diff --git a/docs/use_cases/agents/baby_agi.ipynb b/docs/use_cases/agents/baby_agi.ipynb index 7005fb70..7cfc761f 100644 --- a/docs/use_cases/agents/baby_agi.ipynb +++ b/docs/use_cases/agents/baby_agi.ipynb @@ -38,7 +38,7 @@ "from langchain.llms import BaseLLM\n", "from langchain.vectorstores.base import VectorStore\n", "from pydantic import BaseModel, Field\n", - "from langchain.chains.base import Chain\n" + "from langchain.chains.base import Chain" ] }, { @@ -73,6 +73,7 @@ "embeddings_model = OpenAIEmbeddings()\n", "# Initialize the vectorstore as empty\n", "import faiss\n", + "\n", "embedding_size = 1536\n", "index = faiss.IndexFlatL2(embedding_size)\n", "vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})" @@ -116,7 +117,12 @@ " )\n", " prompt = PromptTemplate(\n", " template=task_creation_template,\n", - " input_variables=[\"result\", \"task_description\", \"incomplete_tasks\", \"objective\"],\n", + " input_variables=[\n", + " \"result\",\n", + " \"task_description\",\n", + " \"incomplete_tasks\",\n", + " \"objective\",\n", + " ],\n", " )\n", " return cls(prompt=prompt, llm=llm, verbose=verbose)" ] @@ -147,7 +153,7 @@ " template=task_prioritization_template,\n", " input_variables=[\"task_names\", \"next_task_id\", \"objective\"],\n", " )\n", - " return cls(prompt=prompt, llm=llm, verbose=verbose)\n" + " return cls(prompt=prompt, llm=llm, verbose=verbose)" ] }, { @@ -173,7 +179,7 @@ " template=execution_template,\n", " input_variables=[\"objective\", \"context\", \"task\"],\n", " )\n", - " return cls(prompt=prompt, llm=llm, verbose=verbose)\n" + " return cls(prompt=prompt, llm=llm, verbose=verbose)" ] }, { @@ -193,11 +199,22 @@ "metadata": {}, "outputs": [], "source": [ - "def get_next_task(task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str) -> List[Dict]:\n", + "def get_next_task(\n", + " task_creation_chain: LLMChain,\n", + " result: Dict,\n", + " task_description: str,\n", + " task_list: List[str],\n", + " objective: str,\n", + ") -> List[Dict]:\n", " \"\"\"Get the next task.\"\"\"\n", " incomplete_tasks = \", \".join(task_list)\n", - " response = task_creation_chain.run(result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective)\n", - " new_tasks = response.split('\\n')\n", + " response = task_creation_chain.run(\n", + " result=result,\n", + " task_description=task_description,\n", + " incomplete_tasks=incomplete_tasks,\n", + " objective=objective,\n", + " )\n", + " new_tasks = response.split(\"\\n\")\n", " return [{\"task_name\": task_name} for task_name in new_tasks if task_name.strip()]" ] }, @@ -208,12 +225,19 @@ "metadata": {}, "outputs": [], "source": [ - "def prioritize_tasks(task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str) -> List[Dict]:\n", + "def prioritize_tasks(\n", + " task_prioritization_chain: LLMChain,\n", + " this_task_id: int,\n", + " task_list: List[Dict],\n", + " objective: str,\n", + ") -> List[Dict]:\n", " \"\"\"Prioritize tasks.\"\"\"\n", " task_names = [t[\"task_name\"] for t in task_list]\n", " next_task_id = int(this_task_id) + 1\n", - " response = task_prioritization_chain.run(task_names=task_names, next_task_id=next_task_id, objective=objective)\n", - " new_tasks = response.split('\\n')\n", + " response = task_prioritization_chain.run(\n", + " task_names=task_names, next_task_id=next_task_id, objective=objective\n", + " )\n", + " new_tasks = response.split(\"\\n\")\n", " prioritized_task_list = []\n", " for task_string in new_tasks:\n", " if not task_string.strip():\n", @@ -239,9 +263,12 @@ " if not results:\n", " return []\n", " sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))\n", - " return [str(item.metadata['task']) for item in sorted_results]\n", + " return [str(item.metadata[\"task\"]) for item in sorted_results]\n", "\n", - "def execute_task(vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5) -> str:\n", + "\n", + "def execute_task(\n", + " vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5\n", + ") -> str:\n", " \"\"\"Execute a task.\"\"\"\n", " context = _get_top_tasks(vectorstore, query=objective, k=k)\n", " return execution_chain.run(objective=objective, context=context, task=task)" @@ -254,7 +281,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "class BabyAGI(Chain, BaseModel):\n", " \"\"\"Controller model for the BabyAGI agent.\"\"\"\n", "\n", @@ -265,9 +291,10 @@ " task_id_counter: int = Field(1)\n", " vectorstore: VectorStore = Field(init=False)\n", " max_iterations: Optional[int] = None\n", - " \n", + "\n", " class Config:\n", " \"\"\"Configuration for this pydantic object.\"\"\"\n", + "\n", " arbitrary_types_allowed = True\n", "\n", " def add_task(self, task: Dict):\n", @@ -285,18 +312,18 @@ " def print_task_result(self, result: str):\n", " print(\"\\033[93m\\033[1m\" + \"\\n*****TASK RESULT*****\\n\" + \"\\033[0m\\033[0m\")\n", " print(result)\n", - " \n", + "\n", " @property\n", " def input_keys(self) -> List[str]:\n", " return [\"objective\"]\n", - " \n", + "\n", " @property\n", " def output_keys(self) -> List[str]:\n", " return []\n", "\n", " def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n", " \"\"\"Run the agent.\"\"\"\n", - " objective = inputs['objective']\n", + " objective = inputs[\"objective\"]\n", " first_task = inputs.get(\"first_task\", \"Make a todo list\")\n", " self.add_task({\"task_id\": 1, \"task_name\": first_task})\n", " num_iters = 0\n", @@ -325,7 +352,11 @@ "\n", " # Step 4: Create new tasks and reprioritize task list\n", " new_tasks = get_next_task(\n", - " self.task_creation_chain, result, task[\"task_name\"], [t[\"task_name\"] for t in self.task_list], objective\n", + " self.task_creation_chain,\n", + " result,\n", + " task[\"task_name\"],\n", + " [t[\"task_name\"] for t in self.task_list],\n", + " objective,\n", " )\n", " for new_task in new_tasks:\n", " self.task_id_counter += 1\n", @@ -333,27 +364,26 @@ " self.add_task(new_task)\n", " self.task_list = deque(\n", " prioritize_tasks(\n", - " self.task_prioritization_chain, this_task_id, list(self.task_list), objective\n", + " self.task_prioritization_chain,\n", + " this_task_id,\n", + " list(self.task_list),\n", + " objective,\n", " )\n", " )\n", " num_iters += 1\n", " if self.max_iterations is not None and num_iters == self.max_iterations:\n", - " print(\"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\")\n", + " print(\n", + " \"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\"\n", + " )\n", " break\n", " return {}\n", "\n", " @classmethod\n", " def from_llm(\n", - " cls,\n", - " llm: BaseLLM,\n", - " vectorstore: VectorStore,\n", - " verbose: bool = False,\n", - " **kwargs\n", + " cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs\n", " ) -> \"BabyAGI\":\n", " \"\"\"Initialize the BabyAGI Controller.\"\"\"\n", - " task_creation_chain = TaskCreationChain.from_llm(\n", - " llm, verbose=verbose\n", - " )\n", + " task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)\n", " task_prioritization_chain = TaskPrioritizationChain.from_llm(\n", " llm, verbose=verbose\n", " )\n", @@ -363,7 +393,7 @@ " task_prioritization_chain=task_prioritization_chain,\n", " execution_chain=execution_chain,\n", " vectorstore=vectorstore,\n", - " **kwargs\n", + " **kwargs,\n", " )" ] }, @@ -405,14 +435,11 @@ "outputs": [], "source": [ "# Logging of LLMChains\n", - "verbose=False\n", + "verbose = False\n", "# If None, will keep on going forever\n", "max_iterations: Optional[int] = 3\n", "baby_agi = BabyAGI.from_llm(\n", - " llm=llm,\n", - " vectorstore=vectorstore,\n", - " verbose=verbose,\n", - " max_iterations=max_iterations\n", + " llm=llm, vectorstore=vectorstore, verbose=verbose, max_iterations=max_iterations\n", ")" ] }, diff --git a/docs/use_cases/agents/baby_agi_with_agent.ipynb b/docs/use_cases/agents/baby_agi_with_agent.ipynb index 7244f7fd..bf71533a 100644 --- a/docs/use_cases/agents/baby_agi_with_agent.ipynb +++ b/docs/use_cases/agents/baby_agi_with_agent.ipynb @@ -34,7 +34,7 @@ "from langchain.llms import BaseLLM\n", "from langchain.vectorstores.base import VectorStore\n", "from pydantic import BaseModel, Field\n", - "from langchain.chains.base import Chain\n" + "from langchain.chains.base import Chain" ] }, { @@ -54,7 +54,9 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install faiss-cpu > /dev/null%pip install google-search-results > /dev/nullfrom langchain.vectorstores import FAISS\n", + "%pip install faiss-cpu > /dev/null\n", + "%pip install google-search-results > /dev/null\n", + "from langchain.vectorstores import FAISS\n", "from langchain.docstore import InMemoryDocstore" ] }, @@ -69,6 +71,7 @@ "embeddings_model = OpenAIEmbeddings()\n", "# Initialize the vectorstore as empty\n", "import faiss\n", + "\n", "embedding_size = 1536\n", "index = faiss.IndexFlatL2(embedding_size)\n", "vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})" @@ -115,7 +118,12 @@ " )\n", " prompt = PromptTemplate(\n", " template=task_creation_template,\n", - " input_variables=[\"result\", \"task_description\", \"incomplete_tasks\", \"objective\"],\n", + " input_variables=[\n", + " \"result\",\n", + " \"task_description\",\n", + " \"incomplete_tasks\",\n", + " \"objective\",\n", + " ],\n", " )\n", " return cls(prompt=prompt, llm=llm, verbose=verbose)" ] @@ -146,7 +154,7 @@ " template=task_prioritization_template,\n", " input_variables=[\"task_names\", \"next_task_id\", \"objective\"],\n", " )\n", - " return cls(prompt=prompt, llm=llm, verbose=verbose)\n" + " return cls(prompt=prompt, llm=llm, verbose=verbose)" ] }, { @@ -158,20 +166,23 @@ "source": [ "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n", "from langchain import OpenAI, SerpAPIWrapper, LLMChain\n", - "todo_prompt = PromptTemplate.from_template(\"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\")\n", + "\n", + "todo_prompt = PromptTemplate.from_template(\n", + " \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n", + ")\n", "todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)\n", "search = SerpAPIWrapper()\n", "tools = [\n", " Tool(\n", - " name = \"Search\",\n", + " name=\"Search\",\n", " func=search.run,\n", - " description=\"useful for when you need to answer questions about current events\"\n", + " description=\"useful for when you need to answer questions about current events\",\n", " ),\n", " Tool(\n", - " name = \"TODO\",\n", + " name=\"TODO\",\n", " func=todo_chain.run,\n", - " description=\"useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!\"\n", - " )\n", + " description=\"useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!\",\n", + " ),\n", "]\n", "\n", "\n", @@ -179,10 +190,10 @@ "suffix = \"\"\"Question: {task}\n", "{agent_scratchpad}\"\"\"\n", "prompt = ZeroShotAgent.create_prompt(\n", - " tools, \n", - " prefix=prefix, \n", - " suffix=suffix, \n", - " input_variables=[\"objective\", \"task\", \"context\",\"agent_scratchpad\"]\n", + " tools,\n", + " prefix=prefix,\n", + " suffix=suffix,\n", + " input_variables=[\"objective\", \"task\", \"context\", \"agent_scratchpad\"],\n", ")" ] }, @@ -203,11 +214,22 @@ "metadata": {}, "outputs": [], "source": [ - "def get_next_task(task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str) -> List[Dict]:\n", + "def get_next_task(\n", + " task_creation_chain: LLMChain,\n", + " result: Dict,\n", + " task_description: str,\n", + " task_list: List[str],\n", + " objective: str,\n", + ") -> List[Dict]:\n", " \"\"\"Get the next task.\"\"\"\n", " incomplete_tasks = \", \".join(task_list)\n", - " response = task_creation_chain.run(result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective)\n", - " new_tasks = response.split('\\n')\n", + " response = task_creation_chain.run(\n", + " result=result,\n", + " task_description=task_description,\n", + " incomplete_tasks=incomplete_tasks,\n", + " objective=objective,\n", + " )\n", + " new_tasks = response.split(\"\\n\")\n", " return [{\"task_name\": task_name} for task_name in new_tasks if task_name.strip()]" ] }, @@ -218,12 +240,19 @@ "metadata": {}, "outputs": [], "source": [ - "def prioritize_tasks(task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str) -> List[Dict]:\n", + "def prioritize_tasks(\n", + " task_prioritization_chain: LLMChain,\n", + " this_task_id: int,\n", + " task_list: List[Dict],\n", + " objective: str,\n", + ") -> List[Dict]:\n", " \"\"\"Prioritize tasks.\"\"\"\n", " task_names = [t[\"task_name\"] for t in task_list]\n", " next_task_id = int(this_task_id) + 1\n", - " response = task_prioritization_chain.run(task_names=task_names, next_task_id=next_task_id, objective=objective)\n", - " new_tasks = response.split('\\n')\n", + " response = task_prioritization_chain.run(\n", + " task_names=task_names, next_task_id=next_task_id, objective=objective\n", + " )\n", + " new_tasks = response.split(\"\\n\")\n", " prioritized_task_list = []\n", " for task_string in new_tasks:\n", " if not task_string.strip():\n", @@ -249,9 +278,12 @@ " if not results:\n", " return []\n", " sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))\n", - " return [str(item.metadata['task']) for item in sorted_results]\n", + " return [str(item.metadata[\"task\"]) for item in sorted_results]\n", "\n", - "def execute_task(vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5) -> str:\n", + "\n", + "def execute_task(\n", + " vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5\n", + ") -> str:\n", " \"\"\"Execute a task.\"\"\"\n", " context = _get_top_tasks(vectorstore, query=objective, k=k)\n", " return execution_chain.run(objective=objective, context=context, task=task)" @@ -264,7 +296,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "class BabyAGI(Chain, BaseModel):\n", " \"\"\"Controller model for the BabyAGI agent.\"\"\"\n", "\n", @@ -275,9 +306,10 @@ " task_id_counter: int = Field(1)\n", " vectorstore: VectorStore = Field(init=False)\n", " max_iterations: Optional[int] = None\n", - " \n", + "\n", " class Config:\n", " \"\"\"Configuration for this pydantic object.\"\"\"\n", + "\n", " arbitrary_types_allowed = True\n", "\n", " def add_task(self, task: Dict):\n", @@ -295,18 +327,18 @@ " def print_task_result(self, result: str):\n", " print(\"\\033[93m\\033[1m\" + \"\\n*****TASK RESULT*****\\n\" + \"\\033[0m\\033[0m\")\n", " print(result)\n", - " \n", + "\n", " @property\n", " def input_keys(self) -> List[str]:\n", " return [\"objective\"]\n", - " \n", + "\n", " @property\n", " def output_keys(self) -> List[str]:\n", " return []\n", "\n", " def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n", " \"\"\"Run the agent.\"\"\"\n", - " objective = inputs['objective']\n", + " objective = inputs[\"objective\"]\n", " first_task = inputs.get(\"first_task\", \"Make a todo list\")\n", " self.add_task({\"task_id\": 1, \"task_name\": first_task})\n", " num_iters = 0\n", @@ -335,7 +367,11 @@ "\n", " # Step 4: Create new tasks and reprioritize task list\n", " new_tasks = get_next_task(\n", - " self.task_creation_chain, result, task[\"task_name\"], [t[\"task_name\"] for t in self.task_list], objective\n", + " self.task_creation_chain,\n", + " result,\n", + " task[\"task_name\"],\n", + " [t[\"task_name\"] for t in self.task_list],\n", + " objective,\n", " )\n", " for new_task in new_tasks:\n", " self.task_id_counter += 1\n", @@ -343,40 +379,41 @@ " self.add_task(new_task)\n", " self.task_list = deque(\n", " prioritize_tasks(\n", - " self.task_prioritization_chain, this_task_id, list(self.task_list), objective\n", + " self.task_prioritization_chain,\n", + " this_task_id,\n", + " list(self.task_list),\n", + " objective,\n", " )\n", " )\n", " num_iters += 1\n", " if self.max_iterations is not None and num_iters == self.max_iterations:\n", - " print(\"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\")\n", + " print(\n", + " \"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\"\n", + " )\n", " break\n", " return {}\n", "\n", " @classmethod\n", " def from_llm(\n", - " cls,\n", - " llm: BaseLLM,\n", - " vectorstore: VectorStore,\n", - " verbose: bool = False,\n", - " **kwargs\n", + " cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs\n", " ) -> \"BabyAGI\":\n", " \"\"\"Initialize the BabyAGI Controller.\"\"\"\n", - " task_creation_chain = TaskCreationChain.from_llm(\n", - " llm, verbose=verbose\n", - " )\n", + " task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)\n", " task_prioritization_chain = TaskPrioritizationChain.from_llm(\n", " llm, verbose=verbose\n", " )\n", " llm_chain = LLMChain(llm=llm, prompt=prompt)\n", " tool_names = [tool.name for tool in tools]\n", " agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)\n", - " agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)\n", + " agent_executor = AgentExecutor.from_agent_and_tools(\n", + " agent=agent, tools=tools, verbose=True\n", + " )\n", " return cls(\n", " task_creation_chain=task_creation_chain,\n", " task_prioritization_chain=task_prioritization_chain,\n", " execution_chain=agent_executor,\n", " vectorstore=vectorstore,\n", - " **kwargs\n", + " **kwargs,\n", " )" ] }, @@ -418,14 +455,11 @@ "outputs": [], "source": [ "# Logging of LLMChains\n", - "verbose=False\n", + "verbose = False\n", "# If None, will keep on going forever\n", "max_iterations: Optional[int] = 3\n", "baby_agi = BabyAGI.from_llm(\n", - " llm=llm,\n", - " vectorstore=vectorstore,\n", - " verbose=verbose,\n", - " max_iterations=max_iterations\n", + " llm=llm, vectorstore=vectorstore, verbose=verbose, max_iterations=max_iterations\n", ")" ] },