From f91ce4eddf701195f59d2db355377471f33fd087 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Fri, 29 Sep 2023 16:19:37 -0400 Subject: [PATCH] Bump deps in langserve (#11234) Bump deps in langserve lockfile --- libs/langserve/poetry.lock | 6 +- .../tests/unit_tests/test_encoders.py | 92 +++++++++++-------- 2 files changed, 56 insertions(+), 42 deletions(-) diff --git a/libs/langserve/poetry.lock b/libs/langserve/poetry.lock index 6db5354cba..0eb502ea4f 100644 --- a/libs/langserve/poetry.lock +++ b/libs/langserve/poetry.lock @@ -1626,7 +1626,7 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v [[package]] name = "langchain" -version = "0.0.304" +version = "0.0.305" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1655,7 +1655,7 @@ clarifai = ["clarifai (>=9.1.0)"] cohere = ["cohere (>=4,<5)"] docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"] embeddings = ["sentence-transformers (>=2,<3)"] -extended-testing = ["amazon-textract-caller (<2)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "dashvector (>=1.0.1,<2.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "openai (>=0,<1)", "openai (>=0,<1)", "openapi-schema-pydantic (>=1.2,<2.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] +extended-testing = ["amazon-textract-caller (<2)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "dashvector (>=1.0.1,<2.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "openai (>=0,<1)", "openai (>=0,<1)", "openapi-schema-pydantic (>=1.2,<2.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] javascript = ["esprima (>=4.0.1,<5.0.0)"] llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"] openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.6.0)"] @@ -1666,7 +1666,7 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"] type = "git" url = "https://github.com/langchain-ai/langchain" reference = "HEAD" -resolved_reference = "4ad0f3de2b1c423d2900d925e26c0097b8741e90" +resolved_reference = "8b4cb4eb60e3935eea895aa955d68ca0afce788c" subdirectory = "libs/langchain" [[package]] diff --git a/libs/langserve/tests/unit_tests/test_encoders.py b/libs/langserve/tests/unit_tests/test_encoders.py index b3c7374b3c..50b041baf4 100644 --- a/libs/langserve/tests/unit_tests/test_encoders.py +++ b/libs/langserve/tests/unit_tests/test_encoders.py @@ -4,6 +4,7 @@ from typing import Any import pytest from langchain.schema.messages import ( HumanMessage, + HumanMessageChunk, SystemMessage, ) @@ -32,6 +33,7 @@ from langserve.serialization import simple_dumps, simple_loads "additional_kwargs": {}, "type": "human", "example": False, + "is_chunk": False, } ] }, @@ -44,6 +46,7 @@ from langserve.serialization import simple_dumps, simple_loads "content": "Hello", "example": False, "type": "human", + "is_chunk": False, }, ), # Test with a list containing mixed elements @@ -55,50 +58,60 @@ from langserve.serialization import simple_dumps, simple_loads "content": "Hello", "example": False, "type": "human", + "is_chunk": False, + }, + { + "additional_kwargs": {}, + "content": "Hi", + "type": "system", + "is_chunk": False, }, - {"additional_kwargs": {}, "content": "Hi", "type": "system"}, 42, "world", ], ), - # # Attention: This test is not correct right now - # # Test with full and chunk messages - # ( - # [HumanMessage(content="Hello"), HumanMessageChunk(content="Hi")], - # [ - # { - # "additional_kwargs": {}, - # "content": "Hello", - # "example": False, - # "type": "human", - # }, - # { - # "additional_kwargs": {}, - # "content": "Hi", - # "example": False, - # "type": "human", - # }, - # ], - # ), - # # Attention: This test is not correct right now - # # Test with full and chunk messages - # ( - # [HumanMessageChunk(content="Hello"), HumanMessage(content="Hi")], - # [ - # { - # "additional_kwargs": {}, - # "content": "Hello", - # "example": False, - # "type": "human", - # }, - # { - # "additional_kwargs": {}, - # "content": "Hi", - # "example": False, - # "type": "human", - # }, - # ], - # ), + # Attention: This test is not correct right now + # Test with full and chunk messages + ( + [HumanMessage(content="Hello"), HumanMessageChunk(content="Hi")], + [ + { + "additional_kwargs": {}, + "content": "Hello", + "example": False, + "type": "human", + "is_chunk": False, + }, + { + "additional_kwargs": {}, + "content": "Hi", + "example": False, + "type": "human", + "is_chunk": True, + }, + ], + ), + # Attention: This test is not correct right now + # Test with full and chunk messages + ( + [HumanMessageChunk(content="Hello"), HumanMessage(content="Hi")], + [ + { + "additional_kwargs": {}, + "content": "Hello", + "example": False, + "type": "human", + "is_chunk": True, + }, + { + "additional_kwargs": {}, + "content": "Hi", + "example": False, + "type": "human", + "is_chunk": False, + }, + ], + ), # Test with a dictionary containing mixed elements ( { @@ -112,6 +125,7 @@ from langserve.serialization import simple_dumps, simple_loads "content": "Greetings", "example": False, "type": "human", + "is_chunk": False, }, "numbers": [1, 2, 3], "boom": "Hello, world!",