notest typos

README.md

@@ -411,9 +411,10 @@ with a postgres db, you will need to:
 ### Set up environment variables (API keys, etc)
 
 To get started, all you need is an OpenAI API Key.
-If you don't have one, see [this OpenAI Page](https://help.openai.com/en/collections/3675940-getting-started-with-openai-api).
-Currently only OpenAI models are supported. Others will be added later
-(Pull Requests welcome!).
+If you don't have one, see [this OpenAI Page](https://platform.openai.com/docs/quickstart).
+(Note that while this is the simplest way to get started, Langroid works with practically any LLM, not just those from OpenAI. 
+See the guides to using [Open/Local LLMs](https://langroid.github.io/langroid/tutorials/local-llm-setup/), 
+and other [non-OpenAI](https://langroid.github.io/langroid/tutorials/non-openai-llms/) proprietary LLMs.) 
 
 In the root of the repo, copy the `.env-template` file to a new file `.env`: 
 ```bash
@@ -441,9 +442,7 @@ All of the following environment variable settings are optional, and some are on
 to use specific features (as noted below).
 
 - **Qdrant** Vector Store API Key, URL. This is only required if you want to use Qdrant cloud.
-  The default vector store in our RAG agent (`DocChatAgent`) is LanceDB which uses file storage,
-  and you do not need to set up any environment variables for that.
-  Alternatively [Chroma](https://docs.trychroma.com/) is also currently supported. 
+  Alternatively [Chroma](https://docs.trychroma.com/) or [LanceDB](https://lancedb.com/) are also currently supported. 
   We use the local-storage version of Chroma, so there is no need for an API key.
 - **Redis** Password, host, port: This is optional, and only needed to cache LLM API responses
   using Redis Cloud. Redis [offers](https://redis.com/try-free/) a free 30MB Redis account
@@ -538,11 +537,12 @@ and they are **not** complete runnable examples! For that we encourage you to
 consult the [`langroid-examples`](https://github.com/langroid/langroid-examples) 
 repository.
 
-:information_source: The various LLM prompts and instructions in Langroid
-have been tested to work well with GPT4.
-Switching to GPT3.5-Turbo is easy via a config flag
-(e.g., `cfg = OpenAIGPTConfig(chat_model=OpenAIChatModel.GPT3_5_TURBO)`),
-and may suffice for some applications, but in general you may see inferior results.
+:information_source:
+The various LLM prompts and instructions in Langroid
+have been tested to work well with GPT-4 (and to some extent GPT-4o).
+Switching to other LLMs (local/open and proprietary) is easy (see guides mentioned above),
+and may suffice for some applications, but in general you may see inferior results
+unless you adjust the prompts and/or the multi-agent setup.
 
 
 :book: Also see the
@@ -817,7 +817,7 @@ config = DocChatAgentConfig(
     "https://en.wikipedia.org/wiki/N-gram_language_model",
     "/path/to/my/notes-on-language-models.txt",
   ],
-  vecdb=lr.vector_store.LanceDBConfig(),
+  vecdb=lr.vector_store.QdrantDBConfig(),
 )
 ```
 

docs/quick-start/setup.md

@@ -65,18 +65,20 @@ For many practical scenarios, you may need additional optional dependencies:
     For the best experience we recommend you work in a nice terminal that supports 
     colored outputs, such as [Iterm2](https://iterm2.com/).    
 
-!!! note "OpenAI GPT4 is required"
+!!! note "OpenAI GPT-4/GPT-4o is required"
     The various LLM prompts and instructions in Langroid 
-    have been tested to work well with GPT4.
-    Switching to GPT3.5-Turbo is easy via a config flag, and may suffice 
-    for some applications, but in general you may see inferior results.
+    have been tested to work well with GPT-4 (and to some extent GPT-4o).
+    Switching to other LLMs (local/open and proprietary) is easy (see guides mentioned below),
+    and may suffice for some applications, but in general you may see inferior results
+    unless you adjust the prompts and/or the multi-agent setup.
 
 ## Set up tokens/keys 
 
 To get started, all you need is an OpenAI API Key.
-If you don't have one, see [this OpenAI Page](https://help.openai.com/en/collections/3675940-getting-started-with-openai-api).
-Currently only OpenAI models are supported. Others will be added later
-(Pull Requests welcome!).
+If you don't have one, see [this OpenAI Page](https://platform.openai.com/docs/quickstart).
+(Note that while this is the simplest way to get started, Langroid works with practically any LLM, not just those from OpenAI.
+See the guides to using [Open/Local LLMs](https://langroid.github.io/langroid/tutorials/local-llm-setup/),
+and other [non-OpenAI](https://langroid.github.io/langroid/tutorials/non-openai-llms/) proprietary LLMs.)
 
 In the root of the repo, copy the `.env-template` file to a new file `.env`:
 ```bash

langroid/agent/task.py

@@ -1059,6 +1059,9 @@ def result(self) -> ChatDocument:
         """
         Get result of task. This is the default behavior.
         Derived classes can override this.
+
+        Note the result of a task is returned as if it is from the User entity.
+
         Returns:
             ChatDocument: result of task
         """

langroid/agent/tool_message.py

@@ -73,7 +73,7 @@ def examples(cls) -> List["ToolMessage" | Tuple[str, "ToolMessage"]]:
         - a tuple (description, ToolMessage instance), where the description is
             a natural language "thought" that leads to the tool usage,
             e.g. ("I want to find the square of 5",  SquareTool(num=5))
-            In some scenarios, ncluding such a description can significantly
+            In some scenarios, including such a description can significantly
             enhance reliability of tool use.
         Returns:
         """