-
Notifications
You must be signed in to change notification settings - Fork 2.1k
/
anthropic_functions.ts
203 lines (183 loc) Β· 6.43 KB
/
anthropic_functions.ts
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
import { XMLParser } from "fast-xml-parser";
import { BaseChatModel, BaseChatModelParams } from "../../chat_models/base.js";
import { CallbackManagerForLLMRun } from "../../callbacks/manager.js";
import {
AIMessage,
BaseMessage,
ChatGenerationChunk,
ChatResult,
SystemMessage,
} from "../../schema/index.js";
import {
ChatAnthropic,
DEFAULT_STOP_SEQUENCES,
type AnthropicInput,
} from "../../chat_models/anthropic.js";
import { BaseFunctionCallOptions } from "../../base_language/index.js";
import { StructuredTool } from "../../tools/base.js";
import { PromptTemplate } from "../../prompts/prompt.js";
import { formatToOpenAIFunction } from "../../tools/convert_to_openai.js";
const TOOL_SYSTEM_PROMPT =
/* #__PURE__ */
PromptTemplate.fromTemplate(`In addition to responding, you can use tools.
You have access to the following tools.
{tools}
In order to use a tool, you can use <tool></tool> to specify the name,
and the <tool_input></tool_input> tags to specify the parameters.
Each parameter should be passed in as <$param_name>$value</$param_name>,
Where $param_name is the name of the specific parameter, and $value
is the value for that parameter.
You will then get back a response in the form <observation></observation>
For example, if you have a tool called 'search' that accepts a single
parameter 'query' that could run a google search, in order to search
for the weather in SF you would respond:
<tool>search</tool><tool_input><query>weather in SF</query></tool_input>
<observation>64 degrees</observation>`);
export interface ChatAnthropicFunctionsCallOptions
extends BaseFunctionCallOptions {
tools?: StructuredTool[];
}
export type AnthropicFunctionsInput = Partial<AnthropicInput> &
BaseChatModelParams & {
llm?: BaseChatModel;
};
export class AnthropicFunctions extends BaseChatModel<ChatAnthropicFunctionsCallOptions> {
llm: BaseChatModel;
stopSequences?: string[];
lc_namespace = ["langchain", "experimental", "chat_models"];
static lc_name(): string {
return "AnthropicFunctions";
}
constructor(fields?: AnthropicFunctionsInput) {
super(fields ?? {});
this.llm = fields?.llm ?? new ChatAnthropic(fields);
this.stopSequences =
fields?.stopSequences ?? (this.llm as ChatAnthropic).stopSequences;
}
invocationParams() {
return this.llm.invocationParams();
}
/** @ignore */
_identifyingParams() {
return this.llm._identifyingParams();
}
async *_streamResponseChunks(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): AsyncGenerator<ChatGenerationChunk> {
yield* this.llm._streamResponseChunks(messages, options, runManager);
}
async _generate(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun | undefined
): Promise<ChatResult> {
let promptMessages = messages;
let forced = false;
let functionCall: string | undefined;
if (options.tools) {
// eslint-disable-next-line no-param-reassign
options.functions = (options.functions ?? []).concat(
options.tools.map(formatToOpenAIFunction)
);
}
if (options.functions !== undefined && options.functions.length > 0) {
const content = await TOOL_SYSTEM_PROMPT.format({
tools: JSON.stringify(options.functions, null, 2),
});
const systemMessage = new SystemMessage({ content });
promptMessages = [systemMessage].concat(promptMessages);
const stopSequences =
options?.stop?.concat(DEFAULT_STOP_SEQUENCES) ??
this.stopSequences ??
DEFAULT_STOP_SEQUENCES;
// eslint-disable-next-line no-param-reassign
options.stop = stopSequences.concat(["</tool_input>"]);
if (options.function_call) {
if (typeof options.function_call === "string") {
functionCall = JSON.parse(options.function_call).name;
} else {
functionCall = options.function_call.name;
}
forced = true;
const matchingFunction = options.functions.find(
(tool) => tool.name === functionCall
);
if (!matchingFunction) {
throw new Error(
`No matching function found for passed "function_call"`
);
}
promptMessages = promptMessages.concat([
new AIMessage({
content: `<tool>${functionCall}</tool>`,
}),
]);
// eslint-disable-next-line no-param-reassign
delete options.function_call;
}
// eslint-disable-next-line no-param-reassign
delete options.functions;
} else if (options.function_call !== undefined) {
throw new Error(
`If "function_call" is provided, "functions" must also be.`
);
}
const chatResult = await this.llm._generate(
promptMessages,
options,
runManager
);
const chatGenerationContent = chatResult.generations[0].message.content;
if (typeof chatGenerationContent !== "string") {
throw new Error("AnthropicFunctions does not support non-string output.");
}
if (forced) {
const parser = new XMLParser();
const result = parser.parse(`${chatGenerationContent}</tool_input>`);
if (functionCall === undefined) {
throw new Error(`Could not parse called function from model output.`);
}
const responseMessageWithFunctions = new AIMessage({
content: "",
additional_kwargs: {
function_call: {
name: functionCall,
arguments: result.tool_input
? JSON.stringify(result.tool_input)
: "",
},
},
});
return {
generations: [{ message: responseMessageWithFunctions, text: "" }],
};
} else if (chatGenerationContent.includes("<tool>")) {
const parser = new XMLParser();
const result = parser.parse(`${chatGenerationContent}</tool_input>`);
const responseMessageWithFunctions = new AIMessage({
content: chatGenerationContent.split("<tool>")[0],
additional_kwargs: {
function_call: {
name: result.tool,
arguments: result.tool_input
? JSON.stringify(result.tool_input)
: "",
},
},
});
return {
generations: [{ message: responseMessageWithFunctions, text: "" }],
};
}
return chatResult;
}
_llmType(): string {
return "anthropic_functions";
}
/** @ignore */
_combineLLMOutput() {
return [];
}
}