Merge branch 'master' of github.com:khoj-ai/khoj into improve-debug-reasoning-and-other-misc-fixes

This commit is contained in:
sabaimran 2024-11-01 14:51:26 -07:00
commit 2b35790165
8 changed files with 125 additions and 111 deletions

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@ -189,7 +189,7 @@ def converse_anthropic(
if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands: if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n" context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results): if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n" context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
context_message = context_message.strip() context_message = context_message.strip()
# Setup Prompt with Primer or Conversation History # Setup Prompt with Primer or Conversation History

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@ -116,8 +116,10 @@ def gemini_send_message_to_model(
messages, system_prompt = format_messages_for_gemini(messages) messages, system_prompt = format_messages_for_gemini(messages)
model_kwargs = {} model_kwargs = {}
if response_type == "json_object":
model_kwargs["response_mime_type"] = "application/json" # Sometimes, this causes unwanted behavior and terminates response early. Disable for now while it's flaky.
# if response_type == "json_object":
# model_kwargs["response_mime_type"] = "application/json"
# Get Response from Gemini # Get Response from Gemini
return gemini_completion_with_backoff( return gemini_completion_with_backoff(
@ -193,7 +195,7 @@ def converse_gemini(
if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands: if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n" context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results): if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n" context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
context_message = context_message.strip() context_message = context_message.strip()
# Setup Prompt with Primer or Conversation History # Setup Prompt with Primer or Conversation History

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@ -160,9 +160,7 @@ def converse_offline(
# Initialize Variables # Initialize Variables
assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured" assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
offline_chat_model = loaded_model or download_model(model, max_tokens=max_prompt_size) offline_chat_model = loaded_model or download_model(model, max_tokens=max_prompt_size)
compiled_references = "\n\n".join({f"# File: {item['file']}\n## {item['compiled']}\n" for item in references})
tracer["chat_model"] = model tracer["chat_model"] = model
current_date = datetime.now() current_date = datetime.now()
if agent and agent.personality: if agent and agent.personality:
@ -204,7 +202,7 @@ def converse_offline(
context_message += f"{prompts.online_search_conversation_offline.format(online_results=yaml_dump(simplified_online_results))}\n\n" context_message += f"{prompts.online_search_conversation_offline.format(online_results=yaml_dump(simplified_online_results))}\n\n"
if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results): if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n" context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
context_message = context_message.strip() context_message = context_message.strip()
# Setup Prompt with Primer or Conversation History # Setup Prompt with Primer or Conversation History

View file

@ -191,7 +191,7 @@ def converse(
if not is_none_or_empty(online_results): if not is_none_or_empty(online_results):
context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n" context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
if not is_none_or_empty(code_results): if not is_none_or_empty(code_results):
context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n" context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
context_message = context_message.strip() context_message = context_message.strip()
# Setup Prompt with Primer or Conversation History # Setup Prompt with Primer or Conversation History

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@ -22,7 +22,7 @@ from langchain.schema import ChatMessage
from llama_cpp.llama import Llama from llama_cpp.llama import Llama
from transformers import AutoTokenizer from transformers import AutoTokenizer
from khoj.database.adapters import ConversationAdapters, ais_user_subscribed from khoj.database.adapters import ConversationAdapters
from khoj.database.models import ChatModelOptions, ClientApplication, KhojUser from khoj.database.models import ChatModelOptions, ClientApplication, KhojUser
from khoj.processor.conversation import prompts from khoj.processor.conversation import prompts
from khoj.processor.conversation.offline.utils import download_model, infer_max_tokens from khoj.processor.conversation.offline.utils import download_model, infer_max_tokens
@ -457,6 +457,11 @@ def clean_json(response: str):
return response.strip().replace("\n", "").removeprefix("```json").removesuffix("```") return response.strip().replace("\n", "").removeprefix("```json").removesuffix("```")
def clean_code_python(code: str):
"""Remove any markdown codeblock and newline formatting if present. Useful for non schema enforceable models"""
return code.strip().removeprefix("```python").removesuffix("```")
def defilter_query(query: str): def defilter_query(query: str):
"""Remove any query filters in query""" """Remove any query filters in query"""
defiltered_query = query defiltered_query = query

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@ -12,6 +12,7 @@ from khoj.database.models import Agent, KhojUser
from khoj.processor.conversation import prompts from khoj.processor.conversation import prompts
from khoj.processor.conversation.utils import ( from khoj.processor.conversation.utils import (
ChatEvent, ChatEvent,
clean_code_python,
clean_json, clean_json,
construct_chat_history, construct_chat_history,
) )
@ -126,13 +127,18 @@ async def execute_sandboxed_python(code: str, sandbox_url: str = SANDBOX_URL) ->
Returns the result of the code execution as a dictionary. Returns the result of the code execution as a dictionary.
""" """
headers = {"Content-Type": "application/json"} headers = {"Content-Type": "application/json"}
data = {"code": code} cleaned_code = clean_code_python(code)
data = {"code": cleaned_code}
async with aiohttp.ClientSession() as session: async with aiohttp.ClientSession() as session:
async with session.post(sandbox_url, json=data, headers=headers) as response: async with session.post(sandbox_url, json=data, headers=headers) as response:
if response.status == 200: if response.status == 200:
result: dict[str, Any] = await response.json() result: dict[str, Any] = await response.json()
result["code"] = code result["code"] = cleaned_code
return result return result
else: else:
return {"code": code, "success": False, "std_err": f"Failed to execute code with {response.status}"} return {
"code": cleaned_code,
"success": False,
"std_err": f"Failed to execute code with {response.status}",
}

View file

@ -710,7 +710,6 @@ async def chat(
meta_log = conversation.conversation_log meta_log = conversation.conversation_log
is_automated_task = conversation_commands == [ConversationCommand.AutomatedTask] is_automated_task = conversation_commands == [ConversationCommand.AutomatedTask]
pending_research = True
researched_results = "" researched_results = ""
online_results: Dict = dict() online_results: Dict = dict()
code_results: Dict = dict() code_results: Dict = dict()
@ -730,6 +729,16 @@ async def chat(
tracer=tracer, tracer=tracer,
) )
# If we're doing research, we don't want to do anything else
if ConversationCommand.Research in conversation_commands:
conversation_commands = [ConversationCommand.Research]
conversation_commands_str = ", ".join([cmd.value for cmd in conversation_commands])
async for result in send_event(
ChatEvent.STATUS, f"**Chose Data Sources to Search:** {conversation_commands_str}"
):
yield result
mode = await aget_relevant_output_modes( mode = await aget_relevant_output_modes(
q, meta_log, is_automated_task, user, uploaded_images, agent, tracer=tracer q, meta_log, is_automated_task, user, uploaded_images, agent, tracer=tracer
) )
@ -759,7 +768,6 @@ async def chat(
): ):
if isinstance(research_result, InformationCollectionIteration): if isinstance(research_result, InformationCollectionIteration):
if research_result.summarizedResult: if research_result.summarizedResult:
pending_research = False
if research_result.onlineContext: if research_result.onlineContext:
online_results.update(research_result.onlineContext) online_results.update(research_result.onlineContext)
if research_result.codeContext: if research_result.codeContext:
@ -773,10 +781,11 @@ async def chat(
yield research_result yield research_result
# researched_results = await extract_relevant_info(q, researched_results, agent) # researched_results = await extract_relevant_info(q, researched_results, agent)
logger.info(f"Researched Results: {researched_results}") logger.info(f"Researched Results: {researched_results}")
pending_research = False for cmd in conversation_commands:
await conversation_command_rate_limiter.update_and_check_if_valid(request, cmd)
q = q.replace(f"/{cmd.value}", "").strip()
used_slash_summarize = conversation_commands == [ConversationCommand.Summarize] used_slash_summarize = conversation_commands == [ConversationCommand.Summarize]
file_filters = conversation.file_filters if conversation else [] file_filters = conversation.file_filters if conversation else []
@ -788,11 +797,9 @@ async def chat(
and not used_slash_summarize and not used_slash_summarize
# but we can't actually summarize # but we can't actually summarize
and len(file_filters) != 1 and len(file_filters) != 1
# not pending research
and not pending_research
): ):
conversation_commands.remove(ConversationCommand.Summarize) conversation_commands.remove(ConversationCommand.Summarize)
elif ConversationCommand.Summarize in conversation_commands and pending_research: elif ConversationCommand.Summarize in conversation_commands:
response_log = "" response_log = ""
agent_has_entries = await EntryAdapters.aagent_has_entries(agent) agent_has_entries = await EntryAdapters.aagent_has_entries(agent)
if len(file_filters) == 0 and not agent_has_entries: if len(file_filters) == 0 and not agent_has_entries:
@ -886,7 +893,7 @@ async def chat(
# Gather Context # Gather Context
## Extract Document References ## Extract Document References
if pending_research: if not ConversationCommand.Research in conversation_commands:
try: try:
async for result in extract_references_and_questions( async for result in extract_references_and_questions(
request, request,
@ -933,7 +940,6 @@ async def chat(
if ConversationCommand.Notes in conversation_commands and is_none_or_empty(compiled_references): if ConversationCommand.Notes in conversation_commands and is_none_or_empty(compiled_references):
conversation_commands.remove(ConversationCommand.Notes) conversation_commands.remove(ConversationCommand.Notes)
if pending_research:
## Gather Online References ## Gather Online References
if ConversationCommand.Online in conversation_commands: if ConversationCommand.Online in conversation_commands:
try: try:
@ -960,7 +966,6 @@ async def chat(
): ):
yield result yield result
if pending_research:
## Gather Webpage References ## Gather Webpage References
if ConversationCommand.Webpage in conversation_commands: if ConversationCommand.Webpage in conversation_commands:
try: try:
@ -999,9 +1004,8 @@ async def chat(
): ):
yield result yield result
if pending_research:
## Gather Code Results ## Gather Code Results
if ConversationCommand.Code in conversation_commands and pending_research: if ConversationCommand.Code in conversation_commands:
try: try:
context = f"# Iteration 1:\n#---\nNotes:\n{compiled_references}\n\nOnline Results:{online_results}" context = f"# Iteration 1:\n#---\nNotes:\n{compiled_references}\n\nOnline Results:{online_results}"
async for result in run_code( async for result in run_code(

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@ -364,7 +364,6 @@ tool_descriptions_for_llm = {
ConversationCommand.Webpage: "To use if the user has directly provided the webpage urls or you are certain of the webpage urls to read.", ConversationCommand.Webpage: "To use if the user has directly provided the webpage urls or you are certain of the webpage urls to read.",
ConversationCommand.Code: "To run Python code in a Pyodide sandbox with no network access. Helpful when need to parse information, run complex calculations, create documents and charts for user. Matplotlib, bs4, pandas, numpy, etc. are available.", ConversationCommand.Code: "To run Python code in a Pyodide sandbox with no network access. Helpful when need to parse information, run complex calculations, create documents and charts for user. Matplotlib, bs4, pandas, numpy, etc. are available.",
ConversationCommand.Summarize: "To retrieve an answer that depends on the entire document or a large text.", ConversationCommand.Summarize: "To retrieve an answer that depends on the entire document or a large text.",
ConversationCommand.Research: "To use when you need to do DEEP research on a topic. This will take longer than usual, but give a more detailed, comprehensive answer.",
} }
function_calling_description_for_llm = { function_calling_description_for_llm = {