v0.0.31

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==0.0.30 gunicorn markdown
+RUN pip install flask praisonai==0.0.31 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docs/api/praisonai/deploy.html

@@ -84,7 +84,7 @@ <h1 class="title">Module <code>praisonai.deploy</code></h1>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==0.0.30 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==0.0.31 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
 
@@ -250,7 +250,7 @@ <h2 id="raises">Raises</h2>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==0.0.30 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==0.0.31 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
 
@@ -416,7 +416,7 @@ <h2 id="raises">Raises</h2>
         file.write(&#34;FROM python:3.11-slim\n&#34;)
         file.write(&#34;WORKDIR /app\n&#34;)
         file.write(&#34;COPY . .\n&#34;)
-        file.write(&#34;RUN pip install flask praisonai==0.0.30 gunicorn markdown\n&#34;)
+        file.write(&#34;RUN pip install flask praisonai==0.0.31 gunicorn markdown\n&#34;)
         file.write(&#34;EXPOSE 8080\n&#34;)
         file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)</code></pre>
 </details>

docs/custom_tools.md

@@ -0,0 +1,135 @@
+# Create Custom Tools
+
+Sure! Let's go through the steps to install and set up the PraisonAI tool.
+
+## Step 1: Install the `praisonai` Package
+
+First, you need to install the `praisonai` package. Open your terminal and run the following command:
+
+```bash
+pip install praisonai
+```
+
+## Step 2: Create the `InternetSearchTool`
+
+Next, create a file named `tools.py` and add the following code to define the `InternetSearchTool`:
+
+```python
+from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
+
+class InternetSearchTool(BaseTool):
+    name: str = "Internet Search Tool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results
+```
+
+## Step 3: Define the Agent Configuration
+
+Create a file named `agents.yaml` and add the following content to configure the agent:
+
+```yaml
+framework: crewai
+topic: research about the causes of lung disease
+roles:
+  research_analyst:
+    backstory: Experienced in analyzing scientific data related to respiratory health.
+    goal: Analyze data on lung diseases
+    role: Research Analyst
+    tasks:
+      data_analysis:
+        description: Gather and analyze data on the causes and risk factors of lung diseases.
+        expected_output: Report detailing key findings on lung disease causes.
+    tools:
+    - InternetSearchTool
+```
+
+## Step 4: Run the PraisonAI Tool
+
+To run the PraisonAI tool, simply type the following command in your terminal:
+
+```bash
+praisonai
+```
+
+If you want to run the `autogen` framework, use:
+
+```bash
+praisonai --framework autogen
+```
+
+## Prerequisites
+
+Ensure you have the `duckduckgo_search` package installed. If not, you can install it using:
+
+```bash
+pip install duckduckgo_search
+```
+
+That's it! You should now have the PraisonAI tool installed and configured.
+
+## Other information
+
+### TL;DR to Create a Custom Tool
+
+```bash
+pip install praisonai duckduckgo-search
+export OPENAI_API_KEY="Enter your API key"
+praisonai --init research about the latest AI News and prepare a detailed report
+```
+
+- Add `- InternetSearchTool` in the agents.yaml file in the tools section. 
+- Create a file called tools.py and add this code [tools.py](./tools.py)
+
+```bash
+praisonai
+```
+
+### Pre-requisite to Create a Custom Tool
+`agents.yaml` file should be present in the current directory. 
+
+If it doesn't exist, create it by running the command `praisonai --init research about the latest AI News and prepare a detailed report`.
+
+#### Step 1 to Create a Custom Tool
+
+Create a file called tools.py in the same directory as the agents.yaml file.
+
+```python
+# example tools.py
+from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
+
+class InternetSearchTool(BaseTool):
+    name: str = "InternetSearchTool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results
+```
+
+#### Step 2 to Create a Custom Tool
+
+Add the tool to the agents.yaml file as show below under the tools section `- InternetSearchTool`.
+
+```yaml
+framework: crewai
+topic: research about the latest AI News and prepare a detailed report
+roles:
+  research_analyst:
+    backstory: Experienced in gathering and analyzing data related to AI news trends.
+    goal: Analyze AI News trends
+    role: Research Analyst
+    tasks:
+      gather_data:
+        description: Conduct in-depth research on the latest AI News trends from reputable
+          sources.
+        expected_output: Comprehensive report on current AI News trends.
+    tools:
+    - InternetSearchTool
+```

docs/firecrawl.md

@@ -0,0 +1,42 @@
+# Firecrawl PraisonAI Integration
+
+## Firecrawl running in Localhost:3002
+
+```
+from firecrawl import FirecrawlApp
+from praisonai_tools import BaseTool
+import re
+
+class WebPageScraperTool(BaseTool):
+    name: str = "Web Page Scraper Tool"
+    description: str = "Scrape and extract information from a given web page URL."
+
+    def _run(self, url: str) -> str:
+        app = FirecrawlApp(api_url='http://localhost:3002')
+        response = app.scrape_url(url=url)
+        content = response["content"]
+        # Remove all content above the line "========================================================"
+        if "========================================================" in content:
+            content = content.split("========================================================", 1)[1]
+
+        # Remove all menu items and similar patterns
+        content = re.sub(r'\*\s+\[.*?\]\(.*?\)', '', content)
+        content = re.sub(r'\[Skip to the content\]\(.*?\)', '', content)
+        content = re.sub(r'\[.*?\]\(.*?\)', '', content)
+        content = re.sub(r'\s*Menu\s*', '', content)
+        content = re.sub(r'\s*Search\s*', '', content)
+        content = re.sub(r'Categories\s*', '', content)
+
+        # Remove all URLs
+        content = re.sub(r'http\S+', '', content)
+        
+        # Remove empty lines or lines with only whitespace
+        content = '\n'.join([line for line in content.split('\n') if line.strip()])
+
+        # Limit to the first 1000 words
+        words = content.split()
+        if len(words) > 1000:
+            content = ' '.join(words[:1000])
+        
+        return content
+```

docs/langchain.md

@@ -0,0 +1,69 @@
+# Langchain Tools
+
+## Integrate Langchain Direct Tools
+
+```
+pip install youtube_search praisonai langchain_community langchain
+```
+
+```
+# tools.py
+from langchain_community.tools import YouTubeSearchTool
+```
+
+```
+# agents.yaml
+framework: crewai
+topic: research about the causes of lung disease
+roles:
+  research_analyst:
+    backstory: Experienced in analyzing scientific data related to respiratory health.
+    goal: Analyze data on lung diseases
+    role: Research Analyst
+    tasks:
+      data_analysis:
+        description: Gather and analyze data on the causes and risk factors of lung
+          diseases.
+        expected_output: Report detailing key findings on lung disease causes.
+    tools:
+    - 'YouTubeSearchTool'
+```
+
+## Integrate Langchain with Wrappers
+
+```
+pip install wikipedia langchain_community
+```
+
+```
+# tools.py
+from langchain_community.utilities import WikipediaAPIWrapper
+class WikipediaSearchTool(BaseTool):
+    name: str = "WikipediaSearchTool"
+    description: str = "Search Wikipedia for relevant information based on a query."
+
+    def _run(self, query: str):
+        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
+        results = api_wrapper.load(query=query)
+        return results
+```
+
+```
+# agents.yaml
+framework: crewai
+topic: research about nvidia growth
+roles:
+  data_collector:
+    backstory: An experienced researcher with the ability to efficiently collect and
+      organize vast amounts of data.
+    goal: Gather information on Nvidia's growth by providing the Ticket Symbol to YahooFinanceNewsTool
+    role: Data Collector
+    tasks:
+      data_collection_task:
+        description: Collect data on Nvidia's growth from various sources such as
+          financial reports, news articles, and company announcements.
+        expected_output: A comprehensive document detailing data points on Nvidia's
+          growth over the years.
+    tools:
+    - 'WikipediaSearchTool'
+```

docs/reddit.md

@@ -0,0 +1,32 @@
+# Reddit PraisonAI Integration
+
+```
+export REDDIT_USER_AGENT=[USER]
+export REDDIT_CLIENT_SECRET=xxxxxx
+export REDDIT_CLIENT_ID=xxxxxx
+```
+
+tools.py
+
+```
+from langchain_community.tools.reddit_search.tool import RedditSearchRun
+```
+
+agents.yaml
+
+```
+framework: crewai
+topic: research about the causes of lung disease
+roles:
+  research_analyst:
+    backstory: Experienced in analyzing scientific data related to respiratory health.
+    goal: Analyze data on lung diseases
+    role: Research Analyst
+    tasks:
+      data_analysis:
+        description: Gather and analyze data on the causes and risk factors of lung
+          diseases.
+        expected_output: Report detailing key findings on lung disease causes.
+    tools:
+    - 'RedditSearchRun'
+```

docs/tavily.md

@@ -0,0 +1,15 @@
+# Tavily PraisonAI Integration
+
+````
+from praisonai_tools import BaseTool
+from langchain.utilities.tavily_search import TavilySearchAPIWrapper
+
+class TavilyTool(BaseTool):
+    name: str = "TavilyTool"
+    description: str = "Search Tavily for relevant information based on a query."
+
+    def _run(self, query: str):
+        api_wrapper = TavilySearchAPIWrapper()
+        results = api_wrapper.results(query=query, max_results=5)
+        return results
+```