🥥 Content Refresher Agent

docs/development/workflows.mdx

@@ -17,4 +17,23 @@ The workflow hierarchy follows a graph-like structure. The frontend models only
 The backend models represent the mechanisms to actually perform work for a given node.
 Each frontend `Node` will have an associated `Block`.
 `Node` represents the frontend view / position while the `Block` represents what will actually happen when that `Node` is run.
-For example, a "SlackMessageBlock" is a `Block` that, when executed, would send a user a message on "Slack".
+For example, a "SlackMessageBlock" is a `Block` that, when executed, would send a user a message on "Slack".
+
+## Adding a new block
+To add a new block, start by updating the frontend:
+- open next/src/services/workflow/node-block-definitions.ts
+- figure it out
+- (soon block definitions on frontend will be set from backend and edits won't be needed here)
+
+Then update the backend:
+- open platform/reworkd_platform/schemas/workflow/blocks
+    - add a new file for your block
+    - define the block's input and output types as classes
+    - add a class for the block with 
+        - attributes: type, description, image_url, and input
+        - async method: `run`
+- install dependencies for your block with `poetry add`
+- open platform/reworkd_platform/web/api/workflow/blocks/web/__init__.py
+    - import your block
+    - add an if branch to get_block_runner
+- `docker compose down; docker compose up --build`

next/src/services/workflow/node-block-definitions.ts

@@ -200,6 +200,33 @@ const WebInteractionAgent: NodeBlockDefinition = {
   output_fields: [],
 };
 
+const ContentRefresherAgent: NodeBlockDefinition = {
+  name: "Content Refresher Agent",
+  type: "ContentRefresherAgent",
+  description: "Refresh the content on an existing page",
+  image_url: "/tools/web.png",
+  icon: FaRobot,
+  input_fields: [
+    {
+      name: "url",
+      description: "The page whose content the agent will refresh",
+      type: "string",
+    },
+  ],
+  output_fields: [
+    {
+      name: "original_content",
+      description: "The original content of the page",
+      type: "string",
+    },
+    {
+      name: "refreshed_content",
+      description: "The refreshed content for the page",
+      type: "string",
+    },
+  ],
+};
+
 export const getNodeBlockDefinitions = (): NodeBlockDefinition[] => {
   return [
     UrlStatusCheckBlockDefinition,
@@ -209,6 +236,7 @@ export const getNodeBlockDefinitions = (): NodeBlockDefinition[] => {
     TriggerBlockDefinition,
     SummaryWebhookBlockDefinition,
     TextInputWebhookBlockDefinition,
+    ContentRefresherAgent
   ];
 };
 

platform/pyproject.toml

@@ -41,6 +41,8 @@ networkx = "^3.1"
 pusher = "^3.3.2"
 pypdf2 = "^3.0.1"
 python-multipart = "^0.0.6"
+scrapingbee = "^1.2.0"
+anthropic = "^0.3.6"
 
 
 [tool.poetry.dev-dependencies]

platform/reworkd_platform/schemas/workflow/blocks/agents/content_refresher_agent.py

@@ -0,0 +1,185 @@
+from loguru import logger
+from reworkd_platform.settings import settings
+from reworkd_platform.schemas.workflow.base import Block, BlockIOBase
+
+import re
+import requests
+from scrapingbee import ScrapingBeeClient
+from bs4 import BeautifulSoup
+import anthropic
+
+
+class ContentRefresherInput(BlockIOBase):
+    url: str
+
+
+class ContentRefresherOutput(ContentRefresherInput):
+    original_content: str
+    refreshed_content: str
+
+
+class ContentRefresherAgent(Block):
+    type = "ContentRefresherAgent"
+    description = "Refresh the content on an existing page"
+    input: ContentRefresherInput
+
+    async def run(self) -> BlockIOBase:
+        logger.info(f"Starting {self.type}")
+        target_url = self.input.url
+
+        target_content = get_page_content(target_url)
+        logger.info(target_content)
+
+        keywords = find_content_kws(target_content)
+        logger.info(keywords)
+
+        source_urls = search_results(keywords)
+        if target_url in source_urls:  # TODO: check based on content overlap
+            source_urls.remove(target_url)
+        logger.info(source_urls)
+
+        source_contents = [
+            get_page_content(url)
+            for url in source_urls[:3]  # TODO: remove limit of 3 sources
+        ]  # TODO: async/multithread the LLM calls
+        source_contents = [
+            content for content in source_contents if content is not None
+        ]
+        logger.info(source_contents)
+
+        new_infos = "\n\n".join(
+            [
+                find_new_info(target_content, source_content)
+                for source_content in source_contents
+            ]
+        )
+        logger.info(new_infos)
+
+        updated_target_content = add_info(target_content, new_infos)
+        logger.info(updated_target_content)
+
+        return ContentRefresherOutput(
+            **self.input.dict(),
+            original_content=target_content,
+            refreshed_content=updated_target_content,
+        )
+
+
+scraper = ScrapingBeeClient(
+    api_key=settings.scrapingbee_api_key,
+)
+claude = anthropic.Anthropic(
+    api_key=settings.anthropic_api_key,
+)
+
+
+def get_page_content(url: str) -> str:
+    page = requests.get(url)
+    if page.status_code != 200:
+        page = scraper.get(url)
+
+    html = BeautifulSoup(page.content, "html.parser")
+
+    pgraphs = html.find_all("p")
+    pgraphs = "\n".join(
+        [
+            f"{i+1}. " + re.sub(r"\s+", " ", p.text).strip()
+            for i, p in enumerate(pgraphs)
+        ]
+    )
+
+    prompt = f"Below is a numbered list of the text in all the <p> tags on a web page:\n{pgraphs}\nSome of these lines may not be part of the main content of the page (e.g. footer text, ads, etc). Please list the line numbers that *are* part of the main content (i.e. the article's paragraphs) of the page. You can list consecutive line numbers as a range (e.g. 23-27) and separated by a comma."
+    response = claude.completions.create(
+        model="claude-2",
+        prompt=f"\n\nHuman: {prompt}\n\nAssistant: Here are the line numbers of the main content:",
+        max_tokens_to_sample=500,
+        temperature=0,
+    )
+    line_nums = response.completion.strip()
+    if len(line_nums) == 0:
+        return ''
+
+    pgraphs = pgraphs.split("\n")
+    content = []
+    for line_num in line_nums.split(","):
+        if "-" in line_num:
+            start, end = map(int, line_num.split("-"))
+            for i in range(start, end + 1):
+                text = ".".join(pgraphs[i - 1].split(".")[1:]).strip()
+                content.append(text)
+        else:
+            text = ".".join(pgraphs[int(line_num) - 1].split(".")[1:]).strip()
+            content.append(text)
+
+    return "\n".join(content)
+
+
+def find_content_kws(content: str) -> str:
+    # Claude: find search keywords that content focuses on
+    prompt = f"Below is content from a web article:\n{content}\nPlease list the keywords that best describe the content of the article. Format them so we can use them to query a search engine effectively."
+    response = claude.completions.create(
+        model="claude-2",
+        prompt=f"\n\nHuman: {prompt}\n\nAssistant: Here is a short search query that best matches the content of the article:",
+        max_tokens_to_sample=20,
+        temperature=0,
+    )
+    response_message = response.completion.strip()
+    return response_message
+
+
+def search_results(search_query: str) -> list[str]:
+    # use SERP API
+    response = requests.post(
+        f"https://google.serper.dev/search",
+        headers={
+            "X-API-KEY": settings.serp_api_key or '',
+            "Content-Type": "application/json",
+        },
+        params={
+            "q": search_query,
+        },
+    )
+    response.raise_for_status()
+    search_results = response.json()
+    urls = [result["link"] for result in search_results["organic"]]
+    return urls
+
+
+def find_new_info(target: str, source: str) -> str:
+    # Claude: info mentioned in source that is not mentioned in target
+    prompt = f"Below is the TARGET article:\n{target}\n----------------\nBelow is the SOURCE article:\n{source}\n----------------\nIn a bullet point list, identify all facts, figures, or ideas that are mentioned in the SOURCE article but not in the TARGET article."
+    response = claude.completions.create(
+        model="claude-2",
+        prompt=f"\n\nHuman: {prompt}\n\nAssistant: Here is a list of claims in the SOURCE that are not in the TARGET:",
+        max_tokens_to_sample=5000,
+        temperature=0,
+    )
+    response_message = response.completion.strip()
+    new_info = "\n".join(response_message.split("\n\n"))
+    return new_info
+
+
+def add_info(target: str, info: str) -> str:
+    # Claude: rewrite target to include the info
+    prompt = f"Below are notes from some SOURCE articles:\n{info}\n----------------\nBelow is the TARGET article:\n{target}\n----------------\nPlease rewrite the TARGET article to include the information from the SOURCE articles."
+    response = claude.completions.create(
+        model="claude-2",
+        prompt=f"\n\nHuman: {prompt}\n\nAssistant: Here is a rewritten version of the target article that incorporates relevant information from the source articles:",
+        max_tokens_to_sample=5000,
+        temperature=0,
+    )
+    response_message = response.completion.strip()
+    return response_message
+
+
+if __name__ == "main":
+    print("MAIN")
+    agent = ContentRefresherAgent(
+        id="test",
+        type="ContentRefresherAgent",
+        input=ContentRefresherInput(
+            url="https://www.science.org/content/article/embattled-physicist-files-patent-unprecedented-ambient-superconductor"
+        )
+    )
+    output = agent.run()
+    print(output)

platform/reworkd_platform/settings.py

@@ -60,6 +60,8 @@ class Settings(BaseSettings):
 
     replicate_api_key: Optional[str] = None
     serp_api_key: Optional[str] = None
+    scrapingbee_api_key: Optional[str] = None
+    anthropic_api_key: Optional[str] = None
 
     # Frontend URL for CORS
     frontend_url: str = "http://localhost:3000"

platform/reworkd_platform/web/api/workflow/blocks/web/__init__.py

@@ -12,13 +12,18 @@
 from reworkd_platform.schemas.workflow.blocks.url_status_check import (
     UrlStatusCheckBlock,
 )
+from reworkd_platform.schemas.workflow.blocks.agents.content_refresher_agent import (
+    ContentRefresherAgent,
+)
 
 
 def get_block_runner(block: Block) -> Block:
     if block.type == "IfCondition":
         return IfCondition(**block.dict())
     if block.type == "WebInteractionAgent":
         return WebInteractionAgent(**block.dict())
+    if block.type == "ContentRefresherAgent":
+        return ContentRefresherAgent(**block.dict())
     if block.type == "ManualTriggerBlock":
         return ManualTriggerBlock(**block.dict())
     if block.type == "UrlStatusCheck":