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Merge branch 'main' of https://github.com/arc53/DocsGPT

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GH Action - Upstream Sync
2025-04-02 01:36:06 +00:00
parent 302458b505 6b92c7eccc
commit 632cba86e9
16 changed files with 703 additions and 334 deletions
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0
application/agents/__init__.py Normal file
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2
application/agents/agent_creator.py
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@@ -1,9 +1,11 @@
from application.agents.classic_agent import ClassicAgent
from application.agents.react_agent import ReActAgent
class AgentCreator:
agents = {
"classic": ClassicAgent,
"react": ReActAgent,
}
@classmethod

128
application/agents/base.py
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@@ -1,4 +1,6 @@
from typing import Dict, Generator
import uuid
from abc import ABC, abstractmethod
from typing import Dict, Generator, List, Optional
from application.agents.llm_handler import get_llm_handler
from application.agents.tools.tool_action_parser import ToolActionParser
@@ -6,19 +8,34 @@ from application.agents.tools.tool_manager import ToolManager
from application.core.mongo_db import MongoDB
from application.llm.llm_creator import LLMCreator
from application.logging import build_stack_data, log_activity, LogContext
from application.retriever.base import BaseRetriever
class BaseAgent:
class BaseAgent(ABC):
def __init__(
self,
endpoint,
llm_name,
gpt_model,
api_key,
user_api_key=None,
decoded_token=None,
endpoint: str,
llm_name: str,
gpt_model: str,
api_key: str,
user_api_key: Optional[str] = None,
prompt: str = "",
chat_history: Optional[List[Dict]] = None,
decoded_token: Optional[Dict] = None,
):
self.endpoint = endpoint
self.llm_name = llm_name
self.gpt_model = gpt_model
self.api_key = api_key
self.user_api_key = user_api_key
self.prompt = prompt
self.decoded_token = decoded_token or {}
self.user: str = decoded_token.get("sub")
self.tool_config: Dict = {}
self.tools: List[Dict] = []
self.tool_calls: List[Dict] = []
self.chat_history: List[Dict] = chat_history if chat_history is not None else []
self.llm = LLMCreator.create_llm(
llm_name,
api_key=api_key,
@@ -26,13 +43,18 @@ class BaseAgent:
decoded_token=decoded_token,
)
self.llm_handler = get_llm_handler(llm_name)
self.gpt_model = gpt_model
self.tools = []
self.tool_config = {}
self.tool_calls = []
def gen(self, *args, **kwargs) -> Generator[Dict, None, None]:
raise NotImplementedError('Method "gen" must be implemented in the child class')
@log_activity()
def gen(
self, query: str, retriever: BaseRetriever, log_context: LogContext = None
) -> Generator[Dict, None, None]:
yield from self._gen_inner(query, retriever, log_context)
@abstractmethod
def _gen_inner(
self, query: str, retriever: BaseRetriever, log_context: LogContext
) -> Generator[Dict, None, None]:
pass
def _get_user_tools(self, user="local"):
mongo = MongoDB.get_client()
@@ -109,14 +131,12 @@ class BaseAgent:
for param, details in action_data[param_type]["properties"].items():
if param not in call_args and "value" in details:
target_dict[param] = details["value"]
for param, value in call_args.items():
for param_type, target_dict in param_types.items():
if param_type in action_data and param in action_data[param_type].get(
"properties", {}
):
target_dict[param] = value
tm = ToolManager(config={})
tool = tm.load_tool(
tool_data["name"],
@@ -151,3 +171,79 @@ class BaseAgent:
self.tool_calls.append(tool_call_data)
return result, call_id
def _build_messages(
self,
system_prompt: str,
query: str,
retrieved_data: List[Dict],
) -> List[Dict]:
docs_together = "\n".join([doc["text"] for doc in retrieved_data])
p_chat_combine = system_prompt.replace("{summaries}", docs_together)
messages_combine = [{"role": "system", "content": p_chat_combine}]
for i in self.chat_history:
if "prompt" in i and "response" in i:
messages_combine.append({"role": "user", "content": i["prompt"]})
messages_combine.append({"role": "assistant", "content": i["response"]})
if "tool_calls" in i:
for tool_call in i["tool_calls"]:
call_id = tool_call.get("call_id") or str(uuid.uuid4())
function_call_dict = {
"function_call": {
"name": tool_call.get("action_name"),
"args": tool_call.get("arguments"),
"call_id": call_id,
}
}
function_response_dict = {
"function_response": {
"name": tool_call.get("action_name"),
"response": {"result": tool_call.get("result")},
"call_id": call_id,
}
}
messages_combine.append(
{"role": "assistant", "content": [function_call_dict]}
)
messages_combine.append(
{"role": "tool", "content": [function_response_dict]}
)
messages_combine.append({"role": "user", "content": query})
return messages_combine
def _retriever_search(
self,
retriever: BaseRetriever,
query: str,
log_context: Optional[LogContext] = None,
) -> List[Dict]:
retrieved_data = retriever.search(query)
if log_context:
data = build_stack_data(retriever, exclude_attributes=["llm"])
log_context.stacks.append({"component": "retriever", "data": data})
return retrieved_data
def _llm_gen(self, messages: List[Dict], log_context: Optional[LogContext] = None):
resp = self.llm.gen_stream(
model=self.gpt_model, messages=messages, tools=self.tools
)
if log_context:
data = build_stack_data(self.llm)
log_context.stacks.append({"component": "llm", "data": data})
return resp
def _llm_handler(
self,
resp,
tools_dict: Dict,
messages: List[Dict],
log_context: Optional[LogContext] = None,
):
resp = self.llm_handler.handle_response(self, resp, tools_dict, messages)
if log_context:
data = build_stack_data(self.llm_handler)
log_context.stacks.append({"component": "llm_handler", "data": data})
return resp

100
application/agents/classic_agent.py
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@@ -1,86 +1,23 @@
import uuid
from typing import Dict, Generator
from application.agents.base import BaseAgent
from application.logging import build_stack_data, log_activity, LogContext
from application.logging import LogContext
from application.retriever.base import BaseRetriever
class ClassicAgent(BaseAgent):
def __init__(
self,
endpoint,
llm_name,
gpt_model,
api_key,
user_api_key=None,
prompt="",
chat_history=None,
decoded_token=None,
):
super().__init__(
endpoint, llm_name, gpt_model, api_key, user_api_key, decoded_token
)
self.user = decoded_token.get("sub")
self.prompt = prompt
self.chat_history = chat_history if chat_history is not None else []
@log_activity()
def gen(
self, query: str, retriever: BaseRetriever, log_context: LogContext = None
) -> Generator[Dict, None, None]:
yield from self._gen_inner(query, retriever, log_context)
def _gen_inner(
self, query: str, retriever: BaseRetriever, log_context: LogContext
) -> Generator[Dict, None, None]:
retrieved_data = self._retriever_search(retriever, query, log_context)
docs_together = "\n".join([doc["text"] for doc in retrieved_data])
p_chat_combine = self.prompt.replace("{summaries}", docs_together)
messages_combine = [{"role": "system", "content": p_chat_combine}]
if len(self.chat_history) > 0:
for i in self.chat_history:
if "prompt" in i and "response" in i:
messages_combine.append({"role": "user", "content": i["prompt"]})
messages_combine.append(
{"role": "assistant", "content": i["response"]}
)
if "tool_calls" in i:
for tool_call in i["tool_calls"]:
call_id = tool_call.get("call_id")
if call_id is None or call_id == "None":
call_id = str(uuid.uuid4())
function_call_dict = {
"function_call": {
"name": tool_call.get("action_name"),
"args": tool_call.get("arguments"),
"call_id": call_id,
}
}
function_response_dict = {
"function_response": {
"name": tool_call.get("action_name"),
"response": {"result": tool_call.get("result")},
"call_id": call_id,
}
}
messages_combine.append(
{"role": "assistant", "content": [function_call_dict]}
)
messages_combine.append(
{"role": "tool", "content": [function_response_dict]}
)
messages_combine.append({"role": "user", "content": query})
tools_dict = self._get_user_tools(self.user)
self._prepare_tools(tools_dict)
resp = self._llm_gen(messages_combine, log_context)
messages = self._build_messages(self.prompt, query, retrieved_data)
resp = self._llm_gen(messages, log_context)
if isinstance(resp, str):
yield {"answer": resp}
@@ -93,7 +30,7 @@ class ClassicAgent(BaseAgent):
yield {"answer": resp.message.content}
return
resp = self._llm_handler(resp, tools_dict, messages_combine, log_context)
resp = self._llm_handler(resp, tools_dict, messages, log_context)
if isinstance(resp, str):
yield {"answer": resp}
@@ -105,7 +42,7 @@ class ClassicAgent(BaseAgent):
yield {"answer": resp.message.content}
else:
completion = self.llm.gen_stream(
model=self.gpt_model, messages=messages_combine, tools=self.tools
model=self.gpt_model, messages=messages, tools=self.tools
)
for line in completion:
if isinstance(line, str):
@@ -113,28 +50,3 @@ class ClassicAgent(BaseAgent):
yield {"sources": retrieved_data}
yield {"tool_calls": self.tool_calls.copy()}
def _retriever_search(self, retriever, query, log_context):
retrieved_data = retriever.search(query)
if log_context:
data = build_stack_data(retriever, exclude_attributes=["llm"])
log_context.stacks.append({"component": "retriever", "data": data})
return retrieved_data
def _llm_gen(self, messages_combine, log_context):
resp = self.llm.gen_stream(
model=self.gpt_model, messages=messages_combine, tools=self.tools
)
if log_context:
data = build_stack_data(self.llm)
log_context.stacks.append({"component": "llm", "data": data})
return resp
def _llm_handler(self, resp, tools_dict, messages_combine, log_context):
resp = self.llm_handler.handle_response(
self, resp, tools_dict, messages_combine
)
if log_context:
data = build_stack_data(self.llm_handler)
log_context.stacks.append({"component": "llm_handler", "data": data})
return resp

121
application/agents/react_agent.py Normal file
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@@ -0,0 +1,121 @@
import os
from typing import Dict, Generator, List
from application.agents.base import BaseAgent
from application.logging import build_stack_data, LogContext
from application.retriever.base import BaseRetriever
current_dir = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
)
with open(
os.path.join(current_dir, "application/prompts", "react_planning_prompt.txt"), "r"
) as f:
planning_prompt = f.read()
with open(
os.path.join(current_dir, "application/prompts", "react_final_prompt.txt"),
"r",
) as f:
final_prompt = f.read()
class ReActAgent(BaseAgent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.plan = ""
self.observations: List[str] = []
def _gen_inner(
self, query: str, retriever: BaseRetriever, log_context: LogContext
) -> Generator[Dict, None, None]:
retrieved_data = self._retriever_search(retriever, query, log_context)
tools_dict = self._get_user_tools(self.user)
self._prepare_tools(tools_dict)
docs_together = "\n".join([doc["text"] for doc in retrieved_data])
plan = self._create_plan(query, docs_together, log_context)
for line in plan:
if isinstance(line, str):
self.plan += line
yield {"thought": line}
prompt = self.prompt + f"\nFollow this plan: {self.plan}"
messages = self._build_messages(prompt, query, retrieved_data)
resp = self._llm_gen(messages, log_context)
if isinstance(resp, str):
self.observations.append(resp)
if (
hasattr(resp, "message")
and hasattr(resp.message, "content")
and resp.message.content is not None
):
self.observations.append(resp.message.content)
resp = self._llm_handler(resp, tools_dict, messages, log_context)
for tool_call in self.tool_calls:
observation = (
f"Action '{tool_call['action_name']}' of tool '{tool_call['tool_name']}' "
f"with arguments '{tool_call['arguments']}' returned: '{tool_call['result']}'"
)
self.observations.append(observation)
if isinstance(resp, str):
self.observations.append(resp)
elif (
hasattr(resp, "message")
and hasattr(resp.message, "content")
and resp.message.content is not None
):
self.observations.append(resp.message.content)
else:
completion = self.llm.gen_stream(
model=self.gpt_model, messages=messages, tools=self.tools
)
for line in completion:
if isinstance(line, str):
self.observations.append(line)
yield {"sources": retrieved_data}
yield {"tool_calls": self.tool_calls.copy()}
final_answer = self._create_final_answer(query, self.observations, log_context)
for line in final_answer:
if isinstance(line, str):
yield {"answer": line}
def _create_plan(
self, query: str, docs_data: str, log_context: LogContext = None
) -> Generator[str, None, None]:
plan_prompt = planning_prompt.replace("{query}", query)
if "{summaries}" in planning_prompt:
summaries = docs_data
plan_prompt = plan_prompt.replace("{summaries}", summaries)
messages = [{"role": "user", "content": plan_prompt}]
print(self.tools)
plan = self.llm.gen_stream(
model=self.gpt_model, messages=messages, tools=self.tools
)
if log_context:
data = build_stack_data(self.llm)
log_context.stacks.append({"component": "planning_llm", "data": data})
return plan
def _create_final_answer(
self, query: str, observations: List[str], log_context: LogContext = None
) -> str:
observation_string = "\n".join(observations)
final_answer_prompt = final_prompt.format(
query=query, observations=observation_string
)
messages = [{"role": "user", "content": final_answer_prompt}]
final_answer = self.llm.gen_stream(model=self.gpt_model, messages=messages)
if log_context:
data = build_stack_data(self.llm)
log_context.stacks.append({"component": "final_answer_llm", "data": data})
return final_answer

13
application/api/answer/routes.py
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@@ -121,6 +121,7 @@ def save_conversation(
conversation_id,
question,
response,
thought,
source_log_docs,
tool_calls,
llm,
@@ -136,6 +137,7 @@ def save_conversation(
"$set": {
f"queries.{index}.prompt": question,
f"queries.{index}.response": response,
f"queries.{index}.thought": thought,
f"queries.{index}.sources": source_log_docs,
f"queries.{index}.tool_calls": tool_calls,
f"queries.{index}.timestamp": current_time,
@@ -155,6 +157,7 @@ def save_conversation(
"queries": {
"prompt": question,
"response": response,
"thought": thought,
"sources": source_log_docs,
"tool_calls": tool_calls,
"timestamp": current_time,
@@ -190,6 +193,7 @@ def save_conversation(
{
"prompt": question,
"response": response,
"thought": thought,
"sources": source_log_docs,
"tool_calls": tool_calls,
"timestamp": current_time,
@@ -230,9 +234,7 @@ def complete_stream(
should_save_conversation=True,
):
try:
response_full = ""
source_log_docs = []
tool_calls = []
response_full, thought, source_log_docs, tool_calls = "", "", [], []
answer = agent.gen(query=question, retriever=retriever)
@@ -258,6 +260,10 @@ def complete_stream(
tool_calls = line["tool_calls"]
data = json.dumps({"type": "tool_calls", "tool_calls": tool_calls})
yield f"data: {data}\n\n"
elif "thought" in line:
thought += line["thought"]
data = json.dumps({"type": "thought", "thought": line["thought"]})
yield f"data: {data}\n\n"
if isNoneDoc:
for doc in source_log_docs:
@@ -275,6 +281,7 @@ def complete_stream(
conversation_id,
question,
response_full,
thought,
source_log_docs,
tool_calls,
llm,

3
application/prompts/react_final_prompt.txt Normal file
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@@ -0,0 +1,3 @@
Query: {query}
Observations: {observations}
Now, using the insights from the observations, formulate a well-structured and precise final answer.

10
application/prompts/react_planning_prompt.txt Normal file
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@@ -0,0 +1,10 @@
You are an AI assistant and talk like you're thinking out loud. Given the following query, outline a concise thought process that includes key steps and considerations necessary for effective analysis and response. Avoid pointwise formatting. The goal is to break down the query into manageable components without excessive detail, focusing on clarity and logical progression.
Include the following elements in your thought process:
1. Identify the main objective of the query.
2. Determine any relevant context or background information needed to understand the query.
3. List potential approaches or methods to address the query.
4. Highlight any critical factors or constraints that may influence the outcome.
Query: {query}
Summaries: {summaries}