Use agents in langchain example (#21)

JavaScript/langchain/README.md

@@ -46,6 +46,14 @@ npm install
 npm run start
 ```
 
-## View logs in Autoblocks
+## View the trace in Autoblocks
 
-After you run the script, you can find the trace on the [explore page](https://app.autoblocks.ai/explore).
+Go to the [explore page](https://app.autoblocks.ai/explore). When you find the trace, switch to the Trace Tree view to
+see a tree of all the spans in the trace.
+
+The first span that is selected will show you the overall question and answer of the LangChain pipeline, but you can also
+drill into individual spans by clicking on them to understand how LangChain is working under the hood.
+
+![explore-trace-top-level-span](https://github.com/autoblocksai/autoblocks-examples/assets/7498009/590e232a-eeaf-46a1-b9e3-3c0a8648234b)
+
+![explore-trace-nested-span](https://github.com/autoblocksai/autoblocks-examples/assets/7498009/b32d7776-8378-49cc-a866-7ba6bd08f5e5)

JavaScript/langchain/package.json

@@ -15,6 +15,6 @@
   "dependencies": {
     "@autoblocks/client": "^0.0.15",
     "dotenv-cli": "^7.3.0",
-    "langchain": "^0.0.166"
+    "langchain": "^0.0.173"
   }
 }

JavaScript/langchain/src/index.js

@@ -1,49 +1,34 @@
 import { AutoblocksCallbackHandler } from '@autoblocks/client/langchain';
-
-import { SimpleSequentialChain, LLMChain } from "langchain/chains";
 import { OpenAI } from "langchain/llms/openai";
-import { PromptTemplate } from "langchain/prompts";
+import { DynamicTool } from "langchain/tools";
+import { Calculator } from "langchain/tools/calculator";
+import { initializeAgentExecutorWithOptions } from "langchain/agents";
 
 const main = async () => {
-  // This is an LLMChain to write a synopsis given a title of a play.
-  const synopsisLLM = new OpenAI({ temperature: 0.7 });
-  const titleTemplate = `You are a playwright. Given the title of a play, it is your job to write a synopsis for that title.
-  
-Title: {title}
-Playwright: This is a synopsis for the above play:`;
-  const synopsisTemplate = new PromptTemplate({
-    template: titleTemplate,
-    inputVariables: ["title"],
-  });
-  const synopsisChain = new LLMChain({ llm: synopsisLLM, prompt: synopsisTemplate });
-
-  // This is an LLMChain to write a review of a play given a synopsis.
-  const reviewLLM = new OpenAI({ temperature: 0.7 });
-  const reviewTemplate = `You are a play critic from the New York Times. Given the synopsis of a play, it is your job to write a review for that play.
-  
-Play Synopsis:
-{synopsis}
-Review from a New York Times play critic of the above play:`;
-  const reviewPromptTemplate = new PromptTemplate({
-    template: reviewTemplate,
-    inputVariables: ["synopsis"],
-  });
-  const reviewChain = new LLMChain({
-    llm: reviewLLM,
-    prompt: reviewPromptTemplate,
-  });
+  const model = new OpenAI({ temperature: 0 });
+  const tools = [
+    new Calculator(),
+    new DynamicTool({
+      name: "Today's Date",
+      description:
+        "call this to get today's date. input should be an empty string.",
+      func: () => new Date().getDate(),
+    }),
+  ];
 
-  // This is the overall chain where we run these two chains in sequence.
-  const overallChain = new SimpleSequentialChain({
-    chains: [synopsisChain, reviewChain],
+  const executor = await initializeAgentExecutorWithOptions(tools, model, {
+    agentType: 'structured-chat-zero-shot-react-description',
   });
 
-  // Run the chain
   const handler = new AutoblocksCallbackHandler();
-  const review = await overallChain.run("Tragedy at sunset on the beach", { callbacks: [handler] });
+  const output = await executor.run(
+    "What is today's date? What is that date divided by 2?",
+    { callbacks: [handler]
+  });
 
-  console.log('Review:');
-  console.log(review);
+  console.log(`Output: ${output}`);
+  console.log('\n');
+  console.log('View your trace: https://app.autoblocks.ai/explore')
 }
 
 main();

Python/langchain/README.md

@@ -46,6 +46,14 @@ poetry install
 poetry run python main.py
 ```
 
-## View logs in Autoblocks
+## View the trace in Autoblocks
 
-Go to the [explore page](https://app.autoblocks.ai/explore) to see the trace.
+Go to the [explore page](https://app.autoblocks.ai/explore). When you find the trace, switch to the Trace Tree view to
+see a tree of all the spans in the trace.
+
+The first span that is selected will show you the overall question and answer of the LangChain pipeline, but you can also
+drill into individual spans by clicking on them to understand how LangChain is working under the hood.
+
+![explore-trace-top-level-span](https://github.com/autoblocksai/autoblocks-examples/assets/7498009/941c09b7-86e9-4e0b-9df4-2a9be0b32771)
+
+![explore-teace-nested-span](https://github.com/autoblocksai/autoblocks-examples/assets/7498009/99f02ba9-c3ea-4645-aa9d-17f6d83be790)

Python/langchain/main.py

@@ -1,39 +1,44 @@
 import dotenv
 from autoblocks.vendor.langchain import AutoblocksCallbackHandler
-from langchain.chains import LLMChain
-from langchain.chains import SimpleSequentialChain
+from langchain.agents import AgentType
+from langchain.agents import initialize_agent
+from langchain.chains import LLMMathChain
 from langchain.llms import OpenAI
-from langchain.prompts import PromptTemplate
+from langchain.tools import Tool
+from langchain.tools import tool
+from datetime import datetime
 
 dotenv.load_dotenv(".env")
 
 
-if __name__ == "__main__":
-    # This is an LLMChain to write a synopsis given a title of a play.
-    synopsis_llm = OpenAI(temperature=0.7)
-    synopsis_template = """You are a playwright. Given the title of a play, it is your job to write a synopsis for that title.
-
-Title: {title}
-Playwright: This is a synopsis for the above play:"""
-    synopsis_prompt_template = PromptTemplate(input_variables=["title"], template=synopsis_template)
-    synopsis_chain = LLMChain(llm=synopsis_llm, prompt=synopsis_prompt_template)
-
-    # This is an LLMChain to write a review of a play given a synopsis.
-    review_llm = OpenAI(temperature=0.7)
-    review_template = """You are a play critic from the New York Times. Given the synopsis of a play, it is your job to write a review for that play.
+@tool
+def todays_date(*args, **kwargs) -> int:
+    """Returns today's date"""
+    return datetime.now().day
 
-Play Synopsis:
-{synopsis}
-Review from a New York Times play critic of the above play:"""
-    review_prompt_template = PromptTemplate(input_variables=["synopsis"], template=review_template)
-    review_chain = LLMChain(llm=review_llm, prompt=review_prompt_template)
 
-    # This is the overall chain where we run these two chains in sequence.
-    overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain])
-
-    # Run the chain
+if __name__ == "__main__":
+    llm = OpenAI(temperature=0)
+    llm_math_chain = LLMMathChain.from_llm(llm)
+    tools = [
+        Tool.from_function(
+            func=llm_math_chain.run,
+            name="Calculator",
+            description="useful for when you need to answer questions about math",
+        ),
+        todays_date,
+    ]
+    agent = initialize_agent(
+        tools,
+        llm,
+        agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+    )
     handler = AutoblocksCallbackHandler()
-    review = overall_chain.run("Tragedy at sunset on the beach", callbacks=[handler])
-
-    print("Review:")
-    print(review)
+    output = agent.run(
+        "What is today's date? What is that date divided by 2?",
+        callbacks=[handler],
+    )
+
+    print(f"Output: {output}")
+    print()
+    print("View your trace: https://app.autoblocks.ai/explore")

Python/langchain/pyproject.toml

@@ -10,5 +10,6 @@ readme = "README.md"
 python = "^3.9"
 autoblocksai = "^0.0.11"
 python-dotenv = "^1.0.0"
-langchain = "^0.0.319"
 openai = "^0.28.1"
+numexpr = "^2.8.7"
+langchain = "^0.0.323"