Langroid

* New Task control signals: PASS_TO, SEND_TO (in addition to the existing PASS, DONE)
- PASS_TO:<recipient> => send current pending message to <recipient>
- SEND_TO:<recipient> <content> => send <content> to <recipient>
(Tests coming soon)
* Agent: tool_messages are now inserted into ChatDocument as soon as they are identified
* [VectorStore](https://github.com/langroid/langroid/blob/main/langroid/vector_store/base.py): `compute_from_docs` method to compute a
pandas-expression on documents
* [LanceRagTaskCreator](https://github.com/langroid/langroid/blob/main/langroid/agent/special/lance_rag/lance_rag_task.py): creates a
3-agent RAG system consisting of: QueryPlanner, QueryPlanCritic, LanceRAGAgent.
This illustrates a fairly complex 3-agent workflow and is a good reference.

* DocChatAgentConfig: remove max_context_tokens param
* misc: remove "nofunc" GPT models

LanceDB Related:
* LanceDB filtered search examples:
https://github.com/langroid/langroid/blob/main/examples/docqa/lance-filtered-movies.py
https://github.com/langroid/langroid/blob/main/examples/docqa/lance-filtered-gh-issues.py

These showcase:
- LanceDB ingest of pandas dataframe directly
- Langroid 2-agent setup where LanceFilterAgent decides on a filter (and retries if no answers) plus possibly rephrased query, and LanceDocChatAgent does the actual answering using RAG on the LanceDB, combining Lancedb-native filtering (via SQL conditions) vector-search, plus lexical (kw/bm25 search).

Minor:
* Tweaks in LanceDocChatAgent prompt
* RecipientTool: return err msg instead of raising

LanceDB Full Text Search, tests

Fix task clone - done_if_*_response

Task.py fix: pass init args to clone