From 65b12c1d8ef06d6f754cfa9b8ee43b8134c80124 Mon Sep 17 00:00:00 2001 From: Leonid Kuligin Date: Tue, 19 Mar 2024 18:14:59 +0100 Subject: [PATCH] moving code to tools (#65) * moving code --------- Co-authored-by: Erick Friis --- libs/tools/.gitignore | 1 + libs/tools/LICENSE | 21 + libs/tools/Makefile | 58 + libs/tools/README.md | 107 ++ libs/tools/langchain_google_tools/__init__.py | 17 + .../bigquery_vector_search.py | 861 ++++++++++++ .../documentai_warehouse.py | 119 ++ .../langchain_google_tools/gmail/__init__.py | 0 .../langchain_google_tools/gmail/loader.py | 110 ++ .../langchain_google_tools/gmail/toolkit.py | 57 + .../vertex_ai_search.py | 441 ++++++ libs/tools/poetry.lock | 1246 +++++++++++++++++ libs/tools/pyproject.toml | 101 ++ .../tools/tests/integration_tests/__init__.py | 0 libs/tools/tests/integration_tests/fake.py | 21 + .../test_bigquery_vector_search.py | 102 ++ .../test_docai_warehoure_retriever.py | 24 + .../test_vertex_ai_search.py | 44 + 18 files changed, 3330 insertions(+) create mode 100644 libs/tools/.gitignore create mode 100644 libs/tools/LICENSE create mode 100644 libs/tools/Makefile create mode 100644 libs/tools/README.md create mode 100644 libs/tools/langchain_google_tools/__init__.py create mode 100644 libs/tools/langchain_google_tools/bigquery_vector_search.py create mode 100644 libs/tools/langchain_google_tools/documentai_warehouse.py create mode 100644 libs/tools/langchain_google_tools/gmail/__init__.py create mode 100644 libs/tools/langchain_google_tools/gmail/loader.py create mode 100644 libs/tools/langchain_google_tools/gmail/toolkit.py create mode 100644 libs/tools/langchain_google_tools/vertex_ai_search.py create mode 100644 libs/tools/poetry.lock create mode 100644 libs/tools/pyproject.toml create mode 100644 libs/tools/tests/integration_tests/__init__.py create mode 100644 libs/tools/tests/integration_tests/fake.py create mode 100644 libs/tools/tests/integration_tests/test_bigquery_vector_search.py create mode 100644 libs/tools/tests/integration_tests/test_docai_warehoure_retriever.py create mode 100644 libs/tools/tests/integration_tests/test_vertex_ai_search.py diff --git a/libs/tools/.gitignore b/libs/tools/.gitignore new file mode 100644 index 00000000..bee8a64b --- /dev/null +++ b/libs/tools/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/tools/LICENSE b/libs/tools/LICENSE new file mode 100644 index 00000000..fc0602fe --- /dev/null +++ b/libs/tools/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/tools/Makefile b/libs/tools/Makefile new file mode 100644 index 00000000..be3f4eb6 --- /dev/null +++ b/libs/tools/Makefile @@ -0,0 +1,58 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +integration_test integration_tests: TEST_FILE = tests/integration_tests/ + +test tests integration_test integration_tests: + poetry run pytest --release $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/tools --name-only --diff-filter=d main | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_google_tools +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_google_tools -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/tools/README.md b/libs/tools/README.md new file mode 100644 index 00000000..101fed80 --- /dev/null +++ b/libs/tools/README.md @@ -0,0 +1,107 @@ +# langchain-google-genai + +This package contains the LangChain integrations for Gemini through their generative-ai SDK. + +## Installation + +```bash +pip install -U langchain-google-genai +``` + +### Image utilities +To use image utility methods, like loading images from GCS urls, install with extras group 'images': + +```bash +pip install -e "langchain-google-genai[images]" +``` + +## Chat Models + +This package contains the `ChatGoogleGenerativeAI` class, which is the recommended way to interface with the Google Gemini series of models. + +To use, install the requirements, and configure your environment. + +```bash +export GOOGLE_API_KEY=your-api-key +``` + +Then initialize + +```python +from langchain_google_genai import ChatGoogleGenerativeAI + +llm = ChatGoogleGenerativeAI(model="gemini-pro") +llm.invoke("Sing a ballad of LangChain.") +``` + +#### Multimodal inputs + +Gemini vision model supports image inputs when providing a single chat message. Example: + +``` +from langchain_core.messages import HumanMessage +from langchain_google_genai import ChatGoogleGenerativeAI + +llm = ChatGoogleGenerativeAI(model="gemini-pro-vision") +# example +message = HumanMessage( + content=[ + { + "type": "text", + "text": "What's in this image?", + }, # You can optionally provide text parts + {"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"}, + ] +) +llm.invoke([message]) +``` + +The value of `image_url` can be any of the following: + +- A public image URL +- An accessible gcs file (e.g., "gcs://path/to/file.png") +- A local file path +- A base64 encoded image (e.g., `data:image/png;base64,abcd124`) +- A PIL image + + + +## Embeddings + +This package also adds support for google's embeddings models. + +``` +from langchain_google_genai import GoogleGenerativeAIEmbeddings + +embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") +embeddings.embed_query("hello, world!") +``` + +## Semantic Retrieval + +Enables retrieval augmented generation (RAG) in your application. + +``` +# Create a new store for housing your documents. +corpus_store = GoogleVectorStore.create_corpus(display_name="My Corpus") + +# Create a new document under the above corpus. +document_store = GoogleVectorStore.create_document( + corpus_id=corpus_store.corpus_id, display_name="My Document" +) + +# Upload some texts to the document. +text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0) +for file in DirectoryLoader(path="data/").load(): + documents = text_splitter.split_documents([file]) + document_store.add_documents(documents) + +# Talk to your entire corpus with possibly many documents. +aqa = corpus_store.as_aqa() +answer = aqa.invoke("What is the meaning of life?") + +# Read the response along with the attributed passages and answerability. +print(response.answer) +print(response.attributed_passages) +print(response.answerable_probability) +``` diff --git a/libs/tools/langchain_google_tools/__init__.py b/libs/tools/langchain_google_tools/__init__.py new file mode 100644 index 00000000..ee1ec9c3 --- /dev/null +++ b/libs/tools/langchain_google_tools/__init__.py @@ -0,0 +1,17 @@ +from langchain_google_tools.bigquery_vector_search import BigQueryVectorSearch +from langchain_google_tools.documentai_warehouse import DocumentAIWarehouseRetriever +from langchain_google_tools.gmail.loader import GMailLoader +from langchain_google_tools.gmail.toolkit import GmailToolkit +from langchain_google_tools.vertex_ai_search import ( + VertexAIMultiTurnSearchRetriever, + VertexAISearchRetriever, +) + +__all__ = [ + "BigQueryVectorSearch", + "DocumentAIWarehouseRetriever", + "GMailLoader", + "GmailToolkit", + "VertexAIMultiTurnSearchRetriever", + "VertexAISearchRetriever", +] diff --git a/libs/tools/langchain_google_tools/bigquery_vector_search.py b/libs/tools/langchain_google_tools/bigquery_vector_search.py new file mode 100644 index 00000000..853555de --- /dev/null +++ b/libs/tools/langchain_google_tools/bigquery_vector_search.py @@ -0,0 +1,861 @@ +"""Vector Store in Google Cloud BigQuery.""" +from __future__ import annotations + +import asyncio +import json +import logging +import sys +import uuid +from datetime import datetime +from functools import partial +from threading import Lock, Thread +from typing import Any, Callable, Dict, List, Optional, Tuple, Type + +import numpy as np +from google.api_core.exceptions import ClientError +from langchain_community.utils.google import get_client_info +from langchain_community.vectorstores.utils import ( + DistanceStrategy, + maximal_marginal_relevance, +) +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + +DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN_DISTANCE +DEFAULT_DOC_ID_COLUMN_NAME = "doc_id" # document id +DEFAULT_TEXT_EMBEDDING_COLUMN_NAME = "text_embedding" # embeddings vectors +DEFAULT_METADATA_COLUMN_NAME = "metadata" # document metadata +DEFAULT_CONTENT_COLUMN_NAME = "content" # text content, do not rename +DEFAULT_TOP_K = 4 # default number of documents returned from similarity search + +_MIN_INDEX_ROWS = 5000 # minimal number of rows for creating an index +_INDEX_CHECK_PERIOD_SECONDS = 60 # Do not check for index more often that this. +_vector_table_lock = Lock() # process-wide BigQueryVectorSearch table lock + + +class BigQueryVectorSearch(VectorStore): + """Google Cloud BigQuery vector store. + + To use, you need the following packages installed: + google-cloud-bigquery + """ + + def __init__( + self, + embedding: Embeddings, + project_id: str, + dataset_name: str, + table_name: str, + location: str = "US", + content_field: str = DEFAULT_CONTENT_COLUMN_NAME, + metadata_field: str = DEFAULT_METADATA_COLUMN_NAME, + text_embedding_field: str = DEFAULT_TEXT_EMBEDDING_COLUMN_NAME, + doc_id_field: str = DEFAULT_DOC_ID_COLUMN_NAME, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + credentials: Optional[Any] = None, + ): + """Constructor for BigQueryVectorSearch. + + Args: + embedding (Embeddings): Text Embedding model to use. + project_id (str): GCP project. + dataset_name (str): BigQuery dataset to store documents and embeddings. + table_name (str): BigQuery table name. + location (str, optional): BigQuery region. Defaults to + `US`(multi-region). + content_field (str): Specifies the column to store the content. + Defaults to `content`. + metadata_field (str): Specifies the column to store the metadata. + Defaults to `metadata`. + text_embedding_field (str): Specifies the column to store + the embeddings vector. + Defaults to `text_embedding`. + doc_id_field (str): Specifies the column to store the document id. + Defaults to `doc_id`. + distance_strategy (DistanceStrategy, optional): + Determines the strategy employed for calculating + the distance between vectors in the embedding space. + Defaults to EUCLIDEAN_DISTANCE. + Available options are: + - COSINE: Measures the similarity between two vectors of an inner + product space. + - EUCLIDEAN_DISTANCE: Computes the Euclidean distance between + two vectors. This metric considers the geometric distance in + the vector space, and might be more suitable for embeddings + that rely on spatial relationships. This is the default behavior + credentials (Credentials, optional): Custom Google Cloud credentials + to use. Defaults to None. + """ + try: + from google.cloud import bigquery + + client_info = get_client_info(module="bigquery-vector-search") + self.bq_client = bigquery.Client( + project=project_id, + location=location, + credentials=credentials, + client_info=client_info, + ) + except ModuleNotFoundError: + raise ImportError( + "Please, install or upgrade the google-cloud-bigquery library: " + "pip install google-cloud-bigquery" + ) + self._logger = logging.getLogger(__name__) + self._creating_index = False + self._have_index = False + self.embedding_model = embedding + self.project_id = project_id + self.dataset_name = dataset_name + self.table_name = table_name + self.location = location + self.content_field = content_field + self.metadata_field = metadata_field + self.text_embedding_field = text_embedding_field + self.doc_id_field = doc_id_field + self.distance_strategy = distance_strategy + self._full_table_id = ( + f"{self.project_id}." f"{self.dataset_name}." f"{self.table_name}" + ) + self._logger.debug("Using table `%s`", self.full_table_id) + with _vector_table_lock: + self.vectors_table = self._initialize_table() + self._last_index_check = datetime.min + self._initialize_vector_index() + + def _initialize_table(self) -> Any: + """Validates or creates the BigQuery table.""" + from google.cloud import bigquery + + table_ref = bigquery.TableReference.from_string(self._full_table_id) + table = self.bq_client.create_table(table_ref, exists_ok=True) + changed_schema = False + schema = table.schema.copy() + columns = {c.name: c for c in schema} + if self.doc_id_field not in columns: + changed_schema = True + schema.append( + bigquery.SchemaField(name=self.doc_id_field, field_type="STRING") + ) + elif ( + columns[self.doc_id_field].field_type != "STRING" + or columns[self.doc_id_field].mode == "REPEATED" + ): + raise ValueError(f"Column {self.doc_id_field} must be of " "STRING type") + if self.metadata_field not in columns: + changed_schema = True + schema.append( + bigquery.SchemaField(name=self.metadata_field, field_type="JSON") + ) + elif ( + columns[self.metadata_field].field_type not in ["JSON", "STRING"] + or columns[self.metadata_field].mode == "REPEATED" + ): + raise ValueError( + f"Column {self.metadata_field} must be of STRING or JSON type" + ) + if self.content_field not in columns: + changed_schema = True + schema.append( + bigquery.SchemaField(name=self.content_field, field_type="STRING") + ) + elif ( + columns[self.content_field].field_type != "STRING" + or columns[self.content_field].mode == "REPEATED" + ): + raise ValueError(f"Column {self.content_field} must be of " "STRING type") + if self.text_embedding_field not in columns: + changed_schema = True + schema.append( + bigquery.SchemaField( + name=self.text_embedding_field, + field_type="FLOAT64", + mode="REPEATED", + ) + ) + elif ( + columns[self.text_embedding_field].field_type not in ("FLOAT", "FLOAT64") + or columns[self.text_embedding_field].mode != "REPEATED" + ): + raise ValueError( + f"Column {self.text_embedding_field} must be of " "ARRAY type" + ) + if changed_schema: + self._logger.debug("Updated table `%s` schema.", self.full_table_id) + table.schema = schema + table = self.bq_client.update_table(table, fields=["schema"]) + return table + + def _initialize_vector_index(self) -> Any: + """ + A vector index in BigQuery table enables efficient + approximate vector search. + """ + from google.cloud import bigquery + + if self._have_index or self._creating_index: + # Already have an index or in the process of creating one. + return + table = self.bq_client.get_table(self.vectors_table) + if (table.num_rows or 0) < _MIN_INDEX_ROWS: + # Not enough rows to create index. + self._logger.debug("Not enough rows to create a vector index.") + return + if ( + datetime.utcnow() - self._last_index_check + ).total_seconds() < _INDEX_CHECK_PERIOD_SECONDS: + return + with _vector_table_lock: + if self._creating_index or self._have_index: + return + self._last_index_check = datetime.utcnow() + # Check if index exists, create if necessary + check_query = ( + f"SELECT 1 FROM `{self.project_id}.{self.dataset_name}" + ".INFORMATION_SCHEMA.VECTOR_INDEXES` WHERE" + f" table_name = '{self.table_name}'" + ) + job = self.bq_client.query( + check_query, api_method=bigquery.enums.QueryApiMethod.QUERY + ) + if job.result().total_rows == 0: + # Need to create an index. Make it in a separate thread. + self._create_index_in_background() + else: + self._logger.debug("Vector index already exists.") + self._have_index = True + + def _create_index_in_background(self): # type: ignore[no-untyped-def] + if self._have_index or self._creating_index: + # Already have an index or in the process of creating one. + return + self._creating_index = True + self._logger.debug("Trying to create a vector index.") + thread = Thread(target=self._create_index, daemon=True) + thread.start() + + def _create_index(self): # type: ignore[no-untyped-def] + table = self.bq_client.get_table(self.vectors_table) + if (table.num_rows or 0) < _MIN_INDEX_ROWS: + # Not enough rows to create index. + return + if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE: + distance_type = "EUCLIDEAN" + elif self.distance_strategy == DistanceStrategy.COSINE: + distance_type = "COSINE" + # Default to EUCLIDEAN_DISTANCE + else: + distance_type = "EUCLIDEAN" + index_name = f"{self.table_name}_langchain_index" + try: + sql = f""" + CREATE VECTOR INDEX IF NOT EXISTS + `{index_name}` + ON `{self.full_table_id}`({self.text_embedding_field}) + OPTIONS(distance_type="{distance_type}", index_type="IVF") + """ + self.bq_client.query(sql).result() + self._have_index = True + except ClientError as ex: + self._logger.debug("Vector index creation failed (%s).", ex.args[0]) + finally: + self._creating_index = False + + def _persist(self, data: Dict[str, Any]) -> None: + """Saves documents and embeddings to BigQuery.""" + from google.cloud import bigquery + + data_len = len(data[list(data.keys())[0]]) + if data_len == 0: + return + + list_of_dicts = [dict(zip(data, t)) for t in zip(*data.values())] + + job_config = bigquery.LoadJobConfig() + job_config.schema = self.vectors_table.schema + job_config.schema_update_options = ( + bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION + ) + job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND + job = self.bq_client.load_table_from_json( + list_of_dicts, self.vectors_table, job_config=job_config + ) + job.result() + + @property + def embeddings(self) -> Optional[Embeddings]: + return self.embedding_model + + @property + def full_table_id(self) -> str: + return self._full_table_id + + def add_texts( # type: ignore[override] + self, + texts: List[str], + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Args: + texts: List of strings to add to the vectorstore. + metadatas: Optional list of metadata associated with the texts. + + Returns: + List of ids from adding the texts into the vectorstore. + """ + embs = self.embedding_model.embed_documents(texts) + return self.add_texts_with_embeddings(texts, embs, metadatas, **kwargs) + + def add_texts_with_embeddings( + self, + texts: List[str], + embs: List[List[float]], + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Args: + texts: List of strings to add to the vectorstore. + embs: List of lists of floats with text embeddings for texts. + metadatas: Optional list of metadata associated with the texts. + + Returns: + List of ids from adding the texts into the vectorstore. + """ + ids = [uuid.uuid4().hex for _ in texts] + values_dict: Dict[str, List[Any]] = { + self.content_field: texts, + self.doc_id_field: ids, + } + if not metadatas: + metadatas = [] + len_diff = len(ids) - len(metadatas) + add_meta = [None for _ in range(0, len_diff)] + metadatas = [m if m is not None else {} for m in metadatas + add_meta] + values_dict[self.metadata_field] = metadatas + values_dict[self.text_embedding_field] = embs + self._persist(values_dict) + return ids + + def get_documents( + self, ids: Optional[List[str]] = None, filter: Optional[Dict[str, Any]] = None + ) -> List[Document]: + """Search documents by their ids or metadata values. + + Args: + ids: List of ids of documents to retrieve from the vectorstore. + filter: Filter on metadata properties, e.g. + { + "str_property": "foo", + "int_property": 123 + } + Returns: + List of ids from adding the texts into the vectorstore. + """ + if ids and len(ids) > 0: + from google.cloud import bigquery + + job_config = bigquery.QueryJobConfig( + query_parameters=[ + bigquery.ArrayQueryParameter("ids", "STRING", ids), + ] + ) + id_expr = f"{self.doc_id_field} IN UNNEST(@ids)" + else: + job_config = None + id_expr = "TRUE" + if filter: + filter_expressions = [] + for i in filter.items(): + if isinstance(i[1], float): + expr = ( + "ABS(CAST(JSON_VALUE(" + f"`{self.metadata_field}`,'$.{i[0]}') " + f"AS FLOAT64) - {i[1]}) " + f"<= {sys.float_info.epsilon}" + ) + else: + val = str(i[1]).replace('"', '\\"') + expr = ( + f"JSON_VALUE(`{self.metadata_field}`,'$.{i[0]}')" f' = "{val}"' + ) + filter_expressions.append(expr) + filter_expression_str = " AND ".join(filter_expressions) + where_filter_expr = f" AND ({filter_expression_str})" + else: + where_filter_expr = "" + + job = self.bq_client.query( + f""" + SELECT * FROM `{self.full_table_id}` WHERE {id_expr} + {where_filter_expr} + """, + job_config=job_config, + ) + docs: List[Document] = [] + for row in job: + metadata = None + if self.metadata_field: + metadata = row[self.metadata_field] + if metadata: + if not isinstance(metadata, dict): + metadata = json.loads(metadata) + else: + metadata = {} + metadata["__id"] = row[self.doc_id_field] + doc = Document(page_content=row[self.content_field], metadata=metadata) + docs.append(doc) + return docs + + def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: + """Delete by vector ID or other criteria. + + Args: + ids: List of ids to delete. + **kwargs: Other keyword arguments that subclasses might use. + + Returns: + Optional[bool]: True if deletion is successful, + False otherwise, None if not implemented. + """ + if not ids or len(ids) == 0: + return True + from google.cloud import bigquery + + job_config = bigquery.QueryJobConfig( + query_parameters=[ + bigquery.ArrayQueryParameter("ids", "STRING", ids), + ] + ) + self.bq_client.query( + f""" + DELETE FROM `{self.full_table_id}` WHERE {self.doc_id_field} + IN UNNEST(@ids) + """, + job_config=job_config, + ).result() + return True + + async def adelete( + self, ids: Optional[List[str]] = None, **kwargs: Any + ) -> Optional[bool]: + """Delete by vector ID or other criteria. + + Args: + ids: List of ids to delete. + **kwargs: Other keyword arguments that subclasses might use. + + Returns: + Optional[bool]: True if deletion is successful, + False otherwise, None if not implemented. + """ + return await asyncio.get_running_loop().run_in_executor( + None, partial(self.delete, **kwargs), ids + ) + + def _search_with_score_and_embeddings_by_vector( + self, + embedding: List[float], + k: int = DEFAULT_TOP_K, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + ) -> List[Tuple[Document, List[float], float]]: + from google.cloud import bigquery + + # Create an index if no index exists. + if not self._have_index and not self._creating_index: + self._initialize_vector_index() + # Prepare filter + filter_expr = "TRUE" + if filter: + filter_expressions = [] + for i in filter.items(): + if isinstance(i[1], float): + expr = ( + "ABS(CAST(JSON_VALUE(" + f"base.`{self.metadata_field}`,'$.{i[0]}') " + f"AS FLOAT64) - {i[1]}) " + f"<= {sys.float_info.epsilon}" + ) + else: + val = str(i[1]).replace('"', '\\"') + expr = ( + f"JSON_VALUE(base.`{self.metadata_field}`,'$.{i[0]}')" + f' = "{val}"' + ) + filter_expressions.append(expr) + filter_expression_str = " AND ".join(filter_expressions) + filter_expr += f" AND ({filter_expression_str})" + # Configure and run a query job. + job_config = bigquery.QueryJobConfig( + query_parameters=[ + bigquery.ArrayQueryParameter("v", "FLOAT64", embedding), + ], + use_query_cache=False, + priority=bigquery.QueryPriority.BATCH, + ) + if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE: + distance_type = "EUCLIDEAN" + elif self.distance_strategy == DistanceStrategy.COSINE: + distance_type = "COSINE" + # Default to EUCLIDEAN_DISTANCE + else: + distance_type = "EUCLIDEAN" + if brute_force: + options_string = ",options => '{\"use_brute_force\":true}'" + elif fraction_lists_to_search: + if fraction_lists_to_search == 0 or fraction_lists_to_search >= 1.0: + raise ValueError( + "`fraction_lists_to_search` must be between " "0.0 and 1.0" + ) + options_string = ( + ',options => \'{"fraction_lists_to_search":' + f"{fraction_lists_to_search}}}'" + ) + else: + options_string = "" + query = f""" + SELECT + base.*, + distance AS _vector_search_distance + FROM VECTOR_SEARCH( + TABLE `{self.full_table_id}`, + "{self.text_embedding_field}", + (SELECT @v AS {self.text_embedding_field}), + distance_type => "{distance_type}", + top_k => {k} + {options_string} + ) + WHERE {filter_expr} + LIMIT {k} + """ + document_tuples: List[Tuple[Document, List[float], float]] = [] + # TODO(vladkol): Use jobCreationMode=JOB_CREATION_OPTIONAL when available. + job = self.bq_client.query( + query, job_config=job_config, api_method=bigquery.enums.QueryApiMethod.QUERY + ) + # Process job results. + for row in job: + metadata = row[self.metadata_field] + if metadata: + if not isinstance(metadata, dict): + metadata = json.loads(metadata) + else: + metadata = {} + metadata["__id"] = row[self.doc_id_field] + metadata["__job_id"] = job.job_id + doc = Document(page_content=row[self.content_field], metadata=metadata) + document_tuples.append( + (doc, row[self.text_embedding_field], row["_vector_search_distance"]) + ) + return document_tuples + + def similarity_search_with_score_by_vector( + self, + embedding: List[float], + k: int = DEFAULT_TOP_K, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Return docs most similar to embedding vector. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Filter on metadata properties, e.g. + { + "str_property": "foo", + "int_property": 123 + } + brute_force: Whether to use brute force search. Defaults to False. + fraction_lists_to_search: Optional percentage of lists to search, + must be in range 0.0 and 1.0, exclusive. + If Node, uses service's default which is 0.05. + + Returns: + List of Documents most similar to the query vector with distance. + """ + del kwargs + document_tuples = self._search_with_score_and_embeddings_by_vector( + embedding, k, filter, brute_force, fraction_lists_to_search + ) + return [(doc, distance) for doc, _, distance in document_tuples] + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = DEFAULT_TOP_K, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs most similar to embedding vector. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Filter on metadata properties, e.g. + { + "str_property": "foo", + "int_property": 123 + } + brute_force: Whether to use brute force search. Defaults to False. + fraction_lists_to_search: Optional percentage of lists to search, + must be in range 0.0 and 1.0, exclusive. + If Node, uses service's default which is 0.05. + + Returns: + List of Documents most similar to the query vector. + """ + tuples = self.similarity_search_with_score_by_vector( + embedding, k, filter, brute_force, fraction_lists_to_search, **kwargs + ) + return [i[0] for i in tuples] + + def similarity_search_with_score( + self, + query: str, + k: int = DEFAULT_TOP_K, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Run similarity search with score. + + Args: + query: search query text. + k: Number of Documents to return. Defaults to 4. + filter: Filter on metadata properties, e.g. + { + "str_property": "foo", + "int_property": 123 + } + brute_force: Whether to use brute force search. Defaults to False. + fraction_lists_to_search: Optional percentage of lists to search, + must be in range 0.0 and 1.0, exclusive. + If Node, uses service's default which is 0.05. + + Returns: + List of Documents most similar to the query vector, with similarity scores. + """ + emb = self.embedding_model.embed_query(query) # type: ignore + return self.similarity_search_with_score_by_vector( + emb, k, filter, brute_force, fraction_lists_to_search, **kwargs + ) + + def similarity_search( + self, + query: str, + k: int = DEFAULT_TOP_K, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Run similarity search. + + Args: + query: search query text. + k: Number of Documents to return. Defaults to 4. + filter: Filter on metadata properties, e.g. + { + "str_property": "foo", + "int_property": 123 + } + brute_force: Whether to use brute force search. Defaults to False. + fraction_lists_to_search: Optional percentage of lists to search, + must be in range 0.0 and 1.0, exclusive. + If Node, uses service's default which is 0.05. + + Returns: + List of Documents most similar to the query vector. + """ + tuples = self.similarity_search_with_score( + query, k, filter, brute_force, fraction_lists_to_search, **kwargs + ) + return [i[0] for i in tuples] + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + if self.distance_strategy == DistanceStrategy.COSINE: + return BigQueryVectorSearch._cosine_relevance_score_fn + else: + raise ValueError( + "Relevance score is not supported " + f"for `{self.distance_strategy}` distance." + ) + + def max_marginal_relevance_search( + self, + query: str, + k: int = DEFAULT_TOP_K, + fetch_k: int = DEFAULT_TOP_K * 5, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + query: search query text. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filter: Filter on metadata properties, e.g. + { + "str_property": "foo", + "int_property": 123 + } + brute_force: Whether to use brute force search. Defaults to False. + fraction_lists_to_search: Optional percentage of lists to search, + must be in range 0.0 and 1.0, exclusive. + If Node, uses service's default which is 0.05. + Returns: + List of Documents selected by maximal marginal relevance. + """ + query_embedding = self.embedding_model.embed_query( # type: ignore + query + ) + doc_tuples = self._search_with_score_and_embeddings_by_vector( + query_embedding, fetch_k, filter, brute_force, fraction_lists_to_search + ) + doc_embeddings = [d[1] for d in doc_tuples] + mmr_doc_indexes = maximal_marginal_relevance( + np.array(query_embedding), doc_embeddings, lambda_mult=lambda_mult, k=k + ) + return [doc_tuples[i][0] for i in mmr_doc_indexes] + + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = DEFAULT_TOP_K, + fetch_k: int = DEFAULT_TOP_K * 5, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filter: Filter on metadata properties, e.g. + { + "str_property": "foo", + "int_property": 123 + } + brute_force: Whether to use brute force search. Defaults to False. + fraction_lists_to_search: Optional percentage of lists to search, + must be in range 0.0 and 1.0, exclusive. + If Node, uses service's default which is 0.05. + Returns: + List of Documents selected by maximal marginal relevance. + """ + doc_tuples = self._search_with_score_and_embeddings_by_vector( + embedding, fetch_k, filter, brute_force, fraction_lists_to_search + ) + doc_embeddings = [d[1] for d in doc_tuples] + mmr_doc_indexes = maximal_marginal_relevance( + np.array(embedding), doc_embeddings, lambda_mult=lambda_mult, k=k + ) + return [doc_tuples[i][0] for i in mmr_doc_indexes] + + async def amax_marginal_relevance_search( + self, + query: str, + k: int = DEFAULT_TOP_K, + fetch_k: int = DEFAULT_TOP_K * 5, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance.""" + + func = partial( + self.max_marginal_relevance_search, + query, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + filter=filter, + brute_force=brute_force, + fraction_lists_to_search=fraction_lists_to_search, + **kwargs, + ) + return await asyncio.get_event_loop().run_in_executor(None, func) + + async def amax_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = DEFAULT_TOP_K, + fetch_k: int = DEFAULT_TOP_K * 5, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + brute_force: bool = False, + fraction_lists_to_search: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance.""" + return await asyncio.get_running_loop().run_in_executor( + None, + partial(self.max_marginal_relevance_search_by_vector, **kwargs), + embedding, + k, + fetch_k, + lambda_mult, + filter, + brute_force, + fraction_lists_to_search, + ) + + @classmethod + def from_texts( + cls: Type["BigQueryVectorSearch"], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> "BigQueryVectorSearch": + """Return VectorStore initialized from texts and embeddings.""" + vs_obj = BigQueryVectorSearch(embedding=embedding, **kwargs) + vs_obj.add_texts(texts, metadatas) + return vs_obj + + def explore_job_stats(self, job_id: str) -> Dict: + """Return the statistics for a single job execution. + + Args: + job_id: The BigQuery Job id. + + Returns: + A dictionary of job statistics for a given job. + """ + return self.bq_client.get_job(job_id)._properties["statistics"] diff --git a/libs/tools/langchain_google_tools/documentai_warehouse.py b/libs/tools/langchain_google_tools/documentai_warehouse.py new file mode 100644 index 00000000..6cbe00c3 --- /dev/null +++ b/libs/tools/langchain_google_tools/documentai_warehouse.py @@ -0,0 +1,119 @@ +"""Retriever wrapper for Google Cloud Document AI Warehouse.""" +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +from langchain_community.utilities.vertexai import get_client_info +from langchain_core.callbacks import CallbackManagerForRetrieverRun +from langchain_core.documents import Document +from langchain_core.pydantic_v1 import root_validator +from langchain_core.retrievers import BaseRetriever +from langchain_core.utils import get_from_dict_or_env + +if TYPE_CHECKING: + from google.cloud.contentwarehouse_v1 import ( + DocumentServiceClient, + RequestMetadata, + SearchDocumentsRequest, + ) + from google.cloud.contentwarehouse_v1.services.document_service.pagers import ( + SearchDocumentsPager, + ) + + +class DocumentAIWarehouseRetriever(BaseRetriever): + """A retriever based on Document AI Warehouse. + + Documents should be created and documents should be uploaded + in a separate flow, and this retriever uses only Document AI + schema_id provided to search for revelant documents. + + More info: https://cloud.google.com/document-ai-warehouse. + """ + + location: str = "us" + """Google Cloud location where Document AI Warehouse is placed.""" + project_number: str + """Google Cloud project number, should contain digits only.""" + schema_id: Optional[str] = None + """Document AI Warehouse schema to query against. + If nothing is provided, all documents in the project will be searched.""" + qa_size_limit: int = 5 + """The limit on the number of documents returned.""" + client: "DocumentServiceClient" = None #: :meta private: + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validates the environment.""" + try: # noqa: F401 + from google.cloud.contentwarehouse_v1 import DocumentServiceClient + except ImportError as exc: + raise ImportError( + "google.cloud.contentwarehouse is not installed." + "Please install it with pip install google-cloud-contentwarehouse" + ) from exc + + values["project_number"] = get_from_dict_or_env( + values, "project_number", "PROJECT_NUMBER" + ) + values["client"] = DocumentServiceClient( + client_info=get_client_info(module="document-ai-warehouse") + ) + return values + + def _prepare_request_metadata(self, user_ldap: str) -> "RequestMetadata": + from google.cloud.contentwarehouse_v1 import RequestMetadata, UserInfo + + user_info = UserInfo(id=f"user:{user_ldap}") + return RequestMetadata(user_info=user_info) + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any + ) -> List[Document]: + request = self._prepare_search_request(query, **kwargs) + response = self.client.search_documents(request=request) + return self._parse_search_response(response=response) + + def _prepare_search_request( + self, query: str, **kwargs: Any + ) -> "SearchDocumentsRequest": + from google.cloud.contentwarehouse_v1 import ( + DocumentQuery, + SearchDocumentsRequest, + ) + + try: + user_ldap = kwargs["user_ldap"] + except KeyError: + raise ValueError("Argument user_ldap should be provided!") + + request_metadata = self._prepare_request_metadata(user_ldap=user_ldap) + schemas = [] + if self.schema_id: + schemas.append( + self.client.document_schema_path( + project=self.project_number, + location=self.location, + document_schema=self.schema_id, + ) + ) + return SearchDocumentsRequest( + parent=self.client.common_location_path(self.project_number, self.location), + request_metadata=request_metadata, + document_query=DocumentQuery( + query=query, is_nl_query=True, document_schema_names=schemas + ), + qa_size_limit=self.qa_size_limit, + ) + + def _parse_search_response( + self, response: "SearchDocumentsPager" + ) -> List[Document]: + documents = [] + for doc in response.matching_documents: + metadata = { + "title": doc.document.title, + "source": doc.document.raw_document_path, + } + documents.append( + Document(page_content=doc.search_text_snippet, metadata=metadata) + ) + return documents diff --git a/libs/tools/langchain_google_tools/gmail/__init__.py b/libs/tools/langchain_google_tools/gmail/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/tools/langchain_google_tools/gmail/loader.py b/libs/tools/langchain_google_tools/gmail/loader.py new file mode 100644 index 00000000..71e54c4f --- /dev/null +++ b/libs/tools/langchain_google_tools/gmail/loader.py @@ -0,0 +1,110 @@ +import base64 +import re +from typing import Any, Iterator + +from googleapiclient.discovery import build +from langchain_community.chat_loaders.base import BaseChatLoader +from langchain_core.chat_sessions import ChatSession +from langchain_core.messages import HumanMessage + + +def _extract_email_content(msg: Any) -> HumanMessage: + from_email = None + for values in msg["payload"]["headers"]: + name = values["name"] + if name == "From": + from_email = values["value"] + if from_email is None: + raise ValueError + for part in msg["payload"]["parts"]: + if part["mimeType"] == "text/plain": + data = part["body"]["data"] + data = base64.urlsafe_b64decode(data).decode("utf-8") + # Regular expression to split the email body at the first + # occurrence of a line that starts with "On ... wrote:" + pattern = re.compile(r"\r\nOn .+(\r\n)*wrote:\r\n") + # Split the email body and extract the first part + newest_response = re.split(pattern, data)[0] + message = HumanMessage( + content=newest_response, additional_kwargs={"sender": from_email} + ) + return message + raise ValueError + + +def _get_message_data(service: Any, message: Any) -> ChatSession: + msg = service.users().messages().get(userId="me", id=message["id"]).execute() + message_content = _extract_email_content(msg) + in_reply_to = None + email_data = msg["payload"]["headers"] + for values in email_data: + name = values["name"] + if name == "In-Reply-To": + in_reply_to = values["value"] + if in_reply_to is None: + raise ValueError + + thread_id = msg["threadId"] + + thread = service.users().threads().get(userId="me", id=thread_id).execute() + messages = thread["messages"] + + response_email = None + for message in messages: + email_data = message["payload"]["headers"] + for values in email_data: + if values["name"] == "Message-ID": + message_id = values["value"] + if message_id == in_reply_to: + response_email = message + if response_email is None: + raise ValueError + starter_content = _extract_email_content(response_email) + return ChatSession(messages=[starter_content, message_content]) + + +class GMailLoader(BaseChatLoader): + """Load data from `GMail`. + + There are many ways you could want to load data from GMail. + This loader is currently fairly opinionated in how to do so. + The way it does it is it first looks for all messages that you have sent. + It then looks for messages where you are responding to a previous email. + It then fetches that previous email, and creates a training example + of that email, followed by your email. + + Note that there are clear limitations here. For example, + all examples created are only looking at the previous email for context. + + To use: + + - Set up a Google Developer Account: + Go to the Google Developer Console, create a project, + and enable the Gmail API for that project. + This will give you a credentials.json file that you'll need later. + """ + + def __init__(self, creds: Any, n: int = 100, raise_error: bool = False) -> None: + super().__init__() + self.creds = creds + self.n = n + self.raise_error = raise_error + + def lazy_load(self) -> Iterator[ChatSession]: + service = build("gmail", "v1", credentials=self.creds) + results = ( + service.users() + .messages() + .list(userId="me", labelIds=["SENT"], maxResults=self.n) + .execute() + ) + messages = results.get("messages", []) + for message in messages: + try: + yield _get_message_data(service, message) + except Exception as e: + # TODO: handle errors better + if self.raise_error: + raise e + else: + pass diff --git a/libs/tools/langchain_google_tools/gmail/toolkit.py b/libs/tools/langchain_google_tools/gmail/toolkit.py new file mode 100644 index 00000000..42be9555 --- /dev/null +++ b/libs/tools/langchain_google_tools/gmail/toolkit.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, List + +from langchain_community.agent_toolkits.base import BaseToolkit +from langchain_community.tools import BaseTool +from langchain_community.tools.gmail.create_draft import GmailCreateDraft +from langchain_community.tools.gmail.get_message import GmailGetMessage +from langchain_community.tools.gmail.get_thread import GmailGetThread +from langchain_community.tools.gmail.search import GmailSearch +from langchain_community.tools.gmail.send_message import GmailSendMessage +from langchain_community.tools.gmail.utils import build_resource_service +from langchain_core.pydantic_v1 import Field + +if TYPE_CHECKING: + # This is for linting and IDE typehints + from googleapiclient.discovery import Resource +else: + try: + # We do this so pydantic can resolve the types when instantiating + from googleapiclient.discovery import Resource + except ImportError: + pass + + +SCOPES = ["https://mail.google.com/"] + + +class GmailToolkit(BaseToolkit): + """Toolkit for interacting with Gmail. + + *Security Note*: This toolkit contains tools that can read and modify + the state of a service; e.g., by reading, creating, updating, deleting + data associated with this service. + + For example, this toolkit can be used to send emails on behalf of the + associated account. + + See https://python.langchain.com/docs/security for more information. + """ + + api_resource: Resource = Field(default_factory=build_resource_service) + + class Config: + """Pydantic config.""" + + arbitrary_types_allowed = True + + def get_tools(self) -> List[BaseTool]: + """Get the tools in the toolkit.""" + return [ + GmailCreateDraft(api_resource=self.api_resource), + GmailSendMessage(api_resource=self.api_resource), + GmailSearch(api_resource=self.api_resource), + GmailGetMessage(api_resource=self.api_resource), + GmailGetThread(api_resource=self.api_resource), + ] diff --git a/libs/tools/langchain_google_tools/vertex_ai_search.py b/libs/tools/langchain_google_tools/vertex_ai_search.py new file mode 100644 index 00000000..42cfc93a --- /dev/null +++ b/libs/tools/langchain_google_tools/vertex_ai_search.py @@ -0,0 +1,441 @@ +"""Retriever wrapper for Google Vertex AI Search. + +Set the following environment variables before the tests: +export PROJECT_ID=... - set to your Google Cloud project ID +export DATA_STORE_ID=... - the ID of the search engine to use for the test +""" + +from __future__ import annotations + +import json +import warnings +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence + +from google.api_core.client_options import ClientOptions +from google.api_core.exceptions import InvalidArgument +from google.protobuf.json_format import MessageToDict +from langchain_community.utilities.vertexai import get_client_info +from langchain_core.callbacks import CallbackManagerForRetrieverRun +from langchain_core.documents import Document +from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator +from langchain_core.retrievers import BaseRetriever +from langchain_core.utils import get_from_dict_or_env + +if TYPE_CHECKING: + from google.cloud.discoveryengine_v1beta import ( + ConversationalSearchServiceClient, + SearchRequest, + SearchResult, + SearchServiceClient, + ) + + +class _BaseVertexAISearchRetriever(BaseModel): + project_id: str + """Google Cloud Project ID.""" + data_store_id: str + """Vertex AI Search data store ID.""" + location_id: str = "global" + """Vertex AI Search data store location.""" + serving_config_id: str = "default_config" + """Vertex AI Search serving config ID.""" + credentials: Any = None + """The default custom credentials (google.auth.credentials.Credentials) to use + when making API calls. If not provided, credentials will be ascertained from + the environment.""" + engine_data_type: int = Field(default=0, ge=0, le=2) + """ Defines the Vertex AI Search data type + 0 - Unstructured data + 1 - Structured data + 2 - Website data + """ + + @root_validator(pre=True) + def validate_environment(cls, values: Dict) -> Dict: + """Validates the environment.""" + try: + from google.cloud import discoveryengine_v1beta # noqa: F401 + except ImportError as exc: + raise ImportError( + "google.cloud.discoveryengine is not installed." + "Please install it with pip install " + "google-cloud-discoveryengine>=0.11.0" + ) from exc + + values["project_id"] = get_from_dict_or_env(values, "project_id", "PROJECT_ID") + + try: + # For backwards compatibility + search_engine_id = get_from_dict_or_env( + values, "search_engine_id", "SEARCH_ENGINE_ID" + ) + + if search_engine_id: + warnings.warn( + "The `search_engine_id` parameter is deprecated. Use `data_store_id` instead.", # noqa: E501 + DeprecationWarning, + ) + values["data_store_id"] = search_engine_id + except: # noqa: E722 + pass + + values["data_store_id"] = get_from_dict_or_env( + values, "data_store_id", "DATA_STORE_ID" + ) + + return values + + @property + def client_options(self) -> "ClientOptions": + return ClientOptions( + api_endpoint=( + f"{self.location_id}-discoveryengine.googleapis.com" + if self.location_id != "global" + else None + ) + ) + + def _convert_structured_search_response( + self, results: Sequence[SearchResult] + ) -> List[Document]: + """Converts a sequence of search results to a list of LangChain documents.""" + documents: List[Document] = [] + + for result in results: + document_dict = MessageToDict( + result.document._pb, preserving_proto_field_name=True + ) + + documents.append( + Document( + page_content=json.dumps(document_dict.get("struct_data", {})), + metadata={"id": document_dict["id"], "name": document_dict["name"]}, + ) + ) + + return documents + + def _convert_unstructured_search_response( + self, results: Sequence[SearchResult], chunk_type: str + ) -> List[Document]: + """Converts a sequence of search results to a list of LangChain documents.""" + documents: List[Document] = [] + + for result in results: + document_dict = MessageToDict( + result.document._pb, preserving_proto_field_name=True + ) + derived_struct_data = document_dict.get("derived_struct_data") + if not derived_struct_data: + continue + + doc_metadata = document_dict.get("struct_data", {}) + doc_metadata["id"] = document_dict["id"] + + if chunk_type not in derived_struct_data: + continue + + for chunk in derived_struct_data[chunk_type]: + doc_metadata["source"] = derived_struct_data.get("link", "") + + if chunk_type == "extractive_answers": + doc_metadata["source"] += f":{chunk.get('pageNumber', '')}" + + documents.append( + Document( + page_content=chunk.get("content", ""), metadata=doc_metadata + ) + ) + + return documents + + def _convert_website_search_response( + self, results: Sequence[SearchResult], chunk_type: str + ) -> List[Document]: + """Converts a sequence of search results to a list of LangChain documents.""" + documents: List[Document] = [] + + for result in results: + document_dict = MessageToDict( + result.document._pb, preserving_proto_field_name=True + ) + derived_struct_data = document_dict.get("derived_struct_data") + if not derived_struct_data: + continue + + doc_metadata = document_dict.get("struct_data", {}) + doc_metadata["id"] = document_dict["id"] + doc_metadata["source"] = derived_struct_data.get("link", "") + + if chunk_type not in derived_struct_data: + continue + + text_field = "snippet" if chunk_type == "snippets" else "content" + + for chunk in derived_struct_data[chunk_type]: + documents.append( + Document( + page_content=chunk.get(text_field, ""), metadata=doc_metadata + ) + ) + + if not documents: + print(f"No {chunk_type} could be found.") # noqa: T201 + if chunk_type == "extractive_answers": + print( # noqa: T201 + "Make sure that your data store is using Advanced Website " + "Indexing.\n" + "https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#advanced-website-indexing" # noqa: E501 + ) + + return documents + + +class VertexAISearchRetriever(BaseRetriever, _BaseVertexAISearchRetriever): + """`Google Vertex AI Search` retriever. + + For a detailed explanation of the Vertex AI Search concepts + and configuration parameters, refer to the product documentation. + https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction + """ + + filter: Optional[str] = None + """Filter expression.""" + get_extractive_answers: bool = False + """If True return Extractive Answers, otherwise return Extractive Segments or Snippets.""" # noqa: E501 + max_documents: int = Field(default=5, ge=1, le=100) + """The maximum number of documents to return.""" + max_extractive_answer_count: int = Field(default=1, ge=1, le=5) + """The maximum number of extractive answers returned in each search result. + At most 5 answers will be returned for each SearchResult. + """ + max_extractive_segment_count: int = Field(default=1, ge=1, le=1) + """The maximum number of extractive segments returned in each search result. + Currently one segment will be returned for each SearchResult. + """ + query_expansion_condition: int = Field(default=1, ge=0, le=2) + """Specification to determine under which conditions query expansion should occur. + 0 - Unspecified query expansion condition. In this case, server behavior defaults + to disabled + 1 - Disabled query expansion. Only the exact search query is used, even if + SearchResponse.total_size is zero. + 2 - Automatic query expansion built by the Search API. + """ + spell_correction_mode: int = Field(default=2, ge=0, le=2) + """Specification to determine under which conditions query expansion should occur. + 0 - Unspecified spell correction mode. In this case, server behavior defaults + to auto. + 1 - Suggestion only. Search API will try to find a spell suggestion if there is any + and put in the `SearchResponse.corrected_query`. + The spell suggestion will not be used as the search query. + 2 - Automatic spell correction built by the Search API. + Search will be based on the corrected query if found. + """ + + _client: SearchServiceClient + _serving_config: str + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.ignore + arbitrary_types_allowed = True + underscore_attrs_are_private = True + + def __init__(self, **kwargs: Any) -> None: + """Initializes private fields.""" + try: + from google.cloud.discoveryengine_v1beta import SearchServiceClient + except ImportError as exc: + raise ImportError( + "google.cloud.discoveryengine is not installed." + "Please install it with pip install google-cloud-discoveryengine" + ) from exc + + super().__init__(**kwargs) + + # For more information, refer to: + # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store + self._client = SearchServiceClient( + credentials=self.credentials, + client_options=self.client_options, + client_info=get_client_info(module="vertex-ai-search"), + ) + + self._serving_config = self._client.serving_config_path( + project=self.project_id, + location=self.location_id, + data_store=self.data_store_id, + serving_config=self.serving_config_id, + ) + + def _create_search_request(self, query: str) -> SearchRequest: + """Prepares a SearchRequest object.""" + from google.cloud.discoveryengine_v1beta import SearchRequest + + query_expansion_spec = SearchRequest.QueryExpansionSpec( + condition=self.query_expansion_condition, + ) + + spell_correction_spec = SearchRequest.SpellCorrectionSpec( + mode=self.spell_correction_mode + ) + + if self.engine_data_type == 0: + if self.get_extractive_answers: + extractive_content_spec = ( + SearchRequest.ContentSearchSpec.ExtractiveContentSpec( + max_extractive_answer_count=self.max_extractive_answer_count, + ) + ) + else: + extractive_content_spec = ( + SearchRequest.ContentSearchSpec.ExtractiveContentSpec( + max_extractive_segment_count=self.max_extractive_segment_count, + ) + ) + content_search_spec = SearchRequest.ContentSearchSpec( + extractive_content_spec=extractive_content_spec + ) + elif self.engine_data_type == 1: + content_search_spec = None + elif self.engine_data_type == 2: + content_search_spec = SearchRequest.ContentSearchSpec( + extractive_content_spec=SearchRequest.ContentSearchSpec.ExtractiveContentSpec( + max_extractive_answer_count=self.max_extractive_answer_count, + ), + snippet_spec=SearchRequest.ContentSearchSpec.SnippetSpec( + return_snippet=True + ), + ) + else: + raise NotImplementedError( + "Only data store type 0 (Unstructured), 1 (Structured)," + "or 2 (Website) are supported currently." + + f" Got {self.engine_data_type}" + ) + + return SearchRequest( + query=query, + filter=self.filter, + serving_config=self._serving_config, + page_size=self.max_documents, + content_search_spec=content_search_spec, + query_expansion_spec=query_expansion_spec, + spell_correction_spec=spell_correction_spec, + ) + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun + ) -> List[Document]: + """Get documents relevant for a query.""" + + search_request = self._create_search_request(query) + + try: + response = self._client.search(search_request) + except InvalidArgument as exc: + raise type(exc)( + exc.message + + " This might be due to engine_data_type not set correctly." + ) + + if self.engine_data_type == 0: + chunk_type = ( + "extractive_answers" + if self.get_extractive_answers + else "extractive_segments" + ) + documents = self._convert_unstructured_search_response( + response.results, chunk_type + ) + elif self.engine_data_type == 1: + documents = self._convert_structured_search_response(response.results) + elif self.engine_data_type == 2: + chunk_type = ( + "extractive_answers" if self.get_extractive_answers else "snippets" + ) + documents = self._convert_website_search_response( + response.results, chunk_type + ) + else: + raise NotImplementedError( + "Only data store type 0 (Unstructured), 1 (Structured)," + "or 2 (Website) are supported currently." + + f" Got {self.engine_data_type}" + ) + + return documents + + +class VertexAIMultiTurnSearchRetriever(BaseRetriever, _BaseVertexAISearchRetriever): + """`Google Vertex AI Search` retriever for multi-turn conversations.""" + + conversation_id: str = "-" + """Vertex AI Search Conversation ID.""" + + _client: ConversationalSearchServiceClient + _serving_config: str + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.ignore + arbitrary_types_allowed = True + underscore_attrs_are_private = True + + def __init__(self, **kwargs: Any): + super().__init__(**kwargs) + from google.cloud.discoveryengine_v1beta import ( + ConversationalSearchServiceClient, + ) + + self._client = ConversationalSearchServiceClient( + credentials=self.credentials, + client_options=self.client_options, + client_info=get_client_info(module="vertex-ai-search"), + ) + + self._serving_config = self._client.serving_config_path( + project=self.project_id, + location=self.location_id, + data_store=self.data_store_id, + serving_config=self.serving_config_id, + ) + + if self.engine_data_type == 1: + raise NotImplementedError( + "Data store type 1 (Structured)" + "is not currently supported for multi-turn search." + + f" Got {self.engine_data_type}" + ) + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun + ) -> List[Document]: + """Get documents relevant for a query.""" + from google.cloud.discoveryengine_v1beta import ( + ConverseConversationRequest, + TextInput, + ) + + request = ConverseConversationRequest( + name=self._client.conversation_path( + self.project_id, + self.location_id, + self.data_store_id, + self.conversation_id, + ), + serving_config=self._serving_config, + query=TextInput(input=query), + ) + response = self._client.converse_conversation(request) + + if self.engine_data_type == 2: + return self._convert_website_search_response( + response.search_results, "extractive_answers" + ) + + return self._convert_unstructured_search_response( + response.search_results, "extractive_answers" + ) diff --git a/libs/tools/poetry.lock b/libs/tools/poetry.lock new file mode 100644 index 00000000..838bc692 --- /dev/null +++ b/libs/tools/poetry.lock @@ -0,0 +1,1246 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = 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+[tool.poetry.urls] +"Source Code" = "https://github.com/langchain-ai/langchain-google/tree/main/libs/tools" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain-core = "^0.1" +google-api-core = "^2.17.1" +google-api-python-client = "^2.122.0" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +numpy = "^1.22.0" + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] +pillow = "^10.1.0" + + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +types-requests = "^2.28.11.5" +types-google-cloud-ndb = "^2.2.0.1" +types-pillow = "^10.1.0.2" +types-protobuf = "^4.24.0.20240302" + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +pillow = "^10.1.0" +types-requests = "^2.31.0.10" +types-pillow = "^10.1.0.2" +types-google-cloud-ndb = "^2.2.0.1" + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[tool.coverage.run] +omit = ["tests/*"] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/tools/tests/integration_tests/__init__.py b/libs/tools/tests/integration_tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/tools/tests/integration_tests/fake.py b/libs/tools/tests/integration_tests/fake.py new file mode 100644 index 00000000..7961bd04 --- /dev/null +++ b/libs/tools/tests/integration_tests/fake.py @@ -0,0 +1,21 @@ +from typing import List + +import numpy as np +from langchain_core.embeddings import Embeddings +from langchain_core.pydantic_v1 import BaseModel + + +class FakeEmbeddings(Embeddings, BaseModel): + """Fake embedding model.""" + + size: int + """The size of the embedding vector.""" + + def _get_embedding(self) -> List[float]: + return list(np.random.normal(size=self.size)) + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + return [self._get_embedding() for _ in texts] + + def embed_query(self, text: str) -> List[float]: + return self._get_embedding() diff --git a/libs/tools/tests/integration_tests/test_bigquery_vector_search.py b/libs/tools/tests/integration_tests/test_bigquery_vector_search.py new file mode 100644 index 00000000..ee3ba8f6 --- /dev/null +++ b/libs/tools/tests/integration_tests/test_bigquery_vector_search.py @@ -0,0 +1,102 @@ +"""Test BigQuery Vector Search. +In order to run this test, you need to install Google Cloud BigQuery SDK +pip install google-cloud-bigquery +Your end-user credentials would be used to make the calls (make sure you've run +`gcloud auth login` first). +""" + +import os +import uuid + +import pytest + +from langchain_google_tools import BigQueryVectorSearch +from tests.integration_tests.fake import FakeEmbeddings + +TEST_TABLE_NAME = "langchain_test_table" + + +@pytest.fixture(scope="class") +def store(request: pytest.FixtureRequest) -> BigQueryVectorSearch: + """BigQueryVectorStore tests context. + + In order to run this test, you define PROJECT environment variable + with GCP project id. + + Example: + export PROJECT=... + """ + from google.cloud import bigquery + + bigquery.Client(location="US").create_dataset( + TestBigQueryVectorStore.dataset_name, exists_ok=True + ) + TestBigQueryVectorStore.store = BigQueryVectorSearch( + project_id=os.environ.get("PROJECT", None), # type: ignore[arg-type] + embedding=FakeEmbeddings(), + dataset_name=TestBigQueryVectorStore.dataset_name, + table_name=TEST_TABLE_NAME, + ) + TestBigQueryVectorStore.store.add_texts( + TestBigQueryVectorStore.texts, TestBigQueryVectorStore.metadatas + ) + + def teardown() -> None: + bigquery.Client(location="US").delete_dataset( + TestBigQueryVectorStore.dataset_name, + delete_contents=True, + not_found_ok=True, + ) + + request.addfinalizer(teardown) + return TestBigQueryVectorStore.store + + +class TestBigQueryVectorStore: + """BigQueryVectorStore tests class.""" + + dataset_name = uuid.uuid4().hex + store: BigQueryVectorSearch + texts = ["apple", "ice cream", "Saturn", "candy", "banana"] + metadatas = [ + { + "kind": "fruit", + }, + { + "kind": "treat", + }, + { + "kind": "planet", + }, + { + "kind": "treat", + }, + { + "kind": "fruit", + }, + ] + + def test_semantic_search(self, store: BigQueryVectorSearch) -> None: + """Test on semantic similarity.""" + docs = store.similarity_search("food", k=4) + print(docs) # noqa: T201 + kinds = [d.metadata["kind"] for d in docs] + assert "fruit" in kinds + assert "treat" in kinds + assert "planet" not in kinds + + def test_semantic_search_filter_fruits(self, store: BigQueryVectorSearch) -> None: + """Test on semantic similarity with metadata filter.""" + docs = store.similarity_search("food", filter={"kind": "fruit"}) + kinds = [d.metadata["kind"] for d in docs] + assert "fruit" in kinds + assert "treat" not in kinds + assert "planet" not in kinds + + def test_get_doc_by_filter(self, store: BigQueryVectorSearch) -> None: + """Test on document retrieval with metadata filter.""" + docs = store.get_documents(filter={"kind": "fruit"}) + kinds = [d.metadata["kind"] for d in docs] + assert "fruit" in kinds + assert "treat" not in kinds + assert "planet" not in kinds diff --git a/libs/tools/tests/integration_tests/test_docai_warehoure_retriever.py b/libs/tools/tests/integration_tests/test_docai_warehoure_retriever.py new file mode 100644 index 00000000..803fd47a --- /dev/null +++ b/libs/tools/tests/integration_tests/test_docai_warehoure_retriever.py @@ -0,0 +1,24 @@ +"""Test Google Cloud Document AI Warehouse retriever.""" +import os + +from langchain_core.documents import Document + +from langchain_google_tools import DocumentAIWarehouseRetriever + + +def test_google_documentai_warehoure_retriever() -> None: + """In order to run this test, you should provide a project_id and user_ldap. + + Example: + export USER_LDAP=... + export PROJECT_NUMBER=... + """ + project_number = os.environ["PROJECT_NUMBER"] + user_ldap = os.environ["USER_LDAP"] + docai_wh_retriever = DocumentAIWarehouseRetriever(project_number=project_number) + documents = docai_wh_retriever.get_relevant_documents( + "What are Alphabet's Other Bets?", user_ldap=user_ldap + ) + assert len(documents) > 0 + for doc in documents: + assert isinstance(doc, Document) diff --git a/libs/tools/tests/integration_tests/test_vertex_ai_search.py b/libs/tools/tests/integration_tests/test_vertex_ai_search.py new file mode 100644 index 00000000..521cfdbc --- /dev/null +++ b/libs/tools/tests/integration_tests/test_vertex_ai_search.py @@ -0,0 +1,44 @@ +"""Test Google Vertex AI Search retriever. + +You need to create a Vertex AI Search app and populate it +with data to run the integration tests. +Follow the instructions in the example notebook: +google_vertex_ai_search.ipynb +to set up the app and configure authentication. + +Set the following environment variables before the tests: +export PROJECT_ID=... - set to your Google Cloud project ID +export DATA_STORE_ID=... - the ID of the search engine to use for the test +""" + + +from langchain_core.documents import Document + +from langchain_google_tools import ( + VertexAIMultiTurnSearchRetriever, + VertexAISearchRetriever, +) + + +def test_google_vertex_ai_search_get_relevant_documents() -> None: + """Test the get_relevant_documents() method.""" + retriever = VertexAIMultiTurnSearchRetriever() + documents = retriever.get_relevant_documents("What are Alphabet's Other Bets?") + assert len(documents) > 0 + for doc in documents: + assert isinstance(doc, Document) + assert doc.page_content + assert doc.metadata["id"] + assert doc.metadata["source"] + + +def test_google_vertex_ai_multiturnsearch_get_relevant_documents() -> None: + """Test the get_relevant_documents() method.""" + retriever = VertexAISearchRetriever() + documents = retriever.get_relevant_documents("What are Alphabet's Other Bets?") + assert len(documents) > 0 + for doc in documents: + assert isinstance(doc, Document) + assert doc.page_content + assert doc.metadata["id"] + assert doc.metadata["source"]