From 879d4dfdf0f02391d634088f7b51f031b95c5bc6 Mon Sep 17 00:00:00 2001 From: Thomas Hjelde Thoresen Date: Wed, 8 May 2024 06:38:42 +0200 Subject: [PATCH 01/16] Add Vespa Vector Store to module guide (#13326) update module guide --- docs/docs/module_guides/storing/vector_stores.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/docs/module_guides/storing/vector_stores.md b/docs/docs/module_guides/storing/vector_stores.md index b5ff6229fb110..d6bf315dbf015 100644 --- a/docs/docs/module_guides/storing/vector_stores.md +++ b/docs/docs/module_guides/storing/vector_stores.md @@ -56,6 +56,7 @@ We are actively adding more integrations and improving feature coverage for each | Typesense | self-hosted / cloud | ✓ | | ✓ | ✓ | | | Upstash | cloud | | | | ✓ | | | Vearch | self-hosted | ✓ | | ✓ | ✓ | | +| Vespa | self-hosted / cloud | ✓ | ✓ | ✓ | ✓ | | | Vertex AI Vector Search | cloud | ✓ | | ✓ | ✓ | | | Weaviate | self-hosted / cloud | ✓ | ✓ | ✓ | ✓ | | @@ -106,6 +107,7 @@ For more details, see [Vector Store Integrations](../../community/integrations/v - [Timesacle](../../examples/vector_stores/Timescalevector.ipynb) - [Upstash](../../examples/vector_stores/UpstashVectorDemo.ipynb) - [Vearch](../../examples/vector_stores/VearchDemo.ipynb) +- [Vespa](../../examples/vector_stores/VespaIndexDemo.ipynb) - [Vertex AI Vector Search](../../examples/vector_stores/VertexAIVectorSearchDemo.ipynb) - [Weaviate](../../examples/vector_stores/WeaviateIndexDemo.ipynb) - [Weaviate Hybrid Search](../../examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb) From d089b78198add300ea06b8c874234fc2c6d8f172 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Wed, 8 May 2024 10:33:32 -0400 Subject: [PATCH 02/16] fix truncate passing for aget_query_embedding and get_text_embedding_batch (#13367) * fix truncate passing for aget_query_embedding and get_text_embedding_batch * bump version to 0.1.1 --- .../llama_index/embeddings/nvidia/base.py | 6 +- .../pyproject.toml | 4 +- .../tests/test_truncate.py | 74 +++++++++++++++++++ 3 files changed, 81 insertions(+), 3 deletions(-) create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py index 4ba973cdcfeb0..c2668478089d8 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py @@ -161,7 +161,9 @@ def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: assert len(texts) <= 259, "The batch size should not be larger than 259." data = self._client.embeddings.create( - input=texts, model=self.model, extra_body={"input_type": "passage"} + input=texts, + model=self.model, + extra_body={"input_type": "passage", "truncate": self.truncate}, ).data return [d.embedding for d in data] @@ -172,7 +174,7 @@ async def _aget_query_embedding(self, query: str) -> List[float]: await self._aclient.embeddings.create( input=[query], model=self.model, - extra_body={"input_type": "query"}, + extra_body={"input_type": "query", "truncate": self.truncate}, ) ) .data[0] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/pyproject.toml index 5d88ba2cc8254..bb337a0c0b705 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-nvidia" readme = "README.md" -version = "0.1.0" +version = "0.1.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" @@ -40,7 +40,9 @@ mypy = "0.991" pre-commit = "3.2.0" pylint = "2.15.10" pytest = "7.2.1" +pytest-asyncio = "^0.23.6" pytest-mock = "3.11.1" +respx = "^0.21.1" ruff = "0.0.292" tree-sitter-languages = "^1.8.0" types-Deprecated = ">=0.1.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py new file mode 100644 index 0000000000000..708c84bebe8b7 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py @@ -0,0 +1,74 @@ +import pytest +import respx +from llama_index.embeddings.nvidia import NVIDIAEmbedding +import re +import httpx +import json + + +@pytest.fixture() +def mocked_route() -> respx.Route: + all_urls = re.compile(r".*/embeddings") + fake_response = httpx.Response( + 200, json={"data": [{"index": 0, "embedding": [1.0, 2.0, 3.0]}]} + ) + with respx.mock: + yield respx.post(all_urls).mock(return_value=fake_response) + + +@pytest.mark.parametrize("method_name", ["get_query_embedding", "get_text_embedding"]) +@pytest.mark.parametrize("truncate", ["END", "START", "NONE"]) +def test_single_tuncate(mocked_route: respx.Route, method_name: str, truncate: str): + # call the method_name method + getattr(NVIDIAEmbedding(truncate=truncate), method_name)("nvidia") + + assert mocked_route.called + request = mocked_route.calls.last.request + request_body = json.loads(request.read()) + assert "truncate" in request_body + assert request_body["truncate"] == truncate + + +@pytest.mark.parametrize("method_name", ["aget_query_embedding", "aget_text_embedding"]) +@pytest.mark.parametrize("truncate", ["END", "START", "NONE"]) +@pytest.mark.asyncio() +async def test_asingle_tuncate( + mocked_route: respx.Route, method_name: str, truncate: str +): + # call the method_name method + await getattr(NVIDIAEmbedding(truncate=truncate), method_name)("nvidia") + + assert mocked_route.called + request = mocked_route.calls.last.request + request_body = json.loads(request.read()) + assert "truncate" in request_body + assert request_body["truncate"] == truncate + + +@pytest.mark.parametrize("method_name", ["get_text_embedding_batch"]) +@pytest.mark.parametrize("truncate", ["END", "START", "NONE"]) +def test_batch_tuncate(mocked_route: respx.Route, method_name: str, truncate: str): + # call the method_name method + getattr(NVIDIAEmbedding(truncate=truncate), method_name)(["nvidia"]) + + assert mocked_route.called + request = mocked_route.calls.last.request + request_body = json.loads(request.read()) + assert "truncate" in request_body + assert request_body["truncate"] == truncate + + +@pytest.mark.parametrize("method_name", ["aget_text_embedding_batch"]) +@pytest.mark.parametrize("truncate", ["END", "START", "NONE"]) +@pytest.mark.asyncio() +async def test_abatch_tuncate( + mocked_route: respx.Route, method_name: str, truncate: str +): + # call the method_name method + await getattr(NVIDIAEmbedding(truncate=truncate), method_name)(["nvidia"]) + + assert mocked_route.called + request = mocked_route.calls.last.request + request_body = json.loads(request.read()) + assert "truncate" in request_body + assert request_body["truncate"] == truncate From a11a953e738cbda93335ede83f012914d53dc4f7 Mon Sep 17 00:00:00 2001 From: Grisha Date: Wed, 8 May 2024 08:19:34 -0700 Subject: [PATCH 03/16] Fix hidden temp directory issue for arxiv reader (#13351) * Fix hidden temp directory issue for arxiv reader * add comment base.py --------- Co-authored-by: Andrei Fajardo <92402603+nerdai@users.noreply.github.com> --- .../readers/llama-index-readers-papers/CHANGELOG.md | 6 ++++++ .../llama_index/readers/papers/arxiv/base.py | 4 +++- .../readers/llama-index-readers-papers/pyproject.toml | 2 +- 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/readers/llama-index-readers-papers/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-papers/CHANGELOG.md index 36bff877abcbe..b3813e3c4c6cd 100644 --- a/llama-index-integrations/readers/llama-index-readers-papers/CHANGELOG.md +++ b/llama-index-integrations/readers/llama-index-readers-papers/CHANGELOG.md @@ -1,5 +1,11 @@ # CHANGELOG +## [0.1.5] - 2024-05-07 + +### Bug Fixes + +- Fix issues with hidden temporary folder (#13165) + ## [0.1.2] - 2024-02-13 - Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-papers/llama_index/readers/papers/arxiv/base.py b/llama-index-integrations/readers/llama-index-readers-papers/llama_index/readers/papers/arxiv/base.py index 84eaea3280fd4..6519b4e78e689 100644 --- a/llama-index-integrations/readers/llama-index-readers-papers/llama_index/readers/papers/arxiv/base.py +++ b/llama-index-integrations/readers/llama-index-readers-papers/llama_index/readers/papers/arxiv/base.py @@ -73,7 +73,9 @@ def get_paper_metadata(filename): return paper_lookup[os.path.basename(filename)] arxiv_documents = SimpleDirectoryReader( - papers_dir, file_metadata=get_paper_metadata + papers_dir, + file_metadata=get_paper_metadata, + exclude_hidden=False, # default directory is hidden ".papers" ).load_data() # Include extra documents containing the abstracts abstract_documents = [] diff --git a/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml index 2a5cdc5a4042a..0cb2c74567e18 100644 --- a/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml @@ -29,7 +29,7 @@ license = "MIT" maintainers = ["thejessezhang"] name = "llama-index-readers-papers" readme = "README.md" -version = "0.1.4" +version = "0.1.5" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 7c656548313598f7ce7e8ce586de8244328a77b0 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Wed, 8 May 2024 11:33:54 -0400 Subject: [PATCH 04/16] set default max_tokens to 1024 (#13371) if unset the service decides and picks a value small enough that users report the model is broken or not accurate --- .../llama-index-llms-nvidia/llama_index/llms/nvidia/base.py | 2 ++ .../llms/llama-index-llms-nvidia/pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py index 173e92d921d65..61d28b47d2787 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py @@ -32,6 +32,7 @@ def __init__( model: str = DEFAULT_MODEL, nvidia_api_key: Optional[str] = None, api_key: Optional[str] = None, + max_tokens: Optional[int] = 1024, **kwargs: Any, ) -> None: api_key = get_from_param_or_env( @@ -45,6 +46,7 @@ def __init__( model=model, api_key=api_key, api_base=BASE_URL, + max_tokens=max_tokens, is_chat_model=True, default_headers={"User-Agent": "llama-index-llms-nvidia"}, **kwargs, diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml index a90fb7fcf040a..e6a493855f714 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml @@ -30,7 +30,7 @@ license = "MIT" name = "llama-index-llms-nvidia" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.1.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 11d796f9ef6ff0eb5b67aabcc451065fe216443c Mon Sep 17 00:00:00 2001 From: James Briggs <35938317+jamescalam@users.noreply.github.com> Date: Thu, 9 May 2024 00:16:55 +0800 Subject: [PATCH 05/16] feat: add attribution tag for pinecone (#13329) --- .../vector_stores/pinecone/base.py | 22 +++++++++++++------ .../legacy/vector_stores/pinecone.py | 22 +++++++++++++------ 2 files changed, 30 insertions(+), 14 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py index 80d4aa6a2edde..47934bc9bd8ab 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py @@ -73,7 +73,8 @@ def _transform_pinecone_filter_operator(operator: str) -> str: def build_dict(input_batch: List[List[int]]) -> List[Dict[str, Any]]: - """Build a list of sparse dictionaries from a batch of input_ids. + """ + Build a list of sparse dictionaries from a batch of input_ids. NOTE: taken from https://www.pinecone.io/learn/hybrid-search-intro/. @@ -97,7 +98,8 @@ def build_dict(input_batch: List[List[int]]) -> List[Dict[str, Any]]: def generate_sparse_vectors( context_batch: List[str], tokenizer: Callable ) -> List[Dict[str, Any]]: - """Generate sparse vectors from a batch of contexts. + """ + Generate sparse vectors from a batch of contexts. NOTE: taken from https://www.pinecone.io/learn/hybrid-search-intro/. @@ -109,7 +111,8 @@ def generate_sparse_vectors( def get_default_tokenizer() -> Callable: - """Get default tokenizer. + """ + Get default tokenizer. NOTE: taken from https://www.pinecone.io/learn/hybrid-search-intro/. @@ -165,7 +168,8 @@ def _to_pinecone_filter(standard_filters: MetadataFilters) -> dict: class PineconeVectorStore(BasePydanticVectorStore): - """Pinecone Vector Store. + """ + Pinecone Vector Store. In this vector store, embeddings and docs are stored within a Pinecone index. @@ -304,7 +308,9 @@ def _initialize_pinecone_client( pinecone.init(api_key=api_key, environment=environment) return pinecone.Index(index_name) else: # If new version of Pinecone client (serverless): - pinecone_instance = pinecone.Pinecone(api_key=api_key) + pinecone_instance = pinecone.Pinecone( + api_key=api_key, source_tag="llamaindex" + ) return pinecone_instance.Index(index_name) @classmethod @@ -352,7 +358,8 @@ def add( nodes: List[BaseNode], **add_kwargs: Any, ) -> List[str]: - """Add nodes to index. + """ + Add nodes to index. Args: nodes: List[BaseNode]: list of nodes with embeddings @@ -412,7 +419,8 @@ def client(self) -> Any: return self._pinecone_index def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: - """Query index for top k most similar nodes. + """ + Query index for top k most similar nodes. Args: query_embedding (List[float]): query embedding diff --git a/llama-index-legacy/llama_index/legacy/vector_stores/pinecone.py b/llama-index-legacy/llama_index/legacy/vector_stores/pinecone.py index a5875db1d7894..1e5acc44fc230 100644 --- a/llama-index-legacy/llama_index/legacy/vector_stores/pinecone.py +++ b/llama-index-legacy/llama_index/legacy/vector_stores/pinecone.py @@ -73,7 +73,8 @@ def _transform_pinecone_filter_operator(operator: str) -> str: def build_dict(input_batch: List[List[int]]) -> List[Dict[str, Any]]: - """Build a list of sparse dictionaries from a batch of input_ids. + """ + Build a list of sparse dictionaries from a batch of input_ids. NOTE: taken from https://www.pinecone.io/learn/hybrid-search-intro/. @@ -97,7 +98,8 @@ def build_dict(input_batch: List[List[int]]) -> List[Dict[str, Any]]: def generate_sparse_vectors( context_batch: List[str], tokenizer: Callable ) -> List[Dict[str, Any]]: - """Generate sparse vectors from a batch of contexts. + """ + Generate sparse vectors from a batch of contexts. NOTE: taken from https://www.pinecone.io/learn/hybrid-search-intro/. @@ -109,7 +111,8 @@ def generate_sparse_vectors( def get_default_tokenizer() -> Callable: - """Get default tokenizer. + """ + Get default tokenizer. NOTE: taken from https://www.pinecone.io/learn/hybrid-search-intro/. @@ -161,7 +164,8 @@ def _to_pinecone_filter(standard_filters: MetadataFilters) -> dict: class PineconeVectorStore(BasePydanticVectorStore): - """Pinecone Vector Store. + """ + Pinecone Vector Store. In this vector store, embeddings and docs are stored within a Pinecone index. @@ -272,7 +276,9 @@ def _initialize_pinecone_client( pinecone.init(api_key=api_key, environment=environment) return pinecone.Index(index_name) else: # If new version of Pinecone client (serverless): - pinecone_instance = pinecone.Pinecone(api_key=api_key) + pinecone_instance = pinecone.Pinecone( + api_key=api_key, source_tag="llamaindex" + ) return pinecone_instance.Index(index_name) @classmethod @@ -320,7 +326,8 @@ def add( nodes: List[BaseNode], **add_kwargs: Any, ) -> List[str]: - """Add nodes to index. + """ + Add nodes to index. Args: nodes: List[BaseNode]: list of nodes with embeddings @@ -380,7 +387,8 @@ def client(self) -> Any: return self._pinecone_index def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: - """Query index for top k most similar nodes. + """ + Query index for top k most similar nodes. Args: query_embedding (List[float]): query embedding From da1732efeb1246fc01aae9a1c1dbfe457215f786 Mon Sep 17 00:00:00 2001 From: Rosie Wood Date: Wed, 8 May 2024 21:44:16 +0100 Subject: [PATCH 06/16] Add ``fail_on_http_error`` arg to github repository reader. (#13366) * catch http error * fix missing variable * add tests * run pants tailor * update tests with mock for all requests * fix tests * skip test if no github_token * lint * vbump --------- Co-authored-by: Andrei Fajardo <92402603+nerdai@users.noreply.github.com> Co-authored-by: Andrei Fajardo --- docs/BUILD | 8 ++ .../github/repository/github_client.py | 13 +++- .../llama-index-readers-github/pyproject.toml | 2 +- .../llama-index-readers-github/tests/BUILD | 3 + .../tests/test_github_repository_reader.py | 75 +++++++++++++++++++ 5 files changed, 99 insertions(+), 2 deletions(-) create mode 100644 llama-index-integrations/readers/llama-index-readers-github/tests/BUILD create mode 100644 llama-index-integrations/readers/llama-index-readers-github/tests/test_github_repository_reader.py diff --git a/docs/BUILD b/docs/BUILD index db46e8d6c978c..589a382606557 100644 --- a/docs/BUILD +++ b/docs/BUILD @@ -1 +1,9 @@ python_sources() + +poetry_requirements( + name="poetry", +) + +python_requirements( + name="reqs", +) diff --git a/llama-index-integrations/readers/llama-index-readers-github/llama_index/readers/github/repository/github_client.py b/llama-index-integrations/readers/llama-index-readers-github/llama_index/readers/github/repository/github_client.py index 86cb7f61ddb4a..1d8057beb316d 100644 --- a/llama-index-integrations/readers/llama-index-readers-github/llama_index/readers/github/repository/github_client.py +++ b/llama-index-integrations/readers/llama-index-readers-github/llama_index/readers/github/repository/github_client.py @@ -11,6 +11,7 @@ from typing import Any, Dict, List, Optional, Protocol from dataclasses_json import DataClassJsonMixin, config +from httpx import HTTPError @dataclass @@ -221,6 +222,7 @@ def __init__( base_url: str = DEFAULT_BASE_URL, api_version: str = DEFAULT_API_VERSION, verbose: bool = False, + fail_on_http_error: bool = True, ) -> None: """ Initialize the GithubClient. @@ -232,6 +234,8 @@ def __init__( - base_url (str): Base URL for the Github API (defaults to "https://api.github.com"). - api_version (str): Github API version (defaults to "2022-11-28"). + - verbose (bool): Whether to print verbose output (defaults to False). + - fail_on_http_error (bool): Whether to raise an exception on HTTP errors (defaults to True). Raises: ValueError: If no Github token is provided. @@ -248,6 +252,7 @@ def __init__( self._base_url = base_url self._api_version = api_version self._verbose = verbose + self._fail_on_http_error = fail_on_http_error self._endpoints = { "getTree": "/repos/{owner}/{repo}/git/trees/{tree_sha}", @@ -294,7 +299,7 @@ async def request( Raises: - ImportError: If the `httpx` library is not installed. - - httpx.HTTPError: If the API request fails. + - httpx.HTTPError: If the API request fails and fail_on_http_error is True. Examples: >>> response = client.request("getTree", "GET", @@ -451,6 +456,12 @@ async def get_blob( except KeyError: print(f"Failed to get blob for {owner}/{repo}/{file_sha}") return None + except HTTPError as excp: + print(f"HTTP Exception for {excp.request.url} - {excp}") + if self._fail_on_http_error: + raise + else: + return None async def get_commit( self, diff --git a/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml index 29f6933598035..176cdb002d50f 100644 --- a/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml @@ -31,7 +31,7 @@ license = "MIT" maintainers = ["ahmetkca", "moncho", "rwood-97"] name = "llama-index-readers-github" readme = "README.md" -version = "0.1.8" +version = "0.1.9" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/readers/llama-index-readers-github/tests/BUILD b/llama-index-integrations/readers/llama-index-readers-github/tests/BUILD new file mode 100644 index 0000000000000..de1e1004dbd49 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-github/tests/BUILD @@ -0,0 +1,3 @@ +python_tests( + dependencies=['llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file'] +) diff --git a/llama-index-integrations/readers/llama-index-readers-github/tests/test_github_repository_reader.py b/llama-index-integrations/readers/llama-index-readers-github/tests/test_github_repository_reader.py new file mode 100644 index 0000000000000..c634338f5ed5e --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-github/tests/test_github_repository_reader.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +from llama_index.readers.github import GithubRepositoryReader +from llama_index.readers.github.repository.github_client import ( + GithubClient, + GitBranchResponseModel, + GitTreeResponseModel, + GitCommitResponseModel, +) +from llama_index.core.schema import Document +import os +import pytest +from httpx import HTTPError + +COMMIT_JSON = '{"sha":"a11a953e738cbda93335ede83f012914d53dc4f7","node_id":"C_kwDOIWuq59oAKGExMWE5NTNlNzM4Y2JkYTkzMzM1ZWRlODNmMDEyOTE0ZDUzZGM0Zjc","commit":{"author":{"name":"Grisha","email":"skvrd@users.noreply.github.com","date":"2024-05-08T15:19:34Z"},"committer":{"name":"GitHub","email":"noreply@github.com","date":"2024-05-08T15:19:34Z"},"message":"Fix hidden temp directory issue for arxiv reader (#13351)\\n\\n* Fix hidden temp directory issue for arxiv reader\\r\\n\\r\\n* add comment base.py\\r\\n\\r\\n---------\\r\\n\\r\\nCo-authored-by: Andrei Fajardo 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(llamahub)"},{"sha":"6519b4e78e6890dc71ecc8140876418bde453309","filename":"llama-index-integrations/readers/llama-index-readers-papers/llama_index/readers/papers/arxiv/base.py","status":"modified","additions":3,"deletions":1,"changes":4,"blob_url":"https://github.com/run-llama/llama_index/blob/a11a953e738cbda93335ede83f012914d53dc4f7/llama-index-integrations%2Freaders%2Fllama-index-readers-papers%2Fllama_index%2Freaders%2Fpapers%2Farxiv%2Fbase.py","raw_url":"https://github.com/run-llama/llama_index/raw/a11a953e738cbda93335ede83f012914d53dc4f7/llama-index-integrations%2Freaders%2Fllama-index-readers-papers%2Fllama_index%2Freaders%2Fpapers%2Farxiv%2Fbase.py","contents_url":"https://api.github.com/repos/run-llama/llama_index/contents/llama-index-integrations%2Freaders%2Fllama-index-readers-papers%2Fllama_index%2Freaders%2Fpapers%2Farxiv%2Fbase.py?ref=a11a953e738cbda93335ede83f012914d53dc4f7","patch":"@@ -73,7 +73,9 @@ def get_paper_metadata(filename):\\n return paper_lookup[os.path.basename(filename)]\\n \\n arxiv_documents = SimpleDirectoryReader(\\n- papers_dir, file_metadata=get_paper_metadata\\n+ papers_dir,\\n+ file_metadata=get_paper_metadata,\\n+ exclude_hidden=False, # default directory is hidden \\".papers\\"\\n ).load_data()\\n # Include extra documents containing the abstracts\\n abstract_documents = 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os.environ.get("GITHUB_TOKEN", None) + + +@pytest.fixture() +def mock_error(monkeypatch): + async def mock_get_blob(self, *args, **kwargs): + if self._fail_on_http_error: + raise HTTPError("Woops") + else: + return + + monkeypatch.setattr(GithubClient, "get_blob", mock_get_blob) + + async def mock_get_commit(self, *args, **kwargs): + return GitCommitResponseModel.from_json(COMMIT_JSON) + + monkeypatch.setattr(GithubClient, "get_commit", mock_get_commit) + + async def mock_get_branch(self, *args, **kwargs): + return GitBranchResponseModel.from_json(BRANCH_JSON) + + monkeypatch.setattr(GithubClient, "get_branch", mock_get_branch) + + async def mock_get_tree(self, *args, **kwargs): + return GitTreeResponseModel.from_json(TREE_JSON) + + monkeypatch.setattr(GithubClient, "get_tree", mock_get_tree) + + +@pytest.mark.skipif(not github_token, reason="No github token") +def test_fail_on_http_error_true(mock_error): + token = os.getenv("GITHUB_TOKEN") + gh_client = GithubClient(token, fail_on_http_error=True) + reader = GithubRepositoryReader(gh_client, "run-llama", "llama_index") + # test for branch + with pytest.raises(HTTPError): + reader.load_data(branch="main") + # test for commit + with pytest.raises(HTTPError): + reader.load_data(commit_sha="a11a953e738cbda93335ede83f012914d53dc4f7") + + +@pytest.mark.skipif(not github_token, reason="No github token") +def test_fail_on_http_error_false(mock_error): + token = os.getenv("GITHUB_TOKEN") + gh_client = GithubClient(token, fail_on_http_error=False) + reader = GithubRepositoryReader(gh_client, "run-llama", "llama_index") + # test for branch + documents = reader.load_data(branch="main") + assert isinstance(documents, list) + assert all(isinstance(doc, Document) for doc in documents) + # test for commit + documents = reader.load_data(commit_sha="a11a953e738cbda93335ede83f012914d53dc4f7") + assert isinstance(documents, list) + assert all(isinstance(doc, Document) for doc in documents) From 649fda11f86ad7f39f459b05b00985e3c4d3a866 Mon Sep 17 00:00:00 2001 From: Logan Date: Wed, 8 May 2024 15:15:57 -0600 Subject: [PATCH 07/16] add sync httpx client support for openai/azure (#13370) --- .../llama_index/llms/azure_openai/base.py | 8 ++++++-- .../llms/llama-index-llms-azure-openai/pyproject.toml | 2 +- .../llama_index/llms/openai/base.py | 11 +++++++---- .../llms/llama-index-llms-openai/pyproject.toml | 2 +- 4 files changed, 15 insertions(+), 8 deletions(-) diff --git a/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py b/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py index 481e80a348b43..5338e6c8ce847 100644 --- a/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py @@ -102,6 +102,7 @@ def __init__( deployment: Optional[str] = None, # custom httpx client http_client: Optional[httpx.Client] = None, + async_http_client: Optional[httpx.AsyncClient] = None, # base class system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, @@ -138,6 +139,7 @@ def __init__( api_version=api_version, callback_manager=callback_manager, http_client=http_client, + async_http_client=async_http_client, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, @@ -182,7 +184,9 @@ def _get_aclient(self) -> AsyncAzureOpenAI: ) return self._aclient - def _get_credential_kwargs(self, **kwargs: Any) -> Dict[str, Any]: + def _get_credential_kwargs( + self, is_async: bool = False, **kwargs: Any + ) -> Dict[str, Any]: if self.use_azure_ad: self._azure_ad_token = refresh_openai_azuread_token(self._azure_ad_token) self.api_key = self._azure_ad_token.token @@ -206,7 +210,7 @@ def _get_credential_kwargs(self, **kwargs: Any) -> Dict[str, Any]: "azure_ad_token_provider": self.azure_ad_token_provider, "api_version": self.api_version, "default_headers": self.default_headers, - "http_client": self._http_client, + "http_client": self._async_http_client if is_async else self._http_client, **kwargs, } diff --git a/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml index 0924c6a43c3fc..7a211c4b89eea 100644 --- a/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml @@ -29,7 +29,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-azure-openai" readme = "README.md" -version = "0.1.7" +version = "0.1.8" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py index f522e68a01c2f..49eeb1b7b16b6 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py @@ -178,6 +178,7 @@ class OpenAI(FunctionCallingLLM): _client: Optional[SyncOpenAI] = PrivateAttr() _aclient: Optional[AsyncOpenAI] = PrivateAttr() _http_client: Optional[httpx.Client] = PrivateAttr() + _async_http_client: Optional[httpx.AsyncClient] = PrivateAttr() def __init__( self, @@ -194,6 +195,7 @@ def __init__( callback_manager: Optional[CallbackManager] = None, default_headers: Optional[Dict[str, str]] = None, http_client: Optional[httpx.Client] = None, + async_http_client: Optional[httpx.AsyncClient] = None, # base class system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, @@ -234,6 +236,7 @@ def __init__( self._client = None self._aclient = None self._http_client = http_client + self._async_http_client = async_http_client def _get_client(self) -> SyncOpenAI: if not self.reuse_client: @@ -245,10 +248,10 @@ def _get_client(self) -> SyncOpenAI: def _get_aclient(self) -> AsyncOpenAI: if not self.reuse_client: - return AsyncOpenAI(**self._get_credential_kwargs()) + return AsyncOpenAI(**self._get_credential_kwargs(is_async=True)) if self._aclient is None: - self._aclient = AsyncOpenAI(**self._get_credential_kwargs()) + self._aclient = AsyncOpenAI(**self._get_credential_kwargs(is_async=True)) return self._aclient def _get_model_name(self) -> str: @@ -331,14 +334,14 @@ def _use_chat_completions(self, kwargs: Dict[str, Any]) -> bool: return kwargs["use_chat_completions"] return self.metadata.is_chat_model - def _get_credential_kwargs(self) -> Dict[str, Any]: + def _get_credential_kwargs(self, is_async: bool = False) -> Dict[str, Any]: return { "api_key": self.api_key, "base_url": self.api_base, "max_retries": self.max_retries, "timeout": self.timeout, "default_headers": self.default_headers, - "http_client": self._http_client, + "http_client": self._async_http_client if is_async else self._http_client, } def _get_model_kwargs(self, **kwargs: Any) -> Dict[str, Any]: diff --git a/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml index bdbd0f82a9c28..30716b32ec083 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml @@ -29,7 +29,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-openai" readme = "README.md" -version = "0.1.17" +version = "0.1.18" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From e64d9e31a46426bb1f3972e72bca17c14cff2ed9 Mon Sep 17 00:00:00 2001 From: Joel Rorseth Date: Wed, 8 May 2024 17:16:13 -0400 Subject: [PATCH 08/16] Fix bug where PDFReader ignores extra_info (#13369) --- .../readers/llama-index-readers-file/BUILD | 1 + .../llama_index/readers/file/docs/base.py | 11 +- .../llama-index-readers-file/pyproject.toml | 3 +- .../tests/test_docs.py | 105 ++++++++++++++++++ 4 files changed, 114 insertions(+), 6 deletions(-) create mode 100644 llama-index-integrations/readers/llama-index-readers-file/tests/test_docs.py diff --git a/llama-index-integrations/readers/llama-index-readers-file/BUILD b/llama-index-integrations/readers/llama-index-readers-file/BUILD index 0896ca890d8bf..09b1d5a6c33e1 100644 --- a/llama-index-integrations/readers/llama-index-readers-file/BUILD +++ b/llama-index-integrations/readers/llama-index-readers-file/BUILD @@ -1,3 +1,4 @@ poetry_requirements( name="poetry", + module_mapping={"fpdf2": ["fpdf"]} ) diff --git a/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py b/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py index 922e9abb7016b..ec7d600c56ba6 100644 --- a/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py +++ b/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py @@ -68,13 +68,14 @@ def load_data( # This block returns a whole PDF as a single Document if self.return_full_document: - text = "" metadata = {"file_name": file.name} + if extra_info is not None: + metadata.update(extra_info) - for page in range(num_pages): - # Extract the text from the page - page_text = pdf.pages[page].extract_text() - text += page_text + # Join text extracted from each page + text = "\n".join( + pdf.pages[page].extract_text() for page in range(num_pages) + ) docs.append(Document(text=text, metadata=metadata)) diff --git a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml index 7230482cc538c..f3ed4a6481a80 100644 --- a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml @@ -50,7 +50,7 @@ license = "MIT" maintainers = ["FarisHijazi", "Haowjy", "ephe-meral", "hursh-desai", "iamarunbrahma", "jon-chuang", "mmaatouk", "ravi03071991", "sangwongenip", "thejessezhang"] name = "llama-index-readers-file" readme = "README.md" -version = "0.1.21" +version = "0.1.22" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" @@ -67,6 +67,7 @@ pymupdf = [ ] [tool.poetry.group.dev.dependencies] +fpdf2 = "2.7.8" ipython = "8.10.0" jupyter = "^1.0.0" mypy = "0.991" diff --git a/llama-index-integrations/readers/llama-index-readers-file/tests/test_docs.py b/llama-index-integrations/readers/llama-index-readers-file/tests/test_docs.py new file mode 100644 index 0000000000000..ae34aabd71c57 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-file/tests/test_docs.py @@ -0,0 +1,105 @@ +import os +import pypdf +import pytest +import tempfile +from fpdf import FPDF +from llama_index.readers.file import PDFReader +from pathlib import Path +from typing import Dict + + +@pytest.fixture() +def multi_page_pdf() -> FPDF: + pdf = FPDF() + pdf.add_page() + pdf.set_font("Helvetica", size=12) + pdf.cell(200, 10, text="Page 1 Content", align="C") + pdf.add_page() + pdf.set_font("Helvetica", size=12) + pdf.cell(200, 10, text="Page 2 Content", align="C") + return pdf + + +@pytest.fixture() +def extra_info() -> Dict[str, str]: + return {"ABC": "abc", "DEF": "def"} + + +def test_pdfreader_loads_data_into_full_document(multi_page_pdf: FPDF) -> None: + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".pdf" + ) as temp_file: + multi_page_pdf.output(temp_file.name) + temp_file_path = Path(temp_file.name) + + reader = PDFReader(return_full_document=True) + docs = reader.load_data(temp_file_path) + + assert len(docs) == 1 + assert docs[0].text == "\n".join( + f"Page {page+1} Content" for page in range(multi_page_pdf.pages_count) + ) + + os.remove(temp_file.name) + + +def test_pdfreader_loads_data_into_multiple_documents(multi_page_pdf: FPDF) -> None: + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".pdf" + ) as temp_file: + multi_page_pdf.output(temp_file.name) + temp_file_path = Path(temp_file.name) + + reader = PDFReader(return_full_document=False) + docs = reader.load_data(temp_file_path) + + assert len(docs) == multi_page_pdf.pages_count + for page in range(multi_page_pdf.pages_count): + assert docs[page].text == f"Page {page+1} Content" + + os.remove(temp_file.name) + + +def test_pdfreader_loads_metadata_into_full_document( + multi_page_pdf: FPDF, extra_info: Dict[str, str] +) -> None: + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".pdf" + ) as temp_file: + multi_page_pdf.output(temp_file.name) + temp_file_path = Path(temp_file.name) + + expected_metadata = {"file_name": temp_file_path.name} + expected_metadata.update(extra_info) + + reader = PDFReader(return_full_document=True) + docs = reader.load_data(temp_file_path, extra_info) + + assert len(docs) == 1 + assert docs[0].metadata == expected_metadata + + os.remove(temp_file.name) + + +def test_pdfreader_loads_metadata_into_multiple_documents( + multi_page_pdf: FPDF, extra_info: Dict[str, str] +) -> None: + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".pdf" + ) as temp_file: + multi_page_pdf.output(temp_file.name) + temp_file_path = Path(temp_file.name) + + expected_metadata = {"file_name": temp_file_path.name} + expected_metadata.update(extra_info) + + reader = PDFReader(return_full_document=False) + docs = reader.load_data(temp_file_path, extra_info) + pypdf_pdf = pypdf.PdfReader(temp_file_path) + + assert len(docs) == multi_page_pdf.pages_count + for page in range(multi_page_pdf.pages_count): + expected_metadata["page_label"] = pypdf_pdf.page_labels[page] + assert docs[page].metadata == expected_metadata + + os.remove(temp_file.name) From 2c1200c4b8682b3ac22dd8c65398e6adda123423 Mon Sep 17 00:00:00 2001 From: Logan Date: Wed, 8 May 2024 15:18:29 -0600 Subject: [PATCH 09/16] use handlers from global default (#13368) --- llama-index-core/llama_index/core/callbacks/base.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/llama-index-core/llama_index/core/callbacks/base.py b/llama-index-core/llama_index/core/callbacks/base.py index cc6b67119513c..fcf27287474b5 100644 --- a/llama-index-core/llama_index/core/callbacks/base.py +++ b/llama-index-core/llama_index/core/callbacks/base.py @@ -68,6 +68,15 @@ def __init__(self, handlers: Optional[List[BaseCallbackHandler]] = None): ) handlers.append(new_handler) + # if we passed in no handlers, use the global default + if len(handlers) == 0: + from llama_index.core.settings import Settings + + # hidden var access to prevent recursion in getter + cb_manager = Settings._callback_manager + if cb_manager is not None: + handlers = cb_manager.handlers + self.handlers = handlers self._trace_map: Dict[str, List[str]] = defaultdict(list) From b0989917756509015a712ac0490e4d07c7811bf4 Mon Sep 17 00:00:00 2001 From: Shukri Date: Thu, 9 May 2024 01:35:11 +0200 Subject: [PATCH 10/16] Update WeaviateVectorStore to use weaviate client version to v4 (#13229) --- .../WeaviateIndexDemo-Hybrid.ipynb | 25 ++-- .../vector_stores/WeaviateIndexDemo.ipynb | 80 ++++++----- .../WeaviateIndex_auto_retriever.ipynb | 136 ++---------------- .../WeaviateIndex_metadata_filter.ipynb | 79 +++++++--- .../vector_stores/weaviate/base.py | 129 +++++++---------- .../vector_stores/weaviate/utils.py | 49 +++---- .../pyproject.toml | 4 +- 7 files changed, 211 insertions(+), 291 deletions(-) diff --git a/docs/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb b/docs/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb index eebbe624a56fb..c0240e1105104 100644 --- a/docs/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb +++ b/docs/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb @@ -72,24 +72,25 @@ { "cell_type": "code", "execution_count": null, - "id": "72a4b618-668d-4713-84c5-6362030e9f19", + "id": "6ac755d4", "metadata": {}, "outputs": [], "source": [ - "import weaviate" + "import os\n", + "import openai\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", + "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" ] }, { "cell_type": "code", "execution_count": null, - "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84", + "id": "72a4b618-668d-4713-84c5-6362030e9f19", "metadata": {}, "outputs": [], "source": [ - "resource_owner_config = weaviate.AuthClientPassword(\n", - " username=\"\",\n", - " password=\"\",\n", - ")" + "import weaviate" ] }, { @@ -100,10 +101,16 @@ "outputs": [], "source": [ "# Connect to cloud instance\n", - "# client = weaviate.Client(\"https://.semi.network/\", auth_client_secret=resource_owner_config)\n", + "cluster_url = \"\"\n", + "api_key = \"\"\n", + "\n", + "client = weaviate.connect_to_wcs(\n", + " cluster_url=cluster_url,\n", + " auth_credentials=weaviate.auth.AuthApiKey(api_key),\n", + ")\n", "\n", "# Connect to local instance\n", - "client = weaviate.Client(\"http://localhost:8080\")" + "# client = weaviate.connect_to_local()" ] }, { diff --git a/docs/docs/examples/vector_stores/WeaviateIndexDemo.ipynb b/docs/docs/examples/vector_stores/WeaviateIndexDemo.ipynb index 6db9b0e8ea932..1cce4a661a184 100644 --- a/docs/docs/examples/vector_stores/WeaviateIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/WeaviateIndexDemo.ipynb @@ -66,7 +66,7 @@ "import os\n", "import openai\n", "\n", - "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY_HERE\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" ] }, @@ -102,17 +102,16 @@ "outputs": [], "source": [ "# cloud\n", - "resource_owner_config = weaviate.AuthClientPassword(\n", - " username=\"\",\n", - " password=\"\",\n", - ")\n", - "client = weaviate.Client(\n", - " \"https://llama-test-ezjahb4m.weaviate.network\",\n", - " auth_client_secret=resource_owner_config,\n", + "cluster_url = \"\"\n", + "api_key = \"\"\n", + "\n", + "client = weaviate.connect_to_wcs(\n", + " cluster_url=cluster_url,\n", + " auth_credentials=weaviate.auth.AuthApiKey(api_key),\n", ")\n", "\n", "# local\n", - "# client = weaviate.Client(\"http://localhost:8080\")" + "# client = connect_to_local()" ] }, { @@ -239,17 +238,16 @@ "metadata": {}, "outputs": [], "source": [ - "resource_owner_config = weaviate.AuthClientPassword(\n", - " username=\"\",\n", - " password=\"\",\n", - ")\n", - "client = weaviate.Client(\n", - " \"https://llama-test-ezjahb4m.weaviate.network\",\n", - " auth_client_secret=resource_owner_config,\n", + "cluster_url = \"\"\n", + "api_key = \"\"\n", + "\n", + "client = weaviate.connect_to_wcs(\n", + " cluster_url=cluster_url,\n", + " auth_credentials=weaviate.auth.AuthApiKey(api_key),\n", ")\n", "\n", "# local\n", - "# client = weaviate.Client(\"http://localhost:8080\")" + "# client = weaviate.connect_to_local()" ] }, { @@ -347,16 +345,7 @@ "execution_count": null, "id": "a0a5b319", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:llama_index.vector_stores.weaviate.base:Successfully deleted index 'LlamaIndex'.\n", - "Successfully deleted index 'LlamaIndex'.\n" - ] - } - ], + "outputs": [], "source": [ "vector_store.delete_index()" ] @@ -366,19 +355,36 @@ "execution_count": null, "id": "71932f10-3783-4f8d-a112-b90538d66971", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:llama_index.vector_stores.weaviate.base:Index 'LlamaIndex' does not exist. No action taken.\n", - "Index 'LlamaIndex' does not exist. No action taken.\n" - ] - } - ], + "outputs": [], "source": [ "vector_store.delete_index() # calling the function again does nothing" ] + }, + { + "cell_type": "markdown", + "id": "6eadc2c1", + "metadata": {}, + "source": [ + "# Connection Termination" + ] + }, + { + "cell_type": "markdown", + "id": "f7d8149a", + "metadata": {}, + "source": [ + "You must ensure your client connections are closed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ac6e54e", + "metadata": {}, + "outputs": [], + "source": [ + "client.close()" + ] } ], "metadata": { diff --git a/docs/docs/examples/vector_stores/WeaviateIndex_auto_retriever.ipynb b/docs/docs/examples/vector_stores/WeaviateIndex_auto_retriever.ipynb index c41b3e1dcf067..4f4659d491edf 100644 --- a/docs/docs/examples/vector_stores/WeaviateIndex_auto_retriever.ipynb +++ b/docs/docs/examples/vector_stores/WeaviateIndex_auto_retriever.ipynb @@ -107,18 +107,7 @@ "execution_count": null, "id": "f2819b6c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:numexpr.utils:Note: NumExpr detected 10 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "Note: NumExpr detected 10 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n", - "NumExpr defaulting to 8 threads.\n" - ] - } - ], + "outputs": [], "source": [ "from llama_index.embeddings.openai import OpenAIEmbedding\n", "from llama_index.llms.openai import OpenAI\n", @@ -143,57 +132,28 @@ "execution_count": null, "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "embedded weaviate is already listening on port 8079\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/lib/python3.11/site-packages/weaviate/warnings.py:121: DeprecationWarning: Dep005: You are using weaviate-client version 3.26.2. The latest version is 4.5.2.\n", - " Please consider upgrading to the latest version. See https://weaviate.io/developers/weaviate/client-libraries/python for details.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "'\\nimport weaviate\\n\\n# cloud\\nresource_owner_config = weaviate.AuthClientPassword(\\n username=\"\",\\n password=\"\",\\n)\\nclient = weaviate.Client(\\n \"https://test.weaviate.network\",\\n auth_client_secret=resource_owner_config,\\n)\\n\\n# local\\n# client = weaviate.Client(\"http://localhost:8081\")\\n'" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import weaviate\n", "from weaviate.embedded import EmbeddedOptions\n", "\n", "# Connect to Weaviate client in embedded mode\n", - "client = weaviate.Client(embedded_options=EmbeddedOptions())\n", + "client = weaviate.connect_to_embedded()\n", "\n", "# Enable this code if you want to use Weaviate Cloud Services instead of Embedded mode.\n", "\"\"\"\n", "import weaviate\n", "\n", "# cloud\n", - "resource_owner_config = weaviate.AuthClientPassword(\n", - " username=\"\",\n", - " password=\"\",\n", - ")\n", - "client = weaviate.Client(\n", - " \"https://test.weaviate.network\",\n", - " auth_client_secret=resource_owner_config,\n", + "cluster_url = \"\"\n", + "api_key = \"\"\n", + "\n", + "client = weaviate.connect_to_wcs(cluster_url=cluster_url,\n", + " auth_credentials=weaviate.auth.AuthApiKey(api_key), \n", ")\n", "\n", "# local\n", - "# client = weaviate.Client(\"http://localhost:8081\")\n", + "# client = weaviate.connect_to_local()\n", "\"\"\"" ] }, @@ -308,16 +268,7 @@ "execution_count": null, "id": "35369eda", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - } - ], + "outputs": [], "source": [ "index = VectorStoreIndex(nodes, storage_context=storage_context)" ] @@ -387,24 +338,7 @@ "execution_count": null, "id": "eeb18e9c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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", - "INFO:llama_index.core.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using query str: Tell me about celebrities\n", - "Using query str: Tell me about celebrities\n", - "INFO:llama_index.core.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using filters: [('country', '==', 'United States')]\n", - "Using filters: [('country', '==', 'United States')]\n", - "INFO:llama_index.core.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using top_k: 2\n", - "Using top_k: 2\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" - ] - } - ], + "outputs": [], "source": [ "response = retriever.retrieve(\"Tell me about celebrities from United States\")" ] @@ -414,20 +348,7 @@ "execution_count": null, "id": "ee543cf6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node ID: a6ba3fa4-3ced-4281-9cf3-df9e73bb92d2\n", - "Text: Angelina Jolie is an American actress, filmmaker, and\n", - "humanitarian. She has received numerous awards for her acting and is\n", - "known for her philanthropic work.\n", - "Score: 0.790\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(response[0])" ] @@ -437,24 +358,7 @@ "execution_count": null, "id": "51f00cde", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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", - "INFO:llama_index.core.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using query str: Sports celebrities\n", - "Using query str: Sports celebrities\n", - "INFO:llama_index.core.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using filters: [('category', '==', 'Sports'), ('country', '==', 'United States')]\n", - "Using filters: [('category', '==', 'Sports'), ('country', '==', 'United States')]\n", - "INFO:llama_index.core.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using top_k: 2\n", - "Using top_k: 2\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" - ] - } - ], + "outputs": [], "source": [ "response = retriever.retrieve(\n", " \"Tell me about Sports celebrities from United States\"\n", @@ -466,19 +370,7 @@ "execution_count": null, "id": "345d3ca3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node ID: 5e689253-2e2c-440d-9f72-6f9513fd2c3a\n", - "Text: Michael Jordan is a retired professional basketball player,\n", - "widely regarded as one of the greatest basketball players of all time.\n", - "Score: 0.797\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(response[0])" ] diff --git a/docs/docs/examples/vector_stores/WeaviateIndex_metadata_filter.ipynb b/docs/docs/examples/vector_stores/WeaviateIndex_metadata_filter.ipynb index 8814f04ef53ec..01f9f0fee7c48 100644 --- a/docs/docs/examples/vector_stores/WeaviateIndex_metadata_filter.ipynb +++ b/docs/docs/examples/vector_stores/WeaviateIndex_metadata_filter.ipynb @@ -66,7 +66,7 @@ "import os\n", "import openai\n", "\n", - "os.environ[\"OPENAI_API_KEY\"] = \"sk-\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" ] }, @@ -89,22 +89,32 @@ "execution_count": null, "id": "df8b27e5-5ad5-4dfe-90c7-0cf1f1d1b37f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/meta \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/meta \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://pypi.org/pypi/weaviate-client/json \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://pypi.org/pypi/weaviate-client/json \"HTTP/1.1 200 OK\"\n" + ] + } + ], "source": [ "import weaviate\n", "\n", "# cloud\n", - "resource_owner_config = weaviate.AuthClientPassword(\n", - " username=\"\",\n", - " password=\"\",\n", - ")\n", - "client = weaviate.Client(\n", - " \"https://test.weaviate.network\",\n", - " auth_client_secret=resource_owner_config,\n", + "cluster_url = \"\"\n", + "api_key = \"\"\n", + "\n", + "client = weaviate.connect_to_wcs(\n", + " cluster_url=cluster_url,\n", + " auth_credentials=weaviate.auth.AuthApiKey(api_key),\n", ")\n", "\n", "# local\n", - "# client = weaviate.Client(\"http://localhost:8081\")" + "# client = weaviate.connect_to_local()" ] }, { @@ -218,8 +228,16 @@ "name": "stdout", "output_type": "stream", "text": [ + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 404 Not Found\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 404 Not Found\"\n", + "INFO:httpx:HTTP Request: POST https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema \"HTTP/1.1 200 OK\"\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" + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/nodes \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/nodes \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/nodes \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/nodes \"HTTP/1.1 200 OK\"\n" ] } ], @@ -244,14 +262,18 @@ "output_type": "stream", "text": [ "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" + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n" ] }, { "data": { "text/plain": [ - 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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=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=1.0)]" ] }, "execution_count": null, @@ -275,14 +297,18 @@ "output_type": "stream", "text": [ "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" + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n" ] }, { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='f63fa125-e2b3-43ec-9f43-e5e2bcdb7688', embedding=[-0.0017992554, -0.023927106, -0.012763222, -0.035462487, -0.0096715875, 0.025614595, -0.0005037285, 0.00077920506, -0.021824038, -0.027831001, 0.023939699, 0.018700922, 0.028435476, -0.0018008294, 0.006447725, 0.015376314, 0.029065136, -0.008059656, 0.010395697, -0.0004836581, 0.0102949515, 0.0069199707, -0.029719982, -0.00061431265, 0.0047161584, -0.0010271339, 0.0043352135, -0.026974663, 0.021484021, -0.017517159, 0.012013925, -0.024078224, -0.0065106913, 0.0020322297, 0.010093461, -0.003085337, 0.005758247, -0.010477553, 0.0009570842, 0.014633314, 0.014003653, 0.007461479, -0.008280038, -0.016849719, 0.0058306577, -0.0027594878, 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Mockingbird', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=1.0)]" ] }, "execution_count": null, @@ -319,14 +345,17 @@ "output_type": "stream", "text": [ "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" + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n" ] }, { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='f63fa125-e2b3-43ec-9f43-e5e2bcdb7688', embedding=[-0.0017992554, -0.023927106, -0.012763222, -0.035462487, -0.0096715875, 0.025614595, -0.0005037285, 0.00077920506, -0.021824038, -0.027831001, 0.023939699, 0.018700922, 0.028435476, -0.0018008294, 0.006447725, 0.015376314, 0.029065136, -0.008059656, 0.010395697, -0.0004836581, 0.0102949515, 0.0069199707, -0.029719982, -0.00061431265, 0.0047161584, -0.0010271339, 0.0043352135, -0.026974663, 0.021484021, -0.017517159, 0.012013925, -0.024078224, -0.0065106913, 0.0020322297, 0.010093461, -0.003085337, 0.005758247, -0.010477553, 0.0009570842, 0.014633314, 0.014003653, 0.007461479, -0.008280038, -0.016849719, 0.0058306577, -0.0027594878, 0.022516666, -0.011724281, -0.01363845, 0.014570348, 0.007058496, 0.03148303, -0.014809619, -0.030173339, 0.020048395, 0.0094763925, 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200 OK\"\n" + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n", + "HTTP Request: GET https://llamaindex-pythonv4-dhqgeqxq.weaviate.network/v1/schema/LlamaIndex_filter \"HTTP/1.1 200 OK\"\n" ] }, { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='9949cb24-4f5e-4532-9eac-eaaa0b368072', embedding=[0.013188333, -0.013766769, -0.039642125, -0.007294717, -0.010765331, 0.017173113, -0.02822766, -0.05074809, -0.021042205, -0.01610622, 0.025476879, 0.014345204, 0.01700601, 0.0012805923, -0.0103797065, -0.00085801294, 0.021916285, -0.011260214, 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-0.014897171407938004, -0.01043056882917881, 0.03343898802995682, -0.023613736033439636, 0.03216463699936867, -0.01985439844429493, -0.02595854364335537, 0.005157938692718744, 0.020822906866669655, -0.0013332904782146215, -0.004982715006917715, -0.03565635904669762], metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=1.0)]" ] }, "execution_count": null, diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py index c0a1ef4da67d2..2dce3c4e12c3a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py @@ -5,7 +5,7 @@ """ import logging -from typing import Any, Dict, List, Optional, cast +from typing import Any, Dict, List, Optional, Union, cast from uuid import uuid4 from llama_index.core.bridge.pydantic import Field, PrivateAttr @@ -28,8 +28,9 @@ to_node, ) -import weaviate # noqa -from weaviate import AuthApiKey, Client +import weaviate +from weaviate import Client +import weaviate.classes as wvc _logger = logging.getLogger(__name__) @@ -37,52 +38,45 @@ def _transform_weaviate_filter_condition(condition: str) -> str: """Translate standard metadata filter op to Chroma specific spec.""" if condition == "and": - return "And" + return wvc.query.Filter.all_of elif condition == "or": - return "Or" + return wvc.query.Filter.any_of else: raise ValueError(f"Filter condition {condition} not supported") def _transform_weaviate_filter_operator(operator: str) -> str: - """Translate standard metadata filter operator to Chroma specific spec.""" + """Translate standard metadata filter operator to Weaviate specific spec.""" if operator == "!=": - return "NotEqual" + return "not_equal" elif operator == "==": - return "Equal" + return "equal" elif operator == ">": - return "GreaterThan" + return "greater_than" elif operator == "<": - return "LessThan" + return "less_than" elif operator == ">=": - return "GreaterThanEqual" + return "greater_or_equal" elif operator == "<=": - return "LessThanEqual" + return "less_or_equal" else: raise ValueError(f"Filter operator {operator} not supported") -def _to_weaviate_filter(standard_filters: MetadataFilters) -> Dict[str, Any]: +def _to_weaviate_filter( + standard_filters: MetadataFilters, +) -> Union[wvc.query.Filter, List[wvc.query.Filter]]: filters_list = [] condition = standard_filters.condition or "and" condition = _transform_weaviate_filter_condition(condition) if standard_filters.filters: for filter in standard_filters.filters: - value_type = "valueText" - if isinstance(filter.value, float): - value_type = "valueNumber" - elif isinstance(filter.value, int): - value_type = "valueInt" - elif isinstance(filter.value, str) and filter.value.isnumeric(): - filter.value = float(filter.value) - value_type = "valueNumber" filters_list.append( - { - "path": filter.key, - "operator": _transform_weaviate_filter_operator(filter.operator), - value_type: filter.value, - } + getattr( + wvc.query.Filter.by_property(filter.key), + _transform_weaviate_filter_operator(filter.operator), + )(filter.value) ) else: return {} @@ -91,7 +85,7 @@ def _to_weaviate_filter(standard_filters: MetadataFilters) -> Dict[str, Any]: # If there is only one filter, return it directly return filters_list[0] - return {"operands": filters_list, "operator": condition} + return condition(filters_list) class WeaviateVectorStore(BasePydanticVectorStore): @@ -153,14 +147,14 @@ def __init__( """Initialize params.""" if weaviate_client is None: if isinstance(auth_config, dict): - auth_config = AuthApiKey(**auth_config) + auth_config = weaviate.auth.AuthApiKey(auth_config) client_kwargs = client_kwargs or {} - self._client = Client( - url=url, auth_client_secret=auth_config, **client_kwargs + self._client = weaviate.WeaviateClient( + auth_client_secret=auth_config, **client_kwargs ) else: - self._client = cast(Client, weaviate_client) + self._client = cast(weaviate.WeaviateClient, weaviate_client) # validate class prefix starts with a capital letter if class_prefix is not None: @@ -233,7 +227,7 @@ def add( """ ids = [r.node_id for r in nodes] - with self._client.batch as batch: + with self._client.batch.dynamic() as batch: for node in nodes: add_node( self._client, @@ -288,7 +282,7 @@ def delete_index(self) -> None: ) return try: - self._client.schema.delete_class(self.index_name) + self._client.collections.delete(self.index_name) _logger.info(f"Successfully deleted index '{self.index_name}'.") except Exception as e: _logger.error(f"Failed to delete index '{self.index_name}': {e}") @@ -297,70 +291,56 @@ def delete_index(self) -> None: def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: """Query index for top k most similar nodes.""" all_properties = get_all_properties(self._client, self.index_name) - - # build query - query_builder = self._client.query.get(self.index_name, all_properties) + collection = self._client.collections.get(self.index_name) + filters = None # list of documents to constrain search if query.doc_ids: - filter_with_doc_ids = { - "operator": "Or", - "operands": [ - {"path": ["doc_id"], "operator": "Equal", "valueText": doc_id} - for doc_id in query.doc_ids - ], - } - query_builder = query_builder.with_where(filter_with_doc_ids) + filters = wvc.query.Filter.by_property("doc_id").contains_any(query.doc_ids) if query.node_ids: - filter_with_node_ids = { - "operator": "Or", - "operands": [ - {"path": ["id"], "operator": "Equal", "valueText": node_id} - for node_id in query.node_ids - ], - } - query_builder = query_builder.with_where(filter_with_node_ids) + filters = wvc.query.Filter.by_property("id").contains_any(query.node_ids) - query_builder = query_builder.with_additional( - ["id", "vector", "distance", "score"] - ) + return_metatada = wvc.query.MetadataQuery(distance=True, score=True) vector = query.query_embedding similarity_key = "distance" if query.mode == VectorStoreQueryMode.DEFAULT: _logger.debug("Using vector search") if vector is not None: - query_builder = query_builder.with_near_vector( - { - "vector": vector, - } - ) + alpha = 1 elif query.mode == VectorStoreQueryMode.HYBRID: _logger.debug(f"Using hybrid search with alpha {query.alpha}") similarity_key = "score" if vector is not None and query.query_str: - query_builder = query_builder.with_hybrid( - query=query.query_str, - alpha=query.alpha, - vector=vector, - ) + alpha = query.alpha if query.filters is not None: - filter = _to_weaviate_filter(query.filters) - query_builder = query_builder.with_where(filter) + filters = _to_weaviate_filter(query.filters) elif "filter" in kwargs and kwargs["filter"] is not None: - query_builder = query_builder.with_where(kwargs["filter"]) + filters = kwargs["filter"] - query_builder = query_builder.with_limit(query.similarity_top_k) + limit = query.similarity_top_k _logger.debug(f"Using limit of {query.similarity_top_k}") # execute query - query_result = query_builder.do() + try: + query_result = collection.query.hybrid( + query=query.query_str, + vector=vector, + alpha=alpha, + limit=limit, + filters=filters, + return_metadata=return_metatada, + return_properties=all_properties, + include_vector=True, + ) + except weaviate.exceptions.WeaviateQueryError as e: + raise ValueError(f"Invalid query, got errors: {e.message}") # parse results - parsed_result = parse_get_response(query_result) - entries = parsed_result[self.index_name] + + entries = query_result.objects similarities = [] nodes: List[BaseNode] = [] @@ -368,8 +348,9 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul for i, entry in enumerate(entries): if i < query.similarity_top_k: - similarities.append(get_node_similarity(entry, similarity_key)) - nodes.append(to_node(entry, text_key=self.text_key)) + entry_as_dict = entry.__dict__ + similarities.append(get_node_similarity(entry_as_dict, similarity_key)) + nodes.append(to_node(entry_as_dict, text_key=self.text_key)) node_ids.append(nodes[-1].node_id) else: break diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/utils.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/utils.py index 82ae48ad5117e..571af5e93d3e9 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/utils.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/utils.py @@ -8,7 +8,8 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, cast if TYPE_CHECKING: - from weaviate import Client + from weaviate import WeaviateClient + from llama_index.core.schema import BaseNode, MetadataMode, TextNode from llama_index.core.vector_stores.utils import ( @@ -47,16 +48,15 @@ def validate_client(client: Any) -> None: """Validate client and import weaviate library.""" try: - import weaviate # noqa - from weaviate import Client + import weaviate - client = cast(Client, client) + client = cast(weaviate.WeaviateClient, client) except ImportError: raise ImportError( "Weaviate is not installed. " "Please install it with `pip install weaviate-client`." ) - cast(Client, client) + cast(weaviate.WeaviateClient, client) def parse_get_response(response: Dict) -> Dict: @@ -73,10 +73,7 @@ def parse_get_response(response: Dict) -> Dict: def class_schema_exists(client: Any, class_name: str) -> bool: """Check if class schema exists.""" validate_client(client) - schema = client.schema.get() - classes = schema["classes"] - existing_class_names = {c["class"] for c in classes} - return class_name in existing_class_names + return client.collections.exists(class_name) def create_default_schema(client: Any, class_name: str) -> None: @@ -87,24 +84,22 @@ def create_default_schema(client: Any, class_name: str) -> None: "description": f"Class for {class_name}", "properties": NODE_SCHEMA, } - client.schema.create_class(class_schema) + client.collections.create_from_dict(class_schema) def get_all_properties(client: Any, class_name: str) -> List[str]: """Get all properties of a class.""" validate_client(client) - schema = client.schema.get() - classes = schema["classes"] - classes_by_name = {c["class"]: c for c in classes} - if class_name not in classes_by_name: + if not client.collections.exists(class_name): raise ValueError(f"{class_name} schema does not exist.") - schema = classes_by_name[class_name] - return [p["name"] for p in schema["properties"]] + + properties = client.collections.get(class_name).config.get().properties + return [p.name for p in properties] def get_node_similarity(entry: Dict, similarity_key: str = "distance") -> float: """Get converted node similarity from distance.""" - distance = entry["_additional"].get(similarity_key, 0.0) + distance = getattr(entry["metadata"], similarity_key) if distance is None: return 1.0 @@ -115,11 +110,13 @@ def get_node_similarity(entry: Dict, similarity_key: str = "distance") -> float: def to_node(entry: Dict, text_key: str = DEFAULT_TEXT_KEY) -> TextNode: """Convert to Node.""" - additional = entry.pop("_additional") - text = entry.pop(text_key, "") - embedding = additional.pop("vector", None) + additional = entry["metadata"].__dict__ + text = entry["properties"].pop(text_key, "") + + embedding = entry["vector"].pop("default", None) + try: - node = metadata_dict_to_node(entry) + node = metadata_dict_to_node(entry["properties"]) node.text = text node.embedding = embedding except Exception as e: @@ -139,7 +136,7 @@ def to_node(entry: Dict, text_key: str = DEFAULT_TEXT_KEY) -> TextNode: def add_node( - client: "Client", + client: "WeaviateClient", node: BaseNode, class_name: str, batch: Optional[Any] = None, @@ -159,6 +156,10 @@ def add_node( # if batch object is provided (via a context manager), use that instead if batch is not None: - batch.add_data_object(metadata, class_name, id, vector) + batch.add_object( + properties=metadata, collection=class_name, uuid=id, vector=vector + ) else: - client.batch.add_data_object(metadata, class_name, id, vector) + client.collections.get(class_name).data.insert( + properties=metadata, uuid=id, vector=vector + ) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml index c4b4f01b7d751..16f17c9919f0e 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-weaviate" readme = "README.md" -version = "0.1.5" +version = "1.0.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" llama-index-core = "^0.10.1" -weaviate-client = "^3.26.2" +weaviate-client = "^4.5.7" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" From 595a84f9df4f524b165445db0188fa539703bf57 Mon Sep 17 00:00:00 2001 From: nassarhayat Date: Wed, 8 May 2024 16:49:21 -0700 Subject: [PATCH 11/16] udpates to multion tool to align with updated api/sdk (#13373) --- .../tools/llama-index-tools-multion/README.md | 2 +- .../examples/multion.ipynb | 93 ++++++++----------- .../llama_index/tools/multion/base.py | 45 ++------- .../llama-index-tools-multion/pyproject.toml | 2 +- 4 files changed, 45 insertions(+), 97 deletions(-) diff --git a/llama-index-integrations/tools/llama-index-tools-multion/README.md b/llama-index-integrations/tools/llama-index-tools-multion/README.md index 8e732a96042bd..64203ee5eb9fc 100644 --- a/llama-index-integrations/tools/llama-index-tools-multion/README.md +++ b/llama-index-integrations/tools/llama-index-tools-multion/README.md @@ -20,7 +20,7 @@ Here's an example usage of the MultionToolSpec. from llama_index.tools.multion import MultionToolSpec from llama_index.agent.openai import OpenAIAgent -multion_tool = MultionToolSpec() +multion_tool = MultionToolSpec(api_key="your-multion-key") agent = OpenAIAgent.from_tools(multion_tool.to_tool_list()) diff --git a/llama-index-integrations/tools/llama-index-tools-multion/examples/multion.ipynb b/llama-index-integrations/tools/llama-index-tools-multion/examples/multion.ipynb index 3473ec1c35750..fefb5c0cfdbf9 100644 --- a/llama-index-integrations/tools/llama-index-tools-multion/examples/multion.ipynb +++ b/llama-index-integrations/tools/llama-index-tools-multion/examples/multion.ipynb @@ -21,7 +21,8 @@ "source": [ "# Set up OpenAI\n", "import openai\n", - "from llama_index.agent import OpenAIAgent\n", + "import llama_index\n", + "from llama_index.agent.openai import OpenAIAgent\n", "\n", "openai.api_key = \"sk-your-key\"" ] @@ -31,7 +32,7 @@ "id": "b4e2a467-bb2b-4642-993d-80b1fc852add", "metadata": {}, "source": [ - "We then import the MultiOn tool and initialize our agent with the tool. When the MultiOn tool is initialized, the user will be prompted to authenticate the session in their browser" + "We then import the MultiOn tool and initialize our agent with the tool." ] }, { @@ -39,20 +40,12 @@ "execution_count": null, "id": "77d5b1ef-876f-4b90-94e4-dfde91e77fed", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Already logged in.\n" - ] - } - ], + "outputs": [], "source": [ "# Set up MultiOn tool\n", - "from llama_index.tools.multion.base import MultionToolSpec\n", + "from llama_index.tools.multion import MultionToolSpec\n", "\n", - "multion_tool = MultionToolSpec()" + "multion_tool = MultionToolSpec(api_key=\"your-multion-key\")" ] }, { @@ -73,8 +66,8 @@ "outputs": [], "source": [ "# Import and initialize our tool spec\n", - "from llama_index.tools.gmail.base import GmailToolSpec\n", - "from llama_index.tools.ondemand_loader_tool import OnDemandLoaderTool\n", + "from llama_index.tools.google import GmailToolSpec\n", + "from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool\n", "\n", "# Initialize the Gmail tool to search our inbox\n", "gmail_tool = GmailToolSpec()\n", @@ -139,27 +132,19 @@ "name": "stdout", "output_type": "stream", "text": [ + "Added user message to memory: browse to the latest email from Julian and open the email\n", "=== Calling Function ===\n", - "Calling function: browse with args: {\n", - " \"instruction\": \"browse to the latest email from Adam and open the email\"\n", - "}\n", - "running new session\n", - "Server Disconnected. Please press connect in the Multion extension popup\n", - "Got output: {'url': 'https://mail.google.com/mail/u/0/', 'status': 'CONTINUE', 'action_completed': 'I am navigating to Gmail to find the latest email from Adam.\\n\\n', 'content': 'Mail\\n\\nChat\\n\\nfo}\\nrR\\n\\nSpaces\\n\\nOr\\n\\nMeet\\n\\n4 Gmail\\n\\n2 Compose\\nfa Inbox\\n\\nyx © Starred\\n© _ Snoozed\\nE& Sent\\n\\nvy More\\nLabels\\n\\nQ Search in mail iz\\nOr ¢G i\\n\\nAdam, me 5\\n\\nMedium\\n\\nMedium\\n\\nEnable desktop notifications for Hypotenuse Mail.\\n\\nMedium Daily Digest\\nLinda Chen\\n\\nCanva\\n\\nZapier\\n\\nSlava Timbaliuc\\nSlava Timbaliuc\\nSlava Timbaliuc\\nMedium Daily Digest\\nSlava Timbaliuc\\nMedium - Programmi.\\n1Password\\n\\nYelp San Francisco\\nMedium Daily Digest\\nLucid\\n\\nDeb at Zapier\\n1Password\\n\\nMedium Daily Digest\\nsimon\\n\\nMedium Daily Digest\\n\\n@rctver @ @ 3 Google @\\n1-50 of 1,926 >\\nHerman Miller chair request - Hi, Okay ... 12:43PM\\nThe Edition: When you talk about Al, co... 10:14AM\\nStats for your stories: Sep 1-Sep 8 - A... 8:40AM\\nExperimenting Llamalndex RouterQuer... 7:40AM\\n\\nInvitation: Donut Friends! @ Wed Sep 1... [J Sep7\\n\\nPromote earth-friendly habits with this... Sep7\\n\\nMULTI-ON\\n\\nAuto | Step\\n\\nZapConnect\\n\\nUpdated invi\\nInvitation: Off\\n\\nUpdated invi Running on mail.google.com\\n\\nbrowse to the latest\\n\\nemail from Adam and\\nLeverage Al open the email\\nJoin yiding@\\nHow | made lam navigating to Gmail to\\nfind the latest email from\\n\\nAdam.\\n\\nUpdated invi\\n\\nSynapses—\\n'}\n", - "========================\n", - "=== Calling Function ===\n", - "Calling function: browse with args: {\n", - " \"instruction\": \"open the latest email from Adam\"\n", - "}\n", - "session updated\n", - "Got output: {'url': 'https://mail.google.com/mail/u/0/', 'status': 'CONTINUE', 'action_completed': 'I am clicking on the latest email from Adam to open it.\\n\\n', 'content': \"M4 Gmail Q Search in mail iz @rctver @ } 3: Google 6\\nG@ Compose € Oo w 4 © G&G D : 10f192% < >\\nInbox Herman Miller chair request (#ethal) inbox x > eG\\nyx © Starred\\n© _ Snoozed Adam Hofmann Hey, I'm interested in having one of these deliv... 12:29PM (1hour ago) +X\\n— Sent\\nv More\\n2\\nLabels +\\n\\nCa] Adam Hofmann. Hi, the price is firm at $480 +\\n\\nAdam Hofmann\\ntome v\\n\\nHi,\\n\\nOkay | can do $500 total for the chair and delive\\n\\n1513 St. Paul Street, St Catharines, Ontario, L2F\\n\\nYou can call me at 905-328-6945\\n\\nThanks\\n\\nSee you on Monday. | | Monday it is.\\n\\nEnable desktop notifications for Hypotenuse Mail.\\n\\nMULTI-ON\\n\\nAuto | Step.\\n\\nRunning on mail.google.com\\n\\nopen the latest email\\nfrom Adam\\n\\n| am clicking on the latest\\nemail from Adam to open it.\\n\"}\n", + "Calling function: gmail_search with args: {\"query\":\"from:Julian\",\"max_results\":1,\"query_str\":\"Browse to the latest email from Julian and open the email\"}\n", + "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=1054044249014-nujv4tvp5q1mshdkjidbvtgifpcflncl.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8080%2F&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fgmail.compose+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fgmail.readonly&state=CvRsDgwgyDkSrqUJxkGpARfzCF1Lue&access_type=offline\n", + "Got output: Open the email from Julian Krispel-Samsel to view the latest communication.\n", "========================\n", - "I have opened the latest email from Adam for you.\n" + "\n", + "I have opened the latest email from Julian Krispel-Samsel for you to view. If you need any specific information or action to be taken, please let me know.\n" ] } ], "source": [ - "print(agent.chat(\"browse to the latest email from adam and open the email\"))" + "print(agent.chat(\"browse to the latest email from Julian and open the email\"))" ] }, { @@ -172,36 +157,28 @@ "name": "stdout", "output_type": "stream", "text": [ + "Added user message to memory: Summarize the email chain with julian and create a response to the last email that confirms all the details\n", "=== Calling Function ===\n", - "Calling function: gmail_search with args: {\n", - " \"query\": \"from:adam\",\n", - " \"max_results\": 5,\n", - " \"query_str\": \"Summarize the email chain with Adam\"\n", - "}\n", - "Got output: The email chain with Adam consists of two emails. In the first email, the sender asks about the soonest delivery date for a chair and proposes a price of $475 with cash payment. In the second email, Adam agrees to the price of $500 for the chair and delivery, provides his address and phone number, and thanks the sender.\n", - "========================\n", - "Based on the summary of the email chain with Adam, here is a response that confirms all the details:\n", - "\n", - "\"Hi Adam,\n", - "\n", - "Thank you for your email. I wanted to confirm that I have received your message regarding the chair. I agree to the price of $500 for the chair and delivery. \n", + "Calling function: gmail_search with args: {\"query\":\"from:Julian\",\"max_results\":1,\"query_str\":\"Summarize the email chain with Julian and create a response to the last email confirming all the details\"}\n", + "Got output: The email chain with Julian involved a change in an event scheduled for Friday, August 6, 2021, from 15:30 to 16:00 United Kingdom Time on Google Meet. The instructions for joining were provided in the description. The email also included contact information for joining the meeting. Julian Krispel-Samsel and Nassar Hayat were listed as attendees, with Julian being the organizer. The email was authenticated and passed SPF and DKIM checks.\n", "\n", - "Here are the details:\n", - "- Price: $500\n", - "- Delivery address: 1513 St. Paul Street, St Catharines, Ontario, L2F\n", - "- Phone number: 905-328-6945\n", + "In response to the last email, I would confirm all the details of the event change, reiterating the date, time, platform (Google Meet), and any specific instructions provided. I would express gratitude for the update and confirm attendance at the revised event timing.\n", + "========================\n", "\n", - "I appreciate your prompt response and I look forward to receiving the chair on Monday as discussed.\n", + "Based on the email chain with Julian, here is a summary:\n", + "- The event scheduled for Friday, August 6, 2021, has been changed from 15:30 to 16:00 United Kingdom Time on Google Meet.\n", + "- Instructions for joining the meeting were provided in the email.\n", + "- Attendees included Julian Krispel-Samsel and Nassar Hayat, with Julian as the organizer.\n", + "- The email passed SPF and DKIM checks.\n", "\n", - "Best regards,\n", - "[Your Name]\"\n" + "To respond and confirm all the details, you can mention the revised event date and time, the platform (Google Meet), and express gratitude for the update. Confirm your attendance at the new timing. Let me know if you would like me to draft the response email for you.\n" ] } ], "source": [ "print(\n", " agent.chat(\n", - " \"Summarize the email chain with adam and create a response to the last email\"\n", + " \"Summarize the email chain with julian and create a response to the last email\"\n", " \" that confirms all the details\"\n", " )\n", ")" @@ -217,14 +194,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "Added user message to memory: pass the entire generated email to the browser and have it send the email as a reply to the chain\n", "=== Calling Function ===\n", - "Calling function: browse with args: {\n", - " \"instruction\": \"compose a reply email and paste the following content:\\n\\n\\\"Hi Adam,\\n\\nThank you for your email. I wanted to confirm that I have received your message regarding the chair. I agree to the price of $500 for the chair and delivery.\\n\\nHere are the details:\\n- Price: $500\\n- Delivery address: 1513 St. Paul Street, St Catharines, Ontario, L2F\\n- Phone number: 905-328-6945\\n\\nI appreciate your prompt response and I look forward to receiving the chair on Monday as discussed.\\n\\nBest regards,\\n[Your Name]\\\"\"\n", - "}\n", - "session updated\n", - "Got output: {'url': 'https://mail.google.com/mail/u/0/', 'status': 'CONTINUE', 'action_completed': 'I am clicking on the \"Reply\" button to start composing the reply email to Adam Hofmann.\\n\\n', 'content': 'Mail\\n\\nChat\\n\\nfo}\\nrR\\n\\nSpaces\\n\\nOr\\n\\nMeet\\n\\nM4 Gmail Q. Search in mail iz @rctver @ } 3: Google 6\\n\\nG Compose € Oo w Y © G&G mo D : 10f1928 << >\\n2\\nfb Inbox\\nyr Starred Ca] Adam Hofmann Hi, the price is firm at $480 + $20 for delivery. ... 12:41PM (1hour ago) +\\n© _ Snoozed\\n—& Sent\\nvy Mor\\nore Adam Hofmann 12:43PM (1hourago) ye © :\\ntome v\\nLabels + regarding the char. |\\n\\nHi,\\n\\nagree to the price of\\n\\n$500 for the chair and\\n\\nOkay | can do $500 total for the chair and delive ,\\ndelivery.\\n\\n1513 St. Paul Street, St Catharines, Ontario, L2F :\\nHere are the details:\\n\\n- Price: $500\\n- Delivery address:\\n1513 St. Paul Street, St\\nCatharines, Ontario,\\n“ee L2F\\n- Phone number: 905-\\n328-6945\\n\\nCa] © vy Adam Hofmann (gmail.com) | appreciate your\\n\\nYou can call me at 905-328-6945\\n\\nThanks\\n\\nprompt response and |\\nlook forward to\\nreceiving the chair on\\nMonday as discussed.\\n\\noO Best regards,\\n[Your Name]\"\\n\\nEnable desktop notifications for Hypotenuse Mail.\\n\\nlam clicking on the \"Reply\"\\n\\nhittan $n ntart namnnninan\\n'}\n", + "Calling function: browse with args: {\"cmd\": \"Compose a reply email to Julian confirming the event change to Fri 6 Aug 2021 from 15:30 to 16:00 UK Time on Google Meet. Express readiness to attend and thank Julian for the details.\"}\n", + "Got output: Error: The read operation timed out\n", "========================\n", - "I have composed the reply email with the provided content. You can review and send the email by clicking on the \"Send\" button.\n" + "\n", + "=== Calling Function ===\n", + "Calling function: gmail_search with args: {\"query\": \"from:Julian\", \"max_results\": 1, \"query_str\": \"Summarize the email chain with Julian and create a response to the last email confirming all the details\"}\n", + "Got output: Summarize the email chain with Julian and create a response to the last email confirming all the details.\n", + "========================\n", + "\n", + "It seems there was a timeout issue with retrieving the email content to generate the response. You may manually draft the email reply based on the summary provided earlier. If you need any further assistance or encounter any other issues, please let me know.\n" ] } ], diff --git a/llama-index-integrations/tools/llama-index-tools-multion/llama_index/tools/multion/base.py b/llama-index-integrations/tools/llama-index-tools-multion/llama_index/tools/multion/base.py index 4254921604b6f..e6aceaef2f15c 100644 --- a/llama-index-integrations/tools/llama-index-tools-multion/llama_index/tools/multion/base.py +++ b/llama-index-integrations/tools/llama-index-tools-multion/llama_index/tools/multion/base.py @@ -1,9 +1,4 @@ """Multion tool spec.""" - -import base64 -from io import BytesIO -from typing import Optional - from llama_index.core.tools.tool_spec.base import BaseToolSpec @@ -12,14 +7,13 @@ class MultionToolSpec(BaseToolSpec): spec_functions = ["browse"] - def __init__(self, token_file: Optional[str] = "multion_token.txt") -> None: + def __init__(self, api_key: str) -> None: """Initialize with parameters.""" - import multion + from multion.client import MultiOn - multion.login() - self.last_tab = None + self.multion = MultiOn(api_key=api_key) - def browse(self, instruction: str): + def browse(self, cmd: str): """ Browse the web using Multion Multion gives the ability for LLMs to control web browsers using natural language instructions. @@ -28,33 +22,6 @@ def browse(self, instruction: str): the final desired state. If the status is 'CONTINUE', reissue the same instruction to continue execution Args: - instruction (str): The detailed and specific natural language instructrion for web browsing + cmd (str): The detailed and specific natural language instructrion for web browsing """ - import multion - - if self.last_tab: - session = multion.update_session(self.last_tab, {"input": instruction}) - else: - session = multion.new_session( - {"input": instruction, "url": "https://google.com"} - ) - self.last_tab = session["tabId"] - - return { - "url": session["url"], - "status": session["status"], - "action_completed": session["message"], - "content": self._read_screenshot(session["screenshot"]), - } - - def _read_screenshot(self, screenshot) -> str: - import pytesseract - from PIL import Image - - image_bytes = screenshot.replace("data:image/png;base64,", "") - image = Image.open(self._bytes_to_image(image_bytes)) - - return pytesseract.image_to_string(image) - - def _bytes_to_image(self, img_bytes): - return BytesIO(base64.b64decode(img_bytes)) + return self.multion.browse(cmd=cmd, local=True) diff --git a/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml index 4198c71b0694f..8629199bac927 100644 --- a/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml @@ -28,7 +28,7 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-multion" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 9b0dc98c5d0490ea304e5e31db1f3061e2fc2216 Mon Sep 17 00:00:00 2001 From: Andrei Fajardo <92402603+nerdai@users.noreply.github.com> Date: Wed, 8 May 2024 23:42:09 -0400 Subject: [PATCH 12/16] Add `start_char_idx` and `end_char_idx`: `MarkdownElementNodeParser` (#13377) --- .../node_parser/relational/base_element.py | 18 ++++++++- .../relational/markdown_element.py | 6 ++- .../node_parser/test_markdown_element.py | 40 +++++++++++++++++++ 3 files changed, 61 insertions(+), 3 deletions(-) diff --git a/llama-index-core/llama_index/core/node_parser/relational/base_element.py b/llama-index-core/llama_index/core/node_parser/relational/base_element.py index 1b7ecab1b93a9..938b9b5227933 100644 --- a/llama-index-core/llama_index/core/node_parser/relational/base_element.py +++ b/llama-index-core/llama_index/core/node_parser/relational/base_element.py @@ -246,6 +246,7 @@ def get_nodes_from_elements( self, elements: List[Element], metadata_inherited: Optional[Dict[str, Any]] = None, + ref_doc_text: Optional[str] = None, ) -> List[BaseNode]: """Get nodes and mappings.""" from llama_index.core.node_parser import SentenceSplitter @@ -300,13 +301,26 @@ def get_nodes_from_elements( for col in table_output.columns: table_summary += f"- {col.col_name}: {col.summary}\n" + # attempt to find start_char_idx for table + # raw table string regardless if perfect or not is stored in element.element + start_char_idx = ref_doc_text.find(str(element.element)) + if start_char_idx >= 0: + end_char_idx = start_char_idx + len(str(element.element)) + else: + start_char_idx = None + end_char_idx = None + # shared index_id and node_id node_id = str(uuid.uuid4()) index_node = IndexNode( text=table_summary, - metadata={"col_schema": col_schema}, + metadata={ + "col_schema": col_schema, + }, excluded_embed_metadata_keys=["col_schema"], index_id=node_id, + start_char_idx=start_char_idx, + end_char_idx=end_char_idx, ) table_str = table_summary + "\n" + table_md @@ -326,6 +340,8 @@ def get_nodes_from_elements( }, excluded_embed_metadata_keys=["table_df", "table_summary"], excluded_llm_metadata_keys=["table_df", "table_summary"], + start_char_idx=start_char_idx, + end_char_idx=end_char_idx, ) nodes.extend([index_node, text_node]) else: diff --git a/llama-index-core/llama_index/core/node_parser/relational/markdown_element.py b/llama-index-core/llama_index/core/node_parser/relational/markdown_element.py index 41e0943addb1b..bd65ac55bd468 100644 --- a/llama-index-core/llama_index/core/node_parser/relational/markdown_element.py +++ b/llama-index-core/llama_index/core/node_parser/relational/markdown_element.py @@ -32,7 +32,9 @@ def get_nodes_from_node(self, node: TextNode) -> List[BaseNode]: self.extract_table_summaries(table_elements) # convert into nodes # will return a list of Nodes and Index Nodes - nodes = self.get_nodes_from_elements(elements, node.metadata) + nodes = self.get_nodes_from_elements( + elements, node.metadata, ref_doc_text=node.get_content() + ) source_document = node.source_node or node.as_related_node_info() for n in nodes: n.relationships[NodeRelationship.SOURCE] = source_document @@ -156,7 +158,7 @@ def extract_elements( elements[idx] = Element( id=f"id_{node_id}_{idx}" if node_id else f"id_{idx}", type="table", - element=element, + element=element.element, table=table, ) else: diff --git a/llama-index-core/tests/node_parser/test_markdown_element.py b/llama-index-core/tests/node_parser/test_markdown_element.py index a15861c0b7814..98a8600508618 100644 --- a/llama-index-core/tests/node_parser/test_markdown_element.py +++ b/llama-index-core/tests/node_parser/test_markdown_element.py @@ -2665,3 +2665,43 @@ def test_extract_ref_doc_id(): assert len(nodes) == 1 assert nodes[0].ref_doc_id == test_document.doc_id + + +def test_start_end_char_idx(): + test_document = Document( + text=""" +# This is a test + +| Year | Benefits | +| ---- | -------- | +| 2020 | 12,000 | +| 2021 | 10,000 | +| 2022 | 130,000 | + +""", + ) + + node_parser = MarkdownElementNodeParser(llm=MockLLM()) + + nodes = node_parser.get_nodes_from_documents([test_document]) + assert len(nodes) == 3 + assert ( + test_document.text[nodes[0].start_char_idx : nodes[0].end_char_idx] + == "This is a test" + ) + assert ( + test_document.text[nodes[1].start_char_idx : nodes[1].end_char_idx] + == """| Year | Benefits | +| ---- | -------- | +| 2020 | 12,000 | +| 2021 | 10,000 | +| 2022 | 130,000 |""" + ) + assert ( + test_document.text[nodes[2].start_char_idx : nodes[2].end_char_idx] + == """| Year | Benefits | +| ---- | -------- | +| 2020 | 12,000 | +| 2021 | 10,000 | +| 2022 | 130,000 |""" + ) From 535225a6a7d121f1390e54ad6b3a8287f8a0fe83 Mon Sep 17 00:00:00 2001 From: Shuiwang Guan Date: Thu, 9 May 2024 11:47:38 +0800 Subject: [PATCH 13/16] Add VectorStore integration for alibaba cloud opensearch vector service (#13286) --- .../community/integrations/vector_stores.md | 21 + .../AlibabaCloudOpenSearchIndexDemo.ipynb | 427 ++++++++++++++++++ .../module_guides/storing/vector_stores.md | 2 + .../llama_index/cli/upgrade/mappings.json | 2 + .../core/command_line/mappings.json | 2 + .../.gitignore | 153 +++++++ .../BUILD | 3 + .../Makefile | 17 + .../README.md | 37 ++ .../alibabacloud_opensearch/BUILD | 1 + .../alibabacloud_opensearch/__init__.py | 6 + .../alibabacloud_opensearch/base.py | 409 +++++++++++++++++ .../pyproject.toml | 63 +++ .../tests/BUILD | 1 + .../tests/__init__.py | 0 ...t_vector_stores_alibabacloud_opensearch.py | 9 + 16 files changed, 1153 insertions(+) create mode 100644 docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/.gitignore create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/BUILD create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/Makefile create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/README.md create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/BUILD create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/__init__.py create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/BUILD create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/__init__.py create mode 100644 llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/test_vector_stores_alibabacloud_opensearch.py diff --git a/docs/docs/community/integrations/vector_stores.md b/docs/docs/community/integrations/vector_stores.md index a6a375f0b749b..9c9abeaaa0301 100644 --- a/docs/docs/community/integrations/vector_stores.md +++ b/docs/docs/community/integrations/vector_stores.md @@ -10,6 +10,7 @@ LlamaIndex offers multiple integration points with vector stores / vector databa LlamaIndex also supports different vector stores as the storage backend for `VectorStoreIndex`. +- Alibaba Cloud OpenSearch (`AlibabaCloudOpenSearchStore`). [QuickStart](https://help.aliyun.com/zh/open-search/vector-search-edition). - Amazon Neptune - Neptune Analytics (`NeptuneAnalyticsVectorStore`). [Working with vector similarity in Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/vector-similarity.html). - Apache Cassandra® and Astra DB through CQL (`CassandraVectorStore`). [Installation](https://cassandra.apache.org/doc/stable/cassandra/getting_started/installing.html) [Quickstart](https://docs.datastax.com/en/astra-serverless/docs/vector-search/overview.html) - Astra DB (`AstraDBVectorStore`). [Quickstart](https://docs.datastax.com/en/astra/home/astra.html). @@ -98,6 +99,25 @@ response = query_engine.query("What did the author do growing up?") Below we show more examples of how to construct various vector stores we support. +**Alibaba Cloud OpenSearch** + +```python +from llama_index.vector_stores.alibabacloud_opensearch import ( + AlibabaCloudOpenSearchStore, + AlibabaCloudOpenSearchConfig, +) + +config = AlibabaCloudOpenSearchConfig( + endpoint="***", + instance_id="***", + username="your_username", + password="your_password", + table_name="llama", +) + +vector_store = AlibabaCloudOpenSearchStore(config) +``` + **Amazon Neptune - Neptune Analytics** ```python @@ -844,6 +864,7 @@ documents = reader.load_data( ## Vector Store Examples +- [Alibaba Cloud OpenSearch](../../examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb) - [Amazon Neptune - Neptune Analytics](../../examples/vector_stores/AmazonNeptuneVectorDemo.ipynb) - [Astra DB](../../examples/vector_stores/AstraDBIndexDemo.ipynb) - [Async Index Creation](../../examples/vector_stores/AsyncIndexCreationDemo.ipynb) diff --git a/docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb b/docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb new file mode 100644 index 0000000000000..5861760bcbec2 --- /dev/null +++ b/docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb @@ -0,0 +1,427 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "600e8429", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "307804a3-c02b-4a57-ac0d-172c30ddc851", + "metadata": {}, + "source": [ + "# Alibaba Cloud OpenSearch Vector Store" + ] + }, + { + "cell_type": "markdown", + "id": "43f9df89", + "metadata": {}, + "source": [ + ">[Alibaba Cloud OpenSearch Vector Search Edition](https://help.aliyun.com/zh/open-search/vector-search-edition/product-overview) is a large-scale distributed search engine that is developed by Alibaba Group. Alibaba Cloud OpenSearch Vector Search Edition provides search services for the entire Alibaba Group, including Taobao, Tmall, Cainiao, Youku, and other e-commerce platforms that are provided for customers in regions outside the Chinese mainland. Alibaba Cloud OpenSearch Vector Search Edition is also a base engine of Alibaba Cloud OpenSearch. After years of development, Alibaba Cloud OpenSearch Vector Search Edition has met the business requirements for high availability, high timeliness, and cost-effectiveness. Alibaba Cloud OpenSearch Vector Search Edition also provides an automated O&M system on which you can build a custom search service based on your business features.\n", + "\n", + "To run, you should have a instance." + ] + }, + { + "cell_type": "markdown", + "id": "5eeaa2f4", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "47bbdd33", + "metadata": {}, + "source": [ + "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bd0b321", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-vector-stores-alibabacloud-opensearch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f6b3761", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d48af8e1", + "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))" + ] + }, + { + "cell_type": "markdown", + "id": "f9074027", + "metadata": {}, + "source": [ + "### Please provide OpenAI access key\n", + "\n", + "In order use embeddings by OpenAI you need to supply an OpenAI API Key:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b6260f3", + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "\n", + "OPENAI_API_KEY = getpass.getpass(\"OpenAI API Key:\")\n", + "openai.api_key = OPENAI_API_KEY" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4b00ea0d", + "metadata": {}, + "source": [ + "#### Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d21ebdc", + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8ee4473a-094f-4d0a-a825-e1213db07240", + "metadata": {}, + "source": [ + "#### Load documents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a2bcc07", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "from IPython.display import Markdown, display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68cbd239-880e-41a3-98d8-dbb3fab55431", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total documents: 1\n" + ] + } + ], + "source": [ + "# load documents\n", + "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\n", + "print(f\"Total documents: {len(documents)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "630c0bdf", + "metadata": {}, + "source": [ + "### Create the Alibaba Cloud OpenSearch Vector Store object:" + ] + }, + { + "cell_type": "markdown", + "id": "2a410b4c", + "metadata": {}, + "source": [ + "To run the next step, you should have a Alibaba Cloud OpenSearch Vector Service instance, and configure a table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "722c4c39", + "metadata": {}, + "outputs": [], + "source": [ + "# if run fllowing cells raise async io exception, run this\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba1558b3", + "metadata": {}, + "outputs": [], + "source": [ + "# initialize without metadata filter\n", + "from llama_index.core import StorageContext, VectorStoreIndex\n", + "from llama_index.vector_stores.alibabacloud_opensearch import (\n", + " AlibabaCloudOpenSearchStore,\n", + " AlibabaCloudOpenSearchConfig,\n", + ")\n", + "\n", + "config = AlibabaCloudOpenSearchConfig(\n", + " endpoint=\"*****\",\n", + " instance_id=\"*****\",\n", + " username=\"your_username\",\n", + " password=\"your_password\",\n", + " table_name=\"llama\",\n", + ")\n", + "\n", + "vector_store = AlibabaCloudOpenSearchStore(config)\n", + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "index = VectorStoreIndex.from_documents(\n", + " documents, storage_context=storage_context\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "04304299-fc3e-40a0-8600-f50c3292767e", + "metadata": {}, + "source": [ + "#### Query Index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35369eda", + "metadata": {}, + "outputs": [], + "source": [ + "# set Logging to DEBUG for more detailed outputs\n", + "query_engine = index.as_query_engine()\n", + "response = query_engine.query(\"What did the author do growing up?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bedbb693-725f-478f-be26-fa7180ea38b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Before college, the author worked on writing and programming. They wrote short stories and tried writing programs on the IBM 1401 in 9th grade using an early version of Fortran." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(f\"{response}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "3e723315", + "metadata": {}, + "source": [ + "### Connecting to an existing store\n", + "\n", + "Since this store is backed by Alibaba Cloud OpenSearch, it is persistent by definition. So, if you want to connect to a store that was created and populated previously, here is how:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b133f816", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.vector_stores.alibabacloud_opensearch import (\n", + " AlibabaCloudOpenSearchStore,\n", + " AlibabaCloudOpenSearchConfig,\n", + ")\n", + "\n", + "config = AlibabaCloudOpenSearchConfig(\n", + " endpoint=\"***\",\n", + " instance_id=\"***\",\n", + " username=\"your_username\",\n", + " password=\"your_password\",\n", + " table_name=\"llama\",\n", + ")\n", + "\n", + "vector_store = AlibabaCloudOpenSearchStore(config)\n", + "\n", + "# Create index from existing stored vectors\n", + "index = VectorStoreIndex.from_vector_store(vector_store)\n", + "query_engine = index.as_query_engine()\n", + "response = query_engine.query(\n", + " \"What did the author study prior to working on AI?\"\n", + ")\n", + "\n", + "display(Markdown(f\"{response}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "bfcbcd74", + "metadata": {}, + "source": [ + "### Metadata filtering\n", + "\n", + "The Alibaba Cloud OpenSearch vector store support metadata filtering at query time. The following cells, which work on a brand new table, demonstrate this feature.\n", + "\n", + "In this demo, for the sake of brevity, a single source document is loaded (the `../data/paul_graham/paul_graham_essay.txt` text file). Nevertheless, you will attach some custom metadata to the document to illustrate how you can can restrict queries with conditions on the metadata attached to the documents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9a7e108", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import StorageContext, VectorStoreIndex\n", + "from llama_index.vector_stores.alibabacloud_opensearch import (\n", + " AlibabaCloudOpenSearchStore,\n", + " AlibabaCloudOpenSearchConfig,\n", + ")\n", + "\n", + "config = AlibabaCloudOpenSearchConfig(\n", + " endpoint=\"****\",\n", + " instance_id=\"****\",\n", + " username=\"your_username\",\n", + " password=\"your_password\",\n", + " table_name=\"llama\",\n", + ")\n", + "\n", + "md_storage_context = StorageContext.from_defaults(\n", + " vector_store=AlibabaCloudOpenSearchStore(config)\n", + ")\n", + "\n", + "\n", + "def my_file_metadata(file_name: str):\n", + " \"\"\"Depending on the input file name, associate a different metadata.\"\"\"\n", + " if \"essay\" in file_name:\n", + " source_type = \"essay\"\n", + " elif \"dinosaur\" in file_name:\n", + " # this (unfortunately) will not happen in this demo\n", + " source_type = \"dinos\"\n", + " else:\n", + " source_type = \"other\"\n", + " return {\"source_type\": source_type}\n", + "\n", + "\n", + "# Load documents and build index\n", + "md_documents = SimpleDirectoryReader(\n", + " \"../data/paul_graham\", file_metadata=my_file_metadata\n", + ").load_data()\n", + "md_index = VectorStoreIndex.from_documents(\n", + " md_documents, storage_context=md_storage_context\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "de69be23", + "metadata": {}, + "source": [ + "Add filter to query engine:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6dd49f3a", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.vector_stores import MetadataFilter, MetadataFilters\n", + "\n", + "md_query_engine = md_index.as_query_engine(\n", + " filters=MetadataFilters(\n", + " filters=[MetadataFilter(key=\"source_type\", value=\"essay\")]\n", + " )\n", + ")\n", + "md_response = md_query_engine.query(\n", + " \"How long it took the author to write his thesis?\"\n", + ")\n", + "\n", + "display(Markdown(f\"{md_response}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "79a14aaf", + "metadata": {}, + "source": [ + "To test that the filtering is at play, try to change it to use only `\"dinos\"` documents... there will be no answer this time :)" + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/module_guides/storing/vector_stores.md b/docs/docs/module_guides/storing/vector_stores.md index d6bf315dbf015..c7056de4a0729 100644 --- a/docs/docs/module_guides/storing/vector_stores.md +++ b/docs/docs/module_guides/storing/vector_stores.md @@ -15,6 +15,7 @@ We are actively adding more integrations and improving feature coverage for each | Vector Store | Type | Metadata Filtering | Hybrid Search | Delete | Store Documents | Async | | ------------------------ | ----------------------- | ------------------ | ------------- | ------ | --------------- | ----- | +| Alibaba Cloud OpenSearch | cloud | ✓ | | ✓ | ✓ | ✓ | | Apache Cassandra® | self-hosted / cloud | ✓ | | ✓ | ✓ | | | Astra DB | cloud | ✓ | | ✓ | ✓ | | | Azure AI Search | cloud | ✓ | ✓ | ✓ | ✓ | | @@ -64,6 +65,7 @@ For more details, see [Vector Store Integrations](../../community/integrations/v ## Example Notebooks +- [Alibaba Cloud OpenSearch](../../examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb) - [Astra DB](../../examples/vector_stores/AstraDBIndexDemo.ipynb) - [Async Index Creation](../../examples/vector_stores/AsyncIndexCreationDemo.ipynb) - [Azure AI Search](../../examples/vector_stores/AzureAISearchIndexDemo.ipynb) diff --git a/llama-index-cli/llama_index/cli/upgrade/mappings.json b/llama-index-cli/llama_index/cli/upgrade/mappings.json index 63172affee039..fd3a01bbc8ca4 100644 --- a/llama-index-cli/llama_index/cli/upgrade/mappings.json +++ b/llama-index-cli/llama_index/cli/upgrade/mappings.json @@ -425,6 +425,8 @@ "set_google_config": "llama_index.vector_stores.google", "GoogleVectorStore": "llama_index.vector_stores.google", "MetalVectorStore": "llama_index.vector_stores.metal", + "AlibabaCloudOpenSearchConfig": "llama_index.vector_stores.alibabacloud_opensearch", + "AlibabaCloudOpenSearchStore": "llama_index.vector_stores.alibabacloud_opensearch", "PathwayRetriever": "llama_index.retrievers.pathway", "AmazonKnowledgeBasesRetriever": "llama_index.retrievers.bedrock", "MongoDBAtlasBM25Retriever": "llama_index.retrievers.mongodb_atlas_bm25_retriever", diff --git a/llama-index-core/llama_index/core/command_line/mappings.json b/llama-index-core/llama_index/core/command_line/mappings.json index 63172affee039..fd3a01bbc8ca4 100644 --- a/llama-index-core/llama_index/core/command_line/mappings.json +++ b/llama-index-core/llama_index/core/command_line/mappings.json @@ -425,6 +425,8 @@ "set_google_config": "llama_index.vector_stores.google", "GoogleVectorStore": "llama_index.vector_stores.google", "MetalVectorStore": "llama_index.vector_stores.metal", + "AlibabaCloudOpenSearchConfig": "llama_index.vector_stores.alibabacloud_opensearch", + "AlibabaCloudOpenSearchStore": "llama_index.vector_stores.alibabacloud_opensearch", "PathwayRetriever": "llama_index.retrievers.pathway", "AmazonKnowledgeBasesRetriever": "llama_index.retrievers.bedrock", "MongoDBAtlasBM25Retriever": "llama_index.retrievers.mongodb_atlas_bm25_retriever", diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/.gitignore b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/.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-alibabacloud-opensearch/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/Makefile b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/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-alibabacloud-opensearch/README.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/README.md new file mode 100644 index 0000000000000..21d31f48b72a3 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/README.md @@ -0,0 +1,37 @@ +# LlamaIndex Vector_Stores Integration: Alibaba Cloud OpenSearch + +Please refer to the [notebook](../../../docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb) for usage of AlibabaCloud OpenSearch as vector store in LlamaIndex. + +## Example Usage + +```sh +pip install llama-index +pip install llama-index-vector-stores-alibabacloud-opensearch +``` + +```python +# Connect to existing instance +from llama_index.core import VectorStoreIndex +from llama_index.vector_stores.alibabacloud_opensearch import ( + AlibabaCloudOpenSearchStore, + AlibabaCloudOpenSearchConfig, +) + +config = AlibabaCloudOpenSearchConfig( + endpoint="***", + instance_id="***", + username="your_username", + password="your_password", + table_name="llama", +) + +vector_store = AlibabaCloudOpenSearchStore(config) + +# Create index from existing stored vectors +index = VectorStoreIndex.from_vector_store(vector_store) +query_engine = index.as_query_engine() +response = query_engine.query( + "What did the author study prior to working on AI?" +) +print(response) +``` diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/__init__.py new file mode 100644 index 0000000000000..fd0d0de54119b --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/__init__.py @@ -0,0 +1,6 @@ +from llama_index.vector_stores.alibabacloud_opensearch.base import ( + AlibabaCloudOpenSearchConfig, + AlibabaCloudOpenSearchStore, +) + +__all__ = ["AlibabaCloudOpenSearchConfig", "AlibabaCloudOpenSearchStore"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py new file mode 100644 index 0000000000000..e8d53298cae9e --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py @@ -0,0 +1,409 @@ +"""Alibaba Cloud OpenSearch Vector Store.""" + +import json +import logging +import asyncio +from typing import Any, List, Dict, Optional + +from llama_index.core.schema import BaseNode, TextNode + +from llama_index.core.vector_stores.types import ( + MetadataFilters, + FilterOperator, + FilterCondition, + VectorStore, + VectorStoreQuery, + VectorStoreQueryMode, + VectorStoreQueryResult, +) +from llama_index.core.vector_stores.utils import ( + DEFAULT_DOC_ID_KEY, + DEFAULT_TEXT_KEY, + DEFAULT_EMBEDDING_KEY, + metadata_dict_to_node, + node_to_metadata_dict, +) + +try: + from alibabacloud_ha3engine_vector import client, models +except ImportError: + raise ImportError( + "`alibabacloud_ha3engine_vector` package not found, " + "please run `pip install alibabacloud_ha3engine_vector`" + ) + +DEFAULT_BATCH_SIZE = 100 +logger = logging.getLogger(__name__) + + +def _to_ha3_filter_operator(op: FilterOperator) -> str: + """Convert FilterOperator to Alibaba Cloud OpenSearch filter operator.""" + if op == FilterOperator.EQ: + return "=" + elif ( + op == FilterOperator.IN + or op == FilterOperator.NIN + or op == FilterOperator.TEXT_MATCH + or op == FilterOperator.CONTAINS + ): + raise ValueError( + "Alibaba Cloud OpenSearch not support filter operator: `in/nin/text_match/contains` yet" + ) + else: + return op.value + + +def _to_ha3_engine_filter( + standard_filters: Optional[MetadataFilters] = None, +) -> str: + """Convert from standard filter to Alibaba Cloud OpenSearch filter spec.""" + if standard_filters is None: + return "" + + filters = [] + for filter in standard_filters.filters: + if isinstance(filter.value, str): + value = f'"{filter.value}"' + else: + value = f"{filter.value}" + filters.append( + f"{filter.key} {_to_ha3_filter_operator(filter.operator)} {value}" + ) + if standard_filters.condition == FilterCondition.AND: + return " AND ".join(filters) + elif standard_filters.condition == FilterCondition.OR: + return " OR ".join(filters) + else: + raise ValueError(f"Unknown filter condition {standard_filters.condition}") + + +class AlibabaCloudOpenSearchConfig: + """`Alibaba Cloud Opensearch` client configuration. + + Attribute: + endpoint (str) : The endpoint of opensearch instance, You can find it + from the console of Alibaba Cloud OpenSearch. + instance_id (str) : The identify of opensearch instance, You can find + it from the console of Alibaba Cloud OpenSearch. + username (str) : The username specified when purchasing the instance. + password (str) : The password specified when purchasing the instance, + After the instance is created, you can modify it on the console. + tablename (str): The table name specified during instance configuration. + namespace (str) : The instance data will be partitioned based on the "namespace" + field. If the namespace is enabled, you need to specify the namespace field + name during initialization, Otherwise, the queries cannot be executed + correctly. + field_mapping (dict[str, str]): The field mapping between llamaindex meta field + and OpenSearch table filed name. OpenSearch has some rules for the field name, + when the meta field name break the rules, can map to another name. + output_fields (list[str]): Specify the field list returned when searching OpenSearch. + id_field (str): The primary key field name in OpenSearch, default is `id`. + embedding_field (list[float]): The field name which stored the embedding. + text_field: The name of the field that stores the key text. + search_config (dict, optional): The configuration used for searching the OpenSearch. + + """ + + def __init__( + self, + endpoint: str, + instance_id: str, + username: str, + password: str, + table_name: str, + namespace: str = "", + field_mapping: Dict[str, str] = None, + output_fields: Optional[List[str]] = None, + id_field: str = "id", + embedding_field: str = DEFAULT_EMBEDDING_KEY, + text_field: str = DEFAULT_TEXT_KEY, + search_config: dict = None, + ) -> None: + self.endpoint = endpoint + self.instance_id = instance_id + self.username = username + self.password = password + self.namespace = namespace + self.table_name = table_name + self.data_source_name = f"{self.instance_id}_{self.table_name}" + self.field_mapping = field_mapping + self.id_field = id_field + self.embedding_field = embedding_field + self.text_field = text_field + self.search_config = search_config + + if output_fields is None: + self.output_fields = ( + list(self.field_mapping.values()) if self.field_mapping else [] + ) + if self.text_field not in self.output_fields: + self.output_fields.append(self.text_field) + + self.inverse_field_mapping: Dict[str, str] = ( + {value: key for key, value in self.field_mapping.items()} + if self.field_mapping + else {} + ) + + def __getitem__(self, item: str) -> Any: + return getattr(self, item) + + +class AlibabaCloudOpenSearchStore(VectorStore): + """The AlibabaCloud OpenSearch Vector Store. + + In this vector store we store the text, its embedding and its metadata + in a OpenSearch table. + + In order to use this you need to have a instance and configure a table. + See the following documentation for details: + https://help.aliyun.com/zh/open-search/vector-search-edition/product-overview + + Args: + config (AlibabaCloudOpenSearchConfig): The instance configuration + + Examples: + `pip install llama-index-vector-stores-alibabacloud_opensearch` + + ```python + from llama_index.vector_stores.alibabacloud_opensearch import ( + AlibabaCloudOpenSearchConfig, + AlibabaCloudOpenSearchStore, + ) + + # Config + config = AlibabaCloudOpenSearchConfig( + endpoint="xxx", + instance_id="ha-cn-******", + username="****", + password="****", + table_name="your_table_name", + ) + + vector_store = AlibabaCloudOpenSearchStore(config) + ``` + + """ + + stores_text: bool = True + flat_metadata: bool = True + + def __init__(self, config: AlibabaCloudOpenSearchConfig) -> None: + """Initialize params.""" + self._config = config + self._client = client.Client( + models.Config( + endpoint=config.endpoint, + instance_id=config.instance_id, + access_user_name=config.username, + access_pass_word=config.password, + ) + ) + + @property + def client(self) -> Any: + """Get client.""" + return self._client + + def add( + self, + nodes: List[BaseNode], + **add_kwargs: Any, + ) -> List[str]: + """Add nodes to vector store. + + Args: + nodes (List[BaseNode]): list of nodes with embeddings + """ + return asyncio.get_event_loop().run_until_complete( + self.async_add(nodes, **add_kwargs) + ) + + async def async_add( + self, + nodes: List[BaseNode], + **add_kwargs: Any, + ) -> List[str]: + """ + Asynchronously add nodes with embedding to vector store. + + Args: + nodes (List[BaseNode]): list of nodes with embeddings + """ + for i in range(0, len(nodes), DEFAULT_BATCH_SIZE): + docs = [] + for node in nodes[i:DEFAULT_BATCH_SIZE]: + doc = { + self._config.id_field: node.node_id, + self._config.embedding_field: node.embedding, + } + if self._config.text_field: + doc[self._config.text_field] = node.get_text() + + meta_fields = node_to_metadata_dict( + node, remove_text=False, flat_metadata=self.flat_metadata + ) + + if self._config.field_mapping: + for key, value in meta_fields.items(): + doc[self._config.field_mapping.get(key, key)] = value + else: + doc.update(meta_fields) + docs.append(doc) + + try: + await self._async_send_data("add", docs) + except Exception as e: + logging.error(f"Add to {self._config.instance_id} failed: {e}") + raise RuntimeError(f"Fail to add docs, error:{e}") + return [node.node_id for node in nodes] + + def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: + """ + Delete nodes using with ref_doc_id. + + Args: + ref_doc_id (str): The doc_id of the document to delete. + + """ + return asyncio.get_event_loop().run_until_complete( + self.adelete(ref_doc_id, **delete_kwargs) + ) + + async def adelete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: + """ + Asynchronously delete nodes using with ref_doc_id. + + Args: + ref_doc_id (str): The doc_id of the document to delete. + + """ + filter = f"{DEFAULT_DOC_ID_KEY}='{ref_doc_id}'" + request = models.FetchRequest(table_name=self._config.table_name, filter=filter) + + response = self._client.fetch(request) + json_response = json.loads(response.body) + err_msg = json_response.get("errorMsg", None) + if err_msg: + raise RuntimeError(f"Failed to query doc by {filter}: {err_msg}") + + docs = [] + for doc in json_response["result"]: + docs.append({"id": doc["id"]}) + await self._async_send_data("delete", docs) + + async def _async_send_data(self, cmd: str, fields_list: List[dict]) -> None: + """ + Asynchronously send data. + + Args: + cmd (str): data operator, add: upsert the doc, delete: delete the doc + fields_list (list[dict]): doc fields list + + """ + docs = [] + for fields in fields_list: + docs.append({"cmd": cmd, "fields": fields}) + request = models.PushDocumentsRequest({}, docs) + await self._client.push_documents_async( + self._config.data_source_name, self._config.id_field, request + ) + + def query( + self, + query: VectorStoreQuery, + **kwargs: Any, + ) -> VectorStoreQueryResult: + """Query vector store.""" + return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs)) + + async def aquery( + self, query: VectorStoreQuery, **kwargs: Any + ) -> VectorStoreQueryResult: + """ + Asynchronously query vector store. + """ + if query.mode != VectorStoreQueryMode.DEFAULT: + raise ValueError( + f"Alibaba Cloud OpenSearch does not support {query.mode} yet." + ) + + request = self._gen_query_request(query) + response = await self._client.query_async(request) + json_response = json.loads(response.body) + logging.debug(f"query result: {json_response}") + + err_msg = json_response.get("errorMsg", None) + if err_msg: + raise RuntimeError( + f"query doc from Alibaba Cloud OpenSearch instance:{self._config.instance_id} failed:" + f"{err_msg}" + ) + + ids = [] + nodes = [] + similarities = [] + for doc in json_response["result"]: + try: + node = metadata_dict_to_node( + { + "_node_content": doc["fields"].get( + self._config.field_mapping.get( + "_node_content", "_node_content" + ), + None, + ), + "_node_type": doc["fields"].get( + self._config.field_mapping.get("_node_type", "_node_type"), + None, + ), + } + ) + except Exception: + text = doc["fields"][self._config.text_field] + metadata = { + self._config.inverse_field_mapping.get(key, key): doc["fields"].get( + key + ) + for key in self._config.output_fields + } + node = TextNode(id_=doc["id"], text=text, metadata=metadata) + + ids.append(doc["id"]) + nodes.append(node) + similarities.append(doc["score"]) + + return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids) + + def _gen_query_request(self, query: VectorStoreQuery) -> models.QueryRequest: + """ + Generate the OpenSearch query request. + + Args: + query (VectorStoreQuery): The vector store query + + Return: + OpenSearch query request + """ + filter = _to_ha3_engine_filter(query.filters) + request = models.QueryRequest( + table_name=self._config.table_name, + namespace=self._config.namespace, + vector=query.query_embedding, + top_k=query.similarity_top_k, + filter=filter, + include_vector=True, + output_fields=self._config.output_fields, + ) + + if self._config.search_config: + request.order = self._config.search_config.get("order", "ASC") + score_threshold: float = self._config.search_config.get( + "score_threshold", None + ) + if score_threshold is not None: + request.score_threshold = score_threshold + search_params = self._config.search_config.get("search_params", None) + if search_params is not None: + request.search_params = json.dumps(search_params) + return request diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml new file mode 100644 index 0000000000000..3bfa1f96095f6 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml @@ -0,0 +1,63 @@ +[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.alibabacloud_opensearch" + +[tool.llamahub.class_authors] +AlibabaCloudOpenSearch = "goctave" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["goctave "] +description = "llama-index vector_stores Alibaba Cloud OpenSearch integration" +exclude = ["**/BUILD"] +license = "MIT" +name = "llama-index-vector-stores-alibabacloud-opensearch" +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.10.1" +alibabacloud_ha3engine_vector = "^1.1.3" + +[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-alibabacloud-opensearch/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/test_vector_stores_alibabacloud_opensearch.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/test_vector_stores_alibabacloud_opensearch.py new file mode 100644 index 0000000000000..76ac57f222cc8 --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/tests/test_vector_stores_alibabacloud_opensearch.py @@ -0,0 +1,9 @@ +from llama_index.core.vector_stores.types import VectorStore +from llama_index.vector_stores.alibabacloud_opensearch import ( + AlibabaCloudOpenSearchStore, +) + + +def test_class(): + names_of_base_classes = [b.__name__ for b in AlibabaCloudOpenSearchStore.__mro__] + assert VectorStore.__name__ in names_of_base_classes From dd18a978ee58d97eb44e921de0c116d7fdc46349 Mon Sep 17 00:00:00 2001 From: alexgiannak <68291030+alexgiannak@users.noreply.github.com> Date: Thu, 9 May 2024 06:52:59 +0300 Subject: [PATCH 14/16] Kdb.ai (#13194) --- .../KDBAI_Advanced_RAG_Demo.ipynb | 49 +++++-------------- .../llama_index/vector_stores/kdbai/base.py | 2 +- .../pyproject.toml | 2 +- 3 files changed, 14 insertions(+), 39 deletions(-) diff --git a/docs/docs/examples/vector_stores/KDBAI_Advanced_RAG_Demo.ipynb b/docs/docs/examples/vector_stores/KDBAI_Advanced_RAG_Demo.ipynb index 427867baeaefa..32fa21966ea57 100644 --- a/docs/docs/examples/vector_stores/KDBAI_Advanced_RAG_Demo.ipynb +++ b/docs/docs/examples/vector_stores/KDBAI_Advanced_RAG_Demo.ipynb @@ -50,15 +50,7 @@ "execution_count": null, "id": "f4ca21f7-819c-4abb-8479-e7c6f3175f34", "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "OpenAI API key: ········\n" - ] - } - ], + "outputs": [], "source": [ "from getpass import getpass\n", "import re\n", @@ -188,8 +180,8 @@ "text": [ "Downloading https://www.govinfo.gov/content/pkg/PLAW-106publ102/pdf/PLAW-106publ102.pdf...\n", "Downloading https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf...\n", - "CPU times: user 33 ms, sys: 25.4 ms, total: 58.4 ms\n", - "Wall time: 6.09 s\n" + "CPU times: user 64.6 ms, sys: 4.44 ms, total: 69 ms\n", + "Wall time: 4.98 s\n" ] } ], @@ -244,8 +236,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3.94 s, sys: 22.5 ms, total: 3.96 s\n", - "Wall time: 3.96 s\n" + "CPU times: user 11.1 s, sys: 56 ms, total: 11.1 s\n", + "Wall time: 11.2 s\n" ] }, { @@ -290,19 +282,12 @@ "id": "4ed27c4b-a979-4d4f-9f17-7c6bd9844d9a", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":4: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1min 23s, sys: 3min 3s, total: 4min 27s\n", - "Wall time: 32.4 s\n" + "CPU times: user 3min 32s, sys: 3.72 s, total: 3min 35s\n", + "Wall time: 4min 41s\n" ] } ], @@ -340,8 +325,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 24.1 ms, sys: 3.74 ms, total: 27.9 ms\n", - "Wall time: 26.6 ms\n" + "CPU times: user 60.2 ms, sys: 766 µs, total: 61 ms\n", + "Wall time: 79.1 ms\n" ] } ], @@ -373,23 +358,13 @@ "id": "1d791e0e-67b0-4179-a4c4-a1f5fd6d765b", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Before the 2008 financial crisis, the main financial regulation in the US included a variety of laws and regulatory measures, but one of the most significant frameworks was established by the Gramm-Leach-Bliley Act of 1999. This act repealed parts of the Glass-Steagall Act of 1933, allowing banks to offer a broader range of financial services, including investment, commercial banking, and insurance services. Other regulatory measures and entities, such as the Securities and Exchange Commission (SEC) and laws like the Sarbanes-Oxley Act of 2002, also played key roles in the financial regulatory landscape prior to the crisis.\n", - "CPU times: user 202 ms, sys: 53.5 ms, total: 256 ms\n", - "Wall time: 18.1 s\n" + "The main financial regulation in the US before the 2008 financial crisis was the Gramm-Leach-Bliley Act.\n", + "CPU times: user 2.28 s, sys: 666 µs, total: 2.28 s\n", + "Wall time: 56.9 s\n" ] } ], diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py index 1242b982ab471..0274c004b4074 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py @@ -167,7 +167,7 @@ def add( f"Failed to insert batch {i} of documents into the datastore: {e}" ) - return df["document_id"].tolist() + return [x.decode("utf-8") for x in df["document_id"].tolist()] except Exception as e: logger.error(f"Error preparing data for KDB.AI: {e}.") diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml index 35ddcea61a499..0213d77630e73 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml @@ -30,7 +30,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-kdbai" readme = "README.md" -version = "0.1.5" +version = "0.1.6" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 362063cd63130c5a9ff2e83811dbf4d60188a1ba Mon Sep 17 00:00:00 2001 From: Antoine Debugne <146744637+co-antwan@users.noreply.github.com> Date: Thu, 9 May 2024 06:10:45 +0200 Subject: [PATCH 15/16] Call Cohere RAG inference with `documents` argument (#13196) --- .../core/prompts/default_prompt_selectors.py | 59 +++++- .../llama_index/llms/cohere/__init__.py | 18 +- .../llama_index/llms/cohere/base.py | 21 +- .../llama_index/llms/cohere/utils.py | 181 +++++++++++++++++- .../tests/test_llms_cohere.py | 78 +++++++- .../tests/test_rag_inference.py | 60 ++++++ 6 files changed, 403 insertions(+), 14 deletions(-) create mode 100644 llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py diff --git a/llama-index-core/llama_index/core/prompts/default_prompt_selectors.py b/llama-index-core/llama_index/core/prompts/default_prompt_selectors.py index 18e2866c7a743..23a9d11912ca9 100644 --- a/llama-index-core/llama_index/core/prompts/default_prompt_selectors.py +++ b/llama-index-core/llama_index/core/prompts/default_prompt_selectors.py @@ -14,22 +14,73 @@ ) from llama_index.core.prompts.utils import is_chat_model +try: + from llama_index.llms.cohere import ( + is_cohere_model, + COHERE_QA_TEMPLATE, + COHERE_REFINE_TEMPLATE, + COHERE_TREE_SUMMARIZE_TEMPLATE, + COHERE_REFINE_TABLE_CONTEXT_PROMPT, + ) +except ImportError: + COHERE_QA_TEMPLATE = None + COHERE_REFINE_TEMPLATE = None + COHERE_TREE_SUMMARIZE_TEMPLATE = None + COHERE_REFINE_TABLE_CONTEXT_PROMPT = None + +# Define prompt selectors for Text QA, Tree Summarize, Refine, and Refine Table. +# Note: Cohere models accept a special argument `documents` for RAG calls. To pass on retrieved documents to the `documents` argument, +# specialised templates have been defined. The conditionals below ensure that these templates are called by default when a retriever +# is called with a Cohere model for generator. + +# Text QA +default_text_qa_conditionals = [(is_chat_model, CHAT_TEXT_QA_PROMPT)] +if COHERE_QA_TEMPLATE is not None: + default_text_qa_conditionals = [ + (is_cohere_model, COHERE_QA_TEMPLATE), + (is_chat_model, CHAT_TEXT_QA_PROMPT), + ] + DEFAULT_TEXT_QA_PROMPT_SEL = SelectorPromptTemplate( default_template=DEFAULT_TEXT_QA_PROMPT, - conditionals=[(is_chat_model, CHAT_TEXT_QA_PROMPT)], + conditionals=default_text_qa_conditionals, ) +# Tree Summarize +default_tree_summarize_conditionals = [(is_chat_model, CHAT_TREE_SUMMARIZE_PROMPT)] +if COHERE_TREE_SUMMARIZE_TEMPLATE is not None: + default_tree_summarize_conditionals = [ + (is_cohere_model, COHERE_TREE_SUMMARIZE_TEMPLATE), + (is_chat_model, CHAT_TREE_SUMMARIZE_PROMPT), + ] + DEFAULT_TREE_SUMMARIZE_PROMPT_SEL = SelectorPromptTemplate( default_template=DEFAULT_TREE_SUMMARIZE_PROMPT, - conditionals=[(is_chat_model, CHAT_TREE_SUMMARIZE_PROMPT)], + conditionals=default_tree_summarize_conditionals, ) +# Refine +default_refine_conditionals = [(is_chat_model, CHAT_REFINE_PROMPT)] +if COHERE_REFINE_TEMPLATE is not None: + default_refine_conditionals = [ + (is_cohere_model, COHERE_REFINE_TEMPLATE), + (is_chat_model, CHAT_REFINE_PROMPT), + ] + DEFAULT_REFINE_PROMPT_SEL = SelectorPromptTemplate( default_template=DEFAULT_REFINE_PROMPT, - conditionals=[(is_chat_model, CHAT_REFINE_PROMPT)], + conditionals=default_refine_conditionals, ) +# Refine Table Context +default_refine_table_conditionals = [(is_chat_model, CHAT_REFINE_TABLE_CONTEXT_PROMPT)] +if COHERE_REFINE_TABLE_CONTEXT_PROMPT is not None: + default_refine_table_conditionals = [ + (is_cohere_model, COHERE_REFINE_TABLE_CONTEXT_PROMPT), + (is_chat_model, CHAT_REFINE_TABLE_CONTEXT_PROMPT), + ] + DEFAULT_REFINE_TABLE_CONTEXT_PROMPT_SEL = SelectorPromptTemplate( default_template=DEFAULT_REFINE_TABLE_CONTEXT_PROMPT, - conditionals=[(is_chat_model, CHAT_REFINE_TABLE_CONTEXT_PROMPT)], + conditionals=default_refine_table_conditionals, ) diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/__init__.py b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/__init__.py index 872737738c460..6218333755b16 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/__init__.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/__init__.py @@ -1,3 +1,19 @@ from llama_index.llms.cohere.base import Cohere +from llama_index.llms.cohere.utils import ( + COHERE_QA_TEMPLATE, + COHERE_REFINE_TEMPLATE, + COHERE_TREE_SUMMARIZE_TEMPLATE, + COHERE_REFINE_TABLE_CONTEXT_PROMPT, + DocumentMessage, + is_cohere_model, +) -__all__ = ["Cohere"] +__all__ = [ + "COHERE_QA_TEMPLATE", + "COHERE_REFINE_TEMPLATE", + "COHERE_TREE_SUMMARIZE_TEMPLATE", + "COHERE_REFINE_TABLE_CONTEXT_PROMPT", + "DocumentMessage", + "is_cohere_model", + "Cohere", +] diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py index 84e4e11598b72..f22b70807ff40 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py @@ -26,6 +26,7 @@ cohere_modelname_to_contextsize, completion_with_retry, messages_to_cohere_history, + remove_documents_from_messages, ) import cohere @@ -47,7 +48,9 @@ class Cohere(LLM): """ model: str = Field(description="The cohere model to use.") - temperature: float = Field(description="The temperature to use for sampling.") + temperature: float = Field( + description="The temperature to use for sampling.", default=None + ) max_retries: int = Field( default=10, description="The maximum number of API retries." ) @@ -61,9 +64,9 @@ class Cohere(LLM): def __init__( self, - model: str = "command", - temperature: float = 0.5, - max_tokens: int = 512, + model: str = "command-r", + temperature: Optional[float] = None, + max_tokens: Optional[int] = 8192, timeout: Optional[float] = None, max_retries: int = 10, api_key: Optional[str] = None, @@ -130,8 +133,10 @@ def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]: @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: - history = messages_to_cohere_history(messages[:-1]) prompt = messages[-1].content + remaining, documents = remove_documents_from_messages(messages[:-1]) + history = messages_to_cohere_history(remaining) + all_kwargs = self._get_all_kwargs(**kwargs) if all_kwargs["model"] not in CHAT_MODELS: raise ValueError(f"{all_kwargs['model']} not supported for chat") @@ -147,6 +152,7 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: chat=True, message=prompt, chat_history=history, + documents=documents, **all_kwargs, ) return ChatResponse( @@ -182,8 +188,10 @@ def complete( def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: - history = messages_to_cohere_history(messages[:-1]) prompt = messages[-1].content + remaining, documents = remove_documents_from_messages(messages[:-1]) + history = messages_to_cohere_history(remaining) + all_kwargs = self._get_all_kwargs(**kwargs) all_kwargs["stream"] = True if all_kwargs["model"] not in CHAT_MODELS: @@ -194,6 +202,7 @@ def stream_chat( chat=True, message=prompt, chat_history=history, + documents=documents, **all_kwargs, ) diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py index a95b7a5b3f101..f85428059ba01 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py @@ -1,7 +1,12 @@ +from collections import Counter import logging -from typing import Any, Callable, Dict, List, Optional, Sequence +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple +import re -from llama_index.core.base.llms.types import ChatMessage +from llama_index.core.base.llms.base import BaseLLM +from llama_index.core.prompts import ChatPromptTemplate, ChatMessage, MessageRole + +from llama_index.core.prompts.chat_prompts import TEXT_QA_SYSTEM_PROMPT from tenacity import ( before_sleep_log, retry, @@ -30,9 +35,103 @@ ALL_AVAILABLE_MODELS = {**COMMAND_MODELS, **GENERATION_MODELS, **REPRESENTATION_MODELS} CHAT_MODELS = {**COMMAND_MODELS} + +def is_cohere_model(llm: BaseLLM) -> bool: + from llama_index.llms.cohere import Cohere + + return isinstance(llm, Cohere) + + logger = logging.getLogger(__name__) +# Cohere models accept a special argument `documents` for RAG calls. To pass on retrieved documents to the `documents` argument +# as intended by the Cohere team, we define: +# 1. A new ChatMessage class, called DocumentMessage +# 2. Specialised prompt templates for Text QA, Refine, Tree Summarize, and Refine Table that leverage DocumentMessage +# These templates are applied by default when a retriever is called with a Cohere LLM via custom logic inside default_prompt_selectors.py. +# See Cohere.chat for details on how the templates are unpackaged. + + +class DocumentMessage(ChatMessage): + role: MessageRole = MessageRole.SYSTEM + + +# Define new templates with DocumentMessage's to leverage Cohere's `documents` argument +# Text QA +COHERE_QA_TEMPLATE = ChatPromptTemplate( + message_templates=[ + TEXT_QA_SYSTEM_PROMPT, + DocumentMessage(content="{context_str}"), + ChatMessage(content="{query_str}", role=MessageRole.USER), + ] +) +# Refine (based on llama_index.core.chat_prompts::CHAT_REFINE_PROMPT) +REFINE_SYSTEM_PROMPT = ChatMessage( + content=( + "You are an expert Q&A system that strictly operates in two modes " + "when refining existing answers:\n" + "1. **Rewrite** an original answer using the new context.\n" + "2. **Repeat** the original answer if the new context isn't useful.\n" + "Never reference the original answer or context directly in your answer.\n" + "When in doubt, just repeat the original answer.\n" + ), + role=MessageRole.USER, +) +COHERE_REFINE_TEMPLATE = ChatPromptTemplate( + message_templates=[ + REFINE_SYSTEM_PROMPT, + DocumentMessage(content="{context_msg}"), + ChatMessage( + content=( + "Query: {query_str}\n" + "Original Answer: {existing_answer}\n" + "New Answer: " + ), + role=MessageRole.USER, + ), + ] +) +# Tree summarize (based on llama_index.core.chat_prompts::CHAT_TREE_SUMMARIZE_PROMPT) +COHERE_TREE_SUMMARIZE_TEMPLATE = ChatPromptTemplate( + message_templates=[ + TEXT_QA_SYSTEM_PROMPT, + DocumentMessage(content="{context_str}"), + ChatMessage( + content=( + "Given the information from multiple sources and not prior knowledge, " + "answer the query.\n" + "Query: {query_str}\n" + "Answer: " + ), + role=MessageRole.USER, + ), + ] +) +# Table context refine (based on llama_index.core.chat_prompts::CHAT_REFINE_TABLE_CONTEXT_PROMPT) +COHERE_REFINE_TABLE_CONTEXT_PROMPT = ChatPromptTemplate( + message_templates=[ + ChatMessage(content="{query_str}", role=MessageRole.USER), + ChatMessage(content="{existing_answer}", role=MessageRole.ASSISTANT), + DocumentMessage(content="{context_msg}"), + ChatMessage( + content=( + "We have provided a table schema below. " + "---------------------\n" + "{schema}\n" + "---------------------\n" + "We have also provided some context information. " + "Given the context information and the table schema, " + "refine the original answer to better " + "answer the question. " + "If the context isn't useful, return the original answer." + ), + role=MessageRole.USER, + ), + ] +) + + def _create_retry_decorator(max_retries: int) -> Callable[[Any], Any]: min_seconds = 4 max_seconds = 10 @@ -119,6 +218,26 @@ def is_chat_model(model: str) -> bool: return model in COMMAND_MODELS +def remove_documents_from_messages( + messages: Sequence[ChatMessage], +) -> Tuple[Sequence[ChatMessage], Optional[List[Dict[str, str]]]]: + """ + Splits messages into two lists: `remaining` and `documents`. + `remaining` contains all messages that aren't of type DocumentMessage (e.g. history, query). + `documents` contains the retrieved documents, formatted as expected by Cohere RAG inference calls. + + NOTE: this will mix turns for multi-turn RAG + """ + documents = [] + remaining = [] + for msg in messages: + if isinstance(msg, DocumentMessage): + documents.append(msg) + else: + remaining.append(msg) + return remaining, messages_to_cohere_documents(documents) + + def messages_to_cohere_history( messages: Sequence[ChatMessage], ) -> List[Dict[str, Optional[str]]]: @@ -135,3 +254,61 @@ def messages_to_cohere_history( {"role": role_map[message.role], "message": message.content} for message in messages ] + + +def messages_to_cohere_documents( + messages: List[DocumentMessage], +) -> Optional[List[Dict[str, str]]]: + """ + Splits out individual documents from `messages` in the format expected by Cohere.chat's `documents`. + Returns None if `messages` is an empty list for compatibility with co.chat (where `documents` is None by default). + """ + if messages == []: + return None + documents = [] + for msg in messages: + documents.extend(document_message_to_cohere_document(msg)) + return documents + + +def document_message_to_cohere_document(message: DocumentMessage) -> Dict: + # By construction, single DocumentMessage contains all retrieved documents + documents: List[Dict[str, str]] = [] + # Capture all key: value pairs. They will be unpacked in separate documents, Cohere-style. + re_known_keywords = re.compile( + r"(file_path|file_name|file_type|file_size|creation_date|last_modified_data|last_accessed_date): (.+)\n+" + ) + # Find most frequent field. We assume that the most frequent field denotes the boundary + # between consecutive documents, and break ties by taking whichever field appears first. + known_keywords = re.findall(re_known_keywords, message.content) + if len(known_keywords) == 0: + # Document doesn't contain expected special fields. Return default formatting. + return [{"text": message.content}] + + fields_counts = Counter([key for key, _ in known_keywords]) + most_frequent = fields_counts.most_common()[0][0] + + # Initialise + document = None + remaining_text = message.content + for key, value in known_keywords: + if key == most_frequent: + # Save current document after extracting text, then reinit `document` to move to next document + if document: # skip first iteration, where document is None + # Extract text up until the most_frequent remaining text, then skip to next document + index = remaining_text.find(key) + document["text"] = remaining_text[:index].strip() + documents.append(document) + document = {} + + # Catch all special fields. Convert them to key: value pairs. + document[key] = value + # Store remaining text, behind the (first instance of) current `value` + remaining_text = remaining_text[ + remaining_text.find(value) + len(value) : + ].strip() + + # Append last document that's in construction + document["text"] = remaining_text.strip() + documents.append(document) + return documents diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py index f18b9558c399b..8097adae624e5 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py @@ -1,7 +1,83 @@ +from typing import Sequence, Optional, List +from unittest import mock + +import pytest +from cohere import NonStreamedChatResponse + from llama_index.core.base.llms.base import BaseLLM -from llama_index.llms.cohere import Cohere +from llama_index.core.base.llms.types import ChatResponse, ChatMessage, MessageRole +from llama_index.core.llms.mock import MockLLM + +from llama_index.llms.cohere import Cohere, DocumentMessage, is_cohere_model + + +def test_is_cohere(): + assert is_cohere_model(Cohere(api_key="mario")) + assert not is_cohere_model(MockLLM()) def test_embedding_class(): names_of_base_classes = [b.__name__ for b in Cohere.__mro__] assert BaseLLM.__name__ in names_of_base_classes + + +@pytest.mark.parametrize( + "messages,expected_chat_history,expected_documents,expected_message", # noqa: PT006 + [ + pytest.param( + [ChatMessage(content="Hello", role=MessageRole.USER)], + [], + None, + "Hello", + id="single user message", + ), + pytest.param( + [ + ChatMessage(content="Earliest message", role=MessageRole.USER), + ChatMessage(content="Latest message", role=MessageRole.USER), + ], + [{"message": "Earliest message", "role": "USER"}], + None, + "Latest message", + id="messages with chat history", + ), + pytest.param( + [ + ChatMessage(content="Earliest message", role=MessageRole.USER), + DocumentMessage(content="Document content"), + ChatMessage(content="Latest message", role=MessageRole.USER), + ], + [{"message": "Earliest message", "role": "USER"}], + [{"text": "Document content"}], + "Latest message", + id="messages with chat history", + ), + ], +) +def test_chat( + messages: Sequence[ChatMessage], + expected_chat_history: Optional[List], + expected_documents: Optional[List], + expected_message: str, +): + # Mock the API client. + with mock.patch("llama_index.llms.cohere.base.cohere.Client", autospec=True): + llm = Cohere(api_key="dummy", temperature=0.3) + # Mock the API response. + llm._client.chat.return_value = NonStreamedChatResponse(text="Placeholder reply") + expected = ChatResponse( + message=ChatMessage(role=MessageRole.ASSISTANT, content="Placeholder reply"), + raw=llm._client.chat.return_value.__dict__, + ) + + actual = llm.chat(messages) + + assert expected == actual + # Assert that the mocked API client was called in the expected way. + llm._client.chat.assert_called_once_with( + chat_history=expected_chat_history, + documents=expected_documents, + message=expected_message, + model="command-r", + temperature=0.3, + ) diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py new file mode 100644 index 0000000000000..2dbdaf040d27b --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py @@ -0,0 +1,60 @@ +import pytest + +from llama_index.core.prompts import MessageRole +from llama_index.llms.cohere import DocumentMessage +from llama_index.llms.cohere.utils import document_message_to_cohere_document + +text1 = "birds flying high" +text2 = "sun in the sky" +text3 = "breeze driftin' on by" +text4 = "fish in the sea" +text5 = "river running free" +texts = [text1, text2, text3, text4, text5] + + +@pytest.mark.parametrize( + "message, expected", # noqa: PT006 + [ + pytest.param( + DocumentMessage( + role=MessageRole.USER, + content="\n\n".join( + [f"file_path: nina.txt\n\n{text}" for text in texts] + ), + additional_kwargs={}, + ), + [{"file_path": "nina.txt", "text": text} for text in texts], + id="single field, multiple documents", + ), + pytest.param( + DocumentMessage( + role=MessageRole.USER, + content="\n\n".join( + [ + f"file_path: nina.txt\n\nfile_name: greatest-hits\n\n{text}" + for text in texts + ] + ), + additional_kwargs={}, + ), + [ + {"file_path": "nina.txt", "file_name": "greatest-hits", "text": text} + for text in texts + ], + id="multiple fields (same count), multiple documents", + ), + pytest.param( + DocumentMessage( + role=MessageRole.USER, + content="\n\n".join(texts), + additional_kwargs={}, + ), + [{"text": "\n\n".join(texts)}], + id="no fields (just text), multiple documents", + ), + ], +) +def test_document_message_to_cohere_document(message, expected): + res = document_message_to_cohere_document(message) + print(res) + assert res == expected From bb91f5219b0fba04e91544a0eb547e6d0cb4441d Mon Sep 17 00:00:00 2001 From: yograjopcito <93268233+yograjopcito@users.noreply.github.com> Date: Thu, 9 May 2024 09:50:26 +0530 Subject: [PATCH 16/16] Pebblo-Llama PebbloSafeReader (#13128) --- .../llama-index-readers-pebblo/.gitignore | 153 +++++++++++ .../readers/llama-index-readers-pebblo/BUILD | 3 + .../llama-index-readers-pebblo/Makefile | 17 ++ .../llama-index-readers-pebblo/README.md | 49 ++++ .../llama_index/readers/pebblo/BUILD | 1 + .../llama_index/readers/pebblo/__init__.py | 5 + .../llama_index/readers/pebblo/base.py | 258 ++++++++++++++++++ .../llama_index/readers/pebblo/utility.py | 226 +++++++++++++++ .../llama-index-readers-pebblo/pyproject.toml | 58 ++++ .../llama-index-readers-pebblo/tests/BUILD | 1 + .../tests/__init__.py | 0 .../tests/test_readers_pebblo.py | 96 +++++++ 12 files changed, 867 insertions(+) create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/.gitignore create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/BUILD create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/Makefile create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/README.md create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/BUILD create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/__init__.py create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/base.py create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/utility.py create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/tests/BUILD create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/tests/__init__.py create mode 100644 llama-index-integrations/readers/llama-index-readers-pebblo/tests/test_readers_pebblo.py diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/.gitignore b/llama-index-integrations/readers/llama-index-readers-pebblo/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/.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/readers/llama-index-readers-pebblo/BUILD b/llama-index-integrations/readers/llama-index-readers-pebblo/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/Makefile b/llama-index-integrations/readers/llama-index-readers-pebblo/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/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/readers/llama-index-readers-pebblo/README.md b/llama-index-integrations/readers/llama-index-readers-pebblo/README.md new file mode 100644 index 0000000000000..867c77aba8dca --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/README.md @@ -0,0 +1,49 @@ +# LlamaIndex Readers Integration: Pebblo + +## Pebblo Safe DocumentReader + +[Pebblo](https://github.com/daxa-ai/pebblo) enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance and security requirements. The project identifies semantic topics and entities found in the loaded data and summarizes them on the UI or a PDF report. + +### Pebblo has two components. + +1. Pebblo Safe DocumentReader for Llama +2. Pebblo Daemon + +This document describes how to augment your existing Llama DocumentReader with Pebblo Safe DocumentReader to get deep data visibility on the types of Topics and Entities ingested into the Gen-AI Llama application. For details on `Pebblo Daemon` see this [pebblo daemon](https://daxa-ai.github.io/pebblo-docs/daemon.html) document. + +Pebblo Safe DocumentReader enables safe data ingestion for Llama `DocumentReader`. This is done by wrapping the document reader call with `Pebblo Safe DocumentReader` + +#### How to Pebblo enable Document Reading? + +Assume a Llama RAG application snippet using `CSVReader` to read a CSV document for inference. + +Here is the snippet of Document loading using `CSVReader` + +``` +from pathlib import Path +from llama_index.readers.file import CSVReader +reader = CSVReader() +documents = reader.load_data(file=Path('data/corp_sens_data.csv')) +print(documents) +``` + +The Pebblo SafeReader can be installed and enabled with few lines of code change to the above snippet. + +##### Install PebbloSafeReader + +``` +pip install llama-index-readers-pebblo +``` + +##### Use PebbloSafeReader + +``` +from pathlib import Path +from llama_index.readers.pebblo import PebbloSafeReader +from llama_index.readers.file import CSVReader +reader = CSVReader() +pebblo_reader = PebbloSafeReader(reader, name="acme-corp-rag-1", # App name (Mandatory) +owner="Joe Smith", # Owner (Optional) +description="Support productivity RAG application") +documents = pebblo_reader.load_data(file=Path('data/corp_sens_data.csv')) +``` diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/BUILD b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/__init__.py b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/__init__.py new file mode 100644 index 0000000000000..ba45226e6889a --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/__init__.py @@ -0,0 +1,5 @@ +from llama_index.readers.pebblo.base import ( + PebbloSafeReader, +) + +__all__ = ["PebbloSafeReader"] diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/base.py b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/base.py new file mode 100644 index 0000000000000..8935034491397 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/base.py @@ -0,0 +1,258 @@ +"""Pebblo's safe dataloader is a wrapper for document loaders.""" + +import logging +import os +import uuid +from http import HTTPStatus +from typing import Any, Dict, List + +import requests +from llama_index.core import Document + +from llama_index.core.readers.base import BaseReader + +from llama_index.readers.pebblo.utility import ( + CLASSIFIER_URL, + PLUGIN_VERSION, + App, + Doc, + get_runtime, + get_reader_full_path, + get_reader_type, +) + +logger = logging.getLogger(__name__) + + +class PebbloSafeReader(BaseReader): + """Pebblo Safe Loader class is a wrapper around document loaders enabling the data + to be scrutinized. + """ + + _discover_sent: bool = False + _loader_sent: bool = False + + def __init__( + self, + llama_reader: BaseReader, + name: str, + owner: str = "", + description: str = "", + ): + if not name or not isinstance(name, str): + raise NameError("Must specify a valid name.") + self.app_name = name + self.load_id = str(uuid.uuid4()) + self.reader = llama_reader + self.owner = owner + self.description = description + self.reader_name = str(type(self.reader)).split(".")[-1].split("'")[0] + self.source_type = get_reader_type(self.reader_name) + self.docs: List[Document] = [] + self.source_aggr_size = 0 + # generate app + self.app = self._get_app_details() + self._send_discover() + + def load_data(self, **kwargs) -> List[Document]: + """Load Documents. + + Returns: + list: Documents fetched from load method of the wrapped `reader`. + """ + self.docs = self.reader.load_data(**kwargs) + self._send_reader_doc(loading_end=True, **kwargs) + return self.docs + + @classmethod + def set_discover_sent(cls) -> None: + cls._discover_sent = True + + @classmethod + def set_reader_sent(cls) -> None: + cls._reader_sent = True + + def _set_reader_details(self, **kwargs) -> None: + self.source_path = get_reader_full_path(self.reader, self.reader_name, **kwargs) + self.source_owner = PebbloSafeReader.get_file_owner_from_path(self.source_path) + self.source_path_size = self.get_source_size(self.source_path) + self.reader_details = { + "loader": self.reader_name, + "source_path": self.source_path, + "source_type": self.source_type, + **( + {"source_path_size": str(self.source_path_size)} + if self.source_path_size > 0 + else {} + ), + } + + def _send_reader_doc(self, loading_end: bool = False, **kwargs) -> None: + """Send documents fetched from reader to pebblo-server. Internal method. + + Args: + loading_end (bool, optional): Flag indicating the halt of data + loading by reader. Defaults to False. + """ + headers = {"Accept": "application/json", "Content-Type": "application/json"} + doc_content = [doc.to_langchain_format().dict() for doc in self.docs] + docs = [] + self._set_reader_details(**kwargs) + for doc in doc_content: + page_content = str(doc.get("page_content")) + page_content_size = self.calculate_content_size(page_content) + self.source_aggr_size += page_content_size + docs.append( + { + "doc": page_content, + "source_path": self.source_path, + "last_modified": doc.get("metadata", {}).get("last_modified", None), + "file_owner": self.source_owner, + **( + {"source_path_size": self.source_path_size} + if self.source_path_size is not None + else {} + ), + } + ) + payload: Dict[str, Any] = { + "name": self.app_name, + "owner": self.owner, + "docs": docs, + "plugin_version": PLUGIN_VERSION, + "load_id": self.load_id, + "loader_details": self.reader_details, + "loading_end": "false", + "source_owner": self.source_owner, + } + if loading_end is True: + payload["loading_end"] = "true" + if "loader_details" in payload: + payload["loader_details"]["source_aggr_size"] = self.source_aggr_size + payload = Doc(**payload).dict(exclude_unset=True) + load_doc_url = f"{CLASSIFIER_URL}/v1/loader/doc" + try: + resp = requests.post( + load_doc_url, headers=headers, json=payload, timeout=20 + ) + if resp.status_code not in [HTTPStatus.OK, HTTPStatus.BAD_GATEWAY]: + logger.warning( + f"Received unexpected HTTP response code: {resp.status_code}" + ) + logger.debug( + f"send_loader_doc: request \ + url {resp.request.url}, \ + body {str(resp.request.body)[:999]} \ + len {len(resp.request.body if resp.request.body else [])} \ + response status{resp.status_code} body {resp.json()}" + ) + except requests.exceptions.RequestException: + logger.warning("Unable to reach pebblo server.") + except Exception: + logger.warning("An Exception caught in _send_loader_doc.") + if loading_end is True: + PebbloSafeReader.set_reader_sent() + + @staticmethod + def calculate_content_size(page_content: str) -> int: + """Calculate the content size in bytes: + - Encode the string to bytes using a specific encoding (e.g., UTF-8) + - Get the length of the encoded bytes. + + Args: + page_content (str): Data string. + + Returns: + int: Size of string in bytes. + """ + # Encode the content to bytes using UTF-8 + encoded_content = page_content.encode("utf-8") + return len(encoded_content) + + def _send_discover(self) -> None: + """Send app discovery payload to pebblo-server. Internal method.""" + headers = {"Accept": "application/json", "Content-Type": "application/json"} + payload = self.app.dict(exclude_unset=True) + app_discover_url = f"{CLASSIFIER_URL}/v1/app/discover" + try: + resp = requests.post( + app_discover_url, headers=headers, json=payload, timeout=20 + ) + logger.debug( + f"send_discover: request \ + url {resp.request.url}, \ + headers {resp.request.headers}, \ + body {str(resp.request.body)[:999]} \ + len {len(resp.request.body if resp.request.body else [])} \ + response status{resp.status_code} body {resp.json()}" + ) + if resp.status_code in [HTTPStatus.OK, HTTPStatus.BAD_GATEWAY]: + PebbloSafeReader.set_discover_sent() + else: + logger.warning( + f"Received unexpected HTTP response code: {resp.status_code}" + ) + except requests.exceptions.RequestException: + logger.warning("Unable to reach pebblo server.") + except Exception: + logger.warning("An Exception caught in _send_discover.") + + def _get_app_details(self) -> App: + """Fetch app details. Internal method. + + Returns: + App: App details. + """ + framework, runtime = get_runtime() + return App( + name=self.app_name, + owner=self.owner, + description=self.description, + load_id=self.load_id, + runtime=runtime, + framework=framework, + plugin_version=PLUGIN_VERSION, + ) + + @staticmethod + def get_file_owner_from_path(file_path: str) -> str: + """Fetch owner of local file path. + + Args: + file_path (str): Local file path. + + Returns: + str: Name of owner. + """ + try: + import pwd + + file_owner_uid = os.stat(file_path).st_uid + file_owner_name = pwd.getpwuid(file_owner_uid).pw_name + except Exception: + file_owner_name = "unknown" + return file_owner_name + + def get_source_size(self, source_path: str) -> int: + """Fetch size of source path. Source can be a directory or a file. + + Args: + source_path (str): Local path of data source. + + Returns: + int: Source size in bytes. + """ + if not source_path: + return 0 + size = 0 + if os.path.isfile(source_path): + size = os.path.getsize(source_path) + elif os.path.isdir(source_path): + total_size = 0 + for dirpath, _, filenames in os.walk(source_path): + for f in filenames: + fp = os.path.join(dirpath, f) + if not os.path.islink(fp): + total_size += os.path.getsize(fp) + size = total_size + return size diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/utility.py b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/utility.py new file mode 100644 index 0000000000000..67512ae927608 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/llama_index/readers/pebblo/utility.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +import logging +import os +import pathlib +import platform +from typing import Optional, Tuple + +from pydantic.v1.main import BaseModel + +from llama_index.core.readers.base import BaseReader + +logger = logging.getLogger(__name__) + +PLUGIN_VERSION = "0.1.0" +CLASSIFIER_URL = os.getenv("PEBBLO_CLASSIFIER_URL", "http://localhost:8000") + +# Supported loaders for Pebblo safe data loading +file_reader = ["CSVReader", "DocxReader", "PDFReader"] +dir_reader = ["SimpleDirectoryReader"] +in_memory = [] + +READER_TYPE_MAPPING = {"file": file_reader, "dir": dir_reader, "in-memory": in_memory} + +SUPPORTED_LOADERS = (*file_reader, *dir_reader, *in_memory) + +logger = logging.getLogger(__name__) + + +class Runtime(BaseModel): + """This class represents a Runtime. + + Args: + type (Optional[str]): Runtime type. Defaults to "" + host (str): Hostname of runtime. + path (str): Current working directory path. + ip (Optional[str]): Ip of current runtime. Defaults to "" + platform (str): Platform details of current runtime. + os (str): OS name. + os_version (str): OS version. + language (str): Runtime kernel. + language_version (str): version of current runtime kernel. + runtime (Optional[str]) More runtime details. Defaults to "" + """ + + type: str = "local" + host: str + path: str + ip: Optional[str] = "" + platform: str + os: str + os_version: str + language: str + language_version: str + runtime: str = "local" + + +class Framework(BaseModel): + """This class represents a Framework instance. + + Args: + name (str): Name of the Framework. + version (str): Version of the Framework. + """ + + name: str + version: str + + +class App(BaseModel): + """This class represents an AI application. + + Args: + name (str): Name of the app. + owner (str): Owner of the app. + description (Optional[str]): Description of the app. + load_id (str): Unique load_id of the app instance. + runtime (Runtime): Runtime details of app. + framework (Framework): Framework details of the app + plugin_version (str): Plugin version used for the app. + """ + + name: str + owner: str + description: Optional[str] + load_id: str + runtime: Runtime + framework: Framework + plugin_version: str + + +class Doc(BaseModel): + """This class represents a pebblo document. + + Args: + name (str): Name of app originating this document. + owner (str): Owner of app. + docs (list): List of documents with its metadata. + plugin_version (str): Pebblo plugin Version + load_id (str): Unique load_id of the app instance. + loader_details (dict): Loader details with its metadata. + loading_end (bool): Boolean, specifying end of loading of source. + source_owner (str): Owner of the source of the loader. + """ + + name: str + owner: str + docs: list + plugin_version: str + load_id: str + loader_details: dict + loading_end: bool + source_owner: str + + +def get_full_path(path: str) -> str: + """Return absolute local path for a local file/directory, + for network related path, return as is. + + Args: + path (str): Relative path to be resolved. + + Returns: + str: Resolved absolute path. + """ + if ( + not path + or ("://" in path) + or (path[0] == "/") + or (path in ["unknown", "-", "in-memory"]) + ): + return path + full_path = pathlib.Path(path).resolve() + return str(full_path) + + +def get_reader_type(reader: str) -> str: + """Return loader type among, file, dir or in-memory. + + Args: + loader (str): Name of the loader, whose type is to be resolved. + + Returns: + str: One of the loader type among, file/dir/in-memory. + """ + for reader_type, readers in READER_TYPE_MAPPING.items(): + if reader in readers: + return reader_type + return "unknown" + + +def get_reader_full_path(reader: BaseReader, reader_type: str, **kwargs) -> str: + """Return absolute source path of source of reader based on the + keys present in Document object from reader. + + Args: + reader (BaseReader): Llama document reader, derived from Baseloader. + """ + location = "-" + if not isinstance(reader, BaseReader): + logger.error( + "loader is not derived from BaseReader, source location will be unknown!" + ) + return location + loader_dict = reader.__dict__ + try: + if reader_type == "SimpleDirectoryReader": + if loader_dict.get("input_dir", None): + location = loader_dict.get("input_dir") + elif kwargs.get("file", None): + location = kwargs.get("file") + elif kwargs.get("input_file", None): + location = kwargs.get("input_file") + except Exception: + pass + return get_full_path(str(location)) + + +def get_runtime() -> Tuple[Framework, Runtime]: + """Fetch the current Framework and Runtime details. + + Returns: + Tuple[Framework, Runtime]: Framework and Runtime for the current app instance. + """ + from importlib.metadata import version + + try: + reader_version = version("llama-index-readers-pebblo") + except Exception: + reader_version = "unknown" + framework = Framework(name="Llama Pebblo Reader", version=reader_version) + uname = platform.uname() + + runtime = Runtime( + host=uname.node, + path=os.environ["PWD"], + platform=platform.platform(), + os=uname.system, + os_version=uname.version, + ip=get_ip(), + language="unknown", + language_version=platform.python_version(), + ) + if "Darwin" in runtime.os: + runtime.type = "desktop" + runtime.runtime = "Mac OSX" + + logger.debug(f"framework {framework}") + logger.debug(f"runtime {runtime}") + return framework, runtime + + +def get_ip() -> str: + """Fetch local runtime IP address. + + Returns: + str: IP address + """ + import socket # lazy imports + + host = socket.gethostname() + try: + public_ip = socket.gethostbyname(host) + except Exception: + public_ip = socket.gethostbyname("localhost") + return public_ip diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml new file mode 100644 index 0000000000000..8575ac2532c76 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml @@ -0,0 +1,58 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.readers.pebblo" + +[tool.llamahub.class_authors] +PebbloReader = "yograjopcito" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Yograj Shisode "] +description = "llama-index readers pebblo integration" +license = "MIT" +name = "llama-index-readers-pebblo" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.10.0" +llama-index = "^0.10.20" +requests = "^2" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +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" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/tests/BUILD b/llama-index-integrations/readers/llama-index-readers-pebblo/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/tests/__init__.py b/llama-index-integrations/readers/llama-index-readers-pebblo/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/tests/test_readers_pebblo.py b/llama-index-integrations/readers/llama-index-readers-pebblo/tests/test_readers_pebblo.py new file mode 100644 index 0000000000000..1d9fa6a142d32 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/tests/test_readers_pebblo.py @@ -0,0 +1,96 @@ +import pytest +import os +from llama_index.core.readers.base import BaseReader +from llama_index.readers.pebblo import PebbloSafeReader +from pathlib import Path +from typing import Dict +from pytest_mock import MockerFixture +from llama_index.readers.file import CSVReader + + +csv_empty_file_name = "test_empty.csv" +csv_file_name = "test_nominal.csv" + + +class MockResponse: + def __init__(self, json_data: Dict, status_code: int): + self.json_data = json_data + self.status_code = status_code + + def json(self) -> Dict: + return self.json_data + + +@pytest.fixture() +def create_empty_file(): + with open(csv_empty_file_name, "w"): + pass + + yield + if os.path.exists(csv_empty_file_name): + os.remove(csv_empty_file_name) + + +@pytest.fixture() +def create_csv_file(): + data = "column1,column2,column3\nvalue1,value2,value3\nvalue4,value5,value6\n" + with open(csv_file_name, "w") as csv_file: + csv_file.write(data) + + yield + if os.path.exists(csv_file_name): + os.remove(csv_file_name) + + +def test_class(): + names_of_base_classes = [b.__name__ for b in PebbloSafeReader.__mro__] + assert BaseReader.__name__ in names_of_base_classes + + +def test_empty_filebased_loader(mocker: MockerFixture, create_empty_file) -> None: + """Test basic file based csv loader.""" + mocker.patch.multiple( + "requests", + get=MockResponse(json_data={"data": ""}, status_code=200), + post=MockResponse(json_data={"data": ""}, status_code=200), + ) + + file_path = f"{Path().resolve()}/{csv_empty_file_name}" + + # Exercise + loader = PebbloSafeReader( + CSVReader(), + "dummy_app_name", + "dummy_owner", + "dummy_description", + ) + result = loader.load_data(file=Path(file_path)) + + # Assert + assert result[0].text == "" + assert result[0].metadata == {"filename": "test_empty.csv", "extension": ".csv"} + + +def test_csv_loader_load_valid_data(mocker: MockerFixture, create_csv_file) -> None: + mocker.patch.multiple( + "requests", + get=MockResponse(json_data={"data": ""}, status_code=200), + post=MockResponse(json_data={"data": ""}, status_code=200), + ) + file_path = f"{Path().resolve()}/test_nominal.csv" + + # Exercise + loader = PebbloSafeReader( + CSVReader(), + "dummy_app_name", + "dummy_owner", + "dummy_description", + ) + result = loader.load_data(file=Path(file_path)) + + # Assert + assert ( + result[0].text + == "column1, column2, column3\nvalue1, value2, value3\nvalue4, value5, value6" + ) + assert result[0].metadata == {"filename": "test_nominal.csv", "extension": ".csv"}