forked from Archives/langchain
Fix Baby AGI notebooks (#2882)
- fix broken notebook cell in
ae485b623d
- Python Black formatting
This commit is contained in:
parent
3c7204d604
commit
1a44b71ddf
@ -38,7 +38,7 @@
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"from langchain.llms import BaseLLM\n",
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"from langchain.vectorstores.base import VectorStore\n",
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"from pydantic import BaseModel, Field\n",
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"from langchain.chains.base import Chain\n"
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"from langchain.chains.base import Chain"
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]
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},
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{
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@ -73,6 +73,7 @@
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"embeddings_model = OpenAIEmbeddings()\n",
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"# Initialize the vectorstore as empty\n",
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"import faiss\n",
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"\n",
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"embedding_size = 1536\n",
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"index = faiss.IndexFlatL2(embedding_size)\n",
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"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
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@ -116,7 +117,12 @@
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" )\n",
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" prompt = PromptTemplate(\n",
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" template=task_creation_template,\n",
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" input_variables=[\"result\", \"task_description\", \"incomplete_tasks\", \"objective\"],\n",
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" input_variables=[\n",
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" \"result\",\n",
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" \"task_description\",\n",
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" \"incomplete_tasks\",\n",
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" \"objective\",\n",
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" ],\n",
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" )\n",
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" return cls(prompt=prompt, llm=llm, verbose=verbose)"
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]
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@ -147,7 +153,7 @@
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" template=task_prioritization_template,\n",
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" input_variables=[\"task_names\", \"next_task_id\", \"objective\"],\n",
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" )\n",
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" return cls(prompt=prompt, llm=llm, verbose=verbose)\n"
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" return cls(prompt=prompt, llm=llm, verbose=verbose)"
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]
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},
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{
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@ -173,7 +179,7 @@
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" template=execution_template,\n",
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" input_variables=[\"objective\", \"context\", \"task\"],\n",
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" )\n",
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" return cls(prompt=prompt, llm=llm, verbose=verbose)\n"
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" return cls(prompt=prompt, llm=llm, verbose=verbose)"
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]
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},
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{
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@ -193,11 +199,22 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_next_task(task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str) -> List[Dict]:\n",
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"def get_next_task(\n",
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" task_creation_chain: LLMChain,\n",
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" result: Dict,\n",
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" task_description: str,\n",
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" task_list: List[str],\n",
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" objective: str,\n",
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") -> List[Dict]:\n",
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" \"\"\"Get the next task.\"\"\"\n",
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" incomplete_tasks = \", \".join(task_list)\n",
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" response = task_creation_chain.run(result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective)\n",
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" new_tasks = response.split('\\n')\n",
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" response = task_creation_chain.run(\n",
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" result=result,\n",
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" task_description=task_description,\n",
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" incomplete_tasks=incomplete_tasks,\n",
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" objective=objective,\n",
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" )\n",
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" new_tasks = response.split(\"\\n\")\n",
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" return [{\"task_name\": task_name} for task_name in new_tasks if task_name.strip()]"
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]
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},
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@ -208,12 +225,19 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def prioritize_tasks(task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str) -> List[Dict]:\n",
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"def prioritize_tasks(\n",
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" task_prioritization_chain: LLMChain,\n",
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" this_task_id: int,\n",
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" task_list: List[Dict],\n",
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" objective: str,\n",
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") -> List[Dict]:\n",
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" \"\"\"Prioritize tasks.\"\"\"\n",
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" task_names = [t[\"task_name\"] for t in task_list]\n",
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" next_task_id = int(this_task_id) + 1\n",
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" response = task_prioritization_chain.run(task_names=task_names, next_task_id=next_task_id, objective=objective)\n",
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" new_tasks = response.split('\\n')\n",
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" response = task_prioritization_chain.run(\n",
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" task_names=task_names, next_task_id=next_task_id, objective=objective\n",
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" )\n",
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" new_tasks = response.split(\"\\n\")\n",
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" prioritized_task_list = []\n",
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" for task_string in new_tasks:\n",
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" if not task_string.strip():\n",
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@ -239,9 +263,12 @@
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" if not results:\n",
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" return []\n",
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" sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))\n",
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" return [str(item.metadata['task']) for item in sorted_results]\n",
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" return [str(item.metadata[\"task\"]) for item in sorted_results]\n",
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"\n",
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"def execute_task(vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5) -> str:\n",
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"\n",
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"def execute_task(\n",
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" vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5\n",
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") -> str:\n",
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" \"\"\"Execute a task.\"\"\"\n",
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" context = _get_top_tasks(vectorstore, query=objective, k=k)\n",
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" return execution_chain.run(objective=objective, context=context, task=task)"
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@ -254,7 +281,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"class BabyAGI(Chain, BaseModel):\n",
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" \"\"\"Controller model for the BabyAGI agent.\"\"\"\n",
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"\n",
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@ -265,9 +291,10 @@
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" task_id_counter: int = Field(1)\n",
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" vectorstore: VectorStore = Field(init=False)\n",
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" max_iterations: Optional[int] = None\n",
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" \n",
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"\n",
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" class Config:\n",
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" \"\"\"Configuration for this pydantic object.\"\"\"\n",
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"\n",
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" arbitrary_types_allowed = True\n",
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"\n",
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" def add_task(self, task: Dict):\n",
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@ -285,18 +312,18 @@
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" def print_task_result(self, result: str):\n",
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" print(\"\\033[93m\\033[1m\" + \"\\n*****TASK RESULT*****\\n\" + \"\\033[0m\\033[0m\")\n",
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" print(result)\n",
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" \n",
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"\n",
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" @property\n",
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" def input_keys(self) -> List[str]:\n",
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" return [\"objective\"]\n",
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" \n",
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"\n",
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" @property\n",
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" def output_keys(self) -> List[str]:\n",
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" return []\n",
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"\n",
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" def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n",
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" \"\"\"Run the agent.\"\"\"\n",
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" objective = inputs['objective']\n",
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" objective = inputs[\"objective\"]\n",
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" first_task = inputs.get(\"first_task\", \"Make a todo list\")\n",
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" self.add_task({\"task_id\": 1, \"task_name\": first_task})\n",
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" num_iters = 0\n",
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@ -325,7 +352,11 @@
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"\n",
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" # Step 4: Create new tasks and reprioritize task list\n",
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" new_tasks = get_next_task(\n",
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" self.task_creation_chain, result, task[\"task_name\"], [t[\"task_name\"] for t in self.task_list], objective\n",
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" self.task_creation_chain,\n",
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" result,\n",
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" task[\"task_name\"],\n",
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" [t[\"task_name\"] for t in self.task_list],\n",
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" objective,\n",
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" )\n",
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" for new_task in new_tasks:\n",
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" self.task_id_counter += 1\n",
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@ -333,27 +364,26 @@
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" self.add_task(new_task)\n",
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" self.task_list = deque(\n",
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" prioritize_tasks(\n",
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" self.task_prioritization_chain, this_task_id, list(self.task_list), objective\n",
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" self.task_prioritization_chain,\n",
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" this_task_id,\n",
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" list(self.task_list),\n",
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" objective,\n",
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" )\n",
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" )\n",
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" num_iters += 1\n",
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" if self.max_iterations is not None and num_iters == self.max_iterations:\n",
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" print(\"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\")\n",
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" print(\n",
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" \"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\"\n",
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" )\n",
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" break\n",
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" return {}\n",
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"\n",
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" @classmethod\n",
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" def from_llm(\n",
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" cls,\n",
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" llm: BaseLLM,\n",
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" vectorstore: VectorStore,\n",
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" verbose: bool = False,\n",
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" **kwargs\n",
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" cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs\n",
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" ) -> \"BabyAGI\":\n",
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" \"\"\"Initialize the BabyAGI Controller.\"\"\"\n",
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" task_creation_chain = TaskCreationChain.from_llm(\n",
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" llm, verbose=verbose\n",
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" )\n",
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" task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)\n",
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" task_prioritization_chain = TaskPrioritizationChain.from_llm(\n",
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" llm, verbose=verbose\n",
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" )\n",
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@ -363,7 +393,7 @@
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" task_prioritization_chain=task_prioritization_chain,\n",
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" execution_chain=execution_chain,\n",
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" vectorstore=vectorstore,\n",
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" **kwargs\n",
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" **kwargs,\n",
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" )"
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]
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},
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@ -405,14 +435,11 @@
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"outputs": [],
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"source": [
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"# Logging of LLMChains\n",
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"verbose=False\n",
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"verbose = False\n",
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"# If None, will keep on going forever\n",
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"max_iterations: Optional[int] = 3\n",
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"baby_agi = BabyAGI.from_llm(\n",
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" llm=llm,\n",
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" vectorstore=vectorstore,\n",
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" verbose=verbose,\n",
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" max_iterations=max_iterations\n",
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" llm=llm, vectorstore=vectorstore, verbose=verbose, max_iterations=max_iterations\n",
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")"
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]
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},
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@ -34,7 +34,7 @@
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"from langchain.llms import BaseLLM\n",
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"from langchain.vectorstores.base import VectorStore\n",
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"from pydantic import BaseModel, Field\n",
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"from langchain.chains.base import Chain\n"
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"from langchain.chains.base import Chain"
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]
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},
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{
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@ -54,7 +54,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install faiss-cpu > /dev/null%pip install google-search-results > /dev/nullfrom langchain.vectorstores import FAISS\n",
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"%pip install faiss-cpu > /dev/null\n",
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"%pip install google-search-results > /dev/null\n",
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"from langchain.vectorstores import FAISS\n",
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"from langchain.docstore import InMemoryDocstore"
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]
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},
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@ -69,6 +71,7 @@
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"embeddings_model = OpenAIEmbeddings()\n",
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"# Initialize the vectorstore as empty\n",
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"import faiss\n",
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"\n",
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"embedding_size = 1536\n",
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"index = faiss.IndexFlatL2(embedding_size)\n",
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"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
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@ -115,7 +118,12 @@
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" )\n",
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" prompt = PromptTemplate(\n",
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" template=task_creation_template,\n",
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" input_variables=[\"result\", \"task_description\", \"incomplete_tasks\", \"objective\"],\n",
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" input_variables=[\n",
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" \"result\",\n",
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" \"task_description\",\n",
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" \"incomplete_tasks\",\n",
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" \"objective\",\n",
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" ],\n",
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" )\n",
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" return cls(prompt=prompt, llm=llm, verbose=verbose)"
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]
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@ -146,7 +154,7 @@
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" template=task_prioritization_template,\n",
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" input_variables=[\"task_names\", \"next_task_id\", \"objective\"],\n",
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" )\n",
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" return cls(prompt=prompt, llm=llm, verbose=verbose)\n"
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" return cls(prompt=prompt, llm=llm, verbose=verbose)"
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]
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},
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{
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@ -158,20 +166,23 @@
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"source": [
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"from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
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"from langchain import OpenAI, SerpAPIWrapper, LLMChain\n",
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"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",
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"\n",
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"todo_prompt = PromptTemplate.from_template(\n",
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" \"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",
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")\n",
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"todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)\n",
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
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" Tool(\n",
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" name = \"Search\",\n",
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" name=\"Search\",\n",
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" func=search.run,\n",
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" description=\"useful for when you need to answer questions about current events\"\n",
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" description=\"useful for when you need to answer questions about current events\",\n",
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" ),\n",
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" Tool(\n",
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" name = \"TODO\",\n",
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" name=\"TODO\",\n",
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" func=todo_chain.run,\n",
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" 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",
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" )\n",
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" 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",
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" ),\n",
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"]\n",
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"\n",
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"\n",
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@ -179,10 +190,10 @@
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"suffix = \"\"\"Question: {task}\n",
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"{agent_scratchpad}\"\"\"\n",
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"prompt = ZeroShotAgent.create_prompt(\n",
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" tools, \n",
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" prefix=prefix, \n",
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" suffix=suffix, \n",
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" input_variables=[\"objective\", \"task\", \"context\",\"agent_scratchpad\"]\n",
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" tools,\n",
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" prefix=prefix,\n",
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" suffix=suffix,\n",
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" input_variables=[\"objective\", \"task\", \"context\", \"agent_scratchpad\"],\n",
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")"
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]
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},
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@ -203,11 +214,22 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_next_task(task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str) -> List[Dict]:\n",
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"def get_next_task(\n",
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" task_creation_chain: LLMChain,\n",
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" result: Dict,\n",
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" task_description: str,\n",
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" task_list: List[str],\n",
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" objective: str,\n",
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") -> List[Dict]:\n",
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" \"\"\"Get the next task.\"\"\"\n",
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" incomplete_tasks = \", \".join(task_list)\n",
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" response = task_creation_chain.run(result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective)\n",
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" new_tasks = response.split('\\n')\n",
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" response = task_creation_chain.run(\n",
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" result=result,\n",
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" task_description=task_description,\n",
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" incomplete_tasks=incomplete_tasks,\n",
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" objective=objective,\n",
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" )\n",
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" new_tasks = response.split(\"\\n\")\n",
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" return [{\"task_name\": task_name} for task_name in new_tasks if task_name.strip()]"
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]
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},
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@ -218,12 +240,19 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def prioritize_tasks(task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str) -> List[Dict]:\n",
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"def prioritize_tasks(\n",
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" task_prioritization_chain: LLMChain,\n",
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" this_task_id: int,\n",
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" task_list: List[Dict],\n",
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" objective: str,\n",
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") -> List[Dict]:\n",
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" \"\"\"Prioritize tasks.\"\"\"\n",
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" task_names = [t[\"task_name\"] for t in task_list]\n",
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" next_task_id = int(this_task_id) + 1\n",
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" response = task_prioritization_chain.run(task_names=task_names, next_task_id=next_task_id, objective=objective)\n",
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" new_tasks = response.split('\\n')\n",
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" response = task_prioritization_chain.run(\n",
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" task_names=task_names, next_task_id=next_task_id, objective=objective\n",
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" )\n",
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" new_tasks = response.split(\"\\n\")\n",
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" prioritized_task_list = []\n",
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" for task_string in new_tasks:\n",
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" if not task_string.strip():\n",
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@ -249,9 +278,12 @@
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" if not results:\n",
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" return []\n",
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" sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))\n",
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" return [str(item.metadata['task']) for item in sorted_results]\n",
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" return [str(item.metadata[\"task\"]) for item in sorted_results]\n",
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"\n",
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"def execute_task(vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5) -> str:\n",
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"\n",
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"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",
|
||||
")"
|
||||
]
|
||||
},
|
||||
|
Loading…
Reference in New Issue
Block a user