json: indent 4 spaces
This commit is contained in:
Olivier Chafik
parent
7628bd8c76
commit
10ee30f1b8
5 changed files with 868 additions and 869 deletions
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@ -177,8 +177,8 @@ private:
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string _visit_pattern(const string& pattern, const string& name) {
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if (!(pattern.front() == '^' && pattern.back() == '$')) {
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_errors.push_back("Pattern must start with '^' and end with '$'");
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return "";
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_errors.push_back("Pattern must start with '^' and end with '$'");
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return "";
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}
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string sub_pattern = pattern.substr(1, pattern.length() - 2);
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unordered_map<string, string> sub_rule_ids;
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@ -212,21 +212,21 @@ private:
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string literal;
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auto flush_literal = [&]() {
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if (literal.empty()) {
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return false;
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}
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ret.push_back(make_pair(literal, true));
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literal.clear();
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return true;
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if (literal.empty()) {
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return false;
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}
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ret.push_back(make_pair(literal, true));
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literal.clear();
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return true;
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};
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for (const auto& item : seq) {
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auto is_literal = item.second;
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if (is_literal) {
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literal += item.first;
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literal += item.first;
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} else {
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flush_literal();
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ret.push_back(item);
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flush_literal();
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ret.push_back(item);
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}
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}
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flush_literal();
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@ -254,7 +254,7 @@ private:
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} else if (c == ')') {
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i++;
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if (start > 0 && sub_pattern[start - 1] != '(') {
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_errors.push_back("Unbalanced parentheses");
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_errors.push_back("Unbalanced parentheses");
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}
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return join_seq();
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} else if (c == '[') {
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@ -270,7 +270,7 @@ private:
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}
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}
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if (i >= length) {
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_errors.push_back("Unbalanced square brackets");
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_errors.push_back("Unbalanced square brackets");
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}
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square_brackets += ']';
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i++;
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@ -289,7 +289,7 @@ private:
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i++;
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}
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if (i >= length) {
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_errors.push_back("Unbalanced curly brackets");
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_errors.push_back("Unbalanced curly brackets");
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}
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curly_brackets += '}';
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i++;
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@ -547,8 +547,8 @@ public:
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for (size_t i = 1; i < tokens.size(); ++i) {
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string sel = tokens[i];
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if (target.is_null() || !target.contains(sel)) {
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_errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump());
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return;
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_errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump());
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return;
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}
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target = target[sel];
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}
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@ -698,8 +698,8 @@ public:
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return _add_rule(rule_name, "object");
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} else {
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if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get<string>()) == PRIMITIVE_RULES.end()) {
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_errors.push_back("Unrecognized schema: " + schema.dump());
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return "";
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_errors.push_back("Unrecognized schema: " + schema.dump());
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return "";
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}
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// TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
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return _add_rule(rule_name == "root" ? "root" : schema_type.get<string>(), PRIMITIVE_RULES.at(schema_type.get<string>()));
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@ -707,12 +707,12 @@ public:
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}
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void check_errors() {
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if (!_errors.empty()) {
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throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
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}
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if (!_warnings.empty()) {
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std::cerr << "WARNING: JSON schema conversion was incomplete: " + join(_warnings.begin(), _warnings.end(), "; ") << std::endl;
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}
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if (!_errors.empty()) {
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throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
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}
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if (!_warnings.empty()) {
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std::cerr << "WARNING: JSON schema conversion was incomplete: " + join(_warnings.begin(), _warnings.end(), "; ") << std::endl;
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}
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}
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string format_grammar() {
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@ -725,10 +725,10 @@ public:
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};
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string json_schema_to_grammar(const json& schema) {
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SchemaConverter converter([](const string&) { return json::object(); }, /* dotall= */ false);
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auto copy = schema;
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converter.resolve_refs(copy, "input");
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converter.visit(copy, "");
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converter.check_errors();
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return converter.format_grammar();
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SchemaConverter converter([](const string&) { return json::object(); }, /* dotall= */ false);
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auto copy = schema;
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converter.resolve_refs(copy, "input");
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converter.visit(copy, "");
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converter.check_errors();
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return converter.format_grammar();
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}
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@ -4,71 +4,71 @@
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#! python json-schema-pydantic-example.py
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from pydantic import BaseModel, TypeAdapter
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from annotated_types import MaxLen, MinLen
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from typing import Annotated, Iterable, List, Optional
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import enum, json, requests
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from annotated_types import MinLen
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from typing import Annotated, List, Optional
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import json, requests
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if True:
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def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs):
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'''
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Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support
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(llama.cpp server, llama-cpp-python, Anyscale / Together...)
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def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs):
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'''
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Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support
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(llama.cpp server, llama-cpp-python, Anyscale / Together...)
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The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below)
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'''
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if response_model:
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type_adapter = TypeAdapter(response_model)
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schema = type_adapter.json_schema()
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messages = [{
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"role": "system",
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"content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}"
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}] + messages
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response_format={"type": "json_object", "schema": schema}
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The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below)
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'''
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if response_model:
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type_adapter = TypeAdapter(response_model)
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schema = type_adapter.json_schema()
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messages = [{
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"role": "system",
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"content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}"
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}] + messages
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response_format={"type": "json_object", "schema": schema}
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data = requests.post(endpoint, headers={"Content-Type": "application/json"},
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json=dict(messages=messages, response_format=response_format, **kwargs)).json()
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if 'error' in data:
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raise Exception(data['error']['message'])
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data = requests.post(endpoint, headers={"Content-Type": "application/json"},
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json=dict(messages=messages, response_format=response_format, **kwargs)).json()
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if 'error' in data:
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raise Exception(data['error']['message'])
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content = data["choices"][0]["message"]["content"]
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return type_adapter.validate_json(content) if type_adapter else content
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content = data["choices"][0]["message"]["content"]
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return type_adapter.validate_json(content) if type_adapter else content
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else:
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# This alternative branch uses Instructor + OpenAI client lib.
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# Instructor support streamed iterable responses, retry & more.
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# (see https://python.useinstructor.com/)
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#! pip install instructor openai
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import instructor, openai
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client = instructor.patch(
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openai.OpenAI(api_key="123", base_url="http://localhost:8080"),
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mode=instructor.Mode.JSON_SCHEMA)
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create_completion = client.chat.completions.create
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# This alternative branch uses Instructor + OpenAI client lib.
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# Instructor support streamed iterable responses, retry & more.
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# (see https://python.useinstructor.com/)
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#! pip install instructor openai
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import instructor, openai
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client = instructor.patch(
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openai.OpenAI(api_key="123", base_url="http://localhost:8080"),
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mode=instructor.Mode.JSON_SCHEMA)
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create_completion = client.chat.completions.create
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if __name__ == '__main__':
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class QAPair(BaseModel):
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question: str
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concise_answer: str
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justification: str
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class QAPair(BaseModel):
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question: str
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concise_answer: str
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justification: str
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class PyramidalSummary(BaseModel):
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title: str
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summary: str
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question_answers: Annotated[List[QAPair], MinLen(2)]
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sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]]
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class PyramidalSummary(BaseModel):
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title: str
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summary: str
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question_answers: Annotated[List[QAPair], MinLen(2)]
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sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]]
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print("# Summary\n", create_completion(
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model="...",
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response_model=PyramidalSummary,
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messages=[{
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"role": "user",
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"content": f"""
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You are a highly efficient corporate document summarizer.
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Create a pyramidal summary of an imaginary internal document about our company processes
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(starting high-level, going down to each sub sections).
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Keep questions short, and answers even shorter (trivia / quizz style).
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"""
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}]))
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print("# Summary\n", create_completion(
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model="...",
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response_model=PyramidalSummary,
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messages=[{
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"role": "user",
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"content": f"""
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You are a highly efficient corporate document summarizer.
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Create a pyramidal summary of an imaginary internal document about our company processes
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(starting high-level, going down to each sub sections).
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Keep questions short, and answers even shorter (trivia / quizz style).
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"""
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}]))
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@ -4,17 +4,17 @@ assert len(sys.argv) >= 2
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[_, pattern, *rest] = sys.argv
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print(subprocess.check_output(
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[
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"python",
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os.path.join(
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os.path.dirname(os.path.realpath(__file__)),
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"json-schema-to-grammar.py"),
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*rest,
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"-",
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"--raw-pattern",
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],
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text=True,
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input=json.dumps({
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"type": "string",
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"pattern": pattern,
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}, indent=2)))
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[
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"python",
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os.path.join(
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os.path.dirname(os.path.realpath(__file__)),
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"json-schema-to-grammar.py"),
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*rest,
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"-",
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"--raw-pattern",
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],
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text=True,
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input=json.dumps({
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"type": "string",
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"pattern": pattern,
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}, indent=2)))
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@ -377,14 +377,14 @@ static json oaicompat_completion_params_parse(
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}
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if (body.contains("response_format")) {
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auto response_format = json_value(body, "response_format", json::object());
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if (response_format.contains("type")) {
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if (response_format["type"] == "json_object") {
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llama_params["json_schema"] = json_value(response_format, "schema", json::object());
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} else {
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throw std::runtime_error("response_format type not supported: " + response_format["type"].dump());
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auto response_format = json_value(body, "response_format", json::object());
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if (response_format.contains("type")) {
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if (response_format["type"] == "json_object") {
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llama_params["json_schema"] = json_value(response_format, "schema", json::object());
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} else {
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throw std::runtime_error("response_format type not supported: " + response_format["type"].dump());
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}
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}
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}
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}
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// Handle 'stop' field
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