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import json
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
import httpx
app = FastAPI()
# Allow all origins for testing (adjust for production)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Replace these with secure methods in production
import os
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
MODEL_NAME = "gpt-4o-mini"
@app.post("/openai_stream")
async def openai_stream(request: Request):
try:
body = await request.json()
except Exception as e:
raise HTTPException(status_code=400, detail="Invalid JSON payload") from e
conversation = body.get("conversation")
if not conversation:
raise HTTPException(status_code=400, detail="Missing 'conversation' in payload")
temperature = body.get("temperature", 0.7)
max_tokens = body.get("max_tokens", 256)
model = body.get("model", MODEL_NAME)
# Using OpenAI's SDK instead of direct API calls
from openai import AsyncOpenAI
# Initialize the client with the API key
client = AsyncOpenAI(api_key=OPENAI_API_KEY)
async def event_generator():
try:
print(f"Starting stream for model: {model}, temperature: {temperature}, max_tokens: {max_tokens}")
line_count = 0
# Use the SDK to create a streaming completion
stream = await client.chat.completions.create(
model=model,
messages=conversation,
temperature=temperature,
max_tokens=max_tokens,
stream=True
)
async for chunk in stream:
if chunk.choices and chunk.choices[0].delta.content is not None:
content = chunk.choices[0].delta.content
line_count += 1
if line_count % 10 == 0:
print(f"Processed {line_count} stream chunks")
# Format the response in the same way as before
response_json = json.dumps({
"choices": [{"delta": {"content": content}}]
})
yield f"data: {response_json}\n\n"
# Send the [DONE] marker
print("Stream completed successfully")
yield "data: [DONE]\n\n"
except Exception as e:
print(f"Error during streaming: {str(e)}")
yield f"data: {{\"error\": \"{str(e)}\"}}\n\n"
finally:
print(f"Stream ended after processing {line_count if 'line_count' in locals() else 0} chunks")
print("Returning StreamingResponse to client")
return StreamingResponse(event_generator(), media_type="text/event-stream")
@app.post("/gemini_stream")
async def gemini_stream(request: Request):
"""
Stream responses from Google's Gemini model using the Gemini SDK.
"""
body = await request.json()
conversation = body.get("messages", [])
temperature = body.get("temperature", 0.7)
max_tokens = body.get("max_tokens", 256)
model = body.get("model", "gemini-pro") # Default to gemini-pro model
# Using Google's Generative AI SDK
import google.generativeai as genai
from google.generativeai.types import HarmCategory, HarmBlockThreshold
# Initialize the client with the API key
genai.configure(api_key=GOOGLE_API_KEY)
# Convert OpenAI message format to Gemini format
gemini_messages = []
for msg in conversation:
role = "user" if msg["role"] == "user" else "model"
gemini_messages.append({"role": role, "parts": [msg["content"]]})
async def event_generator():
try:
print(f"Starting Gemini stream for model: {model}, temperature: {temperature}, max_tokens: {max_tokens}")
line_count = 0
# Create a Gemini model instance
gemini_model = genai.GenerativeModel(
model_name=model,
generation_config={
"temperature": temperature,
"max_output_tokens": max_tokens,
"top_p": 0.95,
},
safety_settings={
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
}
)
# Start the streaming response
response = gemini_model.generate_content(
gemini_messages,
stream=True
)
# Fix: Use synchronous iteration instead of async for
for chunk in response:
if hasattr(chunk, 'text') and chunk.text:
content = chunk.text
line_count += 1
if line_count % 10 == 0:
print(f"Processed {line_count} Gemini stream chunks")
# Format the response to match OpenAI format for client compatibility
response_json = json.dumps({
"choices": [{"delta": {"content": content}}]
})
yield f"data: {response_json}\n\n"
# Send the [DONE] marker
print("Gemini stream completed successfully")
yield "data: [DONE]\n\n"
except Exception as e:
print(f"Error during Gemini streaming: {str(e)}")
yield f"data: {{\"error\": \"{str(e)}\"}}\n\n"
finally:
print(f"Gemini stream ended after processing {line_count if 'line_count' in locals() else 0} chunks")
print("Returning StreamingResponse from Gemini to client")
return StreamingResponse(event_generator(), media_type="text/event-stream")
@app.post("/anthropic_stream")
async def anthropic_stream(request: Request):
"""
Stream responses from Anthropic's Claude models.
"""
print("Received request for Anthropic streaming")
# Parse the request body
body = await request.json()
messages = body.get("messages", [])
temperature = body.get("temperature", 0.7)
max_tokens = body.get("max_tokens", 1024)
model = body.get("model", "claude-3-opus-20240229")
# Load Anthropic API key from environment
anthropic_api_key = ANTHROPIC_API_KEY #os.environ.get("ANTHROPIC_API_KEY")
if not anthropic_api_key:
return JSONResponse(
status_code=500,
content={"error": "ANTHROPIC_API_KEY not found in environment variables"}
)
# Convert messages to Anthropic format
anthropic_messages = []
for msg in messages:
role = "assistant" if msg.get("role") == "assistant" else "user"
content = msg.get("content", "")
anthropic_messages.append({"role": role, "content": content})
line_count = 0
async def event_generator():
try:
import anthropic
# Initialize Anthropic client
client = anthropic.Anthropic(api_key=anthropic_api_key)
# Start the streaming response
with client.messages.stream(
model=model,
messages=anthropic_messages,
max_tokens=max_tokens,
temperature=temperature
) as stream:
for chunk in stream:
if hasattr(chunk, 'delta') and hasattr(chunk.delta, 'text') and chunk.delta.text:
content = chunk.delta.text
nonlocal line_count
line_count += 1
if line_count % 10 == 0:
print(f"Processed {line_count} Anthropic stream chunks")
# Format the response to match OpenAI format for client compatibility
response_json = json.dumps({
"choices": [{"delta": {"content": content}}]
})
yield f"data: {response_json}\n\n"
# Send the [DONE] marker
print("Anthropic stream completed successfully")
yield "data: [DONE]\n\n"
except Exception as e:
print(f"Error during Anthropic streaming: {str(e)}")
yield f"data: {{\"error\": \"{str(e)}\"}}\n\n"
finally:
print(f"Anthropic stream ended after processing {line_count if 'line_count' in locals() else 0} chunks")
print("Returning StreamingResponse from Anthropic to client")
return StreamingResponse(event_generator(), media_type="text/event-stream")
@app.post("/summarize_openai")
async def summarize_openai(request: Request):
try:
body = await request.json()
except Exception as e:
raise HTTPException(status_code=400, detail="Invalid JSON payload") from e
previous_summary = body.get("previous_summary", "")
latest_conversation = body.get("latest_conversation", "")
persona = body.get("persona", "helpful assistant")
temperature = body.get("temperature", 0.7)
max_tokens = body.get("max_tokens", 1024)
model = body.get("model", MODEL_NAME)
# Load the prompt from prompts.toml
import tomli
with open("configs/prompts.toml", "rb") as f:
prompts_config = tomli.load(f)
# Get the prompt and system prompt
prompt_template = prompts_config["summarization"]["prompt"]
system_prompt = prompts_config["summarization"]["system_prompt"]
# Replace variables in the prompt
prompt = prompt_template.replace("$previous_summary", previous_summary).replace("$latest_conversation", latest_conversation)
system_prompt = system_prompt.replace("$persona", persona)
# Using OpenAI's SDK
from openai import AsyncOpenAI
# Initialize the client with the API key
client = AsyncOpenAI(api_key=OPENAI_API_KEY)
try:
print(f"Starting OpenAI summarization for model: {model}")
# Use the SDK to create a completion
response = await client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
],
temperature=temperature,
max_tokens=max_tokens
)
summary = response.choices[0].message.content
print("OpenAI summarization completed successfully")
return {"summary": summary}
except Exception as e:
print(f"Error during OpenAI summarization: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error during summarization: {str(e)}")
@app.post("/summarize_anthropic")
async def summarize_anthropic(request: Request):
try:
body = await request.json()
except Exception as e:
raise HTTPException(status_code=400, detail="Invalid JSON payload") from e
previous_summary = body.get("previous_summary", "")
latest_conversation = body.get("latest_conversation", "")
persona = body.get("persona", "helpful assistant")
temperature = body.get("temperature", 0.7)
max_tokens = body.get("max_tokens", 1024)
model = body.get("model", "claude-3-opus-20240229")
# Load the prompt from prompts.toml
import tomli
with open("configs/prompts.toml", "rb") as f:
prompts_config = tomli.load(f)
# Get the prompt and system prompt
prompt_template = prompts_config["summarization"]["prompt"]
system_prompt = prompts_config["summarization"]["system_prompt"]
# Replace variables in the prompt
prompt = prompt_template.replace("$previous_summary", previous_summary).replace("$latest_conversation", latest_conversation)
system_prompt = system_prompt.replace("$persona", persona)
try:
import anthropic
# Initialize Anthropic client
client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)
print(f"Starting Anthropic summarization for model: {model}")
# Create the response
response = client.messages.create(
model=model,
messages=[
{"role": "user", "content": prompt}
],
system=system_prompt,
max_tokens=max_tokens,
temperature=temperature
)
summary = response.content[0].text
print("Anthropic summarization completed successfully")
return {"summary": summary}
except Exception as e:
print(f"Error during Anthropic summarization: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error during summarization: {str(e)}")
@app.post("/summarize_google")
async def summarize_google(request: Request):
try:
body = await request.json()
except Exception as e:
raise HTTPException(status_code=400, detail="Invalid JSON payload") from e
previous_summary = body.get("previous_summary", "")
latest_conversation = body.get("latest_conversation", "")
persona = body.get("persona", "helpful assistant")
temperature = body.get("temperature", 0.7)
max_tokens = body.get("max_tokens", 1024)
model = body.get("model", "gemini-1.5-pro")
# Load the prompt from prompts.toml
import tomli
with open("configs/prompts.toml", "rb") as f:
prompts_config = tomli.load(f)
# Get the prompt and system prompt
prompt_template = prompts_config["summarization"]["prompt"]
system_prompt = prompts_config["summarization"]["system_prompt"]
# Replace variables in the prompt
prompt = prompt_template.replace("$previous_summary", previous_summary).replace("$latest_conversation", latest_conversation)
system_prompt = system_prompt.replace("$persona", persona)
try:
import google.generativeai as genai
# Configure the Google API
genai.configure(api_key=GOOGLE_API_KEY)
# Initialize the model
model_obj = genai.GenerativeModel(model_name=model)
print(f"Starting Google summarization for model: {model}")
# Combine system prompt and user prompt for Google's API
combined_prompt = f"{system_prompt}\n\n{prompt}"
# Generate the response
response = model_obj.generate_content(
contents=combined_prompt,
generation_config=genai.types.GenerationConfig(
temperature=temperature,
max_output_tokens=max_tokens
)
)
summary = response.text
print("Google summarization completed successfully")
return {"summary": summary}
except Exception as e:
print(f"Error during Google summarization: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error during summarization: {str(e)}")
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