From b624bcc74cddcb5e94f902fa32a03bf9bf0fa5b7 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 08:45:36 +0200 Subject: [PATCH 01/41] working --- .../.gitignore | 153 + .../llama-index-vector-stores-vespa/BUILD | 3 + .../CHANGELOG.md | 9 + .../llama-index-vector-stores-vespa/Makefile | 17 + .../llama-index-vector-stores-vespa/README.md | 3 + .../llama_index/vector_stores/vespa/BUILD | 1 + .../vector_stores/vespa/__init__.py | 5 + .../llama_index/vector_stores/vespa/base.py | 433 ++ .../vector_stores/vespa/vespa_templates.py | 91 + .../poetry.lock | 4564 +++++++++++++++++ .../pyproject.toml | 65 + .../tests/BUILD | 1 + .../tests/__init__.py | 0 13 files changed, 5345 insertions(+) create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/.gitignore create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/CHANGELOG.md create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/Makefile create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/BUILD create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/__init__.py diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/.gitignore b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/CHANGELOG.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/CHANGELOG.md new file mode 100644 index 0000000000000..633b40f215c09 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/CHANGELOG.md @@ -0,0 +1,9 @@ +# CHANGELOG — llama-index-vector-stores-opensearch + +## [0.1.2] + +- Adds OpensearchVectorClient as top-level import + +## [0.1.1] + +- Fixes strict equality in dependency of llama-index-core diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/Makefile b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md new file mode 100644 index 0000000000000..b91ace0ee2d8d --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md @@ -0,0 +1,3 @@ +# LlamaIndex Vector_Stores Integration: Vespa + +See https://docs.vespa.ai/. diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py new file mode 100644 index 0000000000000..ffdeed347bf3c --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py @@ -0,0 +1,5 @@ +from llama_index.vector_stores.vespa.base import ( + VespaVectorStore, +) + +__all__ = ["VespaVectorStore"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py new file mode 100644 index 0000000000000..3a4c877f576b1 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -0,0 +1,433 @@ +"""Vespa vector store.""" + +from typing import Any, List, Optional, Callable + + +from llama_index.core.schema import BaseNode, MetadataMode +from llama_index.core.vector_stores.types import ( + VectorStore, + VectorStoreQuery, + VectorStoreQueryMode, + VectorStoreQueryResult, +) +from llama_index.core.vector_stores.utils import ( + node_to_metadata_dict, + metadata_dict_to_node, +) + +import asyncio +import logging +import json + +try: + from vespa.application import Vespa + from vespa.package import ApplicationPackage + from vespa.io import VespaResponse, VespaQueryResponse + from vespa.deployment import VespaCloud, VespaDocker +except ImportError: + raise ModuleNotFoundError( + "pyvespa not installed. Please install it via `pip install pyvespa`" + ) + +from .vespa_templates import hybrid_template + + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +def callback(response: VespaResponse, id: str): + if not response.is_successful(): + logger.debug( + f"Failed to feed document {id} with status code {response.status_code}: Reason {response.get_json()}" + ) + + +class VespaVectorStore(VectorStore): + """Vespa vector store. + + In this vector store, embeddings and docs are stored in a Vespa application. + + The application must be set up with an embedding field and a doc field. + + During query time, the index queries the Vespa application to get the top k most relevant hits. + + Args: + vespa_application (vespa.application.Vespa): Vespa + instance from `pyvespa` package + schema_name (Optional[str]): Schema name in Vespa application + + Examples: + `pip install llama-index-vector-stores-vespa` + + ```python + from vespa.application import Vespa + + app = Vespa(url="https://api.cord19.vespa.ai") + + vector_store = VespaVectorStore( + vespa_application=app, + ) + ``` + """ + + stores_text: bool = True + is_embedding_query: bool = False + flat_metadata: bool = True + + def __init__( + self, + application_package: ApplicationPackage = hybrid_template, + namespace: str = "default", + default_schema_name: str = "doc", + deployment_target: str = "local", # "local" or "cloud" + local_port: int = 8080, + embeddings_outside_vespa: bool = True, + groupname: Optional[str] = None, + tenant: Optional[str] = None, + application: Optional[str] = None, + key_location: Optional[str] = None, + key_content: Optional[str] = None, + auth_client_token_id: Optional[str] = None, + **kwargs: Any, + ) -> None: + """ + Initialize Vespa vector store. + + Args: + application_package (ApplicationPackage): Application package + deployment_target (str): Deployment target, either `local` or `cloud` + local_port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local` + default_schema_name (str): Schema name in Vespa application + namespace (str): Namespace in Vespa application + embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not. + groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode + tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud` + key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud. + key_content (Optional[str]): Content of the control plane key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available. + auth_client_token_id (Optional[str]): Use token based data plane authentication. This is the token name configured in the Vespa Cloud Console. This is used to configure Vespa services.xml. The token is given read and write permissions. + kwargs (Any): Additional kwargs for Vespa application + + """ + # Verify that application_package is an instance of ApplicationPackage + if not isinstance(application_package, ApplicationPackage): + raise ValueError( + "application_package must be an instance of vespa.package.ApplicationPackage" + ) + # Initialize all parameters + self.application_package = application_package + self.deployment_target = deployment_target + self.default_schema_name = default_schema_name + self.namespace = namespace + self.embeddings_outside_vespa = embeddings_outside_vespa + self.groupname = groupname + self.tenant = tenant + self.key_location = key_location + self.key_content = key_content + self.auth_client_token_id = auth_client_token_id + self.kwargs = kwargs + self.app = self._deploy() + + @property + def client(self) -> Vespa: + """Get client.""" + return self.app + + def _deploy(self) -> Vespa: + if self.deployment_target == "cloud": + app = self._deploy_app_cloud() + elif self.deployment_target == "local": + app = self._deploy_app_local() + else: + raise ValueError( + f"Deployment target {self.deployment_target} not supported. Please choose either `local` or `cloud`." + ) + return app + + def _deploy_app_local(self) -> Vespa: + return VespaDocker(port=8080).deploy(self.application_package) + + def _deploy_app_cloud(self) -> Vespa: + return VespaCloud( + tenant=self.tenant, + application="hybridsearch", + application_package=self.application_package, + key_location=self.key_location, + key_content=self.key_content, + auth_client_token_id=self.auth_client_token_id, + **self.kwargs, + ).deploy() + + def add( + self, + nodes: List[BaseNode], + schema: Optional[str] = None, + callback: Optional[Callable[[VespaResponse, str], None]] = callback, + ) -> List[str]: + """ + Add nodes to vector store. + + Args: + nodes (List[BaseNode]): List of nodes to add + schema (Optional[str]): Schema name in Vespa application to add nodes to. Defaults to `default_schema_name`. + """ + # Create vespa iterable from nodes + ids = [] + data_to_insert = [] + for node in nodes: + metadata = node_to_metadata_dict( + node, remove_text=False, flat_metadata=self.flat_metadata + ) + logger.debug(f"Metadata: {metadata}") + entry = { + "id": node.node_id, + "fields": { + "id": node.node_id, + # TODO: Only if outside. "embedding": node.get_embedding(), + "text": node.get_content(metadata_mode=MetadataMode.NONE) or "", + "metadata": json.dumps(metadata), + }, + } + data_to_insert.append(entry) + ids.append(node.node_id) + + self.app.feed_iterable( + data_to_insert, + schema=schema or self.default_schema_name, + namespace=self.namespace, + operation_type="feed", + callback=callback, + ) + return ids + + async def async_add( + self, + nodes: List[BaseNode], + schema: Optional[str] = None, + callback: Optional[Callable[[VespaResponse, str], None]] = callback, + max_connections: int = 10, + num_concurrent_requests: int = 1000, + total_timeout: int = 60, + **kwargs: Any, + ) -> List[str]: + """ + Add nodes to vector store asynchronously. + + Args: + nodes (List[BaseNode]): List of nodes to add + schema (Optional[str]): Schema name in Vespa application to add nodes to. Defaults to `default_schema_name`. + max_connections (int): Maximum number of connections to Vespa application + num_concurrent_requests (int): Maximum number of concurrent requests + total_timeout (int): Total timeout for all requests + kwargs (Any): Additional kwargs for Vespa application + """ + semaphore = asyncio.Semaphore(max_concurrent_requests) + ids = [] + data_to_insert = [] + for node in nodes: + metadata = node_to_metadata_dict( + node, remove_text=False, flat_metadata=self.flat_metadata + ) + + entry = { + "id": node.node_id, + "fields": { + "embedding": node.get_embedding(), + "body": node.get_content(metadata_mode=MetadataMode.NONE) or "", + **metadata, + }, + } + data_to_insert.append(entry) + ids.append(node.node_id) + + async with self.app.asyncio( + connections=max_connections, total_timeout=total_timeout + ) as async_app: + for doc in data_to_insert: + async with semaphore: + task = asyncio.create_task( + async_app.feed_data_point( + data_id=doc["id"], + fields=doc["fields"], + schema=schema or self.default_schema_name, + namespace=self.namespace, + timeout=10, + ) + ) + tasks.append(task) + + results = await asyncio.wait(tasks, return_when=asyncio.ALL_COMPLETED) + for result in results: + if result.exception(): + raise result.exception + return ids + + def delete( + self, ref_doc_id: str, namespace: Optional[str] = None, **delete_kwargs: Any + ) -> None: + """ + Delete nodes using with ref_doc_id. + """ + self.app.delete_data( + schema=self.default_schema_name, + namespace=namespace or self.namespace, + data_id=ref_doc_id, + kwargs=delete_kwargs, + ) + + async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: + """ + Delete nodes using with ref_doc_id. + NOTE: this is not implemented for all vector stores. If not implemented, + it will just call delete synchronously. + """ + self.delete(ref_doc_id, **delete_kwargs) + + def _create_query_body( + self, + query: VectorStoreQuery, + sources_str: str, + rank_profile: Optional[str] = None, + create_embedding: bool = True, + vector_top_k: int = 10, + ) -> dict: + """ + Create query parameters for Vespa. + + Args: + query (VectorStoreQuery): VectorStoreQuery object + sources_str (str): Sources string + rank_profile (Optional[str]): Rank profile to use. If not provided, default rank profile is used. + create_embedding (bool): Whether to create embedding + vector_top_k (int): Number of top k vectors to return + + Returns: + dict: Query parameters + """ + logging.info(f"Query: {query}") + if query.filters: + logger.warning("Filter support not implemented yet. Will be ignored.") + if query.alpha: + logger.warning( + "Alpha support not implemented. Must be defined in Vespa rank profile. " + "See for example https://pyvespa.readthedocs.io/en/latest/examples/evaluating-with-snowflake-arctic-embed.html" + ) + + input_embedding = ( + f"embed({query.query_str})" if create_embedding else query.query_embedding + ) + # if input_embedding is None and not create_embedding: + # raise ValueError( + # "Input embedding must be provided if embeddings are not created outside Vespa" + # ) + + base_params = { + "hits": query.similarity_top_k, + "ranking.profile": rank_profile or self._default_rank_profile(query.mode), + "query": query.query_str, + "tracelevel": 9, + } + logger.debug(query.mode) + if query.mode in [ + VectorStoreQueryMode.TEXT_SEARCH, + VectorStoreQueryMode.DEFAULT, + ]: + query_params = {"yql": f"select * from {sources_str} where userQuery()"} + elif query.mode in [ + VectorStoreQueryMode.SEMANTIC_HYBRID, + VectorStoreQueryMode.HYBRID, + ]: + if not query.embedding_field: + embedding_field = "embedding" + logger.warning( + f"Embedding field not provided. Using default embedding field {embedding_field}" + ) + query_params = { + "yql": f"select * from {sources_str} where {self._build_query_filter(query.mode, vector_top_k)}", + "input.query(q)": input_embedding, + } + else: + raise NotImplementedError( + f"Query mode {query.mode} not implemented for Vespa yet. Contributions are welcome!" + ) + + return {**base_params, **query_params} + + def _default_rank_profile(self, mode): + return { + VectorStoreQueryMode.TEXT_SEARCH: "bm25", + VectorStoreQueryMode.SEMANTIC_HYBRID: "semantic_hybrid", + VectorStoreQueryMode.HYBRID: "hybrid", + VectorStoreQueryMode.DEFAULT: "bm25", + }.get(mode) + + def _build_query_filter(self, mode, embedding_field, vector_top_k): + if mode == VectorStoreQueryMode.HYBRID: + return f"{{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q) or userQuery()" + return f"{{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q)" + + def query( + self, + query: VectorStoreQuery, + sources: Optional[List[str]] = None, + rank_profile: Optional[str] = None, + vector_top_k: int = 10, + **kwargs: Any, + ) -> VectorStoreQueryResult: + """Query vector store.""" + logger.info(f"Query: {query}") + sources_str = ",".join(sources) if sources else "sources *" + mode = query.mode + body = self._create_query_body( + query=query, + sources_str=sources_str, + rank_profile=rank_profile, + create_embedding=not self.embeddings_outside_vespa, + vector_top_k=vector_top_k, + ) + logger.info(f"Query body:\n {body}") + logger.debug(body) + with self.app.syncio() as session: + response = session.query( + body=body, + # schema=self.default_schema_name, + # namespace=self.namespace, + ) + if not response.is_successful(): + raise ValueError( + f"Query request failed: {response.status_code}, response payload: {response.get_json()}" + ) + logger.debug("Response:") + logger.debug(response.json) + logger.debug("Hits:") + logger.debug(response.hits) + nodes = [] + ids: List[str] = [] + similarities: List[float] = [] + for hit in response.hits: + response_fields: dict = hit.get("fields", {}) + metadata = response_fields.get("metadata", {}) + metadata = json.loads(metadata) + logger.info(f"Metadata: {metadata}") + node = metadata_dict_to_node(metadata) + text = response_fields.get("body", "") + node.set_content(text) + nodes.append(node) + ids.append(response_fields.get("id")) + similarities.append(hit["relevance"]) + return VectorStoreQueryResult(nodes=nodes, ids=ids, similarities=similarities) + + async def aquery( + self, query: VectorStoreQuery, **kwargs: Any + ) -> VectorStoreQueryResult: + """ + Asynchronously query vector store. + NOTE: this is not implemented for all vector stores. If not implemented, + it will just call query synchronously. + """ + return self.query(query, **kwargs) + + def persist( + self, + ) -> None: + return NotImplemented("Persist is not implemented for VespaVectorStore") diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py new file mode 100644 index 0000000000000..f17b7c6625839 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py @@ -0,0 +1,91 @@ +from vespa.package import ( + ApplicationPackage, + Field, + Schema, + Document, + HNSW, + RankProfile, + Component, + Parameter, + FieldSet, + GlobalPhaseRanking, + Function, +) + +hybrid_template = ApplicationPackage( + name="hybridsearch", + schema=[ + Schema( + name="doc", + document=Document( + fields=[ + Field(name="id", type="string", indexing=["summary"]), + Field(name="metadata", type="string", indexing=["summary"]), + Field( + name="text", + type="string", + indexing=["index", "summary"], + index="enable-bm25", + bolding=True, + ), + Field( + name="embedding", + type="tensor(x[384])", + indexing=[ + "input text", + "embed", + "index", + "attribute", + ], + ann=HNSW(distance_metric="angular"), + is_document_field=False, + ), + ] + ), + fieldsets=[FieldSet(name="default", fields=["text", "metadata"])], + rank_profiles=[ + RankProfile( + name="bm25", + inputs=[("query(q)", "tensor(x[384])")], + functions=[Function(name="bm25sum", expression="bm25(text)")], + first_phase="bm25sum", + ), + RankProfile( + name="semantic", + inputs=[("query(q)", "tensor(x[384])")], + first_phase="closeness(field, embedding)", + ), + RankProfile( + name="fusion", + inherits="bm25", + inputs=[("query(q)", "tensor(x[384])")], + first_phase="closeness(field, embedding)", + global_phase=GlobalPhaseRanking( + expression="reciprocal_rank_fusion(bm25sum, closeness(field, embedding))", + rerank_count=1000, + ), + ), + ], + ) + ], + components=[ + Component( + id="e5", + type="hugging-face-embedder", + parameters=[ + Parameter( + "transformer-model", + { + "url": "https://github.com/vespa-engine/sample-apps/raw/master/simple-semantic-search/model/e5-small-v2-int8.onnx" + }, + ), + Parameter( + "tokenizer-model", + { + "url": "https://raw.githubusercontent.com/vespa-engine/sample-apps/master/simple-semantic-search/model/tokenizer.json" + }, + ), + ], + ) + ], +) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock new file mode 100644 index 0000000000000..284ee87b190d8 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock @@ -0,0 +1,4564 @@ +# This file is automatically @generated by 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"pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<4.0" +content-hash = "7ac7522a61395fcf723c8b5be5c70113c338f9b5734a6a7959a69975e6105441" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml new file mode 100644 index 0000000000000..b89ab8ee736d0 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml @@ -0,0 +1,65 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.vector_stores.vespa" + +[tool.llamahub.class_authors] +VespaVectorStore = "llama-index" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Thomas Thoresen "] +description = "llama-index vector_stores vespa integration" +exclude = ["**/BUILD"] +license = "MIT" +name = "llama-index-vector-stores-vespa" +readme = "README.md" +version = "0.0.1" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.10.0" +llama-index-readers-file = "^0.1.0" +pyvespa = "^0.40.0" + + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d From 491c08eed06dcc8dc8cee56179dfd514325eefbc Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 13:22:56 +0200 Subject: [PATCH 02/41] fixes to base --- .../llama_index/vector_stores/vespa/base.py | 75 +++++++++++++------ 1 file changed, 53 insertions(+), 22 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 3a4c877f576b1..cf9f2a1fbc6db 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -81,8 +81,9 @@ def __init__( namespace: str = "default", default_schema_name: str = "doc", deployment_target: str = "local", # "local" or "cloud" - local_port: int = 8080, - embeddings_outside_vespa: bool = True, + port: int = 8080, + embeddings_outside_vespa: bool = False, + url: Optional[str] = None, groupname: Optional[str] = None, tenant: Optional[str] = None, application: Optional[str] = None, @@ -97,10 +98,11 @@ def __init__( Args: application_package (ApplicationPackage): Application package deployment_target (str): Deployment target, either `local` or `cloud` - local_port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local` + port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local` default_schema_name (str): Schema name in Vespa application namespace (str): Namespace in Vespa application embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not. + url (Optional[str]): URL of deployed Vespa application. groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud` key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud. @@ -114,25 +116,44 @@ def __init__( raise ValueError( "application_package must be an instance of vespa.package.ApplicationPackage" ) + if application_package == hybrid_template: + logger.info( + "Using default hybrid template. Please make sure that the Vespa application is set up with the correct schema and rank profile." + ) # Initialize all parameters self.application_package = application_package self.deployment_target = deployment_target self.default_schema_name = default_schema_name self.namespace = namespace self.embeddings_outside_vespa = embeddings_outside_vespa + self.port = port + self.url = url self.groupname = groupname self.tenant = tenant self.key_location = key_location self.key_content = key_content self.auth_client_token_id = auth_client_token_id self.kwargs = kwargs - self.app = self._deploy() + if self.url is None: + self.app = self._deploy() + else: + self.app = self._try_get_running_app() @property def client(self) -> Vespa: """Get client.""" return self.app + def _try_get_running_app(self) -> Vespa: + app = Vespa(url=f"{self.url}:{self.port}") + status = app.get_application_status() + if status.status_code == 200: + return app + else: + raise ConnectionError( + f"Vespa application not running on url {self.url} and port {self.port}. Please start Vespa application first." + ) + def _deploy(self) -> Vespa: if self.deployment_target == "cloud": app = self._deploy_app_cloud() @@ -304,7 +325,7 @@ def _create_query_body( Returns: dict: Query parameters """ - logging.info(f"Query: {query}") + logger.info(f"Query: {query}") if query.filters: logger.warning("Filter support not implemented yet. Will be ignored.") if query.alpha: @@ -313,9 +334,6 @@ def _create_query_body( "See for example https://pyvespa.readthedocs.io/en/latest/examples/evaluating-with-snowflake-arctic-embed.html" ) - input_embedding = ( - f"embed({query.query_str})" if create_embedding else query.query_embedding - ) # if input_embedding is None and not create_embedding: # raise ValueError( # "Input embedding must be provided if embeddings are not created outside Vespa" @@ -323,7 +341,8 @@ def _create_query_body( base_params = { "hits": query.similarity_top_k, - "ranking.profile": rank_profile or self._default_rank_profile(query.mode), + "ranking.profile": rank_profile + or self._get_default_rank_profile(query.mode), "query": query.query_str, "tracelevel": 9, } @@ -343,8 +362,12 @@ def _create_query_body( f"Embedding field not provided. Using default embedding field {embedding_field}" ) query_params = { - "yql": f"select * from {sources_str} where {self._build_query_filter(query.mode, vector_top_k)}", - "input.query(q)": input_embedding, + "yql": f"select * from {sources_str} where {self._build_query_filter(query.mode, embedding_field, vector_top_k, query.similarity_top_k)}", + "input.query(q)": ( + f"embed({query.query_str})" + if create_embedding + else query.query_embedding + ), } else: raise NotImplementedError( @@ -353,18 +376,27 @@ def _create_query_body( return {**base_params, **query_params} - def _default_rank_profile(self, mode): + def _get_default_rank_profile(self, mode): return { VectorStoreQueryMode.TEXT_SEARCH: "bm25", - VectorStoreQueryMode.SEMANTIC_HYBRID: "semantic_hybrid", - VectorStoreQueryMode.HYBRID: "hybrid", + VectorStoreQueryMode.SEMANTIC_HYBRID: "fusion", + VectorStoreQueryMode.HYBRID: "fusion", VectorStoreQueryMode.DEFAULT: "bm25", }.get(mode) - def _build_query_filter(self, mode, embedding_field, vector_top_k): - if mode == VectorStoreQueryMode.HYBRID: - return f"{{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q) or userQuery()" - return f"{{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q)" + def _build_query_filter( + self, mode, embedding_field, vector_top_k, similarity_top_k + ): + """ + Build query filter for Vespa query. + The part after "select * from {sources_str} where" in the query. + """ + if mode == VectorStoreQueryMode.SEMANTIC_HYBRID: + return f"rank({targetHits:{vector_top_k}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}" + elif mode == VectorStoreQueryMode.HYBRID: + return f'title contains "vegetable" and rank({targetHits:{vector_top_k}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}' + else: + raise ValueError(f"Query mode {mode} not supported.") def query( self, @@ -375,7 +407,7 @@ def query( **kwargs: Any, ) -> VectorStoreQueryResult: """Query vector store.""" - logger.info(f"Query: {query}") + logger.debug(f"Query: {query}") sources_str = ",".join(sources) if sources else "sources *" mode = query.mode body = self._create_query_body( @@ -385,8 +417,7 @@ def query( create_embedding=not self.embeddings_outside_vespa, vector_top_k=vector_top_k, ) - logger.info(f"Query body:\n {body}") - logger.debug(body) + logger.debug(f"Query body:\n {body}") with self.app.syncio() as session: response = session.query( body=body, @@ -408,7 +439,7 @@ def query( response_fields: dict = hit.get("fields", {}) metadata = response_fields.get("metadata", {}) metadata = json.loads(metadata) - logger.info(f"Metadata: {metadata}") + logger.debug(f"Metadata: {metadata}") node = metadata_dict_to_node(metadata) text = response_fields.get("body", "") node.set_content(text) From 7f5fd2f3ed9f09e3584c0d2218f9d546954f13ff Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 13:23:29 +0200 Subject: [PATCH 03/41] add notebook draft --- .../vector_stores/VespaIndexDemo.ipynb | 484 ++++++++++++++++++ 1 file changed, 484 insertions(+) create mode 100644 docs/docs/examples/vector_stores/VespaIndexDemo.ipynb diff --git a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb new file mode 100644 index 0000000000000..991d9b6057b1b --- /dev/null +++ b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "23cf319b", + "metadata": {}, + "source": [ + "\"Open\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "307804a3-c02b-4a57-ac0d-172c30ddc851", + "metadata": {}, + "source": [ + "# Vespa Vector Store demo\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5508d8ac", + "metadata": {}, + "source": [ + "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0beb6603", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-vector-stores-vespa" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b05d5956", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index pyvespa" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396", + "metadata": {}, + "source": [ + "#### Setting up API key\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "08ad68ce", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import openai\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n", + "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eccceb71", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import sys\n", + "\n", + "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", + "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8ee4473a-094f-4d0a-a825-e1213db07240", + "metadata": {}, + "source": [ + "#### Load documents, build the VectorStoreIndex\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0a2bcc07", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.vector_stores.vespa import VespaVectorStore\n", + "from IPython.display import Markdown, display" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "3a41a70d", + "metadata": {}, + "source": [ + "## Defining some sample data\n", + "\n", + "Let's insert some documents.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "df6b6d46", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.schema import TextNode\n", + "\n", + "nodes = [\n", + " TextNode(\n", + " text=\"The Shawshank Redemption\",\n", + " metadata={\n", + " \"author\": \"Stephen King\",\n", + " \"theme\": \"Friendship\",\n", + " \"year\": 1994,\n", + " },\n", + " ),\n", + " TextNode(\n", + " text=\"The Godfather\",\n", + " metadata={\n", + " \"director\": \"Francis Ford Coppola\",\n", + " \"theme\": \"Mafia\",\n", + " \"year\": 1972,\n", + " },\n", + " ),\n", + " TextNode(\n", + " text=\"Inception\",\n", + " metadata={\n", + " \"director\": \"Christopher Nolan\",\n", + " \"theme\": \"Fiction\",\n", + " \"year\": 2010,\n", + " },\n", + " ),\n", + " TextNode(\n", + " text=\"To Kill a Mockingbird\",\n", + " metadata={\n", + " \"author\": \"Harper Lee\",\n", + " \"theme\": \"Mafia\",\n", + " \"year\": 1960,\n", + " },\n", + " ),\n", + " TextNode(\n", + " text=\"1984\",\n", + " metadata={\n", + " \"author\": \"George Orwell\",\n", + " \"theme\": \"Totalitarianism\",\n", + " \"year\": 1949,\n", + " },\n", + " ),\n", + " TextNode(\n", + " text=\"The Great Gatsby\",\n", + " metadata={\n", + " \"author\": \"F. Scott Fitzgerald\",\n", + " \"theme\": \"The American Dream\",\n", + " \"year\": 1925,\n", + " },\n", + " ),\n", + " TextNode(\n", + " text=\"Harry Potter and the Sorcerer's Stone\",\n", + " metadata={\n", + " \"author\": \"J.K. Rowling\",\n", + " \"theme\": \"Fiction\",\n", + " \"year\": 1997,\n", + " },\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "30b0b2e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for configuration server, 0/300 seconds...\n", + "Waiting for configuration server, 5/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 0/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 5/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 10/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 15/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 20/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 25/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 30/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 35/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Waiting for application status, 40/300 seconds...\n", + "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", + "Application is up!\n", + "Finished deployment.\n" + ] + } + ], + "source": [ + "from llama_index.core import StorageContext\n", + "\n", + "vector_store = VespaVectorStore()\n", + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "index = VectorStoreIndex(nodes, storage_context=storage_context)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bfe83ebf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + ] + }, + { + "data": { + "text/plain": [ + "[NodeWithScore(node=TextNode(id_='3032156b-21a9-4ef8-95c6-9809e71b5e07', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retriever = index.as_retriever()\n", + "retriever.retrieve(\"Who directed inception?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c1bd18f8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + ] + }, + { + "data": { + "text/plain": [ + "Response(response='Christopher Nolan', source_nodes=[NodeWithScore(node=TextNode(id_='3032156b-21a9-4ef8-95c6-9809e71b5e07', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)], metadata={'3032156b-21a9-4ef8-95c6-9809e71b5e07': {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}})" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query_engine = index.as_query_engine()\n", + "query_engine.query(\"Who directed inception?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0663ab38", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:llama_index.vector_stores.vespa.base:Filter support not implemented yet. Will be ignored.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filter support not implemented yet. Will be ignored.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Metadata: {'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997, '_node_content': '{\"id_\": \"6be7efdc-8c99-4deb-bfbb-46bd6920e69e\", \"embedding\": null, \"metadata\": {\"author\": \"J.K. Rowling\", \"theme\": \"Fiction\", \"year\": 1997}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Harry Potter and the Sorcerer\\'s Stone\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metadata: {'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997, '_node_content': '{\"id_\": \"6be7efdc-8c99-4deb-bfbb-46bd6920e69e\", \"embedding\": null, \"metadata\": {\"author\": \"J.K. Rowling\", \"theme\": \"Fiction\", \"year\": 1997}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Harry Potter and the Sorcerer\\'s Stone\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + ] + }, + { + "data": { + "text/plain": [ + "[NodeWithScore(node=TextNode(id_='6be7efdc-8c99-4deb-bfbb-46bd6920e69e', embedding=None, metadata={'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.1663224434456927)]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from llama_index.core.vector_stores import FilterOperator, FilterCondition\n", + "\n", + "filters = MetadataFilters(\n", + " filters=[\n", + " MetadataFilter(key=\"theme\", value=\"Fiction\"),\n", + " MetadataFilter(key=\"year\", value=1997, operator=FilterOperator.GT),\n", + " ],\n", + " condition=FilterCondition.OR,\n", + ")\n", + "\n", + "retriever = index.as_retriever(filters=filters)\n", + "retriever.retrieve(\"Harry Potter?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fb9bc14", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 7c46e5e6a63550f87945f7c7d95b98dcc1945b4c Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 13:30:09 +0200 Subject: [PATCH 04/41] more text --- .../vector_stores/VespaIndexDemo.ipynb | 121 +++++++++++++++++- 1 file changed, 119 insertions(+), 2 deletions(-) diff --git a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb index 991d9b6057b1b..e362144756e61 100644 --- a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb @@ -185,6 +185,107 @@ "]" ] }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7a9e71f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;31mInit signature:\u001b[0m\n", + "\u001b[0mVespaVectorStore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mapplication_package\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mvespa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mApplicationPackage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mApplicationPackage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hybridsearch'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mSchema\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'doc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDocument\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mField\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'id'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'string'\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mQueryProfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mQueryProfileType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mnamespace\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'default'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mdefault_schema_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'doc'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mdeployment_target\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'local'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m8080\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0membeddings_outside_vespa\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mgroupname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mtenant\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mapplication\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mkey_location\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mkey_content\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0mauth_client_token_id\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mDocstring:\u001b[0m \n", + "Vespa vector store.\n", + "\n", + "In this vector store, embeddings and docs are stored in a Vespa application.\n", + "\n", + "The application must be set up with an embedding field and a doc field.\n", + "\n", + "During query time, the index queries the Vespa application to get the top k most relevant hits.\n", + "\n", + "Args:\n", + " vespa_application (vespa.application.Vespa): Vespa\n", + " instance from `pyvespa` package\n", + " schema_name (Optional[str]): Schema name in Vespa application\n", + "\n", + "Examples:\n", + " `pip install llama-index-vector-stores-vespa`\n", + "\n", + " ```python\n", + " from vespa.application import Vespa\n", + "\n", + " app = Vespa(url=\"https://api.cord19.vespa.ai\")\n", + "\n", + " vector_store = VespaVectorStore(\n", + " vespa_application=app,\n", + " )\n", + " ```\n", + "\u001b[1;31mInit docstring:\u001b[0m\n", + "Initialize Vespa vector store.\n", + "\n", + "Args:\n", + " application_package (ApplicationPackage): Application package\n", + " deployment_target (str): Deployment target, either `local` or `cloud`\n", + " port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local`\n", + " default_schema_name (str): Schema name in Vespa application\n", + " namespace (str): Namespace in Vespa application\n", + " embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not.\n", + " url (Optional[str]): URL of deployed Vespa application.\n", + " groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode\n", + " tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud`\n", + " key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud.\n", + " key_content (Optional[str]): Content of the control plane key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available.\n", + " auth_client_token_id (Optional[str]): Use token based data plane authentication. This is the token name configured in the Vespa Cloud Console. This is used to configure Vespa services.xml. The token is given read and write permissions.\n", + " kwargs (Any): Additional kwargs for Vespa application\n", + "\u001b[1;31mFile:\u001b[0m c:\\users\\thoma\\documents\\repos\\llama_index\\llama-index-integrations\\vector_stores\\llama-index-vector-stores-vespa\\llama_index\\vector_stores\\vespa\\base.py\n", + "\u001b[1;31mType:\u001b[0m _ProtocolMeta\n", + "\u001b[1;31mSubclasses:\u001b[0m " + ] + } + ], + "source": [ + "?VespaVectorStore" + ] + }, + { + "cell_type": "markdown", + "id": "31fe4378", + "metadata": {}, + "source": [ + "### Initilizing the VespaVectorStore\n" + ] + }, + { + "cell_type": "markdown", + "id": "a0be7d09", + "metadata": {}, + "source": [ + "TODO:\n", + "\n", + "- Explain template used\n", + "- Option to define your own application package\n", + "- Option to use own embedding model (also outside), but must then change template\n", + "- Link to pyvespa documentation\n" + ] + }, { "cell_type": "code", "execution_count": 18, @@ -229,6 +330,14 @@ "index = VectorStoreIndex(nodes, storage_context=storage_context)" ] }, + { + "cell_type": "markdown", + "id": "77d2528e", + "metadata": {}, + "source": [ + "## As retriever\n" + ] + }, { "cell_type": "code", "execution_count": 19, @@ -295,9 +404,17 @@ "retriever.retrieve(\"Who directed inception?\")" ] }, + { + "cell_type": "markdown", + "id": "c8aa36e8", + "metadata": {}, + "source": [ + "### As query engine\n" + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "c1bd18f8", "metadata": {}, "outputs": [ @@ -351,7 +468,7 @@ "Response(response='Christopher Nolan', source_nodes=[NodeWithScore(node=TextNode(id_='3032156b-21a9-4ef8-95c6-9809e71b5e07', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)], metadata={'3032156b-21a9-4ef8-95c6-9809e71b5e07': {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}})" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } From 8dd6985666ce51f62c0c00369263fa47b44639d5 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 13:33:46 +0200 Subject: [PATCH 05/41] better doc and fix targetHits --- .../llama_index/vector_stores/vespa/base.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index cf9f2a1fbc6db..1af5c1e27aede 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -61,13 +61,15 @@ class VespaVectorStore(VectorStore): `pip install llama-index-vector-stores-vespa` ```python - from vespa.application import Vespa + from llama_index.core import VectorStoreIndex + from llama_index.vector_stores.vespa import VespaVectorStore - app = Vespa(url="https://api.cord19.vespa.ai") + vector_store = VespaVectorStore() + storage_context = StorageContext.from_defaults(vector_store=vector_store) + index = VectorStoreIndex(nodes, storage_context=storage_context) + retriever = index.as_retriever() + retriever.retrieve("Who directed inception?") - vector_store = VespaVectorStore( - vespa_application=app, - ) ``` """ @@ -392,9 +394,9 @@ def _build_query_filter( The part after "select * from {sources_str} where" in the query. """ if mode == VectorStoreQueryMode.SEMANTIC_HYBRID: - return f"rank({targetHits:{vector_top_k}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}" + return f"rank({{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}" elif mode == VectorStoreQueryMode.HYBRID: - return f'title contains "vegetable" and rank({targetHits:{vector_top_k}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}' + return f'title contains "vegetable" and rank({{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}' else: raise ValueError(f"Query mode {mode} not supported.") From 723d7546e3a0b8e7e5e53ff7345ec8fe65e7c41f Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 13:37:24 +0200 Subject: [PATCH 06/41] fix --- .../llama_index/vector_stores/vespa/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 1af5c1e27aede..210e825587f36 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -419,7 +419,7 @@ def query( create_embedding=not self.embeddings_outside_vespa, vector_top_k=vector_top_k, ) - logger.debug(f"Query body:\n {body}") + logger.info(f"Vespa Query body:\n {body}") with self.app.syncio() as session: response = session.query( body=body, From c6aa2d90bb5f0bc493a02f96ede896a8ef14bced Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 15:24:13 +0200 Subject: [PATCH 07/41] improve docstring --- .../llama_index/vector_stores/vespa/base.py | 55 +++++++++---------- 1 file changed, 25 insertions(+), 30 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 210e825587f36..1cb19b4ceb1ad 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -44,7 +44,8 @@ def callback(response: VespaResponse, id: str): class VespaVectorStore(VectorStore): - """Vespa vector store. + """ + Vespa vector store. In this vector store, embeddings and docs are stored in a Vespa application. @@ -52,10 +53,22 @@ class VespaVectorStore(VectorStore): During query time, the index queries the Vespa application to get the top k most relevant hits. + Initialize Vespa vector store. + Args: - vespa_application (vespa.application.Vespa): Vespa - instance from `pyvespa` package - schema_name (Optional[str]): Schema name in Vespa application + application_package (ApplicationPackage): Application package + deployment_target (str): Deployment target, either `local` or `cloud` + port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local` + default_schema_name (str): Schema name in Vespa application + namespace (str): Namespace in Vespa application + embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not. + url (Optional[str]): URL of deployed Vespa application. + groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode + tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud` + key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud. + key_content (Optional[str]): Content of the control plane key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available. + auth_client_token_id (Optional[str]): Use token based data plane authentication. This is the token name configured in the Vespa Cloud Console. This is used to configure Vespa services.xml. The token is given read and write permissions. + kwargs (Any): Additional kwargs for Vespa application Examples: `pip install llama-index-vector-stores-vespa` @@ -94,25 +107,6 @@ def __init__( auth_client_token_id: Optional[str] = None, **kwargs: Any, ) -> None: - """ - Initialize Vespa vector store. - - Args: - application_package (ApplicationPackage): Application package - deployment_target (str): Deployment target, either `local` or `cloud` - port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local` - default_schema_name (str): Schema name in Vespa application - namespace (str): Namespace in Vespa application - embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not. - url (Optional[str]): URL of deployed Vespa application. - groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode - tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud` - key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud. - key_content (Optional[str]): Content of the control plane key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available. - auth_client_token_id (Optional[str]): Use token based data plane authentication. This is the token name configured in the Vespa Cloud Console. This is used to configure Vespa services.xml. The token is given read and write permissions. - kwargs (Any): Additional kwargs for Vespa application - - """ # Verify that application_package is an instance of ApplicationPackage if not isinstance(application_package, ApplicationPackage): raise ValueError( @@ -206,11 +200,12 @@ def add( "id": node.node_id, "fields": { "id": node.node_id, - # TODO: Only if outside. "embedding": node.get_embedding(), "text": node.get_content(metadata_mode=MetadataMode.NONE) or "", "metadata": json.dumps(metadata), }, } + if self.embeddings_outside_vespa: + entry["fields"]["embedding"] = node.get_embedding() data_to_insert.append(entry) ids.append(node.node_id) @@ -251,15 +246,17 @@ async def async_add( metadata = node_to_metadata_dict( node, remove_text=False, flat_metadata=self.flat_metadata ) - + logger.debug(f"Metadata: {metadata}") entry = { "id": node.node_id, "fields": { - "embedding": node.get_embedding(), - "body": node.get_content(metadata_mode=MetadataMode.NONE) or "", - **metadata, + "id": node.node_id, + "text": node.get_content(metadata_mode=MetadataMode.NONE) or "", + "metadata": json.dumps(metadata), }, } + if self.embeddings_outside_vespa: + entry["fields"]["embedding"] = node.get_embedding() data_to_insert.append(entry) ids.append(node.node_id) @@ -423,8 +420,6 @@ def query( with self.app.syncio() as session: response = session.query( body=body, - # schema=self.default_schema_name, - # namespace=self.namespace, ) if not response.is_successful(): raise ValueError( From ed1f213d7ca592e04b41cafbec95ba44c9380db8 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 15:35:53 +0200 Subject: [PATCH 08/41] improve docs --- .../llama_index/vector_stores/vespa/base.py | 24 ++++++++++++++----- 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 1cb19b4ceb1ad..c014fb72fc317 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -47,14 +47,26 @@ class VespaVectorStore(VectorStore): """ Vespa vector store. - In this vector store, embeddings and docs are stored in a Vespa application. - - The application must be set up with an embedding field and a doc field. - + Can be initialized in several ways: + 1. (Default) Initialize Vespa vector store with default hybrid template and local (docker) deployment. + 2. Initialize by providing an application package created in pyvespa (can be deployed locally or to Vespa cloud). + 3. Initialize from previously deployed Vespa application by providing URL. (Local or cloud deployment). + + The application must be set up with the following fields: + - id: Document id + - text: Text field + - embedding: Field to store embedding vectors. + - metadata: Metadata field (all metadata will be stored here) + + The application must be set up with the following rank profiles: + - bm25: For text search + - semantic: For semantic search + - fusion: For semantic hybrid search + + When creating a VectorStoreIndex from VespaVectorStore, the index will add documents to the Vespa application. + Be ware that the Vespa container will be reused if not deleted between deployments, to avoid data duplication. During query time, the index queries the Vespa application to get the top k most relevant hits. - Initialize Vespa vector store. - Args: application_package (ApplicationPackage): Application package deployment_target (str): Deployment target, either `local` or `cloud` From 19fd4852220f176eeba2025e6c929d1fa374962f Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 15:49:29 +0200 Subject: [PATCH 09/41] update notebook --- .../vector_stores/VespaIndexDemo.ipynb | 282 +++++++----------- 1 file changed, 116 insertions(+), 166 deletions(-) diff --git a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb index e362144756e61..1e21209301ea1 100644 --- a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "08ad68ce", "metadata": {}, "outputs": [], @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "eccceb71", "metadata": {}, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "0a2bcc07", "metadata": {}, "outputs": [], @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "df6b6d46", "metadata": {}, "outputs": [], @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "id": "7a9e71f4", "metadata": {}, "outputs": [ @@ -202,7 +202,7 @@ "\u001b[0m \u001b[0mdefault_schema_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'doc'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", "\u001b[0m \u001b[0mdeployment_target\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'local'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", "\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m8080\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0membeddings_outside_vespa\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", + "\u001b[0m \u001b[0membeddings_outside_vespa\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", "\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", "\u001b[0m \u001b[0mgroupname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", "\u001b[0m \u001b[0mtenant\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", @@ -215,46 +215,55 @@ "\u001b[1;31mDocstring:\u001b[0m \n", "Vespa vector store.\n", "\n", - "In this vector store, embeddings and docs are stored in a Vespa application.\n", + "Can be initialized in several ways:\n", + "1. (Default) Initialize Vespa vector store with default hybrid template and local (docker) deployment.\n", + "2. Initialize by providing an application package created in pyvespa (can be deployed locally or to Vespa cloud).\n", + "3. Initialize from previously deployed Vespa application by providing URL. (Local or cloud deployment).\n", + "\n", + "The application must be set up with the following fields:\n", + "- id: Document id\n", + "- text: Text field\n", + "- embedding: Field to store embedding vectors.\n", + "- metadata: Metadata field (all metadata will be stored here)\n", "\n", - "The application must be set up with an embedding field and a doc field.\n", + "The application must be set up with the following rank profiles:\n", + "- bm25: For text search\n", + "- semantic: For semantic search\n", + "- fusion: For semantic hybrid search\n", "\n", + "When creating a VectorStoreIndex from VespaVectorStore, the index will add documents to the Vespa application.\n", + "Be ware that the Vespa container will be reused if not deleted between deployments, to avoid data duplication.\n", "During query time, the index queries the Vespa application to get the top k most relevant hits.\n", "\n", "Args:\n", - " vespa_application (vespa.application.Vespa): Vespa\n", - " instance from `pyvespa` package\n", - " schema_name (Optional[str]): Schema name in Vespa application\n", + " application_package (ApplicationPackage): Application package\n", + " deployment_target (str): Deployment target, either `local` or `cloud`\n", + " port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local`\n", + " default_schema_name (str): Schema name in Vespa application\n", + " namespace (str): Namespace in Vespa application\n", + " embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not.\n", + " url (Optional[str]): URL of deployed Vespa application.\n", + " groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode\n", + " tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud`\n", + " key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud.\n", + " key_content (Optional[str]): Content of the control plane key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available.\n", + " auth_client_token_id (Optional[str]): Use token based data plane authentication. This is the token name configured in the Vespa Cloud Console. This is used to configure Vespa services.xml. The token is given read and write permissions.\n", + " kwargs (Any): Additional kwargs for Vespa application\n", "\n", "Examples:\n", " `pip install llama-index-vector-stores-vespa`\n", "\n", " ```python\n", - " from vespa.application import Vespa\n", + " from llama_index.core import VectorStoreIndex\n", + " from llama_index.vector_stores.vespa import VespaVectorStore\n", "\n", - " app = Vespa(url=\"https://api.cord19.vespa.ai\")\n", + " vector_store = VespaVectorStore()\n", + " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + " index = VectorStoreIndex(nodes, storage_context=storage_context)\n", + " retriever = index.as_retriever()\n", + " retriever.retrieve(\"Who directed inception?\")\n", "\n", - " vector_store = VespaVectorStore(\n", - " vespa_application=app,\n", - " )\n", " ```\n", - "\u001b[1;31mInit docstring:\u001b[0m\n", - "Initialize Vespa vector store.\n", - "\n", - "Args:\n", - " application_package (ApplicationPackage): Application package\n", - " deployment_target (str): Deployment target, either `local` or `cloud`\n", - " port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local`\n", - " default_schema_name (str): Schema name in Vespa application\n", - " namespace (str): Namespace in Vespa application\n", - " embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not.\n", - " url (Optional[str]): URL of deployed Vespa application.\n", - " groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode\n", - " tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud`\n", - " key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud.\n", - " key_content (Optional[str]): Content of the control plane key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available.\n", - " auth_client_token_id (Optional[str]): Use token based data plane authentication. This is the token name configured in the Vespa Cloud Console. This is used to configure Vespa services.xml. The token is given read and write permissions.\n", - " kwargs (Any): Additional kwargs for Vespa application\n", "\u001b[1;31mFile:\u001b[0m c:\\users\\thoma\\documents\\repos\\llama_index\\llama-index-integrations\\vector_stores\\llama-index-vector-stores-vespa\\llama_index\\vector_stores\\vespa\\base.py\n", "\u001b[1;31mType:\u001b[0m _ProtocolMeta\n", "\u001b[1;31mSubclasses:\u001b[0m " @@ -288,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "30b0b2e3", "metadata": {}, "outputs": [ @@ -296,8 +305,11 @@ "name": "stdout", "output_type": "stream", "text": [ + "INFO:llama_index.vector_stores.vespa.base:Using default hybrid template. Please make sure that the Vespa application is set up with the correct schema and rank profile.\n", + "Using default hybrid template. Please make sure that the Vespa application is set up with the correct schema and rank profile.\n", "Waiting for configuration server, 0/300 seconds...\n", "Waiting for configuration server, 5/300 seconds...\n", + "Waiting for configuration server, 10/300 seconds...\n", "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", "Waiting for application status, 0/300 seconds...\n", "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", @@ -318,7 +330,9 @@ "Waiting for application status, 40/300 seconds...\n", "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", "Application is up!\n", - "Finished deployment.\n" + "Finished deployment.\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" ] } ], @@ -340,67 +354,72 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "bfe83ebf", + "execution_count": 10, + "id": "5a71818e", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query body:\n", + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", + "Vespa Query body:\n", " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" - ] - }, + "data": { + "text/plain": [ + "[NodeWithScore(node=TextNode(id_='c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retriever = index.as_retriever(vector_store_query_mode=\"default\")\n", + "retriever.retrieve(\"Who directed inception?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bfe83ebf", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "WARNING:llama_index.vector_stores.vespa.base:Embedding field not provided. Using default embedding field embedding\n", + "Embedding field not provided. Using default embedding field embedding\n", + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who directed inception?)'}\n", + "Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who directed inception?)'}\n" ] }, { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='3032156b-21a9-4ef8-95c6-9809e71b5e07', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)]" + "[NodeWithScore(node=TextNode(id_='c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03278688524590164),\n", + " NodeWithScore(node=TextNode(id_='830e48de-cef2-4d5c-a03a-dd59e2a0b418', embedding=None, metadata={'author': 'George Orwell', 'theme': 'Totalitarianism', 'year': 1949}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03225806451612903)]" ] }, - "execution_count": 19, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "retriever = index.as_retriever()\n", + "retriever = index.as_retriever(vector_store_query_mode=\"semantic_hybrid\")\n", "retriever.retrieve(\"Who directed inception?\")" ] }, @@ -414,61 +433,31 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 11, "id": "c1bd18f8", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Metadata: {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010, '_node_content': '{\"id_\": \"3032156b-21a9-4ef8-95c6-9809e71b5e07\", \"embedding\": null, \"metadata\": {\"director\": \"Christopher Nolan\", \"theme\": \"Fiction\", \"year\": 2010}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Inception\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", + "Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" ] }, { "data": { "text/plain": [ - "Response(response='Christopher Nolan', source_nodes=[NodeWithScore(node=TextNode(id_='3032156b-21a9-4ef8-95c6-9809e71b5e07', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)], metadata={'3032156b-21a9-4ef8-95c6-9809e71b5e07': {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}})" + "Response(response='Christopher Nolan', source_nodes=[NodeWithScore(node=TextNode(id_='c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)], metadata={'c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0': {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}})" ] }, - "execution_count": 23, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -480,81 +469,42 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "id": "0663ab38", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:llama_index.vector_stores.vespa.base:Filter support not implemented yet. Will be ignored.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Filter support not implemented yet. Will be ignored.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query body:\n", + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", + "WARNING:llama_index.vector_stores.vespa.base:Filter support not implemented yet. Will be ignored.\n", + "Filter support not implemented yet. Will be ignored.\n", + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", + "Vespa Query body:\n", " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Metadata: {'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997, '_node_content': '{\"id_\": \"6be7efdc-8c99-4deb-bfbb-46bd6920e69e\", \"embedding\": null, \"metadata\": {\"author\": \"J.K. Rowling\", \"theme\": \"Fiction\", \"year\": 1997}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Harry Potter and the Sorcerer\\'s Stone\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Metadata: {'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997, '_node_content': '{\"id_\": \"6be7efdc-8c99-4deb-bfbb-46bd6920e69e\", \"embedding\": null, \"metadata\": {\"author\": \"J.K. Rowling\", \"theme\": \"Fiction\", \"year\": 1997}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"Harry Potter and the Sorcerer\\'s Stone\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None', 'ref_doc_id': 'None'}\n" - ] - }, { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='6be7efdc-8c99-4deb-bfbb-46bd6920e69e', embedding=None, metadata={'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.1663224434456927)]" + "[NodeWithScore(node=TextNode(id_='180bb151-3eb8-4bb5-92be-5a7753c23fb7', embedding=None, metadata={'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.1663224434456927)]" ] }, - "execution_count": 21, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from llama_index.core.vector_stores import FilterOperator, FilterCondition\n", + "from llama_index.core.vector_stores import (\n", + " FilterOperator,\n", + " FilterCondition,\n", + " MetadataFilter,\n", + " MetadataFilters,\n", + ")\n", "\n", "filters = MetadataFilters(\n", " filters=[\n", From 38ab426d0aeeb37d6d9eec2833505d77680ebfc5 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 15:58:20 +0200 Subject: [PATCH 10/41] add guard against empty embeddings --- .../llama_index/vector_stores/vespa/base.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index c014fb72fc317..904fd2c93a9ff 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -300,12 +300,16 @@ def delete( """ Delete nodes using with ref_doc_id. """ - self.app.delete_data( + response: VespaResponse = self.app.delete_data( schema=self.default_schema_name, namespace=namespace or self.namespace, data_id=ref_doc_id, kwargs=delete_kwargs, ) + if not response.is_successful(): + raise ValueError( + f"Delete request failed: {response.status_code}, response payload: {response.json}" + ) async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: """ @@ -345,10 +349,10 @@ def _create_query_body( "See for example https://pyvespa.readthedocs.io/en/latest/examples/evaluating-with-snowflake-arctic-embed.html" ) - # if input_embedding is None and not create_embedding: - # raise ValueError( - # "Input embedding must be provided if embeddings are not created outside Vespa" - # ) + if input_embedding is None and not create_embedding: + raise ValueError( + "Input embedding must be provided if embeddings are not created outside Vespa" + ) base_params = { "hits": query.similarity_top_k, From 66a6898ffa2ff698346156d5e691941e9cdc642b Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 17:22:15 +0200 Subject: [PATCH 11/41] rename templates --- .../vector_stores/vespa/__init__.py | 3 +- .../vector_stores/vespa/vespa_templates.py | 91 ------------------- 2 files changed, 2 insertions(+), 92 deletions(-) delete mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py index ffdeed347bf3c..7472eed62961b 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/__init__.py @@ -1,5 +1,6 @@ from llama_index.vector_stores.vespa.base import ( VespaVectorStore, ) +from llama_index.vector_stores.vespa.templates import hybrid_template -__all__ = ["VespaVectorStore"] +__all__ = ["VespaVectorStore", "hybrid_template"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py deleted file mode 100644 index f17b7c6625839..0000000000000 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/vespa_templates.py +++ /dev/null @@ -1,91 +0,0 @@ -from vespa.package import ( - ApplicationPackage, - Field, - Schema, - Document, - HNSW, - RankProfile, - Component, - Parameter, - FieldSet, - GlobalPhaseRanking, - Function, -) - -hybrid_template = ApplicationPackage( - name="hybridsearch", - schema=[ - Schema( - name="doc", - document=Document( - fields=[ - Field(name="id", type="string", indexing=["summary"]), - Field(name="metadata", type="string", indexing=["summary"]), - Field( - name="text", - type="string", - indexing=["index", "summary"], - index="enable-bm25", - bolding=True, - ), - Field( - name="embedding", - type="tensor(x[384])", - indexing=[ - "input text", - "embed", - "index", - "attribute", - ], - ann=HNSW(distance_metric="angular"), - is_document_field=False, - ), - ] - ), - fieldsets=[FieldSet(name="default", fields=["text", "metadata"])], - rank_profiles=[ - RankProfile( - name="bm25", - inputs=[("query(q)", "tensor(x[384])")], - functions=[Function(name="bm25sum", expression="bm25(text)")], - first_phase="bm25sum", - ), - RankProfile( - name="semantic", - inputs=[("query(q)", "tensor(x[384])")], - first_phase="closeness(field, embedding)", - ), - RankProfile( - name="fusion", - inherits="bm25", - inputs=[("query(q)", "tensor(x[384])")], - first_phase="closeness(field, embedding)", - global_phase=GlobalPhaseRanking( - expression="reciprocal_rank_fusion(bm25sum, closeness(field, embedding))", - rerank_count=1000, - ), - ), - ], - ) - ], - components=[ - Component( - id="e5", - type="hugging-face-embedder", - parameters=[ - Parameter( - "transformer-model", - { - "url": "https://github.com/vespa-engine/sample-apps/raw/master/simple-semantic-search/model/e5-small-v2-int8.onnx" - }, - ), - Parameter( - "tokenizer-model", - { - "url": "https://raw.githubusercontent.com/vespa-engine/sample-apps/master/simple-semantic-search/model/tokenizer.json" - }, - ), - ], - ) - ], -) From aadee3fdbc38c18cb9709b07c19108c1a7b7d8cd Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 17:22:26 +0200 Subject: [PATCH 12/41] templates --- .../vector_stores/vespa/templates.py | 91 +++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py new file mode 100644 index 0000000000000..f17b7c6625839 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py @@ -0,0 +1,91 @@ +from vespa.package import ( + ApplicationPackage, + Field, + Schema, + Document, + HNSW, + RankProfile, + Component, + Parameter, + FieldSet, + GlobalPhaseRanking, + Function, +) + +hybrid_template = ApplicationPackage( + name="hybridsearch", + schema=[ + Schema( + name="doc", + document=Document( + fields=[ + Field(name="id", type="string", indexing=["summary"]), + Field(name="metadata", type="string", indexing=["summary"]), + Field( + name="text", + type="string", + indexing=["index", "summary"], + index="enable-bm25", + bolding=True, + ), + Field( + name="embedding", + type="tensor(x[384])", + indexing=[ + "input text", + "embed", + "index", + "attribute", + ], + ann=HNSW(distance_metric="angular"), + is_document_field=False, + ), + ] + ), + fieldsets=[FieldSet(name="default", fields=["text", "metadata"])], + rank_profiles=[ + RankProfile( + name="bm25", + inputs=[("query(q)", "tensor(x[384])")], + functions=[Function(name="bm25sum", expression="bm25(text)")], + first_phase="bm25sum", + ), + RankProfile( + name="semantic", + inputs=[("query(q)", "tensor(x[384])")], + first_phase="closeness(field, embedding)", + ), + RankProfile( + name="fusion", + inherits="bm25", + inputs=[("query(q)", "tensor(x[384])")], + first_phase="closeness(field, embedding)", + global_phase=GlobalPhaseRanking( + expression="reciprocal_rank_fusion(bm25sum, closeness(field, embedding))", + rerank_count=1000, + ), + ), + ], + ) + ], + components=[ + Component( + id="e5", + type="hugging-face-embedder", + parameters=[ + Parameter( + "transformer-model", + { + "url": "https://github.com/vespa-engine/sample-apps/raw/master/simple-semantic-search/model/e5-small-v2-int8.onnx" + }, + ), + Parameter( + "tokenizer-model", + { + "url": "https://raw.githubusercontent.com/vespa-engine/sample-apps/master/simple-semantic-search/model/tokenizer.json" + }, + ), + ], + ) + ], +) From c59dc59ebb83af3f107e628dae18ff1dbebea95c Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 17:22:34 +0200 Subject: [PATCH 13/41] base update --- .../llama_index/vector_stores/vespa/base.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 904fd2c93a9ff..ef7842b415f34 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -15,6 +15,8 @@ metadata_dict_to_node, ) +from llama_index.vector_stores.vespa.templates import hybrid_template + import asyncio import logging import json @@ -29,9 +31,6 @@ "pyvespa not installed. Please install it via `pip install pyvespa`" ) -from .vespa_templates import hybrid_template - - logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) From 5752c557fb2930ac9a644f69d10bdb199cc3f5af Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 17:27:18 +0200 Subject: [PATCH 14/41] bug in emb --- .../llama_index/vector_stores/vespa/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index ef7842b415f34..d0efe9963a3c8 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -348,7 +348,7 @@ def _create_query_body( "See for example https://pyvespa.readthedocs.io/en/latest/examples/evaluating-with-snowflake-arctic-embed.html" ) - if input_embedding is None and not create_embedding: + if query.query_embedding is None and not create_embedding: raise ValueError( "Input embedding must be provided if embeddings are not created outside Vespa" ) From ffe3cdd08bee305b5f62f5695e1d4a1680470a4e Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 17:28:11 +0200 Subject: [PATCH 15/41] textnodes --- .../tests/test_vespavectorstore.py | 115 ++++++++++++++++++ 1 file changed, 115 insertions(+) create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py new file mode 100644 index 0000000000000..7c716863fe2eb --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -0,0 +1,115 @@ +import asyncio +import pytest + +from llama_index.core.schema import TextNode +from llama_index.core.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode +from llama_index.vector_stores.vespa import VespaVectorStore, hybrid_template + + +# Assuming Vespa services are mocked or local Vespa Docker is used +@pytest.fixture(scope="module") +def vespa_app(): + app_package = hybrid_template + return VespaVectorStore(application_package=app_package, deployment_target="local") + + +@pytest.fixture() +def nodes() -> list: + return [ + TextNode( + text="The Shawshank Redemption", + metadata={ + "author": "Stephen King", + "theme": "Friendship", + "year": 1994, + }, + ), + TextNode( + text="The Godfather", + metadata={ + "director": "Francis Ford Coppola", + "theme": "Mafia", + "year": 1972, + }, + ), + TextNode( + text="Inception", + metadata={ + "director": "Christopher Nolan", + "theme": "Fiction", + "year": 2010, + }, + ), + TextNode( + text="To Kill a Mockingbird", + metadata={ + "author": "Harper Lee", + "theme": "Mafia", + "year": 1960, + }, + ), + TextNode( + text="1984", + metadata={ + "author": "George Orwell", + "theme": "Totalitarianism", + "year": 1949, + }, + ), + TextNode( + text="The Great Gatsby", + metadata={ + "author": "F. Scott Fitzgerald", + "theme": "The American Dream", + "year": 1925, + }, + ), + TextNode( + text="Harry Potter and the Sorcerer's Stone", + metadata={ + "author": "J.K. Rowling", + "theme": "Fiction", + "year": 1997, + }, + ), + ] + + +def test_add_nodes(vespa_app, nodes): + # Testing the addition of nodes + ids = vespa_app.add(nodes) + assert len(ids) == 2 + assert all(isinstance(id, str) for id in ids) + + +def test_query(vespa_app): + # Testing querying + query = VectorStoreQuery( + query_str="test", mode=VectorStoreQueryMode.TEXT_SEARCH, similarity_top_k=1 + ) + result = vespa_app.query(query) + assert len(result.nodes) > 0 + assert result.nodes[0].get_content().contains("test") + + +def test_delete_node(vespa_app, nodes): + # Testing deletion of a node + vespa_app.add(nodes) + vespa_app.delete(ref_doc_id=nodes[0].node_id) + query = VectorStoreQuery( + query_str="This is a test.", + mode=VectorStoreQueryMode.TEXT_SEARCH, + similarity_top_k=1, + ) + result = vespa_app.query(query) + assert not any(node.node_id == nodes[0].node_id for node in result.nodes) + + +@pytest.mark.asyncio() +async def test_async_add_and_query(vespa_app, nodes): + # Testing async add and query + await asyncio.gather(*[vespa_app.async_add(nodes)]) + query = VectorStoreQuery(query_embedding=[0.1, 0.2, 0.3], similarity_top_k=1) + result = await vespa_app.aquery(query) + assert len(result.nodes) == 1 + assert result.nodes[0].node_id == "1" From e02d321b81af22ddcac9e9660b9a039f339e00af Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 18:24:46 +0200 Subject: [PATCH 16/41] fix hybrid --- .../llama_index/vector_stores/vespa/base.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index d0efe9963a3c8..c328ec3fa3b5e 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -405,10 +405,8 @@ def _build_query_filter( Build query filter for Vespa query. The part after "select * from {sources_str} where" in the query. """ - if mode == VectorStoreQueryMode.SEMANTIC_HYBRID: + if mode in [VectorStoreQueryMode.SEMANTIC_HYBRID, VectorStoreQueryMode.HYBRID]: return f"rank({{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}" - elif mode == VectorStoreQueryMode.HYBRID: - return f'title contains "vegetable" and rank({{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}' else: raise ValueError(f"Query mode {mode} not supported.") From 90de6430a364ca98dd4edc540b3e25b2f7e62389 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Wed, 1 May 2024 19:06:37 +0200 Subject: [PATCH 17/41] make tests pass --- .../tests/test_vespavectorstore.py | 59 +++++++++++++++---- 1 file changed, 47 insertions(+), 12 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py index 7c716863fe2eb..1bce096245af8 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -5,12 +5,26 @@ from llama_index.core.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode from llama_index.vector_stores.vespa import VespaVectorStore, hybrid_template +try: + # Should be installed as pyvespa-dependency + import docker + + client = docker.from_env() + docker_available = client.ping() +except Exception: + docker_available = False + # Assuming Vespa services are mocked or local Vespa Docker is used @pytest.fixture(scope="module") def vespa_app(): app_package = hybrid_template - return VespaVectorStore(application_package=app_package, deployment_target="local") + vespa_app = VespaVectorStore( + application_package=app_package, deployment_target="local" + ) + yield vespa_app + vespa_app.client.container.stop() + vespa_app.client.container.remove() @pytest.fixture() @@ -19,6 +33,7 @@ def nodes() -> list: TextNode( text="The Shawshank Redemption", metadata={ + "id": "1", "author": "Stephen King", "theme": "Friendship", "year": 1994, @@ -27,6 +42,7 @@ def nodes() -> list: TextNode( text="The Godfather", metadata={ + "id": "2", "director": "Francis Ford Coppola", "theme": "Mafia", "year": 1972, @@ -35,6 +51,7 @@ def nodes() -> list: TextNode( text="Inception", metadata={ + "id": "3", "director": "Christopher Nolan", "theme": "Fiction", "year": 2010, @@ -43,6 +60,7 @@ def nodes() -> list: TextNode( text="To Kill a Mockingbird", metadata={ + "id": "4", "author": "Harper Lee", "theme": "Mafia", "year": 1960, @@ -51,6 +69,7 @@ def nodes() -> list: TextNode( text="1984", metadata={ + "id": "5", "author": "George Orwell", "theme": "Totalitarianism", "year": 1949, @@ -59,6 +78,7 @@ def nodes() -> list: TextNode( text="The Great Gatsby", metadata={ + "id": "6", "author": "F. Scott Fitzgerald", "theme": "The American Dream", "year": 1925, @@ -67,6 +87,7 @@ def nodes() -> list: TextNode( text="Harry Potter and the Sorcerer's Stone", metadata={ + "id": "7", "author": "J.K. Rowling", "theme": "Fiction", "year": 1997, @@ -78,38 +99,52 @@ def nodes() -> list: def test_add_nodes(vespa_app, nodes): # Testing the addition of nodes ids = vespa_app.add(nodes) - assert len(ids) == 2 + assert len(ids) == len(nodes) assert all(isinstance(id, str) for id in ids) -def test_query(vespa_app): - # Testing querying +def test_query_text_search(vespa_app, nodes): query = VectorStoreQuery( - query_str="test", mode=VectorStoreQueryMode.TEXT_SEARCH, similarity_top_k=1 + query_str="Inception", # Ensure the query matches the case used in the nodes + mode="text_search", + similarity_top_k=1, + ) + result = vespa_app.query(query) + assert len(result.nodes) == 1 + node_metadata = result.nodes[0].metadata + assert node_metadata["id"] == "3", "Expected Inception node" + + +def test_query_vector_search(vespa_app, nodes): + query = VectorStoreQuery( + query_str="magic, wizardry", + mode="semantic_hybrid", + similarity_top_k=1, ) result = vespa_app.query(query) - assert len(result.nodes) > 0 - assert result.nodes[0].get_content().contains("test") + assert len(result.nodes) == 1, "Expected 1 result" + node_metadata = result.nodes[0].metadata + print(node_metadata) + assert node_metadata["id"] == "7", "Expected Harry Potter node" def test_delete_node(vespa_app, nodes): # Testing deletion of a node - vespa_app.add(nodes) vespa_app.delete(ref_doc_id=nodes[0].node_id) query = VectorStoreQuery( - query_str="This is a test.", + query_str="Shawshank Redemption", mode=VectorStoreQueryMode.TEXT_SEARCH, similarity_top_k=1, ) result = vespa_app.query(query) - assert not any(node.node_id == nodes[0].node_id for node in result.nodes) + assert len(result.nodes) == 0 @pytest.mark.asyncio() async def test_async_add_and_query(vespa_app, nodes): # Testing async add and query await asyncio.gather(*[vespa_app.async_add(nodes)]) - query = VectorStoreQuery(query_embedding=[0.1, 0.2, 0.3], similarity_top_k=1) + query = VectorStoreQuery(query_str="Harry Potter", similarity_top_k=1) result = await vespa_app.aquery(query) assert len(result.nodes) == 1 - assert result.nodes[0].node_id == "1" + assert result.nodes[0].node_id == "7" From 9a6b011bd57cc595d2800373935b9a065da2f82c Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 06:00:23 +0200 Subject: [PATCH 18/41] more tests --- .../llama_index/vector_stores/vespa/base.py | 1 + .../tests/test_vespavectorstore.py | 44 ++++++++++--------- 2 files changed, 25 insertions(+), 20 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index c328ec3fa3b5e..0f92708e2230d 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -309,6 +309,7 @@ def delete( raise ValueError( f"Delete request failed: {response.status_code}, response payload: {response.json}" ) + logger.info(f"Deleted node with id {ref_doc_id}") async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: """ diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py index 1bce096245af8..d12824de95504 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -19,17 +19,12 @@ @pytest.fixture(scope="module") def vespa_app(): app_package = hybrid_template - vespa_app = VespaVectorStore( - application_package=app_package, deployment_target="local" - ) - yield vespa_app - vespa_app.client.container.stop() - vespa_app.client.container.remove() + return VespaVectorStore(application_package=app_package, deployment_target="local") @pytest.fixture() -def nodes() -> list: - return [ +def nodes_ids() -> tuple: + nodes = [ TextNode( text="The Shawshank Redemption", metadata={ @@ -94,16 +89,21 @@ def nodes() -> list: }, ), ] + # Set node.node_id to node.metadata["id"] + ids = [node.metadata["id"] for node in nodes] + for node in nodes: + node.node_id = node.metadata["id"] + return nodes, ids -def test_add_nodes(vespa_app, nodes): +def test_add_nodes(vespa_app, nodes_ids): # Testing the addition of nodes - ids = vespa_app.add(nodes) - assert len(ids) == len(nodes) - assert all(isinstance(id, str) for id in ids) + nodes, ids = nodes_ids + inserted_ids = vespa_app.add(nodes) + assert inserted_ids == list(ids), "Failed to insert nodes" -def test_query_text_search(vespa_app, nodes): +def test_query_text_search(vespa_app): query = VectorStoreQuery( query_str="Inception", # Ensure the query matches the case used in the nodes mode="text_search", @@ -115,7 +115,7 @@ def test_query_text_search(vespa_app, nodes): assert node_metadata["id"] == "3", "Expected Inception node" -def test_query_vector_search(vespa_app, nodes): +def test_query_vector_search(vespa_app): query = VectorStoreQuery( query_str="magic, wizardry", mode="semantic_hybrid", @@ -128,21 +128,25 @@ def test_query_vector_search(vespa_app, nodes): assert node_metadata["id"] == "7", "Expected Harry Potter node" -def test_delete_node(vespa_app, nodes): - # Testing deletion of a node - vespa_app.delete(ref_doc_id=nodes[0].node_id) +def test_delete_node(vespa_app, nodes_ids): + nodes, ids = nodes_ids + node_to_delete = ids[0] + vespa_app.delete(ref_doc_id=node_to_delete) query = VectorStoreQuery( - query_str="Shawshank Redemption", + query_str="Godfather", mode=VectorStoreQueryMode.TEXT_SEARCH, similarity_top_k=1, ) result = vespa_app.query(query) - assert len(result.nodes) == 0 + assert ( + len(result.nodes) == 0 + ), f"Deletion of node {node_to_delete} failed, found {len(result.nodes)} nodes" @pytest.mark.asyncio() -async def test_async_add_and_query(vespa_app, nodes): +async def test_async_add_and_query(vespa_app, nodes_ids): # Testing async add and query + ids, nodes = zip(*nodes_ids) await asyncio.gather(*[vespa_app.async_add(nodes)]) query = VectorStoreQuery(query_str="Harry Potter", similarity_top_k=1) result = await vespa_app.aquery(query) From 830aa7084202fa7872c677499d35f563c3e2c2f8 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 06:14:22 +0200 Subject: [PATCH 19/41] more tests --- .../llama_index/vector_stores/vespa/base.py | 1 + .../tests/test_vespavectorstore.py | 9 ++------- 2 files changed, 3 insertions(+), 7 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index c328ec3fa3b5e..0f92708e2230d 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -309,6 +309,7 @@ def delete( raise ValueError( f"Delete request failed: {response.status_code}, response payload: {response.json}" ) + logger.info(f"Deleted node with id {ref_doc_id}") async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: """ diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py index 1bce096245af8..68bfd0fe3d13a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -19,15 +19,10 @@ @pytest.fixture(scope="module") def vespa_app(): app_package = hybrid_template - vespa_app = VespaVectorStore( - application_package=app_package, deployment_target="local" - ) - yield vespa_app - vespa_app.client.container.stop() - vespa_app.client.container.remove() + return VespaVectorStore(application_package=app_package, deployment_target="local") -@pytest.fixture() +@pytest.fixture(scope="session") def nodes() -> list: return [ TextNode( From 285cdc263f83e592c454e5d295d360e909342da0 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 06:50:38 +0200 Subject: [PATCH 20/41] update README --- .../llama-index-vector-stores-vespa/README.md | 138 +++++++++++++++++- 1 file changed, 137 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md index b91ace0ee2d8d..caee71a93f376 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md @@ -1,3 +1,139 @@ + + + + #Vespa + + # LlamaIndex Vector_Stores Integration: Vespa -See https://docs.vespa.ai/. +[Vespa.ai](https://vespa.ai/) is an open-source big data serving engine. It is designed for low-latency and high-throughput serving of data and models. Vespa.ai is used by many companies to serve search results, recommendations, and rankings for billions of documents and users, expecting response times in the milliseconds. + +This integration allows you to use Vespa.ai as a vector store for LlamaIndex. Vespa has integrated support for [embedding inference](https://docs.vespa.ai/en/embedding.html), so you don't need to run a separate service for these tasks. + +Huggingface 🤗 embedders are supported, as well as SPLADE and ColBERT. + +## Abstraction level of this integration + +To make it really simple to get started, we provide a template Vespa application that will be deployed upon initializing the vector store. This removes some of the complexity of setting up Vespa for the first time, but for serious use cases, we strongly recommend that you read the [Vespa documentation](docs.vespa.ai) and tailor the application to your needs. + +## The template + +The provided template Vespa application can be seen below: + +```python +from vespa.package import ( + ApplicationPackage, + Field, + Schema, + Document, + HNSW, + RankProfile, + Component, + Parameter, + FieldSet, + GlobalPhaseRanking, + Function, +) + +hybrid_template = ApplicationPackage( + name="hybridsearch", + schema=[ + Schema( + name="doc", + document=Document( + fields=[ + Field(name="id", type="string", indexing=["summary"]), + Field(name="metadata", type="string", indexing=["summary"]), + Field( + name="text", + type="string", + indexing=["index", "summary"], + index="enable-bm25", + bolding=True, + ), + Field( + name="embedding", + type="tensor(x[384])", + indexing=[ + "input text", + "embed", + "index", + "attribute", + ], + ann=HNSW(distance_metric="angular"), + is_document_field=False, + ), + ] + ), + fieldsets=[FieldSet(name="default", fields=["text", "metadata"])], + rank_profiles=[ + RankProfile( + name="bm25", + inputs=[("query(q)", "tensor(x[384])")], + functions=[Function(name="bm25sum", expression="bm25(text)")], + first_phase="bm25sum", + ), + RankProfile( + name="semantic", + inputs=[("query(q)", "tensor(x[384])")], + first_phase="closeness(field, embedding)", + ), + RankProfile( + name="fusion", + inherits="bm25", + inputs=[("query(q)", "tensor(x[384])")], + first_phase="closeness(field, embedding)", + global_phase=GlobalPhaseRanking( + expression="reciprocal_rank_fusion(bm25sum, closeness(field, embedding))", + rerank_count=1000, + ), + ), + ], + ) + ], + components=[ + Component( + id="e5", + type="hugging-face-embedder", + parameters=[ + Parameter( + "transformer-model", + { + "url": "https://github.com/vespa-engine/sample-apps/raw/master/simple-semantic-search/model/e5-small-v2-int8.onnx" + }, + ), + Parameter( + "tokenizer-model", + { + "url": "https://raw.githubusercontent.com/vespa-engine/sample-apps/master/simple-semantic-search/model/tokenizer.json" + }, + ), + ], + ) + ], +) +``` + +Note that the fields `id`, `metadata`, `text`, and `embedding` are required for the integration to work. +The schema name must also be `doc`, and the rank profiles must be named `bm25`, `semantic`, and `fusion`. + +Other than that you are free to modify as you see fit by switching out embedding models, adding more fields, or changing the ranking expressions. + +For more details, check out this Pyvespa example notebook on [hybrid search](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa.html). + +## Going to production + +If you are ready to graduate to a production setup, we highly recommend to check out the [Vespa Cloud](https://cloud.vespa.ai/) service, where we manage all infrastructure and operations for you. Free trials are available. + +## Next steps + +There are many awesome features in Vespa, that are not exposed directly in this integration, check out [Pyvespa examples](https://pyvespa.readthedocs.io/en/latest/examples/pyvespa-examples.html) for some inspiration on what you can do with Vespa. + +Teasers: + +- Binary + Matryoshka embeddings. +- ColBERT. +- ONNX models. +- XGBoost and lightGBM models for ranking. +- Multivector indexing. +- and much more. From 1d1850457cce80e1952a954209d111a5a0dca6a5 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 07:49:22 +0200 Subject: [PATCH 21/41] clean pyproj --- .../llama-index-vector-stores-vespa/pyproject.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml index b89ab8ee736d0..1f3300ef1c4bd 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml @@ -31,8 +31,7 @@ version = "0.0.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-readers-file = "^0.1.0" +llama-index-core = "^0.10.1" pyvespa = "^0.40.0" From 6bfc62dd211733f5bfd4af4a9e0a2f93652cd838 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 08:33:59 +0200 Subject: [PATCH 22/41] working tests --- .../tests/test_vespavectorstore.py | 34 +++++++------------ 1 file changed, 13 insertions(+), 21 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py index 03f6faaf07665..81d0cda13cc17 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -16,13 +16,13 @@ # Assuming Vespa services are mocked or local Vespa Docker is used -@pytest.fixture(scope="module") +@pytest.fixture(scope="session") def vespa_app(): app_package = hybrid_template return VespaVectorStore(application_package=app_package, deployment_target="local") -@pytest.fixture() +@pytest.fixture(scope="session") def nodes() -> list: return [ TextNode( @@ -89,21 +89,15 @@ def nodes() -> list: }, ), ] - # Set node.node_id to node.metadata["id"] - ids = [node.metadata["id"] for node in nodes] - for node in nodes: - node.node_id = node.metadata["id"] - return nodes, ids -def test_add_nodes(vespa_app, nodes_ids): - # Testing the addition of nodes - nodes, ids = nodes_ids - inserted_ids = vespa_app.add(nodes) - assert inserted_ids == list(ids), "Failed to insert nodes" +@pytest.fixture(scope="session") +def added_node_ids(vespa_app, nodes): + return vespa_app.add(nodes) + # Assume returned `inserted_ids` is a list of IDs that match the order of `nodes` -def test_query_text_search(vespa_app): +def test_query_text_search(vespa_app, added_node_ids): query = VectorStoreQuery( query_str="Inception", # Ensure the query matches the case used in the nodes mode="text_search", @@ -115,7 +109,7 @@ def test_query_text_search(vespa_app): assert node_metadata["id"] == "3", "Expected Inception node" -def test_query_vector_search(vespa_app): +def test_query_vector_search(vespa_app, added_node_ids): query = VectorStoreQuery( query_str="magic, wizardry", mode="semantic_hybrid", @@ -128,10 +122,9 @@ def test_query_vector_search(vespa_app): assert node_metadata["id"] == "7", "Expected Harry Potter node" -def test_delete_node(vespa_app, nodes_ids): - nodes, ids = nodes_ids - node_to_delete = ids[0] - vespa_app.delete(ref_doc_id=node_to_delete) +def test_delete_node(vespa_app, added_node_ids): + # Testing the deletion of a node + vespa_app.delete(ref_doc_id=added_node_ids[1]) query = VectorStoreQuery( query_str="Godfather", mode=VectorStoreQueryMode.TEXT_SEARCH, @@ -140,13 +133,12 @@ def test_delete_node(vespa_app, nodes_ids): result = vespa_app.query(query) assert ( len(result.nodes) == 0 - ), f"Deletion of node {node_to_delete} failed, found {len(result.nodes)} nodes" + ), f"Deleted node still present in the vector store: {result.nodes}" @pytest.mark.asyncio() -async def test_async_add_and_query(vespa_app, nodes_ids): +async def test_async_add_and_query(vespa_app, nodes): # Testing async add and query - ids, nodes = zip(*nodes_ids) await asyncio.gather(*[vespa_app.async_add(nodes)]) query = VectorStoreQuery(query_str="Harry Potter", similarity_top_k=1) result = await vespa_app.aquery(query) From a7f328f2578a24eb5fd5570bb8403676626cf4be Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 08:34:20 +0200 Subject: [PATCH 23/41] add pytest-asyncio --- .../poetry.lock | 224 ++++++++++-------- .../pyproject.toml | 1 + 2 files changed, 122 insertions(+), 103 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock index 284ee87b190d8..9780fac0ba89c 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock @@ -1465,13 +1465,13 @@ files = [ [[package]] name = "jsonschema" -version = "4.21.1" +version = "4.22.0" description = "An implementation of JSON Schema validation for Python" optional = false python-versions = ">=3.8" files = [ - {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"}, - {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"}, + {file = "jsonschema-4.22.0-py3-none-any.whl", hash = "sha256:ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802"}, + {file = "jsonschema-4.22.0.tar.gz", hash = "sha256:5b22d434a45935119af990552c862e5d6d564e8f6601206b305a61fdf661a2b7"}, ] [package.dependencies] @@ -1950,13 +1950,13 @@ files = [ [[package]] name = "marshmallow" -version = "3.21.1" +version = "3.21.2" description = "A lightweight library for 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= "3.2.0" pylint = "2.15.10" pytest = "7.2.1" pytest-mock = "3.11.1" +pytest-asyncio = "0.23.6" ruff = "0.0.292" tree-sitter-languages = "^1.8.0" types-Deprecated = ">=0.1.0" From 67b6a720a2acd116f4a495c6a98b9d2fce72c745 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 08:36:07 +0200 Subject: [PATCH 24/41] add skips if not docker available --- .../tests/test_vespavectorstore.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py index 81d0cda13cc17..bb79b2031096e 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -97,6 +97,7 @@ def added_node_ids(vespa_app, nodes): # Assume returned `inserted_ids` is a list of IDs that match the order of `nodes` +@pytest.mark.skipif(not docker_available, reason="Docker not available") def test_query_text_search(vespa_app, added_node_ids): query = VectorStoreQuery( query_str="Inception", # Ensure the query matches the case used in the nodes @@ -109,6 +110,7 @@ def test_query_text_search(vespa_app, added_node_ids): assert node_metadata["id"] == "3", "Expected Inception node" +@pytest.mark.skipif(not docker_available, reason="Docker not available") def test_query_vector_search(vespa_app, added_node_ids): query = VectorStoreQuery( query_str="magic, wizardry", @@ -122,6 +124,7 @@ def test_query_vector_search(vespa_app, added_node_ids): assert node_metadata["id"] == "7", "Expected Harry Potter node" +@pytest.mark.skipif(not docker_available, reason="Docker not available") def test_delete_node(vespa_app, added_node_ids): # Testing the deletion of a node vespa_app.delete(ref_doc_id=added_node_ids[1]) @@ -136,6 +139,7 @@ def test_delete_node(vespa_app, added_node_ids): ), f"Deleted node still present in the vector store: {result.nodes}" +@pytest.mark.skipif(not docker_available, reason="Docker not available") @pytest.mark.asyncio() async def test_async_add_and_query(vespa_app, nodes): # Testing async add and query From b84d2fdbd26b526683e9d270e5f19d26910bf3c9 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 09:14:28 +0200 Subject: [PATCH 25/41] log --- .../llama_index/vector_stores/vespa/base.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 0f92708e2230d..75802d4313f7a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -20,6 +20,7 @@ import asyncio import logging import json +import sys try: from vespa.application import Vespa @@ -32,6 +33,8 @@ ) logger = logging.getLogger(__name__) +handler = logging.StreamHandler(stream=sys.stdout) +logger.addHandler(handler) logger.setLevel(logging.INFO) From 8f9c0deed679fd19464d1117de0640920951c828 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 09:14:38 +0200 Subject: [PATCH 26/41] Better demo --- .../vector_stores/VespaIndexDemo.ipynb | 728 +++++++++++++----- 1 file changed, 549 insertions(+), 179 deletions(-) diff --git a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb index 1e21209301ea1..da7722331d85c 100644 --- a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb @@ -1,12 +1,24 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "d792fb5b", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \"#Vespa\"\n", + "\n" + ] + }, { "attachments": {}, "cell_type": "markdown", "id": "23cf319b", "metadata": {}, "source": [ - "\"Open\n" + "\"Open\n" ] }, { @@ -29,22 +41,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "0beb6603", "metadata": {}, - "outputs": [], - "source": [ - "%pip install llama-index-vector-stores-vespa" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b05d5956", - "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\thoma\\Documents\\Repos\\llama_index\\.venv\\Scripts\\python.exe: No module named pip\n" + ] + } + ], "source": [ - "!pip install llama-index pyvespa" + "%pip install llama-index-vector-stores-vespa llama-index pyvespa" ] }, { @@ -58,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "08ad68ce", "metadata": {}, "outputs": [], @@ -70,20 +87,6 @@ "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eccceb71", - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import sys\n", - "\n", - "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", - "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -95,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "0a2bcc07", "metadata": {}, "outputs": [], @@ -185,95 +188,6 @@ "]" ] }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7a9e71f4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;31mInit signature:\u001b[0m\n", - "\u001b[0mVespaVectorStore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mapplication_package\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mvespa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpackage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mApplicationPackage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mApplicationPackage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hybridsearch'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mSchema\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'doc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDocument\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mField\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'id'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'string'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'summary'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mgroupname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mtenant\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n", - "\u001b[0m \u001b[0mapplication\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mDocstring:\u001b[0m \n", - "Vespa vector store.\n", - "\n", - "Can be initialized in several ways:\n", - "1. (Default) Initialize Vespa vector store with default hybrid template and local (docker) deployment.\n", - "2. Initialize by providing an application package created in pyvespa (can be deployed locally or to Vespa cloud).\n", - "3. Initialize from previously deployed Vespa application by providing URL. (Local or cloud deployment).\n", - "\n", - "The application must be set up with the following fields:\n", - "- id: Document id\n", - "- text: Text field\n", - "- embedding: Field to store embedding vectors.\n", - "- metadata: Metadata field (all metadata will be stored here)\n", - "\n", - "The application must be set up with the following rank profiles:\n", - "- bm25: For text search\n", - "- semantic: For semantic search\n", - "- fusion: For semantic hybrid search\n", - "\n", - "When creating a VectorStoreIndex from VespaVectorStore, the index will add documents to the Vespa application.\n", - "Be ware that the Vespa container will be reused if not deleted between deployments, to avoid data duplication.\n", - "During query time, the index queries the Vespa application to get the top k most relevant hits.\n", - "\n", - "Args:\n", - " application_package (ApplicationPackage): Application package\n", - " deployment_target (str): Deployment target, either `local` or `cloud`\n", - " port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local`\n", - " default_schema_name (str): Schema name in Vespa application\n", - " namespace (str): Namespace in Vespa application\n", - " embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not.\n", - " url (Optional[str]): URL of deployed Vespa application.\n", - " groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode\n", - " tenant (Optional[str]): Tenant for Vespa application. Applicable only if deployment_target is `cloud`\n", - " key_location (Optional[str]): Location of the control plane key used for signing HTTP requests to the Vespa Cloud.\n", - " key_content (Optional[str]): Content of the control plane key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available.\n", - " auth_client_token_id (Optional[str]): Use token based data plane authentication. This is the token name configured in the Vespa Cloud Console. This is used to configure Vespa services.xml. The token is given read and write permissions.\n", - " kwargs (Any): Additional kwargs for Vespa application\n", - "\n", - "Examples:\n", - " `pip install llama-index-vector-stores-vespa`\n", - "\n", - " ```python\n", - " from llama_index.core import VectorStoreIndex\n", - " from llama_index.vector_stores.vespa import VespaVectorStore\n", - "\n", - " vector_store = VespaVectorStore()\n", - " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", - " index = VectorStoreIndex(nodes, storage_context=storage_context)\n", - " retriever = index.as_retriever()\n", - " retriever.retrieve(\"Who directed inception?\")\n", - "\n", - " ```\n", - "\u001b[1;31mFile:\u001b[0m c:\\users\\thoma\\documents\\repos\\llama_index\\llama-index-integrations\\vector_stores\\llama-index-vector-stores-vespa\\llama_index\\vector_stores\\vespa\\base.py\n", - "\u001b[1;31mType:\u001b[0m _ProtocolMeta\n", - "\u001b[1;31mSubclasses:\u001b[0m " - ] - } - ], - "source": [ - "?VespaVectorStore" - ] - }, { "cell_type": "markdown", "id": "31fe4378", @@ -287,12 +201,9 @@ "id": "a0be7d09", "metadata": {}, "source": [ - "TODO:\n", + "To make it really simple to get started, we provide a template Vespa application that will be deployed upon initializing the vector store.\n", "\n", - "- Explain template used\n", - "- Option to define your own application package\n", - "- Option to use own embedding model (also outside), but must then change template\n", - "- Link to pyvespa documentation\n" + "This is a huge abstraction and there are endless opportunities to tailor and customize the Vespa application to your needs. But for now, let's keep it simple and initialize with the default template.\n" ] }, { @@ -305,7 +216,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:llama_index.vector_stores.vespa.base:Using default hybrid template. Please make sure that the Vespa application is set up with the correct schema and rank profile.\n", "Using default hybrid template. Please make sure that the Vespa application is set up with the correct schema and rank profile.\n", "Waiting for configuration server, 0/300 seconds...\n", "Waiting for configuration server, 5/300 seconds...\n", @@ -330,9 +240,7 @@ "Waiting for application status, 40/300 seconds...\n", "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", "Application is up!\n", - "Finished deployment.\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "Finished deployment.\n" ] } ], @@ -344,6 +252,146 @@ "index = VectorStoreIndex(nodes, storage_context=storage_context)" ] }, + { + "cell_type": "markdown", + "id": "71a4a3ec", + "metadata": {}, + "source": [ + "### Deleting documents\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f4637a79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'93526dbf-1a37-4d48-8bfb-39f651ea5e2b'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "node_to_delete = nodes[0].node_id\n", + "node_to_delete" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "84a97903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted node with id 93526dbf-1a37-4d48-8bfb-39f651ea5e2b\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Deleted node with id 93526dbf-1a37-4d48-8bfb-39f651ea5e2b\n" + ] + } + ], + "source": [ + "vector_store.delete(ref_doc_id=node_to_delete)" + ] + }, + { + "cell_type": "markdown", + "id": "03315550", + "metadata": {}, + "source": [ + "## Querying\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "74cabf95", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d6401e25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=1, doc_ids=None, node_ids=None, query_str='Great Gatsby', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=1, doc_ids=None, node_ids=None, query_str='Great Gatsby', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vespa Query body:\n", + " {'hits': 1, 'ranking.profile': 'bm25', 'query': 'Great Gatsby', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 1, 'ranking.profile': 'bm25', 'query': 'Great Gatsby', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + } + ], + "source": [ + "query = VectorStoreQuery(\n", + " query_str=\"Great Gatsby\",\n", + " mode=VectorStoreQueryMode.TEXT_SEARCH,\n", + " similarity_top_k=1,\n", + ")\n", + "result = vector_store.query(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "09f1bf81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "VectorStoreQueryResult(nodes=[TextNode(id_='aa905982-f6df-4404-bf03-57d74661a83a', embedding=None, metadata={'id': '6', 'author': 'F. Scott Fitzgerald', 'theme': 'The American Dream', 'year': 1925}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')], similarities=[4.304484021249592], ids=['aa905982-f6df-4404-bf03-57d74661a83a'])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, { "cell_type": "markdown", "id": "77d2528e", @@ -352,9 +400,17 @@ "## As retriever\n" ] }, + { + "cell_type": "markdown", + "id": "a8d7aca1", + "metadata": {}, + "source": [ + "### Default query mode (text search)\n" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "id": "5a71818e", "metadata": {}, "outputs": [ @@ -362,33 +418,55 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Vespa Query body:\n", " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, { "data": { + "text/markdown": [ + "**Retrieved nodes:**\n", + " [NodeWithScore(node=TextNode(id_='b0d3a693-6868-4ea7-912c-71c501273e9e', embedding=None, metadata={'id': '3', 'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.9082661238647787)]" + ], "text/plain": [ - "[NodeWithScore(node=TextNode(id_='c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)]" + "" ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "retriever = index.as_retriever(vector_store_query_mode=\"default\")\n", - "retriever.retrieve(\"Who directed inception?\")" + "results = retriever.retrieve(\"Who directed inception?\")\n", + "display(Markdown(f\"**Retrieved nodes:**\\n {results}\"))" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 30, "id": "bfe83ebf", "metadata": {}, "outputs": [ @@ -396,31 +474,64 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "WARNING:llama_index.vector_stores.vespa.base:Embedding field not provided. Using default embedding field embedding\n", - "Embedding field not provided. Using default embedding field embedding\n", - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who directed inception?)'}\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who wrote Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who wrote Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding field not provided. Using default embedding field embedding\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:llama_index.vector_stores.vespa.base:Embedding field not provided. Using default embedding field embedding\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who directed inception?)'}\n" + " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who wrote Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who wrote Harry Potter?)'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who wrote Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who wrote Harry Potter?)'}\n" ] }, { "data": { + "text/markdown": [ + "**Retrieved nodes:**\n", + " [NodeWithScore(node=TextNode(id_='89045517-cba4-4fb6-88d2-deed2ca412ea', embedding=None, metadata={'id': '7', 'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03278688524590164)]" + ], "text/plain": [ - "[NodeWithScore(node=TextNode(id_='c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03278688524590164),\n", - " NodeWithScore(node=TextNode(id_='830e48de-cef2-4d5c-a03a-dd59e2a0b418', embedding=None, metadata={'author': 'George Orwell', 'theme': 'Totalitarianism', 'year': 1949}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03225806451612903)]" + "" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "retriever = index.as_retriever(vector_store_query_mode=\"semantic_hybrid\")\n", - "retriever.retrieve(\"Who directed inception?\")" + "results = retriever.retrieve(\"Who wrote Harry Potter?\")\n", + "display(Markdown(f\"**Retrieved nodes:**\\n {results}\"))" ] }, { @@ -433,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 31, "id": "c1bd18f8", "metadata": {}, "outputs": [ @@ -441,61 +552,210 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" ] }, { "data": { + "text/markdown": [ + "**Response:** Christopher Nolan" + ], "text/plain": [ - "Response(response='Christopher Nolan', source_nodes=[NodeWithScore(node=TextNode(id_='c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3017175961610485)], metadata={'c9d6c88f-e4e1-4ea1-9ad7-1dd6754351a0': {'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}})" + "" ] }, - "execution_count": 11, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "query_engine = index.as_query_engine()\n", - "query_engine.query(\"Who directed inception?\")" + "response = query_engine.query(\"Who directed inception?\")\n", + "display(Markdown(f\"**Response:** {response}\"))" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "0663ab38", + "execution_count": 37, + "id": "aede9cf6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n", - "WARNING:llama_index.vector_stores.vespa.base:Filter support not implemented yet. Will be ignored.\n", - "Filter support not implemented yet. Will be ignored.\n", + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='When was the book about the wizard boy published and what was it called?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='When was the book about the wizard boy published and what was it called?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding field not provided. Using default embedding field embedding\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:llama_index.vector_stores.vespa.base:Embedding field not provided. Using default embedding field embedding\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'When was the book about the wizard boy published and what was it called?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(When was the book about the wizard boy published and what was it called?)'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n", + " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'When was the book about the wizard boy published and what was it called?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(When was the book about the wizard boy published and what was it called?)'}\n" + ] + }, + { + "data": { + "text/markdown": [ + "**Response:** The book about the wizard boy was published in 1997 and it was called \"Harry Potter and the Philosopher's Stone.\"" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Sources:** [NodeWithScore(node=TextNode(id_='89045517-cba4-4fb6-88d2-deed2ca412ea', embedding=None, metadata={'id': '7', 'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03278688524590164)]" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query_engine = index.as_query_engine(\n", + " vector_store_query_mode=\"semantic_hybrid\", verbose=True\n", + ")\n", + "response = query_engine.query(\n", + " \"When was the book about the wizard boy published and what was it called?\"\n", + ")\n", + "display(Markdown(f\"**Response:** {response}\"))\n", + "display(Markdown(f\"**Sources:** {response.source_nodes}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "90081efd", + "metadata": {}, + "source": [ + "## Using metadata filters\n", + "\n", + "**NOTE**: This metadata filtering is done by llama-index, outside of vespa. For native and much more performant filtering, you should use Vespa's own filtering capabilities.\n", + "\n", + "See [Vespa's documentation](https://docs.vespa.ai/en/reference/query-language-reference.html) for more information.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "0663ab38", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filter support not implemented yet. Will be ignored.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:llama_index.vector_stores.vespa.base:Filter support not implemented yet. Will be ignored.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", + " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" ] }, { "data": { + "text/markdown": [ + "**Result:** [NodeWithScore(node=TextNode(id_='89045517-cba4-4fb6-88d2-deed2ca412ea', embedding=None, metadata={'id': '7', 'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.762040452832472)]" + ], "text/plain": [ - "[NodeWithScore(node=TextNode(id_='180bb151-3eb8-4bb5-92be-5a7753c23fb7', embedding=None, metadata={'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.1663224434456927)]" + "" ] }, - "execution_count": 13, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -506,6 +766,8 @@ " MetadataFilters,\n", ")\n", "\n", + "# Let's define a filter that will only allow nodes that has the theme \"Fiction\" OR is published after 1997\n", + "\n", "filters = MetadataFilters(\n", " filters=[\n", " MetadataFilter(key=\"theme\", value=\"Fiction\"),\n", @@ -515,16 +777,124 @@ ")\n", "\n", "retriever = index.as_retriever(filters=filters)\n", - "retriever.retrieve(\"Harry Potter?\")" + "result = retriever.retrieve(\"Harry Potter\")\n", + "display(Markdown(f\"**Result:** {result}\"))" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "5fb9bc14", + "cell_type": "markdown", + "id": "414e6d78", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## Abstraction level of this integration\n", + "\n", + "To make it really simple to get started, we provide a template Vespa application that will be deployed upon initializing the vector store. This removes some of the complexity of setting up Vespa for the first time, but for serious use cases, we strongly recommend that you read the [Vespa documentation](docs.vespa.ai) and tailor the application to your needs.\n", + "\n", + "### The template\n", + "\n", + "The provided template Vespa application can be seen below:\n", + "\n", + "```python\n", + "from vespa.package import (\n", + " ApplicationPackage,\n", + " Field,\n", + " Schema,\n", + " Document,\n", + " HNSW,\n", + " RankProfile,\n", + " Component,\n", + " Parameter,\n", + " FieldSet,\n", + " GlobalPhaseRanking,\n", + " Function,\n", + ")\n", + "\n", + "hybrid_template = ApplicationPackage(\n", + " name=\"hybridsearch\",\n", + " schema=[\n", + " Schema(\n", + " name=\"doc\",\n", + " document=Document(\n", + " fields=[\n", + " Field(name=\"id\", type=\"string\", indexing=[\"summary\"]),\n", + " Field(name=\"metadata\", type=\"string\", indexing=[\"summary\"]),\n", + " Field(\n", + " name=\"text\",\n", + " type=\"string\",\n", + " indexing=[\"index\", \"summary\"],\n", + " index=\"enable-bm25\",\n", + " bolding=True,\n", + " ),\n", + " Field(\n", + " name=\"embedding\",\n", + " type=\"tensor(x[384])\",\n", + " indexing=[\n", + " \"input text\",\n", + " \"embed\",\n", + " \"index\",\n", + " \"attribute\",\n", + " ],\n", + " ann=HNSW(distance_metric=\"angular\"),\n", + " is_document_field=False,\n", + " ),\n", + " ]\n", + " ),\n", + " fieldsets=[FieldSet(name=\"default\", fields=[\"text\", \"metadata\"])],\n", + " rank_profiles=[\n", + " RankProfile(\n", + " name=\"bm25\",\n", + " inputs=[(\"query(q)\", \"tensor(x[384])\")],\n", + " functions=[Function(name=\"bm25sum\", expression=\"bm25(text)\")],\n", + " first_phase=\"bm25sum\",\n", + " ),\n", + " RankProfile(\n", + " name=\"semantic\",\n", + " inputs=[(\"query(q)\", \"tensor(x[384])\")],\n", + " first_phase=\"closeness(field, embedding)\",\n", + " ),\n", + " RankProfile(\n", + " name=\"fusion\",\n", + " inherits=\"bm25\",\n", + " inputs=[(\"query(q)\", \"tensor(x[384])\")],\n", + " first_phase=\"closeness(field, embedding)\",\n", + " global_phase=GlobalPhaseRanking(\n", + " expression=\"reciprocal_rank_fusion(bm25sum, closeness(field, embedding))\",\n", + " rerank_count=1000,\n", + " ),\n", + " ),\n", + " ],\n", + " )\n", + " ],\n", + " components=[\n", + " Component(\n", + " id=\"e5\",\n", + " type=\"hugging-face-embedder\",\n", + " parameters=[\n", + " Parameter(\n", + " \"transformer-model\",\n", + " {\n", + " \"url\": \"https://github.com/vespa-engine/sample-apps/raw/master/simple-semantic-search/model/e5-small-v2-int8.onnx\"\n", + " },\n", + " ),\n", + " Parameter(\n", + " \"tokenizer-model\",\n", + " {\n", + " \"url\": \"https://raw.githubusercontent.com/vespa-engine/sample-apps/master/simple-semantic-search/model/tokenizer.json\"\n", + " },\n", + " ),\n", + " ],\n", + " )\n", + " ],\n", + ")\n", + "```\n", + "\n", + "Note that the fields `id`, `metadata`, `text`, and `embedding` are required for the integration to work.\n", + "The schema name must also be `doc`, and the rank profiles must be named `bm25`, `semantic`, and `fusion`.\n", + "\n", + "Other than that you are free to modify as you see fit by switching out embedding models, adding more fields, or changing the ranking expressions.\n", + "\n", + "For more details, check out this Pyvespa example notebook on [hybrid search](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa.html).\n" + ] } ], "metadata": { From 087a9b19f3121c8a360acacecf7ed96ca67b37cf Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 12:52:50 +0200 Subject: [PATCH 27/41] make format --- .../llama-index-vector-stores-vespa/README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md index caee71a93f376..0956439e74517 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/README.md @@ -43,7 +43,9 @@ hybrid_template = ApplicationPackage( document=Document( fields=[ Field(name="id", type="string", indexing=["summary"]), - Field(name="metadata", type="string", indexing=["summary"]), + Field( + name="metadata", type="string", indexing=["summary"] + ), Field( name="text", type="string", @@ -70,7 +72,9 @@ hybrid_template = ApplicationPackage( RankProfile( name="bm25", inputs=[("query(q)", "tensor(x[384])")], - functions=[Function(name="bm25sum", expression="bm25(text)")], + functions=[ + Function(name="bm25sum", expression="bm25(text)") + ], first_phase="bm25sum", ), RankProfile( From 56e51ea27b04691c1eec1d615c732e9402bf63c1 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 13:00:50 +0200 Subject: [PATCH 28/41] make lint --- .../llama-index-vector-stores-vespa/pyproject.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml index 34730638365d6..3d39c19f43bbe 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml @@ -34,7 +34,6 @@ python = ">=3.8.1,<4.0" llama-index-core = "^0.10.1" pyvespa = "^0.40.0" - [tool.poetry.group.dev.dependencies] ipython = "8.10.0" jupyter = "^1.0.0" @@ -42,8 +41,8 @@ mypy = "0.991" pre-commit = "3.2.0" pylint = "2.15.10" pytest = "7.2.1" -pytest-mock = "3.11.1" pytest-asyncio = "0.23.6" +pytest-mock = "3.11.1" ruff = "0.0.292" tree-sitter-languages = "^1.8.0" types-Deprecated = ">=0.1.0" From b9830d6efee4f3fec74b37f483829b513cee9fcd Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 17:26:50 +0200 Subject: [PATCH 29/41] fix lint --- .../llama_index/vector_stores/vespa/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 75802d4313f7a..377c74ca3d0fb 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -25,7 +25,7 @@ try: from vespa.application import Vespa from vespa.package import ApplicationPackage - from vespa.io import VespaResponse, VespaQueryResponse + from vespa.io import VespaResponse from vespa.deployment import VespaCloud, VespaDocker except ImportError: raise ModuleNotFoundError( From f840b2cc6684be62d3a43a71262995d3bc80751f Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Thu, 2 May 2024 17:32:42 +0200 Subject: [PATCH 30/41] add pyvespa as dev-dep --- .../llama-index-vector-stores-vespa/pyproject.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml index 3d39c19f43bbe..9830a544a05c4 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml @@ -8,11 +8,11 @@ check-hidden = true skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" [tool.llamahub] -contains_example = false +contains_example = true import_path = "llama_index.vector_stores.vespa" [tool.llamahub.class_authors] -VespaVectorStore = "llama-index" +VespaVectorStore = "thomasht86" [tool.mypy] disallow_untyped_defs = true @@ -43,6 +43,7 @@ pylint = "2.15.10" pytest = "7.2.1" pytest-asyncio = "0.23.6" pytest-mock = "3.11.1" +pyvespa = "0.40.0" ruff = "0.0.292" tree-sitter-languages = "^1.8.0" types-Deprecated = ">=0.1.0" From 0e745a7da5f8e58bd6a4bda48f78b819dc0851b1 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Fri, 3 May 2024 06:03:04 +0200 Subject: [PATCH 31/41] remove poetry lockfile --- .../poetry.lock | 4582 ----------------- 1 file changed, 4582 deletions(-) delete mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock deleted file mode 100644 index 9780fac0ba89c..0000000000000 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/poetry.lock +++ /dev/null @@ -1,4582 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - 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-[package.dependencies] -idna = ">=2.0" -multidict = ">=4.0" - -[[package]] -name = "zipp" -version = "3.18.1" -description = "Backport of pathlib-compatible object wrapper for zip files" -optional = false -python-versions = ">=3.8" -files = [ - {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"}, - {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, -] - -[package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] - -[metadata] -lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "a3696d87aa9c993b4081bf9bde35a0b28c0a4627495c123b8f4a272b1299afb5" From 56402987395f4f37e853fb054608da226efde4b0 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Fri, 3 May 2024 06:11:50 +0200 Subject: [PATCH 32/41] aquery match query --- .../llama_index/vector_stores/vespa/base.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 377c74ca3d0fb..92acd74a793ba 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -463,14 +463,26 @@ def query( return VectorStoreQueryResult(nodes=nodes, ids=ids, similarities=similarities) async def aquery( - self, query: VectorStoreQuery, **kwargs: Any + self, + query: VectorStoreQuery, + sources: Optional[List[str]] = None, + rank_profile: Optional[str] = None, + vector_top_k: int = 10, + **kwargs: Any, ) -> VectorStoreQueryResult: """ Asynchronously query vector store. NOTE: this is not implemented for all vector stores. If not implemented, it will just call query synchronously. """ - return self.query(query, **kwargs) + logger.info("Async query not implemented. Will call query synchronously.") + return self.query( + query=query, + sources=sources, + rank_profile=rank_profile, + vector_top_k=vector_top_k, + **kwargs, + ) def persist( self, From c214df1188db3153ecb66752a414020da3b8b017 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Fri, 3 May 2024 06:34:00 +0200 Subject: [PATCH 33/41] Make use of namespace more clear --- .../llama_index/vector_stores/vespa/base.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 92acd74a793ba..56ed4e27ea37f 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -74,7 +74,7 @@ class VespaVectorStore(VectorStore): deployment_target (str): Deployment target, either `local` or `cloud` port (int): Port that Vespa application will run on. Only applicable if deployment_target is `local` default_schema_name (str): Schema name in Vespa application - namespace (str): Namespace in Vespa application + namespace (str): Namespace in Vespa application. See https://docs.vespa.ai/en/documents.html#namespace. Defaults to `default`. embeddings_outside_vespa (bool): Whether embeddings are created outside Vespa, or not. url (Optional[str]): URL of deployed Vespa application. groupname (Optional[str]): Group name in Vespa application, only applicable in `streaming` mode, see https://pyvespa.readthedocs.io/en/latest/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.html#A-summary-of-Vespa-streaming-mode @@ -115,7 +115,7 @@ def __init__( url: Optional[str] = None, groupname: Optional[str] = None, tenant: Optional[str] = None, - application: Optional[str] = None, + application: Optional[str] = "hybridsearch", key_location: Optional[str] = None, key_content: Optional[str] = None, auth_client_token_id: Optional[str] = None, @@ -140,6 +140,7 @@ def __init__( self.url = url self.groupname = groupname self.tenant = tenant + self.application = application self.key_location = key_location self.key_content = key_content self.auth_client_token_id = auth_client_token_id @@ -176,12 +177,14 @@ def _deploy(self) -> Vespa: return app def _deploy_app_local(self) -> Vespa: + logger.info(f"Deploying Vespa application {self.application} to Vespa Docker.") return VespaDocker(port=8080).deploy(self.application_package) def _deploy_app_cloud(self) -> Vespa: + logger.info(f"Deploying Vespa application {self.application} to Vespa Cloud.") return VespaCloud( tenant=self.tenant, - application="hybridsearch", + application=self.application, application_package=self.application_package, key_location=self.key_location, key_content=self.key_content, From c6e0905b0e8247e27aa5c61d77df983511fae409 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Fri, 3 May 2024 06:40:12 +0200 Subject: [PATCH 34/41] arg to adelete --- .../llama_index/vector_stores/vespa/base.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index 56ed4e27ea37f..eb956fdff1c2e 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -317,12 +317,15 @@ def delete( ) logger.info(f"Deleted node with id {ref_doc_id}") - async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: + async def adelete( + self, ref_doc_id: str, namespace: Optional[str] = None, **delete_kwargs: Any + ) -> None: """ Delete nodes using with ref_doc_id. NOTE: this is not implemented for all vector stores. If not implemented, it will just call delete synchronously. """ + logger.info("Async delete not implemented. Will call delete synchronously.") self.delete(ref_doc_id, **delete_kwargs) def _create_query_body( From f74f96ef6224e7dab07117763218f18e2a77137b Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Fri, 3 May 2024 18:18:49 +0200 Subject: [PATCH 35/41] strip output from demonotebook --- .../vector_stores/VespaIndexDemo.ipynb | 401 ++---------------- 1 file changed, 29 insertions(+), 372 deletions(-) diff --git a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb index da7722331d85c..2d7861f4d0773 100644 --- a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0beb6603", "metadata": {}, "outputs": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "08ad68ce", "metadata": {}, "outputs": [], @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "0a2bcc07", "metadata": {}, "outputs": [], @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "df6b6d46", "metadata": {}, "outputs": [], @@ -208,42 +208,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "30b0b2e3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using default hybrid template. Please make sure that the Vespa application is set up with the correct schema and rank profile.\n", - "Waiting for configuration server, 0/300 seconds...\n", - "Waiting for configuration server, 5/300 seconds...\n", - "Waiting for configuration server, 10/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 0/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 5/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 10/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 15/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 20/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 25/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 30/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 35/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Waiting for application status, 40/300 seconds...\n", - "Using plain http against endpoint http://localhost:8080/ApplicationStatus\n", - "Application is up!\n", - "Finished deployment.\n" - ] - } - ], + "outputs": [], "source": [ "from llama_index.core import StorageContext\n", "\n", @@ -262,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "f4637a79", "metadata": {}, "outputs": [ @@ -272,7 +240,7 @@ "'93526dbf-1a37-4d48-8bfb-39f651ea5e2b'" ] }, - "execution_count": 8, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "84a97903", "metadata": {}, "outputs": [ @@ -317,51 +285,23 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "74cabf95", "metadata": {}, "outputs": [], "source": [ - "from llama_index.core.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode" + "from llama_index.core.vector_stores.types import (\n", + " VectorStoreQuery,\n", + " VectorStoreQueryMode,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "d6401e25", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=1, doc_ids=None, node_ids=None, query_str='Great Gatsby', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=1, doc_ids=None, node_ids=None, query_str='Great Gatsby', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vespa Query body:\n", - " {'hits': 1, 'ranking.profile': 'bm25', 'query': 'Great Gatsby', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 1, 'ranking.profile': 'bm25', 'query': 'Great Gatsby', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - } - ], + "outputs": [], "source": [ "query = VectorStoreQuery(\n", " query_str=\"Great Gatsby\",\n", @@ -373,21 +313,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "09f1bf81", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VectorStoreQueryResult(nodes=[TextNode(id_='aa905982-f6df-4404-bf03-57d74661a83a', embedding=None, metadata={'id': '6', 'author': 'F. Scott Fitzgerald', 'theme': 'The American Dream', 'year': 1925}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')], similarities=[4.304484021249592], ids=['aa905982-f6df-4404-bf03-57d74661a83a'])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result" ] @@ -410,54 +339,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "5a71818e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "data": { - "text/markdown": [ - "**Retrieved nodes:**\n", - " [NodeWithScore(node=TextNode(id_='b0d3a693-6868-4ea7-912c-71c501273e9e', embedding=None, metadata={'id': '3', 'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.9082661238647787)]" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "retriever = index.as_retriever(vector_store_query_mode=\"default\")\n", "results = retriever.retrieve(\"Who directed inception?\")\n", @@ -466,68 +351,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "bfe83ebf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who wrote Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who wrote Harry Potter?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Embedding field not provided. Using default embedding field embedding\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:llama_index.vector_stores.vespa.base:Embedding field not provided. Using default embedding field embedding\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who wrote Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who wrote Harry Potter?)'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'Who wrote Harry Potter?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(Who wrote Harry Potter?)'}\n" - ] - }, - { - "data": { - "text/markdown": [ - "**Retrieved nodes:**\n", - " [NodeWithScore(node=TextNode(id_='89045517-cba4-4fb6-88d2-deed2ca412ea', embedding=None, metadata={'id': '7', 'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03278688524590164)]" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "retriever = index.as_retriever(vector_store_query_mode=\"semantic_hybrid\")\n", "results = retriever.retrieve(\"Who wrote Harry Potter?\")\n", @@ -544,53 +371,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "c1bd18f8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Who directed inception?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Who directed inception?', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "data": { - "text/markdown": [ - "**Response:** Christopher Nolan" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "query_engine = index.as_query_engine()\n", "response = query_engine.query(\"Who directed inception?\")\n", @@ -599,79 +383,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "aede9cf6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='When was the book about the wizard boy published and what was it called?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='When was the book about the wizard boy published and what was it called?', output_fields=None, embedding_field=None, mode=, alpha=None, filters=None, mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Embedding field not provided. Using default embedding field embedding\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:llama_index.vector_stores.vespa.base:Embedding field not provided. Using default embedding field embedding\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'When was the book about the wizard boy published and what was it called?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(When was the book about the wizard boy published and what was it called?)'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'fusion', 'query': 'When was the book about the wizard boy published and what was it called?', 'tracelevel': 9, 'yql': 'select * from sources * where rank({targetHits:10}nearestNeighbor(embedding,q), userQuery()) limit 2', 'input.query(q)': 'embed(When was the book about the wizard boy published and what was it called?)'}\n" - ] - }, - { - "data": { - "text/markdown": [ - "**Response:** The book about the wizard boy was published in 1997 and it was called \"Harry Potter and the Philosopher's Stone.\"" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Sources:** [NodeWithScore(node=TextNode(id_='89045517-cba4-4fb6-88d2-deed2ca412ea', embedding=None, metadata={'id': '7', 'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03278688524590164)]" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "query_engine = index.as_query_engine(\n", " vector_store_query_mode=\"semantic_hybrid\", verbose=True\n", @@ -697,67 +412,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "0663ab38", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Query: VectorStoreQuery(query_embedding=None, similarity_top_k=2, doc_ids=None, node_ids=[], query_str='Harry Potter', output_fields=None, embedding_field=None, mode=, alpha=None, filters=MetadataFilters(filters=[MetadataFilter(key='theme', value='Fiction', operator=), MetadataFilter(key='year', value=1997, operator='>)], condition=), mmr_threshold=None, sparse_top_k=None, hybrid_top_k=None)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filter support not implemented yet. Will be ignored.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:llama_index.vector_stores.vespa.base:Filter support not implemented yet. Will be ignored.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Vespa Query body:\n", - " {'hits': 2, 'ranking.profile': 'bm25', 'query': 'Harry Potter', 'tracelevel': 9, 'yql': 'select * from sources * where userQuery()'}\n" - ] - }, - { - "data": { - "text/markdown": [ - "**Result:** [NodeWithScore(node=TextNode(id_='89045517-cba4-4fb6-88d2-deed2ca412ea', embedding=None, metadata={'id': '7', 'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.762040452832472)]" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from llama_index.core.vector_stores import (\n", " FilterOperator,\n", @@ -912,8 +570,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" + "pygments_lexer": "ipython3" } }, "nbformat": 4, From ccde6125cc5881e59463f68eb6a9b50def9dc772 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Sat, 4 May 2024 05:58:00 +0200 Subject: [PATCH 36/41] lint --- .../vector_stores/VespaIndexDemo.ipynb | 47 ++----------------- 1 file changed, 3 insertions(+), 44 deletions(-) diff --git a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb index 2d7861f4d0773..9d3c3e59fd194 100644 --- a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb @@ -44,22 +44,7 @@ "execution_count": null, "id": "0beb6603", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\thoma\\Documents\\Repos\\llama_index\\.venv\\Scripts\\python.exe: No module named pip\n" - ] - } - ], + "outputs": [], "source": [ "%pip install llama-index-vector-stores-vespa llama-index pyvespa" ] @@ -233,18 +218,7 @@ "execution_count": null, "id": "f4637a79", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'93526dbf-1a37-4d48-8bfb-39f651ea5e2b'" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "node_to_delete = nodes[0].node_id\n", "node_to_delete" @@ -255,22 +229,7 @@ "execution_count": null, "id": "84a97903", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deleted node with id 93526dbf-1a37-4d48-8bfb-39f651ea5e2b\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.vespa.base:Deleted node with id 93526dbf-1a37-4d48-8bfb-39f651ea5e2b\n" - ] - } - ], + "outputs": [], "source": [ "vector_store.delete(ref_doc_id=node_to_delete)" ] From be0206dfe58b825cb4678b27e5bd446cbde3b372 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Sat, 4 May 2024 06:02:38 +0200 Subject: [PATCH 37/41] black reformat --- .../llama_index/vector_stores/vespa/base.py | 15 ++++++++++++--- .../tests/test_vespavectorstore.py | 5 ++++- 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py index eb956fdff1c2e..74ec8d657dc06 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/base.py @@ -300,7 +300,10 @@ async def async_add( return ids def delete( - self, ref_doc_id: str, namespace: Optional[str] = None, **delete_kwargs: Any + self, + ref_doc_id: str, + namespace: Optional[str] = None, + **delete_kwargs: Any, ) -> None: """ Delete nodes using with ref_doc_id. @@ -318,7 +321,10 @@ def delete( logger.info(f"Deleted node with id {ref_doc_id}") async def adelete( - self, ref_doc_id: str, namespace: Optional[str] = None, **delete_kwargs: Any + self, + ref_doc_id: str, + namespace: Optional[str] = None, + **delete_kwargs: Any, ) -> None: """ Delete nodes using with ref_doc_id. @@ -415,7 +421,10 @@ def _build_query_filter( Build query filter for Vespa query. The part after "select * from {sources_str} where" in the query. """ - if mode in [VectorStoreQueryMode.SEMANTIC_HYBRID, VectorStoreQueryMode.HYBRID]: + if mode in [ + VectorStoreQueryMode.SEMANTIC_HYBRID, + VectorStoreQueryMode.HYBRID, + ]: return f"rank({{targetHits:{vector_top_k}}}nearestNeighbor({embedding_field},q), userQuery()) limit {similarity_top_k}" else: raise ValueError(f"Query mode {mode} not supported.") diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py index bb79b2031096e..b84b700195823 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -2,7 +2,10 @@ import pytest from llama_index.core.schema import TextNode -from llama_index.core.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode +from llama_index.core.vector_stores.types import ( + VectorStoreQuery, + VectorStoreQueryMode, +) from llama_index.vector_stores.vespa import VespaVectorStore, hybrid_template try: From 29047725d5fed5c8a308f8ec09f5d80ba8040c79 Mon Sep 17 00:00:00 2001 From: thomasht86 Date: Sat, 4 May 2024 06:19:48 +0200 Subject: [PATCH 38/41] more explicit imports --- .../vector_stores/vespa/templates.py | 31 +++++++++++-------- .../tests/test_vespavectorstore.py | 3 +- 2 files changed, 20 insertions(+), 14 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py index f17b7c6625839..18570bd9e620c 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/llama_index/vector_stores/vespa/templates.py @@ -1,16 +1,21 @@ -from vespa.package import ( - ApplicationPackage, - Field, - Schema, - Document, - HNSW, - RankProfile, - Component, - Parameter, - FieldSet, - GlobalPhaseRanking, - Function, -) +try: + from vespa.package import ( + ApplicationPackage, + Field, + Schema, + Document, + HNSW, + RankProfile, + Component, + Parameter, + FieldSet, + GlobalPhaseRanking, + Function, + ) +except ImportError: + raise ModuleNotFoundError( + "pyvespa not installed. Please install it via `pip install pyvespa`" + ) hybrid_template = ApplicationPackage( name="hybridsearch", diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py index b84b700195823..44bbc6b90d5b4 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/test_vespavectorstore.py @@ -6,6 +6,7 @@ VectorStoreQuery, VectorStoreQueryMode, ) +from vespa.application import ApplicationPackage from llama_index.vector_stores.vespa import VespaVectorStore, hybrid_template try: @@ -21,7 +22,7 @@ # Assuming Vespa services are mocked or local Vespa Docker is used @pytest.fixture(scope="session") def vespa_app(): - app_package = hybrid_template + app_package: ApplicationPackage = hybrid_template return VespaVectorStore(application_package=app_package, deployment_target="local") From 0711775aa2fdd7b8694b00927907d6b531f60edd Mon Sep 17 00:00:00 2001 From: Logan Markewich Date: Sat, 4 May 2024 14:59:15 -0600 Subject: [PATCH 39/41] fix tests --- .../vector_stores/llama-index-vector-stores-vespa/tests/BUILD | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD index dabf212d7e716..1831dad8031af 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD @@ -1 +1,3 @@ -python_tests() +python_tests( + module_mapping={"pyvespa": ["vespa"]} +) From 4873c4bdab1c78639c7e87954fe8f6b62c7f3f50 Mon Sep 17 00:00:00 2001 From: Logan Markewich Date: Sat, 4 May 2024 14:59:15 -0600 Subject: [PATCH 40/41] fix tests --- .../vector_stores/llama-index-vector-stores-vespa/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD index 0896ca890d8bf..72b97f383ad9b 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/BUILD @@ -1,3 +1,4 @@ poetry_requirements( name="poetry", + module_mapping={"pyvespa": ["vespa"]}, ) From 61ca433300ef40d5921ca5de54153407ba4457d9 Mon Sep 17 00:00:00 2001 From: Logan Markewich Date: Sat, 4 May 2024 15:26:17 -0600 Subject: [PATCH 41/41] fix tests --- .../vector_stores/llama-index-vector-stores-vespa/tests/BUILD | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD index 1831dad8031af..dabf212d7e716 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/tests/BUILD @@ -1,3 +1 @@ -python_tests( - module_mapping={"pyvespa": ["vespa"]} -) +python_tests()