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- Add emojis to the README.md file. (#8)
- Add examples how to use the integration with OpenAI Assistant.
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| Original file line number | Diff line number | Diff line change |
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| # ruff: noqa:T201,SIM115 | ||
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| """ | ||
| - Create OpenAI Assistant and add tools | ||
| - Create a thread and a message | ||
| - Run the Assistant and poll for the results | ||
| - Submit tool outputs | ||
| - Get assistant answer | ||
| """ | ||
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| from __future__ import annotations | ||
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| import json | ||
| from typing import TYPE_CHECKING | ||
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| from apify_client import ApifyClient | ||
| from openai import OpenAI, Stream | ||
| from openai.types.beta.threads.run_submit_tool_outputs_params import ToolOutput | ||
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| if TYPE_CHECKING: | ||
| from openai.types.beta import AssistantStreamEvent | ||
| from openai.types.beta.threads import Run | ||
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| client = OpenAI(api_key="YOUR-OPENAI-API-KEY") | ||
| apify_client = ApifyClient("YOUR-APIFY-API-TOKEN") | ||
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| INSTRUCTIONS = """ | ||
| You are a smart and helpful assistant. Maintain an expert, friendly, and informative tone in your responses. | ||
| Your task is to answer questions based on information from the internet. | ||
| Always call call_rag_web_browser function to retrieve the latest and most relevant online results. | ||
| Never provide answers based solely on your own knowledge. | ||
| For each answer, always include relevant sources whenever possible. | ||
| """ | ||
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| rag_web_browser_function = { | ||
| "type": "function", | ||
| "function": { | ||
| "name": "call_rag_web_browser", | ||
| "description": "Query Google search, scrape the top N pages from the results, and returns their cleaned content as markdown", | ||
| "parameters": { | ||
| "type": "object", | ||
| "properties": { | ||
| "query": {"type": "string", "description": "Use regular search words or enter Google Search URLs. "}, | ||
| "maxResults": {"type": "integer", "description": "The number of top organic search results to return and scrape text from"} | ||
| }, | ||
| "required": ["query"] | ||
| } | ||
| } | ||
| } | ||
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| my_assistant = client.beta.assistants.retrieve("asst_7GXx3q9lWLmhSf9yexA7J1WX") | ||
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| def call_rag_web_browser(query: str, max_results: int) -> list[dict]: | ||
| """ | ||
| Query Google search, scrape the top N pages from the results, and returns their cleaned content as markdown. | ||
| First start the Actor and wait for it to finish. Then fetch results from the Actor run's default dataset. | ||
| """ | ||
| actor_call = apify_client.actor("apify/rag-web-browser").call(run_input={"query": query, "maxResults": max_results}) | ||
| return apify_client.dataset(actor_call["defaultDatasetId"]).list_items().items | ||
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| def submit_tool_outputs(run_: Run) -> Run | Stream[AssistantStreamEvent]: | ||
| """ Submit tool outputs to continue the run """ | ||
| tool_output = [] | ||
| for tool in run_.required_action.submit_tool_outputs.tool_calls: | ||
| if tool.function.name == "call_rag_web_browser": | ||
| d = json.loads(tool.function.arguments) | ||
| output = call_rag_web_browser(query=d["query"], max_results=d["maxResults"]) | ||
| tool_output.append(ToolOutput(tool_call_id=tool.id, output=json.dumps(output))) | ||
| print("RAG-Web-Browser added as a tool output.") | ||
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| return client.beta.threads.runs.submit_tool_outputs_and_poll(thread_id=run_.thread_id, run_id=run_.id, tool_outputs=tool_output) | ||
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| # Runs are asynchronous, which means you'll want to monitor their status by polling the Run object until a terminal status is reached. | ||
| thread = client.beta.threads.create() | ||
| message = client.beta.threads.messages.create( | ||
| thread_id=thread.id, role="user", content="What are the latest LLM news?" | ||
| ) | ||
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| # Run with assistant and poll for the results | ||
| run = client.beta.threads.runs.create_and_poll(thread_id=thread.id, assistant_id=my_assistant.id) | ||
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| if run.status == "requires_action": | ||
| run = submit_tool_outputs(run) | ||
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| print("Assistant response:") | ||
| for m in client.beta.threads.messages.list(thread_id=run.thread_id): | ||
| print(m.content[0].text.value) | ||
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| # Delete the thread | ||
| client.beta.threads.delete(thread.id) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| # ruff: noqa:T201,SIM115 | ||
| """ | ||
| - Create OpenAI Assistant and vVector Store | ||
| - Update the assistant to use the new Vector Store | ||
| - Call Website Content Crawler and crawl docs.apify.com | ||
| - Use OpenAi Vector Store Integration to upload dataset items to the Vector Store | ||
| - Create a thread and a message and get assistant answer | ||
| """ | ||
| from apify_client import ApifyClient | ||
| from openai import OpenAI | ||
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| client = OpenAI(api_key="YOUR-OPENAI-API-KEY") | ||
| apify_client = ApifyClient("YOUR-APIFY-API-TOKEN") | ||
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| my_assistant = client.beta.assistants.create( | ||
| instructions="As a customer support agent at Apify, your role is to assist customers", | ||
| name="Support assistant", | ||
| tools=[{"type": "file_search"}], | ||
| model="gpt-4o-mini", | ||
| ) | ||
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| # Create a vector store | ||
| vector_store = client.beta.vector_stores.create(name="Support assistant vector store") | ||
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| # Update the assistant to use the new Vector Store | ||
| assistant = client.beta.assistants.update( | ||
| assistant_id=my_assistant.id, | ||
| tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}}, | ||
| ) | ||
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| run_input = {"startUrls": [{"url": "https://docs.apify.com/platform"}], "maxCrawlPages": 10, "crawlerType": "cheerio"} | ||
| actor_call_website_crawler = apify_client.actor("apify/website-content-crawler").call(run_input=run_input) | ||
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| dataset_id = actor_call_website_crawler["defaultDatasetId"] | ||
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| run_input_vs = { | ||
| "datasetId": dataset_id, | ||
| "assistantId": my_assistant.id, | ||
| "datasetFields": ["text", "url"], | ||
| "openaiApiKey": "YOUR-OPENAI-API-KEY", | ||
| "vectorStoreId": vector_store.id, | ||
| } | ||
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| apify_client.actor("jiri.spilka/openai-vector-store-integration").call(run_input=run_input_vs) | ||
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| # Create a thread and a message | ||
| thread = client.beta.threads.create() | ||
| message = client.beta.threads.messages.create( | ||
| thread_id=thread.id, role="user", content="How can I scrape a website using Apify?" | ||
| ) | ||
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| # Run with assistant and poll for the results | ||
| run = client.beta.threads.runs.create_and_poll( | ||
| thread_id=thread.id, | ||
| assistant_id=assistant.id, | ||
| tool_choice={"type": "file_search"} | ||
| ) | ||
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| print("Assistant response:") | ||
| for m in client.beta.threads.messages.list(thread_id=run.thread_id): | ||
| print(m.content[0].text.value) |