From 92265363c1ce5d4d7eb138cd60e0f02775d3e479 Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sat, 10 Feb 2024 19:49:05 +0200 Subject: [PATCH 01/11] chore: Poetry + precommit --- .pre-commit-config.yaml | 77 + poetry.lock | 4044 +++++++++++++++++++++++++++++++++++++++ pyproject.toml | 134 ++ 3 files changed, 4255 insertions(+) create mode 100644 .pre-commit-config.yaml create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..b24594f3 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,77 @@ +default_language_version: + python: python3.9 +repos: + - repo: meta + hooks: + - id: check-hooks-apply + - id: check-useless-excludes + + - repo: https://github.com/psf/black + rev: 23.9.1 + hooks: + - id: black + + - repo: https://github.com/asottile/blacken-docs + rev: 1.16.0 + hooks: + - id: blacken-docs + additional_dependencies: [black==22.10.0] + + - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook + rev: v9.11.0 + hooks: + - id: commitlint + stages: [commit-msg] + additional_dependencies: ['@commitlint/config-conventional'] + + - repo: https://github.com/codespell-project/codespell + rev: v2.2.4 + hooks: + - id: codespell + name: Run codespell to check for common misspellings in files + language: python + types: [ text ] + args: [ "--write-changes", "--ignore-words-list", "asend" ] + exclude: "poetry.lock" + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: check-vcs-permalinks + - id: end-of-file-fixer + - id: trailing-whitespace + args: [ --markdown-linebreak-ext=md ] + - id: debug-statements + - id: no-commit-to-branch + - id: check-merge-conflict + - id: check-toml + - id: check-yaml + args: [ '--unsafe' ] # for mkdocs.yml + - id: detect-private-key + + - repo: https://github.com/commitizen-tools/commitizen + rev: v3.13.0 + hooks: + - id: commitizen 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"wcwidth" +version = "0.2.13" +description = "Measures the displayed width of unicode strings in a terminal" +optional = false +python-versions = "*" +files = [ + {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, + {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, +] + [[package]] name = "weaviate-client" version = "3.26.0" @@ -4041,4 +4570,4 @@ fastembed = ["fastembed"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "f4b5f49b6964e5f9f626e4c7522539c637e91e743b70073fd3f6fec4e97757af" +content-hash = "abd8afe21470956cb3a1181306de2a607f2c1eef042ab67ffc2802c8299c2c15" diff --git a/pyproject.toml b/pyproject.toml index 029f3e24..225a8388 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -129,6 +129,9 @@ zipp = "3.17.0" [tool.poetry.extras] fastembed = ["fastembed"] +[tool.poetry.group.dev.dependencies] +ipykernel = "^6.29.2" + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" From d01fe59bff2f2b06a68317358c1e50ffab56a1bb Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sat, 10 Feb 2024 22:35:43 +0200 Subject: [PATCH 03/11] feat: Encoders --- .env.example | 8 ++- .gitignore | 1 - api/ingest.py | 22 +++++++- dev/walkthrough.ipynb | 79 ++++++++++++++++++++++++++++ encoders/__init__.py | 16 ++++++ encoders/base.py | 15 ++++++ encoders/bm25.py | 67 +++++++++++++++++++++++ encoders/cohere.py | 39 ++++++++++++++ encoders/fastembed.py | 51 ++++++++++++++++++ encoders/huggingface.py | 114 ++++++++++++++++++++++++++++++++++++++++ encoders/openai.py | 64 ++++++++++++++++++++++ main.py | 3 ++ models/ingest.py | 13 ++++- service/embedding.py | 15 +++--- 14 files changed, 495 insertions(+), 12 deletions(-) create mode 100644 dev/walkthrough.ipynb create mode 100644 encoders/__init__.py create mode 100644 encoders/base.py create mode 100644 encoders/bm25.py create mode 100644 encoders/cohere.py create mode 100644 encoders/fastembed.py create mode 100644 encoders/huggingface.py create mode 100644 encoders/openai.py diff --git a/.env.example b/.env.example index 35a872cb..34f6a841 100644 --- a/.env.example +++ b/.env.example @@ -1,4 +1,10 @@ API_BASE_URL=https://rag.superagent.sh + +PINECONE_API_KEY= +PINECONE_INDEX= +PINECONE_HOST= + +OPENAI_API_KEY= COHERE_API_KEY= HUGGINGFACE_API_KEY= -JWT_SECRET= \ No newline at end of file +JWT_SECRET= diff --git a/.gitignore b/.gitignore index 961665ff..e427eea2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,4 @@ .venv .env __pycache__/ -dev/ ``` diff --git a/api/ingest.py b/api/ingest.py index 8d9b7498..1672d75c 100644 --- a/api/ingest.py +++ b/api/ingest.py @@ -3,12 +3,16 @@ import requests from fastapi import APIRouter -from models.ingest import RequestPayload +import encoders +from models.ingest import EncoderEnum, RequestPayload from service.embedding import EmbeddingService router = APIRouter() +# Ensure you import the encoders module or specific encoder classes + + @router.post("/ingest") async def ingest(payload: RequestPayload) -> Dict: embedding_service = EmbeddingService( @@ -18,7 +22,21 @@ async def ingest(payload: RequestPayload) -> Dict: ) documents = await embedding_service.generate_documents() chunks = await embedding_service.generate_chunks(documents=documents) - await embedding_service.generate_embeddings(nodes=chunks) + + # Encoder selection based on the payload's encoder value + encoder_mapping = { + EncoderEnum.cohere: encoders.CohereEncoder, + EncoderEnum.openai: encoders.OpenAIEncoder, + EncoderEnum.huggingface: encoders.HuggingFaceEncoder, + EncoderEnum.fastembed: encoders.FastEmbedEncoder, + } + + encoder_class = encoder_mapping.get(payload.encoder) + if encoder_class is None: + raise ValueError(f"Unsupported encoder: {payload.encoder}") + encoder = encoder_class() + + await embedding_service.generate_embeddings(nodes=chunks, encoder=encoder) if payload.webhook_url: requests.post( diff --git a/dev/walkthrough.ipynb b/dev/walkthrough.ipynb new file mode 100644 index 00000000..e19aba4b --- /dev/null +++ b/dev/walkthrough.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "from dotenv import load_dotenv\n", + "load_dotenv()\n", + "\n", + "API_URL = os.environ.get('API_BASE_URL', 'http://localhost:8000')\n", + "PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY', '')\n", + "PINECONE_INDEX = os.environ.get('PINECONE_INDEX', '')\n", + "PINECONE_HOST = os.environ.get('PINECONE_HOST', '')\n", + "\n", + "print(\"API_URL:\", API_URL)\n", + "print(\"PINECONE_API_KEY:\", PINECONE_API_KEY)\n", + "print(\"PINECONE_INDEX:\", PINECONE_INDEX)\n", + "print(\"PINECONE_HOST:\", PINECONE_HOST)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ingest a file\n", + "url = f\"{API_URL}/api/v1/ingest\"\n", + "\n", + "payload = {\n", + " \"files\": [\n", + " {\n", + " \"type\": \"PDF\",\n", + " \"url\": \"https://arxiv.org/pdf/2402.05131.pdf\"\n", + " }\n", + " ],\n", + " \"vector_database\": {\n", + " \"type\": \"pinecone\",\n", + " \"config\": {\n", + " \"api_key\": PINECONE_API_KEY,\n", + " \"host\": PINECONE_HOST,\n", + " }\n", + " },\n", + " \"index_name\": PINECONE_INDEX,\n", + " \"encoder\": \"openai\",\n", + "}\n", + "\n", + "response = requests.post(url, json=payload)\n", + "\n", + "print(response.json())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/encoders/__init__.py b/encoders/__init__.py new file mode 100644 index 00000000..9ff51166 --- /dev/null +++ b/encoders/__init__.py @@ -0,0 +1,16 @@ +from encoders.base import BaseEncoder +from encoders.bm25 import BM25Encoder +from encoders.cohere import CohereEncoder +from encoders.fastembed import FastEmbedEncoder +from encoders.huggingface import HuggingFaceEncoder +from encoders.openai import OpenAIEncoder + + +__all__ = [ + "BaseEncoder", + "CohereEncoder", + "OpenAIEncoder", + "BM25Encoder", + "FastEmbedEncoder", + "HuggingFaceEncoder", +] diff --git a/encoders/base.py b/encoders/base.py new file mode 100644 index 00000000..5c26633f --- /dev/null +++ b/encoders/base.py @@ -0,0 +1,15 @@ +from typing import List + +from pydantic.v1 import BaseModel, Field + + +class BaseEncoder(BaseModel): + name: str + score_threshold: float + type: str = Field(default="base") + + class Config: + arbitrary_types_allowed = True + + def __call__(self, docs: List[str]) -> List[List[float]]: + raise NotImplementedError("Subclasses must implement this method") diff --git a/encoders/bm25.py b/encoders/bm25.py new file mode 100644 index 00000000..1965fb6e --- /dev/null +++ b/encoders/bm25.py @@ -0,0 +1,67 @@ +from typing import Any, Dict, List, Optional + +from semantic_router.encoders import BaseEncoder +from semantic_router.utils.logger import logger + + +class BM25Encoder(BaseEncoder): + model: Optional[Any] = None + idx_mapping: Optional[Dict[int, int]] = None + type: str = "sparse" + + def __init__( + self, + name: str = "bm25", + score_threshold: float = 0.82, + use_default_params: bool = True, + ): + super().__init__(name=name, score_threshold=score_threshold) + try: + from pinecone_text.sparse import BM25Encoder as encoder + except ImportError: + raise ImportError( + "Please install pinecone-text to use BM25Encoder. " + "You can install it with: `pip install 'semantic-router[hybrid]'`" + ) + + self.model = encoder() + + if use_default_params: + logger.info("Downloading and initializing default sBM25 model parameters.") + self.model = encoder.default() + self._set_idx_mapping() + + def _set_idx_mapping(self): + params = self.model.get_params() + doc_freq = params["doc_freq"] + if isinstance(doc_freq, dict): + indices = doc_freq["indices"] + self.idx_mapping = {int(idx): i for i, idx in enumerate(indices)} + else: + raise TypeError("Expected a dictionary for 'doc_freq'") + + def __call__(self, docs: List[str]) -> List[List[float]]: + if self.model is None or self.idx_mapping is None: + raise ValueError("Model or index mapping is not initialized.") + if len(docs) == 1: + sparse_dicts = self.model.encode_queries(docs) + elif len(docs) > 1: + sparse_dicts = self.model.encode_documents(docs) + else: + raise ValueError("No documents to encode.") + + embeds = [[0.0] * len(self.idx_mapping)] * len(docs) + for i, output in enumerate(sparse_dicts): + indices = output["indices"] + values = output["values"] + for idx, val in zip(indices, values): + if idx in self.idx_mapping: + position = self.idx_mapping[idx] + embeds[i][position] = val + return embeds + + def fit(self, docs: List[str]): + if self.model is None: + raise ValueError("Model is not initialized.") + self.model.fit(docs) + self._set_idx_mapping() diff --git a/encoders/cohere.py b/encoders/cohere.py new file mode 100644 index 00000000..ba52fcef --- /dev/null +++ b/encoders/cohere.py @@ -0,0 +1,39 @@ +import os +from typing import List, Optional + +import cohere + +from encoders import BaseEncoder + + +class CohereEncoder(BaseEncoder): + client: Optional[cohere.Client] = None + type: str = "cohere" + + def __init__( + self, + name: Optional[str] = None, + cohere_api_key: Optional[str] = None, + score_threshold: float = 0.3, + ): + if name is None: + name = os.getenv("COHERE_MODEL_NAME", "embed-english-v3.0") + super().__init__(name=name, score_threshold=score_threshold) + cohere_api_key = cohere_api_key or os.getenv("COHERE_API_KEY") + if cohere_api_key is None: + raise ValueError("Cohere API key cannot be 'None'.") + try: + self.client = cohere.Client(cohere_api_key) + except Exception as e: + raise ValueError( + f"Cohere API client failed to initialize. Error: {e}" + ) from e + + def __call__(self, docs: List[str]) -> List[List[float]]: + if self.client is None: + raise ValueError("Cohere client is not initialized.") + try: + embeds = self.client.embed(docs, input_type="search_query", model=self.name) + return embeds.embeddings + except Exception as e: + raise ValueError(f"Cohere API call failed. Error: {e}") from e diff --git a/encoders/fastembed.py b/encoders/fastembed.py new file mode 100644 index 00000000..f50ee83f --- /dev/null +++ b/encoders/fastembed.py @@ -0,0 +1,51 @@ +from typing import Any, List, Optional + +import numpy as np +from pydantic.v1 import PrivateAttr + +from encoders.base import BaseEncoder + + +class FastEmbedEncoder(BaseEncoder): + type: str = "fastembed" + name: str = "BAAI/bge-small-en-v1.5" + max_length: int = 512 + cache_dir: Optional[str] = None + threads: Optional[int] = None + _client: Any = PrivateAttr() + + def __init__( + self, score_threshold: float = 0.5, **data + ): # TODO default score_threshold not thoroughly tested, should optimize + super().__init__(score_threshold=score_threshold, **data) + self._client = self._initialize_client() + + def _initialize_client(self): + try: + from fastembed.embedding import FlagEmbedding as Embedding + except ImportError: + raise ImportError( + "Please install fastembed to use FastEmbedEncoder. " + "You can install it with: " + "`pip install 'semantic-router[fastembed]'`" + ) + + embedding_args = { + "model_name": self.name, + "max_length": self.max_length, + "cache_dir": self.cache_dir, + "threads": self.threads, + } + + embedding_args = {k: v for k, v in embedding_args.items() if v is not None} + + embedding = Embedding(**embedding_args) + return embedding + + def __call__(self, docs: List[str]) -> List[List[float]]: + try: + embeds: List[np.ndarray] = list(self._client.embed(docs)) + embeddings: List[List[float]] = [e.tolist() for e in embeds] + return embeddings + except Exception as e: + raise ValueError(f"FastEmbed embed failed. Error: {e}") from e diff --git a/encoders/huggingface.py b/encoders/huggingface.py new file mode 100644 index 00000000..63dfa54c --- /dev/null +++ b/encoders/huggingface.py @@ -0,0 +1,114 @@ +from typing import Any, List, Optional + +from pydantic.v1 import PrivateAttr + +from encoders import BaseEncoder + + +class HuggingFaceEncoder(BaseEncoder): + name: str = "sentence-transformers/all-MiniLM-L6-v2" + type: str = "huggingface" + score_threshold: float = 0.5 + tokenizer_kwargs: dict = {} + model_kwargs: dict = {} + device: Optional[str] = None + _tokenizer: Any = PrivateAttr() + _model: Any = PrivateAttr() + _torch: Any = PrivateAttr() + + def __init__(self, **data): + super().__init__(**data) + self._tokenizer, self._model = self._initialize_hf_model() + + def _initialize_hf_model(self): + try: + from transformers import AutoModel, AutoTokenizer + except ImportError: + raise ImportError( + "Please install transformers to use HuggingFaceEncoder. " + "You can install it with: " + "`pip install semantic-router[local]`" + ) + + try: + import torch + except ImportError: + raise ImportError( + "Please install Pytorch to use HuggingFaceEncoder. " + "You can install it with: " + "`pip install semantic-router[local]`" + ) + + self._torch = torch + + tokenizer = AutoTokenizer.from_pretrained( + self.name, + **self.tokenizer_kwargs, + ) + + model = AutoModel.from_pretrained(self.name, **self.model_kwargs) + + if self.device: + model.to(self.device) + + else: + device = "cuda" if self._torch.cuda.is_available() else "cpu" + model.to(device) + self.device = device + + return tokenizer, model + + def __call__( + self, + docs: List[str], + batch_size: int = 32, + normalize_embeddings: bool = True, + pooling_strategy: str = "mean", + ) -> List[List[float]]: + all_embeddings = [] + for i in range(0, len(docs), batch_size): + batch_docs = docs[i : i + batch_size] + + encoded_input = self._tokenizer( + batch_docs, padding=True, truncation=True, return_tensors="pt" + ).to(self.device) + + with self._torch.no_grad(): + model_output = self._model(**encoded_input) + + if pooling_strategy == "mean": + embeddings = self._mean_pooling( + model_output, encoded_input["attention_mask"] + ) + elif pooling_strategy == "max": + embeddings = self._max_pooling( + model_output, encoded_input["attention_mask"] + ) + else: + raise ValueError( + "Invalid pooling_strategy. Please use 'mean' or 'max'." + ) + + if normalize_embeddings: + embeddings = self._torch.nn.functional.normalize(embeddings, p=2, dim=1) + + embeddings = embeddings.tolist() + all_embeddings.extend(embeddings) + return all_embeddings + + def _mean_pooling(self, model_output, attention_mask): + token_embeddings = model_output[0] + input_mask_expanded = ( + attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() + ) + return self._torch.sum( + token_embeddings * input_mask_expanded, 1 + ) / self._torch.clamp(input_mask_expanded.sum(1), min=1e-9) + + def _max_pooling(self, model_output, attention_mask): + token_embeddings = model_output[0] + input_mask_expanded = ( + attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() + ) + token_embeddings[input_mask_expanded == 0] = -1e9 + return self._torch.max(token_embeddings, 1)[0] diff --git a/encoders/openai.py b/encoders/openai.py new file mode 100644 index 00000000..0a249802 --- /dev/null +++ b/encoders/openai.py @@ -0,0 +1,64 @@ +import os +from time import sleep +from typing import List, Optional + +import openai +from openai import OpenAIError +from openai.types import CreateEmbeddingResponse + +from encoders import BaseEncoder +from semantic_router.utils.logger import logger + + +class OpenAIEncoder(BaseEncoder): + client: Optional[openai.Client] + type: str = "openai" + + def __init__( + self, + name: Optional[str] = None, + openai_api_key: Optional[str] = None, + score_threshold: float = 0.82, + ): + if name is None: + name = os.getenv("OPENAI_MODEL_NAME", "text-embedding-ada-002") + super().__init__(name=name, score_threshold=score_threshold) + api_key = openai_api_key or os.getenv("OPENAI_API_KEY") + if api_key is None: + raise ValueError("OpenAI API key cannot be 'None'.") + try: + self.client = openai.Client(api_key=api_key) + except Exception as e: + raise ValueError( + f"OpenAI API client failed to initialize. Error: {e}" + ) from e + + def __call__(self, docs: List[str]) -> List[List[float]]: + if self.client is None: + raise ValueError("OpenAI client is not initialized.") + embeds = None + error_message = "" + + # Exponential backoff + for j in range(3): + try: + embeds = self.client.embeddings.create(input=docs, model=self.name) + if embeds.data: + break + except OpenAIError as e: + sleep(2**j) + error_message = str(e) + logger.warning(f"Retrying in {2**j} seconds...") + except Exception as e: + logger.error(f"OpenAI API call failed. Error: {error_message}") + raise ValueError(f"OpenAI API call failed. Error: {e}") from e + + if ( + not embeds + or not isinstance(embeds, CreateEmbeddingResponse) + or not embeds.data + ): + raise ValueError(f"No embeddings returned. Error: {error_message}") + + embeddings = [embeds_obj.embedding for embeds_obj in embeds.data] + return embeddings diff --git a/main.py b/main.py index 6fb1bc98..c266c65d 100644 --- a/main.py +++ b/main.py @@ -1,9 +1,12 @@ from decouple import config +from dotenv import load_dotenv from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from router import router +load_dotenv() + app = FastAPI( title="SuperRag", docs_url="/", diff --git a/models/ingest.py b/models/ingest.py index 69ee61b3..48fda5ed 100644 --- a/models/ingest.py +++ b/models/ingest.py @@ -1,3 +1,4 @@ +from enum import Enum from typing import List, Optional from pydantic import BaseModel @@ -6,8 +7,18 @@ from models.vector_database import VectorDatabase +# Step 1: Define the Encoder Enum +class EncoderEnum(str, Enum): + cohere = "cohere" + openai = "openai" + huggingface = "huggingface" + fastembed = "fastembed" + + +# Step 2: Use the Enum in RequestPayload class RequestPayload(BaseModel): files: List[File] + encoder: EncoderEnum vector_database: VectorDatabase index_name: str - webhook_url: Optional[str] + webhook_url: Optional[str] = None diff --git a/service/embedding.py b/service/embedding.py index 2773e018..4761bfbc 100644 --- a/service/embedding.py +++ b/service/embedding.py @@ -4,11 +4,11 @@ import numpy as np import requests -from fastembed.embedding import FlagEmbedding as Embedding from llama_index import Document, SimpleDirectoryReader from llama_index.node_parser import SimpleNodeParser from tqdm import tqdm +from encoders import BaseEncoder from models.file import File from service.vector_database import get_vector_service @@ -55,17 +55,18 @@ async def generate_chunks( return nodes async def generate_embeddings( - self, - nodes: List[Union[Document, None]], + self, nodes: List[Union[Document, None]], encoder: BaseEncoder ) -> List[tuple[str, list, dict[str, Any]]]: pbar = tqdm(total=len(nodes), desc="Generating embeddings") async def generate_embedding(node): if node is not None: - embedding_model = Embedding( - model_name="sentence-transformers/all-MiniLM-L6-v2", max_length=512 - ) - embeddings: List[np.ndarray] = list(embedding_model.embed(node.text)) + embeddings: List[np.ndarray] = [ + np.array(e) for e in encoder([node.text]) + ] + + print(f"embeddings: {embeddings}") + embedding = ( node.id_, embeddings[0].tolist(), From 21464493ce64875d816594e42645eee51d75b05a Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sat, 10 Feb 2024 22:53:13 +0200 Subject: [PATCH 04/11] chore: Clean notebook outputs --- dev/walkthrough.ipynb | 19 ++----------------- 1 file changed, 2 insertions(+), 17 deletions(-) diff --git a/dev/walkthrough.ipynb b/dev/walkthrough.ipynb index c05fb7c6..e19aba4b 100644 --- a/dev/walkthrough.ipynb +++ b/dev/walkthrough.ipynb @@ -24,17 +24,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'success': True, 'index_name': 'simonas-serverless-1536'}\n" - ] - } - ], + "outputs": [], "source": [ "# Ingest a file\n", "url = f\"{API_URL}/api/v1/ingest\"\n", @@ -61,13 +53,6 @@ "\n", "print(response.json())" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From e48afc39cf836cc2e05db1d528cd35ce3caa6a87 Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sun, 11 Feb 2024 11:56:48 +0200 Subject: [PATCH 05/11] feat: Added encoders to query --- README.md | 4 ++- api/ingest.py | 17 ++------- dev/walkthrough.ipynb | 60 +++++++++++++++++++++++++++++++ encoders/base.py | 1 + encoders/openai.py | 8 +++-- models/query.py | 2 ++ service/embedding.py | 17 +++++++++ service/router.py | 15 +++++--- service/vector_database.py | 72 ++++++++++++++++++++++++++------------ 9 files changed, 153 insertions(+), 43 deletions(-) diff --git a/README.md b/README.md index 70dfe1dc..6d7360c3 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # SuperRag -Super-performant RAG pipeline for AI Agents/Assistants. +Super-performant RAG pipeline for AI Agents/Assistants. ## API @@ -23,6 +23,7 @@ Input example: } }, "index_name": "my_index", + "encoder": "my_encoder" "webhook_url": "https://my-webhook-url" } ``` @@ -41,6 +42,7 @@ Input example: } }, "index_name": "my_index", + "encoder": "my_encoder", } ``` diff --git a/api/ingest.py b/api/ingest.py index 394f54bb..3a05b7e4 100644 --- a/api/ingest.py +++ b/api/ingest.py @@ -4,9 +4,8 @@ import aiohttp from fastapi import APIRouter -import encoders -from models.ingest import EncoderEnum, RequestPayload -from service.embedding import EmbeddingService +from models.ingest import RequestPayload +from service.embedding import EmbeddingService, get_encoder router = APIRouter() @@ -21,17 +20,7 @@ async def ingest(payload: RequestPayload) -> Dict: documents = await embedding_service.generate_documents() chunks = await embedding_service.generate_chunks(documents=documents) - encoder_mapping = { - EncoderEnum.cohere: encoders.CohereEncoder, - EncoderEnum.openai: encoders.OpenAIEncoder, - EncoderEnum.huggingface: encoders.HuggingFaceEncoder, - EncoderEnum.fastembed: encoders.FastEmbedEncoder, - } - - encoder_class = encoder_mapping.get(payload.encoder) - if encoder_class is None: - raise ValueError(f"Unsupported encoder: {payload.encoder}") - encoder = encoder_class() + encoder = get_encoder(encoder_type=payload.encoder) summary_documents = await embedding_service.generate_summary_documents( documents=documents diff --git a/dev/walkthrough.ipynb b/dev/walkthrough.ipynb index e19aba4b..9c229bbc 100644 --- a/dev/walkthrough.ipynb +++ b/dev/walkthrough.ipynb @@ -53,6 +53,66 @@ "\n", "print(response.json())" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Query the index\n", + "query_url = f\"{API_URL}/api/v1/query\"\n", + "\n", + "query_payload = {\n", + " \"input\": \"What is the best chunk strategy?\",\n", + " \"vector_database\": {\n", + " \"type\": \"pinecone\",\n", + " \"config\": {\n", + " \"api_key\": PINECONE_API_KEY,\n", + " \"host\": PINECONE_HOST,\n", + " }\n", + " },\n", + " \"index_name\": PINECONE_INDEX,\n", + " \"encoder\": \"openai\",\n", + "}\n", + "\n", + "query_response = requests.post(query_url, json=query_payload)\n", + "\n", + "print(query_response.json())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the index\n", + "query_url = f\"{API_URL}/api/v1/delete\"\n", + "\n", + "delete_payload = {\n", + " \"file_url\": \"https://arxiv.org/pdf/2402.05131.pdf\",\n", + " \"vector_database\": {\n", + " \"type\": \"pinecone\",\n", + " \"config\": {\n", + " \"api_key\": PINECONE_API_KEY,\n", + " \"host\": PINECONE_HOST,\n", + " }\n", + " },\n", + " \"index_name\": PINECONE_INDEX,\n", + "}\n", + "\n", + "delete_response = requests.delete(query_url, json=delete_payload)\n", + "\n", + "print(delete_response.json())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/encoders/base.py b/encoders/base.py index 5c26633f..8320b486 100644 --- a/encoders/base.py +++ b/encoders/base.py @@ -7,6 +7,7 @@ class BaseEncoder(BaseModel): name: str score_threshold: float type: str = Field(default="base") + dimension: int = Field(default=1536) class Config: arbitrary_types_allowed = True diff --git a/encoders/openai.py b/encoders/openai.py index 0a249802..caa9a494 100644 --- a/encoders/openai.py +++ b/encoders/openai.py @@ -3,16 +3,20 @@ from typing import List, Optional import openai +from dotenv import load_dotenv from openai import OpenAIError from openai.types import CreateEmbeddingResponse +from semantic_router.utils.logger import logger from encoders import BaseEncoder -from semantic_router.utils.logger import logger + +load_dotenv() class OpenAIEncoder(BaseEncoder): client: Optional[openai.Client] type: str = "openai" + dimension: int = 1536 def __init__( self, @@ -21,7 +25,7 @@ def __init__( score_threshold: float = 0.82, ): if name is None: - name = os.getenv("OPENAI_MODEL_NAME", "text-embedding-ada-002") + name = os.getenv("OPENAI_MODEL_NAME", "text-embedding-3-small") super().__init__(name=name, score_threshold=score_threshold) api_key = openai_api_key or os.getenv("OPENAI_API_KEY") if api_key is None: diff --git a/models/query.py b/models/query.py index d6958adb..978bfef5 100644 --- a/models/query.py +++ b/models/query.py @@ -1,6 +1,7 @@ from typing import List, Optional from pydantic import BaseModel +from models.ingest import EncoderEnum from models.vector_database import VectorDatabase @@ -9,6 +10,7 @@ class RequestPayload(BaseModel): input: str vector_database: VectorDatabase index_name: str + encoder: EncoderEnum = EncoderEnum.openai class ResponseData(BaseModel): diff --git a/service/embedding.py b/service/embedding.py index dc269346..1008e37b 100644 --- a/service/embedding.py +++ b/service/embedding.py @@ -10,7 +10,9 @@ from tqdm import tqdm from encoders import BaseEncoder +import encoders from models.file import File +from models.ingest import EncoderEnum from service.vector_database import get_vector_service from utils.summarise import completion @@ -85,6 +87,7 @@ async def generate_embedding(node): vector_service = get_vector_service( index_name=index_name or self.index_name, credentials=self.vector_credentials, + encoder=encoder, ) await vector_service.upsert(embeddings=[e for e in embeddings if e is not None]) @@ -102,3 +105,17 @@ async def generate_summary_documents( pbar.update() pbar.close() return summary_documents + + +def get_encoder(*, encoder_type: EncoderEnum) -> encoders.BaseEncoder: + encoder_mapping = { + EncoderEnum.cohere: encoders.CohereEncoder, + EncoderEnum.openai: encoders.OpenAIEncoder, + EncoderEnum.huggingface: encoders.HuggingFaceEncoder, + EncoderEnum.fastembed: encoders.FastEmbedEncoder, + } + + encoder_class = encoder_mapping.get(encoder_type) + if encoder_class is None: + raise ValueError(f"Unsupported encoder: {encoder_type}") + return encoder_class() diff --git a/service/router.py b/service/router.py index 58c8ef20..82109017 100644 --- a/service/router.py +++ b/service/router.py @@ -6,6 +6,7 @@ from semantic_router.route import Route from models.query import RequestPayload +from service.embedding import get_encoder from service.vector_database import VectorService, get_vector_service @@ -27,7 +28,9 @@ def create_route_layer() -> RouteLayer: return RouteLayer(encoder=encoder, routes=routes) -async def get_documents(vector_service: VectorService, payload: RequestPayload) -> List: +async def get_documents( + *, vector_service: VectorService, payload: RequestPayload +) -> List: chunks = await vector_service.query(input=payload.input, top_k=4) documents = await vector_service.convert_to_rerank_format(chunks=chunks) @@ -41,15 +44,19 @@ async def get_documents(vector_service: VectorService, payload: RequestPayload) async def query(payload: RequestPayload) -> List: rl = create_route_layer() decision = rl(payload.input).name + encoder = get_encoder(encoder_type=payload.encoder) if decision == "summarize": vector_service: VectorService = get_vector_service( index_name=f"{payload.index_name}summary", credentials=payload.vector_database, + encoder=encoder, ) - return await get_documents(vector_service, payload) + return await get_documents(vector_service=vector_service, payload=payload) vector_service: VectorService = get_vector_service( - index_name=payload.index_name, credentials=payload.vector_database + index_name=payload.index_name, + credentials=payload.vector_database, + encoder=encoder, ) - return await get_documents(vector_service, payload) + return await get_documents(vector_service=vector_service, payload=payload) diff --git a/service/vector_database.py b/service/vector_database.py index 6b784ca1..1ea429bf 100644 --- a/service/vector_database.py +++ b/service/vector_database.py @@ -1,24 +1,27 @@ from abc import ABC, abstractmethod -from typing import Any, List, Type +from typing import Any, List -import numpy as np import weaviate from astrapy.db import AstraDB from decouple import config -from fastembed import TextEmbedding from pinecone import Pinecone, ServerlessSpec from qdrant_client import QdrantClient from qdrant_client.http import models as rest from tqdm import tqdm +from encoders.base import BaseEncoder +from encoders.openai import OpenAIEncoder from models.vector_database import VectorDatabase class VectorService(ABC): - def __init__(self, index_name: str, dimension: int, credentials: dict): + def __init__( + self, index_name: str, dimension: int, credentials: dict, encoder: BaseEncoder + ): self.index_name = index_name self.dimension = dimension self.credentials = credentials + self.encoder = encoder @abstractmethod async def upsert(): @@ -37,11 +40,12 @@ async def delete(self, file_url: str): pass async def _generate_vectors(self, input: str): - embedding_model = TextEmbedding( - model_name="sentence-transformers/all-MiniLM-L6-v2" - ) - embeddings: List[np.ndarray] = list(embedding_model.embed(input)) - return embeddings[0].tolist() + # embedding_model = TextEmbedding( + # model_name="sentence-transformers/all-MiniLM-L6-v2" + # ) + # embeddings: List[np.ndarray] = list(embedding_model.embed(input)) + # return embeddings[0].tolist() + return self.encoder([input]) async def rerank(self, query: str, documents: list, top_n: int = 4): from cohere import Client @@ -65,15 +69,20 @@ async def rerank(self, query: str, documents: list, top_n: int = 4): class PineconeVectorService(VectorService): - def __init__(self, index_name: str, dimension: int, credentials: dict): + def __init__( + self, index_name: str, dimension: int, credentials: dict, encoder: BaseEncoder + ): super().__init__( - index_name=index_name, dimension=dimension, credentials=credentials + index_name=index_name, + dimension=dimension, + credentials=credentials, + encoder=encoder, ) pinecone = Pinecone(api_key=credentials["api_key"]) if index_name not in [index.name for index in pinecone.list_indexes()]: pinecone.create_index( name=self.index_name, - dimension=1536, # TODO: make it dynamic based on the encoder + dimension=dimension or 1536, metric="dotproduct", spec=ServerlessSpec(cloud="aws", region="us-west-2"), ) @@ -107,9 +116,14 @@ async def delete(self, file_url: str) -> None: class QdrantService(VectorService): - def __init__(self, index_name: str, dimension: int, credentials: dict): + def __init__( + self, index_name: str, dimension: int, credentials: dict, encoder: BaseEncoder + ): super().__init__( - index_name=index_name, dimension=dimension, credentials=credentials + index_name=index_name, + dimension=dimension, + credentials=credentials, + encoder=encoder, ) self.client = QdrantClient( url=credentials["host"], api_key=credentials["api_key"], https=True @@ -120,7 +134,7 @@ def __init__(self, index_name: str, dimension: int, credentials: dict): collection_name=self.index_name, vectors_config={ "content": rest.VectorParams( - size=1024, distance=rest.Distance.COSINE + size=dimension, distance=rest.Distance.COSINE ) }, optimizers_config=rest.OptimizersConfigDiff( @@ -186,9 +200,14 @@ async def delete(self, file_url: str) -> None: class WeaviateService(VectorService): - def __init__(self, index_name: str, dimension: int, credentials: dict): + def __init__( + self, index_name: str, dimension: int, credentials: dict, encoder: BaseEncoder + ): super().__init__( - index_name=index_name, dimension=dimension, credentials=credentials + index_name=index_name, + dimension=dimension, + credentials=credentials, + encoder=encoder, ) self.client = weaviate.Client( url=credentials["host"], @@ -251,9 +270,14 @@ async def delete(self, file_url: str) -> None: class AstraService(VectorService): - def __init__(self, index_name: str, dimension: int, credentials: dict): + def __init__( + self, index_name: str, dimension: int, credentials: dict, encoder: BaseEncoder + ): super().__init__( - index_name=index_name, dimension=dimension, credentials=credentials + index_name=index_name, + dimension=dimension, + credentials=credentials, + encoder=encoder, ) self.client = AstraDB( token=credentials["api_key"], @@ -302,8 +326,11 @@ async def delete(self, file_url: str) -> None: def get_vector_service( - index_name: str, credentials: VectorDatabase, dimension: int = 1024 -) -> Type[VectorService]: + *, + index_name: str, + credentials: VectorDatabase, + encoder: BaseEncoder = OpenAIEncoder(), +) -> VectorService: services = { "pinecone": PineconeVectorService, "qdrant": QdrantService, @@ -317,6 +344,7 @@ def get_vector_service( raise ValueError(f"Unsupported provider: {credentials.type.value}") return service( index_name=index_name, - dimension=dimension, + dimension=encoder.dimension, credentials=dict(credentials.config), + encoder=encoder, ) From 08160b63e6008446a63789fdca3c76ececd240e0 Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sun, 11 Feb 2024 13:39:02 +0200 Subject: [PATCH 06/11] chore: Added a note to fix delete for Pinecone --- service/vector_database.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/service/vector_database.py b/service/vector_database.py index 1ea429bf..d2e9e449 100644 --- a/service/vector_database.py +++ b/service/vector_database.py @@ -40,11 +40,6 @@ async def delete(self, file_url: str): pass async def _generate_vectors(self, input: str): - # embedding_model = TextEmbedding( - # model_name="sentence-transformers/all-MiniLM-L6-v2" - # ) - # embeddings: List[np.ndarray] = list(embedding_model.embed(input)) - # return embeddings[0].tolist() return self.encoder([input]) async def rerank(self, query: str, documents: list, top_n: int = 4): @@ -112,6 +107,11 @@ async def query(self, input: str, top_k: 4, include_metadata: bool = True): return results["matches"] async def delete(self, file_url: str) -> None: + """ + TODO: Fix the error + "Severless and starter indexes do not support deleting with metadata + filtering.","details":[]} + """ self.index.delete(filter={"file_url": {"$eq": file_url}}) From b17c8c3928fb1a86cf4a2caf297dc0361f49fb13 Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sun, 11 Feb 2024 13:50:47 +0200 Subject: [PATCH 07/11] chore: Linting fix --- service/vector_database.py | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/service/vector_database.py b/service/vector_database.py index d2e9e449..ae004c65 100644 --- a/service/vector_database.py +++ b/service/vector_database.py @@ -39,7 +39,7 @@ async def convert_to_rerank_format(): async def delete(self, file_url: str): pass - async def _generate_vectors(self, input: str): + async def _generate_vectors(self, input: str) -> List[List[float]]: return self.encoder([input]) async def rerank(self, query: str, documents: list, top_n: int = 4): @@ -77,7 +77,7 @@ def __init__( if index_name not in [index.name for index in pinecone.list_indexes()]: pinecone.create_index( name=self.index_name, - dimension=dimension or 1536, + dimension=dimension, metric="dotproduct", spec=ServerlessSpec(cloud="aws", region="us-west-2"), ) @@ -95,12 +95,20 @@ async def convert_to_rerank_format(self, chunks: List): return docs async def upsert(self, embeddings: List[tuple[str, list, dict[str, Any]]]): - self.index.upsert(vectors=tqdm(embeddings, desc="Upserting to Pinecone")) - - async def query(self, input: str, top_k: 4, include_metadata: bool = True): + if self.index is None: + raise ValueError(f"Pinecone index {self.index_name} is not initialized.") + for _ in tqdm( + embeddings, desc=f"Upserting to Pinecone index {self.index_name}" + ): + pass + self.index.upsert(vectors=embeddings) + + async def query(self, input: str, top_k: int = 4, include_metadata: bool = True): + if self.index is None: + raise ValueError(f"Pinecone index {self.index_name} is not initialized.") vectors = await self._generate_vectors(input=input) results = self.index.query( - vector=vectors, + vector=vectors[0], top_k=top_k, include_metadata=include_metadata, ) @@ -112,6 +120,9 @@ async def delete(self, file_url: str) -> None: "Severless and starter indexes do not support deleting with metadata filtering.","details":[]} """ + if self.index is None: + raise ValueError(f"Pinecone index {self.index_name} is not initialized.") + self.index.delete(filter={"file_url": {"$eq": file_url}}) From 3409594b1010b3bc1e66a9b38061e60cb061f4c7 Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sun, 11 Feb 2024 14:55:52 +0200 Subject: [PATCH 08/11] fix: Fix Pinecone deletion by file_url --- service/vector_database.py | 15 +++++++------ utils/logger.py | 43 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 52 insertions(+), 6 deletions(-) create mode 100644 utils/logger.py diff --git a/service/vector_database.py b/service/vector_database.py index ae004c65..2bb15e6b 100644 --- a/service/vector_database.py +++ b/service/vector_database.py @@ -12,6 +12,7 @@ from encoders.base import BaseEncoder from encoders.openai import OpenAIEncoder from models.vector_database import VectorDatabase +from utils.logger import logger class VectorService(ABC): @@ -115,15 +116,17 @@ async def query(self, input: str, top_k: int = 4, include_metadata: bool = True) return results["matches"] async def delete(self, file_url: str) -> None: - """ - TODO: Fix the error - "Severless and starter indexes do not support deleting with metadata - filtering.","details":[]} - """ if self.index is None: raise ValueError(f"Pinecone index {self.index_name} is not initialized.") - self.index.delete(filter={"file_url": {"$eq": file_url}}) + query_response = self.index.query( + vector=[0.0] * self.dimension, + top_k=1000, + filter={"file_url": {"$eq": file_url}}, + ) + chunks = query_response.matches + logger.info(f"Deleting {len(chunks)} chunks from Pinecone") + self.index.delete(ids=[chunk["id"] for chunk in chunks]) class QdrantService(VectorService): diff --git a/utils/logger.py b/utils/logger.py new file mode 100644 index 00000000..607f09d5 --- /dev/null +++ b/utils/logger.py @@ -0,0 +1,43 @@ +import logging + +import colorlog + + +class CustomFormatter(colorlog.ColoredFormatter): + def __init__(self): + super().__init__( + "%(log_color)s%(asctime)s %(levelname)s %(name)s %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + log_colors={ + "DEBUG": "cyan", + "INFO": "green", + "WARNING": "yellow", + "ERROR": "red", + "CRITICAL": "bold_red", + }, + reset=True, + style="%", + ) + + +def add_coloured_handler(logger): + formatter = CustomFormatter() + console_handler = logging.StreamHandler() + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + return logger + + +def setup_custom_logger(name): + logger = logging.getLogger(name) + logger.handlers = [] + + add_coloured_handler(logger) + + logger.setLevel(logging.INFO) + logger.propagate = False + + return logger + + +logger: logging.Logger = setup_custom_logger(__name__) From 11d7a3b88c517ec2adbbdb6bd2e7ef26e8606db0 Mon Sep 17 00:00:00 2001 From: Simonas <20096648+simjak@users.noreply.github.com> Date: Sun, 11 Feb 2024 17:02:27 +0200 Subject: [PATCH 09/11] fix: Pinecone delete --- dev/walkthrough.ipynb | 7 ------- service/vector_database.py | 8 ++++++-- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/dev/walkthrough.ipynb b/dev/walkthrough.ipynb index 9c229bbc..a768a172 100644 --- a/dev/walkthrough.ipynb +++ b/dev/walkthrough.ipynb @@ -106,13 +106,6 @@ "\n", "print(delete_response.json())" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/service/vector_database.py b/service/vector_database.py index 2bb15e6b..f0febb22 100644 --- a/service/vector_database.py +++ b/service/vector_database.py @@ -125,8 +125,12 @@ async def delete(self, file_url: str) -> None: filter={"file_url": {"$eq": file_url}}, ) chunks = query_response.matches - logger.info(f"Deleting {len(chunks)} chunks from Pinecone") - self.index.delete(ids=[chunk["id"] for chunk in chunks]) + logger.info( + f"Deleting {len(chunks)} chunks from Pinecone {self.index_name} index." + ) + + if chunks: + self.index.delete(ids=[chunk["id"] for chunk in chunks]) class QdrantService(VectorService): From 6b6d19b04b1c522228e4fc18738fe12dd19ee69c Mon Sep 17 00:00:00 2001 From: Ismail Pelaseyed Date: Sun, 11 Feb 2024 07:40:53 -0800 Subject: [PATCH 10/11] Small tweaks --- .env.example | 10 ++++------ encoders/cohere.py | 6 +++--- encoders/openai.py | 8 +++----- 3 files changed, 10 insertions(+), 14 deletions(-) diff --git a/.env.example b/.env.example index 34f6a841..2b45ca72 100644 --- a/.env.example +++ b/.env.example @@ -1,10 +1,8 @@ API_BASE_URL=https://rag.superagent.sh +OPENAI_API_KEY= +COHERE_API_KEY= +# Optional for walkthrough PINECONE_API_KEY= -PINECONE_INDEX= PINECONE_HOST= - -OPENAI_API_KEY= -COHERE_API_KEY= -HUGGINGFACE_API_KEY= -JWT_SECRET= +PINECONE_INDEX= \ No newline at end of file diff --git a/encoders/cohere.py b/encoders/cohere.py index ba52fcef..a2e07ec9 100644 --- a/encoders/cohere.py +++ b/encoders/cohere.py @@ -1,5 +1,5 @@ -import os from typing import List, Optional +from decouple import config import cohere @@ -17,9 +17,9 @@ def __init__( score_threshold: float = 0.3, ): if name is None: - name = os.getenv("COHERE_MODEL_NAME", "embed-english-v3.0") + name = config("COHERE_MODEL_NAME", "embed-english-v3.0") super().__init__(name=name, score_threshold=score_threshold) - cohere_api_key = cohere_api_key or os.getenv("COHERE_API_KEY") + cohere_api_key = cohere_api_key or config("COHERE_API_KEY") if cohere_api_key is None: raise ValueError("Cohere API key cannot be 'None'.") try: diff --git a/encoders/openai.py b/encoders/openai.py index caa9a494..32676ade 100644 --- a/encoders/openai.py +++ b/encoders/openai.py @@ -1,6 +1,6 @@ -import os from time import sleep from typing import List, Optional +from decouple import config import openai from dotenv import load_dotenv @@ -10,8 +10,6 @@ from encoders import BaseEncoder -load_dotenv() - class OpenAIEncoder(BaseEncoder): client: Optional[openai.Client] @@ -25,9 +23,9 @@ def __init__( score_threshold: float = 0.82, ): if name is None: - name = os.getenv("OPENAI_MODEL_NAME", "text-embedding-3-small") + name = config("OPENAI_MODEL_NAME", "text-embedding-3-small") super().__init__(name=name, score_threshold=score_threshold) - api_key = openai_api_key or os.getenv("OPENAI_API_KEY") + api_key = openai_api_key or config("OPENAI_API_KEY") if api_key is None: raise ValueError("OpenAI API key cannot be 'None'.") try: From 27a51462fa87b715db68881d8b24a99037e02e71 Mon Sep 17 00:00:00 2001 From: Ismail Pelaseyed Date: Sun, 11 Feb 2024 07:41:39 -0800 Subject: [PATCH 11/11] Fix linting --- encoders/__init__.py | 1 - encoders/cohere.py | 2 +- encoders/openai.py | 3 +-- models/query.py | 2 +- service/embedding.py | 2 +- 5 files changed, 4 insertions(+), 6 deletions(-) diff --git a/encoders/__init__.py b/encoders/__init__.py index 9ff51166..9d65ec09 100644 --- a/encoders/__init__.py +++ b/encoders/__init__.py @@ -5,7 +5,6 @@ from encoders.huggingface import HuggingFaceEncoder from encoders.openai import OpenAIEncoder - __all__ = [ "BaseEncoder", "CohereEncoder", diff --git a/encoders/cohere.py b/encoders/cohere.py index a2e07ec9..f4507199 100644 --- a/encoders/cohere.py +++ b/encoders/cohere.py @@ -1,7 +1,7 @@ from typing import List, Optional -from decouple import config import cohere +from decouple import config from encoders import BaseEncoder diff --git a/encoders/openai.py b/encoders/openai.py index 32676ade..f4857b12 100644 --- a/encoders/openai.py +++ b/encoders/openai.py @@ -1,9 +1,8 @@ from time import sleep from typing import List, Optional -from decouple import config import openai -from dotenv import load_dotenv +from decouple import config from openai import OpenAIError from openai.types import CreateEmbeddingResponse from semantic_router.utils.logger import logger diff --git a/models/query.py b/models/query.py index 978bfef5..6910d28f 100644 --- a/models/query.py +++ b/models/query.py @@ -1,8 +1,8 @@ from typing import List, Optional from pydantic import BaseModel -from models.ingest import EncoderEnum +from models.ingest import EncoderEnum from models.vector_database import VectorDatabase diff --git a/service/embedding.py b/service/embedding.py index 1008e37b..d7b8d8a2 100644 --- a/service/embedding.py +++ b/service/embedding.py @@ -9,8 +9,8 @@ from llama_index.node_parser import SimpleNodeParser from tqdm import tqdm -from encoders import BaseEncoder import encoders +from encoders import BaseEncoder from models.file import File from models.ingest import EncoderEnum from service.vector_database import get_vector_service