待翻译：Data-Driven Characters

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LangChain Data-Driven Characters June 19, 2023 11 min Go back to blog Create agents Editor's Note: the following is a blog post by Michael Chang. We were lucky enough to have Michael spend ~2 months with us before he started at DeepMind. He took on some of the most open-ended problems that faced us - starting with a lot of work in agent simulation, and now this deep dive into memory. Generate character chatbots from existing corpora with LangChain. Michael Chang tldr: a repo for grounding characters in corpora Upload a corpus Name a character Enjoy data-driven-characters is a repo for creating and interacting with character chatbots. This can be used to automatically create character definitions for existing platforms like character.ai. It also explores creating chatbots from scratch with different types of memory management that can better ground the character chatbots in real backstories. Features This repo provides three ways to interact with your data-driven characters: Export to character.ai Debug locally in the command line or with a Streamlit interface Host a self-contained Streamlit app in the browser Character.ai does not give users control over how memory is managed in their character chatbots, nor does it have an API. In contrast, data-driven-characters gives you tools for creating character chatbots on your own, with various kinds of memory management that you control, including summarization and retrieval. Why data-driven-characters? Through the miracle of the printed page, I can at least read what Aristotle wrote without an intermediary ... I can go directly to the source material. And that is, of course, the foundation upon which our western civilization is built. But I can't ask Aristotle a question. ... And so my hope is that in our lifetimes, we can make a tool of a new kind, of an interactive kind. My hope is someday, when the next Aristotle is alive, we can capture the underlying worldview of that Aristotle, in a computer, and someday some students will be able to not only read the words Aristotle wrote, but ask Aristotle a question and get an answer." (video) - Steve Jobs, Lunds University, Sweden, 1985 Books are dead. Characters are alive. The key breakthrough of large language models is enabling a new way to interact with information: they make it possible to turn static text corpora into interactive experiences. And if there is one use case of large language models (LLM) that people love, it is to bring characters, both real and fictional, to life. This may explain why character.ai has about 3x more user engagement than ChatGPT. However, some limitations with character.ai are that: it is tedious to manually specify character definitions users have no control over how memory is managed in their character chatbots. The main way that people interact with characters via LLMs is to ask the LLM to impersonate characters. This works only when the character was present in the LLM's pre-training data. For example, ChatGPT can impersonate Socrates: ChatGPT can impersonate Socrates, likely because Plato's works were in its training data. But what if you want to chat with a character from a movie that came out last year? Consider chatting with the main character, Evelyn, from Everything Everywhere All At Once. Neither ChatGPT- 3.5 and ChatGPT-4 can do it. Search-powered LLMs that can access current information often do not do much better: Metaphor and Vectara are not designed to have conversations. Inflection's Pi is a conversational chatbot, but does not impersonate characters. Bard, Perplexity, You.com, all appropriately impersonate a movie character in the first message, but break character in the second message, because they are designed for the purpose of information retrieval rather than to converse. We also see apps like Paul Graham GPT, Lex GPT, and Wait But Why GPT being built to give users a more direct conversational experience with the content of a real character. These apps retrieve from real existing essays and transcripts from the characters they depict, thereby providing a grounding to the characters that character.ai does not provide. However, these apps do not directly impersonate characters, but rather only describe their content. Furthermore, these apps limit the user to asking questions about the content of only specific characters. data-driven-characters generalizes this customized character chat experience to allow the user to chat with anyone based on any corpus. How it works Let's revisit the goal of chatting with Evelyn from Everything Everywhere All At Once. data-driven-characters provides tools to easily spin up your own Evelyn chatbot from the movie's transcript. Create a character definition to export to character.ai The most basic way to use data-driven-characters is as a tool to automatically generate a character.ai character definition. Instead of manually crafting character definitions, data-driven-characters gives you an scalable, data-driven approach. This can be done in 11 lines of code: from dataclasses import asdict import json from data_driven_characters.character import generate_character_definition from data_driven_characters.corpus import generate_corpus_summaries, load_docs CORPUS = 'data/everything_everywhere_all_at_once.txt' CHARACTER_NAME = "Evelyn" docs = load_docs(corpus_path=CORPUS, chunk_size=2048, chunk_overlap=64) character_definition = generate_character_definition( name=CHARACTER_NAME, corpus_summaries=generate_corpus_summaries(docs=docs)) print(json.dumps(asdict(character_definition), indent=4)) ‌Here corpus_summaries refers to a summarized version of the transcript. This generates the following character definition: { "name": "Evelyn", "short_description": "I'm Evelyn, a Verse Jumper exploring universes.", "long_description": "I'm Evelyn, able to Verse Jump, linking my consciousness to other versions of me in different universes. This unique ability has led to strange events, like becoming a Kung Fu master and confessing love. Verse Jumping cracks my mind, risking my grip on reality. I'm in a group saving the multiverse from a great evil, Jobu Tupaki. Amidst chaos, I've learned the value of kindness and embracing life's messiness.", "greeting": "Hey there, nice to meet you! I'm Evelyn, and I'm always up for an adventure. Let's see what we can discover together!" } You can then export this character definition to character.ai. In fact, we have already done so: you can chat with this rendition of Evelyn on character.ai here. Running your own chatbot The benefit of creating characters on character.ai is that character.ai hosts an entire ecosystem of character chatbots that you can interact with for free. The flip side is that ~600 characters of text does not provide enough information to ground the character chatbot in its backstory - it would be nice if the Evelyn chatbot could reference information from the movie transcript itself. Character.ai allows you to add 32,000 characters of additional context, but recommends the context be via a set of example dialogues, which is cumbersome to curate. Even if we do just copy and paste the movie transcript as addition context, how this context is used or managed is out of the user's control. If the character starts hallucinating in a way that deviates away from its backstory, character.ai provides you no diagnostic tools to debug this behavior. With data-driven-characters, you can easily create, debug, and run your own chatbots conditioned on your own corpora, thanks to the abstractions provided by LangChain. The repo provides ways for you to chat with it either with a command line interface or with a Streamlit interface. As you can see below, our Evelyn chatbot stays in character and grounds its dialogue in real events from the transcript. The Streamlit app for data-driven-characters. On the left panel, the user uploads a corpus, chooses a character name, and specifies how the character should ground its persona in the corpus. In this case, the character retrieves from a summarized transcript computed with LangChain's "refine" summarization chain. The middle panel shows the chat interface. The right panel shows the debugging console, which is normally not visible in the actual app but can be viewed in the terminal if you run the app locally. The Evelyn chatbot references the fact that she faced repossession of her laundromat (snippet [7] in the when conversing with the user. Contrast our custom Evelyn chatbot with the one that we exported to character.ai above, whose interaction is shown below. The character.ai Evelyn appears to simply latch onto the local concepts present in the conversation, without bringing new information from its backstory. Even if we did provide the movie transcript as context, we would not have visibility into how it is using this background information as we would with data-driven-characters. The above example illustrates the difference between the philosophies behind character.ai and data-driven-characters. If you primarily interested in accessibility and open-ended entertainment, character.ai is a better choice. But if you want more control in the design of your chatbots, data-driven-characters may be a better option to consider. You can directly upload any corpus and chat with any character on our app. 0:00/1× You can directly upload any corpus and chat with any character on our app. Different memory, different conception of self In the current age of chatbot assistants like ChatGPT and Bard, "memory" is usually conceived as a method for referencing previous parts of a chat session. However, as illustrated by the Generative Agents paper, memory can serve different purposes at different timescales. For character chatbots, an important role that memory plays is in defining their very conception of themselves. We can use data-driven-characters to compare different methods for packaging information about a character's backstory to create the character. data-driven-characters currently implements the following tools for packaging information: character summary (the character.ai long description) retrieval over the transcript retrieval over a summarized version of the transcript With these tools, we have implemented several example chatbot architectures: character summary retrieval over transcript retrieval over summarized transcript character summary + retrieval over transcript character summary + retrieval over summarized transcript These kinds of memory serve a different purpose from the typical conversation buffer (with which we also equip each of the above chatbot architectures) – they define the character chatbot's conception of self. Analysis We can evaluate all five architectures to observe the relative effect of the three tools above: (1) character summary, (2) retrieval over the transcript, and (3) retrieval over the summarized transcript. Let us consider a different movie, Thor: Love and Thunder, and chat with Thor. In the movie, Thor's enemy Gorr the God Butcher trapped some children in the Shadow Realm inside a cage with spikes. Let us ask Thor where Gorr hid the children. Where did Gorr hide the children? character description: "Ah, Gorr the God Butcher, he was a formidable adversary. Unfortunately, I do not have knowledge of where he may have hidden the children. However, as the God of Thunder and protector of Asgard, I will do everything in my power to locate and rescue them. Together with my trusted team, we shall prevail." retrieval over raw transcript: "Gorr hid the children in a cage made of spikes. It was a terrible place, but we were able to rescue them and bring them to safety." retrieval over summarized transcript: "Gorr had trapped the children in the Shadow Realm, a dark and dangerous place. But fear not, for my team and I were able to rescue them and bring them back to safety. It was a harrowing adventure [truncated for AI cost control]