Better QA Prompts (#409)

backend/danswer/configs/model_configs.py

@@ -77,7 +77,7 @@ GEN_AI_ENDPOINT = os.environ.get("GEN_AI_ENDPOINT", "")
 GEN_AI_HOST_TYPE = os.environ.get("GEN_AI_HOST_TYPE", ModelHostType.HUGGINGFACE.value)
 
 # Set this to be enough for an answer + quotes
-GEN_AI_MAX_OUTPUT_TOKENS = int(os.environ.get("GEN_AI_MAX_OUTPUT_TOKENS", "512"))
+GEN_AI_MAX_OUTPUT_TOKENS = int(os.environ.get("GEN_AI_MAX_OUTPUT_TOKENS", "1024"))
 
 # Danswer custom Deep Learning Models
 INTENT_MODEL_VERSION = "danswer/intent-model"

backend/danswer/direct_qa/llm_utils.py

@@ -18,10 +18,10 @@ from danswer.direct_qa.huggingface import HuggingFaceCompletionQA
 from danswer.direct_qa.interfaces import QAModel
 from danswer.direct_qa.local_transformers import TransformerQA
 from danswer.direct_qa.open_ai import OpenAICompletionQA
-from danswer.direct_qa.qa_block import JsonChatQAHandler
 from danswer.direct_qa.qa_block import QABlock
 from danswer.direct_qa.qa_block import QAHandler
 from danswer.direct_qa.qa_block import SimpleChatQAHandler
+from danswer.direct_qa.qa_block import SingleMessageQAHandler
 from danswer.direct_qa.qa_prompts import WeakModelFreeformProcessor
 from danswer.direct_qa.qa_utils import get_gen_ai_api_key
 from danswer.direct_qa.request_model import RequestCompletionQA
@@ -53,7 +53,7 @@ def check_model_api_key_is_valid(model_api_key: str) -> bool:
 
 def get_default_qa_handler(model: str) -> QAHandler:
     if model == DanswerGenAIModel.OPENAI_CHAT.value:
-        return JsonChatQAHandler()
+        return SingleMessageQAHandler()
 
     return SimpleChatQAHandler()
 

backend/danswer/direct_qa/qa_block.py

@@ -16,7 +16,10 @@ from danswer.direct_qa.interfaces import DanswerAnswer
 from danswer.direct_qa.interfaces import DanswerAnswerPiece
 from danswer.direct_qa.interfaces import DanswerQuotes
 from danswer.direct_qa.interfaces import QAModel
+from danswer.direct_qa.qa_prompts import CODE_BLOCK_PAT
+from danswer.direct_qa.qa_prompts import GENERAL_SEP_PAT
 from danswer.direct_qa.qa_prompts import JsonChatProcessor
+from danswer.direct_qa.qa_prompts import QUESTION_PAT
 from danswer.direct_qa.qa_prompts import SAMPLE_JSON_RESPONSE
 from danswer.direct_qa.qa_prompts import UNCERTAINTY_PAT
 from danswer.direct_qa.qa_prompts import WeakModelFreeformProcessor
@@ -93,6 +96,49 @@ class SimpleChatQAHandler(QAHandler):
         )
 
 
+class SingleMessageQAHandler(QAHandler):
+    def build_prompt(
+        self, query: str, context_chunks: list[InferenceChunk]
+    ) -> list[BaseMessage]:
+        complete_answer_not_found_response = (
+            '{"answer": "' + UNCERTAINTY_PAT + '", "quotes": []}'
+        )
+
+        context_docs_str = "\n".join(
+            f"{CODE_BLOCK_PAT.format(c.content)}" for c in context_chunks
+        )
+
+        prompt: list[BaseMessage] = [
+            HumanMessage(
+                content="You are a question answering system that is constantly learning and improving. "
+                "You can process and comprehend vast amounts of text and utilize this knowledge "
+                "to provide accurate and detailed answers to diverse queries.\n"
+                "You ALWAYS responds in a json containing an answer and quotes that support the answer.\n"
+                "Your responses are as informative and detailed as possible.\n"
+                "If you don't know the answer, respond with "
+                f"{CODE_BLOCK_PAT.format(complete_answer_not_found_response)}"
+                "\nSample response:"
+                f"{CODE_BLOCK_PAT.format(json.dumps(SAMPLE_JSON_RESPONSE))}"
+                f"{GENERAL_SEP_PAT}CONTEXT:\n\n{context_docs_str}"
+                f"{GENERAL_SEP_PAT}{QUESTION_PAT} {query}"
+                "\nHint: Make the answer as informative as possible and use a JSON! "
+                "Quotes MUST be EXACT substrings from provided documents!"
+            )
+        ]
+        return prompt
+
+    def process_response(
+        self,
+        tokens: Iterator[str],
+        context_chunks: list[InferenceChunk],
+    ) -> AnswerQuestionStreamReturn:
+        yield from process_model_tokens(
+            tokens=tokens,
+            context_docs=context_chunks,
+            is_json_prompt=True,
+        )
+
+
 class JsonChatQAUnshackledHandler(QAHandler):
     def build_prompt(
         self, query: str, context_chunks: list[InferenceChunk]

backend/danswer/direct_qa/qa_prompts.py

@@ -6,7 +6,8 @@ from danswer.configs.constants import DocumentSource
 from danswer.connectors.factory import identify_connector_class
 
 
-GENERAL_SEP_PAT = "---\n"
+GENERAL_SEP_PAT = "\n-----\n"
+CODE_BLOCK_PAT = "\n```\n{}\n```\n"
 DOC_SEP_PAT = "---NEW DOCUMENT---"
 DOC_CONTENT_START_PAT = "DOCUMENT CONTENTS:\n"
 QUESTION_PAT = "Query:"

backend/danswer/direct_qa/qa_utils.py

@@ -246,6 +246,9 @@ def process_model_tokens(
             json_answer_ind = model_output.index('{"answer":')
             if json_answer_ind != 0:
                 model_output = model_output[json_answer_ind:]
+            end = model_output.rfind("}")
+            if end != -1:
+                model_output = model_output[: end + 1]
         except ValueError:
             logger.exception("Did not find answer pattern in response for JSON prompt")
 

backend/danswer/secondary_llm_flows/query_validation.py

@@ -4,50 +4,59 @@ from dataclasses import asdict
 
 from danswer.direct_qa.interfaces import DanswerAnswerPiece
 from danswer.direct_qa.qa_block import dict_based_prompt_to_langchain_prompt
+from danswer.direct_qa.qa_prompts import CODE_BLOCK_PAT
 from danswer.llm.build import get_default_llm
 from danswer.server.models import QueryValidationResponse
 from danswer.server.utils import get_json_line
 
+QUERY_PAT = "QUERY: "
 REASONING_PAT = "REASONING: "
 ANSWERABLE_PAT = "ANSWERABLE: "
-COT_PAT = "\nLet's think step by step"
 
 
 def get_query_validation_messages(user_query: str) -> list[dict[str, str]]:
+    ambiguous_example = (
+        f"{QUERY_PAT}What is this Slack channel about?\n"
+        f"{REASONING_PAT}First the system must determine which Slack channel is "
+        f"being referred to. By fetching 5 documents related to Slack channel contents, "
+        f"it is not possible to determine which Slack channel the user is referring to.\n"
+        f"{ANSWERABLE_PAT}False"
+    )
+
+    debug_example = (
+        f"{QUERY_PAT}Danswer is unreachable.\n"
+        f"{REASONING_PAT}The system searches documents related to Danswer being "
+        f"unreachable. Assuming the documents from search contains situations where "
+        f"Danswer is not reachable and contains a fix, the query is answerable.\n"
+        f"{ANSWERABLE_PAT}True"
+    )
+
+    up_to_date_example = (
+        f"{QUERY_PAT}How many customers do we have\n"
+        f"{REASONING_PAT}Assuming the retrieved documents contain up to date customer "
+        f"acquisition information including a list of customers, the query can be answered. "
+        f"It is important to note that if the information only exists in a database, "
+        f"the system is unable to execute SQL and won't find an answer."
+        f"\n{ANSWERABLE_PAT}True"
+    )
+
     messages = [
         {
-            "role": "system",
-            "content": f"You are a helper tool to determine if a query is answerable using retrieval augmented "
+            "role": "user",
+            "content": "You are a helper tool to determine if a query is answerable using retrieval augmented "
             f"generation. A system will try to answer the user query based on ONLY the top 5 most relevant "
             f"documents found from search. Sources contain both up to date and proprietary information for "
             f"the specific team. For named or unknown entities, assume the search will always find "
-            f"consistent knowledge about the entity. Determine if that system should attempt to answer. "
-            f'"{ANSWERABLE_PAT}" must be exactly "True" or "False"',
+            f"consistent knowledge about the entity.\n"
+            f"The system is not tuned for writing code nor for interfacing with structured data "
+            f"via query languages like SQL.\n"
+            f"Determine if that system should attempt to answer. "
+            f'"{ANSWERABLE_PAT}" must be exactly "True" or "False"\n'
+            f"{CODE_BLOCK_PAT.format(ambiguous_example)}\n"
+            f"{CODE_BLOCK_PAT.format(debug_example)}\n"
+            f"{CODE_BLOCK_PAT.format(up_to_date_example)}\n"
+            f"{CODE_BLOCK_PAT.format(QUERY_PAT + user_query)}\n",
         },
-        {"role": "user", "content": "What is this Slack channel about?"},
-        {
-            "role": "assistant",
-            "content": f"{REASONING_PAT}First the system must determine which Slack channel is being referred to."
-            f"By fetching 5 documents related to Slack channel contents, it is not possible to determine"
-            f"which Slack channel the user is referring to.\n{ANSWERABLE_PAT}False",
-        },
-        {
-            "role": "user",
-            "content": f"Danswer is unreachable.{COT_PAT}",
-        },
-        {
-            "role": "assistant",
-            "content": f"{REASONING_PAT}The system searches documents related to Danswer being "
-            f"unreachable. Assuming the documents from search contains situations where Danswer is not "
-            f"reachable and contains a fix, the query is answerable.\n{ANSWERABLE_PAT}True",
-        },
-        {"role": "user", "content": f"How many customers do we have?{COT_PAT}"},
-        {
-            "role": "assistant",
-            "content": f"{REASONING_PAT}Assuming the searched documents contains customer acquisition information"
-            f"including a list of customers, the query can be answered.\n{ANSWERABLE_PAT}True",
-        },
-        {"role": "user", "content": user_query + COT_PAT},
     ]
 
     return messages