Sn1 360 move qa task to web dataset

README.md

@@ -49,24 +49,21 @@ Subnet one utilizes the concept of "Tasks" to control the behavior of miners. Va
 ### 1. **QA (Question Answering)**
 The miner receives a question about a specific section from a Wikipedia page. The miner must then find the original context in the specified section and use it to return an accurate answer. References are generated using the validators privileged knowledge of the context, and miner complestions are scored based on similarity metrics.
 
-### 2. **Summarization**
-Similar to QA, but the miner uses the entire Wikipedia page instead of a specific section. The miner reads the whole page, summarizes it, and provides a concise answer.
-
-### 3. **DateQA**
-The miner receives a question about an event from Wikipedia. The miner must search through Wikipedia for the relevant event and return the correct answer based on the findings. References are again generated with validator's knowledge of the context, and similarity metrics are used to score miner completions.
-
-### 4. **Inference**
+### 2. **Inference**
 A question is given with some pre-seeded information and a random seed. The miner must perform an inference based on this information to provide the correct answer. Completions are scored based on similarity metrics.
 
-### 5. **MultiChoice**
+### 3. **MultiChoice**
 The miner is presented with a question from Wikipedia along with four possible answers (A, B, C, or D). The miner must search Wikipedia and return the correct answer by selecting one of the given options. Miner completions are scored by Regex matching.
 
-### 6. **Programming**
+### 5. **Programming**
 The miner receives a code snippet that is incomplete. The task is to complete the code snippet to perform its intended function. The validator generates a reference using it's internal LLM, and the miner is scored based on its similarity to this reference.
 
-### 7. **Web Retrieval**
+### 6. **Web Retrieval**
 The miner is given a question based on a random web page and must return a scraped website that contains the answer. This requires searching the web to locate the most accurate and reliable source to provide the answer. The miner is scored based on the embedding similarity between the answer it returns and the original website that the validator generated the reference from.
 
+### 7. **Multistep Reasoning**
+The miner is given a complex problem that requires multiple steps to solve. Each step builds upon the previous one, and the miner must provide intermediate results before arriving at the final answer. The validator generates a reference solution using its internal LLM, and the miner is scored based on the accuracy and coherence of the intermediate and final results.
+
 # API Documentation
 
 For detailed information on the available API endpoints, request/response formats, and usage examples, please refer to the [API Documentation](./api/API.md).

docs/SN1_validation.md

@@ -31,7 +31,6 @@ More tooling will be included in future releases.
 # Tasks
 The validation process supports an ever-growing number of tasks. Tasks drive agent behaviour based on specific goals, such as;
 - Question answering
-- Summarization
 - Code debugging
 - Mathematics
  and more.

neurons/validator.py

@@ -22,7 +22,7 @@
 
 torch.multiprocessing.set_start_method("spawn", force=True)
 
-NEURON_SAMPLE_SIZE = 100
+NEURON_SAMPLE_SIZE = 100  # TODO: Should add this to constants.py
 
 
 def create_loop_process(task_queue, scoring_queue, reward_events):

prompting/datasets/random_website.py

@@ -23,15 +23,15 @@ class DDGDataset(BaseDataset):
     english_words: list[str] = None
 
     def search_random_term(self, retries: int = 3) -> tuple[Optional[str], Optional[list[dict[str, str]]]]:
-        try:
-            ddg = PatchedDDGS(proxy=shared_settings.PROXY_URL, verify=False)
-            for _ in range(retries):
-                random_words = " ".join(random.sample(ENGLISH_WORDS, 3))
+        ddg = PatchedDDGS(proxy=shared_settings.PROXY_URL, verify=False)
+        for _ in range(retries):
+            random_words = " ".join(random.sample(ENGLISH_WORDS, 3))
+            try:
                 results = list(ddg.text(random_words))
                 if results:
                     return random_words, results
-        except Exception as ex:
-            logger.error(f"Failed to get search results from DuckDuckGo: {ex}")
+            except Exception as ex:
+                logger.error(f"Failed to get search results from DuckDuckGo: {ex}")
         return None, None
 
     @staticmethod
@@ -44,7 +44,7 @@ def extract_website_content(url: str) -> Optional[str]:
             logger.error(f"Failed to extract content from website {url}: {ex}")
 
     def next(self) -> Optional[DDGDatasetEntry]:
-        search_term, results = self.search_random_term(retries=3)
+        search_term, results = self.search_random_term(retries=5)
         if not results:
             return None
         website_url = results[0]["href"]

prompting/rewards/web_retrieval.py

@@ -60,24 +60,24 @@ def reward(self, reference: str, response_event: DendriteResponseEvent, **kwargs
                 continue
 
             dataset_entry = DDGDatasetEntry.model_validate_json(json.loads(reference))
-            search_term = dataset_entry.search_term
+            query = dataset_entry.query
             reference_content = dataset_entry.website_content
 
             # Similarity between search term and miner's scraped content.
-            search_response_sim = self._cosine_similarity(content1=search_term, content2=response_content)
+            search_response_sim = self._cosine_similarity(content1=query, content2=response_content)
 
             # Similarity between search term and relevant section of content.
             search_relevant_sim = 0
             if response_relevant is not None:
-                search_relevant_sim = self._cosine_similarity(content1=search_term, content2=response_relevant)
+                search_relevant_sim = self._cosine_similarity(content1=query, content2=response_relevant)
 
             # If the URL provided in the completion is valid.
             valid_url_score = 0
             if response_url_scraped is not None:
                 valid_url_score = self._cosine_similarity(content1=response_content, content2=response_url_scraped)
 
             # Similarity between search term and reference content.
-            search_reference_sim = self._cosine_similarity(content1=search_term, content2=reference_content)
+            search_reference_sim = self._cosine_similarity(content1=query, content2=reference_content)
             score = (search_response_sim + valid_url_score + search_relevant_sim) / 3
             if abs(search_response_sim - search_reference_sim) > _SEARCH_TERM_THRESH:
                 logger.info(

prompting/tasks/multi_step_reasoning.py

@@ -9,7 +9,7 @@
 from prompting.llms.apis.llm_wrapper import LLMWrapper
 from prompting.rewards.relevance import RelevanceRewardModel
 from prompting.rewards.reward import BaseRewardConfig, BaseRewardModel
-from prompting.tasks.qa import QuestionAnsweringTask
+from prompting.tasks.qa import WikiQuestionAnsweringTask
 from shared.base import Context
 from shared.timer import Timer
 
@@ -174,7 +174,7 @@ class MultiStepReasoningRewardConfig(BaseRewardConfig):
     ]
 
 
-class MultiStepReasoningTask(QuestionAnsweringTask):
+class MultiStepReasoningTask(WikiQuestionAnsweringTask):
     """QuestionAnsweringTasks must be initialised with an LLM pipeline to generate query and reference plus
     context from a dataset to base the query on"""
 

prompting/tasks/qa.py

@@ -47,11 +47,11 @@ class QARewardConfig(BaseRewardConfig):
     penalty_definition: ClassVar[list[BaseRewardModel]] = [RougeRewardModel(weight=0.5)]
 
 
-class QuestionAnsweringTask(BaseTextTask):
+class WikiQuestionAnsweringTask(BaseTextTask):
     """QuestionAnsweringTasks must be initialised with an LLM pipeline to generate query and reference plus
     context from a dataset to base the query on"""
 
-    name: ClassVar[str] = "qa"
+    name: ClassVar[str] = "wiki_qa"
     query_system_prompt: ClassVar[str] = QUERY_SYSTEM_PROMPT
     reference_system_prompt: ClassVar[str] = REFERENCE_SYSTEM_PROMPT
     augmentation_system_prompt: ClassVar[str] = ""
@@ -67,3 +67,25 @@ def make_reference(self, dataset_entry: Context):
         reference_prompt = REFERENCE_PROMPT_TEMPLATE.format(context=dataset_entry.content, question=self.query)
         self.reference = self.generate_reference(messages=[{"role": "user", "content": reference_prompt}])
         return self.reference
+
+
+class WebQuestionAnsweringTask(BaseTextTask):
+    """QuestionAnsweringTasks must be initialised with an LLM pipeline to generate query and reference plus
+    context from a dataset to base the query on"""
+
+    name: ClassVar[str] = "web_qa"
+    query_system_prompt: ClassVar[str] = QUERY_SYSTEM_PROMPT
+    reference_system_prompt: ClassVar[str] = REFERENCE_SYSTEM_PROMPT
+    augmentation_system_prompt: ClassVar[str] = ""
+    query: str | None = None
+    reference: str | None = None
+
+    def make_query(self, dataset_entry: Context):
+        query_prompt = QUERY_PROMPT_TEMPLATE.format(context=dataset_entry.website_content)
+        self.query = self.generate_query(messages=[query_prompt])
+        return self.query
+
+    def make_reference(self, dataset_entry: Context):
+        reference_prompt = REFERENCE_PROMPT_TEMPLATE.format(context=dataset_entry.website_content, question=self.query)
+        self.reference = self.generate_reference(messages=[{"role": "user", "content": reference_prompt}])
+        return self.reference

prompting/tasks/task_registry.py

@@ -8,21 +8,17 @@
 from prompting.datasets.huggingface_github import HuggingFaceGithubDataset
 from prompting.datasets.random_website import DDGDataset
 from prompting.datasets.sn13 import SN13Dataset
-from prompting.datasets.wiki import WikiDataset, WikiDateDataset
+from prompting.datasets.wiki import WikiDataset
 from prompting.rewards.reward import BaseRewardConfig
 from prompting.tasks.base_task import BaseTextTask
-from prompting.tasks.date_qa import DateQARewardConfig, DateQuestionAnsweringTask
 from prompting.tasks.inference import InferenceRewardConfig, InferenceTask
 from prompting.tasks.multi_choice import MultiChoiceRewardConfig, MultiChoiceTask
 from prompting.tasks.multi_step_reasoning import MultiStepReasoningRewardConfig, MultiStepReasoningTask
 from prompting.tasks.programming_task import ProgrammingRewardConfig, ProgrammingTask
-from prompting.tasks.qa import QARewardConfig, QuestionAnsweringTask
-from prompting.tasks.summarization import SummarizationRewardConfig, SummarizationTask
+from prompting.tasks.qa import QARewardConfig, WebQuestionAnsweringTask, WikiQuestionAnsweringTask
 from prompting.tasks.web_retrieval import WebRetrievalRewardConfig, WebRetrievalTask
 from shared.base import BaseDataset
 
-# from prompting.tasks.
-
 
 class TaskConfig(BaseModel):
     task: type[BaseTextTask]
@@ -38,37 +34,31 @@ def __hash__(self):
 
 class TaskRegistry(BaseModel):
     task_configs: ClassVar[list[TaskConfig]] = [
-        TaskConfig(task=QuestionAnsweringTask, probability=0.15, datasets=[WikiDataset], reward_model=QARewardConfig),
-        TaskConfig(
-            task=SummarizationTask, probability=0.1, datasets=[WikiDataset], reward_model=SummarizationRewardConfig
-        ),
         TaskConfig(
-            task=DateQuestionAnsweringTask,
-            probability=0.1,
-            datasets=[WikiDateDataset],
-            reward_model=DateQARewardConfig,
+            task=WikiQuestionAnsweringTask, probability=0.05, datasets=[WikiDataset], reward_model=QARewardConfig
         ),
+        TaskConfig(task=WebQuestionAnsweringTask, probability=0.25, datasets=[DDGDataset], reward_model=QARewardConfig),
         TaskConfig(
             task=InferenceTask,
-            probability=0.16,
+            probability=0.1,
             datasets=[SN13Dataset],
             reward_model=InferenceRewardConfig,
         ),
         TaskConfig(
             task=MultiChoiceTask,
-            probability=0.26,
+            probability=0.2,
             datasets=[WikiDataset],
             reward_model=MultiChoiceRewardConfig,
         ),
         TaskConfig(
             task=ProgrammingTask,
-            probability=0.1,
+            probability=0.2,
             datasets=[HuggingFaceGithubDataset],
             reward_model=ProgrammingRewardConfig,
         ),
         TaskConfig(
             task=WebRetrievalTask,
-            probability=0.03,
+            probability=0.1,
             datasets=[DDGDataset],
             reward_model=WebRetrievalRewardConfig,
         ),

prompting/tasks/web_retrieval.py

@@ -43,5 +43,7 @@ def make_query(self, dataset_entry: DDGDatasetEntry) -> str:
         return self.query
 
     def make_reference(self, dataset_entry: DDGDatasetEntry) -> str:
-        self.reference = json.dumps(dataset_entry.model_dump_json())
+        ref_dict = dataset_entry.model_dump_json()
+        ref_dict["query"] = self.query
+        self.reference = json.dumps(ref_dict)
         return self.reference

shared/settings.py

@@ -113,6 +113,7 @@ class SharedSettings(BaseSettings):
     SUBTENSOR_NETWORK: Optional[str] = Field(None, env="SUBTENSOR_NETWORK")
     MAX_ALLOWED_VRAM_GB: int = Field(62, env="MAX_ALLOWED_VRAM_GB")
     LLM_MAX_MODEL_LEN: int = Field(4096, env="LLM_MAX_MODEL_LEN")
+    PROXY_URL: Optional[str] = Field(None, env="PROXY_URL")
     LLM_MODEL: str = Field("hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", env="LLM_MODEL")
     SAMPLING_PARAMS: dict[str, Any] = {
         "temperature": 0.7,

validator_api/API_docs.md

@@ -103,7 +103,7 @@ bash run_api.sh
 
 **Parameters:**
 
-- **task** (query, optional): The type of task (e.g., `QuestionAnsweringTask`, `SummarizationTask`, etc.).
+- **task** (query, optional): The type of task (e.g., `QuestionAnsweringTask`, `Programming`, etc.).
 - **model** (query, optional): The specific model (string).
 - **k** (query, optional): The maximum number of results to return (integer, default: 10).