update examples to use constants.AT not @

Author: pchalasani

examples/basic/drug-outcomes.py

@@ -0,0 +1,141 @@
+"""
+ADE (Adverse Drug Event) probability estimation task:
+
+Given a pair of (Drug Category, Adverse Event), have the LLM generate an estimate
+of the probability that the drug category is associated with an increased risk
+of the adverse event.
+
+Run this N times (without caching) to get statistics on the estimates.
+Illustrates the use of `llm_response_batch`.
+
+Default model is GPT4o, see how to specify alternative models below.
+
+Example run:
+
+python3 examples/basic/ drug-outcomes.py \
+    --model litellm/claude-3-5-sonnet-20240620 --temp 0.1 \
+    --pair "(Antibiotics, Acute Liver Injury)" --n 20 --reason true
+
+Interesting models to try:
+- gpt-4o (default)
+- gpt-4
+- litellm/claude-3-5-sonnet-20240620
+- groq/llama3-70b-8192
+
+See reference below for specific (DrugCategory, ADE) pairs to test.
+
+References:
+    - Guides to using Langroid with local and non-OpenAI models:
+        https://langroid.github.io/langroid/tutorials/local-llm-setup/
+        https://langroid.github.io/langroid/tutorials/non-openai-llms/
+    - OMOP Ground Truth table of known Drug-ADE associations:
+        (see page 16 for the table of Drug-ADE pairs)
+        https://www.brookings.edu/wp-content/uploads/2012/04/OMOP-methods-review.pdf
+"""
+
+import langroid as lr
+import langroid.language_models as lm
+from langroid.utils.configuration import settings
+import numpy as np
+import re
+from fire import Fire
+
+# Turn off cache retrieval, to get independent estimates on each run
+settings.cache = False
+
+MODEL = lm.OpenAIChatModel.GPT4o
+TEMP = 0.1
+PAIR = "(Antibiotics, Acute Liver Injury)"
+N = 20
+# should LLM include reasoning along with probability?
+# (meant to test whether including reasoning along with the probability
+# improves accuracy and/or variance of estimates)
+REASON: bool = False
+
+
+def extract_num(x: str) -> int:
+    """
+    Extracts an integer from a string that contains a number.
+
+    Args:
+        x (str): The input string containing the number.
+
+    Returns:
+        int: The extracted integer.
+
+    Raises:
+        ValueError: If no number is found in the expected format.
+    """
+    match = re.search(r"\d+", x)
+    if match:
+        return int(match.group(0))
+    else:
+        return -1
+
+
+def main(
+    model: str = MODEL,
+    temp: float = TEMP,
+    pair: str = PAIR,
+    n: int = N,
+    reason: bool = REASON,
+):
+    REASONING_PROMPT = (
+        """
+            IMPORTANT: Before showing your estimated probability, 
+            you MUST show 2-3 sentences with your REASONING, and THEN give your 
+            percent probability estimate in the range [0,100].
+    """
+        if reason
+        else ""
+    )
+
+    agent = lr.ChatAgent(
+        lr.ChatAgentConfig(
+            llm=lm.OpenAIGPTConfig(
+                temperature=temp,
+                chat_model=model,
+            ),
+            name="ADE-Estimator",
+            system_message=f"""
+            You are a clinician with deep knowledge of Adverse Drug Events (ADEs) 
+            of various drugs and categories of drugs.
+            You will be given a (DRUG CATEGORY, ADVERSE OUTCOME) pair,
+            you have to estimate the probability that this DRUG CATEGORY
+            is associated with INCREASED RISK of the ADVERSE OUTCOME. 
+    
+            {REASONING_PROMPT}
+                
+            You must give your probability estimate as a SINGLE NUMBER e.g. 56, 
+            which means 56%.             
+            DO NOT GIVE A RANGE OF PROBABILITIES, ONLY A SINGLE NUMBER. 
+            """,
+        )
+    )
+
+    results = lr.llm_response_batch(
+        agent,
+        [pair] * n,
+        # ["(Beta Blockers, Mortality after Myocardial Infarction)"]*20,
+    )
+    probs = [extract_num(r.content) for r in results]
+    cached = [r.metadata.cached for r in results]
+    n_cached = sum(cached)
+    # eliminate negatives (due to errs)
+    probs = [p for p in probs if p >= 0]
+    mean = np.mean(probs)
+    std = np.std(probs)
+    std_err = std / np.sqrt(len(probs))
+    hi = max(probs)
+    lo = min(probs)
+    print(f"Stats for {pair} with {model} temp {temp} reason {reason}:")
+    print(
+        f"N: {len(probs)} ({n_cached} cached ) Mean: {mean:.2f}, Std: {std:.2f}, StdErr:"
+        f" {std_err:.2f}, min: {lo:.2f}, max: {hi:.2f}"
+    )
+    toks, cost = agent.llm.tot_tokens_cost()
+    print(f"Tokens: {toks}, Cost: {cost:.2f}")
+
+
+if __name__ == "__main__":
+    Fire(main)

examples/basic/plan-subtasks.py

@@ -16,7 +16,7 @@
 """
 
 import langroid as lr
-from langroid.utils.constants import NO_ANSWER, DONE
+from langroid.utils.constants import NO_ANSWER, DONE, AT
 
 planner = lr.ChatAgent(
     lr.ChatAgentConfig(
@@ -39,11 +39,12 @@
             * Send Divide operation to `Divider`
             
             To clarify who you are sending the message to, preface your message with
-            @<helper_name>, e.g. "@Multiplier multiply with 5" 
+            {AT}<helper_name>, e.g. "{AT}Multiplier multiply with 5" 
             
             When you have the final answer, say {DONE} and show it.
             
-            At the START, ask the user what they need help with, address them as "@user"
+            At the START, ask the user what they need help with, 
+            address them as "{AT}user"
             
         EXAMPLE: 
         ============
@@ -53,7 +54,7 @@
                 1. multiply 4 with 5
                 2. add 1 to the result
                 3. divide result by 3
-            @Multiplier multiply 4 with 5
+            {AT}Multiplier multiply 4 with 5
             [... wait for result, then show your NEW PLAN and send a new request]
             and so on.                         
                         
@@ -102,7 +103,8 @@
 )
 
 
-planner_task = lr.Task(planner, interactive=False)
+task_config = lr.TaskConfig(addressing_prefix=AT)
+planner_task = lr.Task(planner, interactive=False, config=task_config)
 adder_task = lr.Task(adder, interactive=False, single_round=True)
 multiplier_task = lr.Task(multiplier, interactive=False, single_round=True)
 divider_task = lr.Task(divider, interactive=False, single_round=True)

examples/chainlit/chat-search-assistant-local.py

@@ -4,15 +4,26 @@
 Tested and works ok nous-hermes2-mixtral, but may still have issues.
 See that script for details.
 
-You can specify a local in a few different ways, e.g. `-m local/localhost:8000/v1`
-or `-m ollama/mistral` etc. See here how to use Langroid with local LLMs:
+You can specify a local model in a few different ways, e.g. `groq/llama3-70b-8192`
+or `ollama/mistral` etc. See here how to use Langroid with local LLMs:
 https://langroid.github.io/langroid/tutorials/local-llm-setup/
 
+Since chainlit does not take cmd line args in the normal way, you have to specify
+the model via an environment variable, e.g. `MODEL=ollama/mistral` before the
+script is run, e.g.
+
+MODEL=ollama/mistral chainlit run  examples/chainlit/chat-search-assistant-local.py
+
+Note - this is just an example of using an open/local LLM;
+ it does not mean that this will work with ANY local LLM.
+
+You may get good results using `groq/llama3-70b-8192` (see the above-linked guide
+to using open/local LLMs with Langroid for more details).
 
 """
 
 from typing import List, Optional, Type
-
+import os
 from dotenv import load_dotenv
 from textwrap import dedent
 
@@ -279,8 +290,8 @@ async def llm_response_async(
 
 @cl.on_chat_start
 async def main(
-    debug: bool = False,
-    model: str = "ollama/nous-hermes2-mixtral:latest",
+    debug: bool = True,
+    model: str = os.getenv("MODEL", "gpt-4o"),
     nocache: bool = True,
 ) -> None:
     set_global(

examples/docqa/doc-aware-guide-2.py

@@ -29,7 +29,7 @@
 from langroid.agent.chat_agent import ChatAgent, ChatAgentConfig
 from langroid.agent.task import Task
 from langroid.utils.configuration import set_global, Settings
-from langroid.utils.constants import DONE, NO_ANSWER
+from langroid.utils.constants import DONE, NO_ANSWER, AT
 from fire import Fire
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -137,9 +137,11 @@ def main(
     #     recipients=["DocAgent", "User"], default="User"
     # )
     # guide_agent.enable_message(MyRecipientTool)
+    task_config = lr.TaskConfig(addressing_prefix=AT)
     guide_task = Task(
         guide_agent,
         interactive=False,
+        config=task_config,
         system_message=f"""
         You are VERY HELPFUL GUIDE, who wants to help a User with their inquiry.
         
@@ -154,7 +156,7 @@ def main(
           
         Since you could be talking to TWO people, in order to CLARIFY who you are
         addressing, you MUST ALWAYS EXPLICITLY ADDRESS either the 
-        "User" or the "DocAgent" using @User or @DocAgent, respectively.
+        "User" or the "DocAgent" using {AT}User or {AT}DocAgent, respectively.
         
         You must THINK like this at each step after receiving a question from the User:
         

examples/docqa/doc-based-troubleshooting.py

@@ -31,7 +31,7 @@
 from langroid.agent.chat_agent import ChatAgent, ChatAgentConfig
 from langroid.agent.task import Task
 from langroid.utils.configuration import set_global, Settings
-from langroid.utils.constants import DONE, NO_ANSWER
+from langroid.utils.constants import DONE, NO_ANSWER, AT
 from fire import Fire
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -144,9 +144,11 @@ def main(
     #     recipients=["DocAgent", "User"], default="User"
     # )
     # guide_agent.enable_message(MyRecipientTool)
+    task_config = lr.TaskConfig(addressing_prefix=AT)
     guide_task = Task(
         guide_agent,
         interactive=False,
+        config=task_config,
         system_message=f"""
         You are a TROUBLESHOOTER, who wants to help a User with their PROBLEM.
         
@@ -161,7 +163,7 @@ def main(
           
         Since you could be talking to TWO people, in order to CLARIFY who you are
         addressing, you MUST ALWAYS EXPLICITLY ADDRESS either the 
-        "User" or the "DocAgent" using @User or @DocAgent, respectively.
+        "User" or the "DocAgent" using {AT}User or {AT}DocAgent, respectively.
         
         You must THINK like this at each step after receiving a question from the User: