From 038bb9d57a682edfc5f4244ff575f85e3ab93676 Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Tue, 17 Sep 2024 14:15:22 -0700 Subject: [PATCH] Add docstrings, update README --- README.md | 61 +++++++++++---- src/enrichment_agent/graph.py | 137 +++++++++++++++++++++++++++++++--- src/enrichment_agent/tools.py | 48 ++++++++++-- static/config.png | Bin 0 -> 346172 bytes static/overview.png | Bin 0 -> 135602 bytes static/studio_example.png | Bin 0 -> 745136 bytes 6 files changed, 217 insertions(+), 29 deletions(-) create mode 100644 static/config.png create mode 100644 static/overview.png create mode 100644 static/studio_example.png diff --git a/README.md b/README.md index 15ace9f..e5a879d 100644 --- a/README.md +++ b/README.md @@ -3,23 +3,21 @@ [![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) -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). +Producing structured results (e.g., to populate a database or spreadsheet) from open-ended research (e.g., web research) is a common use case that LLM-powered agents are well-suited to handle. Here, we provide a general template for this kind of "data enrichment agent" agent using [LangGraph](https://github.com/langchain-ai/langgraph) in [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio). 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 and structuring the results into a user-defined JSON format. -![Graph view in LangGraph studio UI](./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. +![Overview of agent](./static/overview.png) ## What it does -The enrichment agent: +The enrichment agent defined in `src/enrichment_agent/graph.py` performs the following steps: -1. Takes a research **topic** and requested **extraction_schema** as input. The -2. Searches the web for relevant information +1. Takes a research **topic** and requested **extraction_schema** as user input. +2. The `call_agent_model` graph node uses an LLM with bound tools (defined in `tools.py`) to perform web search (using [Tavily](https://tavily.com/)) or web scraping. 3. Reads and extracts key details from websites 4. Organizes the findings into the requested 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 `extraction_schema` key in the state. +![Graph view in LangGraph studio UI](./static/studio.png) ## Getting Started @@ -87,19 +85,56 @@ OPENAI_API_KEY=your-api-key End setup instructions --> -3. Customize whatever you'd like in the code. -4. Open the folder LangGraph Studio! +3. Consider a research topic and desired extraction schema. + +As an example, here is a research topic we can consider. +``` +"Top 5 chip providers for LLM Training" +``` + +And here is a desired extraction schema. +```json +"extraction_schema": { +"type": "object", +"properties": { +"companies": { +"type": "string", +"description": "Names of top chip providers for LLM training" +}, +"technologies": { +"type": "string", +"description": "Brief summary of key chip technologies used for LLM training" +}, +"market_share": { +"type": "string", +"description": "Overview of market share distribution among top providers" +}, +"future_outlook": { +"type": "string", +"description": "Brief summary of future prospects and developments in the field" +} +}, +"required": ["companies", "technologies", "market_share", "future_outlook"] +} +``` +4. Open the folder LangGraph Studio, and input `topic` and `extraction_schema`. + +![Results In Studio](./static/studio_example.png) ## How to customize -1. **Customize research targets**: Provide a custom `extraction_schema` when calling the graph to gather different types of information. +1. **Customize research targets**: Provide a custom JSON `extraction_schema` when calling the graph to gather different types of information. 2. **Select a different model**: We default to anthropic (sonnet-35). You can select a compatible chat model using `provider/model-name` via configuration. Example: `openai/gpt-4o-mini`. -3. **Customize the prompt**: We provide a default prompt in [prompts.py](./src/enrichment_agent/prompts.py). You can easily update this via configuration in the studio. +3. **Customize the prompt**: We provide a default prompt in [prompts.py](./src/enrichment_agent/prompts.py). You can easily update this via configuration. + +For quick prototyping, these configurations can be set in the studio UI. + +![Config In Studio](./static/config.png) You can also quickly extend this template by: - Adding new tools and API connections in [tools.py](./src/enrichment_agent/tools.py). These are just any python functions. -- Adding additional steps in [graph.py](./src/enrichment_agent/graph.py). Concerned about hallucinatio +- Adding additional steps in [graph.py](./src/enrichment_agent/graph.py). ## Development diff --git a/src/enrichment_agent/graph.py b/src/enrichment_agent/graph.py index 7c9edb8..09767b9 100644 --- a/src/enrichment_agent/graph.py +++ b/src/enrichment_agent/graph.py @@ -19,29 +19,62 @@ from enrichment_agent.utils import init_model # Define the nodes - - async def call_agent_model( state: State, *, config: Optional[RunnableConfig] = None ) -> Dict[str, Any]: - """Call the primary LLM to decide whether and how to continue researching.""" + """ + Call the primary Language Model (LLM) to decide on the next research action. + + This asynchronous function performs the following steps: + 1. Initializes configuration and sets up the 'Info' tool, which is the user-defined extraction schema. + 2. Prepares the prompt and message history for the LLM. + 3. Initializes and configures the LLM with available tools. + 4. Invokes the LLM and processes its response. + 5. Handles the LLM's decision to either continue research or submit final info. + + Args: + state (State): The current state of the research process, including topic and extraction schema. + config (Optional[RunnableConfig]): Configuration for the LLM, if provided. + + Returns: + Dict[str, Any]: A dictionary containing: + - 'messages': List of response messages from the LLM. + - 'info': Extracted information if the LLM decided to submit final info, else None. + - 'loop_step': Incremented step count for the research loop. + + Note: + - The function uses three tools: scrape_website, search, and a dynamic 'Info' tool. + - If the LLM calls the 'Info' tool, it's considered as submitting the final answer. + - If the LLM doesn't call any tool, a prompt to use a tool is appended to the messages. + """ + + # Load configuration from the provided RunnableConfig configuration = Configuration.from_runnable_config(config) + + # Define the 'Info' tool, which is the user-defined extraction schema info_tool = { "name": "Info", "description": "Call this when you have gathered all the relevant info", "parameters": state.extraction_schema, } + # Format the prompt defined in prompts.py with the extraction schema and topic p = configuration.prompt.format( info=json.dumps(state.extraction_schema, indent=2), topic=state.topic ) + + # Create the messages list with the formatted prompt and the previous messages messages = [HumanMessage(content=p)] + state.messages - raw_model = init_model(config) + # Initialize the raw model with the provided configuration and bind the tools + raw_model = init_model(config) model = raw_model.bind_tools([scrape_website, search, info_tool], tool_choice="any") response = cast(AIMessage, await model.ainvoke(messages)) + # Initialize info to None info = None + + # Check if the response has tool calls if response.tool_calls: for tool_call in response.tool_calls: if tool_call["name"] == "Info": @@ -65,7 +98,6 @@ async def call_agent_model( "loop_step": 1, } - class InfoIsSatisfactory(BaseModel): """Validate whether the current extracted info is satisfactory and complete.""" @@ -80,7 +112,35 @@ class InfoIsSatisfactory(BaseModel): async def reflect( state: State, *, config: Optional[RunnableConfig] = None ) -> Dict[str, Any]: - """Validate the quality of the data enrichment agent's calls.""" + """ + Validate the quality of the data enrichment agent's output. + + This asynchronous function performs the following steps: + 1. Prepares the initial prompt using the main prompt template. + 2. Constructs a message history for the model. + 3. Prepares a checker prompt to evaluate the presumed info. + 4. Initializes and configures a language model with structured output. + 5. Invokes the model to assess the quality of the gathered information. + 6. Processes the model's response and determines if the info is satisfactory. + + Args: + state (State): The current state of the research process, including topic, + extraction schema, and gathered information. + config (Optional[RunnableConfig]): Configuration for the language model, if provided. + + Returns: + Dict[str, Any]: A dictionary containing either: + - 'info': The presumed info if it's deemed satisfactory. + - 'messages': A list with a ToolMessage indicating an error or unsatisfactory result. + + Raises: + ValueError: If the last message in the state is not an AIMessage with tool calls. + + Note: + - This function acts as a quality check for the information gathered by the agent. + - It uses a separate language model invocation to critique the gathered info. + - The function can either approve the gathered info or request further research. + """ p = prompts.MAIN_PROMPT.format( info=json.dumps(state.extraction_schema, indent=2), topic=state.topic ) @@ -135,21 +195,79 @@ async def reflect( def route_after_agent( state: State, ) -> Literal["reflect", "tools", "call_agent_model", "__end__"]: - """Schedule the next node after the agent.""" + """ + Schedule the next node after the agent's action. + + This function determines the next step in the research process based on the + last message in the state. It handles three main scenarios: + + 1. Error recovery: If the last message is unexpectedly not an AIMessage. + 2. Info submission: If the agent has called the "Info" tool to submit findings. + 3. Continued research: If the agent has called any other tool. + + Args: + state (State): The current state of the research process, including + the message history. + + Returns: + Literal["reflect", "tools", "call_agent_model", "__end__"]: + - "reflect": If the agent has submitted info for review. + - "tools": If the agent has called a tool other than "Info". + - "call_agent_model": If an unexpected message type is encountered. + + Note: + - The function assumes that normally, the last message should be an AIMessage. + - The "Info" tool call indicates that the agent believes it has gathered + sufficient information to answer the query. + - Any other tool call indicates that the agent needs to continue research. + - The error recovery path (returning "call_agent_model" for non-AIMessages) + serves as a safeguard against unexpected states. + """ last_message = state.messages[-1] + # "If for some reason the last message is not an AIMessage (due to a bug or unexpected behavior elsewhere in the code), + # it ensures the system doesn't crash but instead tries to recover by calling the agent model again. if not isinstance(last_message, AIMessage): return "call_agent_model" + # If the "Into" tool was called, then the model provided its extraction output. Reflect on the result if last_message.tool_calls and last_message.tool_calls[0]["name"] == "Info": return "reflect" + # The last message is a tool call that is not "Info" (extraction output) else: return "tools" - def route_after_checker( state: State, config: RunnableConfig ) -> Literal["__end__", "call_agent_model"]: - """Schedule the next node after the checker.""" + """ + Schedule the next node after the checker's evaluation. + + This function determines whether to continue the research process or end it + based on the checker's evaluation and the current state of the research. + + Args: + state (State): The current state of the research process, including + the message history, info gathered, and loop step count. + config (RunnableConfig): Configuration object containing settings like + the maximum number of allowed loops. + + Returns: + Literal["__end__", "call_agent_model"]: + - "__end__": If the research process should terminate. + - "call_agent_model": If further research is needed. + + The function makes decisions based on the following criteria: + 1. If the maximum number of loops has been reached, it ends the process. + 2. If no info has been gathered yet, it continues the research. + 3. If the last message indicates an error or unsatisfactory result, it continues the research. + 4. If none of the above conditions are met, it assumes the result is satisfactory and ends the process. + + Note: + - The function relies on a Configuration object derived from the RunnableConfig. + - It checks the loop_step against max_loops to prevent infinite loops. + - The presence of info and its quality (determined by the checker) influence the decision. + - An error status in the last message triggers additional research. + """ configurable = Configuration.from_runnable_config(config) last_message = state.messages @@ -164,7 +282,6 @@ def route_after_checker( else: return "__end__" - # Create the graph workflow = StateGraph( State, input=InputState, output=OutputState, config_schema=Configuration diff --git a/src/enrichment_agent/tools.py b/src/enrichment_agent/tools.py index 9e3b2e9..d21ef73 100644 --- a/src/enrichment_agent/tools.py +++ b/src/enrichment_agent/tools.py @@ -23,11 +23,27 @@ async def search( query: str, *, config: Annotated[RunnableConfig, InjectedToolArg] ) -> Optional[list[dict[str, Any]]]: - """Search for general results. - - This function performs a search using the Tavily search engine, which is designed - to provide comprehensive, accurate, and trusted results. It's particularly useful - for answering questions about current events. + """ + Perform a general web search using the Tavily search engine. + + This asynchronous function executes the following steps: + 1. Extracts configuration from the provided RunnableConfig. + 2. Initializes a TavilySearchResults object with a maximum number of results. + 3. Invokes the Tavily search with the given query. + 4. Returns the search results as a list of dictionaries. + + Args: + query (str): The search query string. + config (RunnableConfig): Configuration object containing search parameters. + + Returns: + Optional[list[dict[str, Any]]]: A list of search result dictionaries, or None if the search fails. + Each dictionary typically contains information like title, url, content snippet, etc. + + Note: + This function uses the Tavily search engine, which is designed for comprehensive + and accurate results, particularly useful for current events and factual queries. + The maximum number of results is determined by the configuration. """ configuration = Configuration.from_runnable_config(config) wrapped = TavilySearchResults(max_results=configuration.max_search_results) @@ -56,7 +72,27 @@ async def scrape_website( state: Annotated[State, InjectedState], config: Annotated[RunnableConfig, InjectedToolArg], ) -> str: - """Scrape and summarize content from a given URL.""" + """ + Scrape and summarize content from a given URL. + + This asynchronous function performs the following steps: + 1. Fetches the content of the specified URL. + 2. Formats a prompt using the fetched content and the extraction schema from the state. + 3. Initializes a language model using the provided configuration. + 4. Invokes the model with the formatted prompt to summarize the content. + + Args: + url (str): The URL of the website to scrape. + state (State): Injected state containing the extraction schema. + config (RunnableConfig): Configuration for initializing the language model. + + Returns: + str: A summary of the scraped content, tailored to the extraction schema. + + Note: + The function uses aiohttp for asynchronous HTTP requests and assumes the + existence of a _INFO_PROMPT template and an init_model function. + """ async with aiohttp.ClientSession() as session: async with session.get(url) as response: content = await response.text() diff --git a/static/config.png b/static/config.png new file mode 100644 index 0000000000000000000000000000000000000000..730a388727a550767b385d59160b77ed8625d3cf GIT binary patch literal 346172 zcmeFYXIxX;);4h*YJi2%$(Xp(sU!sHliEktR|??+{TG5hN55krD+IQHu0V z5ReX$-g^lUdT6QZuIN7JJ^y?6`{8`K_seDdLT1)1D|5_Ip7G3?$Q#CbCy$>wPDe*~ 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