16538940978

Committed 26 Jul 2025 10:35AM UTC coverage: 67.584% (+44.9%) from 22.674%

Build # 16538940978

Build Type

Pull #53

github

Committed by

fogfish

Commit Message

update license

Pull Request Pull Request #53: Enable multi-content I/O within prompts & responses

Run Details

596 of 837 new or added lines in 27 files covered. (71.21%)

2 existing lines in 2 files now uncovered.

884 of 1308 relevant lines covered (67.58%)

0.72 hits per line

Source File
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0.0

/chatter.go

//
// Copyright (C) 2024 Dmitry Kolesnikov
//
// This file may be modified and distributed under the terms
// of the MIT license.  See the LICENSE file for details.
// https://github.com/kshard/chatter
//

package chatter

import (
        "context"
        "encoding/json"
)

type Opt = interface{ ChatterOpt() }

// The generic trait to "interact" with LLMs;
type Chatter interface {
        Usage() Usage
        Prompt(context.Context, []Message, ...Opt) (*Reply, error)
}

// LLM Usage stats
type Usage struct {
        InputTokens int `json:"inputTokens"`
        ReplyTokens int `json:"replyTokens"`
}

// LLMs' critical parameter influencing the balance between predictability
// and creativity in generated text. Lower temperatures prioritize exploiting
// learned patterns, yielding more deterministic outputs, while higher
// temperatures encourage exploration, fostering diversity and innovation.
type Temperature float64

func (Temperature) ChatterOpt() {}

// Nucleus Sampling, a parameter used in LLMs, impacts token selection by
// considering only the most likely tokens that together represent
// a cumulative probability mass (e.g., top-p tokens). This limits the
// number of choices to avoid overly diverse or nonsensical outputs while
// maintaining diversity within the top-ranked options.
type TopP float64

func (TopP) ChatterOpt() {}

type TopK float64

func (TopK) ChatterOpt() {}

// Token quota for reply, the model would limit response given number
type MaxTokens int

func (MaxTokens) ChatterOpt() {}

// The stop sequence prevents LLMs from generating more text after a specific
// string appears. Stop sequences make it easy to guarantee concise,
// controlled responses from models.
type StopSequences []string

func (StopSequences) ChatterOpt() {}

// Command registry is a sequence of tools available for LLM usage.
type Registry []Cmd

func (Registry) ChatterOpt() {}

// Command descriptor
type Cmd struct {
        // [Required] A unique name for the command, used as a reference by LLMs (e.g., "bash").
        Cmd string `json:"cmd"`

        // [Required] A detailed, multi-line description to educate the LLM on command usage.
        // Provides contextual information on how and when to use the command.
        About string `json:"about"`

        // [Required] JSON Schema specifies arguments, types, and additional context
        // to guide the LLM on command invokation.
        Schema json.RawMessage `json:"schema"`
}

// Foundational identity of LLMs
type LLM interface {
        // Model ID as defined by the vendor
        ModelID() string

        // Encode prompt to bytes:
        // - encoding prompt as prompt markup supported by LLM
        // - encoding prompt to envelop supported by LLM's hosting platform
        Encode([]Message, ...Opt) ([]byte, error)

        // Decode LLM's reply into pure text
        Decode([]byte) (Reply, error)
}

1	//
2	// Copyright (C) 2024 Dmitry Kolesnikov
3	//
4	// This file may be modified and distributed under the terms
5	// of the MIT license. See the LICENSE file for details.
6	// https://github.com/kshard/chatter
7	//
8
9	package chatter
10
11	import (
12	"context"
13	"encoding/json"
14	)
15
16	type Opt = interface{ ChatterOpt() }
17
18	// The generic trait to "interact" with LLMs;
19	type Chatter interface {
20	Usage() Usage
21	Prompt(context.Context, []Message, ...Opt) (*Reply, error)
22	}
23
24	// LLM Usage stats
25	type Usage struct {
26	InputTokens int `json:"inputTokens"`
27	ReplyTokens int `json:"replyTokens"`
28	}
29
30	// LLMs' critical parameter influencing the balance between predictability
31	// and creativity in generated text. Lower temperatures prioritize exploiting
32	// learned patterns, yielding more deterministic outputs, while higher
33	// temperatures encourage exploration, fostering diversity and innovation.
34	type Temperature float64
35
36	func (Temperature) ChatterOpt() {}	×
37
38	// Nucleus Sampling, a parameter used in LLMs, impacts token selection by
39	// considering only the most likely tokens that together represent
40	// a cumulative probability mass (e.g., top-p tokens). This limits the
41	// number of choices to avoid overly diverse or nonsensical outputs while
42	// maintaining diversity within the top-ranked options.
43	type TopP float64
44
45	func (TopP) ChatterOpt() {}	×
46
47	type TopK float64
48
NEW 49	func (TopK) ChatterOpt() {}	×
50
51	// Token quota for reply, the model would limit response given number
52	type MaxTokens int
53
NEW 54	func (MaxTokens) ChatterOpt() {}	×
55
56	// The stop sequence prevents LLMs from generating more text after a specific
57	// string appears. Stop sequences make it easy to guarantee concise,
58	// controlled responses from models.
59	type StopSequences []string
60
NEW 61	func (StopSequences) ChatterOpt() {}	×
62
63	// Command registry is a sequence of tools available for LLM usage.
64	type Registry []Cmd
65
66	func (Registry) ChatterOpt() {}	×
67
68	// Command descriptor
69	type Cmd struct {
70	// [Required] A unique name for the command, used as a reference by LLMs (e.g., "bash").
71	Cmd string `json:"cmd"`
72
73	// [Required] A detailed, multi-line description to educate the LLM on command usage.
74	// Provides contextual information on how and when to use the command.
75	About string `json:"about"`
76
77	// [Required] JSON Schema specifies arguments, types, and additional context
78	// to guide the LLM on command invokation.
79	Schema json.RawMessage `json:"schema"`
80	}
81
82	// Foundational identity of LLMs
83	type LLM interface {
84	// Model ID as defined by the vendor
85	ModelID() string
86
87	// Encode prompt to bytes:
88	// - encoding prompt as prompt markup supported by LLM
89	// - encoding prompt to envelop supported by LLM's hosting platform
90	Encode([]Message, ...Opt) ([]byte, error)
91
92	// Decode LLM's reply into pure text
93	Decode([]byte) (Reply, error)
94	}

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