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Update Tracing Walkthrough (#4760)

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Add client methods to read / list runs and sessions.

Update walkthrough to:
- Let the user create a dataset from the runs without going to the UI
- Use the new CLI command to start the server

Improve the error message when `docker` isn't found
dynamic_agent_tools
Zander Chase 1 year ago committed by GitHub
parent fc0a3c8500
commit bee136efa4
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 1214 additions and 1119 deletions
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10
langchain/callbacks/tracers/base.py
Unescape Escape View File

@ -56,7 +56,11 @@ class BaseTracer(BaseCallbackHandler, ABC):
raise TracerException(
f"Parent run with UUID {run.parent_run_id} not found."
)
if run.child_execution_order > parent_run.child_execution_order:
if (
run.child_execution_order is not None
and parent_run.child_execution_order is not None
and run.child_execution_order > parent_run.child_execution_order
):
parent_run.child_execution_order = run.child_execution_order
self.run_map.pop(str(run.id))
@ -68,6 +72,10 @@ class BaseTracer(BaseCallbackHandler, ABC):
parent_run = self.run_map.get(parent_run_id)
if parent_run is None:
raise TracerException(f"Parent run with UUID {parent_run_id} not found.")
if parent_run.child_execution_order is None:
raise TracerException(
f"Parent run with UUID {parent_run_id} has no child execution order."
)
return parent_run.child_execution_order + 1

2
langchain/callbacks/tracers/schemas.py
Unescape Escape View File

@ -108,7 +108,7 @@ class RunBase(BaseModel):
extra: dict
error: Optional[str]
execution_order: int
child_execution_order: int
child_execution_order: Optional[int]
serialized: dict
inputs: dict
outputs: Optional[dict]

35
langchain/cli/main.py
Unescape Escape View File

@ -5,6 +5,7 @@ import shutil
import subprocess
from contextlib import contextmanager
from pathlib import Path
from subprocess import CalledProcessError
from typing import Generator, List, Optional
import requests
@ -19,10 +20,29 @@ _DIR = Path(__file__).parent
def get_docker_compose_command() -> List[str]:
if shutil.which("docker-compose") is None:
"""Get the correct docker compose command for this system."""
try:
subprocess.check_call(
["docker", "compose", "--version"],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
)
return ["docker", "compose"]
else:
except (CalledProcessError, FileNotFoundError):
try:
subprocess.check_call(
["docker-compose", "--version"],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
)
return ["docker-compose"]
except (CalledProcessError, FileNotFoundError):
raise ValueError(
"Neither 'docker compose' nor 'docker-compose'"
" commands are available. Please install the Docker"
" server following the instructions for your operating"
" system at https://docs.docker.com/engine/install/"
)
def get_ngrok_url(auth_token: Optional[str]) -> str:
@ -85,6 +105,12 @@ class ServerCommand:
)
self.ngrok_path = Path(__file__).absolute().parent / "docker-compose.ngrok.yaml"
def _open_browser(self, url: str) -> None:
try:
subprocess.run(["open", url])
except FileNotFoundError:
pass
def _start_local(self) -> None:
command = [
*self.docker_compose_command,
@ -107,7 +133,7 @@ class ServerCommand:
)
logger.info("\tLANGCHAIN_TRACING_V2=true")
subprocess.run(["open", "http://localhost"])
self._open_browser("http://localhost")
def _start_and_expose(self, auth_token: Optional[str]) -> None:
with create_ngrok_config(auth_token=auth_token):
@ -138,7 +164,8 @@ class ServerCommand:
)
logger.info("\tLANGCHAIN_TRACING_V2=true")
logger.info(f"\tLANGCHAIN_ENDPOINT={ngrok_url}")
subprocess.run(["open", "http://localhost"])
self._open_browser("http://0.0.0.0:4040")
self._open_browser("http://localhost")
def start(self, *, expose: bool = False, auth_token: Optional[str] = None) -> None:
"""Run the LangChainPlus server locally.

74
langchain/client/langchain.py
Unescape Escape View File

@ -27,9 +27,16 @@ from requests import Response
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.tracers.langchain import LangChainTracer
from langchain.callbacks.tracers.schemas import Run, TracerSession
from langchain.chains.base import Chain
from langchain.chat_models.base import BaseChatModel
from langchain.client.models import Dataset, DatasetCreate, Example, ExampleCreate
from langchain.client.models import (
Dataset,
DatasetCreate,
Example,
ExampleCreate,
ListRunsQueryParams,
)
from langchain.llms.base import BaseLLM
from langchain.schema import ChatResult, LLMResult, messages_from_dict
from langchain.utils import raise_for_status_with_text, xor_args
@ -192,6 +199,71 @@ class LangChainPlusClient(BaseSettings):
raise ValueError(f"Dataset {file_name} already exists")
return Dataset(**result)
def read_run(self, run_id: str) -> Run:
"""Read a run from the LangChain+ API."""
response = self._get(f"/runs/{run_id}")
raise_for_status_with_text(response)
return Run(**response.json())
def list_runs(
self,
*,
session_id: Optional[str] = None,
session_name: Optional[str] = None,
run_type: Optional[str] = None,
**kwargs: Any,
) -> List[Run]:
"""List runs from the LangChain+ API."""
if session_name is not None:
if session_id is not None:
raise ValueError("Only one of session_id or session_name may be given")
session_id = self.read_session(session_name=session_name).id
query_params = ListRunsQueryParams(
session_id=session_id, run_type=run_type, **kwargs
)
filtered_params = {
k: v for k, v in query_params.dict().items() if v is not None
}
response = self._get("/runs", params=filtered_params)
raise_for_status_with_text(response)
return [Run(**run) for run in response.json()]
@xor_args(("session_id", "session_name"))
def read_session(
self, *, session_id: Optional[str] = None, session_name: Optional[str] = None
) -> TracerSession:
"""Read a session from the LangChain+ API."""
path = "/sessions"
params: Dict[str, Any] = {"limit": 1, "tenant_id": self.tenant_id}
if session_id is not None:
path += f"/{session_id}"
elif session_name is not None:
params["name"] = session_name
else:
raise ValueError("Must provide dataset_name or dataset_id")
response = self._get(
path,
params=params,
)
raise_for_status_with_text(response)
response = self._get(
path,
params=params,
)
raise_for_status_with_text(response)
result = response.json()
if isinstance(result, list):
if len(result) == 0:
raise ValueError(f"Dataset {session_name} not found")
return TracerSession(**result[0])
return TracerSession(**response.json())
def list_sessions(self) -> List[TracerSession]:
"""List sessions from the LangChain+ API."""
response = self._get("/sessions")
raise_for_status_with_text(response)
return [TracerSession(**session) for session in response.json()]
def create_dataset(self, dataset_name: str, description: str) -> Dataset:
"""Create a dataset in the LangChain+ API."""
dataset = DatasetCreate(

49
langchain/client/models.py
Unescape Escape View File

@ -2,9 +2,9 @@ from datetime import datetime
from typing import Any, Dict, List, Optional
from uuid import UUID
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, root_validator
from langchain.callbacks.tracers.schemas import Run
from langchain.callbacks.tracers.schemas import Run, RunTypeEnum
class ExampleBase(BaseModel):
@ -52,3 +52,48 @@ class Dataset(DatasetBase):
id: UUID
created_at: datetime
modified_at: Optional[datetime] = Field(default=None)
class ListRunsQueryParams(BaseModel):
"""Query params for GET /runs endpoint."""
class Config:
extra = "forbid"
id: Optional[List[UUID]]
"""Filter runs by id."""
parent_run: Optional[UUID]
"""Filter runs by parent run."""
run_type: Optional[RunTypeEnum]
"""Filter runs by type."""
session: Optional[UUID] = Field(default=None, alias="session_id")
"""Only return runs within a session."""
reference_example: Optional[UUID]
"""Only return runs that reference the specified dataset example."""
execution_order: Optional[int]
"""Filter runs by execution order."""
error: Optional[bool]
"""Whether to return only runs that errored."""
offset: Optional[int]
"""The offset of the first run to return."""
limit: Optional[int]
"""The maximum number of runs to return."""
start_time: Optional[datetime] = Field(
default=None,
alias="start_before",
description="Query Runs that started <= this time",
)
end_time: Optional[datetime] = Field(
default=None,
alias="end_after",
description="Query Runs that ended >= this time",
)
@root_validator
def validate_time_range(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate that start_time <= end_time."""
start_time = values.get("start_time")
end_time = values.get("end_time")
if start_time and end_time and start_time > end_time:
raise ValueError("start_time must be <= end_time")
return values

414
langchain/experimental/client/tracing_datasets.ipynb
Unescape Escape View File

@ -1,30 +1,45 @@
{
"cells": [{
"cells": [
{
"cell_type": "markdown",
"id": "1a4596ea-a631-416d-a2a4-3577c140493d",
"metadata": {},
"metadata": {
"tags": []
},
"source": [
"# Running Chains on Traced Datasets\n",
"# Tracing and Datasets\n",
"\n",
"LangChain makes it easy to get started with Agents and other LLM applications. However, it can be tricky to get right, especially when you need to deliver a full product. To speed up your application development process, and to help monitor your applications in production, LangChain offers additional tracing tooling.\n",
"\n",
"When might you want to use tracing? Some situations we've found it useful include:\n",
"- Quickly debugging a new chain, agent, or set of tools\n",
"- Evaluating a given chain across different LLMs or Chat Models to compare results or improve prompts\n",
"- Running a given chain multiple time on a dataset to ensure it consistently meets a quality bar.\n",
"\n",
"\n",
"Developing applications with language models can be uniquely challenging. To manage this complexity and ensure reliable performance, LangChain provides tracing and evaluation functionality. This notebook demonstrates how to run Chains, which are language model functions, as well as Chat models, and LLMs on previously captured datasets or traces. Some common use cases for this approach include:\n",
"In this notebook, we'll show how to enable tracing in your LangChain applications and walk you a couple common ways to evaluate your agents.\n",
"We'll focus on using Datasets to benchmark Chain behavior.\n",
"\n",
"- Running an evaluation chain to grade previous runs.\n",
"- Comparing different chains, LLMs, and agents on traced datasets.\n",
"- Executing a stochastic chain multiple times over a dataset to generate metrics before deployment.\n",
"Bear in mind that this notebook is designed under the assumption that you're running LangChain+ server locally in the background, and it's set up to work specifically with the V2 endpoints. This is done using the folowing command in your terminal:\n",
"\n",
"Please note that this notebook assumes you have LangChain+ tracing running in the background. It is also configured to work only with the V2 endpoints. To set it up, follow the [tracing directions here](..\\/..\\/tracing\\/local_installation.md).\n",
"\n",
"We'll start by creating a client to connect to LangChain+."
"```\n",
"pip install --upgrade langchain\n",
"langchain server start\n",
"```\n",
"\n",
"Now, let's get started by creating a client to connect to LangChain+."
]
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 1,
"id": "904db9a5-f387-4a57-914c-c8af8d39e249",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
@ -40,7 +55,7 @@
"LangChainPlusClient (API URL: http://localhost:8000)"
]
},
"execution_count": 18,
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@ -64,37 +79,43 @@
"tags": []
},
"source": [
"## Capture traces\n",
"\n",
"If you have been using LangChainPlus already, you may have datasets available. To view all saved datasets, run:\n",
"## Tracing Runs\n",
"\n",
"```\n",
"datasets = client.list_datasets()\n",
"print(datasets)\n",
"```\n",
"\n",
"Datasets can be created in a number of ways, most often by collecting `Run`'s captured through the LangChain tracing API and converting a set of runs to a dataset.\n",
"\n",
"The V2 tracing API is currently accessible using the `tracing_v2_enabled` context manager. Assuming the server was succesfully started above, running LangChain Agents, Chains, LLMs, and other primitives will then automatically capture traces. We'll start with a simple math example.\n",
"\n",
"**Note** You can also use the `LANGCHAIN_TRACING_V2` environment variable to enable tracing for all runs by default, regardless of whether or not those runs happen within the `tracing_v2_enabled` context manager (i.e. `os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"`)"
"The V2 tracing API can be activated by setting the `LANGCHAIN_TRACING_V2` environment variable to true. Assuming you've successfully initiated the server as described earlier, running LangChain Agents, Chains, LLMs, and other primitives will automatically start capturing traces. Let's begin our exploration with a straightforward math example.\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 2,
"id": "4417e0b8-a26f-4a11-b7eb-ba7a18e73885",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.callbacks.manager import tracing_v2_enabled"
"import os\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_SESSION\"] = \"Tracing Walkthrough\"\n",
"# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"http://localhost:8000\" # The default. Update this if you wish to connect to a hosted LangChain instance\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = None # Update if you wish to authenticate with a hosted LangChain instance"
]
},
{
"cell_type": "markdown",
"id": "7935e832-9ae1-4557-8d08-890c425f18e2",
"metadata": {},
"source": [
"**Note** You can also use the `tracing_v2_enabled` context manager to capture sessions within a given context:\n",
"```\n",
"from langchain.callbacks.manager import tracing_v2_enabled\n",
"with tracing_v2_enabled(\"My Session Name\"):\n",
" ...\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 3,
"id": "7c801853-8e96-404d-984c-51ace59cbbef",
"metadata": {
"tags": []
@ -117,32 +138,27 @@
"metadata": {
"tags": []
},
"outputs": [{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/wfh/code/lc/lckg/langchain/callbacks/manager.py:78: UserWarning: The experimental tracing v2 is in development. This is not yet stable and may change in the future.\n",
" warnings.warn(\n"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The current population of Canada as of 2023 is 39,566,248.\n",
"39,566,248\n",
"Anwar Hadid is Dua Lipa's boyfriend and his age raised to the 0.43 power is approximately 3.87.\n",
"LLMMathChain._evaluate(\"\n",
"(age)**0.43\n",
"\") raised error: 'age'. Please try again with a valid numerical expression\n",
"The distance between Paris and Boston is approximately 3448 miles.\n",
"unknown format from LLM: Sorry, I cannot answer this question as it requires information from the future.\n",
"The distance between Paris and Boston is 3448 miles.\n",
"unknown format from LLM: Assuming we don't have any information about the actual number of points scored in the 2023 super bowl, we cannot provide a mathematical expression to solve this problem.\n",
"LLMMathChain._evaluate(\"\n",
"(total number of points scored in the 2023 super bowl)**0.23\n",
"\") raised error: invalid syntax. Perhaps you forgot a comma? (<expr>, line 1). Please try again with a valid numerical expression\n",
"Could not parse LLM output: `The final answer is that there were no more points scored in the 2023 Super Bowl than in the 2022 Super Bowl.`\n",
"15 points were scored more in the 2023 Super Bowl than in the 2022 Super Bowl.\n",
"1.9347796717823205\n",
"77\n",
"0.2791714614499425\n"
"LLMMathChain._evaluate(\"\n",
"round(0.2791714614499425, 2)\n",
"\") raised error: 'VariableNode' object is not callable. Please try again with a valid numerical expression\n"
]
}
],
@ -159,7 +175,7 @@
" \"who is kendall jenner's boyfriend? what is his height (in inches) raised to .13 power?\",\n",
" 'what is 1213 divided by 4345?'\n",
"]\n",
"with tracing_v2_enabled(session_name=\"search_and_math_chain\"):\n",
"\n",
"for input_example in inputs:\n",
" try:\n",
" print(agent.run(input_example))\n",
@ -176,13 +192,7 @@
"source": [
"## Creating the Dataset\n",
"\n",
"Now that you've captured a session entitled 'search_and_math_chain', it's time to create a dataset:\n",
"\n",
" 1. Navigate to the UI by clicking on the link below.\n",
" 2. Select the 'search_and_math_chain' session from the list.\n",
" 3. Next to the fist example, click \"+ to Dataset\".\n",
" 4. Click \"Create Dataset\" and create a title **\"calculator-example-dataset\"**.\n",
" 5. Add the other examples to the dataset as well"
"Now that you've captured a session entitled 'Tracing Walkthrough', it's time to create a dataset. We will do so using the `create_dataset` method below."
]
},
{
@ -200,25 +210,36 @@
{
"cell_type": "code",
"execution_count": 6,
"id": "7bfb4699-62c3-400a-b3e7-7fb8ad3a68ad",
"id": "c0e12629-bca5-4438-8665-890d0cb9cc4a",
"metadata": {
"tags": []
},
"outputs": [{
"data": {
"text/html": [
"<a href=\"http://localhost\", target=\"_blank\" rel=\"noopener\">LangChain+ Client</a>"
],
"text/plain": [
"LangChainPlusClient (API URL: http://localhost:8000)"
"outputs": [],
"source": [
"runs = client.list_runs(\n",
" session_name=os.environ[\"LANGCHAIN_SESSION\"],\n",
" run_type=\"chain\")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}],
{
"cell_type": "code",
"execution_count": 7,
"id": "17580c4b-bd04-4dde-9d21-9d4edd25b00d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"client"
"if dataset_name not in set([dataset.name for dataset in client.list_datasets()]):\n",
" dataset = client.create_dataset(dataset_name, description=\"A calculator example dataset\")\n",
" # List all \"Chain\" runs in the current session \n",
" runs = client.list_runs(\n",
" session_name=os.environ[\"LANGCHAIN_SESSION\"],\n",
" run_type=\"chain\")\n",
" for run in runs:\n",
" if run.name == \"AgentExecutor\":\n",
" # We will only use examples from the top level AgentExecutor run here.\n",
" client.create_example(inputs=run.inputs, outputs=run.outputs, dataset_id=dataset.id)"
]
},
{
@ -229,12 +250,30 @@
"tags": []
},
"source": [
"**Creating a Dataset in the UI** \n",
"\n",
"Alternatively, you could create or edit the dataset in the UI using the following steps:\n",
"\n",
" 1. Navigate to the UI by clicking on the link below.\n",
" 2. Select the 'search_and_math_chain' session from the list.\n",
" 3. Next to the fist example, click \"+ to Dataset\".\n",
" 4. Click \"Create Dataset\" and create a title **\"calculator-example-dataset\"**.\n",
" 5. Add the other examples to the dataset as well\n",
"\n",
"Once you've used LangChain+ for a while, you will have a number of datasets to work with. To view all saved datasets, execute the following code:\n",
"\n",
"```\n",
"datasets = client.list_datasets()\n",
"print(datasets)\n",
"```\n",
"\n",
"\n",
"**Optional:** If you didn't run the trace above, you can also create datasets by uploading dataframes or CSV files."
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"id": "1baa677c-5642-4378-8e01-3aa1647f19d6",
"metadata": {
"tags": []
@ -247,108 +286,12 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"id": "60d14593-c61f-449f-a38f-772ca43707c2",
"metadata": {
"tags": []
},
"outputs": [{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset json (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--agent-search-calculator-8a025c0ce5fb99d2/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c34edde8de5340888b3278d1ac427417",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>input</th>\n",
" <th>output</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>How many people live in canada as of 2023?</td>\n",
" <td>approximately 38,625,801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>who is dua lipa's boyfriend? what is his age r...</td>\n",
" <td>her boyfriend is Romain Gravas. his age raised...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>what is dua lipa's boyfriend age raised to the...</td>\n",
" <td>her boyfriend is Romain Gravas. his age raised...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>how far is it from paris to boston in miles</td>\n",
" <td>approximately 3,435 mi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>what was the total number of points scored in ...</td>\n",
" <td>approximately 2.682651500990882</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" input \\\n",
"0 How many people live in canada as of 2023? \n",
"1 who is dua lipa's boyfriend? what is his age r... \n",
"2 what is dua lipa's boyfriend age raised to the... \n",
"3 how far is it from paris to boston in miles \n",
"4 what was the total number of points scored in ... \n",
"\n",
" output \n",
"0 approximately 38,625,801 \n",
"1 her boyfriend is Romain Gravas. his age raised... \n",
"2 her boyfriend is Romain Gravas. his age raised... \n",
"3 approximately 3,435 mi \n",
"4 approximately 2.682651500990882 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"# import pandas as pd\n",
"# from langchain.evaluation.loading import load_dataset\n",
@ -361,7 +304,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"id": "52a7ea76-79ca-4765-abf7-231e884040d6",
"metadata": {
"tags": []
@ -397,7 +340,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 11,
"id": "c2b59104-b90e-466a-b7ea-c5bd0194263b",
"metadata": {
"tags": []
@ -425,12 +368,13 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 12,
"id": "112d7bdf-7e50-4c1a-9285-5bac8473f2ee",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"data": {
"text/plain": [
"\u001b[0;31mSignature:\u001b[0m\n",
@ -467,14 +411,15 @@
},
"metadata": {},
"output_type": "display_data"
}],
}
],
"source": [
"?client.arun_on_dataset"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 13,
"id": "6e10f823",
"metadata": {
"tags": []
@ -493,20 +438,24 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 14,
"id": "a8088b7d-3ab6-4279-94c8-5116fe7cee33",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed examples: 1\r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/wfh/code/lc/lckg/langchain/callbacks/manager.py:78: UserWarning: The experimental tracing v2 is in development. This is not yet stable and may change in the future.\n",
" warnings.warn(\n",
"Chain failed for example 5523e460-6bb4-4a64-be37-bec0a98699a4. Error: LLMMathChain._evaluate(\"\n",
"(total number of points scored in the 2023 super bowl)**0.23\n",
"\") raised error: invalid syntax. Perhaps you forgot a comma? (<expr>, line 1). Please try again with a valid numerical expression\n"
"Chain failed for example 8d4ff5b4-41fb-4986-80f1-025e6fec96b0. Error: unknown format from LLM: It is impossible to accurately predict the total number of points scored in a future event. Therefore, a mathematical expression cannot be provided.\n"
]
},
{
@ -520,23 +469,25 @@
"name": "stderr",
"output_type": "stream",
"text": [
"Chain failed for example f193a3f6-1147-4ce6-a83e-fab1157dc88d. Error: unknown format from LLM: Assuming we don't have any information about the actual number of points scored in the 2023 super bowl, we cannot provide a mathematical expression to solve this problem.\n"
"Chain failed for example 178081fb-a44a-46d5-a23b-74a830da65f3. Error: LLMMathChain._evaluate(\"\n",
"(age)**0.43\n",
"\") raised error: 'age'. Please try again with a valid numerical expression\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed examples: 6\r"
"Processed examples: 5\r"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Chain failed for example 6d7bbb45-1dc0-4adc-be21-4f76a208a8d2. Error: LLMMathChain._evaluate(\"\n",
"(age ** 0.43)\n",
"\") raised error: 'age'. Please try again with a valid numerical expression\n"
"Chain failed for example 7de97d34-50e2-4ec5-bc49-c8e6287ae73e. Error: LLMMathChain._evaluate(\"\n",
"(total number of points scored in the 2023 super bowl)**0.23\n",
"\") raised error: invalid syntax. Perhaps you forgot a comma? (<expr>, line 1). Please try again with a valid numerical expression\n"
]
},
{
@ -551,6 +502,7 @@
"chain_results = await client.arun_on_dataset(\n",
" dataset_name=dataset_name,\n",
" llm_or_chain_factory=chain_factory,\n",
" concurrency_level=5, # Optional, sets the number of examples to run at a time\n",
" verbose=True\n",
")\n",
"\n",
@ -571,12 +523,13 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 15,
"id": "136db492-d6ca-4215-96f9-439c23538241",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"data": {
"text/html": [
"<a href=\"http://localhost\", target=\"_blank\" rel=\"noopener\">LangChain+ Client</a>"
@ -585,10 +538,11 @@
"LangChainPlusClient (API URL: http://localhost:8000)"
]
},
"execution_count": 13,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}],
}
],
"source": [
"# You can navigate to the UI by clicking on the link below\n",
"client"
@ -614,12 +568,13 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 16,
"id": "64490d7c-9a18-49ed-a3ac-36049c522cb4",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
@ -629,7 +584,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0adb751cec11417b88072963325b481d",
"model_id": "44f3c72015944e2ea4c39516350ea15c",
"version_major": 2,
"version_minor": 0
},
@ -711,7 +666,7 @@
"4 [{'data': {'content': 'Here is the topic for a... "
]
},
"execution_count": 24,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@ -727,7 +682,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 17,
"id": "348acd86-a927-4d60-8d52-02e64585e4fc",
"metadata": {
"tags": []
@ -757,7 +712,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 18,
"id": "a69dd183-ad5e-473d-b631-db90706e837f",
"metadata": {
"tags": []
@ -771,19 +726,12 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 19,
"id": "063da2a9-3692-4b7b-8edb-e474824fe416",
"metadata": {
"tags": []
},
"outputs": [{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/wfh/code/lc/lckg/langchain/callbacks/manager.py:78: UserWarning: The experimental tracing v2 is in development. This is not yet stable and may change in the future.\n",
" warnings.warn(\n"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
@ -820,12 +768,13 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 20,
"id": "5b7a81f2-d19d-438b-a4bb-5678f746b965",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"data": {
"text/html": [
"<a href=\"http://localhost\", target=\"_blank\" rel=\"noopener\">LangChain+ Client</a>"
@ -834,10 +783,11 @@
"LangChainPlusClient (API URL: http://localhost:8000)"
]
},
"execution_count": 19,
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}],
}
],
"source": [
"client"
]
@ -854,7 +804,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 21,
"id": "d6805d0b-4612-4671-bffb-e6978992bd40",
"metadata": {
"tags": []
@ -868,22 +818,23 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 22,
"id": "5d7cb243-40c3-44dd-8158-a7b910441e9f",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset parquet (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___parquet/LangChainDatasets--state-of-the-union-completions-a7eb4af13453cd35/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
"Found cached dataset parquet (/Users/wfh/.cache/huggingface/datasets/LangChainDatasets___parquet/LangChainDatasets--state-of-the-union-completions-5347290a406c64c8/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "189832bd50114f129fb58e590d6e8267",
"model_id": "5ce2168f975241fbae82a76b4d70e4c4",
"version_major": 2,
"version_minor": 0
},
@ -941,15 +892,15 @@
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[[{'generation_info': {'finish_reason': 'lengt...</td>\n",
" <td>With a duty to one another to the American peo...</td>\n",
" <td>Madam Speaker, Madam Vice President, our First...</td>\n",
" <td>[[]]</td>\n",
" <td>And the costs and the threats to America and t...</td>\n",
" <td>Please rise if you are able and show that, Yes...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[[]]</td>\n",
" <td>And the costs and the threats to America and t...</td>\n",
" <td>[[{'generation_info': {'finish_reason': 'stop'...</td>\n",
" <td>Please rise if you are able and show that, Yes...</td>\n",
" <td>Groups of citizens blocking tanks with their b...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@ -960,25 +911,25 @@
"0 [[{'generation_info': {'finish_reason': 'stop'... \n",
"1 [[]] \n",
"2 [[{'generation_info': {'finish_reason': 'stop'... \n",
"3 [[{'generation_info': {'finish_reason': 'lengt... \n",
"4 [[]] \n",
"3 [[]] \n",
"4 [[{'generation_info': {'finish_reason': 'stop'... \n",
"\n",
" ground_truth \\\n",
"0 The pandemic has been punishing. \\n\\nAnd so ma... \n",
"1 With a duty to one another to the American peo... \n",
"2 He thought he could roll into Ukraine and the ... \n",
"3 With a duty to one another to the American peo... \n",
"4 And the costs and the threats to America and t... \n",
"3 And the costs and the threats to America and t... \n",
"4 Please rise if you are able and show that, Yes... \n",
"\n",
" prompt \n",
"0 Putin may circle Kyiv with tanks, but he will ... \n",
"1 Madam Speaker, Madam Vice President, our First... \n",
"2 With a duty to one another to the American peo... \n",
"3 Madam Speaker, Madam Vice President, our First... \n",
"4 Please rise if you are able and show that, Yes... "
"3 Please rise if you are able and show that, Yes... \n",
"4 Groups of citizens blocking tanks with their b... "
]
},
"execution_count": 11,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@ -991,7 +942,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 23,
"id": "c7dcc1b2-7aef-44c0-ba0f-c812279099a5",
"metadata": {
"tags": []
@ -1011,24 +962,17 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 24,
"id": "e946138e-bf7c-43d7-861d-9c5740c933fa",
"metadata": {
"tags": []
},
"outputs": [{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/wfh/code/lc/lckg/langchain/callbacks/manager.py:78: UserWarning: The experimental tracing v2 is in development. This is not yet stable and may change in the future.\n",
" warnings.warn(\n"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"55 processed\r"
"50 processed\r"
]
}
],
@ -1054,12 +998,13 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 25,
"id": "2bf96f17-74c1-4f7d-8458-ae5ab5c6bd36",
"metadata": {
"tags": []
},
"outputs": [{
"outputs": [
{
"data": {
"text/html": [
"<a href=\"http://localhost\", target=\"_blank\" rel=\"noopener\">LangChain+ Client</a>"
@ -1068,10 +1013,11 @@
"LangChainPlusClient (API URL: http://localhost:8000)"
]
},
"execution_count": 24,
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}],
}
],
"source": [
"client"
]

9
langchain/server.py
Unescape Escape View File

@ -1,18 +1,15 @@
"""Script to run langchain-server locally using docker-compose."""
import shutil
import subprocess
from pathlib import Path
from langchain.cli.main import get_docker_compose_command
def main() -> None:
"""Run the langchain server locally."""
p = Path(__file__).absolute().parent / "docker-compose.yaml"
if shutil.which("docker-compose") is None:
docker_compose_command = ["docker", "compose"]
else:
docker_compose_command = ["docker-compose"]
docker_compose_command = get_docker_compose_command()
subprocess.run([*docker_compose_command, "-f", str(p), "pull"])
subprocess.run([*docker_compose_command, "-f", str(p), "up"])

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