File size: 7,724 Bytes
fa8d021
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
#!/usr/bin/env python3
"""Load a locomo dataset into Honcho and test with configurable query and reasoning level."""

import argparse
import json
import os
import time
from datetime import datetime, timedelta, timezone

import httpx
from dotenv import load_dotenv

load_dotenv()

# Use environment variables with defaults matching .env.template
BASE_URL = os.getenv("HONCHO_BASE_URL")
REASONING_LEVELS = ["minimal", "low", "medium", "high", "max"]


def parse_datetime(dt_string: str) -> datetime:
    """Parse datetime string like '1:56 pm on 8 May, 2023' into datetime object."""
    parts = dt_string.split(" on ")
    time_part = parts[0]
    date_part = parts[1]

    time_obj = datetime.strptime(time_part, "%I:%M %p")
    date_obj = datetime.strptime(date_part, "%d %B, %Y")

    return datetime(
        year=date_obj.year,
        month=date_obj.month,
        day=date_obj.day,
        hour=time_obj.hour,
        minute=time_obj.minute,
        tzinfo=timezone.utc,
    )


def load_locomo(
    client: httpx.Client, filepath: str, workspace_id: str
) -> tuple[str, str]:
    """Load locomo dataset into Honcho. Returns (speaker_a, speaker_b)."""
    with open(filepath) as f:
        data = json.load(f)

    convo = data[0]["conversation"]
    speaker_a = convo["speaker_a"]
    speaker_b = convo["speaker_b"]

    print(f"Loading conversation between {speaker_a} and {speaker_b}")

    # Create workspace
    resp = client.post(f"{BASE_URL}/workspaces", json={"id": workspace_id})
    if resp.status_code >= 400:
        print(f"Failed to create workspace: {resp.status_code} {resp.text}")
        return "", ""
    print(f"Created workspace: {workspace_id}")

    # Create peers
    resp = client.post(
        f"{BASE_URL}/workspaces/{workspace_id}/peers", json={"id": speaker_a}
    )
    if resp.status_code >= 400:
        print(f"Failed to create peer {speaker_a}: {resp.status_code} {resp.text}")
        return "", ""
    resp = client.post(
        f"{BASE_URL}/workspaces/{workspace_id}/peers", json={"id": speaker_b}
    )
    if resp.status_code >= 400:
        print(f"Failed to create peer {speaker_b}: {resp.status_code} {resp.text}")
        return "", ""
    print(f"Created peers: {speaker_a}, {speaker_b}")

    session_num = 1
    while f"session_{session_num}" in convo:
        session_key = f"session_{session_num}"
        datetime_key = f"session_{session_num}_date_time"

        messages = convo[session_key]
        base_time = parse_datetime(convo[datetime_key])

        print(f"\n--- Session {session_num}: {convo[datetime_key]} ---")
        print(f"  {len(messages)} messages")

        session_id = f"locomo_session_{session_num}"

        # Create session
        resp = client.post(
            f"{BASE_URL}/workspaces/{workspace_id}/sessions",
            json={"id": session_id},
        )
        if resp.status_code >= 400:
            print(
                f"Failed to create session {session_id}: {resp.status_code} {resp.text}"
            )
            return "", ""

        # Add peers to session
        resp = client.post(
            f"{BASE_URL}/workspaces/{workspace_id}/sessions/{session_id}/peers",
            json={speaker_a: {}, speaker_b: {}},
        )
        if resp.status_code >= 400:
            print(f"Failed to add peers to session: {resp.status_code} {resp.text}")
            return "", ""
        print(f"  Created session: {session_id}")

        # Build message batch
        msg_batch = []
        for i, msg in enumerate(messages):
            msg_time = base_time + timedelta(seconds=i * 2)
            msg_batch.append(
                {
                    "peer_id": msg["speaker"],
                    "content": msg["text"],
                    "created_at": msg_time.isoformat(),
                }
            )

        # Create messages
        resp = client.post(
            f"{BASE_URL}/workspaces/{workspace_id}/sessions/{session_id}/messages",
            json={"messages": msg_batch},
        )
        if resp.status_code >= 400:
            print(f"Failed to create messages: {resp.status_code} {resp.text}")
            return "", ""
        print(f"  Loaded {len(messages)} messages")
        session_num += 1

    print(f"\nDone! Loaded {session_num - 1} sessions.")
    return speaker_a, speaker_b


def chat(
    client: httpx.Client, workspace_id: str, peer_id: str, query: str, level: str
) -> dict:
    """Call the chat endpoint with a specific reasoning level."""
    resp = client.post(
        f"{BASE_URL}/workspaces/{workspace_id}/peers/{peer_id}/chat",
        json={
            "query": query,
            "reasoning_level": level,
        },
    )
    if resp.status_code >= 400:
        return {"error": f"{resp.status_code} {resp.text}"}
    return resp.json()


def main():
    parser = argparse.ArgumentParser(
        description="Load a locomo dataset into Honcho and test with a query."
    )
    parser.add_argument(
        "filepath",
        type=str,
        help="Path to the locomo JSON file",
    )
    parser.add_argument(
        "--workspace",
        "-w",
        type=str,
        default=None,
        help="Workspace ID (default: auto-generated from timestamp)",
    )
    parser.add_argument(
        "--query",
        "-q",
        type=str,
        default="What do you know about this person?",
        help="The query to send to the chat endpoint",
    )
    parser.add_argument(
        "--peer",
        "-p",
        type=str,
        default=None,
        help="The peer ID to query (default: first speaker from dataset)",
    )
    parser.add_argument(
        "--level",
        "-l",
        type=str,
        choices=REASONING_LEVELS,
        default="medium",
        help="Reasoning level to use (default: medium)",
    )
    parser.add_argument(
        "--skip-load",
        action="store_true",
        help="Skip loading data, just run the query (requires --workspace and --peer)",
    )
    args = parser.parse_args()

    if not BASE_URL:
        print(
            "Error: HONCHO_BASE_URL is not set. Please set it in your environment or .env."
        )
        return

    # Generate workspace ID if not provided
    workspace_id = (
        args.workspace or f"locomo_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
    )

    with httpx.Client(timeout=None) as client:
        if args.skip_load:
            if not args.workspace or not args.peer:
                print(
                    "Error: --skip-load requires --workspace and --peer to be specified"
                )
                return
            speaker_a = args.peer
        else:
            # Load the dataset
            speaker_a, speaker_b = load_locomo(client, args.filepath, workspace_id)
            if not speaker_a:
                return

            print(f"\nPeers available: {speaker_a}, {speaker_b}")

        # Determine which peer to query
        peer_id = args.peer or speaker_a

        print("\n" + "=" * 60)
        print("Testing chat endpoint")
        print("=" * 60)
        print(f"Workspace: {workspace_id}")
        print(f"Peer: {peer_id}")
        print(f"Query: {args.query}")
        print(f"Level: {args.level}")
        print("=" * 60)

        start_time = time.time()
        result = chat(client, workspace_id, peer_id, args.query, args.level)
        elapsed = time.time() - start_time

        print(f"\nTime: {elapsed:.2f}s")

        if "error" in result:
            print(f"Error: {result['error']}")
        else:
            content = result.get("content", "")
            print(f"\nResponse ({len(content)} chars):")
            print("-" * 60)
            print(content)
            print("-" * 60)


if __name__ == "__main__":
    main()