Add SSE example, move folders around, clean secrets (#104)

.gitignore

@@ -1 +1,4 @@
-.DS_Store
+.hume/
+__pycache__/
+.venv/
+.DS_Store

evi-custom-language-model-sse/README.md

@@ -0,0 +1,25 @@
+# Custom Language Model SSE Client
+
+A Python client library for handling Server-Sent Events (SSE) with Hume Custom Language Models, specifically designed to work with OpenAI-compatible APIs.
+
+## Features
+
+- Server-Sent Events (SSE) client implementation
+- Compatible with OpenAI-style streaming responses
+- Support for custom language model endpoints
+- Easy-to-use async interface
+
+## Installation
+
+```bash
+poetry install
+```
+
+## Usage
+
+Run the server:
+```bash
+poetry run python openai_sse.py
+```
+
+Spin it up behind ngrok and use the ngrok URL in your config.

evi-custom-language-model-sse/openai_sse.py

@@ -0,0 +1,75 @@
+from typing import AsyncIterable, Optional
+import fastapi
+from fastapi.responses import StreamingResponse
+from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+import openai
+import os
+from fastapi import HTTPException, Security
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+
+app = fastapi.FastAPI()
+"""
+This would be the server that Hume would send requests to, that would then 
+get streamed back to us.
+
+uvicorn openai_sse:app --reload
+"""
+
+client = openai.AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])
+
+
+async def get_response(
+    raw_messages: list[dict],
+    custom_session_id: Optional[str],
+) -> AsyncIterable[str]:
+    """
+    Stream a response from OpenAI back to Hume.
+    """
+    messages: list[ChatCompletionMessageParam] = [
+        {"role": m["role"], "content": m["content"]} for m in raw_messages
+    ]
+
+    chat_completion_chunk_stream = await client.chat.completions.create(
+        messages=messages,
+        model="gpt-4o",
+        stream=True,
+    )
+
+    async for chunk in chat_completion_chunk_stream:
+        yield "data: " + chunk.model_dump_json(exclude_none=True) + "\n\n"
+    yield "data: [DONE]\n\n"
+
+
+security = HTTPBearer()
+API_KEY = "your-secret-key-here"  # In production, use environment variables
+
+
+async def verify_token(credentials: HTTPAuthorizationCredentials = Security(security)):
+    if credentials.credentials != API_KEY:
+        raise HTTPException(status_code=401, detail="Invalid authentication token")
+    return credentials.credentials
+
+
+@app.post("/chat/completions", response_class=StreamingResponse)
+async def root(
+    request: fastapi.Request,
+    # token: str = Security(verify_token)
+):
+    """Chat completions endpoint with Bearer token authentication"""
+    request_json = await request.json()
+    messages = request_json["messages"]
+    print(messages)
+
+    custom_session_id = request.query_params.get("custom_session_id")
+    print(custom_session_id)
+
+    return StreamingResponse(
+        get_response(messages, custom_session_id=custom_session_id),
+        media_type="text/event-stream",
+    )
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

evi-custom-language-model-sse/pyproject.toml

@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "evi-custom-language-model-sse"
+version = "0.1.0"
+description = ""
+authors = ["[email protected]"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+fastapi = "^0.115.5"
+uvicorn = "^0.32.0"
+openai = "^1.54.4"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

evi-custom-language-model/docs/detailed-install-instructions-mac.md → evi-custom-language-model-wss/docs/detailed-install-instructions-mac.md

@@ -84,15 +84,15 @@ You can sign up for a free SerpApi key that is good for 100 free searches. Once
 * Sign up for an account [here.](https://serpapi.com/users/sign_up)
 
 ```text
-SERPAPI_API_KEY=9a71b5441a2983748991d9e47b8aa54eb235f282d058e7e6d95dad854e315433
-
+SERPAPI_API_KEY=<your-serpapi-api-key>
 ```
+
 ### 9. Sign up (if you haven't already) for an OpenAI API key and also add it to the `.env` file
 
 Your `.env` file should look something like this:
 ```text
-SERPAPI_API_KEY=9a71b5441a2983748991d9e47b8aa5482d058e7e6d95dad854e315433
-OPENAI_API_KEY=sk-pFgGbNkxB95472398475932NLfT3BlbkFJBC6YKGKjjjjkHezP48ZK7
+SERPAPI_API_KEY=<your-serpapi-api-key>
+OPENAI_API_KEY=<your-openai-api-key>
 
 ```
 

evi-wss-clm-endpoint/app.py

@@ -0,0 +1,37 @@
+import json
+
+import uvicorn
+from agent import eliza_response
+
+from fastapi import FastAPI, WebSocket
+
+eliza_app = FastAPI()
+
+
+@eliza_app.get("/")
+async def root():
+    return {"message": "Hello World"}
+
+
+@eliza_app.websocket("/ws")
+async def websocket_handler(websocket: WebSocket) -> None:
+    await websocket.accept()
+    while True:
+        data = await websocket.receive_text()
+
+        hume_payload = json.loads(data)
+
+        print(hume_payload)
+
+        last_message = hume_payload["messages"][-1]["message"]["content"]
+
+        user_text = last_message.split("{")[0] or ""
+
+        await websocket.send_text(
+            json.dumps({"type": "assistant_input", "text": eliza_response(user_text)})
+        )
+        await websocket.send_text(json.dumps({"type": "assistant_end"}))
+
+
+if __name__ == "__main__":
+    uvicorn.run(eliza_app, host="0.0.0.0", port=8000)

evi-wss-clm-endpoint/cdk/cdk.out/ElizaStack.assets.json

@@ -0,0 +1,45 @@
+{
+  "version": "36.0.0",
+  "files": {
+    "ee7de53d64cc9d6248fa6aa550f92358f6c907b5efd6f3298aeab1b5e7ea358a": {
+      "source": {
+        "path": "asset.ee7de53d64cc9d6248fa6aa550f92358f6c907b5efd6f3298aeab1b5e7ea358a",
+        "packaging": "zip"
+      },
+      "destinations": {
+        "current_account-current_region": {
+          "bucketName": "cdk-hnb659fds-assets-${AWS::AccountId}-${AWS::Region}",
+          "objectKey": "ee7de53d64cc9d6248fa6aa550f92358f6c907b5efd6f3298aeab1b5e7ea358a.zip",
+          "assumeRoleArn": "arn:${AWS::Partition}:iam::${AWS::AccountId}:role/cdk-hnb659fds-file-publishing-role-${AWS::AccountId}-${AWS::Region}"
+        }
+      }
+    },
+    "914151f6f3dff61235ecc07604e20d47eefdda2a4051d47aff607ccea64c12dd": {
+      "source": {
+        "path": "ElizaStack.template.json",
+        "packaging": "file"
+      },
+      "destinations": {
+        "current_account-current_region": {
+          "bucketName": "cdk-hnb659fds-assets-${AWS::AccountId}-${AWS::Region}",
+          "objectKey": "914151f6f3dff61235ecc07604e20d47eefdda2a4051d47aff607ccea64c12dd.json",
+          "assumeRoleArn": "arn:${AWS::Partition}:iam::${AWS::AccountId}:role/cdk-hnb659fds-file-publishing-role-${AWS::AccountId}-${AWS::Region}"
+        }
+      }
+    }
+  },
+  "dockerImages": {
+    "689e46f5ffafa1e0f81f114b5dfd7694d2d1e291d9bd855e4f7b601d2b2403d0": {
+      "source": {
+        "directory": "asset.689e46f5ffafa1e0f81f114b5dfd7694d2d1e291d9bd855e4f7b601d2b2403d0"
+      },
+      "destinations": {
+        "current_account-current_region": {
+          "repositoryName": "cdk-hnb659fds-container-assets-${AWS::AccountId}-${AWS::Region}",
+          "imageTag": "689e46f5ffafa1e0f81f114b5dfd7694d2d1e291d9bd855e4f7b601d2b2403d0",
+          "assumeRoleArn": "arn:${AWS::Partition}:iam::${AWS::AccountId}:role/cdk-hnb659fds-image-publishing-role-${AWS::AccountId}-${AWS::Region}"
+        }
+      }
+    }
+  }
+}