Merge pull request #72 from mindvalley/feat/inject-employee-context-t…

…o-prompt

feat/inject employee context to prompt

.gitignore

@@ -7,3 +7,5 @@
 .vscode/
 *.sw?
 /backend/tests/regression/answer_quality/search_test_config.yaml
+env.sh
+.cursorrules

backend/danswer/chat/personas.yaml

@@ -19,7 +19,7 @@ personas:
     # Default number of chunks to include as context, set to 0 to disable retrieval
     # Remove the field to set to the system default number of chunks/tokens to pass to Gen AI
     # Each chunk is 512 tokens long
-    num_chunks: 20
+    num_chunks: 50
     # Enable/Disable usage of the LLM chunk filter feature whereby each chunk is passed to the LLM to determine
     # if the chunk is useful or not towards the latest user query
     # This feature can be overriden for all personas via DISABLE_LLM_DOC_RELEVANCE env variable
@@ -80,7 +80,6 @@ personas:
     is_visible: true
     internet_search: true
 
-
   - id: 3
     name: "Art"
     description: >
@@ -92,8 +91,8 @@ personas:
     llm_filter_extraction: false
     recency_bias: "no_decay"
     document_sets: []
-    icon_shape: 234124 
+    icon_shape: 234124
     icon_color: "#9B59B6"
-    image_generation: true 
+    image_generation: true
     display_priority: 3
     is_visible: false

backend/danswer/chat/process_message.py

@@ -4,8 +4,6 @@
 from functools import partial
 from typing import cast
 
-from sqlalchemy.orm import Session
-
 from danswer.chat.chat_utils import create_chat_chain
 from danswer.chat.models import AllCitations
 from danswer.chat.models import CitationInfo
@@ -80,12 +78,8 @@
 from danswer.tools.images.image_generation_tool import IMAGE_GENERATION_RESPONSE_ID
 from danswer.tools.images.image_generation_tool import ImageGenerationResponse
 from danswer.tools.images.image_generation_tool import ImageGenerationTool
-from danswer.tools.internet_search.internet_search_tool import (
-    INTERNET_SEARCH_RESPONSE_ID,
-)
-from danswer.tools.internet_search.internet_search_tool import (
-    internet_search_response_to_search_docs,
-)
+from danswer.tools.internet_search.internet_search_tool import INTERNET_SEARCH_RESPONSE_ID
+from danswer.tools.internet_search.internet_search_tool import internet_search_response_to_search_docs
 from danswer.tools.internet_search.internet_search_tool import InternetSearchResponse
 from danswer.tools.internet_search.internet_search_tool import InternetSearchTool
 from danswer.tools.models import DynamicSchemaInfo
@@ -101,6 +95,7 @@
 from danswer.tools.utils import explicit_tool_calling_supported
 from danswer.utils.logger import setup_logger
 from danswer.utils.timing import log_generator_function_time
+from sqlalchemy.orm import Session
 
 logger = setup_logger()
 
@@ -286,6 +281,8 @@ def stream_chat_message_objects(
     try:
         user_id = user.id if user is not None else None
 
+        user_email = user.email if user is not None else None
+
         chat_session = get_chat_session_by_id(
             chat_session_id=new_msg_req.chat_session_id,
             user_id=user_id,
@@ -628,6 +625,7 @@ def stream_chat_message_objects(
 
         # LLM prompt building, response capturing, etc.
         answer = Answer(
+            user_email=user_email,
             is_connected=is_connected,
             question=final_msg.message,
             latest_query_files=latest_query_files,

backend/danswer/chat/prompts.yaml

@@ -7,21 +7,26 @@ prompts:
     description: "Answers user questions using retrieved context!"
     # System Prompt (as shown in UI)
     system: >
-      You are a question answering system that is constantly learning and improving.
-      The current date is DANSWER_DATETIME_REPLACEMENT.
-      
-      You can process and comprehend vast amounts of text and utilize this knowledge to provide
-      grounded, accurate, and concise answers to diverse queries.
-      
+      You are an advanced AI assistant, Mindvalley’s AI Assistant, designed to assist in various tasks and provide information relevant to my role and my department. The current date is DANSWER_DATETIME_REPLACEMENT.
+
+      You can process and comprehend vast amounts of text and utilize this knowledge to provide grounded, accurate, and concise answers to diverse queries, specifically tailored to MindValley’s operations and me.
+
+      Below is my employee information in Mindvalley:
+
+      DANSWER_EMPLOYEE_REPLACEMENT
+
+      When answering questions, always keep it relavent to me and my role and you can address me with my name.
+
       You always clearly communicate ANY UNCERTAINTY in your answer.
     # Task Prompt (as shown in UI)
     task: >
       Answer my query based on the documents provided.
+
       The documents may not all be relevant, ignore any documents that are not directly relevant
       to the most recent user query.
-      
+
       I have not read or seen any of the documents and do not want to read them.
-      
+
       If there are no relevant documents, refer to the chat history and your internal knowledge.
     # Inject a statement at the end of system prompt to inform the LLM of the current date/time
     # If the DANSWER_DATETIME_REPLACEMENT is set, the date/time is inserted there instead
@@ -30,20 +35,20 @@ prompts:
     # Prompts the LLM to include citations in the for [1], [2] etc.
     # which get parsed to match the passed in sources
     include_citations: true
-  
+
   - name: "ImageGeneration"
     description: "Generates images based on user prompts!"
     system: >
       You are an advanced image generation system capable of creating diverse and detailed images.
-      
+
       You can interpret user prompts and generate high-quality, creative images that match their descriptions.
-      
+
       You always strive to create safe and appropriate content, avoiding any harmful or offensive imagery.
     task: >
       Generate an image based on the user's description.
-      
+
       Provide a detailed description of the generated image, including key elements, colors, and composition.
-      
+
       If the request is not possible or appropriate, explain why and suggest alternatives.
     datetime_aware: true
     include_citations: false
@@ -70,7 +75,7 @@ prompts:
       You are a text summarizing assistant that highlights the most important knowledge from the
       context provided, prioritizing the information that relates to the user query.
       The current date is DANSWER_DATETIME_REPLACEMENT.
-      
+
       You ARE NOT creative and always stick to the provided documents.
       If there are no documents, refer to the conversation history.
 
@@ -104,7 +109,7 @@ prompts:
 
   - name: "InternetSearch"
     description: "Use this Assistant to search the Internet for you (via Bing) and getting the answer"
-    system:  >
+    system: >
       You are an intelligent AI agent designed to assist users by providing accurate and relevant information through internet searches. Your primary objectives are:
       Information Retrieval: Search the internet to find reliable and up-to-date information based on user queries. Ensure that the sources you reference are credible and trustworthy.
       Context Understanding: Analyze user questions to understand context and intent. Provide answers that are directly related to the user's needs, offering additional context when necessary.

backend/danswer/configs/app_configs.py

@@ -72,6 +72,12 @@
     if _VALID_EMAIL_DOMAINS_STR
     else []
 )
+
+# Airtable Config To Get MV Employee Info
+AIRTABLE_API_TOKEN = os.environ.get("AIRTABLE_API_TOKEN")
+AIRTABLE_EMPLOYEE_BASE_ID = os.environ.get("AIRTABLE_EMPLOYEE_BASE_ID")
+AIRTABLE_EMPLOYEE_TABLE_NAME_OR_ID = os.environ.get("AIRTABLE_EMPLOYEE_TABLE_NAME_OR_ID")
+
 # OAuth Login Flow
 # Used for both Google OAuth2 and OIDC flows
 OAUTH_CLIENT_ID = (

backend/danswer/llm/answering/answer.py

@@ -4,10 +4,6 @@
 from typing import cast
 from uuid import uuid4
 
-from langchain.schema.messages import BaseMessage
-from langchain_core.messages import AIMessageChunk
-from langchain_core.messages import HumanMessage
-
 from danswer.chat.chat_utils import llm_doc_from_inference_section
 from danswer.chat.models import AnswerQuestionPossibleReturn
 from danswer.chat.models import CitationInfo
@@ -24,9 +20,7 @@
 from danswer.llm.answering.prompts.build import AnswerPromptBuilder
 from danswer.llm.answering.prompts.build import default_build_system_message
 from danswer.llm.answering.prompts.build import default_build_user_message
-from danswer.llm.answering.prompts.citations_prompt import (
-    build_citations_system_message,
-)
+from danswer.llm.answering.prompts.citations_prompt import build_citations_system_message
 from danswer.llm.answering.prompts.citations_prompt import build_citations_user_message
 from danswer.llm.answering.prompts.quotes_prompt import build_quotes_user_message
 from danswer.llm.answering.stream_processing.citation_processing import (
@@ -59,16 +53,16 @@
 from danswer.tools.search.search_tool import SearchTool
 from danswer.tools.tool import Tool
 from danswer.tools.tool import ToolResponse
-from danswer.tools.tool_runner import (
-    check_which_tools_should_run_for_non_tool_calling_llm,
-)
+from danswer.tools.tool_runner import check_which_tools_should_run_for_non_tool_calling_llm
 from danswer.tools.tool_runner import ToolCallFinalResult
 from danswer.tools.tool_runner import ToolCallKickoff
 from danswer.tools.tool_runner import ToolRunner
 from danswer.tools.tool_selection import select_single_tool_for_non_tool_calling_llm
 from danswer.tools.utils import explicit_tool_calling_supported
 from danswer.utils.logger import setup_logger
-
+from langchain.schema.messages import BaseMessage
+from langchain_core.messages import AIMessageChunk
+from langchain_core.messages import HumanMessage
 
 logger = setup_logger()
 
@@ -99,6 +93,7 @@ def _get_answer_stream_processor(
 class Answer:
     def __init__(
         self,
+        user_email: str,
         question: str,
         answer_style_config: AnswerStyleConfig,
         llm: LLM,
@@ -125,7 +120,7 @@ def __init__(
             raise ValueError(
                 "Cannot provide both `message_history` and `single_message_history`"
             )
-
+        self.user_email = user_email
         self.question = question
         self.is_connected: Callable[[], bool] | None = is_connected
 
@@ -166,7 +161,7 @@ def _update_prompt_builder_for_search_tool(
     ) -> None:
         if self.answer_style_config.citation_config:
             prompt_builder.update_system_prompt(
-                build_citations_system_message(self.prompt_config)
+                build_citations_system_message(self.prompt_config, self.user_email)
             )
             prompt_builder.update_user_prompt(
                 build_citations_user_message(

backend/danswer/llm/answering/prompts/citations_prompt.py

@@ -1,34 +1,41 @@
-from langchain.schema.messages import HumanMessage
-from langchain.schema.messages import SystemMessage
-
 from danswer.chat.models import LlmDoc
 from danswer.configs.model_configs import GEN_AI_SINGLE_USER_MESSAGE_EXPECTED_MAX_TOKENS
 from danswer.db.models import Persona
 from danswer.db.persona import get_default_prompt__read_only
 from danswer.db.search_settings import get_multilingual_expansion
 from danswer.file_store.utils import InMemoryChatFile
 from danswer.llm.answering.models import PromptConfig
-from danswer.llm.factory import get_llms_for_persona
-from danswer.llm.factory import get_main_llm_from_tuple
+from danswer.llm.factory import get_llms_for_persona, get_main_llm_from_tuple
 from danswer.llm.interfaces import LLMConfig
-from danswer.llm.utils import build_content_with_imgs
-from danswer.llm.utils import check_number_of_tokens
-from danswer.llm.utils import get_max_input_tokens
+from danswer.llm.utils import (
+    build_content_with_imgs,
+    check_number_of_tokens,
+    get_max_input_tokens,
+)
 from danswer.prompts.chat_prompts import REQUIRE_CITATION_STATEMENT
 from danswer.prompts.constants import DEFAULT_IGNORE_STATEMENT
-from danswer.prompts.direct_qa_prompts import CITATIONS_PROMPT
-from danswer.prompts.direct_qa_prompts import CITATIONS_PROMPT_FOR_TOOL_CALLING
-from danswer.prompts.prompt_utils import add_date_time_to_prompt
-from danswer.prompts.prompt_utils import build_complete_context_str
-from danswer.prompts.prompt_utils import build_task_prompt_reminders
-from danswer.prompts.token_counts import ADDITIONAL_INFO_TOKEN_CNT
+from danswer.prompts.direct_qa_prompts import (
+    CITATIONS_PROMPT,
+    CITATIONS_PROMPT_FOR_TOOL_CALLING,
+)
+from danswer.prompts.prompt_utils import (
+    add_date_time_to_prompt,
+    add_employee_context_to_prompt,
+    build_complete_context_str,
+    build_task_prompt_reminders,
+)
 from danswer.prompts.token_counts import (
+    ADDITIONAL_INFO_TOKEN_CNT,
     CHAT_USER_PROMPT_WITH_CONTEXT_OVERHEAD_TOKEN_CNT,
+    CITATION_REMINDER_TOKEN_CNT,
+    CITATION_STATEMENT_TOKEN_CNT,
+    LANGUAGE_HINT_TOKEN_CNT,
 )
-from danswer.prompts.token_counts import CITATION_REMINDER_TOKEN_CNT
-from danswer.prompts.token_counts import CITATION_STATEMENT_TOKEN_CNT
-from danswer.prompts.token_counts import LANGUAGE_HINT_TOKEN_CNT
 from danswer.search.models import InferenceChunk
+from danswer.utils.logger import setup_logger
+from langchain.schema.messages import HumanMessage, SystemMessage
+
+logger = setup_logger()
 
 
 def get_prompt_tokens(prompt_config: PromptConfig) -> int:
@@ -117,12 +124,17 @@ def compute_max_llm_input_tokens(llm_config: LLMConfig) -> int:
 
 def build_citations_system_message(
     prompt_config: PromptConfig,
+    user_email: str | None = None,
 ) -> SystemMessage:
     system_prompt = prompt_config.system_prompt.strip()
     if prompt_config.include_citations:
         system_prompt += REQUIRE_CITATION_STATEMENT
     if prompt_config.datetime_aware:
         system_prompt = add_date_time_to_prompt(prompt_str=system_prompt)
+    if user_email:
+        system_prompt = add_employee_context_to_prompt(
+            prompt_str=system_prompt, user_email=user_email
+        )
 
     return SystemMessage(content=system_prompt)
 

backend/danswer/prompts/direct_qa_prompts.py

@@ -2,12 +2,13 @@
 # It is used also for the one shot direct QA flow
 import json
 
-from danswer.prompts.constants import DEFAULT_IGNORE_STATEMENT
-from danswer.prompts.constants import FINAL_QUERY_PAT
-from danswer.prompts.constants import GENERAL_SEP_PAT
-from danswer.prompts.constants import QUESTION_PAT
-from danswer.prompts.constants import THOUGHT_PAT
-
+from danswer.prompts.constants import (
+    DEFAULT_IGNORE_STATEMENT,
+    FINAL_QUERY_PAT,
+    GENERAL_SEP_PAT,
+    QUESTION_PAT,
+    THOUGHT_PAT,
+)
 
 ONE_SHOT_SYSTEM_PROMPT = """
 You are a question answering system that is constantly learning and improving.
@@ -90,7 +91,8 @@
 # similar to the chat flow, but with the option of including a
 # "conversation history" block
 CITATIONS_PROMPT = f"""
-Refer to the following context documents when responding to me.{DEFAULT_IGNORE_STATEMENT}
+Refer to the following context documents when responding to me. \
+Make sure you take into account my employee information in the system message.{DEFAULT_IGNORE_STATEMENT}
 CONTEXT:
 {GENERAL_SEP_PAT}
 {{context_docs_str}}
@@ -106,7 +108,8 @@
 # NOTE: need to add the extra line about "getting right to the point" since the
 # tool calling models from OpenAI tend to be more verbose
 CITATIONS_PROMPT_FOR_TOOL_CALLING = f"""
-Refer to the provided context documents when responding to me.{DEFAULT_IGNORE_STATEMENT} \
+Refer to the provided context documents when responding to me. \
+Make sure you take into account my employee information in the system message.{DEFAULT_IGNORE_STATEMENT} \
 You should always get right to the point, and never use extraneous language.
 
 {{task_prompt}}