diff --git a/agents-api/agents_api/embed_models_registry.py b/agents-api/agents_api/embed_models_registry.py new file mode 100644 index 000000000..061668eb8 --- /dev/null +++ b/agents-api/agents_api/embed_models_registry.py @@ -0,0 +1,143 @@ +import tiktoken +import numpy as np +from typing import TypedDict, Any +from dataclasses import dataclass +from transformers import PreTrainedTokenizer +from agents_api.clients.model import openai_client +from agents_api.clients.embed import embed +from agents_api.exceptions import ModelNotSupportedError, PromptTooBigError, UnknownTokenizerError + + +def normalize_l2(x): + x = np.array(x) + if x.ndim == 1: + norm = np.linalg.norm(x) + if norm == 0: + return x + return x / norm + else: + norm = np.linalg.norm(x, 2, axis=1, keepdims=True) + return np.where(norm == 0, x, x / norm) + + +class EmbeddingInput(TypedDict): + instruction: str | None + text: str + + +@dataclass +class EmbeddingModel: + embedding_provider: str + embedding_model_name: str + original_embedding_dimensions: int + output_embedding_dimensions: int + context_window: int + tokenizer: Any + + @classmethod + def from_model_name(cls, model_name: str): + try: + return _embedding_model_registry[model_name] + except KeyError: + raise ModelNotSupportedError(model_name) + + def _token_count(self, text: str) -> int: + tokenize = getattr(self.tokenizer, "tokenize", None) + if tokenize: + return len(tokenize(text)) + + encode = getattr(self.tokenizer, "encode", None) + if encode: + return len(encode(text)) + + raise UnknownTokenizerError + + def preprocess(self, inputs: list[EmbeddingInput]) -> list[str]: + """Maybe use this function from embed() to truncate (if needed) or raise an error""" + result: list[str] = [] + + for i in inputs: + instruction = i.get('instruction', '') + sep = ' ' if len(instruction) else '' + result.append(f"{instruction}{sep}{i['text']}") + + token_count = self._token_count(" ".join(result)) + if token_count > self.context_window: + raise PromptTooBigError(token_count, self.context_window) + + return result + + async def embed( + self, inputs: list[EmbeddingInput] + ) -> list[np.NDArray | list[float]]: + input = self.preprocess(inputs) + embeddings: list[np.NDArray | list[float]] = [] + + if self.embedding_provider == "julep": + embeddings = await embed(input) + elif self.embedding_provider == "openai": + embeddings = ( + await openai_client.embeddings.create( + input=input, model=self.embedding_model_name + ) + .data[0] + .embedding + ) + + return self.normalize(embeddings) + + def normalize( + self, embeddings: list[np.NDArray | list[float]] + ) -> list[np.NDArray | list[float]]: + return [ + ( + e + if len(e) <= self.original_embedding_dimensions + else normalize_l2(e[: self.original_embedding_dimensions]) + ) + for e in embeddings + ] + + +_embedding_model_registry = { + "text-embeddings-3-small": EmbeddingModel( + embedding_provider="openai", + embedding_model_name="text-embeddings-3-small", + original_embedding_dimensions=1024, + output_embedding_dimensions=1024, + context_window=8192, + tokenizer=tiktoken.encoding_for_model("text-embeddings-3-small"), + ), + "text-embeddings-3-large": EmbeddingModel( + embedding_provider="openai", + embedding_model_name="text-embeddings-3-large", + original_embedding_dimensions=1024, + output_embedding_dimensions=1024, + context_window=8192, + tokenizer=tiktoken.encoding_for_model("text-embeddings-3-large"), + ), + "Alibaba-NLP/gte-large-en-v1.5": EmbeddingModel( + embedding_provider="julep", + embedding_model_name="Alibaba-NLP/gte-large-en-v1.5", + original_embedding_dimensions=1024, + output_embedding_dimensions=1024, + context_window=8192, + tokenizer=PreTrainedTokenizer.from_pretrained("Alibaba-NLP/gte-large-en-v1.5"), + ), + "BAAI/bge-m3": EmbeddingModel( + embedding_provider="julep", + embedding_model_name="BAAI/bge-m3", + original_embedding_dimensions=1024, + output_embedding_dimensions=1024, + context_window=8192, + tokenizer=PreTrainedTokenizer.from_pretrained("BAAI/bge-m3"), + ), + "BAAI/llm-embedder": EmbeddingModel( + embedding_provider="julep", + embedding_model_name="BAAI/llm-embedder", + original_embedding_dimensions=1024, + output_embedding_dimensions=1024, + context_window=8192, + tokenizer=PreTrainedTokenizer.from_pretrained("BAAI/llm-embedder"), + ), +} diff --git a/agents-api/agents_api/exceptions.py b/agents-api/agents_api/exceptions.py new file mode 100644 index 000000000..f4a218f23 --- /dev/null +++ b/agents-api/agents_api/exceptions.py @@ -0,0 +1,17 @@ +class BaseException(Exception): + pass + + +class ModelNotSupportedError(BaseException): + def __init__(self, model_name): + super().__init__(f"model {model_name} is not supported") + + +class PromptTooBigError(BaseException): + def __init__(self, token_count, max_tokens): + super().__init__(f"prompt is too big, {max_tokens} required, but actual length is {token_count}") + + +class UnknownTokenizerError(BaseException): + def __init__(self): + super().__init__("unknown tokenizer") diff --git a/agents-api/agents_api/routers/agents/routers.py b/agents-api/agents_api/routers/agents/routers.py index f8071d026..08bdbdef2 100644 --- a/agents-api/agents_api/routers/agents/routers.py +++ b/agents-api/agents_api/routers/agents/routers.py @@ -66,6 +66,8 @@ PatchToolRequest, PatchAgentRequest, ) +from agents_api.env import embedding_model_id +from agents_api.embed_models_registry import EmbeddingModel class AgentList(BaseModel): @@ -319,9 +321,13 @@ async def create_docs(agent_id: UUID4, request: CreateDoc) -> ResourceCreatedRes ) indices, snippets = list(zip(*enumerate(content))) - embeddings = await embed( + model = EmbeddingModel.from_model_name(embedding_model_id) + embeddings = await model.embed( [ - snippet_embed_instruction + request.title + "\n\n" + snippet + { + "instruction": snippet_embed_instruction, + "text": request.title + "\n\n" + snippet, + } for snippet in snippets ] ) diff --git a/agents-api/migrations/migrate_1714566760_change_embeddings_dimensions.py b/agents-api/migrations/migrate_1714566760_change_embeddings_dimensions.py new file mode 100644 index 000000000..86f2c9543 --- /dev/null +++ b/agents-api/migrations/migrate_1714566760_change_embeddings_dimensions.py @@ -0,0 +1,118 @@ +# /usr/bin/env python3 + +MIGRATION_ID = "change_embeddings_dimensions" +CREATED_AT = 1714566760.731964 + + +change_dimensions = { + "up": """ + ?[ + doc_id, + snippet_idx, + title, + snippet, + embed_instruction, + embedding, + ] := + *information_snippets{ + snippet_idx, + title, + snippet, + embed_instruction, + embedding, + additional_info_id: doc_id, + } + + :replace information_snippets { + doc_id: Uuid, + snippet_idx: Int, + => + title: String, + snippet: String, + embed_instruction: String default 'Encode this passage for retrieval: ', + embedding: ? default null, + } + """, + "down": """ + ?[ + doc_id, + snippet_idx, + title, + snippet, + embed_instruction, + embedding, + ] := + *information_snippets{ + snippet_idx, + title, + snippet, + embed_instruction, + embedding, + additional_info_id: doc_id, + } + + :replace information_snippets { + doc_id: Uuid, + snippet_idx: Int, + => + title: String, + snippet: String, + embed_instruction: String default 'Encode this passage for retrieval: ', + embedding: ? default null, + } + """, +} + +information_snippets_hnsw_index = dict( + up=""" + ::hnsw create information_snippets:embedding_space { + fields: [embedding], + filter: !is_null(embedding), + dim: 1024, + distance: Cosine, + m: 64, + ef_construction: 256, + extend_candidates: false, + keep_pruned_connections: false, + } + """, + down=""" + + ::hnsw create information_snippets:embedding_space { + fields: [embedding], + filter: !is_null(embedding), + dim: 768, + distance: Cosine, + m: 64, + ef_construction: 256, + extend_candidates: false, + keep_pruned_connections: false, + } + """, +) + +drop_index = { + "up": """ + ::hnsw drop information_snippets:embedding_space + """, + "down": """ + ::hnsw drop information_snippets:embedding_space + """, +} + + +queries_to_run = [ + drop_index, + change_dimensions, + information_snippets_hnsw_index, +] + + +def up(client): + for q in queries_to_run: + client.run(q["up"]) + + +def down(client): + for q in reversed(queries_to_run): + client.run(q["down"]) diff --git a/agents-api/poetry.lock b/agents-api/poetry.lock index 9ca654918..76197e50a 100644 --- a/agents-api/poetry.lock +++ b/agents-api/poetry.lock @@ -677,13 +677,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth [[package]] name = "fastapi" -version = "0.110.2" +version = "0.110.3" description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production" optional = false python-versions = ">=3.8" files = [ - {file = "fastapi-0.110.2-py3-none-any.whl", hash = "sha256:239403f2c0a3dda07a9420f95157a7f014ddb2b770acdbc984f9bdf3ead7afdb"}, - {file = "fastapi-0.110.2.tar.gz", hash = "sha256:b53d673652da3b65e8cd787ad214ec0fe303cad00d2b529b86ce7db13f17518d"}, + {file = "fastapi-0.110.3-py3-none-any.whl", hash = "sha256:fd7600612f755e4050beb74001310b5a7e1796d149c2ee363124abdfa0289d32"}, + {file = "fastapi-0.110.3.tar.gz", hash = "sha256:555700b0159379e94fdbfc6bb66a0f1c43f4cf7060f25239af3d84b63a656626"}, ] [package.dependencies] @@ -692,7 +692,7 @@ starlette = ">=0.37.2,<0.38.0" typing-extensions = ">=4.8.0" [package.extras] -all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"] +all = ["email_validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings 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(>=2.20.0,<3.0.0.dev0)"] [[package]] name = "google-cloud-aiplatform" -version = "1.48.0" +version = "1.49.0" description = "Vertex AI API client library" optional = false python-versions = ">=3.8" files = [ - {file = "google-cloud-aiplatform-1.48.0.tar.gz", hash = "sha256:70fa242247693092412270e26109729e0d2ef3f3fe8caf0ce823208f8dc156c7"}, - {file = "google_cloud_aiplatform-1.48.0-py2.py3-none-any.whl", hash = "sha256:bf7b0fd6b6bd9f95082c0489c3f29ba9ee560e1aaeff64a574719304eeb68c85"}, + {file = "google-cloud-aiplatform-1.49.0.tar.gz", hash = "sha256:e6e6d01079bb5def49e4be4db4d12b13c624b5c661079c869c13c855e5807429"}, + {file = "google_cloud_aiplatform-1.49.0-py2.py3-none-any.whl", hash = "sha256:8072d9e0c18d8942c704233d1a93b8d6312fc7b278786a283247950e28ae98df"}, ] [package.dependencies] @@ -932,9 +932,9 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"] cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"] datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"] endpoint = ["requests (>=2.28.1)"] -full = ["cloudpickle (<3.0)", "cloudpickle (>=2.2.1,<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pydantic (<3)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"] +full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"] langchain = ["langchain (>=0.1.13,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<0.2)"] -langchain-testing = ["langchain (>=0.1.13,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<0.2)", "pytest-xdist"] +langchain-testing = ["absl-py", "cloudpickle (>=2.2.1,<3.0)", "langchain (>=0.1.13,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<0.2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"] lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"] metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"] pipelines = ["pyyaml (>=5.3.1,<7)"] @@ -944,9 +944,9 @@ private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"] rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"] ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)"] ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", 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+sagemaker = ["sagemaker (>=2.31.0)"] +sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] +serving = ["fastapi", "pydantic", "starlette", "uvicorn"] +sigopt = ["sigopt"] +sklearn = ["scikit-learn"] +speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] +timm = ["timm"] +tokenizers = ["tokenizers (>=0.19,<0.20)"] +torch = ["accelerate (>=0.21.0)", "torch"] +torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] +torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] +torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] +video = ["av (==9.2.0)", "decord (==0.6.0)"] +vision = ["Pillow (>=10.0.1,<=15.0)"] + [[package]] name = "typer" version = "0.12.3" @@ -3673,4 +3855,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.11" -content-hash = "54549ef11aa0cf0d908c1d2fc053962c6df53912285f75945c85cd7fef673ab3" +content-hash = "c9a0f03637a0f69f931480bf8e0d08b7a2f4d121054b7f431d75c9562316fac7" diff --git a/agents-api/pyproject.toml b/agents-api/pyproject.toml index 41d8366c7..5ff6e0a4d 100644 --- a/agents-api/pyproject.toml +++ b/agents-api/pyproject.toml @@ -26,6 +26,9 @@ jinja2 = "^3.1.3" jinja2schema = "^0.1.4" jsonschema = "^4.21.1" litellm = "^1.35.32" +numpy = "^1.26.4" +transformers = "^4.40.1" +tiktoken = "^0.6.0" [tool.poetry.group.dev.dependencies]