kamp/anything-llm
1
0
Fork 0
mirror of https://github.com/Mintplex-Labs/anything-llm.git synced 2024-11-11 01:10:11 +01:00
Code Issues Packages Projects Releases Wiki Activity

Add Anthropic agent support with new API and tool_calling (#1116)

Browse Source 
* Add Anthropic agent support with new API and tool_calling

* patch useProviderHook to unset default models on provider change
This commit is contained in:
Timothy Carambat 2024-04-17 11:54:58 -07:00 committed by GitHub
parent a025dfd76e
commit 9449fcd737
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 153 additions and 96 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
frontend/src/hooks/useGetProvidersModels.js
View File

@ -47,8 +47,12 @@ export default function useGetProviderModels(provider = null) {
if (
PROVIDER_DEFAULT_MODELS.hasOwnProperty(provider) &&
!groupedProviders.includes(provider)
)
) {
setDefaultModels(PROVIDER_DEFAULT_MODELS[provider]);
} else {
setDefaultModels([]);
}
groupedProviders.includes(provider)
? setCustomModels(groupModels(models))
: setCustomModels(models);

5
frontend/src/pages/WorkspaceSettings/AgentConfig/AgentLLMSelection/index.jsx
View File

@ -5,10 +5,7 @@ import { AVAILABLE_LLM_PROVIDERS } from "@/pages/GeneralSettings/LLMPreference";
import { CaretUpDown, MagnifyingGlass, X } from "@phosphor-icons/react";
import AgentModelSelection from "../AgentModelSelection";
const ENABLED_PROVIDERS = [
"openai",
// "anthropic"
];
const ENABLED_PROVIDERS = ["openai", "anthropic"];
const LLM_DEFAULT = {
name: "Please make a selection",

237
server/utils/agents/aibitat/providers/anthropic.js
View File

@ -25,6 +25,90 @@ class AnthropicProvider extends Provider {
this.model = model;
}
// For Anthropic we will always need to ensure the message sequence is role,content
// as we can attach any data to message nodes and this keeps the message property
// sent to the API always in spec.
#sanitize(chats) {
const sanitized = [...chats];
// If the first message is not a USER, Anthropic will abort so keep shifting the
// message array until that is the case.
while (sanitized.length > 0 && sanitized[0].role !== "user")
sanitized.shift();
return sanitized.map((msg) => {
const { role, content } = msg;
return { role, content };
});
}
#normalizeChats(messages = []) {
if (!messages.length) return messages;
const normalized = [];
[...messages].forEach((msg, i) => {
if (msg.role !== "function") return normalized.push(msg);
// If the last message is a role "function" this is our special aibitat message node.
// and we need to remove it from the array of messages.
// Since Anthropic needs to have the tool call resolved, we look at the previous chat to "function"
// and go through its content "thought" from ~ln:143 and get the tool_call id so we can resolve
// this tool call properly.
const functionCompletion = msg;
const toolCallId = messages[i - 1]?.content?.find(
(msg) => msg.type === "tool_use"
)?.id;
// Append the Anthropic acceptable node to the message chain so function can resolve.
normalized.push({
role: "user",
content: [
{
type: "tool_result",
tool_use_id: toolCallId,
content: functionCompletion.content,
},
],
});
});
return normalized;
}
// Anthropic handles system message as a property, so here we split the system message prompt
// from all the chats and then normalize them so they will be useable in case of tool_calls or general chat.
#parseSystemPrompt(messages = []) {
const chats = [];
let systemPrompt =
"You are a helpful ai assistant who can assist the user and use tools available to help answer the users prompts and questions.";
for (const msg of messages) {
if (msg.role === "system") {
systemPrompt = msg.content;
continue;
}
chats.push(msg);
}
return [systemPrompt, this.#normalizeChats(chats)];
}
// Anthropic does not use the regular schema for functions so here we need to ensure it is in there specific format
// so that the call can run correctly.
#formatFunctions(functions = []) {
return functions.map((func) => {
const { name, description, parameters, required } = func;
const { type, properties } = parameters;
return {
name,
description,
input_schema: {
type,
properties,
required,
},
};
});
}
/**
* Create a completion based on the received messages.
*
@ -32,89 +116,78 @@ class AnthropicProvider extends Provider {
* @param functions
* @returns The completion.
*/
async complete(messages, functions) {
// clone messages to avoid mutating the original array
const promptMessages = [...messages];
if (functions) {
const functionPrompt = this.getFunctionPrompt(functions);
// add function prompt after the first message
promptMessages.splice(1, 0, {
content: functionPrompt,
role: "system",
});
}
const prompt = promptMessages
.map((message) => {
const { content, role } = message;
switch (role) {
case "system":
return content
? `${Anthropic.HUMAN_PROMPT} <admin>${content}</admin>`
: "";
case "function":
case "user":
return `${Anthropic.HUMAN_PROMPT} ${content}`;
case "assistant":
return `${Anthropic.AI_PROMPT} ${content}`;
default:
return content;
}
})
.filter(Boolean)
.join("\n")
.concat(` ${Anthropic.AI_PROMPT}`);
async complete(messages, functions = null) {
try {
const response = await this.client.completions.create({
model: this.model,
max_tokens_to_sample: 3000,
stream: false,
prompt,
});
const [systemPrompt, chats] = this.#parseSystemPrompt(messages);
const response = await this.client.messages.create(
{
model: this.model,
max_tokens: 4096,
system: systemPrompt,
messages: this.#sanitize(chats),
stream: false,
...(Array.isArray(functions) && functions?.length > 0
? { tools: this.#formatFunctions(functions) }
: {}),
},
{ headers: { "anthropic-beta": "tools-2024-04-04" } } // Required to we can use tools.
);
const result = response.completion.trim();
// TODO: get cost from response
const cost = 0;
// We know that we need to call a tool. So we are about to recurse through completions/handleExecution
// https://docs.anthropic.com/claude/docs/tool-use#how-tool-use-works
if (response.stop_reason === "tool_use") {
// Get the tool call explicitly.
const toolCall = response.content.find(
(res) => res.type === "tool_use"
);
// Handle function calls if the model returns a function call
if (result.includes("function_name") && functions) {
let functionCall;
try {
functionCall = JSON.parse(result);
} catch (error) {
// call the complete function again in case it gets a json error
return await this.complete(
[
...messages,
{
role: "function",
content: `You gave me this function call: ${result} but I couldn't parse it.
${error?.message}
Please try again.`,
},
],
functions
);
}
// Here we need the chain of thought the model may or may not have generated alongside the call.
// this needs to be in a very specific format so we always ensure there is a 2-item content array
// so that we can ensure the tool_call content is correct. For anthropic all text items must not
// be empty, but the api will still return empty text so we need to make 100% sure text is not empty
// or the tool call will fail.
// wtf.
let thought = response.content.find((res) => res.type === "text");
thought =
thought?.content?.length > 0
? {
role: thought.role,
content: [
{ type: "text", text: thought.content },
{ ...toolCall },
],
}
: {
role: "assistant",
content: [
{
type: "text",
text: `Okay, im going to use ${toolCall.name} to help me.`,
},
{ ...toolCall },
],
};
// Modify messages forcefully by adding system thought so that tool_use/tool_result
// messaging works with Anthropic's disastrous tool calling API.
messages.push(thought);
const functionArgs = toolCall.input;
return {
result: null,
functionCall,
cost,
functionCall: {
name: toolCall.name,
arguments: functionArgs,
},
cost: 0,
};
}
const completion = response.content.find((msg) => msg.type === "text");
return {
result,
cost,
result:
completion?.text ?? "I could not generate a response from this.",
cost: 0,
};
} catch (error) {
// If invalid Auth error we need to abort because no amount of waiting
@ -132,24 +205,6 @@ class AnthropicProvider extends Provider {
throw error;
}
}
getFunctionPrompt(functions = []) {
const functionPrompt = `<functions>You have been trained to directly call a Javascript function passing a JSON Schema parameter as a response to this chat. This function will return a string that you can use to keep chatting.
Here is a list of functions available to you:
${JSON.stringify(functions, null, 2)}
When calling any of those function in order to complete your task, respond only this JSON format. Do not include any other information or any other stuff.
Function call format:
{
function_name: "givenfunctionname",
parameters: {}
}
</functions>`;
return functionPrompt;
}
}
module.exports = AnthropicProvider;

1
server/utils/agents/index.js
View File

@ -50,6 +50,7 @@ class AgentHandler {
from: USER_AGENT.name,
to: WORKSPACE_AGENT.name,
content: chatLog.prompt,
state: "success",
},
{
from: WORKSPACE_AGENT.name,