Share context builder for chat final response across model types

The context building logic was nearly identical across all model
types.

This change extracts that logic into a shared function and calls it
once in the `agenerate_chat_response', the entrypoint to the converse
methods for all 3 model types.

Main differences handled are
- Gemini system prompt had additional verbosity instructions. Keep it
- Pass system messsage via chatml messages list to anthropic, gemini
  models as well (like openai models) instead of passing it as
  separate arg to chat_completion_* funcs.

  The model specific message formatters for both already extract
  system instruction from the messages list. So system messages wil be
  automatically extracted from the chat_completion_* funcs to pass as
  separate arg required by anthropic, gemini api libraries.

src/khoj/processor/conversation/anthropic/anthropic_chat.py

@@ -1,22 +1,16 @@
 import logging
-from datetime import datetime
+from typing import AsyncGenerator, List, Optional
-from typing import AsyncGenerator, Dict, List, Optional
+
+from langchain_core.messages.chat import ChatMessage
 
-from khoj.database.models import Agent, ChatMessageModel, ChatModel
-from khoj.processor.conversation import prompts
 from khoj.processor.conversation.anthropic.utils import (
     anthropic_chat_completion_with_backoff,
     anthropic_completion_with_backoff,
 )
 from khoj.processor.conversation.utils import (
-    OperatorRun,
     ResponseWithThought,
-    generate_chatml_messages_with_context,
     messages_to_print,
 )
-from khoj.utils.helpers import is_none_or_empty, truncate_code_context
-from khoj.utils.rawconfig import LocationData
-from khoj.utils.yaml import yaml_dump
 
 logger = logging.getLogger(__name__)
 
@@ -52,91 +46,17 @@ def anthropic_send_message_to_model(
 
 async def converse_anthropic(
     # Query
-    user_query: str,
+    messages: List[ChatMessage],
-    # Context
-    references: list[dict],
-    online_results: Optional[Dict[str, Dict]] = None,
-    code_results: Optional[Dict[str, Dict]] = None,
-    operator_results: Optional[List[OperatorRun]] = None,
-    query_images: Optional[list[str]] = None,
-    query_files: str = None,
-    program_execution_context: Optional[List[str]] = None,
-    generated_asset_results: Dict[str, Dict] = {},
-    location_data: LocationData = None,
-    user_name: str = None,
-    chat_history: List[ChatMessageModel] = [],
     # Model
     model: Optional[str] = "claude-3-7-sonnet-latest",
     api_key: Optional[str] = None,
     api_base_url: Optional[str] = None,
-    max_prompt_size=None,
-    tokenizer_name=None,
-    agent: Agent = None,
-    vision_available: bool = False,
     deepthought: Optional[bool] = False,
     tracer: dict = {},
 ) -> AsyncGenerator[ResponseWithThought, None]:
     """
     Converse with user using Anthropic's Claude
     """
-    # Initialize Variables
-    current_date = datetime.now()
-
-    if agent and agent.personality:
-        system_prompt = prompts.custom_personality.format(
-            name=agent.name,
-            bio=agent.personality,
-            current_date=current_date.strftime("%Y-%m-%d"),
-            day_of_week=current_date.strftime("%A"),
-        )
-    else:
-        system_prompt = prompts.personality.format(
-            current_date=current_date.strftime("%Y-%m-%d"),
-            day_of_week=current_date.strftime("%A"),
-        )
-
-    if location_data:
-        location_prompt = prompts.user_location.format(location=f"{location_data}")
-        system_prompt = f"{system_prompt}\n{location_prompt}"
-
-    if user_name:
-        user_name_prompt = prompts.user_name.format(name=user_name)
-        system_prompt = f"{system_prompt}\n{user_name_prompt}"
-
-    context_message = ""
-    if not is_none_or_empty(references):
-        context_message = f"{prompts.notes_conversation.format(query=user_query, references=yaml_dump(references))}\n\n"
-    if not is_none_or_empty(online_results):
-        context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
-    if not is_none_or_empty(code_results):
-        context_message += (
-            f"{prompts.code_executed_context.format(code_results=truncate_code_context(code_results))}\n\n"
-        )
-    if not is_none_or_empty(operator_results):
-        operator_content = [
-            {"query": oc.query, "response": oc.response, "webpages": oc.webpages} for oc in operator_results
-        ]
-        context_message += (
-            f"{prompts.operator_execution_context.format(operator_results=yaml_dump(operator_content))}\n\n"
-        )
-    context_message = context_message.strip()
-
-    # Setup Prompt with Primer or Conversation History
-    messages = generate_chatml_messages_with_context(
-        user_query,
-        context_message=context_message,
-        chat_history=chat_history,
-        model_name=model,
-        max_prompt_size=max_prompt_size,
-        tokenizer_name=tokenizer_name,
-        query_images=query_images,
-        vision_enabled=vision_available,
-        model_type=ChatModel.ModelType.ANTHROPIC,
-        query_files=query_files,
-        generated_asset_results=generated_asset_results,
-        program_execution_context=program_execution_context,
-    )
-
     logger.debug(f"Conversation Context for Claude: {messages_to_print(messages)}")
 
     # Get Response from Claude
@@ -146,8 +66,6 @@ async def converse_anthropic(
         temperature=0.2,
         api_key=api_key,
         api_base_url=api_base_url,
-        system_prompt=system_prompt,
-        max_prompt_size=max_prompt_size,
         deepthought=deepthought,
         tracer=tracer,
     ):

src/khoj/processor/conversation/anthropic/utils.py

@@ -184,8 +184,7 @@ async def anthropic_chat_completion_with_backoff(
     temperature: float,
     api_key: str | None,
     api_base_url: str,
-    system_prompt: str,
+    system_prompt: str = "",
-    max_prompt_size: int | None = None,
     deepthought: bool = False,
     model_kwargs: dict | None = None,
     tracer: dict = {},

src/khoj/processor/conversation/google/gemini_chat.py

@@ -1,22 +1,16 @@
 import logging
-from datetime import datetime
+from typing import AsyncGenerator, List, Optional
-from typing import AsyncGenerator, Dict, List, Optional
+
+from langchain_core.messages.chat import ChatMessage
 
-from khoj.database.models import Agent, ChatMessageModel, ChatModel
-from khoj.processor.conversation import prompts
 from khoj.processor.conversation.google.utils import (
     gemini_chat_completion_with_backoff,
     gemini_completion_with_backoff,
 )
 from khoj.processor.conversation.utils import (
-    OperatorRun,
     ResponseWithThought,
-    generate_chatml_messages_with_context,
     messages_to_print,
 )
-from khoj.utils.helpers import is_none_or_empty, truncate_code_context
-from khoj.utils.rawconfig import LocationData
-from khoj.utils.yaml import yaml_dump
 
 logger = logging.getLogger(__name__)
 
@@ -61,93 +55,18 @@ def gemini_send_message_to_model(
 
 async def converse_gemini(
     # Query
-    user_query: str,
+    messages: List[ChatMessage],
-    # Context
-    references: list[dict],
-    online_results: Optional[Dict[str, Dict]] = None,
-    code_results: Optional[Dict[str, Dict]] = None,
-    operator_results: Optional[List[OperatorRun]] = None,
-    query_images: Optional[list[str]] = None,
-    query_files: str = None,
-    generated_asset_results: Dict[str, Dict] = {},
-    program_execution_context: List[str] = None,
-    location_data: LocationData = None,
-    user_name: str = None,
-    chat_history: List[ChatMessageModel] = [],
     # Model
     model: Optional[str] = "gemini-2.5-flash",
     api_key: Optional[str] = None,
     api_base_url: Optional[str] = None,
     temperature: float = 1.0,
-    max_prompt_size=None,
-    tokenizer_name=None,
-    agent: Agent = None,
-    vision_available: bool = False,
     deepthought: Optional[bool] = False,
     tracer={},
 ) -> AsyncGenerator[ResponseWithThought, None]:
     """
     Converse with user using Google's Gemini
     """
-    # Initialize Variables
-    current_date = datetime.now()
-
-    if agent and agent.personality:
-        system_prompt = prompts.custom_personality.format(
-            name=agent.name,
-            bio=agent.personality,
-            current_date=current_date.strftime("%Y-%m-%d"),
-            day_of_week=current_date.strftime("%A"),
-        )
-    else:
-        system_prompt = prompts.personality.format(
-            current_date=current_date.strftime("%Y-%m-%d"),
-            day_of_week=current_date.strftime("%A"),
-        )
-
-    system_prompt += f"\n\n{prompts.gemini_verbose_language_personality}"
-    if location_data:
-        location_prompt = prompts.user_location.format(location=f"{location_data}")
-        system_prompt += f"\n{location_prompt}"
-
-    if user_name:
-        user_name_prompt = prompts.user_name.format(name=user_name)
-        system_prompt += f"\n{user_name_prompt}"
-
-    context_message = ""
-    if not is_none_or_empty(references):
-        context_message = f"{prompts.notes_conversation.format(query=user_query, references=yaml_dump(references))}\n\n"
-    if not is_none_or_empty(online_results):
-        context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
-    if not is_none_or_empty(code_results):
-        context_message += (
-            f"{prompts.code_executed_context.format(code_results=truncate_code_context(code_results))}\n\n"
-        )
-    if not is_none_or_empty(operator_results):
-        operator_content = [
-            {"query": oc.query, "response": oc.response, "webpages": oc.webpages} for oc in operator_results
-        ]
-        context_message += (
-            f"{prompts.operator_execution_context.format(operator_results=yaml_dump(operator_content))}\n\n"
-        )
-    context_message = context_message.strip()
-
-    # Setup Prompt with Primer or Conversation History
-    messages = generate_chatml_messages_with_context(
-        user_query,
-        context_message=context_message,
-        chat_history=chat_history,
-        model_name=model,
-        max_prompt_size=max_prompt_size,
-        tokenizer_name=tokenizer_name,
-        query_images=query_images,
-        vision_enabled=vision_available,
-        model_type=ChatModel.ModelType.GOOGLE,
-        query_files=query_files,
-        generated_asset_results=generated_asset_results,
-        program_execution_context=program_execution_context,
-    )
-
     logger.debug(f"Conversation Context for Gemini: {messages_to_print(messages)}")
 
     # Get Response from Google AI
@@ -157,7 +76,6 @@ async def converse_gemini(
         temperature=temperature,
         api_key=api_key,
         api_base_url=api_base_url,
-        system_prompt=system_prompt,
         deepthought=deepthought,
         tracer=tracer,
     ):

src/khoj/processor/conversation/google/utils.py

@@ -308,7 +308,7 @@ async def gemini_chat_completion_with_backoff(
     temperature: float,
     api_key: str,
     api_base_url: str,
-    system_prompt: str,
+    system_prompt: str = "",
     model_kwargs=None,
     deepthought=False,
     tracer: dict = {},

src/khoj/processor/conversation/openai/gpt.py

@@ -1,9 +1,8 @@
 import logging
-from datetime import datetime
 from typing import Any, AsyncGenerator, Dict, List, Optional
 
-from khoj.database.models import Agent, ChatMessageModel, ChatModel
+from langchain_core.messages.chat import ChatMessage
-from khoj.processor.conversation import prompts
+
 from khoj.processor.conversation.openai.utils import (
     chat_completion_with_backoff,
     clean_response_schema,
@@ -15,15 +14,11 @@ from khoj.processor.conversation.openai.utils import (
     to_openai_tools,
 )
 from khoj.processor.conversation.utils import (
-    OperatorRun,
     ResponseWithThought,
     StructuredOutputSupport,
-    generate_chatml_messages_with_context,
     messages_to_print,
 )
-from khoj.utils.helpers import ToolDefinition, is_none_or_empty, truncate_code_context
+from khoj.utils.helpers import ToolDefinition
-from khoj.utils.rawconfig import LocationData
-from khoj.utils.yaml import yaml_dump
 
 logger = logging.getLogger(__name__)
 
@@ -96,92 +91,18 @@ def send_message_to_model(
 
 async def converse_openai(
     # Query
-    user_query: str,
+    messages: List[ChatMessage],
-    # Context
+    # Model
-    references: list[dict],
-    online_results: Optional[Dict[str, Dict]] = None,
-    code_results: Optional[Dict[str, Dict]] = None,
-    operator_results: Optional[List[OperatorRun]] = None,
-    query_images: Optional[list[str]] = None,
-    query_files: str = None,
-    generated_asset_results: Dict[str, Dict] = {},
-    program_execution_context: List[str] = None,
-    location_data: LocationData = None,
-    chat_history: list[ChatMessageModel] = [],
     model: str = "gpt-4.1-mini",
     api_key: Optional[str] = None,
     api_base_url: Optional[str] = None,
     temperature: float = 0.6,
-    max_prompt_size=None,
-    tokenizer_name=None,
-    user_name: str = None,
-    agent: Agent = None,
-    vision_available: bool = False,
     deepthought: Optional[bool] = False,
     tracer: dict = {},
 ) -> AsyncGenerator[ResponseWithThought, None]:
     """
     Converse with user using OpenAI's ChatGPT
     """
-    # Initialize Variables
-    current_date = datetime.now()
-
-    if agent and agent.personality:
-        system_prompt = prompts.custom_personality.format(
-            name=agent.name,
-            bio=agent.personality,
-            current_date=current_date.strftime("%Y-%m-%d"),
-            day_of_week=current_date.strftime("%A"),
-        )
-    else:
-        system_prompt = prompts.personality.format(
-            current_date=current_date.strftime("%Y-%m-%d"),
-            day_of_week=current_date.strftime("%A"),
-        )
-
-    if location_data:
-        location_prompt = prompts.user_location.format(location=f"{location_data}")
-        system_prompt = f"{system_prompt}\n{location_prompt}"
-
-    if user_name:
-        user_name_prompt = prompts.user_name.format(name=user_name)
-        system_prompt = f"{system_prompt}\n{user_name_prompt}"
-
-    context_message = ""
-    if not is_none_or_empty(references):
-        context_message = f"{prompts.notes_conversation.format(references=yaml_dump(references))}\n\n"
-    if not is_none_or_empty(online_results):
-        context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
-    if not is_none_or_empty(code_results):
-        context_message += (
-            f"{prompts.code_executed_context.format(code_results=truncate_code_context(code_results))}\n\n"
-        )
-    if not is_none_or_empty(operator_results):
-        operator_content = [
-            {"query": oc.query, "response": oc.response, "webpages": oc.webpages} for oc in operator_results
-        ]
-        context_message += (
-            f"{prompts.operator_execution_context.format(operator_results=yaml_dump(operator_content))}\n\n"
-        )
-
-    context_message = context_message.strip()
-
-    # Setup Prompt with Primer or Conversation History
-    messages = generate_chatml_messages_with_context(
-        user_query,
-        system_prompt,
-        chat_history,
-        context_message=context_message,
-        model_name=model,
-        max_prompt_size=max_prompt_size,
-        tokenizer_name=tokenizer_name,
-        query_images=query_images,
-        vision_enabled=vision_available,
-        model_type=ChatModel.ModelType.OPENAI,
-        query_files=query_files,
-        generated_asset_results=generated_asset_results,
-        program_execution_context=program_execution_context,
-    )
     logger.debug(f"Conversation Context for GPT: {messages_to_print(messages)}")
 
     # Get Response from GPT

src/khoj/routers/helpers.py

@@ -33,6 +33,7 @@ from apscheduler.triggers.cron import CronTrigger
 from asgiref.sync import sync_to_async
 from django.utils import timezone as django_timezone
 from fastapi import Depends, Header, HTTPException, Request, UploadFile, WebSocket
+from langchain_core.messages.chat import ChatMessage
 from pydantic import BaseModel, EmailStr, Field
 from starlette.authentication import has_required_scope
 from starlette.requests import URL
@@ -124,6 +125,7 @@ from khoj.utils.helpers import (
     mode_descriptions_for_llm,
     timer,
     tool_descriptions_for_llm,
+    truncate_code_context,
 )
 from khoj.utils.rawconfig import (
     ChatRequestBody,
@@ -132,6 +134,7 @@ from khoj.utils.rawconfig import (
     SearchResponse,
 )
 from khoj.utils.state import SearchType
+from khoj.utils.yaml import yaml_dump
 
 logger = logging.getLogger(__name__)
 
@@ -1598,6 +1601,105 @@ def send_message_to_model_wrapper_sync(
         raise HTTPException(status_code=500, detail="Invalid conversation config")
 
 
+def build_conversation_context(
+    # Query and Context
+    user_query: str,
+    references: List[Dict],
+    online_results: Dict[str, Dict],
+    code_results: Dict[str, Dict],
+    operator_results: List[OperatorRun],
+    query_files: str = None,
+    query_images: Optional[List[str]] = None,
+    generated_asset_results: Dict[str, Dict] = {},
+    program_execution_context: List[str] = None,
+    chat_history: List[ChatMessageModel] = [],
+    location_data: LocationData = None,
+    user_name: str = None,
+    # Model config
+    agent: Agent = None,
+    model_name: str = None,
+    model_type: ChatModel.ModelType = None,
+    max_prompt_size: int = None,
+    tokenizer_name: str = None,
+    vision_available: bool = False,
+) -> List[ChatMessage]:
+    """
+    Construct system, context and chatml messages for chat response.
+    Share common logic across different model types.
+
+    Returns:
+        List of ChatMessages with context
+    """
+    # Initialize Variables
+    current_date = datetime.now()
+
+    # Build system prompt
+    if agent and agent.personality:
+        system_prompt = prompts.custom_personality.format(
+            name=agent.name,
+            bio=agent.personality,
+            current_date=current_date.strftime("%Y-%m-%d"),
+            day_of_week=current_date.strftime("%A"),
+        )
+    else:
+        system_prompt = prompts.personality.format(
+            current_date=current_date.strftime("%Y-%m-%d"),
+            day_of_week=current_date.strftime("%A"),
+        )
+
+    # Add Gemini-specific personality enhancement
+    if model_type == ChatModel.ModelType.GOOGLE:
+        system_prompt += f"\n\n{prompts.gemini_verbose_language_personality}"
+
+    # Add location context if available
+    if location_data:
+        location_prompt = prompts.user_location.format(location=f"{location_data}")
+        system_prompt += f"\n{location_prompt}"
+
+    # Add user name context if available
+    if user_name:
+        user_name_prompt = prompts.user_name.format(name=user_name)
+        system_prompt += f"\n{user_name_prompt}"
+
+    # Build context message
+    context_message = ""
+    if not is_none_or_empty(references):
+        context_message = f"{prompts.notes_conversation.format(references=yaml_dump(references))}\n\n"
+    if not is_none_or_empty(online_results):
+        context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
+    if not is_none_or_empty(code_results):
+        context_message += (
+            f"{prompts.code_executed_context.format(code_results=truncate_code_context(code_results))}\n\n"
+        )
+    if not is_none_or_empty(operator_results):
+        operator_content = [
+            {"query": oc.query, "response": oc.response, "webpages": oc.webpages} for oc in operator_results
+        ]
+        context_message += (
+            f"{prompts.operator_execution_context.format(operator_results=yaml_dump(operator_content))}\n\n"
+        )
+    context_message = context_message.strip()
+
+    # Generate the chatml messages
+    messages = generate_chatml_messages_with_context(
+        user_message=user_query,
+        query_files=query_files,
+        query_images=query_images,
+        context_message=context_message,
+        generated_asset_results=generated_asset_results,
+        program_execution_context=program_execution_context,
+        chat_history=chat_history,
+        system_message=system_prompt,
+        model_name=model_name,
+        model_type=model_type,
+        max_prompt_size=max_prompt_size,
+        tokenizer_name=tokenizer_name,
+        vision_enabled=vision_available,
+    )
+
+    return messages
+
+
 async def agenerate_chat_response(
     q: str,
     chat_history: List[ChatMessageModel],
@@ -1645,33 +1747,39 @@ async def agenerate_chat_response(
                 chat_model = vision_enabled_config
                 vision_available = True
 
+        # Build shared conversation context and generate chatml messages
+        messages = build_conversation_context(
+            user_query=query_to_run,
+            references=compiled_references,
+            online_results=online_results,
+            code_results=code_results,
+            operator_results=operator_results,
+            query_files=query_files,
+            query_images=query_images,
+            generated_asset_results=generated_asset_results,
+            program_execution_context=program_execution_context,
+            chat_history=chat_history,
+            location_data=location_data,
+            user_name=user_name,
+            agent=agent,
+            model_type=chat_model.model_type,
+            model_name=chat_model.name,
+            max_prompt_size=max_prompt_size,
+            tokenizer_name=chat_model.tokenizer,
+            vision_available=vision_available,
+        )
+
         if chat_model.model_type == ChatModel.ModelType.OPENAI:
             openai_chat_config = chat_model.ai_model_api
             api_key = openai_chat_config.api_key
             chat_model_name = chat_model.name
             chat_response_generator = converse_openai(
-                # Query
+                # Query + Context Messages
-                query_to_run,
+                messages,
-                # Context
-                references=compiled_references,
-                online_results=online_results,
-                code_results=code_results,
-                operator_results=operator_results,
-                query_images=query_images,
-                query_files=query_files,
-                generated_asset_results=generated_asset_results,
-                program_execution_context=program_execution_context,
-                location_data=location_data,
-                user_name=user_name,
-                chat_history=chat_history,
                 # Model
                 model=chat_model_name,
                 api_key=api_key,
                 api_base_url=openai_chat_config.api_base_url,
-                max_prompt_size=max_prompt_size,
-                tokenizer_name=chat_model.tokenizer,
-                agent=agent,
-                vision_available=vision_available,
                 deepthought=deepthought,
                 tracer=tracer,
             )
@@ -1680,28 +1788,12 @@ async def agenerate_chat_response(
             api_key = chat_model.ai_model_api.api_key
             api_base_url = chat_model.ai_model_api.api_base_url
             chat_response_generator = converse_anthropic(
-                # Query
+                # Query + Context Messages
-                query_to_run,
+                messages,
-                # Context
-                references=compiled_references,
-                online_results=online_results,
-                code_results=code_results,
-                operator_results=operator_results,
-                query_images=query_images,
-                query_files=query_files,
-                generated_asset_results=generated_asset_results,
-                program_execution_context=program_execution_context,
-                location_data=location_data,
-                user_name=user_name,
-                chat_history=chat_history,
                 # Model
                 model=chat_model.name,
                 api_key=api_key,
                 api_base_url=api_base_url,
-                max_prompt_size=max_prompt_size,
-                tokenizer_name=chat_model.tokenizer,
-                agent=agent,
-                vision_available=vision_available,
                 deepthought=deepthought,
                 tracer=tracer,
             )
@@ -1709,28 +1801,12 @@ async def agenerate_chat_response(
             api_key = chat_model.ai_model_api.api_key
             api_base_url = chat_model.ai_model_api.api_base_url
             chat_response_generator = converse_gemini(
-                # Query
+                # Query + Context Messages
-                query_to_run,
+                messages,
-                # Context
-                references=compiled_references,
-                online_results=online_results,
-                code_results=code_results,
-                operator_results=operator_results,
-                query_images=query_images,
-                query_files=query_files,
-                generated_asset_results=generated_asset_results,
-                program_execution_context=program_execution_context,
-                location_data=location_data,
-                user_name=user_name,
-                chat_history=chat_history,
                 # Model
                 model=chat_model.name,
                 api_key=api_key,
                 api_base_url=api_base_url,
-                max_prompt_size=max_prompt_size,
-                tokenizer_name=chat_model.tokenizer,
-                agent=agent,
-                vision_available=vision_available,
                 deepthought=deepthought,
                 tracer=tracer,
             )