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DineFilter
ohlava/dinefilter
Say what you want to eat and where, we'll find it
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.dockerignore
1.env
2mise.toml
3.venv
4storage
5node_modules
6.DS_Store
7__pycache__
.gitignore
1storage
2node_modules
3.venv
4.env
5mise.toml
6.DS_Store
7__pycache__
8presentation/
poetry.lock
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2
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3424
3425[package.dependencies]
3426cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
3427
3428[package.extras]
3429cffi = ["cffi (>=1.11)"]
3430
3431[metadata]
3432lock-version = "2.1"
3433python-versions = ">=3.12,<4.0"
3434content-hash = "20b9db716688f7c3932d2634e51bfcfe3f8c00fc8169eb16d550ad6c08e9b51d"
pyproject.toml
1[project]
2name = "dinefilter"
3version = "0.1.0"
4description = ""
5authors = [
6 {name = "Your Name",email = "you@example.com"}
7]
8readme = "README.md"
9requires-python = ">=3.12,<4.0"
10dependencies = [
11 "langchain (>=0.3.19,<0.4.0)",
12 "langchain-openai (>=0.3.6,<0.4.0)",
13 "langgraph (>=0.2.74,<0.3.0)",
14 "langgraph-cli (>=0.1.73,<0.2.0)",
15 "apify (>=2.3.0,<3.0.0)",
16 "pydantic (>=2.10.6,<3.0.0)"
17]
18
19
20[build-system]
21requires = ["poetry-core>=2.0.0,<3.0.0"]
22build-backend = "poetry.core.masonry.api"
23
24[tool.poetry]
25package-mode = false
26
27[tool.poetry.group.dev.dependencies]
28ruff = "^0.9.6"
.actor/Dockerfile
1# First, specify the base Docker image.
2# You can see the Docker images from Apify at https://hub.docker.com/r/apify/.
3# You can also use any other image from Docker Hub.
4FROM apify/actor-python:3.12
5
6RUN apt update && apt install -yq git && rm -rf /var/lib/apt/lists/*
7
8RUN pip install -U pip setuptools \
9 && pip install poetry \
10 && poetry self add poetry-plugin-export
11
12# Second, copy just poetry.lock and pyproject.toml into the Actor image,
13# since those should be the only files that affects the dependency install in the next step,
14# in order to speed up the build
15COPY pyproject.toml ./
16COPY poetry.lock ./
17
18# Install the dependencies
19RUN echo "Python version:" \
20 && python --version \
21 && echo "Installing dependencies:" \
22 # Export packages from poetry.lock
23 && poetry export -f requirements.txt --without-hashes | \
24 # Install everything using pip (ignore dependency checks - the lockfile is correct, period)
25 pip install -r /dev/stdin --no-dependencies \
26 && echo "All installed Python packages:" \
27 && pip freeze
28
29# Next, copy the remaining files and directories with the source code.
30# Since we do this after installing the dependencies, quick build will be really fast
31# for most source file changes.
32COPY . ./
33
34# Use compileall to ensure the runnability of the Actor Python code.
35RUN python3 -m compileall -q .
36
37# Specify how to launch the source code of your Actor.
38# By default, the "python3 -m ." command is run
39CMD ["python3", "-m", "src"]
.actor/actor.json
1{
2 "actorSpecification": 1,
3 "name": "dinefilter",
4 "version": "0.0",
5 "buildTag": "latest",
6 "environmentVariables": {"OPENAI_API_KEY": "@openaiToken"},
7 "input": {
8 "title": "Dinefilter settings",
9 "description": "Say what you want to eat, we'll find it",
10 "type": "object",
11 "schemaVersion": 1,
12 "properties": {
13 "prompt": {
14 "title": "Prompt",
15 "description": "A specification of what you want to look for",
16 "type": "string",
17 "editor": "textfield",
18 "prefill": "Find me a place for lunch at Václavské náměstí"
19 }
20 }
21 },
22 "storages": {
23 "dataset": {
24 "actorSpecification": 1,
25 "views": {
26 "recommendations": {
27 "title": "Recommendations",
28 "transformation": {},
29 "display": {
30 "component": "table",
31 "properties": {
32 "name": {
33 "label": "Restaurant Name",
34 "format": "text"
35 },
36 "cuisine": {
37 "label": "Cuisine",
38 "format": "text"
39 },
40 "address": {
41 "label": "Restaurant Address",
42 "format": "text"
43 },
44 "map_url": {
45 "label": "Google Maps Link",
46 "format": "link"
47 },
48 "rating": {
49 "label": "Rating",
50 "format": "text"
51 },
52 "price_level": {
53 "label": "Price level",
54 "format": "text"
55 },
56 "menu_url": {
57 "label": "Menu Link",
58 "format": "link"
59 },
60 "web_url": {
61 "label": "Website Link",
62 "format": "link"
63 },
64 "review_summary": {
65 "label": "Review summary",
66 "format": "text"
67 },
68 "menu_highlights": {
69 "label": "Menu highlights",
70 "format": "text"
71 },
72 "location": {
73 "label": "Restaurant Longtitude and Latitude",
74 "format": "text"
75 }
76 }
77 }
78 }
79 }
80 }
81 }
82}
src/__main__.py
1from langchain_core.prompts import ChatPromptTemplate
2import operator
3from itertools import chain
4from datetime import timedelta
5import asyncio
6from langchain_openai import ChatOpenAI
7from langgraph.graph import END, StateGraph
8from langchain_core.output_parsers import StrOutputParser
9from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
10import os
11import json
12from typing import Dict, List, Optional, TypedDict, Annotated, Union, Literal, Annotated
13from dotenv import load_dotenv
14from apify import Actor
15from pydantic import BaseModel, Field
16import requests
17
18load_dotenv()
19
20class RagWebBrowserClient:
21 def __init__(self, api_token: str):
22 self.api_token = api_token
23 self.base_url = "https://rag-web-browser.apify.actor"
24
25 def search(self,
26 query: str,
27 max_results: int = 3,
28 output_formats: str = "markdown",
29 request_timeout_secs: int = 30) -> List[dict]:
30
31 params = {
32 'query': query,
33 'maxResults': max_results,
34 'outputFormats': output_formats,
35 'requestTimeoutSecs': request_timeout_secs
36 }
37
38 headers = {
39 'Authorization': f'Bearer {self.api_token}',
40 'Content-Type': 'application/json'
41 }
42
43 try:
44 response = requests.get(
45 f'{self.base_url}/search',
46 params=params,
47 headers=headers,
48 timeout=request_timeout_secs
49 )
50 response.raise_for_status()
51 return response.json()
52
53 except requests.exceptions.RequestException as e:
54 print(f"Error making request: {e}")
55 return []
56
57class Settings(BaseModel):
58 openai_api_key: Annotated[str, Field(alias="OPENAI_API_KEY")]
59
60settings = Settings.model_validate(os.environ)
61
62# Type definitions
63class RestaurantQuery(TypedDict):
64 location: str
65 restaurant_name: Optional[str]
66 date: Optional[str]
67 cuisine_type: Optional[str]
68
69class RestaurantData(TypedDict):
70 name: str
71 address: str
72 location: Optional[Dict[str, float]]
73 google_rating: Optional[float]
74 google_reviews_count: Optional[int]
75 google_map_url: str
76 web_url: Optional[str]
77 web_url_content: Optional[str]
78 menu_url: Optional[str]
79 menu_url_content: Optional[str]
80 top_positive_reviews: List[str]
81 top_negative_reviews: List[str]
82 price_level: Optional[str]
83 cuisine: List[str]
84
85class AgentState(TypedDict):
86 query: Annotated[RestaurantQuery, lambda _, new: new]
87 restaurants: Annotated[list[RestaurantData], lambda _, new: new]
88 top_matches: Annotated[list[RestaurantData], lambda _, new: new]
89 current_step: Annotated[str, lambda _, new: new]
90 error: Annotated[Optional[str], lambda _, new: new]
91 final_response: Annotated[Optional[str], lambda _, new: new]
92
93# APIFY actors
94GOOGLE_MAPS_SCRAPER = "compass/crawler-google-places"
95RAG_WEB_BROWSER = "apify/rag-web-browser"
96
97# LLM setup
98llm = ChatOpenAI(model="gpt-4", temperature=0, api_key=settings.openai_api_key)
99
100# State transformation functions
101async def parse_user_query(state: AgentState) -> AgentState:
102 """Parse the user query to extract location, restaurant name, date, etc."""
103 print("Parsing user query")
104 user_message = state["query"].get("user_input", "")
105
106 prompt = ChatPromptTemplate.from_messages([
107 ("system", """
108 Extract the following information from the user query:
109 1. Location (city, neighborhood, address) - add country name
110 2. Restaurant name (if specified)
111 3. Date (if specified)
112 4. Cuisine type (if specified)
113
114 Format your response as JSON with keys: location, restaurant_name, date, cuisine_type.
115 If information is not provided, use null for that field.
116 """),
117 ("human", "{user_input}")
118 ])
119
120 formatted_prompt = prompt.invoke({"user_input": user_message})
121 response = await llm.ainvoke(formatted_prompt.to_messages())
122 parsed_query = json.loads(response.content)
123
124 # Create a new query object that preserves the original user_input
125 updated_query = {
126 **state["query"], # Keep all existing fields
127 **parsed_query, # Add the parsed fields
128 }
129
130 return {**state, "query": updated_query, "current_step": "query_parsed"}
131
132async def search_google_maps(state: AgentState) -> AgentState:
133 """Use APIFY Google Maps Scraper to find restaurants, with caching for debugging."""
134 print("Searching Google Maps")
135 try:
136 location = state["query"]["location"]
137 restaurant_name = state["query"].get("restaurant_name")
138 cuisine_type = state["query"].get("cuisine_type")
139
140 search_term = restaurant_name or f"{cuisine_type} restaurants" if cuisine_type else "restaurants"
141
142 run_input = {
143 "searchStringsArray": [f"{search_term} in {location}"],
144 "maxCrawledPlaces": 5,
145 "language": "en",
146 "maxImages": 0,
147 "includeReviews": True,
148 "maxReviews": 15,
149 }
150
151 run = await Actor.call(GOOGLE_MAPS_SCRAPER, run_input=run_input, timeout=timedelta(seconds=90))
152 items = (await Actor.apify_client.dataset(run.default_dataset_id).list_items()).items
153
154 restaurants = []
155 for item in items:
156 rest_data = {
157 "name": item.get("title", ""),
158 "address": item.get("address", ""),
159 "location": item.get("location"),
160 "google_rating": item.get("totalScore"),
161 "google_reviews_count": item.get("reviewsCount"),
162 "google_map_url": item.get("url", ""),
163 "web_url": item.get("website", ""),
164 "menu_url": item.get("menu", ""),
165 "price_level": item.get("priceLevel"),
166 "cuisine": item.get("categories", []),
167 "top_positive_reviews": [r.get("text", "") for r in item.get("reviews", []) if r.get("stars", 0) >= 4][:3],
168 "top_negative_reviews": [r.get("text", "") for r in item.get("reviews", []) if r.get("stars", 0) <= 2][:3],
169 }
170 restaurants.append(rest_data)
171
172 if restaurants:
173 await Actor.charge({"eventName": "google_maps_search"})
174
175 return {**state, "restaurants": restaurants, "current_step": "google_maps_completed"}
176
177 except Exception as e:
178 return {**state, "error": f"Error in Google Maps search: {str(e)}", "current_step": "error"}
179
180async def crawl_websites(state: AgentState) -> AgentState:
181 print("Website crawling")
182 """Use RAG Web Browser in standby mode to fetch information about the restaurants."""
183 try:
184 if not state.get("top_matches"):
185 return {
186 **state,
187 "error": "No restaurants to crawl websites for",
188 "current_step": "error",
189 }
190
191 rag_client = RagWebBrowserClient(os.environ["APIFY_TOKEN"])
192 updated_restaurants = []
193
194 for restaurant in state["top_matches"]:
195 restaurant_copy = {**restaurant}
196 updated_restaurants.append(restaurant_copy)
197
198 url_to_crawl = restaurant.get("menu_url") or restaurant.get("web_url")
199 if url_to_crawl:
200 try:
201 results = rag_client.search(
202 query=url_to_crawl,
203 max_results=1,
204 output_formats="markdown",
205 request_timeout_secs=45,
206 dynamic_content_wait_secs=10
207 )
208 if results:
209
210 await Actor.charge({"eventName": "website_crawl"})
211
212 if restaurant.get("menu_url"):
213 restaurant_copy["menu_url_content"] = results[0].get("markdown", "")
214 else:
215 restaurant_copy["web_url_content"] = results[0].get("markdown", "")
216 except Exception as e:
217 print(f"Error crawling URL {url_to_crawl}: {str(e)}")
218
219 return {
220 **state,
221 "top_matches": updated_restaurants,
222 "current_step": "content_crawl_completed",
223 }
224 except Exception as e:
225 return {
226 **state,
227 "error": f"Error in RAG Web Browser crawl: {str(e)}",
228 "current_step": "error",
229 }
230
231async def analyze_and_summarize(state: AgentState) -> AgentState:
232 """Use LLM to analyze and summarize the restaurant data."""
233 print("Analysing and summarizing")
234 try:
235 if not state.get("restaurants"):
236 return {
237 **state,
238 "error": "No restaurants to analyze",
239 "current_step": "error",
240 }
241
242 analyzed_restaurants = []
243
244 analysis_prompt = ChatPromptTemplate.from_messages([
245 ("system", """
246 Analyze the restaurant data provided and create a comprehensive summary that includes:
247 1. Key themes from positive reviews
248 2. Key themes from negative reviews
249
250 Focus on being objective and data-driven in your analysis, make it relatively consise.
251 Don't complain about not having much data. Answer straight to the point.
252 """),
253 ("human", "Restaurant data:\n{restaurant_info}")
254 ])
255
256 for restaurant in state["restaurants"]:
257 # Prepare the restaurant data for the LLM
258 restaurant_info = restaurant.get("top_positive_reviews", [])
259 restaurant_info += restaurant.get("top_negative_reviews", [])
260
261 # Format the prompt with this restaurant's data
262 formatted_prompt = analysis_prompt.invoke({"restaurant_info": restaurant_info})
263 response = await llm.ainvoke(formatted_prompt.to_messages())
264
265 # Add the analysis
266 restaurant["review_analysis"] = response.content
267 analyzed_restaurants.append(restaurant)
268
269 if analyzed_restaurants:
270 await Actor.charge({"eventName": "restaurant_analysis"})
271
272 return {
273 **state,
274 "restaurants": analyzed_restaurants,
275 "current_step": "analysis_completed",
276 }
277
278 except Exception as e:
279 return {
280 **state,
281 "error": f"Error in analysis: {str(e)}",
282 "current_step": "error",
283 }
284
285async def pick_top_restaurants(state: AgentState) -> AgentState:
286 print("Selecting top restaurants")
287 try:
288 data = [
289 {
290 "restaurant_index": index,
291 "review_analysis": item["review_analysis"],
292 "google_rating": item["google_rating"],
293 "price_level": item["price_level"],
294 "address": item["address"],
295 "name": item["name"],
296 "cuisine": item["cuisine"],
297 } for index, item in enumerate(state["restaurants"])
298 ]
299
300 prompt = ChatPromptTemplate.from_messages([
301 ("human", """
302 Pick up to three restaurants that best match the user criteria. If the criteria are too vague, pick the restaurants with the best quality overall.
303 The result should be a JSON list of numbers that contains the `restaurant_index` values of the selected restaurants with no extra characters around it.
304
305 User criteria:
306 {user_criteria}
307
308 Restaurant data:
309 {restaurant_data}
310 """)
311 ])
312
313 formatted_prompt = prompt.invoke({
314 "user_criteria": json.dumps(state["query"], indent=2, ensure_ascii=False),
315 "restaurant_data": json.dumps(data, indent=2, ensure_ascii=False)
316 })
317
318 response = await llm.ainvoke(formatted_prompt.to_messages())
319 top_matches = [state["restaurants"][index] for index in json.loads(response.content)]
320
321 return {**state, "top_matches": top_matches, "current_step": "pick_top_restaurants_done"}
322 except Exception as e:
323 return {
324 **state,
325 "error": f"Error when picking top restaurants: {str(e)}",
326 "current_step": "error",
327 }
328
329async def format_final_response(state: AgentState) -> AgentState:
330 """Format the final response to the user with concise review summaries and menu highlights."""
331 print("Formatting final response")
332 if state.get("error"):
333 return state
334
335 try:
336 restaurants = state.get("top_matches", [])
337 query = state.get("query", {})
338
339 sorted_restaurants = sorted(
340 restaurants,
341 key=lambda x: x.get("google_rating") if x.get("google_rating") is not None else 0,
342 reverse=True
343 )
344
345 restaurant_summaries = []
346 for restaurant in sorted_restaurants:
347 # Truncate menu/web content
348 menu_content = restaurant.get("menu_url_content") or restaurant.get("web_url_content", "")
349 if menu_content:
350 menu_content = menu_content[:1000] + "..." if len(menu_content) > 1000 else menu_content
351
352 summary = {
353 "name": restaurant.get("name", ""),
354 "address": restaurant.get("address", ""),
355 "rating": restaurant.get("google_rating", "N/A"),
356 "price_level": restaurant.get("price_level", "N/A"),
357 "cuisine": restaurant.get("cuisine", []),
358 "map_url": restaurant.get("google_map_url", ""),
359 "menu_url": restaurant.get("menu_url", ""),
360 "web_url": restaurant.get("web_url", ""),
361 "review_analysis": restaurant.get("review_analysis", "No review analysis available"),
362 "menu_content": menu_content
363 }
364 restaurant_summaries.append(summary)
365
366 response_prompt = ChatPromptTemplate.from_messages([
367 ("human", """
368 You are an assistant that generates structured JSON responses.
369
370 Summarize the top restaurant options for {location}.
371
372 Your response must be a JSON object with the following structure:
373 {{
374 "restaurants": [
375 {{
376 "name": "Restaurant Name",
377 "address": "Restaurant Address",
378 "location": "Restaurant Longtitude and Latitude",
379 "rating": "4.5",
380 "price_level": "$$",
381 "cuisine": ["Cuisine1", "Cuisine2"],
382 "map_url": "Google Maps Link",
383 "menu_url": "Menu Link",
384 "web_url": "Website Link",
385 "review_summary": "Brief review summary",
386 "menu_highlights": "Key menu items if available"
387 }}
388 ],
389 "notes": "Mention reservation recommendations if a date is provided: {date}.
390 Prioritize restaurants matching cuisine type: {cuisine_type}."
391 If the cuisine type in the input Restaurant data is too vague, adjust it in the output to fit the name and the reviews.
392 }}
393
394 DO NOT return additional text, only a valid JSON response.
395
396 Restaurant data:
397 {restaurant_data}
398 """)
399 ])
400
401 formatted_prompt = response_prompt.invoke({
402 "location": query.get("location", "the requested location"),
403 "date": query.get("date"),
404 "cuisine_type": query.get("cuisine_type"),
405 "restaurant_data": json.dumps(restaurant_summaries, indent=2, ensure_ascii=False)
406 })
407
408 response = await llm.ainvoke(formatted_prompt.to_messages())
409 final = json.loads(response.content)
410 if final.get("restaurants"):
411 await Actor.charge({"eventName": "successful_completion"})
412
413 return {
414 **state,
415 "final_response": final,
416 "current_step": "completed",
417 }
418 except Exception as e:
419 return {
420 **state,
421 "error": f"Error formatting response: {str(e)}",
422 "current_step": "error",
423 }
424
425async def handle_error(state: AgentState) -> Dict:
426 """Handle any errors that occurred during processing."""
427 error_message = state.get("error", "An unknown error occurred")
428 print(f"Handling error: {error_message}")
429
430 prompt = f"""
431 The restaurant search encountered an error: {error_message}
432
433 Please generate a helpful response to the user explaining what went wrong
434 and suggesting alternative actions they could take.
435 """
436
437 response = await llm.ainvoke([HumanMessage(content=prompt)])
438
439 return {
440 **state,
441 "final_response": response.content,
442 "current_step": "error_handled",
443 }
444
445# Define the graph
446def build_restaurant_agent():
447 """Build and return the restaurant agent graph."""
448 workflow = StateGraph(AgentState)
449
450 workflow.add_node("parse_query", parse_user_query)
451 workflow.add_node("search_google_maps", search_google_maps)
452 workflow.add_node("analyze", analyze_and_summarize)
453 workflow.add_node("pick_top_restaurants", pick_top_restaurants)
454 workflow.add_node("crawl_websites", crawl_websites)
455 workflow.add_node("format_response", format_final_response)
456 workflow.add_node("handle_error", handle_error)
457
458 workflow.add_edge("parse_query", "search_google_maps")
459 workflow.add_edge("search_google_maps", "analyze")
460 workflow.add_edge("analyze", "pick_top_restaurants")
461 workflow.add_edge("pick_top_restaurants", "crawl_websites")
462 workflow.add_edge("crawl_websites", "format_response")
463 workflow.add_edge("format_response", END)
464
465 # Error handling
466 workflow.add_conditional_edges(
467 "parse_query",
468 lambda state: "handle_error" if state.get("error") else "search_google_maps",
469 )
470 workflow.add_conditional_edges(
471 "search_google_maps",
472 lambda state: "handle_error" if state.get("error") else "analyze",
473 )
474 workflow.add_conditional_edges(
475 "analyze",
476 lambda state: "handle_error" if state.get("error") else "pick_top_restaurants",
477 )
478 workflow.add_conditional_edges(
479 "pick_top_restaurants",
480 lambda state: "handle_error" if state.get("error") else "crawl_websites",
481 )
482 workflow.add_conditional_edges(
483 "crawl_websites",
484 lambda state: "handle_error" if state.get("error") else "format_response",
485 )
486 workflow.add_conditional_edges(
487 "format_response",
488 lambda state: "handle_error" if state.get("error") else END,
489 )
490
491 workflow.add_edge("handle_error", END)
492
493 workflow.set_entry_point("parse_query")
494
495 return workflow.compile()
496
497async def get_restaurant_recommendations(user_query: str):
498 """Run the restaurant agent with a user query."""
499
500 await Actor.charge({"eventName": "init"})
501
502 agent = build_restaurant_agent()
503
504 initial_state = {
505 "query": {"user_input": user_query},
506 "restaurants": [],
507 "top_matches": [],
508 "current_step": "started",
509 "error": None,
510 }
511
512 result = await agent.ainvoke(initial_state)
513 await Actor.set_value("final_state", result)
514
515 if "final_response" in result:
516 return result["final_response"]
517 elif "error" in result:
518 raise RuntimeError(f"There was an error: {result['error']}")
519 else:
520 raise RuntimeError("Unable to process your request. Please try again with more specific details.")
521
522class Input(BaseModel):
523 prompt: Annotated[str, Field()]
524
525async def main() -> None:
526 async with Actor:
527 input = Input.model_validate(await Actor.get_input())
528 try:
529 response = await get_restaurant_recommendations(input.prompt)
530 if isinstance(response, dict) and "restaurants" in response:
531 for item in response["restaurants"]:
532 await Actor.push_data(item)
533 else:
534 print(f"Unexpected response format: {response}")
535 await Actor.push_data({"error": "Failed to retrieve restaurant recommendations"})
536 except Exception as ex:
537 print(str(ex))
538 await Actor.push_data({"error": str(ex)})
539
540if __name__ == "__main__":
541 asyncio.run(main())
Pricing
Pricing model
Pay per eventThis Actor is paid per result. You are not charged for the Apify platform usage, but only a fixed price for each dataset of 1,000 items in the Actor outputs.
Initial setup
$0.100
Initial setup of your restaurant search
Search Google maps
$0.200
Searching Google Maps for restaurants
Scrape web content
$0.100
Fetching restaurant website content
Analyze restaurants and reviews
$0.100
Analyzing restaurant reviews and data
Recommend best
$0.100
Finalizing restaurant recommendations