TSPostPatch

docs/tracing/faq/customizing_trace_attributes.mdx

@@ -81,8 +81,8 @@ chat_completion = client.chat.completions.create(
     messages=messages,
 )
 # End and submit the run
-rt.end(outputs=chat_completion)
-rt.post()
+await rt.end(outputs=chat_completion)
+rt.postRun()
 `),
     TypeScriptBlock(`import OpenAI from "openai";
 import { RunTree } from "langsmith";\n
@@ -100,13 +100,14 @@ const rt = new RunTree({
     // highlight-next-line
     project_name: "My Project"
 })
+await rt.postRun();
 const chatCompletion = await client.chat.completions.create({
     model: "gpt-3.5-turbo",
     messages: messages,
 });
 // End and submit the run
-rt.end(chatCompletion)
-await rt.postRun()`),
+await rt.end(chatCompletion);
+await rt.patchRun();`),
     LangChainPyBlock(`# You can set the project name for a specific tracer instance:
 from langchain.callbacks.tracers import LangChainTracer\n
 tracer = LangChainTracer(project_name="My Project")
@@ -185,8 +186,8 @@ chat_completion = client.chat.completions.create(
     messages=messages,
 )
 # End and submit the run
-rt.end(outputs=chat_completion)
-rt.post()`),
+await rt.end(outputs=chat_completion)
+rt.postRun()`),
     TypeScriptBlock(`import OpenAI from "openai";
 import { RunTree } from "langsmith";\n
 const client = new OpenAI();\n
@@ -204,13 +205,14 @@ const rt = new RunTree({
     // highlight-next-line
     extra: {metadata: {"my-key": "my-value"}}
 })
+await rt.postRun();
 const chatCompletion = await client.chat.completions.create({
     model: "gpt-3.5-turbo",
     messages: messages,
 });
 // End and submit the run
-rt.end(chatCompletion)
-await rt.postRun()`),
+await rt.end(chatCompletion)
+await rt.patchRun()`),
     LangChainPyBlock(`from langchain_openai import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser\n
@@ -282,8 +284,8 @@ chat_completion = client.chat.completions.create(
     messages=messages,
 )
 # End and submit the run
-rt.end(outputs=chat_completion)
-rt.post()`),
+await rt.end(outputs=chat_completion)
+rt.postRun()`),
     TypeScriptBlock(`import OpenAI from "openai";
 import { RunTree } from "langsmith";\n
 const client = new OpenAI();\n
@@ -298,13 +300,14 @@ const rt = new RunTree({
     name: "OpenAI Call RunTree",
     inputs: { messages },
 })
+await rt.postRun()
 const chatCompletion = await client.chat.completions.create({
     model: "gpt-3.5-turbo",
     messages: messages,
 });
 // End and submit the run
-rt.end(chatCompletion)
-await rt.postRun()`),
+await rt.end(chatCompletion)
+await rt.patchRun()`),
     LangChainPyBlock(`# When tracing within LangChain, run names default to the class name of the traced object (e.g., 'ChatOpenAI').
 # (Note: this is not currently supported directly on LLM objects.)
 ...

docs/tracing/faq/logging_and_viewing.mdx

@@ -187,6 +187,7 @@ pipeline = RunTree(
     run_type="chain",
     inputs={"question": question}
 )
+await pipeline.postRun();
 # This can be retrieved in a retrieval step
 context = "During this morning's meeting, we solved all world conflict."
 messages = [
@@ -206,9 +207,9 @@ chat_completion = client.chat.completions.create(
 )
 # End the runs and log them
 child_llm_run.end(outputs=chat_completion)
-child_llm_run.post()
+child_llm_run.postRun()
 pipeline.end(outputs={"answer": chat_completion.choices[0].message.content})
-pipeline.post()`),
+pipeline.postRun()`),
     TypeScriptBlock(`import OpenAI from "openai";
 import { RunTree } from "langsmith";
 // This can be a user input to your app
@@ -239,8 +240,8 @@ const chatCompletion = await client.chat.completions.create({
 // End the runs and log them
 childRun.end(chatCompletion);
 await childRun.postRun();
-pipeline.end({ outputs: { answer: chatCompletion.choices[0].message.content } });
-await pipeline.postRun();`),
+await pipeline.end({ outputs: { answer: chatCompletion.choices[0].message.content } });
+await pipeline.patchRun();`),
   ]}
   groupId="client-language"
 />
@@ -369,7 +370,7 @@ chat_completion = client.chat.completions.create(
     model="gpt-3.5-turbo", messages=messages
 )
 rt.end(outputs=chat_completion)
-rt.post()
+rt.postRun()
 print("RunTree Run ID: ", run_id)\n
 # Collect run ID using openai_wrapper
 run_id = uuid4()
@@ -415,12 +416,13 @@ const rt = new RunTree({
     inputs: { messages },
     id: runId
 })
+await rt.postRun();
 const chatCompletion = await client.chat.completions.create({
     model: "gpt-3.5-turbo",
     messages: messages,
 });
 rt.end(chatCompletion)
-await rt.postRun()
+await rt.patchRun()
 console.log("Run ID: ", runId);`),
     APIBlock(`import openai
 import requests\n

versioned_docs/version-2.0/how_to_guides/tracing/add_metadata_tags.mdx

@@ -128,13 +128,14 @@ const rt = new RunTree({
    // highlight-next-line
   extra: { metadata: { "my-key": "my-value" } },
 });
+await rt.postRun();
 const chatCompletion = await client.chat.completions.create({
   model: "gpt-3.5-turbo",
   messages: messages,
 });
 // End and submit the run
-rt.end(chatCompletion);
-await rt.postRun();`),
+await rt.end(chatCompletion);
+await rt.patchRun();`),
   ]}
   groupId="client-language"
 />

versioned_docs/version-2.0/how_to_guides/tracing/annotate_code.mdx

@@ -225,9 +225,9 @@ chat_completion = client.chat.completions.create(
 )
 # End the runs and log them
 child_llm_run.end(outputs=chat_completion)
-child_llm_run.post()
+child_llm_run.postRun()
 pipeline.end(outputs={"answer": chat_completion.choices[0].message.content})
-pipeline.post()`),
+pipeline.postRun()`),
     TypeScriptBlock(`import OpenAI from "openai";
 import { RunTree } from "langsmith";
 // This can be a user input to your app
@@ -237,6 +237,7 @@ const pipeline = new RunTree({
     run_type: "chain",
     inputs: { question }
 });
+await pipeline.postRun();\n
 // This can be retrieved in a retrieval step
 const context = "During this morning's meeting, we solved all world conflict.";
 const messages = [
@@ -259,7 +260,7 @@ const chatCompletion = await client.chat.completions.create({
 childRun.end(chatCompletion);
 await childRun.postRun();
 pipeline.end({ outputs: { answer: chatCompletion.choices[0].message.content } });
-await pipeline.postRun();`),
+await pipeline.patchRun();`),
   ]}
   groupId="client-language"
 />

versioned_docs/version-2.0/how_to_guides/tracing/log_traces_to_project.mdx

@@ -92,7 +92,7 @@ chat_completion = client.chat.completions.create(
 )
 # End and submit the run
 rt.end(outputs=chat_completion)
-rt.post()
+rt.postRun()
 `),
     TypeScriptBlock(`import OpenAI from "openai";
 import { traceable } from "langsmith/traceable";
@@ -124,10 +124,11 @@ const rt = new RunTree({
     inputs: { messages },
     // highlight-next-line
     project_name: "My Project"
-});\n
+});
+await rt.postRun();\n
 // Execute a chat completion and handle it within RunTree
 rt.end({outputs: chatCompletion});
-await rt.postRun();`),
+await rt.patchRun();`),
   ]}
   groupId="client-language"
 />