From 823a44ef80c601134c5ccf3240c09b62a0f996b7 Mon Sep 17 00:00:00 2001
From: Harrison Chase <hw.chase.17@gmail.com>
Date: Fri, 6 Jan 2023 07:58:38 -0800
Subject: [PATCH] bump to 0058 (#556)

---
 docs/modules/memory/how_to_guides.rst | 2 ++
 docs/use_cases/chatbots.md            | 1 +
 langchain/llms/openai.py              | 4 ++--
 pyproject.toml                        | 2 +-
 4 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/modules/memory/how_to_guides.rst b/docs/modules/memory/how_to_guides.rst
index f0e359f7..3240aa76 100644
--- a/docs/modules/memory/how_to_guides.rst
+++ b/docs/modules/memory/how_to_guides.rst
@@ -15,6 +15,8 @@ The examples here all highlight how to use memory in different ways.
 
 `Adding Memory to Agents <./examples/agent_with_memory.html>`_: How to add a memory component to any agent.
 
+`Conversation Agent <./examples/conversational_agent.html>`_: Example of a conversation agent, which combines memory with agents and a conversation focused prompt.
+
 
 .. toctree::
    :maxdepth: 1
diff --git a/docs/use_cases/chatbots.md b/docs/use_cases/chatbots.md
index 1a277533..e2196b36 100644
--- a/docs/use_cases/chatbots.md
+++ b/docs/use_cases/chatbots.md
@@ -7,6 +7,7 @@ Most chat based applications rely on remembering what happened in previous inter
 The following resources exist:
 - [ChatGPT Clone](../modules/memory/examples/chatgpt_clone.ipynb): A notebook walking through how to recreate a ChatGPT-like experience with LangChain.
 - [Conversation Memory](../modules/memory/getting_started.ipynb): A notebook walking through how to use different types of conversational memory.
+- [Conversation Agent](../modules/memory/examples/conversational_agent.ipynb): A notebook walking through how to create an agent optimized for conversation.
 
 
 Additional related resources include:
diff --git a/langchain/llms/openai.py b/langchain/llms/openai.py
index 7f1d360c..476d7dca 100644
--- a/langchain/llms/openai.py
+++ b/langchain/llms/openai.py
@@ -171,8 +171,8 @@ class BaseOpenAI(BaseLLM, BaseModel):
                     Generation(
                         text=choice["text"],
                         generation_info=dict(
-                            finish_reason=choice["finish_reason"],
-                            logprobs=choice["logprobs"],
+                            finish_reason=choice.get("finish_reason"),
+                            logprobs=choice.get("logprobs"),
                         ),
                     )
                     for choice in sub_choices
diff --git a/pyproject.toml b/pyproject.toml
index f9746eeb..63e6b895 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.57"
+version = "0.0.58"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"