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@@ -539,7 +539,6 @@ async def chat_completion_files_handler(
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if len(queries) == 0:
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queries = [get_last_user_message(body["messages"])]
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- print(f"{queries=}")
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sources = get_sources_from_files(
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files=files,
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@@ -970,7 +969,7 @@ app.add_middleware(SecurityHeadersMiddleware)
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@app.middleware("http")
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async def commit_session_after_request(request: Request, call_next):
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response = await call_next(request)
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- log.debug("Commit session after request")
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+ #log.debug("Commit session after request")
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Session.commit()
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return response
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@@ -1177,6 +1176,8 @@ async def get_all_models():
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model["actions"].extend(
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get_action_items_from_module(action_function, function_module)
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)
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+ log.debug(f"get_all_models() returned {len(models)} models")
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+
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return models
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@@ -1214,6 +1215,8 @@ async def get_models(user=Depends(get_verified_user)):
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filtered_models.append(model)
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models = filtered_models
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+ log.debug(f"/api/models returned filtered models accessible to the user: {json.dumps([model['id'] for model in models])}")
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+
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return {"data": models}
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@@ -1704,7 +1707,6 @@ async def update_task_config(form_data: TaskConfigForm, user=Depends(get_admin_u
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@app.post("/api/task/title/completions")
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async def generate_title(form_data: dict, user=Depends(get_verified_user)):
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- print("generate_title")
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model_list = await get_all_models()
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models = {model["id"]: model for model in model_list}
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@@ -1725,9 +1727,7 @@ async def generate_title(form_data: dict, user=Depends(get_verified_user)):
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models,
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)
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- print(task_model_id)
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-
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- model = models[task_model_id]
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+ log.debug(f"generating chat title using model {task_model_id} for user {user.email} ")
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if app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE != "":
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template = app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE
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@@ -1766,10 +1766,12 @@ Artificial Intelligence in Healthcare
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"max_completion_tokens": 50,
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}
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),
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- "chat_id": form_data.get("chat_id", None),
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- "metadata": {"task": str(TASKS.TITLE_GENERATION), "task_body": form_data},
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+ "metadata": {
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+ "task": str(TASKS.TITLE_GENERATION),
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+ "task_body": form_data,
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+ "chat_id": form_data.get("chat_id", None)
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+ },
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}
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- log.debug(payload)
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# Handle pipeline filters
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try:
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@@ -1793,7 +1795,7 @@ Artificial Intelligence in Healthcare
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@app.post("/api/task/tags/completions")
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async def generate_chat_tags(form_data: dict, user=Depends(get_verified_user)):
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- print("generate_chat_tags")
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+
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if not app.state.config.ENABLE_TAGS_GENERATION:
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return JSONResponse(
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status_code=status.HTTP_200_OK,
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@@ -1818,7 +1820,8 @@ async def generate_chat_tags(form_data: dict, user=Depends(get_verified_user)):
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app.state.config.TASK_MODEL_EXTERNAL,
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models,
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)
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- print(task_model_id)
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+
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+ log.debug(f"generating chat tags using model {task_model_id} for user {user.email} ")
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if app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE != "":
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template = app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE
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@@ -1849,9 +1852,12 @@ JSON format: { "tags": ["tag1", "tag2", "tag3"] }
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"model": task_model_id,
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"messages": [{"role": "user", "content": content}],
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"stream": False,
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- "metadata": {"task": str(TASKS.TAGS_GENERATION), "task_body": form_data},
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+ "metadata": {
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+ "task": str(TASKS.TAGS_GENERATION),
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+ "task_body": form_data,
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+ "chat_id": form_data.get("chat_id", None)
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+ }
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}
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- log.debug(payload)
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# Handle pipeline filters
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try:
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@@ -1875,7 +1881,7 @@ JSON format: { "tags": ["tag1", "tag2", "tag3"] }
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@app.post("/api/task/queries/completions")
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async def generate_queries(form_data: dict, user=Depends(get_verified_user)):
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- print("generate_queries")
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+
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type = form_data.get("type")
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if type == "web_search":
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if not app.state.config.ENABLE_SEARCH_QUERY_GENERATION:
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@@ -1908,9 +1914,8 @@ async def generate_queries(form_data: dict, user=Depends(get_verified_user)):
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app.state.config.TASK_MODEL_EXTERNAL,
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models,
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)
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- print(task_model_id)
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-
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- model = models[task_model_id]
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+
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+ log.debug(f"generating {type} queries using model {task_model_id} for user {user.email}")
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if app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE != "":
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template = app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE
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@@ -1925,9 +1930,8 @@ async def generate_queries(form_data: dict, user=Depends(get_verified_user)):
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"model": task_model_id,
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"messages": [{"role": "user", "content": content}],
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"stream": False,
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- "metadata": {"task": str(TASKS.QUERY_GENERATION), "task_body": form_data},
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+ "metadata": {"task": str(TASKS.QUERY_GENERATION), "task_body": form_data, "chat_id": form_data.get("chat_id", None)},
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}
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- log.debug(payload)
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# Handle pipeline filters
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try:
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@@ -1951,7 +1955,6 @@ async def generate_queries(form_data: dict, user=Depends(get_verified_user)):
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@app.post("/api/task/emoji/completions")
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async def generate_emoji(form_data: dict, user=Depends(get_verified_user)):
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- print("generate_emoji")
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model_list = await get_all_models()
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models = {model["id"]: model for model in model_list}
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@@ -1971,9 +1974,8 @@ async def generate_emoji(form_data: dict, user=Depends(get_verified_user)):
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app.state.config.TASK_MODEL_EXTERNAL,
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models,
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)
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- print(task_model_id)
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- model = models[task_model_id]
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+ log.debug(f"generating emoji using model {task_model_id} for user {user.email} ")
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template = '''
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Your task is to reflect the speaker's likely facial expression through a fitting emoji. Interpret emotions from the message and reflect their facial expression using fitting, diverse emojis (e.g., 😊, 😢, 😡, 😱).
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@@ -2003,7 +2005,6 @@ Message: """{{prompt}}"""
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"chat_id": form_data.get("chat_id", None),
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"metadata": {"task": str(TASKS.EMOJI_GENERATION), "task_body": form_data},
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}
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- log.debug(payload)
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# Handle pipeline filters
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try:
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@@ -2027,7 +2028,6 @@ Message: """{{prompt}}"""
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@app.post("/api/task/moa/completions")
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async def generate_moa_response(form_data: dict, user=Depends(get_verified_user)):
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- print("generate_moa_response")
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model_list = await get_all_models()
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models = {model["id"]: model for model in model_list}
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@@ -2047,9 +2047,8 @@ async def generate_moa_response(form_data: dict, user=Depends(get_verified_user)
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app.state.config.TASK_MODEL_EXTERNAL,
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models,
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)
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- print(task_model_id)
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-
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- model = models[task_model_id]
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+
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+ log.debug(f"generating MOA model {task_model_id} for user {user.email} ")
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template = """You have been provided with a set of responses from various models to the latest user query: "{{prompt}}"
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@@ -2073,7 +2072,6 @@ Responses from models: {{responses}}"""
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"task_body": form_data,
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},
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}
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- log.debug(payload)
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try:
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payload = filter_pipeline(payload, user, models)
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@@ -2108,7 +2106,7 @@ Responses from models: {{responses}}"""
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async def get_pipelines_list(user=Depends(get_admin_user)):
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responses = await get_openai_models_responses()
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- print(responses)
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+ log.debug(f"get_pipelines_list: get_openai_models_responses returned {responses}")
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urlIdxs = [
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idx
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for idx, response in enumerate(responses)
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