├── .gitignore ├── README.md ├── assets └── img │ └── demo-cover.png ├── backend ├── .gitignore ├── README.md ├── app │ ├── __init__.py │ ├── api │ │ ├── __init__.py │ │ └── routers │ │ │ ├── __init__.py │ │ │ ├── chat.py │ │ │ └── index.py │ ├── conn_llm.py │ ├── context.py │ ├── engine │ │ ├── __init__.py │ │ ├── agents.py │ │ ├── constants.py │ │ ├── generate.py │ │ ├── index.py │ │ ├── loader.py │ │ ├── query_tools.py │ │ └── utils.py │ ├── public │ │ ├── 404.html │ │ ├── favicon.ico │ │ ├── index.html │ │ ├── index.txt │ │ └── llama.png │ ├── static │ │ └── _next │ │ │ └── static │ │ │ ├── a0F5iAmesR9XNB9QY5ZMN │ │ │ ├── _buildManifest.js │ │ │ └── _ssgManifest.js │ │ │ ├── chunks │ │ │ ├── 69-2dd335a42df48c81.js │ │ │ ├── 77-6cbd386b7cb57e62.js │ │ │ ├── app │ │ │ │ ├── _not-found-aeb74ca2b03c8470.js │ │ │ │ ├── layout-386c8c158172a3ec.js │ │ │ │ └── page-887ea91a8c950aff.js │ │ │ ├── fd9d1056-16b896b82b5ac953.js │ │ │ ├── framework-b370f160bb96059c.js │ │ │ ├── main-app-1444d65fd19930be.js │ │ │ ├── main-f1b04c206ba955e4.js │ │ │ ├── pages │ │ │ │ ├── _app-d21e88acd55d90f1.js │ │ │ │ └── _error-d6107f1aac0c574c.js │ │ │ ├── polyfills-c67a75d1b6f99dc8.js │ │ │ └── webpack-d074f59e6fb5743e.js │ │ │ ├── css │ │ │ └── 3ffa7081b01d7ba0.css │ │ │ └── media │ │ │ ├── 05a31a2ca4975f99-s.woff2 │ │ │ ├── 513657b02c5c193f-s.woff2 │ │ │ ├── 51ed15f9841b9f9d-s.woff2 │ │ │ ├── c9a5bc6a7c948fb0-s.p.woff2 │ │ │ ├── d6b16ce4a6175f26-s.woff2 │ │ │ ├── ec159349637c90ad-s.woff2 │ │ │ └── fd4db3eb5472fc27-s.woff2 │ └── storage.py ├── config.json ├── main.py ├── refresh_index.py └── tests │ └── __init__.py ├── dist ├── app-0.1.0-py3-none-any.whl ├── app-0.1.0.tar.gz ├── docker-entrypoint.sh └── requirements.txt ├── dockerfile ├── dockerfile-amd64 ├── frontend ├── .gitignore ├── README.md ├── app │ ├── components │ │ ├── chat-section.tsx │ │ ├── header.tsx │ │ ├── transform.ts │ │ └── ui │ │ │ ├── README.md │ │ │ ├── button.tsx │ │ │ ├── chat │ │ │ ├── chat-actions.tsx │ │ │ ├── chat-avatar.tsx │ │ │ ├── chat-input.tsx │ │ │ ├── chat-message.tsx │ │ │ ├── chat-messages.tsx │ │ │ ├── chat.interface.ts │ │ │ ├── codeblock.tsx │ │ │ ├── index.ts │ │ │ ├── markdown.tsx │ │ │ └── use-copy-to-clipboard.tsx │ │ │ ├── file-uploader.tsx │ │ │ ├── input.tsx │ │ │ ├── lib │ │ │ └── utils.ts │ │ │ └── upload-image-preview.tsx │ ├── favicon.ico │ ├── globals.css │ ├── layout.tsx │ └── page.tsx ├── next.config.js ├── package-lock.json ├── package.json ├── postcss.config.js ├── public │ └── llama.png ├── tailwind.config.ts └── tsconfig.json ├── poetry.lock └── pyproject.toml /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | storage 3 | .env 4 | chromadb 5 | cache_folder 6 | .venv 7 | .vscode 8 | 9 | 10 | # See https://help.github.com/articles/ignoring-files/ for more about ignoring files. 11 | 12 | # dependencies 13 | /node_modules 14 | /.pnp 15 | .pnp.js 16 | 17 | # testing 18 | /coverage 19 | 20 | # next.js 21 | /.next/ 22 | /out/ 23 | 24 | # production 25 | /build 26 | 27 | # misc 28 | .DS_Store 29 | *.pem 30 | 31 | # debug 32 | npm-debug.log* 33 | yarn-debug.log* 34 | yarn-error.log* 35 | 36 | # local env files 37 | .env*.local 38 | 39 | # vercel 40 | .vercel 41 | 42 | # typescript 43 | *.tsbuildinfo 44 | next-env.d.ts 45 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This is a [LlamaIndex](https://www.llamaindex.ai/) project using [FastAPI](https://fastapi.tiangolo.com/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama). 2 | 3 | **Demo** 4 | 5 | 6 | 7 | ## Getting Started 8 | ### Quickstart 9 | #### Option 1) Clone project and setup by yourself 10 | ```bash 11 | git clone https://github.com/thisishugow/create-llama-ollama.git 12 | cd create-llama-ollama 13 | poetry install 14 | ``` 15 | Download and install `Ollama`([Guide](https://ollama.com)). 16 | ```bash 17 | # After Ollama is installed. 18 | ollama pull llama3 19 | ollama serve & 20 | ``` 21 | 22 | Start the app: 23 | ```bash 24 | poetry install 25 | poetry run ./backend/main.py -c ./backend/config.json # you can make your configuration. 26 | ``` 27 | Then visit [http://localhost:8080](http://localhost:8080) with your browser to see the result. 28 | 29 | #### Optional 2) Docker container 30 | 31 | ```bash 32 | # for Windows WSL2/Linux 33 | docker run --name my-offline-llama -p 8080:8080 thisisyuwang/create-llama-ollama:latest-linux-amd64 34 | 35 | # for Mac arm64 36 | docker run --name my-offline-llama -p 8080:8080 thisisyuwang/create-llama-ollama:latest-arm64 37 | ``` 38 | > ⚠️ WARNING: It will take minutes to download LLM at the first time. 39 | 40 | Then visit [http://localhost:8080](http://localhost:8080) with your browser to see the result. 41 | 42 | 43 | 44 | ### For Development 45 | Startup the backend as described in the [backend README](./backend/README.md). 46 | 47 | Second, run the development server of the frontend as described in the [frontend README](./frontend/README.md). 48 | 49 | Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. 50 | 51 | First, setup the environment: 52 | 53 | ``` 54 | poetry install 55 | poetry shell 56 | ``` 57 | 58 | By default, we use the OpenAI LLM (though you can customize, see `app/context.py`). As a result you need to specify an `OPENAI_API_KEY` in an .env file in this directory. 59 | 60 | Example `.env` file: 61 | 62 | ``` 63 | OPENAI_API_KEY= 64 | ``` 65 | 66 | Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step): 67 | 68 | ``` 69 | python app/engine/generate.py 70 | ``` 71 | 72 | Third, run the development server: 73 | 74 | ``` 75 | python main.py 76 | ``` 77 | 78 | Then call the API endpoint `/api/chat` to see the result: 79 | 80 | ``` 81 | curl --location 'localhost:8000/api/chat' \ 82 | --header 'Content-Type: application/json' \ 83 | --data '{ "messages": [{ "role": "user", "content": "Hello" }] }' 84 | ``` 85 | 86 | You can start editing the API by modifying `app/api/routers/chat.py`. The endpoint auto-updates as you save the file. 87 | 88 | Open [http://localhost:8000/docs](http://localhost:8000/docs) with your browser to see the Swagger UI of the API. 89 | 90 | The API allows CORS for all origins to simplify development. You can change this behavior by setting the `ENVIRONMENT` environment variable to `prod`: 91 | 92 | ``` 93 | ENVIRONMENT=prod uvicorn main:app 94 | ``` 95 | 96 | ## Learn More 97 | 98 | To learn more about LlamaIndex, take a look at the following resources: 99 | 100 | - [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex. 101 | - [LlamaIndexTS Documentation](https://ts.llamaindex.ai) - learn about LlamaIndex (Typescript features). 102 | 103 | You can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome! 104 | -------------------------------------------------------------------------------- /assets/img/demo-cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thisishugow/create-llama-ollama/b45eee69b27ace1dcfe32609586f3d47f29faf0a/assets/img/demo-cover.png -------------------------------------------------------------------------------- /backend/.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | storage 3 | .env 4 | -------------------------------------------------------------------------------- /backend/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thisishugow/create-llama-ollama/b45eee69b27ace1dcfe32609586f3d47f29faf0a/backend/README.md -------------------------------------------------------------------------------- /backend/app/__init__.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | 4 | from llama_index.core import Settings 5 | from app.conn_llm import get_sys_embedding, get_sys_llm 6 | from app.engine.constants import CHUNK_SIZE, CHUNK_OVERLAP 7 | from app.engine.utils import read_json_config 8 | 9 | 10 | 11 | def get_args(): 12 | parser = argparse.ArgumentParser() 13 | parser.add_argument( 14 | "-c", 15 | "--config", 16 | help="Config path", 17 | type=str, 18 | required=True, 19 | ) 20 | args = parser.parse_args() 21 | return args 22 | if "CREATE_LLAMA_APP_CONF" not in os.environ.keys(): 23 | args = get_args() 24 | os.environ['CREATE_LLAMA_APP_CONF'] = args.config 25 | 26 | else: 27 | conf = read_json_config() 28 | 29 | Settings.llm = get_sys_llm() 30 | Settings.embed_model = get_sys_embedding() 31 | Settings.chunk_size = conf.get('chunk_size', CHUNK_SIZE) 32 | Settings.chunk_overlap = conf.get('chunk_size', CHUNK_OVERLAP) -------------------------------------------------------------------------------- /backend/app/api/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thisishugow/create-llama-ollama/b45eee69b27ace1dcfe32609586f3d47f29faf0a/backend/app/api/__init__.py -------------------------------------------------------------------------------- /backend/app/api/routers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thisishugow/create-llama-ollama/b45eee69b27ace1dcfe32609586f3d47f29faf0a/backend/app/api/routers/__init__.py -------------------------------------------------------------------------------- /backend/app/api/routers/chat.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | from fastapi.responses import StreamingResponse 4 | from llama_index.core.chat_engine.types import BaseChatEngine 5 | 6 | from app.engine import get_chat_engine 7 | 8 | from fastapi import APIRouter, Depends, HTTPException, Request, status 9 | from llama_index.core.llms import ChatMessage 10 | from llama_index.core.types import MessageRole 11 | from pydantic import BaseModel 12 | 13 | chat_router = r = APIRouter() 14 | 15 | 16 | class _Message(BaseModel): 17 | role: MessageRole 18 | content: str 19 | 20 | 21 | class _ChatData(BaseModel): 22 | messages: List[_Message] 23 | 24 | 25 | @r.post("") 26 | async def chat( 27 | request: Request, 28 | data: _ChatData, 29 | chat_engine: BaseChatEngine = Depends(get_chat_engine), 30 | ): 31 | # check preconditions and get last message 32 | if len(data.messages) == 0: 33 | raise HTTPException( 34 | status_code=status.HTTP_400_BAD_REQUEST, 35 | detail="No messages provided", 36 | ) 37 | lastMessage = data.messages.pop() 38 | if lastMessage.role != MessageRole.USER: 39 | raise HTTPException( 40 | status_code=status.HTTP_400_BAD_REQUEST, 41 | detail="Last message must be from user", 42 | ) 43 | # convert messages coming from the request to type ChatMessage 44 | messages = [ 45 | ChatMessage( 46 | role=m.role, 47 | content=m.content, 48 | ) 49 | for m in data.messages 50 | ] 51 | # query chat engine 52 | # query_engine = get_compound_chat_engine() 53 | response = await chat_engine.astream_chat(lastMessage.content, messages) 54 | 55 | # stream response 56 | async def event_generator(): 57 | async for token in response.async_response_gen(): 58 | # If client closes connection, stop sending events 59 | if await request.is_disconnected(): 60 | break 61 | yield token 62 | 63 | return StreamingResponse(event_generator(), media_type="text/plain") 64 | -------------------------------------------------------------------------------- /backend/app/api/routers/index.py: -------------------------------------------------------------------------------- 1 | import app 2 | 3 | from fastapi import APIRouter, HTTPException, Request, status, Response 4 | from fastapi.responses import HTMLResponse, RedirectResponse, FileResponse 5 | from fastapi.staticfiles import StaticFiles 6 | from fastapi.templating import Jinja2Templates 7 | from fastapi.responses import HTMLResponse 8 | import logging, os 9 | LIB_ROOT:os.PathLike = os.path.dirname(os.path.realpath(app.__file__)) 10 | router = APIRouter() 11 | templates = Jinja2Templates(directory=os.path.join(LIB_ROOT, 'public')) 12 | router.mount("/_next", StaticFiles(directory=os.path.join(LIB_ROOT, 'static/_next/')),) 13 | router.mount("/static", StaticFiles(directory=os.path.join(LIB_ROOT, 'static')),) 14 | 15 | 16 | @router.get("/", response_class=HTMLResponse) 17 | async def redirect_homepage(request: Request): 18 | return templates.TemplateResponse("index.html", context={"request":request,}) 19 | 20 | @router.get("/llama.png") 21 | async def get_favicon(request: Request): 22 | favicon_path = os.path.join(os.path.join(LIB_ROOT, 'public'), "llama.png") 23 | return FileResponse(path=favicon_path, media_type="image/vnd.microsoft.icon") 24 | 25 | @router.get("/favicon.ico") 26 | async def get_favicon(request: Request): 27 | favicon_path = os.path.join(os.path.join(LIB_ROOT, 'public'), "favicon.ico") 28 | return FileResponse(path=favicon_path, media_type="image/vnd.microsoft.icon") -------------------------------------------------------------------------------- /backend/app/conn_llm.py: -------------------------------------------------------------------------------- 1 | import os 2 | from logging import getLogger 3 | from llama_index.llms.ollama import Ollama 4 | from transformers import AutoTokenizer 5 | from llama_index.embeddings.huggingface import HuggingFaceEmbedding 6 | from llama_index.llms.langchain import LangChainLLM 7 | from llama_index.core.chat_engine.types import BaseChatEngine 8 | from langchain.chat_models.ollama import ChatOllama 9 | 10 | from app.engine.utils import read_json_config 11 | 12 | 13 | 14 | 15 | conf:dict = read_json_config() 16 | log = getLogger('uvicorn') 17 | EMBDEDING_MODEL:str = "sentence-transformers/all-mpnet-base-v2" 18 | HF_TOKEN:str = os.environ.get('HF_TOKEN', '') 19 | hf_token_to_log:str = '' 20 | if HF_TOKEN: 21 | hf_token_to_log:str = f"{HF_TOKEN[0:10]}*********" 22 | 23 | 24 | def get_sys_tokenizer(): 25 | conf:dict = read_json_config() 26 | log.debug(f'You are using HF_TOKEN: {hf_token_to_log}') 27 | tokenizer = AutoTokenizer.from_pretrained( 28 | conf.get('embedding', EMBDEDING_MODEL), 29 | tokenizer_config={ 30 | "token": HF_TOKEN, 31 | },) 32 | return tokenizer 33 | 34 | def get_sys_llm()->LangChainLLM: 35 | conf:dict = read_json_config() 36 | # sys_llm: Ollama = Ollama(model=conf.get('llm', "mistral")) 37 | sys_llm = LangChainLLM(ChatOllama(model=conf.get('llm', "llama3"))) 38 | return sys_llm 39 | 40 | def get_sys_embedding() -> HuggingFaceEmbedding: 41 | conf:dict = read_json_config() 42 | tokenizer = get_sys_tokenizer() 43 | sys_embedding: HuggingFaceEmbedding = HuggingFaceEmbedding( 44 | model_name=conf.get('embedding', EMBDEDING_MODEL), 45 | tokenizer=tokenizer, 46 | cache_folder=conf.get("cache_folder", "cache_folder"), 47 | max_length=512, 48 | # device="mps", 49 | ) 50 | return sys_embedding 51 | -------------------------------------------------------------------------------- /backend/app/context.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from llama_index.core import ServiceContext 4 | from llama_index.llms.openai import OpenAI 5 | from llama_index.core import Settings, ServiceContext 6 | 7 | 8 | def create_base_context(): 9 | # model = os.getenv("MODEL", "gpt-3.5-turbo") 10 | # return ServiceContext.from_defaults( 11 | # llm=OpenAI(model=model), 12 | # ) 13 | return ServiceContext.from_defaults(llm=Settings.llm, embed_model=Settings.embed_model ) -------------------------------------------------------------------------------- /backend/app/engine/__init__.py: -------------------------------------------------------------------------------- 1 | from llama_index.core.chat_engine.types import BaseChatEngine 2 | from llama_index.core.chat_engine import CondensePlusContextChatEngine 3 | from llama_index.core.agent import ReActAgent 4 | from app.engine.index import get_index 5 | from app.engine.query_tools import get_tools 6 | def get_chat_engine()->BaseChatEngine: 7 | chat_engine = get_index().as_chat_engine( 8 | similarity_top_k=3, 9 | chat_mode="condense_plus_context", 10 | ) 11 | 12 | return chat_engine 13 | -------------------------------------------------------------------------------- /backend/app/engine/agents.py: -------------------------------------------------------------------------------- 1 | from llama_index.core.selectors import LLMSingleSelector 2 | from llama_index.core.query_engine import RouterQueryEngine 3 | from llama_index.core.agent import ReActAgent 4 | from app.engine.index import get_index 5 | from app.engine.query_tools import get_tools 6 | 7 | 8 | def get_compound_chat_engine(): 9 | chat_engine = RouterQueryEngine( 10 | selector=LLMSingleSelector.from_defaults(), 11 | query_engine_tools=get_tools() 12 | ) 13 | return chat_engine 14 | -------------------------------------------------------------------------------- /backend/app/engine/constants.py: -------------------------------------------------------------------------------- 1 | DATA_DIR = "data" # directory containing the documents to index 2 | CHUNK_SIZE = 1024 3 | CHUNK_OVERLAP = 20 4 | PGVECTOR_SCHEMA = "public" 5 | PGVECTOR_TABLE = "llamaindex_embedding" -------------------------------------------------------------------------------- /backend/app/engine/generate.py: -------------------------------------------------------------------------------- 1 | from typing import Literal 2 | from dotenv import load_dotenv 3 | 4 | load_dotenv() 5 | import logging 6 | 7 | from app.engine.utils import init_pg_vector_store_from_env 8 | from app.engine.loader import get_documents 9 | from app.storage import chroma_vector_store 10 | from app.engine.utils import read_json_config 11 | from app.conn_llm import get_sys_embedding, get_sys_llm 12 | from app.engine.constants import CHUNK_SIZE, CHUNK_OVERLAP 13 | from app.engine.utils import read_json_config 14 | 15 | from llama_index.core import ( 16 | SimpleDirectoryReader, 17 | VectorStoreIndex, 18 | StorageContext, 19 | Settings, 20 | ) 21 | 22 | logging.basicConfig(level=logging.INFO) 23 | logger = logging.getLogger() 24 | 25 | 26 | def generate_datasource(from_:Literal["postgres", "chroma"]='chroma'): 27 | conf = read_json_config() 28 | Settings.llm = get_sys_llm() 29 | Settings.embed_model = get_sys_embedding() 30 | Settings.chunk_size = conf.get('chunk_size', CHUNK_SIZE) 31 | Settings.chunk_overlap = conf.get('chunk_size', CHUNK_OVERLAP) 32 | logger.info("Creating new index") 33 | # load the documents and create the index 34 | documents = get_documents() 35 | 36 | if from_ == 'postgres': 37 | logger.info("Connecting to index from PGVector...") 38 | store = init_pg_vector_store_from_env() 39 | elif from_ == 'chroma': 40 | logger.info("Connecting to index from ChromaDB...") 41 | store = chroma_vector_store 42 | else: 43 | ValueError(f"arg from_ must be in ['postgres', 'chroma']. Found='{from_}'") 44 | 45 | storage_context = StorageContext.from_defaults(vector_store=store) 46 | VectorStoreIndex.from_documents( 47 | documents, 48 | storage_context=storage_context, 49 | show_progress=True, # this will show you a progress bar as the embeddings are created 50 | ) 51 | logger.info( 52 | f"Successfully created embeddings in the vector store" 53 | ) 54 | 55 | 56 | if __name__ == "__main__": 57 | generate_datasource() 58 | -------------------------------------------------------------------------------- /backend/app/engine/index.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from typing import Literal 3 | from llama_index.core import ( 4 | VectorStoreIndex, 5 | ) 6 | from app.engine.utils import init_pg_vector_store_from_env 7 | from app.storage import chroma_vector_store 8 | 9 | def get_index(from_:Literal["postgres", "chroma"]="chroma"): 10 | logger = logging.getLogger("uvicorn") 11 | if from_ == 'postgres': 12 | logger.info("Connecting to index from PGVector...") 13 | store = init_pg_vector_store_from_env() 14 | elif from_ == 'chroma': 15 | logger.info("Connecting to index from ChromaDB...") 16 | store = chroma_vector_store 17 | else: 18 | ValueError(f"arg from_ must be in ['postgres', 'chroma']. Found='{from_}'") 19 | index = VectorStoreIndex.from_vector_store(store) 20 | logger.info("Finished connecting to index from Vector Store.") 21 | return index 22 | -------------------------------------------------------------------------------- /backend/app/engine/loader.py: -------------------------------------------------------------------------------- 1 | import os 2 | from app.engine.constants import DATA_DIR 3 | from app.engine.utils import read_json_config 4 | from llama_index.core import VectorStoreIndex, download_loader 5 | from llama_index.core import SimpleDirectoryReader 6 | conf = read_json_config() 7 | def get_documents(): 8 | return SimpleDirectoryReader(conf.get('data_dir', DATA_DIR)).load_data() 9 | -------------------------------------------------------------------------------- /backend/app/engine/query_tools.py: -------------------------------------------------------------------------------- 1 | from llama_index.core.tools import QueryEngineTool, ToolMetadata 2 | from app.engine.index import get_index 3 | 4 | 5 | def get_tools()->list[QueryEngineTool]: 6 | query_engine = get_index().as_query_engine() 7 | tools: list[QueryEngineTool] = [ 8 | QueryEngineTool( 9 | query_engine=query_engine, 10 | metadata=ToolMetadata( 11 | name='local-knowledge-base-retriever', 12 | description=( 13 | "It is useful when the given question cannot be answered in current chat context." 14 | "This retriever will query the related information from the local vector store and response." 15 | ), 16 | ) 17 | ), 18 | QueryEngineTool( 19 | query_engine=query_engine, 20 | metadata=ToolMetadata( 21 | name='my-good-friend', 22 | description=( 23 | "When the question cannot be found in the context, " 24 | "then just make a causal talk with user, just like his/her best friend." 25 | ), 26 | ) 27 | ), 28 | ] 29 | return tools -------------------------------------------------------------------------------- /backend/app/engine/utils.py: -------------------------------------------------------------------------------- 1 | import os, json 2 | from llama_index.vector_stores.postgres import PGVectorStore 3 | from urllib.parse import urlparse 4 | from app.engine.constants import PGVECTOR_SCHEMA, PGVECTOR_TABLE 5 | 6 | 7 | def init_pg_vector_store_from_env(): 8 | original_conn_string = os.environ.get("PG_CONNECTION_STRING") 9 | if original_conn_string is None or original_conn_string == "": 10 | raise ValueError("PG_CONNECTION_STRING environment variable is not set.") 11 | 12 | # The PGVectorStore requires both two connection strings, one for psycopg2 and one for asyncpg 13 | # Update the configured scheme with the psycopg2 and asyncpg schemes 14 | original_scheme = urlparse(original_conn_string).scheme + "://" 15 | conn_string = original_conn_string.replace( 16 | original_scheme, "postgresql+psycopg2://" 17 | ) 18 | async_conn_string = original_conn_string.replace( 19 | original_scheme, "postgresql+asyncpg://" 20 | ) 21 | conf = read_json_config() 22 | return PGVectorStore( 23 | connection_string=conn_string, 24 | async_connection_string=async_conn_string, 25 | schema_name=conf.get('pgvector_schema', PGVECTOR_SCHEMA), 26 | table_name=conf.get('pgvector_table', PGVECTOR_TABLE), 27 | ) 28 | 29 | def read_json_config(env:str='CREATE_LLAMA_APP_CONF')->dict: 30 | fp:os.PathLike = os.environ.get(env, None) 31 | if fp is None: 32 | return {} 33 | with open(fp, 'r') as f: 34 | res:dict = json.load(f) 35 | return res -------------------------------------------------------------------------------- /backend/app/public/404.html: -------------------------------------------------------------------------------- 1 | 404: This page could not be found.Create Llama App

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