blob42
/
langchain
forked from Archives/langchain
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add in the async methods and link the run id (#5810)

Browse Source
searx_updates
Zander Chase 12 months ago committed by GitHub
parent ce7c11625f
commit d9fcc45d05
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 110 additions and 174 deletions
Show Stats Download Patch File Download Diff File

41
langchain/evaluation/run_evaluators/base.py
Unescape Escape View File

@ -6,10 +6,13 @@ from typing import Any, Dict, List, Optional
from langchainplus_sdk import EvaluationResult, RunEvaluator from langchainplus_sdk import EvaluationResult, RunEvaluator
from langchainplus_sdk.schemas import Example, Run from langchainplus_sdk.schemas import Example, Run
from langchain.callbacks.manager import CallbackManagerForChainRun from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.base import Chain from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain from langchain.chains.llm import LLMChain
from langchain.schema import BaseOutputParser from langchain.schema import RUN_KEY, BaseOutputParser
class RunEvalInputMapper: class RunEvalInputMapper:
@ -59,8 +62,33 @@ class RunEvaluatorChain(Chain, RunEvaluator):
example: Optional[Example] = inputs.get("example") example: Optional[Example] = inputs.get("example")
chain_input = self.input_mapper.map(run, example) chain_input = self.input_mapper.map(run, example)
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
chain_output = self.eval_chain(chain_input, callbacks=_run_manager.get_child()) callbacks = _run_manager.get_child()
chain_output = self.eval_chain(
chain_input, callbacks=callbacks, include_run_info=True
)
run_info = chain_output[RUN_KEY]
feedback = self.output_parser.parse_chain_output(chain_output) feedback = self.output_parser.parse_chain_output(chain_output)
feedback.evaluator_info[RUN_KEY] = run_info
return {"feedback": feedback}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: AsyncCallbackManagerForChainRun | None = None,
) -> Dict[str, Any]:
run: Run = inputs["run"]
example: Optional[Example] = inputs.get("example")
chain_input = self.input_mapper.map(run, example)
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
chain_output = await self.eval_chain.acall(
chain_input,
callbacks=callbacks,
include_run_info=True,
)
run_info = chain_output[RUN_KEY]
feedback = self.output_parser.parse_chain_output(chain_output)
feedback.evaluator_info[RUN_KEY] = run_info
return {"feedback": feedback} return {"feedback": feedback}
def evaluate_run( def evaluate_run(
@ -68,3 +96,10 @@ class RunEvaluatorChain(Chain, RunEvaluator):
) -> EvaluationResult: ) -> EvaluationResult:
"""Evaluate an example.""" """Evaluate an example."""
return self({"run": run, "example": example})["feedback"] return self({"run": run, "example": example})["feedback"]
async def aevaluate_run(
self, run: Run, example: Optional[Example] = None
) -> EvaluationResult:
"""Evaluate an example."""
result = await self.acall({"run": run, "example": example})
return result["feedback"]

2
langchain/evaluation/run_evaluators/implementations.py
Unescape Escape View File

@ -43,7 +43,7 @@ class StringRunEvalInputMapper(RunEvalInputMapper, BaseModel):
def map(self, run: Run, example: Optional[Example] = None) -> Dict[str, str]: def map(self, run: Run, example: Optional[Example] = None) -> Dict[str, str]:
"""Maps the Run and Optional[Example] to a dictionary""" """Maps the Run and Optional[Example] to a dictionary"""
if run.outputs is None: if run.outputs is None:
raise ValueError("Run outputs cannot be None.") raise ValueError(f"Run {run.id} has no outputs.")
data = { data = {
value: run.outputs.get(key) for key, value in self.prediction_map.items() value: run.outputs.get(key) for key, value in self.prediction_map.items()

169
langchain/experimental/client/tracing_datasets.ipynb
Unescape Escape View File

@ -120,11 +120,11 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from langchain.llms import OpenAI\n", "from langchain.chat_models import ChatOpenAI\n",
"from langchain.agents import initialize_agent, load_tools\n", "from langchain.agents import initialize_agent, load_tools\n",
"from langchain.agents import AgentType\n", "from langchain.agents import AgentType\n",
"\n", "\n",
"llm = OpenAI(temperature=0)\n", "llm = ChatOpenAI(temperature=0)\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n", "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
"agent = initialize_agent(\n", "agent = initialize_agent(\n",
" tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False\n", " tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False\n",
@ -138,51 +138,7 @@
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n",
"Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"unknown format from LLM: This question cannot be answered using the numexpr library, as it does not involve any mathematical expressions.\n"
]
},
{
"data": {
"text/plain": [
"['39,566,248 people live in Canada as of 2023.',\n",
" \"Romain Gavras is Dua Lipa's boyfriend and his age raised to the .43 power is 4.9373857399466665.\",\n",
" '3.991298452658078',\n",
" 'The shortest distance (air line) between Boston and Paris is 3,437.00 mi (5,531.32 km).',\n",
" 'The total number of points scored in the 2023 Super Bowl raised to the .23 power is 2.3086081644669734.',\n",
" ValueError('unknown format from LLM: This question cannot be answered using the numexpr library, as it does not involve any mathematical expressions.'),\n",
" 'The 2023 Super Bowl scored 3 more points than the 2022 Super Bowl.',\n",
" '1.9347796717823205',\n",
" 'Devin Booker, Kendall Jenner\\'s boyfriend, is 6\\' 5\" tall and his height raised to the .13 power is 1.27335715306192.',\n",
" '1213 divided by 4345 is 0.2791714614499425']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"import asyncio\n", "import asyncio\n",
"\n", "\n",
@ -206,13 +162,12 @@
" return await agent.arun(input_example)\n", " return await agent.arun(input_example)\n",
" except Exception as e:\n", " except Exception as e:\n",
" # The agent sometimes makes mistakes! These will be captured by the tracing.\n", " # The agent sometimes makes mistakes! These will be captured by the tracing.\n",
" print(e)\n",
" return e\n", " return e\n",
"\n", "\n",
"\n", "\n",
"for input_example in inputs:\n", "for input_example in inputs:\n",
" results.append(arun(agent, input_example))\n", " results.append(arun(agent, input_example))\n",
"await asyncio.gather(*results)" "results = await asyncio.gather(*results)"
] ]
}, },
{ {
@ -479,27 +434,6 @@
"tags": [] "tags": []
}, },
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Chain failed for example fb07a1d4-e96e-45fe-a3cd-5113e174b017. Error: unknown format from LLM: Sorry, I cannot answer this question as it requires information that is not currently available.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed examples: 2\r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Chain failed for example f088cda6-3745-4f83-b8fa-e5c1038e81b2. Error: unknown format from LLM: Sorry, as an AI language model, I do not have access to personal information such as someone's age. Please provide a different math problem.\n"
]
},
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
@ -511,36 +445,16 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Chain failed for example abb7259c-8136-4903-80b3-04644eebcc82. Error: Parsing LLM output produced both a final answer and a parse-able action: I need to use the search engine to find out who Dua Lipa's boyfriend is and then use the calculator to raise his age to the .43 power.\n", "Chain failed for example 59fb1b4d-d935-4e43-b2a7-bc33fde841bb. Error: LLMMathChain._evaluate(\"\n",
"Action 1: Search\n", "round(0.2791714614499425, 2)\n",
"Action Input 1: \"Dua Lipa boyfriend\"\n", "\") raised error: 'VariableNode' object is not callable. Please try again with a valid numerical expression\n"
"Observation 1: Anwar Hadid is Dua Lipa's boyfriend.\n",
"Action 2: Calculator\n",
"Action Input 2: 21^0.43\n",
"Observation 2: Anwar Hadid's age raised to the 0.43 power is approximately 3.87.\n",
"Thought: I now know the final answer.\n",
"Final Answer: Anwar Hadid is Dua Lipa's boyfriend and his age raised to the 0.43 power is approximately 3.87.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed examples: 7\r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Chain failed for example 2123b7f1-3d3d-4eca-ba30-faf0dff75399. Error: Could not parse LLM output: `I need to subtract the score of the`\n"
] ]
}, },
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Processed examples: 9\r" "Processed examples: 5\r"
] ]
} }
], ],
@ -622,7 +536,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 14,
"id": "35db4025-9183-4e5f-ba14-0b1b380f49c7", "id": "35db4025-9183-4e5f-ba14-0b1b380f49c7",
"metadata": { "metadata": {
"tags": [] "tags": []
@ -644,39 +558,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 27,
"id": "20ab5a84-1d34-4532-8b4f-b12407f42a0e", "id": "4c94a738-dcd3-442e-b8e7-dd36459f56e3",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<a href=\"https://dev.langchain.plus\", target=\"_blank\" rel=\"noopener\">LangChain+ Client</a>"
],
"text/plain": [
"LangChainPlusClient (API URL: https://dev.api.langchain.plus)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# TODO: Use this one above as well\n",
"from langchainplus_sdk import LangChainPlusClient\n",
"\n",
"client = LangChainPlusClient()\n",
"runs = list(client.list_runs(session_name=evaluation_session_name, execution_order=1, error=False))\n",
"client"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58c23a51-1e0a-46d8-b04b-0e0627983232",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
@ -684,12 +567,12 @@
{ {
"data": { "data": {
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"model_id": "ddf4e207965345c7b1ac27a5e3e677e8", "model_id": "a185493c1af74cbaa0f9b10f32cf81c6",
"version_major": 2, "version_major": 2,
"version_minor": 0 "version_minor": 0
}, },
"text/plain": [ "text/plain": [
" 0%| | 0/44 [00:00<?, ?it/s]" "0it [00:00, ?it/s]"
] ]
}, },
"metadata": {}, "metadata": {},
@ -698,19 +581,37 @@
], ],
"source": [ "source": [
"from tqdm.notebook import tqdm\n", "from tqdm.notebook import tqdm\n",
"feedbacks = []\n",
"runs = client.list_runs(session_name=evaluation_session_name, execution_order=1, error=False)\n",
"for run in tqdm(runs):\n", "for run in tqdm(runs):\n",
" eval_feedback = []\n",
" for evaluator in evaluators:\n", " for evaluator in evaluators:\n",
" feedback = client.evaluate_run(run, evaluator)" " eval_feedback.append(client.aevaluate_run(run, evaluator))\n",
" feedbacks.extend(await asyncio.gather(*eval_feedback)) "
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 29,
"id": "8696f167-dc75-4ef8-8bb3-ac1ce8324f30", "id": "8696f167-dc75-4ef8-8bb3-ac1ce8324f30",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [], "outputs": [
{
"data": {
"text/html": [
"<a href=\"https://dev.langchain.plus\", target=\"_blank\" rel=\"noopener\">LangChain+ Client</a>"
],
"text/plain": [
"LangChainPlusClient (API URL: https://dev.api.langchain.plus)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"client" "client"
] ]
@ -718,7 +619,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "daf7dc7f-a5b0-49be-a695-2a87e283e588", "id": "a5037e54-2c5a-4993-9b46-2a98773d3079",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []

70
poetry.lock generated
Unescape Escape View File

@ -1685,13 +1685,13 @@ files = [
[[package]] [[package]]
name = "deeplake" name = "deeplake"
version = "3.5.4" version = "3.6.0"
description = "Activeloop Deep Lake" description = "Activeloop Deep Lake"
category = "main" category = "main"
optional = false optional = false
python-versions = "*" python-versions = "*"
files = [ files = [
{file = "deeplake-3.5.4.tar.gz", hash = "sha256:a05eba10141fe1e5be5f0ed115a42da1320d6675d644b49790dc28485a87aa32"}, {file = "deeplake-3.6.0.tar.gz", hash = "sha256:bf502ed4fcd19624e750c649b8dd2fb892529a29384c8a816bbb09005b763db1"},
] ]
[package.dependencies] [package.dependencies]
@ -1708,11 +1708,12 @@ pyjwt = "*"
tqdm = "*" tqdm = "*"
[package.extras] [package.extras]
all = ["IPython", "av (>=8.1.0)", "flask", "google-api-python-client (>=2.31.0,<2.32.0)", "google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)", "laspy", "libdeeplake (==0.0.54)", "nibabel", "oauth2client (>=4.1.3,<4.2.0)", "pydicom"] all = ["IPython", "av (>=8.1.0)", "azure-cli", "azure-identity", "azure-storage-blob", "flask", "google-api-python-client (>=2.31.0,<2.32.0)", "google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)", "laspy", "libdeeplake (==0.0.55)", "nibabel", "oauth2client (>=4.1.3,<4.2.0)", "pydicom"]
audio = ["av (>=8.1.0)"] audio = ["av (>=8.1.0)"]
av = ["av (>=8.1.0)"] av = ["av (>=8.1.0)"]
azure = ["azure-cli", "azure-identity", "azure-storage-blob"]
dicom = ["nibabel", "pydicom"] dicom = ["nibabel", "pydicom"]
enterprise = ["libdeeplake (==0.0.54)", "pyjwt"] enterprise = ["libdeeplake (==0.0.55)", "pyjwt"]
gcp = ["google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)"] gcp = ["google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)"]
gdrive = ["google-api-python-client (>=2.31.0,<2.32.0)", "google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "oauth2client (>=4.1.3,<4.2.0)"] gdrive = ["google-api-python-client (>=2.31.0,<2.32.0)", "google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "oauth2client (>=4.1.3,<4.2.0)"]
medical = ["nibabel", "pydicom"] medical = ["nibabel", "pydicom"]
@ -2524,14 +2525,14 @@ grpc = ["grpcio (>=1.44.0,<2.0.0dev)"]
[[package]] [[package]]
name = "gptcache" name = "gptcache"
version = "0.1.29.1" version = "0.1.30"
description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications." description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications."
category = "main" category = "main"
optional = false optional = false
python-versions = ">=3.8.1" python-versions = ">=3.8.1"
files = [ files = [
{file = "gptcache-0.1.29.1-py3-none-any.whl", hash = "sha256:e281f1c7bf1f2cc72e0193d6e15ad3d50e7cb358d04332ef96b98f732aee8a74"}, {file = "gptcache-0.1.30-py3-none-any.whl", hash = "sha256:57c37babe85161fbbe547cb036f6780b33232d70557ae085daccf3c032bc7b14"},
{file = "gptcache-0.1.29.1.tar.gz", hash = "sha256:c7c07c5300422feed302f4a140ff5e25e01f04885c81fb3388e07b3c3a82647a"}, {file = "gptcache-0.1.30.tar.gz", hash = "sha256:a69f600e286dee3e7f3b151c8b269778a4e6a7d5da409c01fbfbedf3239d0cd9"},
] ]
[package.dependencies] [package.dependencies]
@ -3969,14 +3970,14 @@ tests = ["pytest", "pytest-mock"]
[[package]] [[package]]
name = "langchainplus-sdk" name = "langchainplus-sdk"
version = "0.0.4" version = "0.0.6"
description = "Client library to connect to the LangChainPlus LLM Tracing and Evaluation Platform." description = "Client library to connect to the LangChainPlus LLM Tracing and Evaluation Platform."
category = "main" category = "main"
optional = false optional = false
python-versions = ">=3.8.1,<4.0" python-versions = ">=3.8.1,<4.0"
files = [ files = [
{file = "langchainplus_sdk-0.0.4-py3-none-any.whl", hash = "sha256:ba72276b0e9cb148f572d531fbed41017d3a0072dc5a1110d8d2f3af06efce75"}, {file = "langchainplus_sdk-0.0.6-py3-none-any.whl", hash = "sha256:43fe01c66442b88403c969b8812f6be81e023c0d2a6d5d3572a8d87961438658"},
{file = "langchainplus_sdk-0.0.4.tar.gz", hash = "sha256:0c09af62e81975e33561c655ba9b32777d79834218c9377d28578a9558740044"}, {file = "langchainplus_sdk-0.0.6.tar.gz", hash = "sha256:c911a98fd2d02baa48f742b7d700fd6a55f11c9a545ee5d66b08825940c9a32e"},
] ]
[package.dependencies] [package.dependencies]
@ -5591,14 +5592,14 @@ sympy = "*"
[[package]] [[package]]
name = "openai" name = "openai"
version = "0.27.7" version = "0.27.8"
description = "Python client library for the OpenAI API" description = "Python client library for the OpenAI API"
category = "main" category = "main"
optional = false optional = false
python-versions = ">=3.7.1" python-versions = ">=3.7.1"
files = [ files = [
{file = "openai-0.27.7-py3-none-any.whl", hash = "sha256:788fb7fa85bf7caac6c1ed7eea5984254a1bdaf09ef485acf0e5718c8b2dc25a"}, {file = "openai-0.27.8-py3-none-any.whl", hash = "sha256:e0a7c2f7da26bdbe5354b03c6d4b82a2f34bd4458c7a17ae1a7092c3e397e03c"},
{file = "openai-0.27.7.tar.gz", hash = "sha256:bca95fd4c3054ef38924def096396122130454442ec52005915ecf8269626b1d"}, {file = "openai-0.27.8.tar.gz", hash = "sha256:2483095c7db1eee274cebac79e315a986c4e55207bb4fa7b82d185b3a2ed9536"},
] ]
[package.dependencies] [package.dependencies]
@ -6340,20 +6341,20 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
[[package]] [[package]]
name = "pinecone-client" name = "pinecone-client"
version = "2.2.1" version = "2.2.2"
description = "Pinecone client and SDK" description = "Pinecone client and SDK"
category = "main" category = "main"
optional = false optional = false
python-versions = ">=3.6" python-versions = ">=3.8"
files = [ files = [
{file = "pinecone-client-2.2.1.tar.gz", hash = "sha256:0878dcaee447c46c8d1b3d71c854689daa7e548e5009a171780907c7d4e74789"}, {file = "pinecone-client-2.2.2.tar.gz", hash = "sha256:391fe413754efd4e0ef00154b44271d63c4cdd4bedf088d23111a5725d863210"},
{file = "pinecone_client-2.2.1-py3-none-any.whl", hash = "sha256:6976a22aee57a9813378607506c8c36b0317dfa36a08a5397aaaeab2eef66c1b"}, {file = "pinecone_client-2.2.2-py3-none-any.whl", hash = "sha256:21fddb752668efee4d3c6b706346d9580e36a8b06b8d97afd60bd33ef2536e7e"},
] ]
[package.dependencies] [package.dependencies]
dnspython = ">=2.0.0" dnspython = ">=2.0.0"
loguru = ">=0.5.0" loguru = ">=0.5.0"
numpy = "*" numpy = ">=1.22.0"
python-dateutil = ">=2.5.3" python-dateutil = ">=2.5.3"
pyyaml = ">=5.4" pyyaml = ">=5.4"
requests = ">=2.19.0" requests = ">=2.19.0"
@ -6362,7 +6363,7 @@ typing-extensions = ">=3.7.4"
urllib3 = ">=1.21.1" urllib3 = ">=1.21.1"
[package.extras] [package.extras]
grpc = ["googleapis-common-protos (>=1.53.0)", "grpc-gateway-protoc-gen-openapiv2 (==0.1.0)", "grpcio (>=1.44.0)", "lz4 (>=3.1.3)", "protobuf (==3.19.3)"] grpc = ["googleapis-common-protos (>=1.53.0)", "grpc-gateway-protoc-gen-openapiv2 (==0.1.0)", "grpcio (>=1.44.0)", "lz4 (>=3.1.3)", "protobuf (>=3.19.5,<3.20.0)"]
[[package]] [[package]]
name = "pinecone-text" name = "pinecone-text"
@ -7159,16 +7160,15 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
[[package]] [[package]]
name = "pylance" name = "pylance"
version = "0.4.18" version = "0.4.19"
description = "python wrapper for lance-rs" description = "python wrapper for lance-rs"
category = "main" category = "main"
optional = true optional = true
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "pylance-0.4.18-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:9741880ede522b4f80ae9a43b499265096dca4eb2a2a2d72690cb01eb6ef6fed"}, {file = "pylance-0.4.19-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:7f5dbf2c384de6c499a2d876be4aec5cbb8f8ef65d490ac93fbd84251ae61e7c"},
{file = "pylance-0.4.18-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:8b2e2baf0dfeb65da8f993872f4c375d6ad16ada4472756f888f033ce72dbd86"}, {file = "pylance-0.4.19-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:b2125d50ef28af11b5473e72f82ce9af30756ae655b982582c58267b72216043"},
{file = "pylance-0.4.18-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c81f891a6230fc38e60f01f16923263a082f7fd1646c15aad1e940d29c4e8a83"}, {file = "pylance-0.4.19-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f98fc8cbc5f66d35ea7701eae691c195df4e45cca8c8f33d9e1a71f24077d7a6"},
{file = "pylance-0.4.18-cp38-abi3-win_amd64.whl", hash = "sha256:eda511361a4545bb32fb98bcb270a1a41aaf022dd63cc306fdf5698310ba81ce"},
] ]
[package.dependencies] [package.dependencies]
@ -8905,20 +8905,20 @@ themes = ["myst-parser (>=0.12.9,<0.13.0)", "pydata-sphinx-theme (>=0.4.0,<0.5.0
[[package]] [[package]]
name = "sphinx-rtd-theme" name = "sphinx-rtd-theme"
version = "1.2.1" version = "1.2.2"
description = "Read the Docs theme for Sphinx" description = "Read the Docs theme for Sphinx"
category = "dev" category = "dev"
optional = false optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
files = [ files = [
{file = "sphinx_rtd_theme-1.2.1-py2.py3-none-any.whl", hash = "sha256:2cc9351176cbf91944ce44cefd4fab6c3b76ac53aa9e15d6db45a3229ad7f866"}, {file = "sphinx_rtd_theme-1.2.2-py2.py3-none-any.whl", hash = "sha256:6a7e7d8af34eb8fc57d52a09c6b6b9c46ff44aea5951bc831eeb9245378f3689"},
{file = "sphinx_rtd_theme-1.2.1.tar.gz", hash = "sha256:cf9a7dc0352cf179c538891cb28d6fad6391117d4e21c891776ab41dd6c8ff70"}, {file = "sphinx_rtd_theme-1.2.2.tar.gz", hash = "sha256:01c5c5a72e2d025bd23d1f06c59a4831b06e6ce6c01fdd5ebfe9986c0a880fc7"},
] ]
[package.dependencies] [package.dependencies]
docutils = "<0.19" docutils = "<0.19"
sphinx = ">=1.6,<7" sphinx = ">=1.6,<7"
sphinxcontrib-jquery = {version = ">=2.0.0,<3.0.0 || >3.0.0", markers = "python_version > \"3\""} sphinxcontrib-jquery = ">=4,<5"
[package.extras] [package.extras]
dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"] dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
@ -9219,14 +9219,14 @@ full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyam
[[package]] [[package]]
name = "steamship" name = "steamship"
version = "2.17.5" version = "2.17.6"
description = "The fastest way to add language AI to your product." description = "The fastest way to add language AI to your product."
category = "main" category = "main"
optional = true optional = true
python-versions = "*" python-versions = "*"
files = [ files = [
{file = "steamship-2.17.5-py3-none-any.whl", hash = "sha256:4782281aca0d69f6c050623355687eb4a48d5a2108ac274d96e029779aad82f0"}, {file = "steamship-2.17.6-py3-none-any.whl", hash = "sha256:7d25db57f19d228f82ce445e15ace66b2a2e3ac25307d69c4828c27026e8e44c"},
{file = "steamship-2.17.5.tar.gz", hash = "sha256:e0a6d0e46744b3208b306dffe87d20ecf46113d333bc76515191b0d5c9eb28bb"}, {file = "steamship-2.17.6.tar.gz", hash = "sha256:ff3af96d7f0eef2036c222513af51ac356f0753123a7bf44bdb31eccd2d957df"},
] ]
[package.dependencies] [package.dependencies]
@ -10385,14 +10385,14 @@ yarl = "*"
[[package]] [[package]]
name = "wasabi" name = "wasabi"
version = "1.1.1" version = "1.1.2"
description = "A lightweight console printing and formatting toolkit" description = "A lightweight console printing and formatting toolkit"
category = "main" category = "main"
optional = true optional = true
python-versions = ">=3.6" python-versions = ">=3.6"
files = [ files = [
{file = "wasabi-1.1.1-py3-none-any.whl", hash = "sha256:32e44649d99a64e08e40c1c96cddb69fad460bd0cc33802a53cab6714dfb73f8"}, {file = "wasabi-1.1.2-py3-none-any.whl", hash = "sha256:0a3f933c4bf0ed3f93071132c1b87549733256d6c8de6473c5f7ed2e171b5cf9"},
{file = "wasabi-1.1.1.tar.gz", hash = "sha256:f5ee7c609027811bd16e620f2fd7a7319466005848e41b051a62053ab8fd70d6"}, {file = "wasabi-1.1.2.tar.gz", hash = "sha256:1aaef3aceaa32edb9c91330d29d3936c0c39fdb965743549c173cb54b16c30b5"},
] ]
[package.dependencies] [package.dependencies]
@ -11241,4 +11241,4 @@ text-helpers = ["chardet"]
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.8.1,<4.0" python-versions = ">=3.8.1,<4.0"
content-hash = "190059b07b111e19ec5b516be227b81728163fee14928c92cba2c0212d35ead0" content-hash = "8c0ab1bdc8b506e38e6fa4cba40dcf2df47473212d47fa1086c6aae8ddf2c021"

2
pyproject.toml
Unescape Escape View File

@ -103,7 +103,7 @@ momento = {version = "^1.5.0", optional = true}
bibtexparser = {version = "^1.4.0", optional = true} bibtexparser = {version = "^1.4.0", optional = true}
pyspark = {version = "^3.4.0", optional = true} pyspark = {version = "^3.4.0", optional = true}
tigrisdb = {version = "^1.0.0b6", optional = true} tigrisdb = {version = "^1.0.0b6", optional = true}
langchainplus-sdk = "^0.0.4" langchainplus-sdk = ">=0.0.6"
[tool.poetry.group.docs.dependencies] [tool.poetry.group.docs.dependencies]
autodoc_pydantic = "^1.8.0" autodoc_pydantic = "^1.8.0"

Write Preview
Loading…
Cancel
Save