From b048ea4f4105ba0c058d87b117222124e9937f10 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 2 Aug 2024 09:51:06 +0530 Subject: [PATCH 01/20] some changes --- bolna/ingestion_server/datachecks/__init__.py | 2 +- bolna/ingestion_server/main.py | 64 +++++++++++++++---- 2 files changed, 54 insertions(+), 12 deletions(-) diff --git a/bolna/ingestion_server/datachecks/__init__.py b/bolna/ingestion_server/datachecks/__init__.py index 7b0b27fe..3bede1da 100644 --- a/bolna/ingestion_server/datachecks/__init__.py +++ b/bolna/ingestion_server/datachecks/__init__.py @@ -46,4 +46,4 @@ class RAGTask(BaseModel): _status:str = RAGTaskStatus.WAIT _message:str = "" _index:str = "" - _nodes:list = [] + _nodes:list = [] \ No newline at end of file diff --git a/bolna/ingestion_server/main.py b/bolna/ingestion_server/main.py index f37ec5ba..e1e6c875 100644 --- a/bolna/ingestion_server/main.py +++ b/bolna/ingestion_server/main.py @@ -1,10 +1,13 @@ -from fastapi import FastAPI, UploadFile +import time +import json +import dotenv + +from fastapi import FastAPI, File, UploadFile, Form +from typing import Dict, Any, Optional, Union + from bolna.ingestion_server.datachecks import RAGConfig from bolna.ingestion_server import RAGFactory import uvicorn -import time -from typing import Dict -import dotenv dotenv.load_dotenv() DB: Dict[str, RAGConfig] = {} @@ -22,10 +25,8 @@ def heartbeat() -> float: @app.post("/create-rag") def create_rag(request: RAGConfig) -> Dict[str, str]: """Create a RAG configuration and return its ID. - Args: request (RAGConfig): The RAG configuration to create. - Returns: Dict[str, str]: A dictionary containing the created RAG ID. """ @@ -37,11 +38,9 @@ def create_rag(request: RAGConfig) -> Dict[str, str]: @app.post("/rag-upload-file/{rag_id}") async def rag_upload_file(file: UploadFile, rag_id: str) -> Dict[str, str]: """Upload a file for a specific RAG ID. - Args: file (UploadFile): The file to upload. rag_id (str): The ID of the RAG to associate with the file. - Returns: Dict[str, str]: A dictionary containing the upload status and index. """ @@ -50,21 +49,64 @@ async def rag_upload_file(file: UploadFile, rag_id: str) -> Dict[str, str]: return {"index": task._index, "status": task._status, "message": "DONE"} except Exception as e: return {"index": None, "status": "ERROR", "message": f"{e}"} + +@app.post("/make-rag") +async def make_rag( + file: UploadFile = File(...), + config: str = Form(...) +) -> Dict[str, Any]: + """ + Create a RAG configuration, return its ID, and ingest the uploaded file. + + Args: + file (UploadFile): The file to upload and ingest. + config (str): The RAG configuration as a JSON string. + Returns: + Dict[str, Any]: A dictionary containing the created RAG ID, upload status, and index. + """ + try: + # Parse the JSON string into a RAGConfig object + rag_config = RAGConfig.parse_raw(config) + + # Create RAG configuration + rag_id = rag_factory.make_rag(rag_config) + + # Ingest the file + task = await rag_factory.file_ingest(rag_name=rag_id, file=file) + + return { + "rag_id": rag_id, + "index": task._index, + "status": task._status, + "message": "RAG created and file ingested successfully" + } + except json.JSONDecodeError: + return { + "rag_id": None, + "index": None, + "status": "ERROR", + "message": "Invalid JSON in config parameter" + } + except Exception as e: + return { + "rag_id": None, + "index": None, + "status": "ERROR", + "message": f"Error creating RAG or ingesting file: {str(e)}" + } @app.get("/rag-retrive/{rag_id}/{index}") async def rag_retrive(query: str, rag_id: str, index: str) -> list: """Retrieve documents based on a query for a specific RAG ID and index. - Args: query (str): The query string to search for. rag_id (str): The ID of the RAG to search in. index (str): The index to search in. - Returns: list: A list of documents matching the query. """ - docs = await rag_factory.retrive_query(rag_name=rag_id, index=index, query=query) + docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) send_filter = [{"text": node.text, "score": node.score} for node in docs] return send_filter From 431f324aa703def06a1dc56f1e00c65544761e35 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 2 Aug 2024 18:36:59 +0530 Subject: [PATCH 02/20] adding extra config schema in the LLM , missed --- bolna/models.py | 1 + local_setup/ngrok-config.yml | 8 ++++---- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/bolna/models.py b/bolna/models.py index 186f8b1b..2cf20f42 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -173,6 +173,7 @@ class LLM(BaseModel): presence_penalty: Optional[float] = 0.0 provider: Optional[str] = "openai" base_url: Optional[str] = None + extra_config: Optional[ExtraConfig] = None class SIMPLE_LLM_AGENT(LLM): diff --git a/local_setup/ngrok-config.yml b/local_setup/ngrok-config.yml index 4907d3d9..dde130d4 100644 --- a/local_setup/ngrok-config.yml +++ b/local_setup/ngrok-config.yml @@ -1,6 +1,6 @@ region: us version: '2' -authtoken: +authtoken: 2idcq5RCgwCkpZpSTiHKxX7RV41_5Sux1mFFCUmgCihBVfab6 tunnels: twilio-app: addr: twilio-app:8001 @@ -11,7 +11,7 @@ tunnels: bolna-app: addr: bolna-app:5001 proto: http - ingestion-app: - addr: ingestion-app:8000 - proto: http + # ingestion-app: + # addr: ingestion-app:8000 + # proto: http From fd154fff4aa79533ecf83b9ef002e37cbed4c563 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Sat, 3 Aug 2024 16:59:01 +0530 Subject: [PATCH 03/20] some minor fixes --- local_setup/ngrok-config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/local_setup/ngrok-config.yml b/local_setup/ngrok-config.yml index dde130d4..2aedb97c 100644 --- a/local_setup/ngrok-config.yml +++ b/local_setup/ngrok-config.yml @@ -1,6 +1,6 @@ region: us version: '2' -authtoken: 2idcq5RCgwCkpZpSTiHKxX7RV41_5Sux1mFFCUmgCihBVfab6 +authtoken: tunnels: twilio-app: addr: twilio-app:8001 From 7bb3487e4370cce0e018f5e893f706897ef8ce6a Mon Sep 17 00:00:00 2001 From: Marmik Pandya Date: Mon, 5 Aug 2024 09:03:27 +0530 Subject: [PATCH 04/20] change llama_index_agent to knowledgebase agent --- bolna/agent_manager/task_manager.py | 6 +++--- bolna/models.py | 8 +++++--- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/bolna/agent_manager/task_manager.py b/bolna/agent_manager/task_manager.py index 4e1643a5..19348bff 100644 --- a/bolna/agent_manager/task_manager.py +++ b/bolna/agent_manager/task_manager.py @@ -561,8 +561,8 @@ def __get_agent_object(self, llm, agent_type, assistant_config = None ): def __setup_tasks(self, llm = None, agent_type = None, assistant_config= None): if self.task_config["task_type"] == "conversation" and not self.__is_multiagent(): self.tools["llm_agent"] = self.__get_agent_object(llm, agent_type, assistant_config) - if agent_type == "llama-index-rag": - logger.info("#### Setting up llama-index-rag agent ####") + if agent_type == "knowledgebase_agent": + logger.info("#### Setting up knowledgebase_agent agent ####") extra_config = self.task_config["tools_config"]["llm_agent"].get("extra_config", {}) vector_store_config = extra_config.get("vector_store", {}) self.tools["llm_agent"] = LlamaIndexRag( @@ -608,7 +608,7 @@ def __get_final_prompt(self, prompt, today): async def load_prompt(self, assistant_name, task_id, local, **kwargs): logger.info("prompt and config setup started") agent_type = self.task_config["tools_config"]["llm_agent"].get("agent_type", "simple_llm_agent") - if self.task_config["task_type"] == "webhook" or agent_type in ["openai_assistant", "llamaindex_rag_agent"]: + if self.task_config["task_type"] == "webhook" or agent_type in ["openai_assistant", "knowledgebase_agent"]: return self.is_local = local today = datetime.now().strftime("%A, %B %d, %Y") diff --git a/bolna/models.py b/bolna/models.py index 186f8b1b..7bcc58aa 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -156,8 +156,6 @@ class VectorStore(BaseModel): provider: str provider_config: Union[LanceDBProviderConfig, MongoDBProviderConfig] -class ExtraConfig(BaseModel): - vector_store : VectorStore class LLM(BaseModel): model: Optional[str] = "gpt-3.5-turbo" @@ -181,6 +179,7 @@ class SIMPLE_LLM_AGENT(LLM): extraction_details: Optional[str] = None summarization_details: Optional[str] = None + class Node(BaseModel): id: str type: str #Can be router or conversation for now @@ -209,12 +208,15 @@ class MultiAgent(BaseModel): default_agent: str embedding_model: Optional[str] = "Snowflake/snowflake-arctic-embed-l" +class KnowledgebaseAgent(LLM): + vector_store: VectorStore + class LLM_AGENT(BaseModel): agent_flow_type: str agent_type: str #can be llamaindex_rag, simple_llm_agent, router_agent, dag_agent, openai_assistant, custom, etc #extra_config: Union[OpenaiAssistants, LLM_AGENT_GRAPH, MultiAgent, LLM, SIMPLE_LLM_AGENT] guardrails: Optional[Routes] = None #Just to reduce confusion - extra_config: Union[OpenaiAssistants, LLM_AGENT_GRAPH, MultiAgent, LLM] + extra_config: Union[OpenaiAssistants, LLM_AGENT_GRAPH, MultiAgent, KnowledgebaseAgent, SIMPLE_LLM_AGENT, LLM] class MessagingModel(BaseModel): From 507cf0621907205db25abacdbc83fdfd56f3f955 Mon Sep 17 00:00:00 2001 From: Marmik Pandya Date: Tue, 6 Aug 2024 22:48:12 +0530 Subject: [PATCH 05/20] refactor rag agent --- bolna/agent_manager/task_manager.py | 83 +++++++++++++--------- bolna/agent_types/llama_index_rag_agent.py | 5 +- bolna/helpers/utils.py | 1 + 3 files changed, 52 insertions(+), 37 deletions(-) diff --git a/bolna/agent_manager/task_manager.py b/bolna/agent_manager/task_manager.py index 19348bff..22c9ff6b 100644 --- a/bolna/agent_manager/task_manager.py +++ b/bolna/agent_manager/task_manager.py @@ -60,10 +60,10 @@ def __init__(self, assistant_name, task_id, task, ws, input_parameters=None, con self.kwargs['assistant_id'] = task['tools_config']["llm_agent"]['extra_config']['assistant_id'] logger.info(f"Assistant id for agent is {self.kwargs['assistant_id']}") - if self.__has_extra_config(): - pass - #self.kwargs['assistant_id'] = task['tools_config']["llm_agent"]['extra_config']['assistant_id'] - #logger.info(f"Assistant id for agent is {self.kwargs['assistant_id']}") + # if self.__has_extra_config(): + # pass + # #self.kwargs['assistant_id'] = task['tools_config']["llm_agent"]['extra_config']['assistant_id'] + # #logger.info(f"Assistant id for agent is {self.kwargs['assistant_id']}") if self.__is_openai_assistant(): self.kwargs['assistant_id'] = task['tools_config']["llm_agent"]['extra_config']['assistant_id'] @@ -187,21 +187,33 @@ def __init__(self, assistant_name, task_id, task, ws, input_parameters=None, con self.llm_config_map[agent]['buffer_size'] = self.task_config["tools_config"]["synthesizer"]['buffer_size'] if 'assistant_id' in config: self.llm_config_map[agent]['agent_type'] = "openai_assistant" - elif 'vectorstore_id' in config: - self.llm_config_map[agent]['agent_type'] = "knowledgebase_agent" elif not self.__is_openai_assistant(): logger.info(f"NOT OPEN AI ASSISTANT") - if self.task_config["tools_config"]["llm_agent"] is not None: - agent_type = self.task_config["tools_config"]["llm_agent"].get("agent_type", None) - llm_config = self.task_config["tools_config"]["llm_agent"] if not agent_type else self.task_config["tools_config"]["llm_agent"]['extra_config'] - self.llm_agent_config = llm_config.copy() - logger.info(f"SETTING FOLLOW UP TASK DETAILS") - self.llm_config = { - "model": llm_config['model'], - "max_tokens": llm_config['max_tokens'], - "provider": llm_config['provider'], - } + if self.task_config["tools_config"]["llm_agent"] is not None: + if self.__is_knowledgebase_agent(): + self.llm_agent_config = self.task_config["tools_config"]["llm_agent"] + self.llm_config = { + "model": self.llm_agent_config['extra_config']['model'], + "max_tokens": self.llm_agent_config['extra_config']['max_tokens'], + "provider": self.llm_agent_config['extra_config']['provider'], + } + else: + agent_type = self.task_config["tools_config"]["llm_agent"].get("agent_type", None) + if not agent_type: + self.llm_agent_config = self.task_config["tools_config"]["llm_agent"] + else: + self.llm_agent_config = self.task_config["tools_config"]["llm_agent"]['extra_config'] + + self.llm_config = { + "model": self.llm_agent_config['model'], + "max_tokens": self.llm_agent_config['max_tokens'], + "provider": self.llm_agent_config['provider'], + } + + logger.info(f"SETTING FOLLOW UP TASK DETAILS {self.llm_agent_config}") + + # Output stuff @@ -268,9 +280,7 @@ def __init__(self, assistant_name, task_id, task, ws, input_parameters=None, con guardrails_meta = self.kwargs.pop('routes', None) self.agent_routing = guardrails_meta['agent_routing_config']['route_layer'] self.default_agent = task['tools_config']['llm_agent']['extra_config']['default_agent'] - logger.info(f"Inisialised with default agent {self.default_agent}, agent_routing {self.agent_routing}") - - + logger.info(f"Inisialised with default agent {self.default_agent}, agent_routing {self.agent_routing}") # for long pauses and rushing if conversation_config is not None: self.minimum_wait_duration = self.task_config["tools_config"]["transcriber"]["endpointing"] @@ -352,7 +362,7 @@ def __init__(self, assistant_name, task_id, task, ws, input_parameters=None, con # setting llm if self.llm_config is not None: - logger.info(f"LLM CONFIG IS NONE {self.task_config['task_type']}") + logger.info(f"LLM CONFIG IS NOT NONE {self.task_config['task_type']} llm agent config {self.llm_agent_config}") llm = self.__setup_llm(self.llm_config) #Setup tasks agent_params = { @@ -394,6 +404,12 @@ def __is_multiagent(self): agent_type = self.task_config['tools_config']["llm_agent"].get("agent_type", None) return agent_type == "multiagent" + def __is_knowledgebase_agent(self): + if self.task_config["task_type"] == "webhook": + return False + agent_type = self.task_config['tools_config']["llm_agent"].get("agent_type", None) + return agent_type == "knowledgebase_agent" + def __setup_routes(self, routes): embedding_model = routes.get("embedding_model", os.getenv("ROUTE_EMBEDDING_MODEL")) self.route_encoder = FastEmbedEncoder(name=embedding_model) @@ -550,10 +566,24 @@ def __setup_llm(self, llm_config): def __get_agent_object(self, llm, agent_type, assistant_config = None ): if agent_type == "simple_llm_agent": + logger.info(f"Simple llm agent") llm_agent = StreamingContextualAgent(llm) elif agent_type == "openai_assistant": logger.info(f"setting up backend as openai_assistants {assistant_config}") llm_agent = OpenAIAssistantAgent(**assistant_config) + elif agent_type == "knowledgebase_agent": + logger.info("#### Setting up knowledgebase_agent agent ####") + extra_config = self.task_config["tools_config"]["llm_agent"].get("extra_config", {}) + vector_store_config = extra_config.get("vector_store", {}) + llm_agent = LlamaIndexRag( + vector_id=vector_store_config.get("vector_id"), + temperature=extra_config.get("temperature", 0.1), + model=extra_config.get("model", "gpt-3.5-turbo-16k"), + buffer=40, + max_tokens=100, # You might want to make this configurable + provider_config=vector_store_config + ) + logger.info("Llama-index rag agent is created") else: raise(f"{agent_type} Agent type is not created yet") return llm_agent @@ -561,19 +591,6 @@ def __get_agent_object(self, llm, agent_type, assistant_config = None ): def __setup_tasks(self, llm = None, agent_type = None, assistant_config= None): if self.task_config["task_type"] == "conversation" and not self.__is_multiagent(): self.tools["llm_agent"] = self.__get_agent_object(llm, agent_type, assistant_config) - if agent_type == "knowledgebase_agent": - logger.info("#### Setting up knowledgebase_agent agent ####") - extra_config = self.task_config["tools_config"]["llm_agent"].get("extra_config", {}) - vector_store_config = extra_config.get("vector_store", {}) - self.tools["llm_agent"] = LlamaIndexRag( - vector_id=vector_store_config.get("vector_id"), - temperature=extra_config.get("temperature", 0.1), - model=extra_config.get("model", "gpt-3.5-turbo-16k"), - buffer=40, - max_tokens=100, # You might want to make this configurable - provider_config=vector_store_config - ) - logger.info("Llama-index rag agent is created") elif self.__is_multiagent(): return self.__get_agent_object(llm, agent_type, assistant_config) elif self.task_config["task_type"] == "extraction": diff --git a/bolna/agent_types/llama_index_rag_agent.py b/bolna/agent_types/llama_index_rag_agent.py index 1ae40578..e51d052e 100644 --- a/bolna/agent_types/llama_index_rag_agent.py +++ b/bolna/agent_types/llama_index_rag_agent.py @@ -65,8 +65,6 @@ def __init__(self, vector_id: str, temperature: float, model: str, buffer: int = self.max_tokens = max_tokens self.provider_config = provider_config self.OPENAI_KEY = os.getenv('OPENAI_API_KEY') - # self.LANCE_DB = "/Users/vipul/Nova/Work/Bolna/LlamaIndex-MutiRAG/DataBase/dev" - self.LANCE_DB = os.getenv(LANCEDB_DIR) self.provider = None self.query_engine = None @@ -101,8 +99,7 @@ def _setup_provider(self): # Add more providers here as elif statements else: logging.info(f"LanceDB RAG") - logging.info(f"URI : LanceDB {self.LANCE_DB}") - self.vector_store = LanceDBVectorStore(uri=self.LANCE_DB , table_name=self.provider_config['provider_config'].get('vector_id')) + self.vector_store = LanceDBVectorStore(table_name=self.provider_config['provider_config'].get('vector_id')) logging.info(f"Table params : {self.provider_config['provider_config'].get('vector_id')}") storage_context = StorageContext.from_defaults(vector_store=self.vector_store) self.vector_index = VectorStoreIndex([], storage_context=storage_context) diff --git a/bolna/helpers/utils.py b/bolna/helpers/utils.py index 25152291..20fbdac2 100644 --- a/bolna/helpers/utils.py +++ b/bolna/helpers/utils.py @@ -151,6 +151,7 @@ async def delete_s3_file_by_prefix(bucket_name,file_key): async def store_file(bucket_name=None, file_key=None, file_data=None, content_type="json", local=False, preprocess_dir=None): if not local: + logger.info(f"Saving at {bucket_name}") session = AioSession() async with AsyncExitStack() as exit_stack: From 3449352ace5baf63072b758f4a9803e26b686ca7 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Wed, 7 Aug 2024 18:03:06 +0530 Subject: [PATCH 06/20] changes in quickstart_ingestion_server script --- local_setup/quickstart_ingestion_server.py | 48 +++++++++++++++++++++- 1 file changed, 47 insertions(+), 1 deletion(-) diff --git a/local_setup/quickstart_ingestion_server.py b/local_setup/quickstart_ingestion_server.py index 592294dc..dfda2d6a 100644 --- a/local_setup/quickstart_ingestion_server.py +++ b/local_setup/quickstart_ingestion_server.py @@ -66,4 +66,50 @@ async def rag_retrive(query: str, rag_id: str, index: str) -> list: """ docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) send_filter = [{"text": node.text, "score": node.score} for node in docs] - return send_filter \ No newline at end of file + return send_filter + +@app.post("/make-rag") +async def make_rag( + file: UploadFile = File(...), + config: str = Form(...) +) -> Dict[str, Any]: + """ + Create a RAG configuration, return its ID, and ingest the uploaded file. + + Args: + file (UploadFile): The file to upload and ingest. + config (str): The RAG configuration as a JSON string. + + Returns: + Dict[str, Any]: A dictionary containing the created RAG ID, upload status, and index. + """ + try: + # Parse the JSON string into a RAGConfig object + rag_config = RAGConfig.parse_raw(config) + + # Create RAG configuration + rag_id = rag_factory.make_rag(rag_config) + + # Ingest the file + task = await rag_factory.file_ingest(rag_name=rag_id, file=file) + + return { + "rag_id": rag_id, + "index": task._index, + "status": task._status, + "message": "RAG created and file ingested successfully" + } + except json.JSONDecodeError: + return { + "rag_id": None, + "index": None, + "status": "ERROR", + "message": "Invalid JSON in config parameter" + } + except Exception as e: + return { + "rag_id": None, + "index": None, + "status": "ERROR", + "message": f"Error creating RAG or ingesting file: {str(e)}" + } \ No newline at end of file From 6b2a7ba1f6c0e9404ca7c1257ae03e797136fdbb Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Wed, 7 Aug 2024 18:25:22 +0530 Subject: [PATCH 07/20] some changes for LANCEDB rag --- bolna/agent_types/llama_index_rag_agent.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/bolna/agent_types/llama_index_rag_agent.py b/bolna/agent_types/llama_index_rag_agent.py index e51d052e..c588725b 100644 --- a/bolna/agent_types/llama_index_rag_agent.py +++ b/bolna/agent_types/llama_index_rag_agent.py @@ -67,6 +67,7 @@ def __init__(self, vector_id: str, temperature: float, model: str, buffer: int = self.OPENAI_KEY = os.getenv('OPENAI_API_KEY') self.provider = None self.query_engine = None + self.LANCE_DB = os.getenv(LANCEDB_DIR) self._setup_llm() self._setup_provider() @@ -99,7 +100,8 @@ def _setup_provider(self): # Add more providers here as elif statements else: logging.info(f"LanceDB RAG") - self.vector_store = LanceDBVectorStore(table_name=self.provider_config['provider_config'].get('vector_id')) + logging.info(f"URI : LanceDB {self.LANCE_DB}") + self.vector_store = LanceDBVectorStore(uri=self.LANCE_DB, table_name=self.provider_config['provider_config'].get('vector_id')) logging.info(f"Table params : {self.provider_config['provider_config'].get('vector_id')}") storage_context = StorageContext.from_defaults(vector_store=self.vector_store) self.vector_index = VectorStoreIndex([], storage_context=storage_context) From 962c658a01544a502f7cb9ffe2c9433451a44c5d Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Thu, 8 Aug 2024 10:28:49 +0530 Subject: [PATCH 08/20] Revert "some changes for LANCEDB rag" This reverts commit 6b2a7ba1f6c0e9404ca7c1257ae03e797136fdbb. --- bolna/agent_types/llama_index_rag_agent.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/bolna/agent_types/llama_index_rag_agent.py b/bolna/agent_types/llama_index_rag_agent.py index c588725b..e51d052e 100644 --- a/bolna/agent_types/llama_index_rag_agent.py +++ b/bolna/agent_types/llama_index_rag_agent.py @@ -67,7 +67,6 @@ def __init__(self, vector_id: str, temperature: float, model: str, buffer: int = self.OPENAI_KEY = os.getenv('OPENAI_API_KEY') self.provider = None self.query_engine = None - self.LANCE_DB = os.getenv(LANCEDB_DIR) self._setup_llm() self._setup_provider() @@ -100,8 +99,7 @@ def _setup_provider(self): # Add more providers here as elif statements else: logging.info(f"LanceDB RAG") - logging.info(f"URI : LanceDB {self.LANCE_DB}") - self.vector_store = LanceDBVectorStore(uri=self.LANCE_DB, table_name=self.provider_config['provider_config'].get('vector_id')) + self.vector_store = LanceDBVectorStore(table_name=self.provider_config['provider_config'].get('vector_id')) logging.info(f"Table params : {self.provider_config['provider_config'].get('vector_id')}") storage_context = StorageContext.from_defaults(vector_store=self.vector_store) self.vector_index = VectorStoreIndex([], storage_context=storage_context) From 5007c27308681d005ca1bbcdbbb34150d71c15b0 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Thu, 8 Aug 2024 10:29:32 +0530 Subject: [PATCH 09/20] Revert "Merge branch 'dev/proxy-server' into develop" This reverts commit 6022691b004550576b0e22f5fd7c932dd8195907, reversing changes made to 5d2984533b7c522d20d0336b2aa8ccdf83eb8b2e. --- local_setup/quickstart_ingestion_server.py | 48 +--------------------- 1 file changed, 1 insertion(+), 47 deletions(-) diff --git a/local_setup/quickstart_ingestion_server.py b/local_setup/quickstart_ingestion_server.py index dfda2d6a..592294dc 100644 --- a/local_setup/quickstart_ingestion_server.py +++ b/local_setup/quickstart_ingestion_server.py @@ -66,50 +66,4 @@ async def rag_retrive(query: str, rag_id: str, index: str) -> list: """ docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) send_filter = [{"text": node.text, "score": node.score} for node in docs] - return send_filter - -@app.post("/make-rag") -async def make_rag( - file: UploadFile = File(...), - config: str = Form(...) -) -> Dict[str, Any]: - """ - Create a RAG configuration, return its ID, and ingest the uploaded file. - - Args: - file (UploadFile): The file to upload and ingest. - config (str): The RAG configuration as a JSON string. - - Returns: - Dict[str, Any]: A dictionary containing the created RAG ID, upload status, and index. - """ - try: - # Parse the JSON string into a RAGConfig object - rag_config = RAGConfig.parse_raw(config) - - # Create RAG configuration - rag_id = rag_factory.make_rag(rag_config) - - # Ingest the file - task = await rag_factory.file_ingest(rag_name=rag_id, file=file) - - return { - "rag_id": rag_id, - "index": task._index, - "status": task._status, - "message": "RAG created and file ingested successfully" - } - except json.JSONDecodeError: - return { - "rag_id": None, - "index": None, - "status": "ERROR", - "message": "Invalid JSON in config parameter" - } - except Exception as e: - return { - "rag_id": None, - "index": None, - "status": "ERROR", - "message": f"Error creating RAG or ingesting file: {str(e)}" - } \ No newline at end of file + return send_filter \ No newline at end of file From 20cd84217b6c0fa236040cf9899797f747671f11 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Thu, 8 Aug 2024 10:31:33 +0530 Subject: [PATCH 10/20] Revert "Merge branch 'dev/proxy-server' into develop" This reverts commit 5d2984533b7c522d20d0336b2aa8ccdf83eb8b2e, reversing changes made to 507cf0621907205db25abacdbc83fdfd56f3f955. --- bolna/ingestion_server/datachecks/__init__.py | 2 +- bolna/ingestion_server/main.py | 64 ++++--------------- bolna/models.py | 1 - local_setup/ngrok-config.yml | 6 +- 4 files changed, 15 insertions(+), 58 deletions(-) diff --git a/bolna/ingestion_server/datachecks/__init__.py b/bolna/ingestion_server/datachecks/__init__.py index 3bede1da..7b0b27fe 100644 --- a/bolna/ingestion_server/datachecks/__init__.py +++ b/bolna/ingestion_server/datachecks/__init__.py @@ -46,4 +46,4 @@ class RAGTask(BaseModel): _status:str = RAGTaskStatus.WAIT _message:str = "" _index:str = "" - _nodes:list = [] \ No newline at end of file + _nodes:list = [] diff --git a/bolna/ingestion_server/main.py b/bolna/ingestion_server/main.py index e1e6c875..f37ec5ba 100644 --- a/bolna/ingestion_server/main.py +++ b/bolna/ingestion_server/main.py @@ -1,13 +1,10 @@ -import time -import json -import dotenv - -from fastapi import FastAPI, File, UploadFile, Form -from typing import Dict, Any, Optional, Union - +from fastapi import FastAPI, UploadFile from bolna.ingestion_server.datachecks import RAGConfig from bolna.ingestion_server import RAGFactory import uvicorn +import time +from typing import Dict +import dotenv dotenv.load_dotenv() DB: Dict[str, RAGConfig] = {} @@ -25,8 +22,10 @@ def heartbeat() -> float: @app.post("/create-rag") def create_rag(request: RAGConfig) -> Dict[str, str]: """Create a RAG configuration and return its ID. + Args: request (RAGConfig): The RAG configuration to create. + Returns: Dict[str, str]: A dictionary containing the created RAG ID. """ @@ -38,9 +37,11 @@ def create_rag(request: RAGConfig) -> Dict[str, str]: @app.post("/rag-upload-file/{rag_id}") async def rag_upload_file(file: UploadFile, rag_id: str) -> Dict[str, str]: """Upload a file for a specific RAG ID. + Args: file (UploadFile): The file to upload. rag_id (str): The ID of the RAG to associate with the file. + Returns: Dict[str, str]: A dictionary containing the upload status and index. """ @@ -49,64 +50,21 @@ async def rag_upload_file(file: UploadFile, rag_id: str) -> Dict[str, str]: return {"index": task._index, "status": task._status, "message": "DONE"} except Exception as e: return {"index": None, "status": "ERROR", "message": f"{e}"} - -@app.post("/make-rag") -async def make_rag( - file: UploadFile = File(...), - config: str = Form(...) -) -> Dict[str, Any]: - """ - Create a RAG configuration, return its ID, and ingest the uploaded file. - - Args: - file (UploadFile): The file to upload and ingest. - config (str): The RAG configuration as a JSON string. - Returns: - Dict[str, Any]: A dictionary containing the created RAG ID, upload status, and index. - """ - try: - # Parse the JSON string into a RAGConfig object - rag_config = RAGConfig.parse_raw(config) - - # Create RAG configuration - rag_id = rag_factory.make_rag(rag_config) - - # Ingest the file - task = await rag_factory.file_ingest(rag_name=rag_id, file=file) - - return { - "rag_id": rag_id, - "index": task._index, - "status": task._status, - "message": "RAG created and file ingested successfully" - } - except json.JSONDecodeError: - return { - "rag_id": None, - "index": None, - "status": "ERROR", - "message": "Invalid JSON in config parameter" - } - except Exception as e: - return { - "rag_id": None, - "index": None, - "status": "ERROR", - "message": f"Error creating RAG or ingesting file: {str(e)}" - } @app.get("/rag-retrive/{rag_id}/{index}") async def rag_retrive(query: str, rag_id: str, index: str) -> list: """Retrieve documents based on a query for a specific RAG ID and index. + Args: query (str): The query string to search for. rag_id (str): The ID of the RAG to search in. index (str): The index to search in. + Returns: list: A list of documents matching the query. """ - docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) + docs = await rag_factory.retrive_query(rag_name=rag_id, index=index, query=query) send_filter = [{"text": node.text, "score": node.score} for node in docs] return send_filter diff --git a/bolna/models.py b/bolna/models.py index 3c76b12b..7bcc58aa 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -171,7 +171,6 @@ class LLM(BaseModel): presence_penalty: Optional[float] = 0.0 provider: Optional[str] = "openai" base_url: Optional[str] = None - extra_config: Optional[ExtraConfig] = None class SIMPLE_LLM_AGENT(LLM): diff --git a/local_setup/ngrok-config.yml b/local_setup/ngrok-config.yml index 2aedb97c..4907d3d9 100644 --- a/local_setup/ngrok-config.yml +++ b/local_setup/ngrok-config.yml @@ -11,7 +11,7 @@ tunnels: bolna-app: addr: bolna-app:5001 proto: http - # ingestion-app: - # addr: ingestion-app:8000 - # proto: http + ingestion-app: + addr: ingestion-app:8000 + proto: http From bd4a72ac73e8ed456e4932e8d52777abe953485c Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Thu, 8 Aug 2024 12:36:43 +0530 Subject: [PATCH 11/20] fixed some issues for RAG --- bolna/agent_manager/task_manager.py | 9 +++++---- bolna/agent_types/llama_index_rag_agent.py | 4 +++- bolna/ingestion_server/main.py | 2 +- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/bolna/agent_manager/task_manager.py b/bolna/agent_manager/task_manager.py index 22c9ff6b..16019ba5 100644 --- a/bolna/agent_manager/task_manager.py +++ b/bolna/agent_manager/task_manager.py @@ -210,6 +210,7 @@ def __init__(self, assistant_name, task_id, task, ws, input_parameters=None, con "max_tokens": self.llm_agent_config['max_tokens'], "provider": self.llm_agent_config['provider'], } + logger.info(f"SETTING FOLLOW UP TASK DETAILS {self.llm_agent_config}") @@ -1001,7 +1002,7 @@ def __store_into_history(self, meta_info, messages, llm_response, should_trigger logger.info("##### User spoke while LLM was generating response") else: self.llm_response_generated = True - convert_to_request_log(message=llm_response, meta_info= meta_info, component="llm", direction="response", model=self.llm_agent_config["model"], run_id= self.run_id) + convert_to_request_log(message=llm_response, meta_info= meta_info, component="llm", direction="response", model=self.llm_config['model'], run_id= self.run_id) if should_trigger_function_call: #Now, we need to consider 2 things here #1. There was silence between function call and now @@ -1115,8 +1116,8 @@ async def _process_conversation_task(self, message, sequence, meta_info): logger.info(f"Route {route} has a vector cache") relevant_utterance = self.vector_caches[route].get(message['data']) cache_response = self.route_responses_dict[route][relevant_utterance] - convert_to_request_log(message = message['data'], meta_info= meta_info, component="llm", direction="request", model=self.llm_agent_config["model"], run_id= self.run_id) - convert_to_request_log(message = message['data'], meta_info= meta_info, component="llm", direction="response", model=self.llm_agent_config["model"], is_cached= True, run_id= self.run_id) + convert_to_request_log(message = message['data'], meta_info= meta_info, component="llm", direction="request", model=self.llm_config["model"], run_id= self.run_id) + convert_to_request_log(message = message['data'], meta_info= meta_info, component="llm", direction="response", model=self.llm_config["model"], is_cached= True, run_id= self.run_id) messages = copy.deepcopy(self.history) messages += [{'role': 'user', 'content': message['data']},{'role': 'assistant', 'content': cache_response}] self.interim_history = copy.deepcopy(messages) @@ -1150,7 +1151,7 @@ async def _process_conversation_task(self, message, sequence, meta_info): {'role': 'system', 'content': self.check_for_completion_prompt}, {'role': 'user', 'content': format_messages(self.history, use_system_prompt= True)}] logger.info(f"##### Answer from the LLM {answer}") - convert_to_request_log(message=format_messages(prompt, use_system_prompt= True), meta_info= meta_info, component="llm", direction="request", model=self.llm_agent_config["model"], run_id= self.run_id) + convert_to_request_log(message=format_messages(prompt, use_system_prompt= True), meta_info= meta_info, component="llm", direction="request", model=self.llm_config["model"], run_id= self.run_id) convert_to_request_log(message=answer, meta_info= meta_info, component="llm", direction="response", model= self.check_for_completion_llm, run_id= self.run_id) if should_hangup: diff --git a/bolna/agent_types/llama_index_rag_agent.py b/bolna/agent_types/llama_index_rag_agent.py index e51d052e..1d2335cc 100644 --- a/bolna/agent_types/llama_index_rag_agent.py +++ b/bolna/agent_types/llama_index_rag_agent.py @@ -67,6 +67,8 @@ def __init__(self, vector_id: str, temperature: float, model: str, buffer: int = self.OPENAI_KEY = os.getenv('OPENAI_API_KEY') self.provider = None self.query_engine = None + self.LANCEDB_DIR = os.getenv('LANCEDB_DIR') + logger.info(f"LANCEDB DIR : {self.LANCEDB_DIR}") self._setup_llm() self._setup_provider() @@ -99,7 +101,7 @@ def _setup_provider(self): # Add more providers here as elif statements else: logging.info(f"LanceDB RAG") - self.vector_store = LanceDBVectorStore(table_name=self.provider_config['provider_config'].get('vector_id')) + self.vector_store = LanceDBVectorStore(uri=self.LANCEDB_DIR, table_name=self.provider_config['provider_config'].get('vector_id')) logging.info(f"Table params : {self.provider_config['provider_config'].get('vector_id')}") storage_context = StorageContext.from_defaults(vector_store=self.vector_store) self.vector_index = VectorStoreIndex([], storage_context=storage_context) diff --git a/bolna/ingestion_server/main.py b/bolna/ingestion_server/main.py index f37ec5ba..8024f7f7 100644 --- a/bolna/ingestion_server/main.py +++ b/bolna/ingestion_server/main.py @@ -64,7 +64,7 @@ async def rag_retrive(query: str, rag_id: str, index: str) -> list: Returns: list: A list of documents matching the query. """ - docs = await rag_factory.retrive_query(rag_name=rag_id, index=index, query=query) + docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) send_filter = [{"text": node.text, "score": node.score} for node in docs] return send_filter From c9c2f6b5283d58e33fc352cc15aa708314b198e2 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 9 Aug 2024 15:49:41 +0530 Subject: [PATCH 12/20] Merge dev/proxy-server into develop --- bolna/ingestion_server/main.py | 48 +++++++++++++++++++++++++++++++--- 1 file changed, 45 insertions(+), 3 deletions(-) diff --git a/bolna/ingestion_server/main.py b/bolna/ingestion_server/main.py index 8024f7f7..e3e7e855 100644 --- a/bolna/ingestion_server/main.py +++ b/bolna/ingestion_server/main.py @@ -50,17 +50,14 @@ async def rag_upload_file(file: UploadFile, rag_id: str) -> Dict[str, str]: return {"index": task._index, "status": task._status, "message": "DONE"} except Exception as e: return {"index": None, "status": "ERROR", "message": f"{e}"} - @app.get("/rag-retrive/{rag_id}/{index}") async def rag_retrive(query: str, rag_id: str, index: str) -> list: """Retrieve documents based on a query for a specific RAG ID and index. - Args: query (str): The query string to search for. rag_id (str): The ID of the RAG to search in. index (str): The index to search in. - Returns: list: A list of documents matching the query. """ @@ -68,6 +65,51 @@ async def rag_retrive(query: str, rag_id: str, index: str) -> list: send_filter = [{"text": node.text, "score": node.score} for node in docs] return send_filter +@app.post("/make-rag") +async def make_rag( + file: UploadFile = File(...), + config: str = Form(...) +) -> Dict[str, Any]: + """ + Create a RAG configuration, return its ID, and ingest the uploaded file. + + Args: + file (UploadFile): The file to upload and ingest. + config (str): The RAG configuration as a JSON string. + + Returns: + Dict[str, Any]: A dictionary containing the created RAG ID, upload status, and index. + """ + try: + # Parse the JSON string into a RAGConfig object + rag_config = RAGConfig.parse_raw(config) + + # Create RAG configuration + rag_id = rag_factory.make_rag(rag_config) + + # Ingest the file + task = await rag_factory.file_ingest(rag_name=rag_id, file=file) + + return { + "rag_id": rag_id, + "index": task._index, + "status": task._status, + "message": "RAG created and file ingested successfully" + } + except json.JSONDecodeError: + return { + "rag_id": None, + "index": None, + "status": "ERROR", + "message": "Invalid JSON in config parameter" + } + except Exception as e: + return { + "rag_id": None, + "index": None, + "status": "ERROR", + "message": f"Error creating RAG or ingesting file: {str(e)}" + } if __name__ == "__main__": uvicorn.run("main:app", port=8000, host="0.0.0.0", reload=True) \ No newline at end of file From e7333fa9a534ec592b9fe41894a93207f1066b28 Mon Sep 17 00:00:00 2001 From: Marmik Pandya Date: Tue, 13 Aug 2024 15:12:39 +0530 Subject: [PATCH 13/20] Update requirements.txt --- requirements.txt | 111 +++++++++-------------------------------------- 1 file changed, 20 insertions(+), 91 deletions(-) diff --git a/requirements.txt b/requirements.txt index f4291b3d..ac741456 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,100 +1,29 @@ -# Web Framework and API -fastapi==0.108.0 -uvicorn==0.22.0 -starlette==0.32.0.post1 - -# Data Validation and Settings Management -pydantic==2.8.2 -python-dotenv==1.0.0 - -# Async Libraries -aiohttp==3.9.1 aiobotocore==2.9.0 aiofiles==23.2.1 -aiocsv==1.3.1 -aiodynamo==23.10.1 -aiohttp-retry==2.8.3 -aioitertools==0.11.0 -aiormq==6.8.0 -aiosignal==1.3.1 - -# Database and Storage -redis==5.0.1 -pymongo==4.8.0 -lancedb==0.10.2 -SQLAlchemy==2.0.31 - -# Machine Learning and NLP -numpy==1.26.1 -scipy==1.11.4 -scikit-learn==1.5.1 -torch==2.1.2 -torchaudio==2.1.2 -transformers==4.39.3 -sentence-transformers==3.0.1 +aiohttp==3.9.1 +azure-cognitiveservices-speech==1.38.0 +daily-python==0.9.1 +fastapi==0.108.0 fastembed==0.2.7 -onnxruntime==1.18.1 -nltk==3.8.1 - -# LLM and Related Libraries -openai>=1.10.0 litellm==1.40.20 -llama-index==0.10.57 -llama-index-vector-stores-lancedb==0.1.7 -llama-index-vector-stores-mongodb==0.1.8 -cohere==5.3.2 - -# Text Processing -tiktoken>=0.6.0 -tokenizers==0.15.2 - -# Audio Processing -azure-cognitiveservices-speech==1.38.0 +numpy==1.26.1 +openai>=1.10.0 +pydantic==2.5.3 pydub==0.25.1 -soundfile==0.12.1 - -# Date and Time Handling python-dateutil==2.8.2 -pytz==2024.1 - -# HTTP and Networking +python-dotenv==1.0.0 +redis==5.0.1 requests==2.31.0 -httpx==0.25.2 -websockets==10.4 - -# File Handling and Parsing -python-multipart==0.0.6 -Pillow==10.4.0 -pypdf==4.3.1 - -# API Clients +tiktoken>=0.6.0 +torchaudio==2.0.1 twilio==8.9.0 -daily-python==0.9.1 -slack_sdk==3.31.0 -stripe==8.0.0 - -# Task Queue and Job Processing -celery==5.3.6 -taskiq==0.11.0 - -# Logging and Monitoring -loguru==0.7.2 -sentry-sdk==2.9.0 - -# Development and Testing +uvicorn==0.22.0 +websockets==10.4 +onnxruntime>=1.16.3 +scipy==1.11.4 uvloop==0.19.0 -pytest==7.4.0 - -# Data Processing and Analysis -pandas==2.2.2 -pyarrow==15.0.0 - -# Utilities -python-dateutil==2.8.2 -tenacity==8.5.0 -ratelimiter==1.2.0.post0 - -# Optional Dependencies (uncomment if needed) -huggingface-hub==0.23.2 -semantic-router==0.0.46 -supabase==2.3.3 \ No newline at end of file +tokenizers +huggingface-hub +semantic-router +sentence_transformers +optimum[onnxruntime] From 1a15427d89420c765a729fba64abe45d78990057 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 16 Aug 2024 16:42:38 +0530 Subject: [PATCH 14/20] rag is added with bolna package --- .gitignore | 3 +- bolna/agent_types/llama_index_rag_agent.py | 53 +----- bolna/ingestion_server/__init__.py | 177 ------------------ bolna/ingestion_server/datachecks/__init__.py | 49 ----- bolna/ingestion_server/embeddings/__init__.py | 9 - bolna/ingestion_server/embeddings/base.py | 10 - .../embeddings/openai_emded.py | 31 --- bolna/ingestion_server/main.py | 115 ------------ bolna/ingestion_server/rags/__init__.py | 10 - bolna/ingestion_server/rags/base.py | 156 --------------- bolna/ingestion_server/rags/lancedb_rag.py | 90 --------- bolna/ingestion_server/rags/mongoDB_rag.py | 96 ---------- bolna/ingestion_server/utils/__init__.py | 1 - bolna/ingestion_server/utils/log.py | 31 --- bolna/models.py | 17 +- bolna/rag/base.py | 14 +- bolna/rag/mongodb_rag.py | 47 +---- bolna/rag/testing_connection.py | 27 --- requirements.txt | 6 + 19 files changed, 28 insertions(+), 914 deletions(-) delete mode 100644 bolna/ingestion_server/__init__.py delete mode 100644 bolna/ingestion_server/datachecks/__init__.py delete mode 100644 bolna/ingestion_server/embeddings/__init__.py delete mode 100644 bolna/ingestion_server/embeddings/base.py delete mode 100644 bolna/ingestion_server/embeddings/openai_emded.py delete mode 100644 bolna/ingestion_server/main.py delete mode 100644 bolna/ingestion_server/rags/__init__.py delete mode 100644 bolna/ingestion_server/rags/base.py delete mode 100644 bolna/ingestion_server/rags/lancedb_rag.py delete mode 100644 bolna/ingestion_server/rags/mongoDB_rag.py delete mode 100644 bolna/ingestion_server/utils/__init__.py delete mode 100644 bolna/ingestion_server/utils/log.py delete mode 100644 bolna/rag/testing_connection.py diff --git a/.gitignore b/.gitignore index f6bdcb66..0d9b3b65 100644 --- a/.gitignore +++ b/.gitignore @@ -169,4 +169,5 @@ agent_data/**/mp3 */__pycache__/ */*/__pycache__/ logs/ -agent_data/ \ No newline at end of file +agent_data/ +testing/ \ No newline at end of file diff --git a/bolna/agent_types/llama_index_rag_agent.py b/bolna/agent_types/llama_index_rag_agent.py index 1d2335cc..fb4b32e1 100644 --- a/bolna/agent_types/llama_index_rag_agent.py +++ b/bolna/agent_types/llama_index_rag_agent.py @@ -19,7 +19,8 @@ from bolna.helpers.data_ingestion_pipe import lance_db from bolna.rag.base import DatabaseConnector -from bolna.rag.mongodb_rag import MongoDBConfig, MongoDBConnector, RAGEngine as MongoDBRAGEngine +from bolna.models import * +from bolna.rag.mongodb_rag import MongoDBConnector, RAGEngine as MongoDBRAGEngine load_dotenv(find_dotenv(), override=True) @@ -67,8 +68,8 @@ def __init__(self, vector_id: str, temperature: float, model: str, buffer: int = self.OPENAI_KEY = os.getenv('OPENAI_API_KEY') self.provider = None self.query_engine = None + self.LANCEDB_DIR = os.getenv('LANCEDB_DIR') - logger.info(f"LANCEDB DIR : {self.LANCEDB_DIR}") self._setup_llm() self._setup_provider() @@ -87,20 +88,18 @@ def _setup_provider(self): """Based on the relevant provider config, set up the provider.""" provider_name = self.provider_config.get('provider') - logging.info(f"Provider Name : {provider_name}") if provider_name == 'mongodb': logging.info(f"Setting up {provider_name} RAG") - config = MongoDBConfig(**self.provider_config['provider_config']) + config = MongoDBProviderConfig(**self.provider_config['provider_config']) connector = MongoDBConnector(config) connector.connect() connector.verify_data() self.provider = MongoDBRAGEngine(connector) self.provider.setup() logger.info(f"{provider_name.capitalize()} RAG engine initialized") - # Add more providers here as elif statements else: - logging.info(f"LanceDB RAG") + logging.info(f"Setting up {provider_name} RAG") self.vector_store = LanceDBVectorStore(uri=self.LANCEDB_DIR, table_name=self.provider_config['provider_config'].get('vector_id')) logging.info(f"Table params : {self.provider_config['provider_config'].get('vector_id')}") storage_context = StorageContext.from_defaults(vector_store=self.vector_store) @@ -108,47 +107,6 @@ def _setup_provider(self): self.query_engine = self.vector_index.as_query_engine() logger.info("LanceDB vector store initialized") - # def _setup_vector_store(self): - # """Set up the vector store and index.""" - # logger.info(f"LLAMA INDEX VALUES: {(lance_db, self.vector_id)}") - - - # #We want to incorprate the latest functionality of MONGODB in our llama-index-rag agent script : Task 1 - # #Lightweight server : Backlog : Task 2 - # #Trying out different set of dbs for making sure that bolna rag agent script (e.g llama-index-rag script) is modular and can be utilized for the various scopes - # # Proxy db : Task 3 -> We want to have a proxy db which can be utilized for any kind of db.. - - # #Extras : 1. Payload config might be different based on the dbs that we are trying right now... - # # 2. Ansh is doing with the filler improvements : line number 60 / constants.py - # # 3. - - - # # check for the provider here - # # logic is : check if the user has provided some provider config and if it is then we will - # # check for the relevant provider using the self. - - - # if self.provider_config and self.provider_config.get('provider') == 'mongodb': - # config = self.provider_config['provider_config'] - # client = MongoClient(config['connection_string']) - # self.vector_store = MongoDBAtlasVectorSearch( - # client=client, - # db_name=config['db_name'], - # collection_name=config['collection_name'], - # index_name=config['index_name'] - # ) - # logger.info("MongoDB vector store initialized") - # else: - # self.vector_store = LanceDBVectorStore(uri=lance_db, table_name=self.vector_id) - # logger.info("LanceDB vector store initialized") - - # storage_context = StorageContext.from_defaults(vector_store=self.vector_store) - # self.vector_index = VectorStoreIndex([], storage_context=storage_context) - - - - # self.query_engine = self.vector_index.as_query_engine() - def _setup_agent(self): """Set up the OpenAI agent with the query engine tool.""" tools = [ @@ -199,6 +157,7 @@ async def generate(self, message: List[dict], **kwargs) -> AsyncGenerator[Tuple[ message, history = await asyncio.to_thread(self.convert_history, message) start_time = time.time() + # mongodb if self.provider: # Use provider-specific query method response = self.provider.query(message.content) diff --git a/bolna/ingestion_server/__init__.py b/bolna/ingestion_server/__init__.py deleted file mode 100644 index 052da0fb..00000000 --- a/bolna/ingestion_server/__init__.py +++ /dev/null @@ -1,177 +0,0 @@ -from bolna.ingestion_server.embeddings import EmbedProviders -from bolna.ingestion_server.rags import RAGProviders, BaseRAG -from bolna.ingestion_server.datachecks import RAGConfig, RAGTask, RAGTaskStatus -import asyncio -from threading import Thread -from bolna.ingestion_server.utils import configure_logger -from typing import Dict -from uuid import uuid4 -import tempfile -import os - -logger = configure_logger(__name__) - -class RAG: - """ - Retrieval-Augmented Generation (RAG) implementation. - - This class handles the ingestion and storage of documents for RAG. - """ - - def __init__(self, VectorDB: BaseRAG, Workers: int = 2) -> None: - """ - Initialize the RAG instance. - - Args: - VectorDB (BaseRAG): The vector database to use. - Workers (int, optional): Number of worker threads. Defaults to 2. - """ - self.file_process_task_queue: asyncio.Queue = asyncio.Queue() - self.file_store_task_queue: asyncio.Queue = asyncio.Queue() - - self.VectorDB: BaseRAG = VectorDB - self.Workers: int = Workers - - self.RAG_THREAD = Thread(target=self.start) - self.shutdown = False - - async def __shutdown_loop(self): - """Monitor the shutdown flag.""" - while not self.shutdown: - await asyncio.sleep(0.5) - - async def __ingestion_task(self): - """Process ingestion tasks from the queue.""" - while not self.shutdown: - task: RAGTask = await self.file_process_task_queue.get() - task._status = RAGTaskStatus.PROCESSING - try: - nodes = await self.VectorDB.generate_nodes_sentence_splitter(task.file_loc) - except Exception as e: - logger.error(f"ERROR in {e}") - task._status = RAGTaskStatus.ERROR - continue - task._nodes = nodes - await self.file_store_task_queue.put(task) - - async def __nodes_storage(self): - """Store processed nodes in the vector database.""" - while not self.shutdown: - task: RAGTask = await self.file_store_task_queue.get() - try: - index = await self.VectorDB.add_index(task._nodes) - except Exception as e: - logger.error(f"ERROR in {e}") - task._status = RAGTaskStatus.ERROR - continue - task._index = index - task._status = RAGTaskStatus.SUCESSFUL - - def start(self): - """Start the RAG processing loop.""" - loop = asyncio.new_event_loop() - ingestion_task_pool = [loop.create_task(self.__ingestion_task()) for _ in range(self.Workers)] - file_storage = loop.create_task(self.__nodes_storage()) - loop.run_until_complete(self.__shutdown_loop()) - file_storage.cancel() - for t in ingestion_task_pool: - t.cancel() - loop.close() - - -class RAGFactory: - """ - Factory class for creating and managing RAG instances. - """ - - def __init__(self) -> None: - """Initialize the RAGFactory.""" - self.RAGS: Dict[str, RAG] = dict() - - def make_rag(self, config: RAGConfig): - """ - Create a new RAG instance. - - Args: - config (RAGConfig): Configuration for the RAG instance. - - Returns: - str: Unique identifier for the created RAG instance. - """ - rag_name = f"RAG-{uuid4()}" - embedding_name = EmbedProviders[config.provider_config.embedding_name.provider](config.provider_config.embedding_name.embedding_model_name) - vector_db = RAGProviders[config.provider](embedding_name, config.provider_config) - rag = RAG(vector_db, config.provider_config.worker) - rag.RAG_THREAD.start() - self.RAGS[rag_name] = rag - return rag_name - - def stop_all(self): - """Stop all RAG instances.""" - for rag in self.RAGS.values(): - rag.shutdown = True - - def stop(self, rag_name: str): - """ - Stop a specific RAG instance. - - Args: - rag_name (str): Identifier of the RAG instance to stop. - - Raises: - ValueError: If the specified RAG instance doesn't exist. - """ - if rag_name in self.RAGS.keys(): - self.RAGS[rag_name].shutdown = True - self.RAGS.pop(rag_name) - else: - raise ValueError("No RAG with that ID exists") - - async def file_ingest(self, rag_name, file) -> RAGTask: - """ - Ingest a file into a RAG instance. - - Args: - rag_name (str): Identifier of the RAG instance. - file: File object to ingest. - - Returns: - RAGTask: Task object representing the ingestion process. - - Raises: - ValueError: If the specified RAG instance doesn't exist or if the file type is unsupported. - """ - if rag_name not in self.RAGS.keys(): - raise ValueError(f"RAG: {rag_name} does not exist") - if file.content_type not in ["application/pdf", "application/x-pdf"]: - raise ValueError("Only PDF files are supported for now") - - task_id = str(uuid4()) - temp_file = tempfile.NamedTemporaryFile() - temp_file.write(await file.read()) - prev = temp_file.name - file_name = f"/tmp/{task_id}.pdf" - os.rename(prev, file_name) - task = RAGTask(file_loc=file_name) - await self.RAGS[rag_name].file_process_task_queue.put(task) - - while task._status in [RAGTaskStatus.WAIT, RAGTaskStatus.PROCESSING]: - await asyncio.sleep(0.4) - - os.rename(file_name, prev) - return task - - async def retrieve_query(self, rag_name: str, index: str, query: str): - """ - Retrieve documents based on a query. - - Args: - rag_name (str): Identifier of the RAG instance. - index (str): Index to search in. - query (str): Query string. - - Returns: - List of relevant documents. - """ - rag = self.RAGS[rag_name] - return await rag.VectorDB.get_docs_index(query=query, index=index) \ No newline at end of file diff --git a/bolna/ingestion_server/datachecks/__init__.py b/bolna/ingestion_server/datachecks/__init__.py deleted file mode 100644 index 7b0b27fe..00000000 --- a/bolna/ingestion_server/datachecks/__init__.py +++ /dev/null @@ -1,49 +0,0 @@ -from pydantic import BaseModel -from typing import Union, Optional - -# Embedding config -class embeddings(BaseModel): - provider:str - embedding_model_name:Optional[str] = "" - -# DB Configs -class LanceDBConfig(BaseModel): - loc: Optional[str] = "" - -class MongoDBConfig(BaseModel): - uri: Optional[str] = "" - db: Optional[str] = "" - collection_name: Optional[str] = "" - -# Provider Configs -class ProviderConfig(BaseModel): - embedding_name:embeddings - chunk_size:int - overlapping:int - worker:int - similarity_top_k:int - rag: Union[LanceDBConfig,MongoDBConfig] - -# Rag Config -class RAGConfig(BaseModel): - provider:str - provider_config:ProviderConfig - -class Query(BaseModel): - provider:str - index:str - query:str - -# Utility checks for RAG -class RAGTaskStatus: - WAIT = "WAITING" - PROCESSING = "PROCESSING" - ERROR = "ERROR" - SUCESSFUL = "SUCESSFUL" - -class RAGTask(BaseModel): - file_loc:str - _status:str = RAGTaskStatus.WAIT - _message:str = "" - _index:str = "" - _nodes:list = [] diff --git a/bolna/ingestion_server/embeddings/__init__.py b/bolna/ingestion_server/embeddings/__init__.py deleted file mode 100644 index 8fcce722..00000000 --- a/bolna/ingestion_server/embeddings/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from typing import Dict - -from bolna.ingestion_server.embeddings.openai_emded import OpenAI -from bolna.ingestion_server.embeddings.base import BaseEmbed - -# EmbedProviders dictionary with the key being the name of the provider and the value being the class of the provider -EmbedProviders : Dict[str, BaseEmbed] = { - "OpenAI": OpenAI -} \ No newline at end of file diff --git a/bolna/ingestion_server/embeddings/base.py b/bolna/ingestion_server/embeddings/base.py deleted file mode 100644 index 37b4dd94..00000000 --- a/bolna/ingestion_server/embeddings/base.py +++ /dev/null @@ -1,10 +0,0 @@ -from abc import ABC, abstractmethod - -class BaseEmbed(ABC): - def __init__(self, name: str) -> None: - self.name = name - - @abstractmethod - def get_embedding(self): - """Method to retrieve the embedding representation.""" - raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/bolna/ingestion_server/embeddings/openai_emded.py b/bolna/ingestion_server/embeddings/openai_emded.py deleted file mode 100644 index cc0bcdc9..00000000 --- a/bolna/ingestion_server/embeddings/openai_emded.py +++ /dev/null @@ -1,31 +0,0 @@ -from bolna.ingestion_server.embeddings.base import BaseEmbed -from llama_index.embeddings.openai import OpenAIEmbedding -import dotenv -import os - -dotenv.load_dotenv() - -class OpenAI(BaseEmbed): - def __init__(self, model: str) -> None: - """Initialize the OpenAI embedding provider. - - Args: - model (str): The model name to be used for embeddings. - - Raises: - ValueError: If the OpenAI API key is not found in the environment variables. - """ - super().__init__("OpenAI") - self.model = model - api_key = os.getenv("OPENAI_API_KEY") - if api_key is None: - raise ValueError("OPENAI KEY IS NOT FOUND") - self.embedding_instance = OpenAIEmbedding(model=model, api_key=api_key) - - def get_embedding(self): - """Retrieve the embedding instance. - - Returns: - OpenAIEmbedding: The instance of the OpenAI embedding. - """ - return self.embedding_instance \ No newline at end of file diff --git a/bolna/ingestion_server/main.py b/bolna/ingestion_server/main.py deleted file mode 100644 index e3e7e855..00000000 --- a/bolna/ingestion_server/main.py +++ /dev/null @@ -1,115 +0,0 @@ -from fastapi import FastAPI, UploadFile -from bolna.ingestion_server.datachecks import RAGConfig -from bolna.ingestion_server import RAGFactory -import uvicorn -import time -from typing import Dict -import dotenv - -dotenv.load_dotenv() -DB: Dict[str, RAGConfig] = {} - -rag_factory = RAGFactory() -app = FastAPI() - - -@app.get("/") -def heartbeat() -> float: - """Health check endpoint that returns the current server time.""" - return time.time() - - -@app.post("/create-rag") -def create_rag(request: RAGConfig) -> Dict[str, str]: - """Create a RAG configuration and return its ID. - - Args: - request (RAGConfig): The RAG configuration to create. - - Returns: - Dict[str, str]: A dictionary containing the created RAG ID. - """ - print(request) - rag_id = rag_factory.make_rag(request) - return {"rag_id": rag_id} - - -@app.post("/rag-upload-file/{rag_id}") -async def rag_upload_file(file: UploadFile, rag_id: str) -> Dict[str, str]: - """Upload a file for a specific RAG ID. - - Args: - file (UploadFile): The file to upload. - rag_id (str): The ID of the RAG to associate with the file. - - Returns: - Dict[str, str]: A dictionary containing the upload status and index. - """ - try: - task = await rag_factory.file_ingest(rag_name=rag_id, file=file) - return {"index": task._index, "status": task._status, "message": "DONE"} - except Exception as e: - return {"index": None, "status": "ERROR", "message": f"{e}"} - -@app.get("/rag-retrive/{rag_id}/{index}") -async def rag_retrive(query: str, rag_id: str, index: str) -> list: - """Retrieve documents based on a query for a specific RAG ID and index. - Args: - query (str): The query string to search for. - rag_id (str): The ID of the RAG to search in. - index (str): The index to search in. - Returns: - list: A list of documents matching the query. - """ - docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) - send_filter = [{"text": node.text, "score": node.score} for node in docs] - return send_filter - -@app.post("/make-rag") -async def make_rag( - file: UploadFile = File(...), - config: str = Form(...) -) -> Dict[str, Any]: - """ - Create a RAG configuration, return its ID, and ingest the uploaded file. - - Args: - file (UploadFile): The file to upload and ingest. - config (str): The RAG configuration as a JSON string. - - Returns: - Dict[str, Any]: A dictionary containing the created RAG ID, upload status, and index. - """ - try: - # Parse the JSON string into a RAGConfig object - rag_config = RAGConfig.parse_raw(config) - - # Create RAG configuration - rag_id = rag_factory.make_rag(rag_config) - - # Ingest the file - task = await rag_factory.file_ingest(rag_name=rag_id, file=file) - - return { - "rag_id": rag_id, - "index": task._index, - "status": task._status, - "message": "RAG created and file ingested successfully" - } - except json.JSONDecodeError: - return { - "rag_id": None, - "index": None, - "status": "ERROR", - "message": "Invalid JSON in config parameter" - } - except Exception as e: - return { - "rag_id": None, - "index": None, - "status": "ERROR", - "message": f"Error creating RAG or ingesting file: {str(e)}" - } - -if __name__ == "__main__": - uvicorn.run("main:app", port=8000, host="0.0.0.0", reload=True) \ No newline at end of file diff --git a/bolna/ingestion_server/rags/__init__.py b/bolna/ingestion_server/rags/__init__.py deleted file mode 100644 index 3cd6f199..00000000 --- a/bolna/ingestion_server/rags/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -from bolna.ingestion_server.rags.lancedb_rag import LanceDB -from bolna.ingestion_server.rags.mongoDB_rag import MongoDB -from bolna.ingestion_server.rags.base import BaseRAG - -from typing import Dict - -RAGProviders : Dict[str, BaseRAG] = { - "LanceDB": LanceDB, - "MongoDB": MongoDB -} \ No newline at end of file diff --git a/bolna/ingestion_server/rags/base.py b/bolna/ingestion_server/rags/base.py deleted file mode 100644 index 4b9c8cba..00000000 --- a/bolna/ingestion_server/rags/base.py +++ /dev/null @@ -1,156 +0,0 @@ -import os -import dotenv -from uuid import uuid4 - -from llama_parse import LlamaParse -from llama_index.core.node_parser import ( - MarkdownElementNodeParser, - SentenceSplitter, - TextSplitter -) -from llama_index.llms.openai import OpenAI -from llama_index.core import Settings - -from bolna.ingestion_server.embeddings import BaseEmbed -from bolna.ingestion_server.utils import configure_logger - - -dotenv.load_dotenv() - -logger = configure_logger(__name__) - -class BaseRAG: - """ - Base class for Retrieval-Augmented Generation (RAG) systems. - - Attributes: - provider (str): The provider for the RAG system. - base_embed (BaseEmbed): The embedding model used. - embeding_model: The actual embedding model instance. - chunk_size (int): Size of the chunks for splitting documents. - overlapping (int): Overlap size for chunking. - LLAMA_CLOUD (str): API key for Llama Cloud. - parse (LlamaParse): Instance of LlamaParse for data parsing. - OPENAI_API_KEY (str): API key for OpenAI. - llm (OpenAI): Instance of OpenAI model. - """ - - def __init__(self, provider: str, embedding_name: BaseEmbed, chunk_size: int, overlapping: int) -> None: - """ - Initializes the BaseRAG instance with the specified parameters. - - Args: - provider (str): The provider for the RAG system. - embedding_name (BaseEmbed): The embedding model used. - chunk_size (int): Size of the chunks for splitting documents. - overlapping (int): Overlap size for chunking. - - Raises: - ValueError: If required environment variables are not set. - """ - self.provider = provider - self.base_embed: BaseEmbed = embedding_name - self.embeding_model = self.base_embed.get_embedding() - self.chunk_size = chunk_size - self.overlapping = overlapping - self.LLAMA_CLOUD = os.getenv("LLAMA_CLOUD_API_KEY") - - if self.LLAMA_CLOUD is None: - raise ValueError("LLAMA_CLOUD_API_KEY is not set in .env") - - self.parse = LlamaParse(api_key=self.LLAMA_CLOUD, result_type="markdown") - - self.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") - - if self.OPENAI_API_KEY is None: - raise ValueError("OPENAI_API_KEY is not set in .env") - - self.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.2, api_key=self.OPENAI_API_KEY) - - Settings.embed_model = self.embeding_model - Settings.llm = self.llm - - def generate_index_name(self) -> str: - """Generates a unique index name using UUID. - - Returns: - str: A unique index name. - """ - return str(uuid4()) - - async def generate_nodes_sentence_splitter(self, file_loc: str): - """Generates nodes using a sentence splitter. - - Args: - file_loc (str): The file location to load data from. - - Returns: - nodes: The generated nodes after processing. - """ - docs = await self.parse.aload_data(file_path=file_loc) - node_parser = MarkdownElementNodeParser(num_workers=8, llm=self.llm) - nodes = await node_parser.aget_nodes_from_documents(docs) - nodes, _ = node_parser.get_nodes_and_objects(nodes) - nodes = await SentenceSplitter(chunk_size=self.chunk_size, chunk_overlap=self.overlapping).aget_nodes_from_documents(nodes) - return nodes - - async def generate_nodes_text_splitter(self, file_loc: str): - """Generates nodes using a text splitter. - - Args: - file_loc (str): The file location to load data from. - - Returns: - nodes: The generated nodes after processing. - """ - docs = await self.parse.aload_data(file_path=file_loc) - node_parser = MarkdownElementNodeParser(num_workers=8, llm=self.llm) - nodes = await node_parser.aget_nodes_from_documents(docs) - nodes, _ = node_parser.get_nodes_and_objects(nodes) - nodes = await TextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.overlapping).aget_nodes_from_documents(nodes) - return nodes - - async def append_index(self, nodes) -> str: - """Appends nodes to the existing index. - - Args: - nodes: The nodes to append. - - Raises: - NotImplementedError: This method should be implemented in subclasses. - """ - raise NotImplementedError - - async def add_index(self, nodes) -> str: - """Adds nodes to the index. - - Args: - nodes: The nodes to add. - - Raises: - NotImplementedError: This method should be implemented in subclasses. - """ - raise NotImplementedError - - async def delete_index(self, index: str) -> bool: - """Deletes an index. - - Args: - index (str): The index to delete. - - Raises: - NotImplementedError: This method should be implemented in subclasses. - """ - raise NotImplementedError - - async def get_docs_index(self, query: str, index: str): - """Retrieves documents from the index based on a query. - - Args: - query (str): The query to search for. - index (str): The index to search in. - - Raises: - NotImplementedError: This method should be implemented in subclasses. - """ - raise NotImplementedError \ No newline at end of file diff --git a/bolna/ingestion_server/rags/lancedb_rag.py b/bolna/ingestion_server/rags/lancedb_rag.py deleted file mode 100644 index 42e5cdef..00000000 --- a/bolna/ingestion_server/rags/lancedb_rag.py +++ /dev/null @@ -1,90 +0,0 @@ -from typing import Any, Coroutine -from bolna.ingestion_server.embeddings.base import BaseEmbed -from bolna.ingestion_server.rags.base import BaseRAG -from llama_index.vector_stores.lancedb import LanceDBVectorStore -from llama_index.core import VectorStoreIndex, StorageContext -from bolna.ingestion_server.datachecks import ProviderConfig, LanceDBConfig -from llama_index.core.retrievers import VectorIndexRetriever - -class LanceDB(BaseRAG): - """ - LanceDB class for managing vector storage and retrieval using LanceDB. - - Attributes: - similarity_top_k (int): Number of top similar items to retrieve. - config (LanceDBConfig): Configuration for LanceDB. - loc (str): Location for the vector database. - path (str): Path to the vector database data. - """ - - def __init__(self, embedding_name: BaseEmbed, config: ProviderConfig) -> None: - """ - Initializes the LanceDB instance. - - Args: - embedding_name (BaseEmbed): The embedding model used. - config (ProviderConfig): Configuration for the provider. - """ - super().__init__("LanceDB", embedding_name, config.chunk_size, config.overlapping) - self.similarity_top_k = config.similarity_top_k - self.config: LanceDBConfig = config.rag - self.loc = self.config.loc - self.path = f"vectordb_data/{self.loc}" - - async def append_index(self, nodes) -> Coroutine[Any, Any, str]: - """Appends nodes to the existing index. - - Args: - nodes: The nodes to append. - - Returns: - Coroutine: A coroutine that returns None. - """ - return None - - async def add_index(self, nodes) -> str: - """Adds nodes to the index and creates a new table. - - Args: - nodes: The nodes to add. - - Returns: - str: The name of the created table. - """ - table_name = self.generate_index_name() - # TODO: add reranking in the DB - vector_store = LanceDBVectorStore(self.path, table_name=table_name) - storage_context = StorageContext.from_defaults(vector_store=vector_store) - vector_index = VectorStoreIndex(nodes=nodes, storage_context=storage_context, embed_model=self.embeding_model) - return table_name - - async def delete_index(self, index: str) -> bool: - """Deletes an index. - - Args: - index (str): The index to delete. - - Returns: - bool: Result of the deletion operation. - """ - return await super().delete_index(index) - - async def get_docs_index(self, query: str, index: str): - """Retrieves documents from the index based on a query. - - Args: - query (str): The query to search for. - index (str): The index to search in. - - Returns: - Retrieved documents based on the query. - """ - vector_store = LanceDBVectorStore(uri=self.path, table_name=index) - storage_context = StorageContext.from_defaults(vector_store=vector_store) - vector_index = VectorStoreIndex(nodes=[], storage_context=storage_context) - query_engine = VectorIndexRetriever(vector_index, similarity_top_k=self.similarity_top_k) - return query_engine.retrieve(query) - - # query_engine = vector_index.as_query_engine(llm=self.llm) - - diff --git a/bolna/ingestion_server/rags/mongoDB_rag.py b/bolna/ingestion_server/rags/mongoDB_rag.py deleted file mode 100644 index 713f53ef..00000000 --- a/bolna/ingestion_server/rags/mongoDB_rag.py +++ /dev/null @@ -1,96 +0,0 @@ -from typing import Any, Coroutine -from pymongo import MongoClient -from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch - -from bolna.ingestion_server.embeddings.base import BaseEmbed -from bolna.ingestion_server.rags.base import BaseRAG -from bolna.ingestion_server.datachecks import ProviderConfig, MongoDBConfig - -from llama_index.core import VectorStoreIndex, StorageContext -from llama_index.core.retrievers import VectorIndexRetriever - -class MongoDB(BaseRAG): - """ - MongoDB class for managing vector storage and retrieval using MongoDB. - - Attributes: - similarity_top_k (int): Number of top similar items to retrieve. - config (MongoDBConfig): Configuration for MongoDB. - client (MongoClient): MongoDB client instance. - """ - - def __init__(self, embedding_name: BaseEmbed, config: ProviderConfig) -> None: - """ - Initializes the MongoDB instance. - - Args: - embedding_name (BaseEmbed): The embedding model used. - config (ProviderConfig): Configuration for the provider. - """ - super().__init__("MongoDB", embedding_name, config.chunk_size, config.overlapping) - self.similarity_top_k = config.similarity_top_k - self.config: MongoDBConfig = config.rag - self.client = MongoClient(self.config.uri) - - async def append_index(self, nodes) -> Coroutine[Any, Any, str]: - """Appends nodes to the existing index. - - Args: - nodes: The nodes to append. - - Returns: - Coroutine: A coroutine that calls the base class method. - """ - return await super().append_index(nodes) - - async def get_docs_index(self, query: str, index: str): - """Retrieves documents from the index based on a query. - - Args: - query (str): The query to search for. - index (str): The index to search in. - - Returns: - Retrieved documents based on the query. - """ - vector_store = MongoDBAtlasVectorSearch( - self.client, - db_name=self.config.db, - collection_name=self.config.collection_name, - vector_index_name=index - ) - vector_store_context = StorageContext.from_defaults(vector_store=vector_store) - vector_store_index = VectorStoreIndex(nodes=[], storage_context=vector_store_context) - vector_store_retriever = VectorIndexRetriever(index=vector_store_index, similarity_top_k=self.similarity_top_k) - return vector_store_retriever.retrieve(query) - - async def delete_index(self, index: str) -> Coroutine[Any, Any, bool]: - """Deletes an index. - - Args: - index (str): The index to delete. - - Returns: - Coroutine: A coroutine that calls the base class method. - """ - return await super().delete_index(index) - - async def add_index(self, nodes) -> str: - """Adds nodes to the index and creates a new index. - - Args: - nodes: The nodes to add. - - Returns: - str: The name of the created index. - """ - index = self.generate_index_name() - vector_store = MongoDBAtlasVectorSearch( - self.client, - db_name=self.config.db, - collection_name=self.config.collection_name, - index_name=index - ) - vector_store_context = StorageContext.from_defaults(vector_store=vector_store) - vector_store_index = VectorStoreIndex(nodes=nodes, storage_context=vector_store_context, embed_model=self.embeding_model) - return index \ No newline at end of file diff --git a/bolna/ingestion_server/utils/__init__.py b/bolna/ingestion_server/utils/__init__.py deleted file mode 100644 index 6f2c0c32..00000000 --- a/bolna/ingestion_server/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from bolna.ingestion_server.utils.log import configure_logger \ No newline at end of file diff --git a/bolna/ingestion_server/utils/log.py b/bolna/ingestion_server/utils/log.py deleted file mode 100644 index acc08150..00000000 --- a/bolna/ingestion_server/utils/log.py +++ /dev/null @@ -1,31 +0,0 @@ -import logging - -VALID_LOGGING_LEVELS = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] - -def configure_logger(file_name, enabled=True, logging_level='INFO'): - """ - Configures a logger for the specified file. - - Parameters: - - file_name (str): The name of the file for which the logger is being configured. - - enabled (bool): Flag to enable or disable the logger. Default is True. - - logging_level (str): The logging level to set. Must be one of the valid levels. Default is 'INFO'. - - Returns: - - logger (logging.Logger): Configured logger instance. - """ - if logging_level not in VALID_LOGGING_LEVELS: - logging_level = "INFO" - - logging.basicConfig( - level=logging_level, - format="%(asctime)s.%(msecs)03d %(levelname)s {%(module)s} [%(funcName)s] %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - ) - - logger = logging.getLogger(file_name) - - if not enabled: - logger.disabled = True - - return logger \ No newline at end of file diff --git a/bolna/models.py b/bolna/models.py index 7bcc58aa..085b51ee 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -3,7 +3,6 @@ from pydantic import BaseModel, Field, validator, ValidationError, Json from pydantic_core import PydanticCustomError -from bolna.agent_types.base_agent import BaseAgent from .providers import * AGENT_WELCOME_MESSAGE = "This call is being recorded for quality assurance and training. Please speak now." @@ -145,17 +144,17 @@ class MongoDBProviderConfig(BaseModel): db_name: Optional[str] = None collection_name: Optional[str] = None index_name: Optional[str] = None - llm_model: Optional[str] = None - embedding_model: Optional[str] = None - embedding_dimensions: Optional[str] = None + llm_model: Optional[str] = "gpt-3.5-turbo" + embedding_model: Optional[str] = "text-embedding-3-small" + embedding_dimensions: Optional[int] = 256 class LanceDBProviderConfig(BaseModel): vector_id: str class VectorStore(BaseModel): provider: str - provider_config: Union[LanceDBProviderConfig, MongoDBProviderConfig] - + provider_config: MongoDBProviderConfig + # provider_config: Union[MongoDBProviderConfig, LanceDBProviderConfig] class LLM(BaseModel): model: Optional[str] = "gpt-3.5-turbo" @@ -210,13 +209,15 @@ class MultiAgent(BaseModel): class KnowledgebaseAgent(LLM): vector_store: VectorStore + provider: Optional[str] = "openai" + model: Optional[str] = "gpt-3.5-turbo" class LLM_AGENT(BaseModel): agent_flow_type: str agent_type: str #can be llamaindex_rag, simple_llm_agent, router_agent, dag_agent, openai_assistant, custom, etc - #extra_config: Union[OpenaiAssistants, LLM_AGENT_GRAPH, MultiAgent, LLM, SIMPLE_LLM_AGENT] guardrails: Optional[Routes] = None #Just to reduce confusion - extra_config: Union[OpenaiAssistants, LLM_AGENT_GRAPH, MultiAgent, KnowledgebaseAgent, SIMPLE_LLM_AGENT, LLM] + extra_config: KnowledgebaseAgent + #extra_config: Union[OpenaiAssistants, LLM_AGENT_GRAPH, MultiAgent, LLM, SIMPLE_LLM_AGENT, KnowledgebaseAgent] class MessagingModel(BaseModel): diff --git a/bolna/rag/base.py b/bolna/rag/base.py index 0ce0c51d..47d62fea 100644 --- a/bolna/rag/base.py +++ b/bolna/rag/base.py @@ -44,16 +44,4 @@ def query(self, query_text, similarity_top_k=5): if not self.index: raise ValueError("Index not created. Call create_index() first.") query_engine = self.index.as_query_engine(similarity_top_k=similarity_top_k) - return query_engine.query(query_text) - -# def run_queries(search_engine, queries): -# for query in queries: -# response = search_engine.query(query) -# print(f"Query: {query}") -# print(f"Response: {response}") -# print("Source nodes:") -# for node in response.source_nodes: -# print(f"Score: {node.score}") -# print(f"Content: {node.node.get_content()[:200]}...") -# print(f"Metadata: {node.node.metadata}") -# print() \ No newline at end of file + return query_engine.query(query_text) \ No newline at end of file diff --git a/bolna/rag/mongodb_rag.py b/bolna/rag/mongodb_rag.py index c41f1578..25cad53e 100644 --- a/bolna/rag/mongodb_rag.py +++ b/bolna/rag/mongodb_rag.py @@ -10,23 +10,15 @@ from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch +from bolna.models import * from bolna.rag.base import DatabaseConnector # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) -class MongoDBConfig(BaseModel): - connection_string: str - db_name: str - collection_name: str - index_name: str - llm_model: Optional[str] = "gpt-3.5-turbo" # Default LLM model - embedding_model: Optional[str] = "text-embedding-3-small" # Default embedding model - embedding_dimensions: Optional[int] = 256 # Default dimensions - class MongoDBConnector(DatabaseConnector): - def __init__(self, config: MongoDBConfig): + def __init__(self, config: MongoDBProviderConfig): super().__init__(config.db_name) self.config = config self.client = None @@ -62,7 +54,7 @@ def setup_vector_store(self): self.client, db_name=self.config.db_name, collection_name=self.config.collection_name, - index_name=self.config.index_name + vector_index_name=self.config.index_name ) class RAGEngine: @@ -91,35 +83,4 @@ def query(self, query_text: str, similarity_top_k: int = 5): logger.info(f"Source - Score: {node.score}, Content: {node.node.get_content()[:10]}") #logger.info(f"Source - Score: {node.score}, Content: {node.node.get_content()[:10]}..., Metadata: {node.node.metadata}") - return response - -# def main(): -# OPENAI_API_KEY = "***" -# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY - -# config = MongoDBConfig( -# connection_string="**", -# db_name="**", -# collection_name="**", -# index_name="**", -# ) - -# try: -# db_connector = MongoDBConnector(config) -# db_connector.connect() -# db_connector.verify_data() - -# rag_engine = RAGEngine(db_connector) -# rag_engine.setup() - -# query = "Any romantic movie for me? you can give anything you want?" -# rag_engine.query(query) - -# except Exception as e: -# logger.error(f"An error occurred: {e}") -# finally: -# if 'db_connector' in locals(): -# db_connector.disconnect() - -# if __name__ == "__main__": -# main() + return response \ No newline at end of file diff --git a/bolna/rag/testing_connection.py b/bolna/rag/testing_connection.py deleted file mode 100644 index ab103cae..00000000 --- a/bolna/rag/testing_connection.py +++ /dev/null @@ -1,27 +0,0 @@ - -import pymongo - -def get_mongo_client(mongo_uri): - """Establish connection to the MongoDB.""" - try: - client = pymongo.MongoClient(mongo_uri) - print("Connection to MongoDB successful") - return client - except pymongo.errors.ConnectionFailure as e: - print(f"Connection failed: {e}") - return None - -mongo_uri = "mongodb+srv://vipul:qqgr4bwAYl5pZSU9@testing-rag.nqaknom.mongodb.net/" - -if not mongo_uri: - print("MONGO_URI not set in environment variables") - -mongo_client = get_mongo_client(mongo_uri) - -DB_NAME="movies" -COLLECTION_NAME="movies_records" - -db = mongo_client[DB_NAME] -collection = db[COLLECTION_NAME] -print(type(db)) -print(type(collection)) diff --git a/requirements.txt b/requirements.txt index ac741456..1e2f61d3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -27,3 +27,9 @@ huggingface-hub semantic-router sentence_transformers optimum[onnxruntime] +lancedb==0.12.0 +llama-index==0.10.65 +llama-index-vector-stores-lancedb==0.1.7 +llama-index-vector-stores-mongodb==0.1.8 +python-multipart==0.0.9 +sentry_sdk==2.13.0 \ No newline at end of file From 1d91dedac703903215b29fbb4fa34fa43d53d12f Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 16 Aug 2024 17:38:21 +0530 Subject: [PATCH 15/20] Union issue is resolved, uffff. --- bolna/models.py | 53 ++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 46 insertions(+), 7 deletions(-) diff --git a/bolna/models.py b/bolna/models.py index 085b51ee..0ee72713 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -1,6 +1,6 @@ import json from typing import Optional, List, Union, Dict -from pydantic import BaseModel, Field, validator, ValidationError, Json +from pydantic import BaseModel, Field, validator, field_validator, ValidationError, Json from pydantic_core import PydanticCustomError from .providers import * @@ -8,6 +8,11 @@ AGENT_WELCOME_MESSAGE = "This call is being recorded for quality assurance and training. Please speak now." +# Configure logging +import logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + def validate_attribute(value, allowed_values): if value not in allowed_values: raise ValidationError(f"Invalid provider {value}. Supported values: {allowed_values}") @@ -153,8 +158,14 @@ class LanceDBProviderConfig(BaseModel): class VectorStore(BaseModel): provider: str - provider_config: MongoDBProviderConfig - # provider_config: Union[MongoDBProviderConfig, LanceDBProviderConfig] + provider_config: Union[LanceDBProviderConfig, MongoDBProviderConfig] + + @field_validator('provider_config', mode='before') + def validate_provider_config(cls, value, info): + vector_provider = info.data.get('provider') + if vector_provider not in ['mongodb', 'lancedb']: + raise ValueError('Unsupported provider for vector_store') + return value class LLM(BaseModel): model: Optional[str] = "gpt-3.5-turbo" @@ -212,12 +223,40 @@ class KnowledgebaseAgent(LLM): provider: Optional[str] = "openai" model: Optional[str] = "gpt-3.5-turbo" + class LLM_AGENT(BaseModel): agent_flow_type: str - agent_type: str #can be llamaindex_rag, simple_llm_agent, router_agent, dag_agent, openai_assistant, custom, etc - guardrails: Optional[Routes] = None #Just to reduce confusion - extra_config: KnowledgebaseAgent - #extra_config: Union[OpenaiAssistants, LLM_AGENT_GRAPH, MultiAgent, LLM, SIMPLE_LLM_AGENT, KnowledgebaseAgent] + agent_type: str + guardrails: Optional[Routes] = None + extra_config: Union[OpenaiAssistants, KnowledgebaseAgent] + + @field_validator('extra_config', mode='before') + def validate_extra_config(cls, value, info): + agent_type = info.data.get('agent_type') + print(f"Agent type: {agent_type}") + print(f"Value type: {type(value)}") + print(f"Value: {value}") + + valid_config_types = { + 'openai_assistant': OpenaiAssistants, + 'knowledgebase_agent': KnowledgebaseAgent, + 'llm_agent_graph': LLM_AGENT_GRAPH, + 'multiagent': MultiAgent, + 'simple_llm_agent': SIMPLE_LLM_AGENT, + } + + if agent_type not in valid_config_types: + raise ValueError(f'Unsupported agent_type: {agent_type}') + + expected_type = valid_config_types[agent_type] + + if not isinstance(value, dict): + raise ValueError(f"extra_config must be a dict, got {type(value)}") + + try: + return expected_type(**value) + except Exception as e: + raise ValueError(f"Failed to create {expected_type.__name__} from extra_config: {str(e)}") class MessagingModel(BaseModel): From 7b0c3387c66417e8f23db4bbd499720331596560 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 16 Aug 2024 17:45:53 +0530 Subject: [PATCH 16/20] changes --- bolna/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bolna/models.py b/bolna/models.py index 0ee72713..3625af95 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -228,7 +228,7 @@ class LLM_AGENT(BaseModel): agent_flow_type: str agent_type: str guardrails: Optional[Routes] = None - extra_config: Union[OpenaiAssistants, KnowledgebaseAgent] + extra_config: Union[OpenaiAssistants, KnowledgebaseAgent, LLM_AGENT_GRAPH, MultiAgent, SIMPLE_LLM_AGENT] @field_validator('extra_config', mode='before') def validate_extra_config(cls, value, info): From e33c1ca9434844c7ec74ff2f3cc8f20f90277f60 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Mon, 26 Aug 2024 16:37:27 +0530 Subject: [PATCH 17/20] RAG in local_setup --- .env.sample | 10 +- bolna/models.py | 10 - bolna/rag/mongodb_rag.py | 7 - examples/batch-calling/README.md | 1 - examples/batch-calling/batch_calling.py | 110 ------- examples/function-calling/example_json.json | 216 ------------- examples/openai-assistants/README.md | 233 -------------- examples/webrtc/README.md | 125 -------- examples/webrtc/docker-compose.yml | 28 -- .../dockerfiles/bolna_server.Dockerfile | 19 -- examples/webrtc/quickstart_server.py | 122 ------- examples/webrtc/requirements.txt | 7 - examples/whisper-melo-llama3/.env-sample | 21 -- examples/whisper-melo-llama3/Readme.md | 297 ------------------ .../audio/audio_sample/EN-AU.wav | Bin 256894 -> 0 bytes .../audio/audio_sample/EN-BR.wav | Bin 223086 -> 0 bytes .../audio/audio_sample/EN-Default.wav | Bin 216770 -> 0 bytes .../audio/audio_sample/EN_INDIA.wav | Bin 237946 -> 0 bytes .../audio/audio_sample/EN_US.wav | Bin 257638 -> 0 bytes .../whisper-melo-llama3/audio/demo_audio.mp3 | Bin 450980 -> 0 bytes .../whisper-melo-llama3/docker-compose.yml | 83 ----- .../dockerfiles/bolna_server.Dockerfile | 19 -- .../dockerfiles/melo_server.Dockerfile | 13 - .../dockerfiles/twilio_server.Dockerfile | 11 - .../dockerfiles/whisper_server.Dockerfile | 16 - .../whisper-melo-llama3/img/agent_res.png | Bin 5578 -> 0 bytes .../whisper-melo-llama3/img/docker_dw.png | Bin 38474 -> 0 bytes .../whisper-melo-llama3/img/docker_up.png | Bin 35326 -> 0 bytes examples/whisper-melo-llama3/ngrok-config.yml | 10 - .../whisper-melo-llama3/quickstart_server.py | 96 ------ examples/whisper-melo-llama3/requirements.txt | 5 - .../telephony_server/twilio_api_server.py | 94 ------ examples/with-fillers/README.md | 21 -- examples/with-fillers/example_agent.json | 0 examples/with-guardrails/README.md | 31 -- examples/with-guardrails/example_agent.json | 199 ------------ local_setup/docker-compose.yml | 30 +- .../dockerfiles/bolna_server.Dockerfile | 3 +- .../dockerfiles/ingestion_server.Dockerfile | 6 +- local_setup/ingestion_server/__init__.py | 177 +++++++++++ .../ingestion_server/datachecks/__init__.py | 60 ++++ .../ingestion_server/embeddings/__init__.py | 9 + .../ingestion_server/embeddings/base.py | 10 + .../embeddings/openai_emded.py | 31 ++ local_setup/ingestion_server/payload.markdown | 19 ++ .../quickstart_ingestion_server.py | 261 +++++++++++++++ local_setup/ingestion_server/rags/__init__.py | 10 + local_setup/ingestion_server/rags/base.py | 156 +++++++++ .../ingestion_server/rags/lancedb_rag.py | 105 +++++++ .../ingestion_server/rags/mongoDB_rag.py | 96 ++++++ .../ingestion_server/utils/__init__.py | 1 + local_setup/ingestion_server/utils/log.py | 31 ++ local_setup/ngrok-config.yml | 8 +- local_setup/quick_scripts/buildup_docker.sh | 15 +- local_setup/quickstart_ingestion_server.py | 69 ---- local_setup/requirements.txt | 9 +- .../telephony_server/twilio_api_server.py | 2 +- 57 files changed, 1017 insertions(+), 1895 deletions(-) delete mode 100644 examples/batch-calling/README.md delete mode 100644 examples/batch-calling/batch_calling.py delete mode 100644 examples/function-calling/example_json.json delete mode 100644 examples/openai-assistants/README.md delete mode 100644 examples/webrtc/README.md delete mode 100644 examples/webrtc/docker-compose.yml delete mode 100644 examples/webrtc/dockerfiles/bolna_server.Dockerfile delete mode 100644 examples/webrtc/quickstart_server.py delete mode 100644 examples/webrtc/requirements.txt delete mode 100644 examples/whisper-melo-llama3/.env-sample delete mode 100644 examples/whisper-melo-llama3/Readme.md delete mode 100644 examples/whisper-melo-llama3/audio/audio_sample/EN-AU.wav delete mode 100644 examples/whisper-melo-llama3/audio/audio_sample/EN-BR.wav delete mode 100644 examples/whisper-melo-llama3/audio/audio_sample/EN-Default.wav delete mode 100644 examples/whisper-melo-llama3/audio/audio_sample/EN_INDIA.wav delete mode 100644 examples/whisper-melo-llama3/audio/audio_sample/EN_US.wav delete mode 100644 examples/whisper-melo-llama3/audio/demo_audio.mp3 delete mode 100644 examples/whisper-melo-llama3/docker-compose.yml delete mode 100644 examples/whisper-melo-llama3/dockerfiles/bolna_server.Dockerfile delete mode 100644 examples/whisper-melo-llama3/dockerfiles/melo_server.Dockerfile delete mode 100644 examples/whisper-melo-llama3/dockerfiles/twilio_server.Dockerfile delete mode 100644 examples/whisper-melo-llama3/dockerfiles/whisper_server.Dockerfile delete mode 100644 examples/whisper-melo-llama3/img/agent_res.png delete mode 100644 examples/whisper-melo-llama3/img/docker_dw.png delete mode 100644 examples/whisper-melo-llama3/img/docker_up.png delete mode 100644 examples/whisper-melo-llama3/ngrok-config.yml delete mode 100644 examples/whisper-melo-llama3/quickstart_server.py delete mode 100644 examples/whisper-melo-llama3/requirements.txt delete mode 100644 examples/whisper-melo-llama3/telephony_server/twilio_api_server.py delete mode 100644 examples/with-fillers/README.md delete mode 100644 examples/with-fillers/example_agent.json delete mode 100644 examples/with-guardrails/README.md delete mode 100644 examples/with-guardrails/example_agent.json create mode 100644 local_setup/ingestion_server/__init__.py create mode 100644 local_setup/ingestion_server/datachecks/__init__.py create mode 100644 local_setup/ingestion_server/embeddings/__init__.py create mode 100644 local_setup/ingestion_server/embeddings/base.py create mode 100644 local_setup/ingestion_server/embeddings/openai_emded.py create mode 100644 local_setup/ingestion_server/payload.markdown create mode 100644 local_setup/ingestion_server/quickstart_ingestion_server.py create mode 100644 local_setup/ingestion_server/rags/__init__.py create mode 100644 local_setup/ingestion_server/rags/base.py create mode 100644 local_setup/ingestion_server/rags/lancedb_rag.py create mode 100644 local_setup/ingestion_server/rags/mongoDB_rag.py create mode 100644 local_setup/ingestion_server/utils/__init__.py create mode 100644 local_setup/ingestion_server/utils/log.py delete mode 100644 local_setup/quickstart_ingestion_server.py diff --git a/.env.sample b/.env.sample index f42c3842..5327fb3d 100644 --- a/.env.sample +++ b/.env.sample @@ -5,13 +5,21 @@ OPENAI_MODEL=gpt-3.5-turbo REDIS_URL=redis://redis:6379 WHISPER_URL= STYLE_TTS= +BUCKET_NAME=bolna-user-prompts-sandbox + +# RAG credentials +LANCEDB_DIR= +LLAMA_CLOUD_API_KEY= + +# AWS credentials +AWS_ACCESS_KEY_ID= +AWS_SECRET_ACCESS_KEY= # Twilio credentials TWILIO_ACCOUNT_SID= TWILIO_AUTH_TOKEN= TWILIO_PHONE_NUMBER= - # Plivo credentials PLIVO_AUTH_ID= PLIVO_AUTH_TOKEN= diff --git a/bolna/models.py b/bolna/models.py index 3625af95..1ac4d4e1 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -7,18 +7,11 @@ AGENT_WELCOME_MESSAGE = "This call is being recorded for quality assurance and training. Please speak now." - -# Configure logging -import logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -logger = logging.getLogger(__name__) - def validate_attribute(value, allowed_values): if value not in allowed_values: raise ValidationError(f"Invalid provider {value}. Supported values: {allowed_values}") return value - class PollyConfig(BaseModel): voice: str engine: str @@ -233,9 +226,6 @@ class LLM_AGENT(BaseModel): @field_validator('extra_config', mode='before') def validate_extra_config(cls, value, info): agent_type = info.data.get('agent_type') - print(f"Agent type: {agent_type}") - print(f"Value type: {type(value)}") - print(f"Value: {value}") valid_config_types = { 'openai_assistant': OpenaiAssistants, diff --git a/bolna/rag/mongodb_rag.py b/bolna/rag/mongodb_rag.py index 25cad53e..4ab060fc 100644 --- a/bolna/rag/mongodb_rag.py +++ b/bolna/rag/mongodb_rag.py @@ -76,11 +76,4 @@ def query(self, query_text: str, similarity_top_k: int = 5): raise ValueError("Index not created. Call setup() first.") query_engine = self.index.as_query_engine(similarity_top_k=similarity_top_k) response = query_engine.query(query_text) - - logger.info(f"Query: {query_text}") - logger.info(f"Response: {response.response}") - for node in response.source_nodes: - logger.info(f"Source - Score: {node.score}, Content: {node.node.get_content()[:10]}") - #logger.info(f"Source - Score: {node.score}, Content: {node.node.get_content()[:10]}..., Metadata: {node.node.metadata}") - return response \ No newline at end of file diff --git a/examples/batch-calling/README.md b/examples/batch-calling/README.md deleted file mode 100644 index 86623e57..00000000 --- a/examples/batch-calling/README.md +++ /dev/null @@ -1 +0,0 @@ -https://docs.bolna.dev/batch-calling#example-request \ No newline at end of file diff --git a/examples/batch-calling/batch_calling.py b/examples/batch-calling/batch_calling.py deleted file mode 100644 index 84719c1d..00000000 --- a/examples/batch-calling/batch_calling.py +++ /dev/null @@ -1,110 +0,0 @@ -import asyncio -import os -from dotenv import load_dotenv -import aiohttp - -# Load environment variables from .env file -load_dotenv() - -# Load from .env -host = "https://api.bolna.dev" -api_key = os.getenv("api_key", None) -agent_id = 'ee153a6c-19f8-3a61-989a-9146a31c7834' #agent_id in which we want to create the batch -file_path = '/path/of/csv/file' -schedule_time = '2024-06-01T04:10:00+05:30' - - -async def schedule_batch(api_key, agent_id, batch_id=None, scheduled_at=None): - print("now scheduling batch for batch id : {}".format(batch_id)) - url = f"{host}/batches/schedule" - headers = {'Authorization': f'Bearer {api_key}'} - data = { - 'agent_id': agent_id, - 'batch_id': batch_id, - 'scheduled_at': scheduled_at - } - - try: - async with aiohttp.ClientSession() as session: - async with session.post(url, headers=headers, data=data) as response: - response_data = await response.json() - if response.status == 200: - return response_data - else: - raise Exception(f"Error scheduling batch: {response_data}") - except aiohttp.ClientError as e: - print(f"HTTP Client Error: {str(e)}") - except Exception as e: - print(f"Unexpected error: {str(e)}") - - -async def get_batch_status(api_key, agent_id, batch_id=None): - print("now getting batch status for batch id : {}".format(batch_id)) - url = f"{host}/batches/{agent_id}/{batch_id}" - headers = {'Authorization': f'Bearer {api_key}'} - - try: - async with aiohttp.ClientSession() as session: - async with session.get(url, headers=headers) as response: - response_data = await response.json() - if response.status == 200: - return response_data - else: - raise Exception(f"Error getting batch status: {response_data}") - except aiohttp.ClientError as e: - print(f"HTTP Client Error: {str(e)}") - except Exception as e: - print(f"Unexpected error: {str(e)}") - - -async def get_batch_executions(api_key, agent_id, batch_id=None): - print("now getting batch executions for batch id : {}".format(batch_id)) - url = f"{host}/batches/{agent_id}/{batch_id}/executions" - headers = {'Authorization': f'Bearer {api_key}'} - - try: - async with aiohttp.ClientSession() as session: - async with session.get(url, headers=headers) as response: - response_data = await response.json() - if response.status == 200: - return response_data - else: - raise Exception(f"Error getting batch executions: {response_data}") - except aiohttp.ClientError as e: - print(f"HTTP Client Error: {str(e)}") - except Exception as e: - print(f"Unexpected error: {str(e)}") - - -async def create_batch(): - url = f"{host}/batches" - headers = {'Authorization': f'Bearer {api_key}'} - - with open(file_path, 'rb') as f: - form_data = aiohttp.FormData() - form_data.add_field('agent_id', agent_id) - form_data.add_field('file', f, filename=os.path.basename(file_path), content_type='application/octet-stream') - - async with aiohttp.ClientSession() as session: - async with session.post(url, headers=headers, data=form_data) as response: - response_data = await response.json() - if response_data.get('state') == 'created': - batch_id = response_data.get('batch_id') - res = await schedule_batch(api_key, agent_id, batch_id, scheduled_at=schedule_time) - if res.get('state') == 'scheduled': - check = True - while check: - # Checking the current status every 1 minute - await asyncio.sleep(60) - res = await get_batch_status(api_key, agent_id, batch_id) - if res.get('status') == 'completed': - check = False - break - if not check: - res = await get_batch_executions(api_key, agent_id, batch_id) - print(res) - return res - - -if __name__ == "__main__": - asyncio.run(create_batch()) diff --git a/examples/function-calling/example_json.json b/examples/function-calling/example_json.json deleted file mode 100644 index b2298e1f..00000000 --- a/examples/function-calling/example_json.json +++ /dev/null @@ -1,216 +0,0 @@ -{ - "agent_config": { - "agent_name": "An agent with routes", - "agent_welcome_message": "This call is being recorded for quality assurance and training. Please speak now.", - "tasks": [ - { - "tools_config": { - "output": { - "format": "wav", - "provider": "twilio" - }, - "input": { - "format": "wav", - "provider": "twilio" - }, - "synthesizer": { - "audio_format": "wav", - "provider": "polly", - "stream": true, - "caching": true, - "provider_config": { - "voice": "Danielle", - "engine": "neural", - "language": "en-US" - }, - "buffer_size": 100.0 - }, - "llm_agent": { - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": null, - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": false, - "routes": { - "embedding_model": "Snowflake/snowflake-arctic-embed-l", - "routes": [ - { - "route_name": "politics", - "utterances": [ - "Are you a Trump supporter", - "How many black people live in my neighborhood", - "Are you a democrat?", - "Are you a republican", - "Are you black?", - "What is the gender split of my society", - "Are you a democrat?", - "Tell me about your political ideologies", - "Who is winning the elections this year?", - "Are there hispanics in the area", - "I do not like democrats", - "I don't want faggots", - "Don't give me homosexuals", - "I need a white hair dresser only" - ], - "response": "Hey, thanks but I don't want to entertain hate speech", - "score_threshold": 0.90 - } - ] - }, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.2, - "model": "gpt-4o", - "family": "openai" - }, - "transcriber": { - "sampling_rate": 16000.0, - "endpointing": 400.0, - "task": "transcribe", - "keywords": "", - "stream": true, - "modeltype": "nova-2", - "model": "deepgram", - "language": "en", - "encoding": "linear16" - }, - "api_tools": { - "tools": "[{\"name\":\"check_availability_of_slots\",\"description\":\"Fetch the available free slots of appointment booking before booking the appointment\",\"parameters\":{\"type\":\"object\",\"properties\":{\"startTime\":{\"type\":\"string\",\"description\":\"It is an ISO FORMATTED DATE on which time user is available (convert it automatically to hr:min such as 3:30 PM is 15:30)\"},\"endTime\":{\"type\":\"string\",\"description\":\"It is an ISO FORMATTED DATE. endDate is always 15 minutes more than startDate always i.e. increment one day to the startDate. such if startDate is 2024-05-15T16:30:00.000Z then endDate is 2024-05-15T16:45:00.000Z\"},\"eventTypeId\":{\"type\":\"integer\",\"description\":\"it is the type of event. For women's haircut use event type id as 814889 and for men's haircut use 798483 \"}},\"required\":[\"startTime\",\"eventTypeId\", \"endTime\"]}},{\"name\":\"book_appointment\",\"description\":\"Use this tool to book an appointment with given details and save the appointment in the calendar.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"name\":{\"type\":\"string\",\"description\":\"name of the person.\"},\"email\":{\"type\":\"string\",\"description\":\"email name of the person.\"},\"address\":{\"type\":\"string\",\"description\":\"address name of the person.\"},\"preferred_date\":{\"type\":\"string\",\"description\":\"Preferred date provided by the person, ask the date when user wants to book an appointment such as tomorrow or day after tomorrow. and convert the user's response into a python readable format i.e. yyyy-mm-dd\"},\"preferred_time\":{\"type\":\"string\",\"description\":\"Preferred time provided by the person, ask the time when users wants to book an appointment such as 9am or 10:30am and convert it to python readable format for example if users said 9am then it is 09:00 or 1:30PM then it 13:30 i.e. in hr:min \"},\"timezone\":{\"type\":\"string\",\"description\":\"fetch timezone by yourself, don't ask user, find timezone based on the address provided by the user\"},\"eventTypeId\":{\"type\":\"integer\",\"description\":\"it is the type of event. For women's haircut use event type id as 814889 and for men's haircut use 798483 \"}},\"required\":[\"name\",\"email\",\"address\",\"eventTypeId\",\"preferred_date\",\"preferred_time\",\"timezone\"]}}, {\"name\":\"transfer_call\",\"description\":\"Transfer calls to human agent if user asks to transfer the call or if it's not in your instructions on how to deal with a certain question user is asking\"}]", - "tools_params": { - "check_availability_of_slots": { - "url": "https://api.cal.com/v1/slots?apiKey=", - "method": "GET", - "api_token": null, - "param": "{\"eventTypeId\":%(eventTypeId)d,\"startTime\":\"%(startTime)s\",\"endTime\":\"%(endTime)s\"}" - }, - "book_appointment": { - "url": "https://api.cal.com/v1/bookings?apiKey=", - "method": "POST", - "api_token": null, - "param": "{\"responses\":{\"name\":\"%(name)s\",\"email\":\"%(email)s\",\"location\":{\"optionValue\":\"\",\"value\":\"inPerson\"}},\"start\":\"%(preferred_date)sT%(preferred_time)s:00.000Z\",\"eventTypeId\":%(eventTypeId)d,\"timeZone\":\"%(timezone)s\",\"language\":\"en\",\"metadata\":{}}" - } - } - } - }, - "task_config": { - "hangup_after_LLMCall": false, - "hangup_after_silence": 10.0, - "interruption_backoff_period": 0.0, - "backchanneling": true, - "backchanneling_start_delay": 5.0, - "optimize_latency": true, - "incremental_delay": 100.0, - "call_cancellation_prompt": null, - "number_of_words_for_interruption": 3.0, - "backchanneling_message_gap": 5.0 - }, - "task_type": "conversation", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "transcriber", - "llm", - "synthesizer" - ] - ] - } - }, - { - "tools_config": { - "output": null, - "input": null, - "synthesizer": null, - "llm_agent": { - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": null, - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": true, - "routes": null, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.1, - "model": "gpt-3.5-turbo-1106", - "family": "openai" - }, - "transcriber": null, - "api_tools": null - }, - "task_config": {}, - "task_type": "summarization", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "llm" - ] - ] - } - }, - { - "tools_config": { - "output": null, - "input": null, - "synthesizer": null, - "llm_agent": { - "extraction_json": "{\n\"slot\": \"The slot booked by the user\"\n}", - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": "slot: Slot booked by user", - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": true, - "routes": null, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.1, - "model": "gpt-3.5-turbo-1106", - "family": "openai" - }, - "transcriber": null, - "api_tools": null - }, - "task_config": {}, - "task_type": "extraction", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "llm" - ] - ] - } - } - ], - "agent_type": "Lead Qualification" - }, - "agent_prompts": { - "task_1": { - "system_prompt": "### Agent Description You're an, Mellisa, a helpful agent whose job is to book appointments for Boston Barber Co. at Beacon Hill. There are two type of appointments available - 1. Haircut for men. event id - 798483 2. Appointment for women - 814889 ### About store - Shop is opened Tuesday to Sunday from 9 am to 9pm. - For premium treatment one beer is on the house ### Flow Users can ask you to find available slots & booking for an appointment. You will ask the users about their availability i.e. when they are available the date and time and check if that slot is available or not then you will ask other details as mentioned in function calling and proceed with this information to do the function calling for finding available slots. If slots are available then you must tell only those slots or that slot to user which is or are very close to the user's availability. ### You have access to following functions/tools 1. **check_availability_of_slots** - To check availability of slots from the calendar before booking the appointment. 2. **book_appointment** - Use this tool to book an appointment with given details and save the appointment in the calendar. ### Important instructions 1. MAKE SURE YOU GET ALL THE REQUIRED DETAILS BEFORE DOING A FUNCTION CALL. 2. PLEASE MAKES SURE YOUR RESPONSES ARE GEARED TO BE SYNTHESIZED BY THE SYNTHESISER IN AN EXPRESSIVE WAY. 3. Just speak 1 sentence at a time" - }, - "task_2": { - "system_prompt": "You are given a conversation. Kindly summarize it in a professional tone" - }, - "task_3": { - "system_prompt": "Extract the information from the given conversation betweenn an bot and a human. \n### JSON structure {\n\"slot\": \"The slot booked by the user\"\n}" - } - } -} \ No newline at end of file diff --git a/examples/openai-assistants/README.md b/examples/openai-assistants/README.md deleted file mode 100644 index 15b860db..00000000 --- a/examples/openai-assistants/README.md +++ /dev/null @@ -1,233 +0,0 @@ -## Create a RAG based assistant - -### Step 1 :Create an assistant via the API (only openai supported yet) - -```sh -curl --request POST \ - --url https://api.bolna.dev/assistant \ - --header 'Authorization: Bearer bn-***' \ - --header 'Content-Type: multipart/form-data' \ - --header 'User-Agent: insomnia/8.5.1' \ - --form 'name=A simple insurance agent assistant with human fallback' \ - --form 'instructions=You are a Max Bupa Life Insurance virtual agent. Answer policy questions using policy documents, check available appointment slots, and book appointments. Greet users warmly, provide clear policy details, confirm appointments, and maintain professionalism and user confidentiality.' \ - --form model=gpt-3.5-turbo \ - --form 'tools=[{"name":"check_availability_of_slots","description":"Fetch the available free slots of appointment booking before booking the appointment","parameters":{"type":"object","properties":{"startTime":{"type":"string","description":"It is an ISO FORMATTED DATE on which time user is available (convert it automatically to hr:min such as 3:30 PM is 15:30)"},"endTime":{"type":"string","description":"It is an ISO FORMATTED DATE. endDate is always 15 minutes more than startDate always i.e. increment one day to the startDate. such if startDate is 2024-05-15T16:30:00.000Z then endDate is 2024-05-15T16:45:00.000Z"},"eventTypeId":{"type":"integer","description":"it is the type of event. use event type id as 814889 "}},"required":["startTime","eventTypeId", "endTime"]}},{"name":"book_appointment","description":"Use this tool to book an appointment with given details and save the appointment in the calendar.","parameters":{"type":"object","properties":{"name":{"type":"string","description":"name of the person."},"email":{"type":"string","description":"email name of the person."},"address":{"type":"string","description":"address name of the person."},"preferred_date":{"type":"string","description":"Preferred date provided by the person, ask the date when user wants to book an appointment such as tomorrow or day after tomorrow. and convert the user'\''s response into a python readable format i.e. yyyy-mm-dd"},"preferred_time":{"type":"string","description":"Preferred time provided by the person, ask the time when users wants to book an appointment such as 9am or 10:30am and convert it to python readable format for example if users said 9am then it is 09:00 or 1:30PM then it 13:30 i.e. in hr:min "},"timezone":{"type":"string","description":"fetch timezone by yourself, don'\''t ask user, find timezone based on the address provided by the user"},"eventTypeId":{"type":"integer","description":"it is the type of event. use event type id as 814889"}},"required":["name","email","address","eventTypeId","preferred_date","preferred_time","timezone"]}}, {"name":"transfer_call","description":"Transfer calls to human agent if user asks to transfer the call or if it'\''s not in your instructions on how to deal with a certain question user is asking"}]' \ - --form 'file=@/Users/marmikpandya/Desktop/indie-hacking/Health Companion-Health Insurance Plan_GEN617.pdf' -``` - -### Step 2 - Create an assistant - -```json -{ - "agent_config": { - "agent_name": "Openai assistant agent agent with call transfer", - "agent_status": "processed", - "agent_welcome_message": "This call is being recorded for quality assurance and training. Please speak now.", - "tasks": [ - { - "tools_config": { - "output": { - "format": "wav", - "provider": "twilio" - }, - "input": { - "format": "wav", - "provider": "twilio" - }, - "synthesizer": { - "audio_format": "wav", - "provider": "deepgram", - "stream": true, - "caching": true, - "provider_config": { - "voice": "Asteria", - "model": "aura-asteria-en" - }, - "buffer_size": 100.0 - }, - "llm_agent": { - "agent_flow_type": "openai_assistant", - "extra_config": { - "name": "a Insurance rag agent", - "assistant_id": "asst_Djy3W6i1n4fZpTMcylSiyVpE" - } - }, - "transcriber": { - "sampling_rate": 16000, - "endpointing": 123.0, - "task": "transcribe", - "keywords": null, - "stream": true, - "provider": "deepgram", - "model": "nova2", - "language": "en", - "encoding": "linear16" - }, - "api_tools": { - "tools": "[{\"name\":\"check_availability_of_slots\",\"description\":\"Fetch the available free slots of appointment booking before booking the appointment\",\"parameters\":{\"type\":\"object\",\"properties\":{\"startTime\":{\"type\":\"string\",\"description\":\"It is an ISO FORMATTED DATE on which time user is available (convert it automatically to hr:min such as 3:30 PM is 15:30)\"},\"endTime\":{\"type\":\"string\",\"description\":\"It is an ISO FORMATTED DATE. endDate is always 15 minutes more than startDate always i.e. increment one day to the startDate. such if startDate is 2024-05-15T16:30:00.000Z then endDate is 2024-05-15T16:45:00.000Z\"},\"eventTypeId\":{\"type\":\"integer\",\"description\":\"it is the type of event. For women's haircut use event type id as 814889 and for men's haircut use 798483 \"}},\"required\":[\"startTime\",\"eventTypeId\", \"endTime\"]}},{\"name\":\"book_appointment\",\"description\":\"Use this tool to book an appointment with given details and save the appointment in the calendar.\",\"parameters\":{\"type\":\"object\",\"properties\":{\"name\":{\"type\":\"string\",\"description\":\"name of the person.\"},\"email\":{\"type\":\"string\",\"description\":\"email name of the person.\"},\"address\":{\"type\":\"string\",\"description\":\"address name of the person.\"},\"preferred_date\":{\"type\":\"string\",\"description\":\"Preferred date provided by the person, ask the date when user wants to book an appointment such as tomorrow or day after tomorrow. and convert the user's response into a python readable format i.e. yyyy-mm-dd\"},\"preferred_time\":{\"type\":\"string\",\"description\":\"Preferred time provided by the person, ask the time when users wants to book an appointment such as 9am or 10:30am and convert it to python readable format for example if users said 9am then it is 09:00 or 1:30PM then it 13:30 i.e. in hr:min \"},\"timezone\":{\"type\":\"string\",\"description\":\"fetch timezone by yourself, don't ask user, find timezone based on the address provided by the user\"},\"eventTypeId\":{\"type\":\"integer\",\"description\":\"it is the type of event. For women's haircut use event type id as 814889 and for men's haircut use 798483 \"}},\"required\":[\"name\",\"email\",\"address\",\"eventTypeId\",\"preferred_date\",\"preferred_time\",\"timezone\"]}}, {\"name\":\"transfer_call\",\"description\":\"Transfer calls to human agent if user asks to transfer the call or if it's not in your instructions on how to deal with a certain question user is asking\"}]", - "tools_params": { - "book_appointment": { - "method": "POST", - "param": "{\"responses\":{\"name\":\"%(name)s\",\"email\":\"%(email)s\",\"location\":{\"optionValue\":\"\",\"value\":\"inPerson\"}},\"start\":\"%(preferred_date)sT%(preferred_time)s:00.000Z\",\"eventTypeId\":%(eventTypeId)d,\"timeZone\":\"%(timezone)s\",\"language\":\"en\",\"metadata\":{}}", - "url": "https://api.cal.com/v1/bookings?apiKey=", - "api_token": null - }, - "check_availability_of_slots": { - "method": "GET", - "param": "{\"eventTypeId\":%(eventTypeId)d,\"startTime\":\"%(startTime)s\",\"endTime\":\"%(endTime)s\"}", - "url": "https://api.cal.com/v1/slots?apiKey=", - "api_token": null - }, - "transfer_call": { - "method": "POST", - "param": null, - "url": "https://webhook.site/c5dfe764-8888-4be5-a28c-8cfe4d54d475", - "api_token": null - } - } - } - }, - "task_config": { - "ambient_noise_track": "office-ambience", - "hangup_after_LLMCall": false, - "hangup_after_silence": 10.0, - "ambient_noise": false, - "interruption_backoff_period": 0.0, - "backchanneling": false, - "backchanneling_start_delay": 5.0, - "optimize_latency": true, - "incremental_delay": 100.0, - "call_cancellation_prompt": null, - "number_of_words_for_interruption": 3.0, - "backchanneling_message_gap": 5.0, - "use_fillers": false - - }, - "task_type": "conversation", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "transcriber", - "llm", - "synthesizer" - ] - ] - } - }, - { - "tools_config": { - "output": null, - "input": null, - "synthesizer": null, - "llm_agent": { - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": null, - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": true, - "routes": null, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.1, - "model": "gpt-3.5-turbo-1106", - "family": "openai" - }, - "transcriber": null, - "api_tools": null - }, - "task_config": { - "ambient_noise_track": "convention_hall", - "hangup_after_LLMCall": false, - "hangup_after_silence": 30.0, - "ambient_noise": true, - "interruption_backoff_period": 100.0, - "backchanneling": false, - "backchanneling_start_delay": 5.0, - "optimize_latency": true, - "incremental_delay": 100.0, - "call_cancellation_prompt": null, - "number_of_words_for_interruption": 1.0, - "backchanneling_message_gap": 5.0, - "call_terminate": 300 - }, - "task_type": "summarization", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "llm" - ] - ] - } - }, - { - "tools_config": { - "output": null, - "input": null, - "synthesizer": null, - "llm_agent": { - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": "slot: Slot booked by user", - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": true, - "routes": null, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.1, - "model": "gpt-3.5-turbo-1106", - "family": "openai" - }, - "transcriber": null, - "api_tools": null - }, - "task_config": { - "ambient_noise_track": "office-ambience", - "hangup_after_LLMCall": false, - "hangup_after_silence": 10.0, - "ambient_noise": true, - "interruption_backoff_period": 100.0, - "backchanneling": false, - "backchanneling_start_delay": 5.0, - "optimize_latency": true, - "incremental_delay": 100.0, - "call_cancellation_prompt": null, - "number_of_words_for_interruption": 1.0, - "backchanneling_message_gap": 5.0 - }, - "task_type": "extraction", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "llm" - ] - ] - } - } - ], - "agent_type": "Lead Qualification" - }, - "agent_prompts": { - "task_2" : { - "system_prompt" : "summarize the conversation" - }, - "task_3" : { - "system_prompt" : "Extract the user sentiment in json with the key 'sentiment' " - } - } -} -``` \ No newline at end of file diff --git a/examples/webrtc/README.md b/examples/webrtc/README.md deleted file mode 100644 index 3644f3c3..00000000 --- a/examples/webrtc/README.md +++ /dev/null @@ -1,125 +0,0 @@ -# Bolna With Daily -Seamlessly merge [Bolna](https://github.com/bolna-ai/bolna) with [Daily](https://github.com/daily-co) for websocket connection. This is docker compose by which you can host bolna server Daily together in cloud just by clone this repo and follow these simple steps to deploy, but before that you have to make sure that you have [docker](https://docs.docker.com/engine/install/) and [docker compose](https://docs.docker.com/compose/install/) and make a .env file refer to .env-sample. - -in .env file kindly save -DAILY_API_KEY = YOUR_DAILY_API_KEY - -### Start Serices -```shell -docker compose up -d -``` - -note: make sure that your all service were runing - -`let assume your server IP is 192.168.1.10` - -### Creating Agent -for creating agent you have to execute following command mention below -```shell -curl --location 'http://192.168.1.10:5001/agent' \ ---header 'Content-Type: application/json' \ ---data '{ - "agent_config": { - "agent_name": "Alfred", - "agent_type": "other", - "agent_welcome_message": "Welcome", - "tasks": [ - { - "task_type": "conversation", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "transcriber", - "llm", - "synthesizer" - ] - ] - }, - "tools_config": { - "input": { - "format": "wav", - "provider": "daily" - }, - "output": { - "format": "wav", - "provider": "daily" - }, - "llm_agent": { - "max_tokens": 100, - "presence_penalty": 0, - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": false, - "min_p": 0.1, - "frequency_penalty": 0, - "provider": "openai", - "top_k": 0, - "temperature": 0.2, - "model": "gpt-3.5-turbo", - "family": "openai" - }, - "synthesizer": { - "audio_format": "wav", - "buffer_size": 150, - "caching": true, - "provider": "polly", - "provider_config": { - "engine": "neural", - "language": "en-US", - "voice": "Danielle" - }, - "stream": true - }, - "transcriber": { - "encoding": "linear16", - "endpointing": 100, - "keywords": "", - "language": "en", - "model": "nova-2", - "provider": "deepgram", - "sampling_rate": 16000, - "stream": true, - "task": "transcribe" - } - }, - "task_config": { - "hangup_after_silence": 30 - } - } - ] - }, - "agent_prompts": { - "task_1": { - "system_prompt": "You are assistant at Dr. Sharma clinic you have to book an appointment" - } - } -} -' - -``` -below given is the response -``` -{"agent_id":"dcfe02de-bOdf-4589-b15b-64c76f0077d0", "state" : "created" } -``` -save / copy the agent_id we have to use in next step - -### Connect frontend to the websocket call - -Example frontent - https://codefile.io/f/rWIsJ0GztJ - -1. Connect your frontend with the (’/chat/v1/{agent_id}’) websocket and use the agent_id you saved in above step. It's that easy! -2. Our daily web-socket call will be up and running, so you can start chatting with the agent through your frontend! - -### Stop Services -```shell -docker compose down -``` - -### Conservation DEMO -This is demo using below prompt to the LLM -```json -"task_1": { - "system_prompt": "You are assistant at Dr. Sharma clinic you have to book an appointment" -} -``` diff --git a/examples/webrtc/docker-compose.yml b/examples/webrtc/docker-compose.yml deleted file mode 100644 index 3ed46100..00000000 --- a/examples/webrtc/docker-compose.yml +++ /dev/null @@ -1,28 +0,0 @@ -services: - - # main bolna service - bolna-app: - image: bolna-app:latest - build: - context: . - dockerfile: dockerfiles/bolna_server.Dockerfile - ports: - - "5001:5001" - depends_on: - - redis - env_file: - - .env - volumes: - - ../agent_data:/app/agent_data - - $HOME/.aws/credentials:/root/.aws/credentials:ro - - $HOME/.aws/config:/root/.aws/config:ro - environment: - - REDIS_URL=redis://redis:6379 - - # redis service used as a persistent storage - redis: - image: redis:latest - ports: - - "6379:6379" - - \ No newline at end of file diff --git a/examples/webrtc/dockerfiles/bolna_server.Dockerfile b/examples/webrtc/dockerfiles/bolna_server.Dockerfile deleted file mode 100644 index 414aca2c..00000000 --- a/examples/webrtc/dockerfiles/bolna_server.Dockerfile +++ /dev/null @@ -1,19 +0,0 @@ -FROM python:3.10.13-slim - -WORKDIR /app -COPY ./requirements.txt /app -COPY ./quickstart_server.py /app - -RUN apt-get update && apt-get install libgomp1 git -y -RUN apt-get -y update && apt-get -y upgrade && apt-get install -y --no-install-recommends ffmpeg -RUN pip install -r requirements.txt -RUN pip install --force-reinstall git+https://github.com/bolna-ai/bolna@master -RUN pip install scipy==1.11.0 -RUN pip install torch==2.0.1 -RUN pip install torchaudio==2.0.1 -RUN pip install pydub==0.25.1 -RUN pip install ffprobe -RUN pip install aiofiles - -EXPOSE 5001 -CMD ["uvicorn", "quickstart_server:app", "--host", "0.0.0.0", "--port", "5001"] diff --git a/examples/webrtc/quickstart_server.py b/examples/webrtc/quickstart_server.py deleted file mode 100644 index 07e2882b..00000000 --- a/examples/webrtc/quickstart_server.py +++ /dev/null @@ -1,122 +0,0 @@ -import os -import asyncio -import time -import uuid -import traceback -import aiohttp -from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException, Query -from fastapi.middleware.cors import CORSMiddleware -import redis.asyncio as redis -from dotenv import load_dotenv -from bolna.helpers.utils import store_file -from bolna.prompts import * -from bolna.helpers.logger_config import configure_logger -from bolna.models import * -from bolna.llms import LiteLLM -from bolna.agent_manager.assistant_manager import AssistantManager - -load_dotenv() -logger = configure_logger(__name__) - -redis_pool = redis.ConnectionPool.from_url(os.getenv('REDIS_URL'), decode_responses=True) -redis_client = redis.Redis.from_pool(redis_pool) -active_websockets: List[WebSocket] = [] - -app = FastAPI() - -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"] -) - - -class CreateAgentPayload(BaseModel): - agent_config: AgentModel - agent_prompts: Optional[Dict[str, Dict[str, str]]] - - -@app.post("/agent") -async def create_agent(agent_data: CreateAgentPayload): - agent_uuid = str(uuid.uuid4()) - data_for_db = agent_data.agent_config.model_dump() - data_for_db["assistant_status"] = "seeding" - agent_prompts = agent_data.agent_prompts - logger.info(f'Data for DB {data_for_db}') - - if len(data_for_db['tasks']) > 0: - logger.info("Setting up follow up tasks") - for index, task in enumerate(data_for_db['tasks']): - if task['task_type'] == "extraction": - extraction_prompt_llm = os.getenv("EXTRACTION_PROMPT_GENERATION_MODEL") - extraction_prompt_generation_llm = LiteLLM(model=extraction_prompt_llm, max_tokens=2000) - extraction_prompt = await extraction_prompt_generation_llm.generate( - messages=[ - {'role': 'system', 'content': EXTRACTION_PROMPT_GENERATION_PROMPT}, - {'role': 'user', 'content': data_for_db["tasks"][index]['tools_config']["llm_agent"]['extraction_details']} - ]) - data_for_db["tasks"][index]["tools_config"]["llm_agent"]['extraction_json'] = extraction_prompt - - stored_prompt_file_path = f"{agent_uuid}/conversation_details.json" - await asyncio.gather( - redis_client.set(agent_uuid, json.dumps(data_for_db)), - store_file(file_key=stored_prompt_file_path, file_data=agent_prompts, local=True) - ) - - return {"agent_id": agent_uuid, "state": "created"} - - -############################################################################################# -# Websocket -############################################################################################# -@app.websocket("/chat/v1/{agent_id}") -async def websocket_endpoint(agent_id: str, websocket: WebSocket, user_agent: str = Query(None)): - logger.info("Connected to ws") - await websocket.accept() - active_websockets.append(websocket) - agent_config, context_data = None, None - try: - retrieved_agent_config = await redis_client.get(agent_id) - logger.info( - f"Retrieved agent config: {retrieved_agent_config}") - async with aiohttp.ClientSession() as session: - token = os.getenv('DAILY_API_KEY') - headers = { - 'Content-Type': 'application/json', - 'Authorization': f'Bearer {token}' - } - payload = { - 'properties': { - 'max_participants': 2, - 'start_video_off': True, - 'enable_screenshare': False, - 'enable_recording': 'cloud', - 'exp': int(time.time()) + 3600 # room will expire after 1 hour automatically, - } - } - - async with session.post("https://api.daily.co/v1/rooms/", headers=headers, json=payload) as response: - response_json = await response.json() - if response.status != 200: - res = {"connection": False, "type": "closing"} - else: - room_url = response_json['url'] - res = {"connection": True, "type": "setup", "room_url": room_url} - active_websockets.remove(websocket) - await websocket.send_json(res) - agent_config = json.loads(retrieved_agent_config) - except Exception as e: - traceback.print_exc() - raise HTTPException(status_code=404, detail="Agent not found") - assistant_manager = AssistantManager(agent_config, websocket, agent_id, room_url=room_url) - - try: - async for index, task_output in assistant_manager.run(local=True): - logger.info(task_output) - except WebSocketDisconnect: - active_websockets.remove(websocket) - except Exception as e: - traceback.print_exc() - logger.error(f"error in executing {e}") \ No newline at end of file diff --git a/examples/webrtc/requirements.txt b/examples/webrtc/requirements.txt deleted file mode 100644 index c0522987..00000000 --- a/examples/webrtc/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -python-dotenv==1.0.0 -fastapi==0.108.0 -plivo==4.47.0 -redis==5.0.1 -twilio==8.9.0 -uvicorn==0.22.0 -daily-python==0.9.1 \ No newline at end of file diff --git a/examples/whisper-melo-llama3/.env-sample b/examples/whisper-melo-llama3/.env-sample deleted file mode 100644 index e66bb509..00000000 --- a/examples/whisper-melo-llama3/.env-sample +++ /dev/null @@ -1,21 +0,0 @@ -TWILIO_ACCOUNT_SID= -TWILIO_AUTH_TOKEN= -TWILIO_PHONE_NUMBER= - -DEEPGRAM_AUTH_TOKEN= -DEEPGRAM_API_KEY= - -ELEVENLABS_API_KEY= - -OPENAI_API_KEY= -OPENAI_MODEL=gpt-3.5-turbo - -ENVIRONMENT=local -WEBSOCKET_URL= -APP_CALLBACK_URL= - -REDIS_URL=redis://redis:6379 - -WHISPER_URL=ws://whisper-app:9000 - -MELO_TTS=http://melo-app:8000/connection \ No newline at end of file diff --git a/examples/whisper-melo-llama3/Readme.md b/examples/whisper-melo-llama3/Readme.md deleted file mode 100644 index 7b6bdf3d..00000000 --- a/examples/whisper-melo-llama3/Readme.md +++ /dev/null @@ -1,297 +0,0 @@ -# Bolna With MeloTTS and WhisperASR -Introducing our Dockerized solution! Seamlessly merge [Bolna](https://github.com/bolna-ai/bolna) with [Whisper ASR](https://github.com/bolna-ai/streaming-whisper-server) and [Melo TTS](https://github.com/anshjoseph/MiloTTS-Server) for telephone provider we use Twillo and for tunning we use ngrok. This is docker compose by which you can host bolna server Whisper ASR, Melo TTS together in cloud just by clone this repo and follow these simple steps to deploy ,but before that you have to make sure that you have [docker](https://docs.docker.com/engine/install/) and [docker compose](https://docs.docker.com/compose/install/) and make a .env file refer to .env-sample and also put ngrok auth token in ngrok-config.yml file - - -### Start Serices -```shell -docker compose up -d -``` -the output something like this -![alt text](./img/docker_up.png "docker compose up -d") - -note: make sure that your all service were runing - -`let assume your server IP is 192.168.1.10` - -### Creating Agent -for creating agent you have to execute following command mention below -```shell -curl --location 'http://192.168.1.10:5001/agent' \ ---header 'Content-Type: application/json' \ ---data '{ - "agent_config": { - "agent_name": "Test agent with a transfer call prompt", - "agent_welcome_message": "Hey how are you!", - "tasks": [ - { - "tools_config": { - "output": { - "format": "wav", - "provider": "twilio" - }, - "input": { - "format": "wav", - "provider": "twilio" - }, - "synthesizer": { - "provider": "melotts", - "provider_config": { - "voice": "Alex", - "sample_rate": 8000, - "sdp_ratio" : 0.2, - "noise_scale" : 0.6, - "noise_scale_w" : 0.8, - "speed" : 1.0 - }, - "stream": true, - "buffer_size": 123, - "audio_format": "pcm" - }, - "llm_agent": { - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": null, - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": false, - "routes": null, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.2, - "model": "gpt-3.5-turbo", - "family": "openai", - "extra_config": null - }, - "transcriber": { - "encoding": "linear16", - "language": "en", - "model": "whisper", - "stream": true, - "modeltype":"distil-large-v3", - "keywords":"ansh,joseph,hola", - "task": "transcribe", - "provider":"whisper" - }, - "api_tools": { - "tools": [ - { - "name": "check_availability_of_slots", - "description": "Fetch the available free slots of appointment booking before booking the appointment", - "parameters": { - "type": "object", - "properties": { - "startTime": { - "type": "string", - "description": "It is an ISO FORMATTED DATE on which time user is available (convert it automatically to hr:min such as 3:30 PM is 15:30)" - }, - "endTime": { - "type": "string", - "description": "It is an ISO FORMATTED DATE. endDate is always 15 minutes more than startDate always i.e. increment one day to the startDate. such if startDate is 2024-05-15T16:30:00.000Z then endDate is 2024-05-15T16:45:00.000Z" - }, - "eventTypeId": { - "type": "integer", - "description": "it is the type of event. For women'\''s haircut use event type id as 814889 and for men'\''s haircut use 798483 " - } - }, - "required": [ - "startTime", - "eventTypeId", - "endTime" - ] - } - }, - { - "name": "book_appointment", - "description": "Use this tool to book an appointment with given details and save the appointment in the calendar.", - "parameters": { - "type": "object", - "properties": { - "name": { - "type": "string", - "description": "name of the person." - }, - "email": { - "type": "string", - "description": "email name of the person." - }, - "address": { - "type": "string", - "description": "address name of the person." - }, - "preferred_date": { - "type": "string", - "description": "Preferred date provided by the person, ask the date when user wants to book an appointment such as tomorrow or day after tomorrow. and convert the user'\''s response into a python readable format i.e. yyyy-mm-dd" - }, - "preferred_time": { - "type": "string", - "description": "Preferred time provided by the person, ask the time when users wants to book an appointment such as 9am or 10:30am and convert it to python readable format for example if users said 9am then it is 09:00 or 1:30PM then it 13:30 i.e. in hr:min " - }, - "timezone": { - "type": "string", - "description": "fetch timezone by yourself, don'\''t ask user, find timezone based on the address provided by the user" - }, - "eventTypeId": { - "type": "integer", - "description": "it is the type of event. For women'\''s haircut use event type id as 814889 and for men'\''s haircut use 798483 " - } - }, - "required": [ - "name", - "email", - "address", - "eventTypeId", - "preferred_date", - "preferred_time", - "timezone" - ] - } - }, - { - "name": "transfer_call", - "description": "Transfer calls to human agent if user asks to transfer the call or if it'\''s not in your instructions on how to deal with a certain question user is asking. Call can be routed either to '\''hair_dresser'\'' or a '\''recruiter'\''. Make sure you have this information. It can only be either of the two", - "parameters": { - "type": "object", - "properties": { - "humanAgentType": { - "type": "string", - "description": "This is the department the call should be routed to. Make sure we know the user has mentioned if they want the call to be routed to '\''hair_dresser'\'' or a '\''recruiter'\'' before sending. It can only be either of the two" - } - }, - "required": [ - "humanAgentType" - ] - } - } - ], - "tools_params": { - "book_appointment": { - "method": "POST", - "param": "{\"responses\":{\"name\":\"%(name)s\",\"email\":\"%(email)s\",\"location\":{\"optionValue\":\"\",\"value\":\"inPerson\"}},\"start\":\"%(preferred_date)sT%(preferred_time)s:00.000Z\",\"eventTypeId\":%(eventTypeId)d,\"timeZone\":\"%(timezone)s\",\"language\":\"en\",\"metadata\":{}}", - "url": "https://api.cal.com/v1/bookings?apiKey=API_KEY", - "api_token": null - }, - "check_availability_of_slots": { - "method": "GET", - "param": "{\"eventTypeId\":%(eventTypeId)d,\"startTime\":\"%(startTime)s\",\"endTime\":\"%(endTime)s\"}", - "url": "https://api.cal.com/v1/slots?apiKey=API_KEY", - "api_token": null - }, - "transfer_call": { - "method": "POST", - "param": "{\"humanAgentType\": \"(humanAgentType)s\"}", - "url": "https://webhook.site/3174ce9e-b398-4ec6-bbd5-f3a61473dfce", - "api_token": null - } - } - } - }, - "task_config": { - "ambient_noise_track": "office-ambience", - "hangup_after_LLMCall": false, - "hangup_after_silence": 10.0, - "ambient_noise": false, - "interruption_backoff_period": 0.0, - "backchanneling": false, - "backchanneling_start_delay": 5.0, - "optimize_latency": true, - "incremental_delay": 100.0, - "call_cancellation_prompt": null, - "number_of_words_for_interruption": 3.0, - "backchanneling_message_gap": 5.0, - "use_fillers": false - }, - "task_type": "conversation", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "transcriber", - "llm", - "synthesizer" - ] - ] - } - } - ], - "agent_type": "Lead Qualification" - }, - "agent_prompts": { - "task_1": { - "system_prompt": "### Agent Description You'\''re an, Mellisa, a helpful agent whose job is to book appointments for Boston Barber Co. at Beacon Hill. There are two type of appointments available - 1. Haircut for men. event id - 798483 2. Appointment for women - 814889 ### About store - Shop is opened Tuesday to Sunday from 9 am to 9pm. - For premium treatment one beer is on the house ### Flow Users can ask you to find available slots & booking for an appointment. You will ask the users about their availability i.e. when they are available the date and time and check if that slot is available or not then you will ask other details as mentioned in function calling and proceed with this information to do the function calling for finding available slots. If slots are available then you must tell only those slots or that slot to user which is or are very close to the user'\''s availability. ### You have access to following functions/tools 1. *check_availability_of_slots* - To check availability of slots from the calendar before booking the appointment. 2. *book_appointment* - Use this tool to book an appointment with given details and save the appointment in the calendar. 3.*transfer_call* - Use this tool to transfer the call to human agent whenever required\n\n### Important instructions 1. MAKE SURE YOU GET ALL THE REQUIRED DETAILS BEFORE DOING A FUNCTION CALL. 2. PLEASE MAKES SURE YOUR RESPONSES ARE GEARED TO BE SYNTHESIZED BY THE SYNTHESISER IN AN EXPRESSIVE WAY. 3. Just speak 1 sentence at a time. If the user says to transfer call MAKE SURE YOU KNOW IF THE CALL SHOULD BE TRANSFERRED FOR haircut or recruitment. It can only be either of the two!" - } - } -}' - -``` -below given is the response -![alt text](./img/agent_res.png "agent response") -copy the agent_id we have to use in next step - -if you want to [Change voice](#change-voice) - -### Make call -```shell -curl --location 'http://192.168.1.10:8001/call' \ ---header 'Content-Type: application/json' \ ---data '{ - "agent_id": "bf2a9e9c-6038-4104-85c4-b71a0d1478c9", - "recipient_phone_number": "+91XXXXXXXXXX" -}' -``` -it gonna give output `Done` for succees - -note: if you are using trial account use you register phone no - -### Stop Services -```shell -docker compose down -``` -![alt text](./img/docker_dw.png "docker compose up -d") - - -### Changing the voice MeloTTS - -by default we resrtict Melo EN but there were 5 option for voice as mention below -- ['EN-US'](./audio/audio_sample/EN_US.wav) -- ['EN-BR'](./audio/audio_sample/EN-BR.wav) -- ['EN-AU'](./audio/audio_sample/EN-AU.wav) -- ['EN-Default'](./audio/audio_sample/EN-Default.wav) -- ['EN_INDIA'](./audio/audio_sample/EN_INDIA.wav) - -you have to just change the following section mention below -```JSON -"synthesizer": { - "provider": "melo", - "provider_config": { - "voice": "", - "sample_rate": 8000, - "sdp_ratio" : 0.2, - "noise_scale" : 0.6, - "noise_scale_w" : 0.8, - "speed" : 1.0 - }, - "stream": true, - "buffer_size": 123, - "audio_format": "pcm" - } -``` -and rest of the config gonna be same mention above - -### Conservation DENO -This is demo using below prompt to the LLM -```json -"task_1": { - "system_prompt": "You are assistant at Dr. Sharma clinic you have to book an appointment" -} -``` - - - -[chat GPT 3.5 turbo 16k demo](./audio/demo_audio.mp3) - -you can give prompt as per your use case \ No newline at end of file diff --git a/examples/whisper-melo-llama3/audio/audio_sample/EN-AU.wav b/examples/whisper-melo-llama3/audio/audio_sample/EN-AU.wav deleted file mode 100644 index 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$HOME/.aws/credentials:/root/.aws/credentials:ro - - $HOME/.aws/config:/root/.aws/config:ro - - # redis service used as a persistent storage - redis: - image: redis:latest - ports: - - "6379:6379" - - # ngrok for local tunneling - ngrok: - image: ngrok/ngrok:latest - restart: unless-stopped - command: - - "start" - - "--all" - - "--config" - - "/etc/ngrok.yml" - volumes: - - ./ngrok-config.yml:/etc/ngrok.yml - ports: - - 4040:4040 - - ### Telephony servers ### - twilio-app: - image: twilio-app:latest - build: - context: . - dockerfile: dockerfiles/twilio_server.Dockerfile - ports: - - "8001:8001" - depends_on: - - redis - env_file: - - .env - - ### whisper servers ### - whisper-app: - image: whisper-app:latest - build: - context: . - dockerfile: dockerfiles/whisper_server.Dockerfile - ports: - - "9002:9000" - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - ### Melo TTS ### - melo-app: - image: melo-app:latest - build: - context: . - dockerfile: dockerfiles/melo_server.Dockerfile - ports: - - "8002:8000" - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - diff --git a/examples/whisper-melo-llama3/dockerfiles/bolna_server.Dockerfile b/examples/whisper-melo-llama3/dockerfiles/bolna_server.Dockerfile deleted file mode 100644 index 414aca2c..00000000 --- a/examples/whisper-melo-llama3/dockerfiles/bolna_server.Dockerfile +++ /dev/null @@ -1,19 +0,0 @@ -FROM python:3.10.13-slim - -WORKDIR /app -COPY ./requirements.txt /app -COPY ./quickstart_server.py /app - -RUN apt-get update && apt-get install libgomp1 git -y -RUN apt-get -y update && apt-get -y upgrade && apt-get install -y --no-install-recommends ffmpeg -RUN pip install -r requirements.txt -RUN pip install --force-reinstall git+https://github.com/bolna-ai/bolna@master -RUN pip install scipy==1.11.0 -RUN pip install torch==2.0.1 -RUN pip install torchaudio==2.0.1 -RUN pip install pydub==0.25.1 -RUN pip install ffprobe -RUN pip install aiofiles - -EXPOSE 5001 -CMD ["uvicorn", "quickstart_server:app", "--host", "0.0.0.0", "--port", "5001"] diff --git a/examples/whisper-melo-llama3/dockerfiles/melo_server.Dockerfile b/examples/whisper-melo-llama3/dockerfiles/melo_server.Dockerfile deleted file mode 100644 index f016c63e..00000000 --- a/examples/whisper-melo-llama3/dockerfiles/melo_server.Dockerfile +++ /dev/null @@ -1,13 +0,0 @@ -FROM python:3.10.13-slim -WORKDIR /app - -RUN apt-get update && apt-get install libgomp1 git -y -RUN apt-get -y update && apt-get -y upgrade && apt-get install -y --no-install-recommends ffmpeg -RUN git clone https://github.com/bolna-ai/MeloTTS -RUN pip install fastapi uvicorn torchaudio -RUN cp -a MeloTTS/. . -RUN python -m pip cache purge -RUN pip install --no-cache-dir txtsplit torch torchaudio cached_path transformers==4.27.4 mecab-python3==1.0.5 num2words==0.5.12 unidic_lite unidic mecab-python3==1.0.5 pykakasi==2.2.1 fugashi==1.3.0 g2p_en==2.1.0 anyascii==0.3.2 jamo==0.4.1 gruut[de,es,fr]==2.2.3 g2pkk>=0.1.1 librosa==0.9.1 pydub==0.25.1 eng_to_ipa==0.0.2 inflect==7.0.0 unidecode==1.3.7 pypinyin==0.50.0 cn2an==0.5.22 jieba==0.42.1 langid==1.1.6 tqdm tensorboard==2.16.2 loguru==0.7.2 -RUN python -m unidic download -EXPOSE 8000 -CMD ["python3", "Server.py"] diff --git a/examples/whisper-melo-llama3/dockerfiles/twilio_server.Dockerfile b/examples/whisper-melo-llama3/dockerfiles/twilio_server.Dockerfile deleted file mode 100644 index d368fbb9..00000000 --- a/examples/whisper-melo-llama3/dockerfiles/twilio_server.Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM python:3.10.13-slim - -WORKDIR /app -COPY ./requirements.txt /app -COPY ./telephony_server/twilio_api_server.py /app - -RUN pip install --no-cache-dir -r requirements.txt - -EXPOSE 8001 - -CMD ["uvicorn", "twilio_api_server:app", "--host", "0.0.0.0", "--port", "8001"] diff --git a/examples/whisper-melo-llama3/dockerfiles/whisper_server.Dockerfile b/examples/whisper-melo-llama3/dockerfiles/whisper_server.Dockerfile deleted file mode 100644 index bed2f67b..00000000 --- a/examples/whisper-melo-llama3/dockerfiles/whisper_server.Dockerfile +++ /dev/null @@ -1,16 +0,0 @@ -FROM python:3.10.13-slim - -RUN apt-get update && apt-get install libgomp1 git -y -RUN apt-get -y update && apt-get -y upgrade && apt-get install -y --no-install-recommends ffmpeg -RUN apt-get -y install build-essential -RUN apt-get -y install portaudio19-dev -RUN git clone https://github.com/bolna-ai/streaming-whisper-server.git -WORKDIR streaming-whisper-server -RUN pip install -e . -RUN pip install git+https://github.com/SYSTRAN/faster-whisper.git -RUN pip install transformers - -RUN ct2-transformers-converter --model openai/whisper-small --copy_files preprocessor_config.json --output_dir ./Server/ASR/whisper_small --quantization float16 -WORKDIR Server -EXPOSE 9000 -CMD ["python3", "Server.py", "-p", "9000"] diff --git 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z1=rPA);2at85x3(SQZu*D7AwR$`(B3S~oV6_xoYLtOpLw-%INV0!!?x-a -tunnels: - twilio-app: - addr: twilio-app:8001 - proto: http - bolna-app: - addr: bolna-app:5001 - proto: http diff --git a/examples/whisper-melo-llama3/quickstart_server.py b/examples/whisper-melo-llama3/quickstart_server.py deleted file mode 100644 index e0399703..00000000 --- a/examples/whisper-melo-llama3/quickstart_server.py +++ /dev/null @@ -1,96 +0,0 @@ -import os -import asyncio -import uuid -import traceback -from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException, Query -from fastapi.middleware.cors import CORSMiddleware -import redis.asyncio as redis -from dotenv import load_dotenv -from bolna.helpers.utils import store_file -from bolna.prompts import * -from bolna.helpers.logger_config import configure_logger -from bolna.models import * -from bolna.llms import LiteLLM -from bolna.agent_manager.assistant_manager import AssistantManager - -load_dotenv() -logger = configure_logger(__name__) - -redis_pool = redis.ConnectionPool.from_url(os.getenv('REDIS_URL'), decode_responses=True) -redis_client = redis.Redis.from_pool(redis_pool) -active_websockets: List[WebSocket] = [] - -app = FastAPI() - -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"] -) - - -class CreateAgentPayload(BaseModel): - agent_config: AgentModel - agent_prompts: Optional[Dict[str, Dict[str, str]]] - - -@app.post("/agent") -async def create_agent(agent_data: CreateAgentPayload): - agent_uuid = str(uuid.uuid4()) - data_for_db = agent_data.agent_config.model_dump() - data_for_db["assistant_status"] = "seeding" - agent_prompts = agent_data.agent_prompts - logger.info(f'Data for DB {data_for_db}') - - if len(data_for_db['tasks']) > 0: - logger.info("Setting up follow up tasks") - for index, task in enumerate(data_for_db['tasks']): - if task['task_type'] == "extraction": - extraction_prompt_llm = os.getenv("EXTRACTION_PROMPT_GENERATION_MODEL") - extraction_prompt_generation_llm = LiteLLM(model=extraction_prompt_llm, max_tokens=2000) - extraction_prompt = await extraction_prompt_generation_llm.generate( - messages=[ - {'role': 'system', 'content': EXTRACTION_PROMPT_GENERATION_PROMPT}, - {'role': 'user', 'content': data_for_db["tasks"][index]['tools_config']["llm_agent"]['extraction_details']} - ]) - data_for_db["tasks"][index]["tools_config"]["llm_agent"]['extraction_json'] = extraction_prompt - - stored_prompt_file_path = f"{agent_uuid}/conversation_details.json" - await asyncio.gather( - redis_client.set(agent_uuid, json.dumps(data_for_db)), - store_file(file_key=stored_prompt_file_path, file_data=agent_prompts, local=True) - ) - - return {"agent_id": agent_uuid, "state": "created"} - - -############################################################################################# -# Websocket -############################################################################################# -@app.websocket("/chat/v1/{agent_id}") -async def websocket_endpoint(agent_id: str, websocket: WebSocket, user_agent: str = Query(None)): - logger.info("Connected to ws") - await websocket.accept() - active_websockets.append(websocket) - agent_config, context_data = None, None - try: - retrieved_agent_config = await redis_client.get(agent_id) - logger.info( - f"Retrieved agent config: {retrieved_agent_config}") - agent_config = json.loads(retrieved_agent_config) - except Exception as e: - traceback.print_exc() - raise HTTPException(status_code=404, detail="Agent not found") - - assistant_manager = AssistantManager(agent_config, websocket, agent_id) - - try: - async for index, task_output in assistant_manager.run(local=True): - logger.info(task_output) - except WebSocketDisconnect: - active_websockets.remove(websocket) - except Exception as e: - traceback.print_exc() - logger.error(f"error in executing {e}") diff --git a/examples/whisper-melo-llama3/requirements.txt b/examples/whisper-melo-llama3/requirements.txt deleted file mode 100644 index 7e9bdc5f..00000000 --- a/examples/whisper-melo-llama3/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -python-dotenv==1.0.0 -fastapi==0.108.0 -redis==5.0.1 -twilio==8.9.0 -uvicorn==0.22.0 diff --git a/examples/whisper-melo-llama3/telephony_server/twilio_api_server.py b/examples/whisper-melo-llama3/telephony_server/twilio_api_server.py deleted file mode 100644 index bcdd3912..00000000 --- a/examples/whisper-melo-llama3/telephony_server/twilio_api_server.py +++ /dev/null @@ -1,94 +0,0 @@ -import os -import json -import requests -import uuid -from twilio.twiml.voice_response import VoiceResponse, Connect -from twilio.rest import Client -from dotenv import load_dotenv -import redis.asyncio as redis -from fastapi import FastAPI, HTTPException, Query, Request -from fastapi.responses import PlainTextResponse -from logging import Logger -logger = Logger(__name__) -app = FastAPI() -load_dotenv() -port = 8001 - -twilio_account_sid = os.getenv('TWILIO_ACCOUNT_SID') -twilio_auth_token = os.getenv('TWILIO_AUTH_TOKEN') -twilio_phone_number = os.getenv('TWILIO_PHONE_NUMBER') - -# Initialize Twilio client -twilio_client = Client(twilio_account_sid, twilio_auth_token) - - -def populate_ngrok_tunnels(): - response = requests.get("http://ngrok:4040/api/tunnels") # ngrok interface - app_callback_url, websocket_url = None, None - - if response.status_code == 200: - data = response.json() - - for tunnel in data['tunnels']: - if tunnel['name'] == 'twilio-app': - app_callback_url = tunnel['public_url'] - elif tunnel['name'] == 'bolna-app': - websocket_url = tunnel['public_url'].replace('https:', 'wss:') - - return app_callback_url, websocket_url - else: - print(f"Error: Unable to fetch data. Status code: {response.status_code}") - - -@app.post('/call') -async def make_call(request: Request): - try: - call_details = await request.json() - agent_id = call_details.get('agent_id', None) - - if not agent_id: - raise HTTPException(status_code=404, detail="Agent not provided") - - if not call_details or "recipient_phone_number" not in call_details: - raise HTTPException(status_code=404, detail="Recipient phone number not provided") - - app_callback_url, websocket_url = populate_ngrok_tunnels() - - print(f'app_callback_url: {app_callback_url}') - print(f'websocket_url: {websocket_url}') - print("tokens") - print(f"{twilio_account_sid} {twilio_auth_token} {twilio_phone_number}") - try: - call = twilio_client.calls.create( - to=call_details.get('recipient_phone_number'), - from_=twilio_phone_number, - url=f"{app_callback_url}/twilio_callback?ws_url={websocket_url}&agent_id={agent_id}", - method="POST", - record=False - ) - except Exception as e: - print(e) - return PlainTextResponse(f"error {e}", status_code=200) - - return PlainTextResponse("done", status_code=200) - - except Exception as e: - print(f"Exception occurred in make_call: {e}") - raise HTTPException(status_code=500, detail="Internal Server Error") - - -@app.post('/twilio_callback') -async def twilio_callback(ws_url: str = Query(...), agent_id: str = Query(...)): - try: - response = VoiceResponse() - - connect = Connect() - websocket_twilio_route = f'{ws_url}/chat/v1/{agent_id}' - connect.stream(url=websocket_twilio_route) - print(f"websocket connection done to {websocket_twilio_route}") - response.append(connect) - - return PlainTextResponse(str(response), status_code=200, media_type='text/xml') - - except Exception as e: - print(f"Exception occurred in twilio_callback: {e}") diff --git a/examples/with-fillers/README.md b/examples/with-fillers/README.md deleted file mode 100644 index ba2aae2a..00000000 --- a/examples/with-fillers/README.md +++ /dev/null @@ -1,21 +0,0 @@ -### With Fillers - -Fillers can be used to give an agent time to think or to make sure with expressive TTS like elevenlabs, you're able to ensir elow latency. This is the example task config fot the agent to enable the same. - -```json - "task_config": { - "ambient_noise_track": "office-ambience", - "hangup_after_LLMCall": false, - "hangup_after_silence": 10.0, - "ambient_noise": true, - "interruption_backoff_period": 0.0, - "backchanneling": true, - "backchanneling_start_delay": 5.0, - "optimize_latency": true, - "incremental_delay": 300.0, - "call_cancellation_prompt": null, - "number_of_words_for_interruption": 3.0, - "backchanneling_message_gap": 5.0, - "use_fillers": true - }, -``` \ No newline at end of file diff --git a/examples/with-fillers/example_agent.json b/examples/with-fillers/example_agent.json deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/with-guardrails/README.md b/examples/with-guardrails/README.md deleted file mode 100644 index 074530b9..00000000 --- a/examples/with-guardrails/README.md +++ /dev/null @@ -1,31 +0,0 @@ -### With Guardrails - -You can add guardrails to make sure agent doesn't respond to unwanted queries -```json - "routes": { - "embedding_model": "Snowflake/snowflake-arctic-embed-l", - "routes": [ - { - "route_name": "politics", - "utterances": [ - "Are you a Trump supporter", - "How many black people live in my neighborhood", - "Are you a democrat?", - "Are you a republican", - "Are you black?", - "What is the gender split of my society", - "Are you a democrat?", - "Tell me about your political ideologies", - "Who is winning the elections this year?", - "Are there hispanics in the area", - "I do not like democrats", - "I don't want faggots", - "Don't give me homosexuals", - "I need a white hair dresser only" - ], - "response": "Hey, thanks but I don't want to entertain hate speech", - "score_threshold": 0.90 - } - ] - }, -``` \ No newline at end of file diff --git a/examples/with-guardrails/example_agent.json b/examples/with-guardrails/example_agent.json deleted file mode 100644 index 88c73ce0..00000000 --- a/examples/with-guardrails/example_agent.json +++ /dev/null @@ -1,199 +0,0 @@ -{ - "agent_config": { - "agent_name": "An agent with routes", - "agent_welcome_message": "This call is being recorded for quality assurance and training. Please speak now.", - "tasks": [ - { - "tools_config": { - "output": { - "format": "wav", - "provider": "twilio" - }, - "input": { - "format": "wav", - "provider": "twilio" - }, - "synthesizer": { - "audio_format": "wav", - "provider": "polly", - "stream": true, - "caching": true, - "provider_config": { - "voice": "Danielle", - "engine": "neural", - "language": "en-US" - }, - "buffer_size": 100.0 - }, - "llm_agent": { - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": null, - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": false, - "routes": { - "embedding_model": "Snowflake/snowflake-arctic-embed-l", - "routes": [ - { - "route_name": "politics", - "utterances": [ - "Are you a Trump supporter", - "How many black people live in my neighborhood", - "Are you a democrat?", - "Are you a republican", - "Are you black?", - "What is the gender split of my society", - "Are you a democrat?", - "Tell me about your political ideologies", - "Who is winning the elections this year?", - "Are there hispanics in the area", - "I do not like democrats", - "I don't want faggots", - "Don't give me homosexuals", - "I need a white hair dresser only" - ], - "response": "Hey, thanks but I don't want to entertain hate speech", - "score_threshold": 0.90 - } - ] - }, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.2, - "model": "gpt-4o", - "family": "openai" - }, - "transcriber": { - "sampling_rate": 16000.0, - "endpointing": 400.0, - "task": "transcribe", - "keywords": "", - "stream": true, - "modeltype": "nova-2", - "model": "deepgram", - "language": "en", - "encoding": "linear16" - } - }, - "task_config": { - "hangup_after_LLMCall": false, - "hangup_after_silence": 10.0, - "interruption_backoff_period": 0.0, - "backchanneling": true, - "backchanneling_start_delay": 5.0, - "optimize_latency": true, - "incremental_delay": 100.0, - "call_cancellation_prompt": null, - "number_of_words_for_interruption": 3.0, - "backchanneling_message_gap": 5.0 - }, - "task_type": "conversation", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "transcriber", - "llm", - "synthesizer" - ] - ] - } - }, - { - "tools_config": { - "output": null, - "input": null, - "synthesizer": null, - "llm_agent": { - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": null, - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": true, - "routes": null, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.1, - "model": "gpt-3.5-turbo-1106", - "family": "openai" - }, - "transcriber": null, - "api_tools": null - }, - "task_config": {}, - "task_type": "summarization", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "llm" - ] - ] - } - }, - { - "tools_config": { - "output": null, - "input": null, - "synthesizer": null, - "llm_agent": { - "extraction_json": "{\n\"slot\": \"The slot booked by the user\"\n}", - "max_tokens": 100.0, - "presence_penalty": 0.0, - "summarization_details": null, - "base_url": null, - "extraction_details": "slot: Slot booked by user", - "top_p": 0.9, - "agent_flow_type": "streaming", - "request_json": true, - "routes": null, - "min_p": 0.1, - "frequency_penalty": 0.0, - "stop": null, - "provider": "openai", - "top_k": 0.0, - "temperature": 0.1, - "model": "gpt-3.5-turbo-1106", - "family": "openai" - }, - "transcriber": null, - "api_tools": null - }, - "task_config": {}, - "task_type": "extraction", - "toolchain": { - "execution": "parallel", - "pipelines": [ - [ - "llm" - ] - ] - } - } - ], - "agent_type": "Lead Qualification" - }, - "agent_prompts": { - "task_1": { - "system_prompt": "### Agent Description You're an, Mellisa, a helpful agent whose job is to book appointments for Boston Barber Co. at Beacon Hill. There are two type of appointments available - 1. Haircut for men. event id - 798483 2. Appointment for women - 814889 ### About store - Shop is opened Tuesday to Sunday from 9 am to 9pm. - For premium treatment one beer is on the house ### Flow Users can ask you to find available slots & booking for an appointment. You will ask the users about their availability i.e. when they are available the date and time and check if that slot is available or not then you will ask other details as mentioned in function calling and proceed with this information to do the function calling for finding available slots. If slots are available then you must tell only those slots or that slot to user which is or are very close to the user's availability. ### You have access to following functions/tools 1. **check_availability_of_slots** - To check availability of slots from the calendar before booking the appointment. 2. **book_appointment** - Use this tool to book an appointment with given details and save the appointment in the calendar. ### Important instructions 1. MAKE SURE YOU GET ALL THE REQUIRED DETAILS BEFORE DOING A FUNCTION CALL. 2. PLEASE MAKES SURE YOUR RESPONSES ARE GEARED TO BE SYNTHESIZED BY THE SYNTHESISER IN AN EXPRESSIVE WAY. 3. Just speak 1 sentence at a time" - }, - "task_2": { - "system_prompt": "You are given a conversation. Kindly summarize it in a professional tone" - }, - "task_3": { - "system_prompt": "Extract the information from the given conversation betweenn an bot and a human. \n### JSON structure {\n\"slot\": \"The slot booked by the user\"\n}" - } - } -} \ No newline at end of file diff --git a/local_setup/docker-compose.yml b/local_setup/docker-compose.yml index b9ad7989..7fbf98cb 100644 --- a/local_setup/docker-compose.yml +++ b/local_setup/docker-compose.yml @@ -7,7 +7,7 @@ services: context: . dockerfile: dockerfiles/bolna_server.Dockerfile args: - BRANCH: ${BRANCH:-master} + BRANCH: ${BRANCH:-develop} ports: - "5001:5001" depends_on: @@ -17,7 +17,7 @@ services: volumes: - ../agent_data:/app/agent_data - $HOME/.aws/credentials:/root/.aws/credentials:ro - - $HOME/.aws/config:/root/.aws/config:ro + - $HOME/.aws/config:/root/.aws/credentials:ro ingestion-app: image: ingestion-app:latest @@ -68,16 +68,16 @@ services: env_file: - .env - plivo-app: - image: plivo-app:latest - build: - context: . - dockerfile: dockerfiles/plivo_server.Dockerfile - ports: - - "8002:8002" - depends_on: - - redis - - ngrok - - bolna-app - env_file: - - .env + # plivo-app: + # image: plivo-app:latest + # build: + # context: . + # dockerfile: dockerfiles/plivo_server.Dockerfile + # ports: + # - "8002:8002" + # depends_on: + # - redis + # - ngrok + # - bolna-app + # env_file: + # - .env \ No newline at end of file diff --git a/local_setup/dockerfiles/bolna_server.Dockerfile b/local_setup/dockerfiles/bolna_server.Dockerfile index 966a9c87..48175706 100644 --- a/local_setup/dockerfiles/bolna_server.Dockerfile +++ b/local_setup/dockerfiles/bolna_server.Dockerfile @@ -8,10 +8,9 @@ RUN apt-get update && apt-get install libgomp1 git -y RUN apt-get -y update && apt-get -y upgrade && apt-get install -y --no-install-recommends ffmpeg RUN pip install -r requirements.txt -ARG BRANCH=master +ARG BRANCH=develop RUN pip install --force-reinstall git+https://github.com/bolna-ai/bolna@${BRANCH} -RUN pip install scipy==1.11.0 RUN pip install torch==2.0.1 RUN pip install torchaudio==2.0.1 RUN pip install pydub==0.25.1 diff --git a/local_setup/dockerfiles/ingestion_server.Dockerfile b/local_setup/dockerfiles/ingestion_server.Dockerfile index 4b410135..28ecb5b4 100644 --- a/local_setup/dockerfiles/ingestion_server.Dockerfile +++ b/local_setup/dockerfiles/ingestion_server.Dockerfile @@ -2,11 +2,15 @@ FROM python:3.10.13-slim WORKDIR /app COPY ./requirements.txt /app -COPY ./quickstart_ingestion_server.py /app +COPY ./ingestion_server /app/ingestion_server RUN apt-get update && apt-get install -y --no-install-recommends gcc RUN pip install --no-cache-dir -r requirements.txt +ENV PYTHONPATH=/app + +WORKDIR /app/ingestion_server + EXPOSE 8000 CMD ["uvicorn", "quickstart_ingestion_server:app", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/local_setup/ingestion_server/__init__.py b/local_setup/ingestion_server/__init__.py new file mode 100644 index 00000000..5a6641ab --- /dev/null +++ b/local_setup/ingestion_server/__init__.py @@ -0,0 +1,177 @@ +from embeddings import EmbedProviders +from rags import RAGProviders, BaseRAG +from datachecks import RAGConfig, RAGTask, RAGTaskStatus +import asyncio +from threading import Thread +from utils import configure_logger +from typing import Dict +from uuid import uuid4 +import tempfile +import os + +logger = configure_logger(__name__) + +class RAG: + """ + Retrieval-Augmented Generation (RAG) implementation. + + This class handles the ingestion and storage of documents for RAG. + """ + + def __init__(self, VectorDB: BaseRAG, Workers: int = 2) -> None: + """ + Initialize the RAG instance. + + Args: + VectorDB (BaseRAG): The vector database to use. + Workers (int, optional): Number of worker threads. Defaults to 2. + """ + self.file_process_task_queue: asyncio.Queue = asyncio.Queue() + self.file_store_task_queue: asyncio.Queue = asyncio.Queue() + + self.VectorDB: BaseRAG = VectorDB + self.Workers: int = Workers + + self.RAG_THREAD = Thread(target=self.start) + self.shutdown = False + + async def __shutdown_loop(self): + """Monitor the shutdown flag.""" + while not self.shutdown: + await asyncio.sleep(0.5) + + async def __ingestion_task(self): + """Process ingestion tasks from the queue.""" + while not self.shutdown: + task: RAGTask = await self.file_process_task_queue.get() + task._status = RAGTaskStatus.PROCESSING + try: + nodes = await self.VectorDB.generate_nodes_sentence_splitter(task.file_loc) + except Exception as e: + logger.error(f"ERROR in {e}") + task._status = RAGTaskStatus.ERROR + continue + task._nodes = nodes + await self.file_store_task_queue.put(task) + + async def __nodes_storage(self): + """Store processed nodes in the vector database.""" + while not self.shutdown: + task: RAGTask = await self.file_store_task_queue.get() + try: + index = await self.VectorDB.add_index(task._nodes) + except Exception as e: + logger.error(f"ERROR in {e}") + task._status = RAGTaskStatus.ERROR + continue + task._index = index + task._status = RAGTaskStatus.SUCESSFUL + + def start(self): + """Start the RAG processing loop.""" + loop = asyncio.new_event_loop() + ingestion_task_pool = [loop.create_task(self.__ingestion_task()) for _ in range(self.Workers)] + file_storage = loop.create_task(self.__nodes_storage()) + loop.run_until_complete(self.__shutdown_loop()) + file_storage.cancel() + for t in ingestion_task_pool: + t.cancel() + loop.close() + + +class RAGFactory: + """ + Factory class for creating and managing RAG instances. + """ + + def __init__(self) -> None: + """Initialize the RAGFactory.""" + self.RAGS: Dict[str, RAG] = dict() + + def make_rag(self, config: RAGConfig): + """ + Create a new RAG instance. + + Args: + config (RAGConfig): Configuration for the RAG instance. + + Returns: + str: Unique identifier for the created RAG instance. + """ + rag_name = f"RAG-{uuid4()}" + embedding_name = EmbedProviders[config.provider_config.embedding_name.provider](config.provider_config.embedding_name.embedding_model_name) + vector_db = RAGProviders[config.provider](embedding_name, config.provider_config) + rag = RAG(vector_db, config.provider_config.worker) + rag.RAG_THREAD.start() + self.RAGS[rag_name] = rag + return rag_name + + def stop_all(self): + """Stop all RAG instances.""" + for rag in self.RAGS.values(): + rag.shutdown = True + + def stop(self, rag_name: str): + """ + Stop a specific RAG instance. + + Args: + rag_name (str): Identifier of the RAG instance to stop. + + Raises: + ValueError: If the specified RAG instance doesn't exist. + """ + if rag_name in self.RAGS.keys(): + self.RAGS[rag_name].shutdown = True + self.RAGS.pop(rag_name) + else: + raise ValueError("No RAG with that ID exists") + + async def file_ingest(self, rag_name, file) -> RAGTask: + """ + Ingest a file into a RAG instance. + + Args: + rag_name (str): Identifier of the RAG instance. + file: File object to ingest. + + Returns: + RAGTask: Task object representing the ingestion process. + + Raises: + ValueError: If the specified RAG instance doesn't exist or if the file type is unsupported. + """ + if rag_name not in self.RAGS.keys(): + raise ValueError(f"RAG: {rag_name} does not exist") + if file.content_type not in ["application/pdf", "application/x-pdf"]: + raise ValueError("Only PDF files are supported for now") + + task_id = str(uuid4()) + temp_file = tempfile.NamedTemporaryFile() + temp_file.write(await file.read()) + prev = temp_file.name + file_name = f"/tmp/{task_id}.pdf" + os.rename(prev, file_name) + task = RAGTask(file_loc=file_name) + await self.RAGS[rag_name].file_process_task_queue.put(task) + + while task._status in [RAGTaskStatus.WAIT, RAGTaskStatus.PROCESSING]: + await asyncio.sleep(0.4) + + os.rename(file_name, prev) + return task + + async def retrieve_query(self, rag_name: str, index: str, query: str): + """ + Retrieve documents based on a query. + + Args: + rag_name (str): Identifier of the RAG instance. + index (str): Index to search in. + query (str): Query string. + + Returns: + List of relevant documents. + """ + rag = self.RAGS[rag_name] + return await rag.VectorDB.get_docs_index(query=query, index=index) \ No newline at end of file diff --git a/local_setup/ingestion_server/datachecks/__init__.py b/local_setup/ingestion_server/datachecks/__init__.py new file mode 100644 index 00000000..88ee7511 --- /dev/null +++ b/local_setup/ingestion_server/datachecks/__init__.py @@ -0,0 +1,60 @@ +from pydantic import BaseModel, Field, root_validator +from typing import Union, Optional + +# Embedding config +class embeddings(BaseModel): + provider:str + embedding_model_name:Optional[str] = "" + +# DB Configs +class LanceDBConfig(BaseModel): + loc: Optional[str] = "" + +class MongoDBConfig(BaseModel): + index: Optional[str] = "" + uri: Optional[str] = "" + db: Optional[str] = "" + collection_name: Optional[str] = "" + +# Provider Configs +class ProviderConfig(BaseModel): + embedding_name:embeddings + chunk_size:int + overlapping:int + worker:int + similarity_top_k:int + rag: Union[MongoDBConfig, LanceDBConfig] = Field(union_mode="left_to_right") + + @root_validator(pre=True) + def check_config_type(cls, values): + config = values.get('rag') + if isinstance(config, dict): + if 'uri' in config and 'collection_name' in config: + values['rag'] = MongoDBConfig(**config) + else: + values['rag'] = LanceDBConfig(**config) + return values + +# Rag Config +class RAGConfig(BaseModel): + provider:str + provider_config:ProviderConfig + +class Query(BaseModel): + provider:str + index:str + query:str + +# Utility checks for RAG +class RAGTaskStatus: + WAIT = "WAITING" + PROCESSING = "PROCESSING" + ERROR = "ERROR" + SUCESSFUL = "SUCESSFUL" + +class RAGTask(BaseModel): + file_loc:str + _status:str = RAGTaskStatus.WAIT + _message:str = "" + _index:str = "" + _nodes:list = [] \ No newline at end of file diff --git a/local_setup/ingestion_server/embeddings/__init__.py b/local_setup/ingestion_server/embeddings/__init__.py new file mode 100644 index 00000000..2a0487f5 --- /dev/null +++ b/local_setup/ingestion_server/embeddings/__init__.py @@ -0,0 +1,9 @@ +from typing import Dict + +from embeddings.openai_emded import OpenAI +from embeddings.base import BaseEmbed + +# EmbedProviders dictionary with the key being the name of the provider and the value being the class of the provider +EmbedProviders : Dict[str, BaseEmbed] = { + "OpenAI": OpenAI +} \ No newline at end of file diff --git a/local_setup/ingestion_server/embeddings/base.py b/local_setup/ingestion_server/embeddings/base.py new file mode 100644 index 00000000..37b4dd94 --- /dev/null +++ b/local_setup/ingestion_server/embeddings/base.py @@ -0,0 +1,10 @@ +from abc import ABC, abstractmethod + +class BaseEmbed(ABC): + def __init__(self, name: str) -> None: + self.name = name + + @abstractmethod + def get_embedding(self): + """Method to retrieve the embedding representation.""" + raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/local_setup/ingestion_server/embeddings/openai_emded.py b/local_setup/ingestion_server/embeddings/openai_emded.py new file mode 100644 index 00000000..0a562e8e --- /dev/null +++ b/local_setup/ingestion_server/embeddings/openai_emded.py @@ -0,0 +1,31 @@ +from embeddings.base import BaseEmbed +from llama_index.embeddings.openai import OpenAIEmbedding +import dotenv +import os + +dotenv.load_dotenv() + +class OpenAI(BaseEmbed): + def __init__(self, model: str) -> None: + """Initialize the OpenAI embedding provider. + + Args: + model (str): The model name to be used for embeddings. + + Raises: + ValueError: If the OpenAI API key is not found in the environment variables. + """ + super().__init__("OpenAI") + self.model = model + api_key = os.getenv("OPENAI_API_KEY") + if api_key is None: + raise ValueError("OPENAI KEY IS NOT FOUND") + self.embedding_instance = OpenAIEmbedding(model=model, api_key=api_key) + + def get_embedding(self): + """Retrieve the embedding instance. + + Returns: + OpenAIEmbedding: The instance of the OpenAI embedding. + """ + return self.embedding_instance \ No newline at end of file diff --git a/local_setup/ingestion_server/payload.markdown b/local_setup/ingestion_server/payload.markdown new file mode 100644 index 00000000..1f456649 --- /dev/null +++ b/local_setup/ingestion_server/payload.markdown @@ -0,0 +1,19 @@ + +## LanceDB + +{ + "provider": "LanceDB", + "provider_config": { + "embedding_name": { + "provider": "OpenAI", + "embedding_model_name": "text-embedding-3-small" + }, + "chunk_size": 512, + "overlapping": 200, + "worker": 2, + "similarity_top_k": 2, + "rag": { + "loc": "dev" + } + } +} \ No newline at end of file diff --git a/local_setup/ingestion_server/quickstart_ingestion_server.py b/local_setup/ingestion_server/quickstart_ingestion_server.py new file mode 100644 index 00000000..1c71b503 --- /dev/null +++ b/local_setup/ingestion_server/quickstart_ingestion_server.py @@ -0,0 +1,261 @@ +import json +import logging +import time +import dotenv + +from fastapi import FastAPI, File, UploadFile, Form +from typing import Dict, Any, Optional, Union + +import uvicorn + +from embeddings import EmbedProviders +from rags import RAGProviders, BaseRAG +from datachecks import RAGConfig, RAGTask, RAGTaskStatus +import asyncio +from threading import Thread +from utils import configure_logger +from uuid import uuid4 +import tempfile +import os + +logger = configure_logger(__name__) + + +dotenv.load_dotenv() +DB: Dict[str, RAGConfig] = {} + + + +class RAG: + """ + Retrieval-Augmented Generation (RAG) implementation. + + This class handles the ingestion and storage of documents for RAG. + """ + + def __init__(self, VectorDB: BaseRAG, Workers: int = 2) -> None: + """ + Initialize the RAG instance. + + Args: + VectorDB (BaseRAG): The vector database to use. + Workers (int, optional): Number of worker threads. Defaults to 2. + """ + self.file_process_task_queue: asyncio.Queue = asyncio.Queue() + self.file_store_task_queue: asyncio.Queue = asyncio.Queue() + + self.VectorDB: BaseRAG = VectorDB + self.Workers: int = Workers + + self.RAG_THREAD = Thread(target=self.start) + self.shutdown = False + + async def __shutdown_loop(self): + """Monitor the shutdown flag.""" + while not self.shutdown: + await asyncio.sleep(0.5) + + async def __ingestion_task(self): + """Process ingestion tasks from the queue.""" + while not self.shutdown: + task: RAGTask = await self.file_process_task_queue.get() + task._status = RAGTaskStatus.PROCESSING + try: + nodes = await self.VectorDB.generate_nodes_sentence_splitter(task.file_loc) + except Exception as e: + logger.error(f"ERROR in {e}") + task._status = RAGTaskStatus.ERROR + continue + task._nodes = nodes + await self.file_store_task_queue.put(task) + + async def __nodes_storage(self): + """Store processed nodes in the vector database.""" + while not self.shutdown: + task: RAGTask = await self.file_store_task_queue.get() + try: + index = await self.VectorDB.add_index(task._nodes) + except Exception as e: + logger.error(f"ERROR in {e}") + task._status = RAGTaskStatus.ERROR + continue + task._index = index + task._status = RAGTaskStatus.SUCESSFUL + + def start(self): + """Start the RAG processing loop.""" + loop = asyncio.new_event_loop() + ingestion_task_pool = [loop.create_task(self.__ingestion_task()) for _ in range(self.Workers)] + file_storage = loop.create_task(self.__nodes_storage()) + loop.run_until_complete(self.__shutdown_loop()) + file_storage.cancel() + for t in ingestion_task_pool: + t.cancel() + loop.close() + + +class RAGFactory: + """ + Factory class for creating and managing RAG instances. + """ + + def __init__(self) -> None: + """Initialize the RAGFactory.""" + self.RAGS: Dict[str, RAG] = dict() + + def make_rag(self, config: RAGConfig): + """ + Create a new RAG instance. + + Args: + config (RAGConfig): Configuration for the RAG instance. + + Returns: + str: Unique identifier for the created RAG instance. + """ + rag_name = f"RAG-{uuid4()}" + embedding_name = EmbedProviders[config.provider_config.embedding_name.provider](config.provider_config.embedding_name.embedding_model_name) + vector_db = RAGProviders[config.provider](embedding_name, config.provider_config) + rag = RAG(vector_db, config.provider_config.worker) + rag.RAG_THREAD.start() + self.RAGS[rag_name] = rag + return rag_name + + def stop_all(self): + """Stop all RAG instances.""" + for rag in self.RAGS.values(): + rag.shutdown = True + + def stop(self, rag_name: str): + """ + Stop a specific RAG instance. + + Args: + rag_name (str): Identifier of the RAG instance to stop. + + Raises: + ValueError: If the specified RAG instance doesn't exist. + """ + if rag_name in self.RAGS.keys(): + self.RAGS[rag_name].shutdown = True + self.RAGS.pop(rag_name) + else: + raise ValueError("No RAG with that ID exists") + + async def file_ingest(self, rag_name, file) -> RAGTask: + """ + Ingest a file into a RAG instance. + + Args: + rag_name (str): Identifier of the RAG instance. + file: File object to ingest. + + Returns: + RAGTask: Task object representing the ingestion process. + + Raises: + ValueError: If the specified RAG instance doesn't exist or if the file type is unsupported. + """ + if rag_name not in self.RAGS.keys(): + raise ValueError(f"RAG: {rag_name} does not exist") + if file.content_type not in ["application/pdf", "application/x-pdf"]: + raise ValueError("Only PDF files are supported for now") + + task_id = str(uuid4()) + temp_file = tempfile.NamedTemporaryFile() + temp_file.write(await file.read()) + prev = temp_file.name + file_name = f"/tmp/{task_id}.pdf" + os.rename(prev, file_name) + task = RAGTask(file_loc=file_name) + await self.RAGS[rag_name].file_process_task_queue.put(task) + + while task._status in [RAGTaskStatus.WAIT, RAGTaskStatus.PROCESSING]: + await asyncio.sleep(0.4) + + os.rename(file_name, prev) + return task + + async def retrieve_query(self, rag_name: str, index: str, query: str): + """ + Retrieve documents based on a query. + + Args: + rag_name (str): Identifier of the RAG instance. + index (str): Index to search in. + query (str): Query string. + + Returns: + List of relevant documents. + """ + rag = self.RAGS[rag_name] + return await rag.VectorDB.get_docs_index(query=query, index=index) + +rag_factory = RAGFactory() +app = FastAPI() + +@app.get("/") +def heartbeat() -> float: + """Health check endpoint that returns the current server time.""" + return time.time() + +@app.post("/make-rag") +async def make_rag( + file: UploadFile = File(...), + config: str = Form(...) +) -> Dict[str, Any]: + """ + Create a RAG configuration, return its ID, and ingest the uploaded file. + + Args: + file (UploadFile): The file to upload and ingest. + config (str): The RAG configuration as a JSON string. + + Returns: + Dict[str, Any]: A dictionary containing the created RAG ID, upload status, and index. + """ + try: + # Parse the JSON string into a RAGConfig object + config = json.loads(config) + logger.info(f"Ingestion Config : {config}") + rag_config = RAGConfig(**config) + logger.info(f"RAG Config : {rag_config}") + + # Create RAG configuration + rag_id = rag_factory.make_rag(rag_config) + logging.info(f"Rag id {rag_id}") + # Ingest the file + task = await rag_factory.file_ingest(rag_name=rag_id, file=file) + + return { + "rag_id": rag_id, + "index": task._index, + "status": task._status, + "message": "RAG created and file ingested successfully" + } + except Exception as e: + logging.error(f"Something went wrong {e}") + return { + "rag_id": None, + "index": None, + "status": "ERROR", + "message": "Invalid JSON in config parameter" + } + +@app.get("/rag-retrive/{rag_id}/{index}") +async def rag_retrive(query: str, rag_id: str, index: str) -> list: + """Retrieve documents based on a query for a specific RAG ID and index. + Args: + query (str): The query string to search for. + rag_id (str): The ID of the RAG to search in. + index (str): The index to search in. + Returns: + list: A list of documents matching the query. + """ + docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) + send_filter = [{"text": node.text, "score": node.score} for node in docs] + return send_filter + + +if __name__ == "__main__": + uvicorn.run("quickstart_ingestion_server:app", port=8000, host="0.0.0.0", reload=True) \ No newline at end of file diff --git a/local_setup/ingestion_server/rags/__init__.py b/local_setup/ingestion_server/rags/__init__.py new file mode 100644 index 00000000..8588c135 --- /dev/null +++ b/local_setup/ingestion_server/rags/__init__.py @@ -0,0 +1,10 @@ +from rags.lancedb_rag import LanceDB +from rags.mongoDB_rag import MongoDB +from rags.base import BaseRAG + +from typing import Dict + +RAGProviders : Dict[str, BaseRAG] = { + "LanceDB": LanceDB, + "MongoDB": MongoDB +} \ No newline at end of file diff --git a/local_setup/ingestion_server/rags/base.py b/local_setup/ingestion_server/rags/base.py new file mode 100644 index 00000000..1da99427 --- /dev/null +++ b/local_setup/ingestion_server/rags/base.py @@ -0,0 +1,156 @@ +import os +import dotenv +from uuid import uuid4 + +from llama_parse import LlamaParse +from llama_index.core.node_parser import ( + MarkdownElementNodeParser, + SentenceSplitter, + TextSplitter +) +from llama_index.llms.openai import OpenAI +from llama_index.core import Settings + +from embeddings import BaseEmbed +from utils import configure_logger + + +dotenv.load_dotenv() + +logger = configure_logger(__name__) + +class BaseRAG: + """ + Base class for Retrieval-Augmented Generation (RAG) systems. + + Attributes: + provider (str): The provider for the RAG system. + base_embed (BaseEmbed): The embedding model used. + embeding_model: The actual embedding model instance. + chunk_size (int): Size of the chunks for splitting documents. + overlapping (int): Overlap size for chunking. + LLAMA_CLOUD (str): API key for Llama Cloud. + parse (LlamaParse): Instance of LlamaParse for data parsing. + OPENAI_API_KEY (str): API key for OpenAI. + llm (OpenAI): Instance of OpenAI model. + """ + + def __init__(self, provider: str, embedding_name: BaseEmbed, chunk_size: int, overlapping: int) -> None: + """ + Initializes the BaseRAG instance with the specified parameters. + + Args: + provider (str): The provider for the RAG system. + embedding_name (BaseEmbed): The embedding model used. + chunk_size (int): Size of the chunks for splitting documents. + overlapping (int): Overlap size for chunking. + + Raises: + ValueError: If required environment variables are not set. + """ + self.provider = provider + self.base_embed: BaseEmbed = embedding_name + self.embeding_model = self.base_embed.get_embedding() + self.chunk_size = chunk_size + self.overlapping = overlapping + self.LLAMA_CLOUD = os.getenv("LLAMA_CLOUD_API_KEY") + + if self.LLAMA_CLOUD is None: + raise ValueError("LLAMA_CLOUD_API_KEY is not set in .env") + + self.parse = LlamaParse(api_key=self.LLAMA_CLOUD, result_type="markdown") + + self.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") + + if self.OPENAI_API_KEY is None: + raise ValueError("OPENAI_API_KEY is not set in .env") + + self.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.2, api_key=self.OPENAI_API_KEY) + + Settings.embed_model = self.embeding_model + Settings.llm = self.llm + + def generate_index_name(self) -> str: + """Generates a unique index name using UUID. + + Returns: + str: A unique index name. + """ + return str(uuid4()) + + async def generate_nodes_sentence_splitter(self, file_loc: str): + """Generates nodes using a sentence splitter. + + Args: + file_loc (str): The file location to load data from. + + Returns: + nodes: The generated nodes after processing. + """ + docs = await self.parse.aload_data(file_path=file_loc) + node_parser = MarkdownElementNodeParser(num_workers=8, llm=self.llm) + nodes = await node_parser.aget_nodes_from_documents(docs) + nodes, _ = node_parser.get_nodes_and_objects(nodes) + nodes = await SentenceSplitter(chunk_size=self.chunk_size, chunk_overlap=self.overlapping).aget_nodes_from_documents(nodes) + return nodes + + async def generate_nodes_text_splitter(self, file_loc: str): + """Generates nodes using a text splitter. + + Args: + file_loc (str): The file location to load data from. + + Returns: + nodes: The generated nodes after processing. + """ + docs = await self.parse.aload_data(file_path=file_loc) + node_parser = MarkdownElementNodeParser(num_workers=8, llm=self.llm) + nodes = await node_parser.aget_nodes_from_documents(docs) + nodes, _ = node_parser.get_nodes_and_objects(nodes) + nodes = await TextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.overlapping).aget_nodes_from_documents(nodes) + return nodes + + async def append_index(self, nodes) -> str: + """Appends nodes to the existing index. + + Args: + nodes: The nodes to append. + + Raises: + NotImplementedError: This method should be implemented in subclasses. + """ + raise NotImplementedError + + async def add_index(self, nodes) -> str: + """Adds nodes to the index. + + Args: + nodes: The nodes to add. + + Raises: + NotImplementedError: This method should be implemented in subclasses. + """ + raise NotImplementedError + + async def delete_index(self, index: str) -> bool: + """Deletes an index. + + Args: + index (str): The index to delete. + + Raises: + NotImplementedError: This method should be implemented in subclasses. + """ + raise NotImplementedError + + async def get_docs_index(self, query: str, index: str): + """Retrieves documents from the index based on a query. + + Args: + query (str): The query to search for. + index (str): The index to search in. + + Raises: + NotImplementedError: This method should be implemented in subclasses. + """ + raise NotImplementedError \ No newline at end of file diff --git a/local_setup/ingestion_server/rags/lancedb_rag.py b/local_setup/ingestion_server/rags/lancedb_rag.py new file mode 100644 index 00000000..bac1cfed --- /dev/null +++ b/local_setup/ingestion_server/rags/lancedb_rag.py @@ -0,0 +1,105 @@ +import os +from typing import Any, Coroutine +from embeddings.base import BaseEmbed +from rags.base import BaseRAG +from llama_index.vector_stores.lancedb import LanceDBVectorStore +from llama_index.core import VectorStoreIndex, StorageContext +from datachecks import ProviderConfig, LanceDBConfig +from llama_index.core.retrievers import VectorIndexRetriever + +class LanceDB(BaseRAG): + """ + LanceDB class for managing vector storage and retrieval using LanceDB. + + Attributes: + similarity_top_k (int): Number of top similar items to retrieve. + config (LanceDBConfig): Configuration for LanceDB. + loc (str): Location for the vector database. + path (str): Path to the vector database data. + """ + + def __init__(self, embedding_name: BaseEmbed, config: ProviderConfig) -> None: + """ + Initializes the LanceDB instance. + + Args: + embedding_name (BaseEmbed): The embedding model used. + config (ProviderConfig): Configuration for the provider. + """ + super().__init__("LanceDB", embedding_name, config.chunk_size, config.overlapping) + self.similarity_top_k = config.similarity_top_k + self.config: LanceDBConfig = config.rag + self.loc = self.config.loc + + # self.path = f"s3://lancedb-example-data/{self.loc}" : use this to config your lancedb dir path + + self.path = os.getenv('LANCEDB_DIR') + print(f"Initialized LanceDB with path: {self.path}") + self.verify_shared_directory() + + def verify_shared_directory(self): + if os.path.exists(self.path): + print(f"Directory {self.path} exists.") + print("Contents of the directory:") + for item in os.listdir(self.path): + print(item) + else: + print(f"Directory {self.path} does not exist.") + + async def append_index(self, nodes) -> Coroutine[Any, Any, str]: + """Appends nodes to the existing index. + + Args: + nodes: The nodes to append. + + Returns: + Coroutine: A coroutine that returns None. + """ + return None + + async def add_index(self, nodes) -> str: + """Adds nodes to the index and creates a new table. + + Args: + nodes: The nodes to add. + + Returns: + str: The name of the created table. + """ + table_name = self.generate_index_name() + # TODO: add reranking in the DB + print(f"LanceDB Dir Path is : {self.path}") + vector_store = LanceDBVectorStore(self.path, table_name=table_name) + storage_context = StorageContext.from_defaults(vector_store=vector_store) + vector_index = VectorStoreIndex(nodes=nodes, storage_context=storage_context, embed_model=self.embeding_model) + return table_name + + async def delete_index(self, index: str) -> bool: + """Deletes an index. + + Args: + index (str): The index to delete. + + Returns: + bool: Result of the deletion operation. + """ + return await super().delete_index(index) + + async def get_docs_index(self, query: str, index: str): + """Retrieves documents from the index based on a query. + + Args: + query (str): The query to search for. + index (str): The index to search in. + + Returns: + Retrieved documents based on the query. + """ + vector_store = LanceDBVectorStore(uri=self.path, table_name=index) + storage_context = StorageContext.from_defaults(vector_store=vector_store) + vector_index = VectorStoreIndex(nodes=[], storage_context=storage_context) + query_engine = VectorIndexRetriever(vector_index, similarity_top_k=self.similarity_top_k) + return query_engine.retrieve(query) + + # query_engine = vector_index.as_query_engine(llm=self.llm) + diff --git a/local_setup/ingestion_server/rags/mongoDB_rag.py b/local_setup/ingestion_server/rags/mongoDB_rag.py new file mode 100644 index 00000000..be7abead --- /dev/null +++ b/local_setup/ingestion_server/rags/mongoDB_rag.py @@ -0,0 +1,96 @@ +from typing import Any, Coroutine +from pymongo import MongoClient +from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch + +from embeddings.base import BaseEmbed +from rags.base import BaseRAG +from datachecks import ProviderConfig, MongoDBConfig + +from llama_index.core import VectorStoreIndex, StorageContext +from llama_index.core.retrievers import VectorIndexRetriever + +class MongoDB(BaseRAG): + """ + MongoDB class for managing vector storage and retrieval using MongoDB. + + Attributes: + similarity_top_k (int): Number of top similar items to retrieve. + config (MongoDBConfig): Configuration for MongoDB. + client (MongoClient): MongoDB client instance. + """ + + def __init__(self, embedding_name: BaseEmbed, config: ProviderConfig) -> None: + """ + Initializes the MongoDB instance. + + Args: + embedding_name (BaseEmbed): The embedding model used. + config (ProviderConfig): Configuration for the provider. + """ + super().__init__("MongoDB", embedding_name, config.chunk_size, config.overlapping) + self.similarity_top_k = config.similarity_top_k + self.config: MongoDBConfig = config.rag + self.client = MongoClient(self.config.uri) + + async def append_index(self, nodes) -> Coroutine[Any, Any, str]: + """Appends nodes to the existing index. + + Args: + nodes: The nodes to append. + + Returns: + Coroutine: A coroutine that calls the base class method. + """ + return await super().append_index(nodes) + + async def get_docs_index(self, query: str, index: str): + """Retrieves documents from the index based on a query. + + Args: + query (str): The query to search for. + index (str): The index to search in. + + Returns: + Retrieved documents based on the query. + """ + vector_store = MongoDBAtlasVectorSearch( + self.client, + db_name=self.config.db, + collection_name=self.config.collection_name, + vector_index_name=self.config.index + ) + vector_store_context = StorageContext.from_defaults(vector_store=vector_store) + vector_store_index = VectorStoreIndex(nodes=[], storage_context=vector_store_context) + vector_store_retriever = VectorIndexRetriever(index=vector_store_index, similarity_top_k=self.similarity_top_k) + return vector_store_retriever.retrieve(query) + + async def delete_index(self, index: str) -> Coroutine[Any, Any, bool]: + """Deletes an index. + + Args: + index (str): The index to delete. + + Returns: + Coroutine: A coroutine that calls the base class method. + """ + return await super().delete_index(index) + + async def add_index(self, nodes) -> str: + """Adds nodes to the index and creates a new index. + + Args: + nodes: The nodes to add. + + Returns: + str: The name of the created index. + """ + # index = self.generate_index_name() + vector_store = MongoDBAtlasVectorSearch( + self.client, + db_name=self.config.db, + collection_name=self.config.collection_name, + index_name=self.config.index + ) + vector_store_context = StorageContext.from_defaults(vector_store=vector_store) + vector_store_index = VectorStoreIndex(nodes=nodes, storage_context=vector_store_context, embed_model=self.embeding_model) + return self.config.index \ No newline at end of file diff --git a/local_setup/ingestion_server/utils/__init__.py b/local_setup/ingestion_server/utils/__init__.py new file mode 100644 index 00000000..80c88a37 --- /dev/null +++ b/local_setup/ingestion_server/utils/__init__.py @@ -0,0 +1 @@ +from .log import configure_logger \ No newline at end of file diff --git a/local_setup/ingestion_server/utils/log.py b/local_setup/ingestion_server/utils/log.py new file mode 100644 index 00000000..acc08150 --- /dev/null +++ b/local_setup/ingestion_server/utils/log.py @@ -0,0 +1,31 @@ +import logging + +VALID_LOGGING_LEVELS = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] + +def configure_logger(file_name, enabled=True, logging_level='INFO'): + """ + Configures a logger for the specified file. + + Parameters: + - file_name (str): The name of the file for which the logger is being configured. + - enabled (bool): Flag to enable or disable the logger. Default is True. + - logging_level (str): The logging level to set. Must be one of the valid levels. Default is 'INFO'. + + Returns: + - logger (logging.Logger): Configured logger instance. + """ + if logging_level not in VALID_LOGGING_LEVELS: + logging_level = "INFO" + + logging.basicConfig( + level=logging_level, + format="%(asctime)s.%(msecs)03d %(levelname)s {%(module)s} [%(funcName)s] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + logger = logging.getLogger(file_name) + + if not enabled: + logger.disabled = True + + return logger \ No newline at end of file diff --git a/local_setup/ngrok-config.yml b/local_setup/ngrok-config.yml index 4907d3d9..11335ddd 100644 --- a/local_setup/ngrok-config.yml +++ b/local_setup/ngrok-config.yml @@ -1,6 +1,6 @@ region: us version: '2' -authtoken: +authtoken: 2idcq5RCgwCkpZpSTiHKxX7RV41_5Sux1mFFCUmgCihBVfab6 tunnels: twilio-app: addr: twilio-app:8001 @@ -10,8 +10,4 @@ tunnels: proto: http bolna-app: addr: bolna-app:5001 - proto: http - ingestion-app: - addr: ingestion-app:8000 - proto: http - + proto: http \ No newline at end of file diff --git a/local_setup/quick_scripts/buildup_docker.sh b/local_setup/quick_scripts/buildup_docker.sh index dbc74825..c188a35c 100755 --- a/local_setup/quick_scripts/buildup_docker.sh +++ b/local_setup/quick_scripts/buildup_docker.sh @@ -3,13 +3,18 @@ # Prompt the user for the branch name read -p "Enter the branch name: " BRANCH -# Build the images with the specified branch name -BRANCH=$BRANCH docker-compose build --no-cache +# Build and run containers for bolna_server +BRANCH=$BRANCH docker-compose build --no-cache bolna-app +docker-compose up -d --build bolna-app -# Run the containers in detached mode -docker-compose up -d +# Build and run containers for other services +for dockerfile in local_setup/dockerfiles/*.Dockerfile; do + service_name=$(basename "$dockerfile" .Dockerfile) + docker-compose build --no-cache "$service_name" + docker-compose up -d --build "$service_name" +done # Append logs to log.txt docker-compose logs -f >> log.txt & -echo "All Docker images are built, and containers are up and running. Logs are being appended to log.txt." +echo "All Docker images are built, and containers are up and running. Logs are being appended to log.txt." \ No newline at end of file diff --git a/local_setup/quickstart_ingestion_server.py b/local_setup/quickstart_ingestion_server.py deleted file mode 100644 index 592294dc..00000000 --- a/local_setup/quickstart_ingestion_server.py +++ /dev/null @@ -1,69 +0,0 @@ -from fastapi import FastAPI, UploadFile -from bolna.ingestion_server.datachecks import RAGConfig -from bolna.ingestion_server import RAGFactory -import uvicorn -import time -from typing import Dict -import dotenv - -dotenv.load_dotenv() -DB: Dict[str, RAGConfig] = {} - -rag_factory = RAGFactory() -app = FastAPI() - - -@app.get("/") -def heartbeat() -> float: - """Health check endpoint that returns the current server time.""" - return time.time() - - -@app.post("/create-rag") -def create_rag(request: RAGConfig) -> Dict[str, str]: - """Create a RAG configuration and return its ID. - - Args: - request (RAGConfig): The RAG configuration to create. - - Returns: - Dict[str, str]: A dictionary containing the created RAG ID. - """ - print(request) - rag_id = rag_factory.make_rag(request) - return {"rag_id": rag_id} - - -@app.post("/rag-upload-file/{rag_id}") -async def rag_upload_file(file: UploadFile, rag_id: str) -> Dict[str, str]: - """Upload a file for a specific RAG ID. - - Args: - file (UploadFile): The file to upload. - rag_id (str): The ID of the RAG to associate with the file. - - Returns: - Dict[str, str]: A dictionary containing the upload status and index. - """ - try: - task = await rag_factory.file_ingest(rag_name=rag_id, file=file) - return {"index": task._index, "status": task._status, "message": "DONE"} - except Exception as e: - return {"index": None, "status": "ERROR", "message": f"{e}"} - - -@app.get("/rag-retrive/{rag_id}/{index}") -async def rag_retrive(query: str, rag_id: str, index: str) -> list: - """Retrieve documents based on a query for a specific RAG ID and index. - - Args: - query (str): The query string to search for. - rag_id (str): The ID of the RAG to search in. - index (str): The index to search in. - - Returns: - list: A list of documents matching the query. - """ - docs = await rag_factory.retrieve_query(rag_name=rag_id, index=index, query=query) - send_filter = [{"text": node.text, "score": node.score} for node in docs] - return send_filter \ No newline at end of file diff --git a/local_setup/requirements.txt b/local_setup/requirements.txt index de8a1882..74f5165b 100644 --- a/local_setup/requirements.txt +++ b/local_setup/requirements.txt @@ -3,4 +3,11 @@ fastapi==0.108.0 plivo==4.47.0 redis==5.0.1 twilio==8.9.0 -uvicorn==0.22.0 \ No newline at end of file +uvicorn==0.22.0 +pylance==0.16.0 +pymongo==4.8.0 +python-multipart==0.0.6 +llama-index==0.10.65 +llama-index-vector-stores-lancedb==0.1.7 +llama-index-vector-stores-mongodb==0.1.8 +llama-index-llms-openai==0.1.29 \ No newline at end of file diff --git a/local_setup/telephony_server/twilio_api_server.py b/local_setup/telephony_server/twilio_api_server.py index 67bd873c..1098fdf6 100644 --- a/local_setup/telephony_server/twilio_api_server.py +++ b/local_setup/telephony_server/twilio_api_server.py @@ -22,7 +22,7 @@ def populate_ngrok_tunnels(): - response = requests.get("http://localhost:4040/api/tunnels") # ngrok interface + response = requests.get("http://ngrok:4040/api/tunnels") # ngrok interface app_callback_url, websocket_url = None, None telephony_url, bolna_url = None, None From 33f8466f8a06dde35fb536fd7c0847d4d97a2091 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Mon, 26 Aug 2024 16:41:43 +0530 Subject: [PATCH 18/20] changes --- local_setup/ngrok-config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/local_setup/ngrok-config.yml b/local_setup/ngrok-config.yml index 11335ddd..7d27206c 100644 --- a/local_setup/ngrok-config.yml +++ b/local_setup/ngrok-config.yml @@ -1,6 +1,6 @@ region: us version: '2' -authtoken: 2idcq5RCgwCkpZpSTiHKxX7RV41_5Sux1mFFCUmgCihBVfab6 +authtoken: tunnels: twilio-app: addr: twilio-app:8001 From 4a94aadb77fc28f804e93d3612e9dee65bccf16e Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Wed, 28 Aug 2024 12:03:01 +0530 Subject: [PATCH 19/20] some changes in local setup for RAG --- local_setup/.env.sample | 28 ++++++++++++ local_setup/quickstart_server.py | 2 +- .../telephony_server/twilio_api_server.py | 45 ++++++++++++++++++- 3 files changed, 72 insertions(+), 3 deletions(-) create mode 100644 local_setup/.env.sample diff --git a/local_setup/.env.sample b/local_setup/.env.sample new file mode 100644 index 00000000..1216f976 --- /dev/null +++ b/local_setup/.env.sample @@ -0,0 +1,28 @@ +DEEPGRAM_AUTH_TOKEN= +ELEVENLABS_API_KEY= +OPENAI_API_KEY= +OPENAI_MODEL=gpt-3.5-turbo +REDIS_URL=redis://redis:6379 +WHISPER_URL= +STYLE_TTS= +BUCKET_NAME=bolna-user-prompts-sandbox + +# RAG credentials +LANCEDB_DIR= +LLAMA_CLOUD_API_KEY= + +# AWS credentials +AWS_ACCESS_KEY_ID= +AWS_SECRET_ACCESS_KEY= + +# You can make a call with either of Twilio or Plivo providers. + +# Twilio credentials +TWILIO_ACCOUNT_SID= +TWILIO_AUTH_TOKEN= +TWILIO_PHONE_NUMBER= + +# Plivo credentials +PLIVO_AUTH_ID= +PLIVO_AUTH_TOKEN= +PLIVO_PHONE_NUMBER= \ No newline at end of file diff --git a/local_setup/quickstart_server.py b/local_setup/quickstart_server.py index 475e8b1d..3b04367b 100644 --- a/local_setup/quickstart_server.py +++ b/local_setup/quickstart_server.py @@ -17,7 +17,7 @@ import threading from typing import Dict import os -load_dotenv() +load_dotenv(override=True) logger = configure_logger(__name__) redis_pool = redis.ConnectionPool.from_url(os.getenv('REDIS_URL'), decode_responses=True) diff --git a/local_setup/telephony_server/twilio_api_server.py b/local_setup/telephony_server/twilio_api_server.py index 1098fdf6..17e88a9f 100644 --- a/local_setup/telephony_server/twilio_api_server.py +++ b/local_setup/telephony_server/twilio_api_server.py @@ -6,11 +6,11 @@ from twilio.rest import Client from dotenv import load_dotenv import redis.asyncio as redis -from fastapi import FastAPI, HTTPException, Query, Request +from fastapi import FastAPI, HTTPException, Query, Request, Header from fastapi.responses import PlainTextResponse app = FastAPI() -load_dotenv() +load_dotenv(override=True) port = 8001 twilio_account_sid = os.getenv('TWILIO_ACCOUNT_SID') @@ -75,6 +75,47 @@ async def make_call(request: Request): raise HTTPException(status_code=500, detail="Internal Server Error") +# # Use this Endpoint to call the playground endpoint directly! +# @app.post('/call') +# async def make_call_v2(request: Request, authorization: str = Header(...)): +# try: +# call_details = await request.json() +# agent_id = call_details.get('agent_id') +# print(f"Agent ID : {agent_id}") +# recipient_phone_number = call_details.get('recipient_phone_number') +# user_data = call_details.get('user_data', {}) + +# if not agent_id: +# raise HTTPException(status_code=400, detail="Agent ID not provided") + +# if not recipient_phone_number: +# raise HTTPException(status_code=400, detail="Recipient phone number not provided") + +# # Prepare the payload for the new endpoint +# payload = { +# "agent_id": agent_id, +# "recipient_phone_number": recipient_phone_number, +# } + +# headers = { +# 'Authorization': authorization, +# 'Content-Type': 'application/json' +# } + +# # Send the request to the new endpoint +# response = requests.post("https://api.bolna.dev/call", headers=headers, json=payload) +# print(f"Response status code: {response.status_code}") +# print(f"Response content: {response.content}") + +# if response.status_code != 200: +# raise HTTPException(status_code=response.status_code, detail=f"Failed to make call: {response.text}") + +# return PlainTextResponse("Call initiated successfully", status_code=200) + +# except Exception as e: +# print(f"Exception occurred in make_call_v2: {e}") +# raise HTTPException(status_code=500, detail=str(e)) + @app.post('/twilio_connect') async def twilio_connect(bolna_host: str = Query(...), agent_id: str = Query(...)): try: From 4673b4f931f490c8c8af1dad310a588924b975e3 Mon Sep 17 00:00:00 2001 From: Marmik Pandya Date: Wed, 28 Aug 2024 17:25:06 +0530 Subject: [PATCH 20/20] bring models up to speed with develop --- bolna/models.py | 116 +++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 105 insertions(+), 11 deletions(-) diff --git a/bolna/models.py b/bolna/models.py index 4872a929..25b14266 100644 --- a/bolna/models.py +++ b/bolna/models.py @@ -1,18 +1,17 @@ import json from typing import Optional, List, Union, Dict -from pydantic import BaseModel, Field, validator, ValidationError, Json +from pydantic import BaseModel, Field, validator, field_validator, ValidationError, Json from pydantic_core import PydanticCustomError + from .providers import * AGENT_WELCOME_MESSAGE = "This call is being recorded for quality assurance and training. Please speak now." - def validate_attribute(value, allowed_values): if value not in allowed_values: raise ValidationError(f"Invalid provider {value}. Supported values: {allowed_values}") return value - class PollyConfig(BaseModel): voice: str engine: str @@ -108,7 +107,7 @@ def validate_model(cls, value): class IOModel(BaseModel): provider: str - format: str + format: Optional[str] = "wav" @validator("provider") def validate_provider(cls, value): @@ -127,16 +126,43 @@ class Route(BaseModel): # Routes can be used for FAQs caching, prompt routing, guard rails, agent assist function calling class Routes(BaseModel): embedding_model: Optional[str] = "Snowflake/snowflake-arctic-embed-l" - routes: List[Route] + routes: Optional[List[Route]] = [] class OpenaiAssistants(BaseModel): name: Optional[str] = None assistant_id: str = None + max_tokens: Optional[int] =100 + temperature: Optional[float] = 0.2 + buffer_size: Optional[int] = 100 + provider: Optional[str] = "openai" + model: Optional[str] = "gpt-3.5-turbo" + +class MongoDBProviderConfig(BaseModel): + connection_string: Optional[str] = None + db_name: Optional[str] = None + collection_name: Optional[str] = None + index_name: Optional[str] = None + llm_model: Optional[str] = "gpt-3.5-turbo" + embedding_model: Optional[str] = "text-embedding-3-small" + embedding_dimensions: Optional[int] = 256 + +class LanceDBProviderConfig(BaseModel): + vector_id: str + +class VectorStore(BaseModel): + provider: str + provider_config: Union[LanceDBProviderConfig, MongoDBProviderConfig] + + @field_validator('provider_config', mode='before') + def validate_provider_config(cls, value, info): + vector_provider = info.data.get('provider') + if vector_provider not in ['mongodb', 'lancedb']: + raise ValueError('Unsupported provider for vector_store') + return value class LLM(BaseModel): model: Optional[str] = "gpt-3.5-turbo" max_tokens: Optional[int] = 100 - agent_flow_type: Optional[str] = "streaming" family: Optional[str] = "openai" temperature: Optional[float] = 0.1 request_json: Optional[bool] = False @@ -148,11 +174,79 @@ class LLM(BaseModel): presence_penalty: Optional[float] = 0.0 provider: Optional[str] = "openai" base_url: Optional[str] = None - routes: Optional[Routes] = None + + +class SIMPLE_LLM_AGENT(LLM): + agent_flow_type: Optional[str] = "streaming" #It is used for backwards compatibility + routes: Optional[Routes] = None extraction_details: Optional[str] = None summarization_details: Optional[str] = None - backend: Optional[str] = "bolna" - extra_config: Optional[OpenaiAssistants] = None + + +class Node(BaseModel): + id: str + type: str #Can be router or conversation for now + llm: LLM + exit_criteria: str + exit_response: Optional[str] = None + exit_prompt: Optional[str] = None + is_root: Optional[bool] = False + +class Edge(BaseModel): + start_node: str # Node ID + end_node: str + condition: Optional[tuple] = None #extracted value from previous step and it's value + +class LLM_AGENT_GRAPH(BaseModel): + nodes: List[Node] + edges: List[Edge] + +class AGENT_ROUTE_CONFIG(BaseModel): + utterances: List[str] + threshold: Optional[float] = 0.85 + +class MultiAgent(BaseModel): + agent_map: Dict[str, Union[LLM, OpenaiAssistants]] + agent_routing_config: Dict[str, AGENT_ROUTE_CONFIG] + default_agent: str + embedding_model: Optional[str] = "Snowflake/snowflake-arctic-embed-l" + +class KnowledgebaseAgent(LLM): + vector_store: VectorStore + provider: Optional[str] = "openai" + model: Optional[str] = "gpt-3.5-turbo" + + +class LLM_AGENT(BaseModel): + agent_flow_type: str + agent_type: str + guardrails: Optional[Routes] = None + extra_config: Union[OpenaiAssistants, KnowledgebaseAgent, LLM_AGENT_GRAPH, MultiAgent, SIMPLE_LLM_AGENT] + + @field_validator('extra_config', mode='before') + def validate_extra_config(cls, value, info): + agent_type = info.data.get('agent_type') + + valid_config_types = { + 'openai_assistant': OpenaiAssistants, + 'knowledgebase_agent': KnowledgebaseAgent, + 'llm_agent_graph': LLM_AGENT_GRAPH, + 'multiagent': MultiAgent, + 'simple_llm_agent': SIMPLE_LLM_AGENT, + } + + if agent_type not in valid_config_types: + raise ValueError(f'Unsupported agent_type: {agent_type}') + + expected_type = valid_config_types[agent_type] + + if not isinstance(value, dict): + raise ValueError(f"extra_config must be a dict, got {type(value)}") + + try: + return expected_type(**value) + except Exception as e: + raise ValueError(f"Failed to create {expected_type.__name__} from extra_config: {str(e)}") class MessagingModel(BaseModel): @@ -184,7 +278,7 @@ class ToolModel(BaseModel): tools_params: Dict[str, APIParams] class ToolsConfig(BaseModel): - llm_agent: Optional[LLM] = None + llm_agent: Optional[Union[LLM_AGENT, SIMPLE_LLM_AGENT]] = None synthesizer: Optional[Synthesizer] = None transcriber: Optional[Transcriber] = None input: Optional[IOModel] = None @@ -232,4 +326,4 @@ class AgentModel(BaseModel): agent_name: str agent_type: str = "other" tasks: List[Task] - agent_welcome_message: Optional[str] = AGENT_WELCOME_MESSAGE + agent_welcome_message: Optional[str] = AGENT_WELCOME_MESSAGE \ No newline at end of file

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