Added fastpi

app.py

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+import os
+from dotenv import load_dotenv
+
+from langchain import PromptTemplate
+from langchain.agents import initialize_agent, Tool
+from langchain.agents import AgentType
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import MessagesPlaceholder
+from langchain.memory import ConversationSummaryBufferMemory
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.summarize import load_summarize_chain
+from langchain.tools import BaseTool
+from pydantic import BaseModel, Field
+from typing import Type
+from bs4 import BeautifulSoup
+import requests
+import json
+from langchain.schema import SystemMessage
+from fastapi import FastAPI
+
+load_dotenv()
+brwoserless_api_key = os.getenv("BROWSERLESS_API_KEY")
+serper_api_key = os.getenv("SERP_API_KEY")
+
+# 1. Tool for search
+
+
+def search(query):
+    url = "https://google.serper.dev/search"
+
+    payload = json.dumps({
+        "q": query
+    })
+
+    headers = {
+        'X-API-KEY': serper_api_key,
+        'Content-Type': 'application/json'
+    }
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+
+    print(response.text)
+
+    return response.text
+
+
+# 2. Tool for scraping
+def scrape_website(objective: str, url: str):
+    # scrape website, and also will summarize the content based on objective if the content is too large
+    # objective is the original objective & task that user give to the agent, url is the url of the website to be scraped
+
+    print("Scraping website...")
+    # Define the headers for the request
+    headers = {
+        'Cache-Control': 'no-cache',
+        'Content-Type': 'application/json',
+    }
+
+    # Define the data to be sent in the request
+    data = {
+        "url": url
+    }
+
+    # Convert Python object to JSON string
+    data_json = json.dumps(data)
+
+    # Send the POST request
+    post_url = f"https://chrome.browserless.io/content?token={brwoserless_api_key}"
+    response = requests.post(post_url, headers=headers, data=data_json)
+
+    # Check the response status code
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.content, "html.parser")
+        text = soup.get_text()
+        print("CONTENTTTTTT:", text)
+
+        if len(text) > 10000:
+            output = summary(objective, text)
+            return output
+        else:
+            return text
+    else:
+        print(f"HTTP request failed with status code {response.status_code}")
+
+
+def summary(objective, content):
+    llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500)
+    docs = text_splitter.create_documents([content])
+    map_prompt = """
+    Write a summary of the following text for {objective}:
+    "{text}"
+    SUMMARY:
+    """
+    map_prompt_template = PromptTemplate(
+        template=map_prompt, input_variables=["text", "objective"])
+
+    summary_chain = load_summarize_chain(
+        llm=llm,
+        chain_type='map_reduce',
+        map_prompt=map_prompt_template,
+        combine_prompt=map_prompt_template,
+        verbose=True
+    )
+
+    output = summary_chain.run(input_documents=docs, objective=objective)
+
+    return output
+
+
+class ScrapeWebsiteInput(BaseModel):
+    """Inputs for scrape_website"""
+    objective: str = Field(
+        description="The objective & task that users give to the agent")
+    url: str = Field(description="The url of the website to be scraped")
+
+
+class ScrapeWebsiteTool(BaseTool):
+    name = "scrape_website"
+    description = "useful when you need to get data from a website url, passing both url and objective to the function; DO NOT make up any url, the url should only be from the search results"
+    args_schema: Type[BaseModel] = ScrapeWebsiteInput
+
+    def _run(self, objective: str, url: str):
+        return scrape_website(objective, url)
+
+    def _arun(self, url: str):
+        raise NotImplementedError("error here")
+
+
+# 3. Create langchain agent with the tools above
+tools = [
+    Tool(
+        name="Search",
+        func=search,
+        description="useful for when you need to answer questions about current events, data. You should ask targeted questions"
+    ),
+    ScrapeWebsiteTool(),
+]
+
+system_message = SystemMessage(
+    content="""You are a world class researcher, who can do detailed research on any topic and produce facts based results; 
+            you do not make things up, you will try as hard as possible to gather facts & data to back up the research
+            
+            Please make sure you complete the objective above with the following rules:
+            1/ You should do enough research to gather as much information as possible about the objective
+            2/ If there are url of relevant links & articles, you will scrape it to gather more information
+            3/ After scraping & search, you should think "is there any new things i should search & scraping based on the data I collected to increase research quality?" If answer is yes, continue; But don't do this more than 3 iteratins
+            4/ You should not make things up, you should only write facts & data that you have gathered
+            5/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research
+            6/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research"""
+)
+
+agent_kwargs = {
+    "extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
+    "system_message": system_message,
+}
+
+llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
+memory = ConversationSummaryBufferMemory(
+    memory_key="memory", return_messages=True, llm=llm, max_token_limit=1000)
+
+agent = initialize_agent(
+    tools,
+    llm,
+    agent=AgentType.OPENAI_FUNCTIONS,
+    verbose=True,
+    agent_kwargs=agent_kwargs,
+    memory=memory,
+)
+
+
+# 4. Use streamlit to create a web app
+# def main():
+#     st.set_page_config(page_title="AI research agent", page_icon=":bird:")
+
+#     st.header("AI research agent :bird:")
+#     query = st.text_input("Research goal")
+
+#     if query:
+#         st.write("Doing research for ", query)
+
+#         result = agent({"input": query})
+
+#         st.info(result['output'])
+
+
+# if __name__ == '__main__':
+#     main()
+
+
+# 5. Set this as an API endpoint via FastAPI
+app = FastAPI()
+
+
+class Query(BaseModel):
+    query: str
+
+
+@app.post("/")
+def researchAgent(query: Query):
+    query = query.query
+    content = agent({"input": query})
+    actual_content = content['output']
+    return actual_content

requirements.txt

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+fastapi[all]
+openai
+python-dotenv
+pydantic
+langchain
+bs4