Merge pull request #880 from cheshire-cat-ai/develop

1.7.1

.env.example

@@ -6,6 +6,12 @@
 # Decide to use https / wss secure protocols
 # CCAT_CORE_USE_SECURE_PROTOCOLS=true
 
+# Uvicorn and FastAPI operating behind https proxy
+# CCAT_HTTPS_PROXY_MODE=true
+
+# Comma separated list of IPs to trust with proxy headers. A wildcard '*' means always trust.
+# CCAT_CORS_FORWARDED_ALLOW_IPS="*"
+
 # Protect endpoints with an access token
 # CCAT_API_KEY=meow
 # CCAT_API_KEY_WS=meow2

.github/workflows/pr.yml

@@ -23,8 +23,8 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       - name: Cat up
-        run: docker-compose up -d
+        run: docker compose up -d
       - name: Test
         run: docker exec cheshire_cat_core python -m pytest --color=yes .
       - name: Cat down
-        run: docker-compose down
+        run: docker compose down

CHANGELOG.md

@@ -1,5 +1,16 @@
 # Changelog
 
+## 1.7.1 ( 2024-08-01 )
+
+New in version 1.7
+
+- User system
+- permission system
+- JWT support
+- Custom Auth support
+- White Rabbit
+- StrayCat.classify
+
 ## 1.5.0 ( 2024-03-07 )
 
 New in version 1.5.0: **Sir Punctilious Cat, the IV**! 

compose.yml

@@ -6,8 +6,8 @@ services:
       context: ./core
     container_name: cheshire_cat_core
     # Uncomment the two lines below to use your .env (see .env.example)
-    # env_file:
-    #   - .env
+    #env_file:
+    #  - .env
     ports:
       - ${CCAT_CORE_PORT:-1865}:80
       - 5678:5678 # only for development purposes (take away in production)

core/cat/agents/__init__.py

@@ -0,0 +1,3 @@
+from .base_agent import BaseAgent, AgentOutput
+
+__all__ = ['BaseAgent', 'AgentOutput']

core/cat/agents/base_agent.py

@@ -1,12 +1,11 @@
 from typing import List
 from abc import ABC, abstractmethod
 
-from langchain_core.utils import get_colored_text
 
 from cat.utils import BaseModelDict
 
 class AgentOutput(BaseModelDict):
-    output: str
+    output: str | None = None
     intermediate_steps: List = []
     return_direct: bool = False
 
@@ -15,25 +14,4 @@ class BaseAgent(ABC):
 
     @abstractmethod
     async def execute(*args, **kwargs) -> AgentOutput:
-        pass
-
-    # TODO: this is here to debug langchain, take it away
-    def _log_prompt(self, langchain_prompt, title):
-        print("\n")
-        print(get_colored_text(f"==================== {title} ====================", "green"))
-        for m in langchain_prompt.messages:
-            print(get_colored_text(type(m).__name__, "green"))
-            print(m.content)
-        print(get_colored_text("========================================", "green"))
-        return langchain_prompt
-
-    # TODO: this is here to debug langchain, take it away
-    def _log_output(self, langchain_output, title):
-        print("\n")
-        print(get_colored_text(f"==================== {title} ====================", "blue"))
-        if hasattr(langchain_output, 'content'):
-            print(langchain_output.content)
-        else:
-            print(langchain_output)
-        print(get_colored_text("========================================", "blue"))
-        return langchain_output
+        pass

core/cat/agents/form_agent.py

@@ -0,0 +1,38 @@
+import traceback
+from cat.experimental.form import CatFormState
+from cat.agents import BaseAgent, AgentOutput
+from cat.log import log
+
+class FormAgent(BaseAgent):
+
+    async def execute(self, stray) -> AgentOutput:
+
+        # get active form from working memory
+        active_form = stray.working_memory.active_form
+
+        if not active_form:
+            # no active form
+            return AgentOutput()
+        elif active_form._state == CatFormState.CLOSED:
+            # form is closed, delete it from working memory
+            stray.working_memory.active_form = None
+            return AgentOutput()
+        else:
+            # continue form
+            try:
+                form_output = active_form.next() # form should be async and should be awaited
+                return AgentOutput(
+                    output=form_output["output"],
+                    return_direct=True, # we assume forms always do a return_direct
+                    intermediate_steps=[
+                        ((active_form.name, ""), form_output["output"])
+                    ]
+                )
+
+            except Exception as e:
+                log.error(e)
+                traceback.print_exc()
+                return AgentOutput()
+
+
+

core/cat/agents/main_agent.py

@@ -8,7 +8,7 @@
 from cat.looking_glass import prompts
 from cat.utils import verbal_timedelta, BaseModelDict
 from cat.env import get_env
-from cat.agents.base_agent import BaseAgent, AgentOutput
+from cat.agents import BaseAgent, AgentOutput
 from cat.agents.memory_agent import MemoryAgent
 from cat.agents.procedures_agent import ProceduresAgent
 
@@ -51,8 +51,10 @@ async def execute(self, stray) -> AgentOutput:
         fast_reply = self.mad_hatter.execute_hook(
             "agent_fast_reply", fast_reply, cat=stray
         )
-        if len(fast_reply.keys()) > 0:
+        if isinstance(fast_reply, AgentOutput):
             return fast_reply
+        if isinstance(fast_reply, dict) and "output" in fast_reply:
+            return AgentOutput(**fast_reply)
 
         # obtain prompt parts from plugins
         prompt_prefix = self.mad_hatter.execute_hook(
@@ -114,14 +116,15 @@ def format_agent_input(self, stray):
         )
 
         # format conversation history to be inserted in the prompt
-        # conversation_history_formatted_content = stray.stringify_chat_history()
+        # TODOV2: take away
+        conversation_history_formatted_content = stray.stringify_chat_history()
 
         return BaseModelDict(**{
-            "input": stray.working_memory.user_message_json.text,  # TODO: deprecate, since it is included in chat history
             "episodic_memory": episodic_memory_formatted_content,
             "declarative_memory": declarative_memory_formatted_content,
-            # "chat_history": conversation_history_formatted_content,
             "tools_output": "",
+            "input": stray.working_memory.user_message_json.text,  # TODOV2: take away
+            "chat_history": conversation_history_formatted_content, # TODOV2: take away
         })
 
     def agent_prompt_episodic_memories(

core/cat/agents/memory_agent.py

@@ -5,33 +5,40 @@
 from langchain_core.output_parsers.string import StrOutputParser
 
 from cat.looking_glass.callbacks import NewTokenHandler, ModelInteractionHandler
-from cat.agents.base_agent import BaseAgent, AgentOutput
+from cat.agents import BaseAgent, AgentOutput
+from cat import utils
+
 
 class MemoryAgent(BaseAgent):
 
     async def execute(self, stray, prompt_prefix, prompt_suffix) -> AgentOutput:
-
-        final_prompt = ChatPromptTemplate(
+
+        prompt_variables = stray.working_memory.agent_input.model_dump()
+        sys_prompt = prompt_prefix + prompt_suffix
+
+        # ensure prompt variables and placeholders match
+        prompt_variables, sys_prompt = utils.match_prompt_variables(prompt_variables, sys_prompt)
+
+        prompt = ChatPromptTemplate(
             messages=[
                 SystemMessagePromptTemplate.from_template(
-                    template=prompt_prefix + prompt_suffix
+                    template=sys_prompt
                 ),
                 *(stray.langchainfy_chat_history()),
             ]
         )
 
-        memory_chain = (
-            final_prompt
-            | RunnableLambda(lambda x: self._log_prompt(x, "MAIN PROMPT"))
+        chain = (
+            prompt
+            | RunnableLambda(lambda x: utils.langchain_log_prompt(x, "MAIN PROMPT"))
             | stray._llm
-            | RunnableLambda(lambda x: self._log_output(x, "MAIN PROMPT OUTPUT"))
+            | RunnableLambda(lambda x: utils.langchain_log_output(x, "MAIN PROMPT OUTPUT"))
             | StrOutputParser()
         )
 
-        output = memory_chain.invoke(
+        output = chain.invoke(
             # convert to dict before passing to langchain
-            # TODO: ensure dict keys and prompt placeholders map, so there are no issues on mismatches
-            stray.working_memory.agent_input.model_dump(),
+            prompt_variables,
             config=RunnableConfig(callbacks=[NewTokenHandler(stray), ModelInteractionHandler(stray, self.__class__.__name__)])
         )