d60c..0000000 --- a/react_agent/utils/state.py +++ /dev/null @@ -1,52 +0,0 @@ -from typing import Sequence - -from langchain_core.messages import AnyMessage -from langgraph.graph import add_messages -from langgraph.managed import IsLastStep -from typing_extensions import Annotated, TypedDict - - -# Optional, the InputState is a restricted version of the State that is used to -# define a narrower interface to the outside world vs. what is maintained -# internally. -class InputState(TypedDict): - messages: Annotated[Sequence[AnyMessage], add_messages] - """Messages track the primary execution state of the agent. - - Typically accumulates a pattern of: - - 1. HumanMessage - user input - 2. AIMessage with .tool_calls - agent picking tool(s) to use to collect - information - 3. ToolMessage(s) - the responses (or errors) from the executed tools - - (... repeat steps 2 and 3 as needed ...) - 4. AIMessage without .tool_calls - agent responding in unstructured - format to the user. - - 5. HumanMessage - user responds with the next conversational turn. - - (... repeat steps 2-5 as needed ... ) - - Merges two lists of messages, updating existing messages by ID. - - By default, this ensures the state is "append-only", unless the - new message has the same ID as an existing message. - - Returns: - A new list of messages with the messages from `right` merged into `left`. - If a message in `right` has the same ID as a message in `left`, the - message from `right` will replace the message from `left`.""" - - -# This is the primary state of your agent, where you can store any information -class State(InputState): - """The state of your graph / agent.""" - - is_last_step: IsLastStep - """Set to 'true' if the step is recursion_limit - 1 (meaning it's the last step before the graph will raise an error) - - This is a 'managed' variable (meaning it is managed by the state machine rather than your code).""" - - # Feel free to add additional attributes to your state as needed. - # Common examples include retrieved documents, extracted entities, API connections, etc. diff --git a/react_agent/utils/tools.py b/react_agent/utils/tools.py deleted file mode 100644 index aacfb98..0000000 --- a/react_agent/utils/tools.py +++ /dev/null @@ -1,82 +0,0 @@ -import httpx - -from react_agent.utils.configuration import ensure_configurable -from langchain_core.runnables import RunnableConfig - -from langchain.chat_models import init_chat_model -from datetime import datetime, timezone - -from react_agent.utils.utils import get_message_text - - -# note that arguments typed as "RunnableConfig" in tools will be excluded from the schema generated -# for the model. -# They are treated as "injected arguments" -async def scrape_webpage(url: str, instructions: str, *, config: RunnableConfig) -> str: - """Scrape the given webpage and return a summary of text based on the instructions. - - Args: - url: The URL of the webpage to scrape. - instructions: The instructions to give to the scraper. An LLM will be used to respond using the - instructions and the scraped text. - """ - async with httpx.AsyncClient() as client: - response = await client.get(url) - web_text = response.text - - configuration = ensure_configurable(config) - model = init_chat_model(configuration["model_name"]) - response_msg = await model.ainvoke( - [ - ( - "system", - "You are a helpful web scraper AI assistant. You are working in extractive Q&A mode, meaning you refrain from making overly abstractive responses." - "Respond to the user's instructions." - " Based on the provided webpage. If you are unable to answer the question, let the user know. Do not guess." - " Provide citations and direct quotes when possible." - f" \n\n\n{web_text}\n" - f"\n\nSystem time: {datetime.now(tz=timezone.utc)}", - ), - ("user", instructions), - ] - ) - return get_message_text(response_msg) - - -# Note, in a real use case, you'd want to use a more robust search API. -async def search_duckduckgo(query: str) -> dict: - """Search DuckDuckGo for the given query and return the JSON response. - - Results are limited, as this is the free public API. - """ - async with httpx.AsyncClient() as client: - response = await client.get( - "https://api.duckduckgo.com/", params={"q": query, "format": "json"} - ) - result = response.json() - - result.pop("meta", None) - return result - - -async def search_wikipedia(query: str) -> dict: - """Search Wikipedia for the given query and return the JSON response.""" - url = "https://en.wikipedia.org/w/api.php" - async with httpx.AsyncClient() as client: - response = await client.get( - url, - params={ - "action": "query", - "list": "search", - "srsearch": query, - "format": "json", - }, - ) - return response.json() - - -TOOLS = [ - scrape_webpage, - search_duckduckgo, - search_wikipedia, -] diff --git a/react_agent/utils/utils.py b/react_agent/utils/utils.py deleted file mode 100644 index 4e1972c..0000000 --- a/react_agent/utils/utils.py +++ /dev/null @@ -1,13 +0,0 @@ -from langchain_core.messages import AnyMessage - - -def get_message_text(msg: AnyMessage) -> str: - """Get the text content of a message.""" - content = msg.content - if isinstance(content, str): - return content - elif isinstance(content, dict): - return content.get("text", "") - else: - txts = [c if isinstance(c, str) else (c.get("text") or "") for c in content] - return "".join(txts).strip() diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..487ad0b --- /dev/null +++ b/setup.py @@ -0,0 +1,32 @@ +import os +import shutil +from setuptools import setup +from setuptools.command.build_py import build_py + + +class CustomBuildPy(build_py): + def run(self): + build_py.run(self) + build_dir = self.build_lib + nested_dir = os.path.join(build_dir, "langgraph", "templates") + os.makedirs(nested_dir, exist_ok=True) + + # Move all directories from src to nested structure + for item in os.listdir(build_dir): + item_path = os.path.join(build_dir, item) + if os.path.isdir(item_path) and item != "langgraph": + shutil.move(item_path, os.path.join(nested_dir, item)) + + open(os.path.join(build_dir, "langgraph", "__init__.py"), "a").close() + open(os.path.join(nested_dir, "__init__.py"), "a").close() + + print( + "Custom build completed. Packages restructured under langgraph.templates." + ) + + +setup( + cmdclass={ + "build_py": CustomBuildPy, + }, +) diff --git a/src/enrichment_agent/__init__.py b/src/enrichment_agent/__init__.py new file mode 100644 index 0000000..a144e12 --- /dev/null +++ b/src/enrichment_agent/__init__.py @@ -0,0 +1,3 @@ +from enrichment_agent.graph import graph + +__all__ = ["graph"] diff --git a/src/enrichment_agent/configuration.py b/src/enrichment_agent/configuration.py new file mode 100644 index 0000000..4e45273 --- /dev/null +++ b/src/enrichment_agent/configuration.py @@ -0,0 +1,25 @@ +"""Define the configurable parameters for the agent.""" + +from __future__ import annotations +from dataclasses import dataclass, fields +from typing import Optional + +from langchain_core.runnables import RunnableConfig, ensure_config + + +@dataclass(kw_only=True) +class Configuration: + """The configuration for the agent.""" + + model_name: str = "claude-3-5-sonnet-20240620" + max_search_results: int = 10 + max_info_tool_calls: int = 3 + + @classmethod + def from_runnable_config( + cls, config: Optional[RunnableConfig] = None + ) -> Configuration: + config = ensure_config(config) + configurable = config.get("configurable") or {} + _fields = {f.name for f in fields(cls) if f.init} + return cls(**{k: v for k, v in configurable.items() if k in _fields}) diff --git a/src/enrichment_agent/graph.py b/src/enrichment_agent/graph.py new file mode 100644 index 0000000..83a06c6 --- /dev/null +++ b/src/enrichment_agent/graph.py @@ -0,0 +1,191 @@ +import json +from typing import List, Literal, Optional, cast, Dict, Any + +from enrichment_agent.configuration import Configuration +from enrichment_agent.state import State, InputState, OutputState +from enrichment_agent.utils import init_model +from enrichment_agent.tools import scrape_website, search +from langchain_core.messages import AIMessage, HumanMessage, ToolMessage, BaseMessage +from langchain_core.runnables import RunnableConfig +from langgraph.graph import StateGraph +from langgraph.prebuilt import ToolNode +from pydantic import BaseModel, Field + +main_prompt = """You are doing web research on behalf of a user. You are trying to figure out this information: + + +{info} + + +You have access to the following tools: + +- `Search`: call a search tool and get back some results +- `ScrapeWebsite`: scrape a website and get relevant notes about the given request. This will update the notes above. +- `Info`: call this when you are done and have gathered all the relevant info + +Here is the information you have about the topic you are researching: + +Topic: {topic}""" + + +# Define the nodes + + +async def call_model( + state: State, *, config: Optional[RunnableConfig] = None +) -> Dict[str, Any]: + info_tool = { + "name": "Info", + "description": "Call this when you have gathered all the relevant info", + "parameters": state["template_schema"], + } + + p = main_prompt.format( + info=json.dumps(state["template_schema"], indent=2), topic=state["topic"] + ) + messages = [HumanMessage(content=p)] + state["messages"] + raw_model = init_model(config) + + model = raw_model.bind_tools([scrape_website, search, info_tool]) + response = cast(AIMessage, await model.ainvoke(messages)) + + info = None + if response.tool_calls: + for tool_call in response.tool_calls: + if tool_call["name"] == "Info": + info = tool_call["args"] + break + + return {"messages": [response], "info": info} + + +class InfoIsSatisfactory(BaseModel): + reason: List[str] = Field( + description="First, provide reasoning for why this is either good or bad as a final result. Must include at least 3 reasons." + ) + is_satisfactory: bool = Field( + description="After providing your reasoning, provide a value indicating whether the result is satisfactory. If not, you will continue researching." + ) + + +async def call_checker( + state: State, *, config: Optional[RunnableConfig] = None +) -> Dict[str, Any]: + p = main_prompt.format( + info=json.dumps(state["template_schema"], indent=2), topic=state["topic"] + ) + messages = [HumanMessage(content=p)] + state["messages"][:-1] + presumed_info = state["info"] + checker_prompt = """I am thinking of calling the info tool with the info below. \ +Is this good? Give your reasoning as well. \ +You can encourage the Assistant to look at specific URLs if that seems relevant, or do more searches. +If you don't think it is good, you should be very specific about what could be improved. + +{presumed_info}""" + p1 = checker_prompt.format(presumed_info=json.dumps(presumed_info or {}, indent=2)) + messages.append(HumanMessage(content=p1)) + raw_model = init_model(config) + bound_model = raw_model.with_structured_output(InfoIsSatisfactory) + response = cast(InfoIsSatisfactory, await bound_model.ainvoke(messages)) + last_message = state["messages"][-1] + if not isinstance(last_message, AIMessage): + raise ValueError( + f"{call_checker.__name__} expects the last message in the state to be an AI message with tool calls." + f" Got: {type(last_message)}" + ) + + if response.is_satisfactory: + try: + return {"info": presumed_info} + except Exception as e: + return { + "messages": [ + ToolMessage( + tool_call_id=last_message.tool_calls[0]["id"], + content=f"Invalid response: {e}", + name="Info", + ) + ] + } + else: + return { + "messages": [ + ToolMessage( + tool_call_id=last_message.tool_calls[0]["id"], + content=str(response), + name="Info", + additional_kwargs={"artifact": response.dict()}, + ) + ] + } + + +def create_correction_response(state: State) -> Dict[str, List[BaseMessage]]: + last_message = state["messages"][-1] + if isinstance(last_message, AIMessage) and last_message.tool_calls: + return { + "messages": [ + ToolMessage( + tool_call_id=call["id"], + content="You must call one, and only one, tool!", + name=call["name"], + ) + for call in last_message.tool_calls + ] + } + return { + "messages": [ + HumanMessage( + content="You must call one, and only one, tool! You can call the `Info` tool to finish the task." + ) + ] + } + + +def route_after_agent( + state: State, +) -> Literal["create_correction_response", "call_checker", "tools", "__end__"]: + last_message = state["messages"][-1] + num_rounds = sum( + 1 for m in state["messages"] if isinstance(m, ToolMessage) and m.name == "Info" + ) + + if ( + not isinstance(last_message, AIMessage) + or not last_message.tool_calls + or ( + len(last_message.tool_calls) != 1 + and any(tc["name"] == "Info" for tc in last_message.tool_calls) + ) + ): + return "create_correction_response" + elif last_message.tool_calls[0]["name"] == "Info": + if num_rounds > 2: + return "__end__" + return "call_checker" + else: + return "tools" + + +def route_after_checker(state: State) -> Literal["__end__", "call_model"]: + if state["info"]: + return "__end__" + return "call_model" + + +# Create the graph +workflow = StateGraph( + State, input=InputState, output=OutputState, config_schema=Configuration +) +workflow.add_node(call_model) +workflow.add_node(call_checker) +workflow.add_node(create_correction_response) +workflow.add_node(ToolNode([search, scrape_website])) +workflow.add_edge("__start__", "call_model") +workflow.add_conditional_edges("call_model", route_after_agent) +workflow.add_edge("tools", "call_model") +workflow.add_conditional_edges("call_checker", route_after_checker) +workflow.add_edge("create_correction_response", "call_model") + +graph = workflow.compile() +graph.name = "ResearchTopic" diff --git a/src/enrichment_agent/state.py b/src/enrichment_agent/state.py new file mode 100644 index 0000000..fb6d9ee --- /dev/null +++ b/src/enrichment_agent/state.py @@ -0,0 +1,70 @@ +from typing import Any, List, Optional, TypedDict, Annotated +from langchain_core.messages import BaseMessage +from langgraph.graph import add_messages + + +class InputState(TypedDict): + topic: str + template_schema: dict[str, Any] + "The json schema defines the information the agent is tasked with filling out." + info: Optional[dict[str, Any]] # This is primarily populated by the agent + "The info state tracks the current extracted data for the given topic, conforming to the provided schema." + + +class State(InputState): + """ + A graph's STate defines three main things: + 1. The structure of the data to be passed between nodes (which "channels" to read from/write to and their types) + 2. Default values for each field + 3. Reducers for the state's fields. Reducers are functions that determine how to apply updates to the state. + See [Reducers](https://langchain-ai.github.io/langgraphjs/concepts/low_level/#reducers) for more information. + """ + + messages: Annotated[List[BaseMessage], add_messages] + """ + Messages track the primary execution state of the agent. + + Typically accumulates a pattern of: + + 1. HumanMessage - user input + 2. AIMessage with .tool_calls - agent picking tool(s) to use to collect + information + 3. ToolMessage(s) - the responses (or errors) from the executed tools + + (... repeat steps 2 and 3 as needed ...) + 4. AIMessage without .tool_calls - agent responding in unstructured + format to the user. + + 5. HumanMessage - user responds with the next conversational turn. + + (... repeat steps 2-5 as needed ... ) + + Merges two lists of messages, updating existing messages by ID. + + By default, this ensures the state is "append-only", unless the + new message has the same ID as an existing message. + + Returns: + A new list of messages with the messages from `right` merged into `left`. + If a message in `right` has the same ID as a message in `left`, the + message from `right` will replace the message from `left`. + """ + + # Feel free to add additional attributes to your state as needed. + # Common examples include retrieved documents, extracted entities, API connections, etc. + + +class OutputState(TypedDict): + """ + Represents the subset of the graph's state that is returned to the end user. + + This class defines the structure of the output that will be provided + to the user after the graph's execution is complete. + """ + + info: dict[str, Any] + """ + A dictionary containing the extracted and processed information + based on the user's query and the graph's execution. + This is the primary output of the enrichment process. + """ diff --git a/src/enrichment_agent/tools.py b/src/enrichment_agent/tools.py new file mode 100644 index 0000000..84a4a9f --- /dev/null +++ b/src/enrichment_agent/tools.py @@ -0,0 +1,56 @@ +import json +from typing import Any, Optional, cast +from typing_extensions import Annotated + +import aiohttp +from enrichment_agent.configuration import Configuration +from enrichment_agent.utils import init_model +from langchain_community.tools.tavily_search import TavilySearchResults +from langchain_core.tools import InjectedToolArg +from langchain_core.runnables import RunnableConfig + + +async def search( + query: str, *, config: Annotated[RunnableConfig, InjectedToolArg] +) -> Optional[list[dict[str, Any]]]: + """A search engine optimized for comprehensive, accurate, and trusted results. + Useful for when you need to answer questions about current events. + Input should be a search query.""" + configuration = Configuration.from_runnable_config(config) + wrapped = TavilySearchResults(max_results=configuration.max_search_results) + result = await wrapped.ainvoke({"query": query}) + return cast(list[dict[str, Any]], result) + + +_INFO_PROMPT = """You are doing web research on behalf of a user. You are trying to find out this information: + + +{info} + + +You just scraped the following website: {url} + +Based on the website content below, jot down some notes about the website. + +{content}""" + + +async def scrape_website( + url: str, + *, + state: Annotated[dict[str, Any], InjectedToolArg], + config: Annotated[RunnableConfig, InjectedToolArg], +) -> str: + """Return a natural language response from the provided URL""" + async with aiohttp.ClientSession() as session: + async with session.get(url) as response: + content = await response.text() + + p = _INFO_PROMPT.format( + info=json.dumps(state.get("schema", {}), indent=2) if state else "", + url=url, + content=content, + ) + raw_model = init_model(config) + result = await raw_model.ainvoke(p) + return str(result.content) diff --git a/src/enrichment_agent/utils.py b/src/enrichment_agent/utils.py new file mode 100644 index 0000000..7fa88a4 --- /dev/null +++ b/src/enrichment_agent/utils.py @@ -0,0 +1,25 @@ +from langchain_core.messages import AnyMessage +from typing import Optional + +from enrichment_agent.configuration import Configuration +from langchain.chat_models import init_chat_model +from langchain_core.runnables import RunnableConfig +from langchain_core.language_models import BaseChatModel + + +def get_message_text(msg: AnyMessage) -> str: + """Get the text content of a message.""" + content = msg.content + if isinstance(content, str): + return content + elif isinstance(content, dict): + return content.get("text", "") + else: + txts = [c if isinstance(c, str) else (c.get("text") or "") for c in content] + return "".join(txts).strip() + + +def init_model(config: Optional[RunnableConfig] = None) -> BaseChatModel: + """Initialize the configured chat model.""" + configuration = Configuration.from_runnable_config(config) + return init_chat_model(configuration.model_name) diff --git a/tests/integration_tests/test_graph.py b/tests/integration_tests/test_graph.py new file mode 100644 index 0000000..f3eca2e --- /dev/null +++ b/tests/integration_tests/test_graph.py @@ -0,0 +1,23 @@ +import pytest +from enrichment_agent import graph +from pydantic import BaseModel, Field + + +class EnrichmentSchema(BaseModel): + founder: str = Field(description="The name of the company founder.") + websiteUrl: str = Field( + description="Website URL of the company, e.g.: https://openai.com/, or https://microsoft.com" + ) + + +@pytest.mark.asyncio +async def test_researcher_simple_runthrough(): + res = await graph.ainvoke( + { + "topic": "LangChain", + "template_schema": EnrichmentSchema.model_json_schema(), + } + ) + + assert res["info"] is not None + assert "harrison" in res["info"]["founder"].lower() diff --git a/tests/unit_tests/test_configuration.py b/tests/unit_tests/test_configuration.py new file mode 100644 index 0000000..8eee4aa --- /dev/null +++ b/tests/unit_tests/test_configuration.py @@ -0,0 +1,5 @@ +from enrichment_agent.configuration import Configuration + + +def test_configuration_from_none(): + Configuration.from_runnable_config()