feat(rai_cli): improved UX

config.toml

@@ -0,0 +1,19 @@
+[vendor]
+name = "openai" # openai, aws, ollama
+
+[aws]
+simple_model = "anthropic.claude-3-haiku-20240307-v1:0"
+complex_model = "anthropic.claude-3-5-sonnet-20240620-v1:0"
+embeddings_model = "amazon.titan-embed-text-v1"
+region_name = "us-east-1"
+
+[openai]
+simple_model = "gpt-4o-mini"
+complex_model = "gpt-4o"
+embeddings_model = "text-embedding-ada-002"
+
+[ollama]
+simple_model = "llama3.1"
+complex_model = "llama3.1:70b"
+embeddings_model = "llama3.1"
+base_url = "http://localhost:11434"

pyproject.toml

@@ -47,6 +47,7 @@ langchain-ollama = "^0.1.1"
 
 streamlit = "^1.37.1"
 deprecated = "^1.2.14"
+tomli = "^2.0.1"
 [tool.poetry.group.dev.dependencies]
 ipykernel = "^6.29.4"
 

src/rai/rai/cli/rai_cli.py

@@ -15,16 +15,22 @@
 
 import argparse
 import glob
+import logging
 import subprocess
 from pathlib import Path
 
+import coloredlogs
 from langchain_community.vectorstores import FAISS
 from langchain_core.messages import SystemMessage
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 
 from rai.apps.talk_to_docs import ingest_documentation
 from rai.messages import preprocess_image
 from rai.messages.multimodal import HumanMultimodalMessage
+from rai.utils.model_initialization import get_embeddings_model, get_llm_model
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+coloredlogs.install(level="INFO")  # type: ignore
 
 
 def parse_whoami_package():
@@ -35,42 +41,79 @@ def parse_whoami_package():
         "documentation_root", type=str, help="Path to the root of the documentation"
     )
     parser.add_argument(
-        "output",
+        "--output",
         type=str,
+        required=False,
+        default=None,
         help="Path to the output directory",
     )
     args = parser.parse_args()
+    save_dir = args.output if args.output is not None else args.documentation_root
+
+    llm = get_llm_model(model_type="simple_model")
+    embeddings_model = get_embeddings_model()
+
+    def build_docs_vector_store():
+        logger.info("Building the robot docs vector store...")
+        faiss_index = FAISS.from_documents(docs, embeddings_model)
+        faiss_index.add_documents(docs)
+        faiss_index.save_local(save_dir)
+
+    def build_robot_identity():
+        logger.info("Building the robot identity...")
+        prompt = (
+            "You will be given a robot's documentation. "
+            "Your task is to identify the robot's identity. "
+            "The description should cover the most important aspects of the robot with respect to human interaction, "
+            "as well as the robot's capabilities and limitations including sensor and actuator information. "
+            "If there are any images provided, make sure to take them into account by thoroughly analyzing them. "
+            "Your description should be thorough and detailed."
+            "Your reply should start with I am a ..."
+        )
+
+        images = glob.glob(args.documentation_root + "/images/*")
+
+        messages = [SystemMessage(content=prompt)] + [
+            HumanMultimodalMessage(
+                content=documentation,
+                images=[preprocess_image(image) for image in images],
+            )
+        ]
+        output = llm.invoke(messages)
+        assert isinstance(output.content, str), "Malformed output"
+
+        with open(save_dir + "/robot_identity.txt", "w") as f:
+            f.write(output.content)
+        logger.info("Done")
 
     docs = ingest_documentation(
         documentation_root=args.documentation_root + "/documentation"
     )
-    faiss_index = FAISS.from_documents(docs, OpenAIEmbeddings())
-    faiss_index.add_documents(docs)
-    save_dir = args.output
-    faiss_index.save_local(save_dir)
-
-    prompt = (
-        "You will be given a robot's documentation. "
-        "Your task is to identify the robot's identity. "
-        "The description should cover the most important aspects of the robot with respect to human interaction, "
-        "as well as the robot's capabilities and limitations including sensor and actuator information. "
-        "If there are any images provided, make sure to take them into account by thoroughly analyzing them. "
-        "Your description should be thorough and detailed."
-        "Your reply should start with I am a ..."
+    documentation = str([doc.page_content for doc in docs])
+    n_tokens = len(documentation) // 4.0
+    logger.info(
+        "Building the robot docs vector store... "
+        f"The documentation's length is {len(documentation)} chars, "
+        f"approximately {n_tokens} tokens"
     )
-    llm = ChatOpenAI(model="gpt-4o-mini")
-
-    images = glob.glob(args.documentation_root + "/images/*")
-
-    messages = [SystemMessage(content=prompt)] + [
-        HumanMultimodalMessage(
-            content=str([doc.page_content for doc in docs]),
-            images=[preprocess_image(image) for image in images],
+    logger.warn("Do you want to continue? (y/n)")
+    if input() == "y":
+        build_docs_vector_store()
+    else:
+        logger.info("Skipping the robot docs vector store creation.")
+
+    logger.info(
+        "Building the robot identity... "
+        f"You can do it manually by creating {save_dir}/robot_identity.txt "
+    )
+    logger.warn("Do you want to continue? (y/n)")
+    if input() == "y":
+        build_robot_identity()
+    else:
+        logger.info(
+            f"Skipping the robot identity creation. "
+            f"You can do it manually by creating {save_dir}/robot_identity.txt"
         )
-    ]
-    output = llm.invoke(messages)
-    with open(save_dir + "/robot_identity.txt", "w") as f:
-        f.write(output.content)
 
 
 def create_rai_ws():
@@ -125,7 +168,7 @@ def create_rai_ws():
 
 # TODO: Refactor this hacky solution
 # NOTE (mkotynia) fast solution, worth refactor in the future and testing if it generic for all setup.py file confgurations
-def modify_setup_py(setup_py_path):
+def modify_setup_py(setup_py_path: Path):
     with open(setup_py_path, "r") as file:
         setup_content = file.read()
 
@@ -157,7 +200,3 @@ def modify_setup_py(setup_py_path):
 
     with open(setup_py_path, "w") as file:
         file.write(modified_script)
-
-
-if __name__ == "__main__":
-    create_rai_ws()

src/rai/rai/utils/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (C) 2024 Robotec.AI
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#         http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

src/rai/rai/utils/model_initialization.py

@@ -0,0 +1,110 @@
+# Copyright (C) 2024 Robotec.AI
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#         http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Literal
+
+import tomli
+
+
+@dataclass
+class VendorConfig:
+    name: str
+
+
+@dataclass
+class ModelConfig:
+    simple_model: str
+    complex_model: str
+    embeddings_model: str
+
+
+@dataclass
+class AWSConfig(ModelConfig):
+    region_name: str
+
+
+@dataclass
+class OllamaConfig(ModelConfig):
+    base_url: str
+
+
+@dataclass
+class RAIConfig:
+    vendor: VendorConfig
+    aws: AWSConfig
+    openai: ModelConfig
+    ollama: OllamaConfig
+
+
+def load_config() -> RAIConfig:
+    with open("config.toml", "rb") as f:
+        config_dict = tomli.load(f)
+    return RAIConfig(
+        vendor=VendorConfig(**config_dict["vendor"]),
+        aws=AWSConfig(**config_dict["aws"]),
+        openai=ModelConfig(**config_dict["openai"]),
+        ollama=OllamaConfig(**config_dict["ollama"]),
+    )
+
+
+def get_llm_model(model_type: Literal["simple_model", "complex_model"]):
+    config = load_config()
+    vendor = config.vendor.name
+    model_config = getattr(config, vendor)
+
+    if vendor == "openai":
+        from langchain_openai import ChatOpenAI
+
+        return ChatOpenAI(model=getattr(model_config, model_type))
+    elif vendor == "aws":
+        from langchain_aws import ChatBedrock
+
+        return ChatBedrock(
+            model_id=getattr(model_config, model_type),
+            region_name=model_config.region_name,
+        )
+    elif vendor == "ollama":
+        from langchain_ollama import ChatOllama
+
+        return ChatOllama(
+            model=getattr(model_config, model_type), base_url=model_config.base_url
+        )
+    else:
+        raise ValueError(f"Unknown LLM vendor: {vendor}")
+
+
+def get_embeddings_model():
+    config = load_config()
+    vendor = config.vendor.name
+    model_config = getattr(config, vendor)
+
+    if vendor == "openai":
+        from langchain_openai import OpenAIEmbeddings
+
+        return OpenAIEmbeddings(model=model_config.embeddings_model)
+    elif vendor == "aws":
+        from langchain_aws import BedrockEmbeddings
+
+        return BedrockEmbeddings(
+            model_id=model_config.embeddings_model, region_name=model_config.region_name
+        )
+    elif vendor == "ollama":
+        from langchain_ollama import OllamaEmbeddings
+
+        return OllamaEmbeddings(
+            model=model_config.embeddings_model, base_url=model_config.base_url
+        )
+    else:
+        raise ValueError(f"Unknown embeddings vendor: {vendor}")