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Update eval set

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This commit is contained in:
Lance Martin
2024-01-26 11:12:14 -08:00
parent 8cbca25ca9
commit 39ed87b559
9 changed files with 1176 additions and 207 deletions
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README.md
+5
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@@ -82,6 +82,11 @@ Add your app dependencies to `pyproject.toml` and `poetry.lock`:
poetry add weaviate-client
poetry add langchainhub
poetry add openai
poetry add pandas
poetry add jupyter
poetry add tiktoken
poetry add scikit-learn
poetry add langchain_openai
```
Update enviorment based on the updated lock file:
app/base_rag_chain.py
+65
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@@ -0,0 +1,65 @@
import weaviate
from langchain_openai import ChatOpenAI
from langchain.vectorstores import Weaviate
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.embeddings.voyageai import VoyageEmbeddings
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_core.pydantic_v1 import BaseModel
# Keys
import os
WEAVIATE_URL = os.environ["WEAVIATE_URL"]
WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]
WEAVIATE_DOCS_INDEX_NAME = "LangChain_agent_docs"
# Fine-tuned embd and vectorstore
def get_embeddings_model():
if os.environ.get("VOYAGE_API_KEY") and os.environ.get("VOYAGE_AI_MODEL"):
return VoyageEmbeddings(model=os.environ["VOYAGE_AI_MODEL"])
return OpenAIEmbeddings(chunk_size=200)
def get_retriever():
weaviate_client = weaviate.Client(
url=WEAVIATE_URL,
auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY),
)
weaviate_client = Weaviate(
client=weaviate_client,
index_name=WEAVIATE_DOCS_INDEX_NAME,
text_key="text",
embedding=get_embeddings_model(),
by_text=False,
attributes=["source", "title"],
)
return weaviate_client.as_retriever(search_kwargs=dict(k=6))
# Retriever
retriever = get_retriever()
# Prompt
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# LLM
model = ChatOpenAI(model="gpt-4-1106-preview")
# Chain
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
# Add typing for input
class Question(BaseModel):
__root__: str
chain = chain.with_types(input_type=Question)
app/context_stuffing_chain.py
+1 -1
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@@ -1,6 +1,6 @@
from bs4 import BeautifulSoup as Soup
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader
from langchain.chat_models import ChatOpenAI
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
app/chain.py → app/multi_query_chain.py
+8 -2
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@@ -23,7 +23,7 @@ from langchain.schema.runnable import (
)
from langchain.vectorstores import Weaviate
from langchain_core.runnables import RunnablePassthrough
from pydantic import BaseModel
from langchain_core.pydantic_v1 import BaseModel
# Prompts
from .prompts import REPHRASE_TEMPLATE, RESPONSE_TEMPLATE
@@ -231,4 +231,10 @@ chain = (
| prompt
| llm
| StrOutputParser()
)
)
# Add typing for input
class Question(BaseModel):
__root__: str
chain = chain.with_types(input_type=Question)
eval/build_eval_set.ipynb
+717
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@@ -0,0 +1,717 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fc4d2f37-d4af-4076-a14f-a004af5fc7da",
"metadata": {},
"source": [
"## Get LCEL-related questions from `chat-langchain`\n",
"\n",
"See [here](https://raw.githubusercontent.com/hinthornw/lspopscripts/main/download_runs.py) if you want code the get full traces."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a20d3f7e-a435-4816-a5bc-36ae4c1a570a",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = \"ls__844575bca0324625ae10cd97bb9c2888\" "
]
},
{
"cell_type": "markdown",
"id": "c2adea52-baea-4634-a692-f49a1df571c7",
"metadata": {},
"source": [
"## Get Questions\n",
"\n",
"* Extract from `chat-langchain`"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "052a5d8c-23cb-47ea-8a52-9c030c3c6a74",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"152090it [42:48, 59.22it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved to fetched_data.csv\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import datetime\n",
"import csv\n",
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"from itertools import islice\n",
"\n",
"import langsmith\n",
"from tqdm import tqdm\n",
"\n",
"client = langsmith.Client()\n",
"\n",
"def download_data(\n",
" project_name: str,\n",
" nested: bool = False,\n",
" since: datetime.datetime = yesterday,\n",
" exclude_followups: bool = True,\n",
" filename: str = \"fetched_data.csv\",\n",
"):\n",
" \"\"\"\n",
" Downloads and saves data from Langsmith runs to a CSV file.\n",
"\n",
" This function retrieves run data from the Langsmith project specified by 'project_name'.\n",
" It extracts 'question' and 'output' from each run's inputs and outputs, respectively,\n",
" and saves them into a CSV file. The function can handle both nested and non-nested runs.\n",
" Follow-up runs can be excluded if desired.\n",
"\n",
" Parameters:\n",
" project_name (str): The name of the Langsmith project to retrieve data from.\n",
" nested (bool): Set to True to handle nested runs; False by default.\n",
" since (datetime): The start time from which to retrieve runs; defaults to yesterday.\n",
" exclude_followups (bool): Set to True to exclude follow-up runs; True by default.\n",
" filename (str): The name of the file to save the data to; defaults to 'fetched_data.csv'.\n",
" \"\"\"\n",
" traces = client.list_runs(\n",
" project_name=project_name, start_time=since, execution_order=1\n",
" )\n",
" batch_size = 10\n",
" executor = ThreadPoolExecutor(max_workers=batch_size) if nested else None\n",
"\n",
" with open(filename, 'w', newline='', encoding='utf-8') as file_handle:\n",
" csv_writer = csv.writer(file_handle)\n",
" # Write the header\n",
" csv_writer.writerow(['question', 'output'])\n",
"\n",
" try:\n",
" if nested:\n",
" pbar = tqdm()\n",
" while True:\n",
" batch = list(islice(traces, batch_size))\n",
" if not batch:\n",
" break\n",
" futures = [\n",
" executor.submit(client.read_run, run.id, load_child_runs=True)\n",
" for run in batch\n",
" ]\n",
" for future in as_completed(futures):\n",
" loaded_run = future.result()\n",
" loaded_run_json=loaded_run.json()\n",
" loaded_run_json = json.loads(loaded_run_json)\n",
" question = loaded_run_json['inputs'].get('question', '')\n",
" output = loaded_run_json['outputs'].get('output', '')\n",
" csv_writer.writerow([question, output])\n",
" pbar.update(len(batch))\n",
" else:\n",
" for run in tqdm(traces):\n",
" if exclude_followups and run.inputs.get(\"chat_history\"):\n",
" continue\n",
" run_json = run.json()\n",
" run_json = json.loads(run_json)\n",
" question = run_json['inputs'].get('question', '')\n",
" output = run_json['outputs'].get('output', '')\n",
" csv_writer.writerow([question, output])\n",
"\n",
" finally:\n",
" if executor:\n",
" executor.shutdown()\n",
" \n",
" print(f\"Saved to {filename}\")\n",
"\n",
"# Call the function\n",
"yesterday = datetime.datetime.now() - datetime.timedelta(days=1)\n",
"window_30_day = datetime.datetime.now() - datetime.timedelta(days=30)\n",
"download_data(project_name=\"chat-langchain\",\n",
" since=window_30_day)"
]
},
{
"cell_type": "markdown",
"id": "a9592ccc-5776-4e1d-b062-f3264929e023",
"metadata": {},
"source": [
"## Read Extracted QA Pairs"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "c3406211-321a-40ff-8c9d-9ba48d83da23",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(447, 2)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Replace 'fetched_data.csv' with the path to your CSV file\n",
"filename = 'fetched_data.csv'\n",
"\n",
"# Read the CSV file into a DataFrame\n",
"df = pd.read_csv(filename)\n",
"\n",
"search_term = 'LCEL'\n",
"filtered_df = df[df['question'].str.contains(search_term, case=False, na=False)]\n",
"filtered_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "b583a444-c256-4466-879f-2de8b9bbd972",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(63797, 2)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "0863bb39-759a-49f5-8668-5daf544c38a9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2126.5666666666666"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"63797 / 30"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "b7b282e0-e114-4856-8924-cc62f075d5a3",
"metadata": {},
"outputs": [
{
"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>question</th>\n",
" <th>output</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>I know that there is QA.chain in langchahin, w...</td>\n",
" <td>In addition to the `QA.chain` in Langchain, th...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" question \\\n",
"0 NaN \n",
"1 NaN \n",
"2 I know that there is QA.chain in langchahin, w... \n",
"\n",
" output \n",
"0 NaN \n",
"1 NaN \n",
"2 In addition to the `QA.chain` in Langchain, th... "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(3)"
]
},
{
"cell_type": "markdown",
"id": "68f2e2d3-b2b3-45cd-a253-c48e19936ff7",
"metadata": {},
"source": [
"## Extract Questions "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "93d4415c-e916-4b5c-9fd0-8c86a034675c",
"metadata": {},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"\n",
"# Group by unique instances of 'question' and then reset index\n",
"unique_questions_df = filtered_df.drop_duplicates(subset='question')\n",
"\n",
"# Extract the 'question' column and convert it to a list\n",
"unique_questions = unique_questions_df['question'].tolist()"
]
},
{
"cell_type": "markdown",
"id": "f037e6e0-6f04-41a1-971f-7843bcb36d51",
"metadata": {},
"source": [
"## Cluster\n",
"\n",
"Some of the questions are highly verbose and contain large code blocks.\n",
"\n",
"Let's try to cluster so that they are at least grouped when we summarize."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7d3a67c4-c33e-42d4-9be7-25694e2b345c",
"metadata": {},
"outputs": [],
"source": [
"# Embed and cluster \n",
"\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"embd = OpenAIEmbeddings()\n",
"question_embeddings = embd.embed_documents(unique_questions)\n",
"\n",
"from sklearn.cluster import KMeans\n",
"clustering_model = KMeans(n_clusters=5, random_state=0)\n",
"clusters = clustering_model.fit_predict(question_embeddings)\n",
"unique_questions_df['cluster'] = clusters"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "c2dabff7-3272-4143-af54-141f798cf00b",
"metadata": {},
"outputs": [],
"source": [
"# Format\n",
"\n",
"def fmt_qus(df):\n",
"\n",
" unique_questions = df['question'].tolist()\n",
" formatted_unique_questions = '--- --- \\n --- --- '.join(unique_questions)\n",
" return formatted_unique_questions\n",
"\n",
"# Get unique values in the 'cluster' column\n",
"all_clusters = unique_questions_df['cluster'].unique()\n",
"\n",
"# Process each cluster\n",
"cluster_context=[]\n",
"for i in all_clusters:\n",
" df_cluster = unique_questions_df[unique_questions_df['cluster'] == i]\n",
" formatted_questions = fmt_qus(df_cluster)\n",
" cluster_context.append(formatted_questions)"
]
},
{
"cell_type": "markdown",
"id": "7d0884c6-1b3d-49f2-b247-f7e9854b598d",
"metadata": {},
"source": [
"## Summarize\n",
"\n",
"Summarize major question themes."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "c8001a4c-c697-4f85-97fc-85c459d7a4fa",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rlm/Desktop/Code/code-langchain/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:115: LangChainDeprecationWarning: The class `ChatOpenAI` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use langchain_openai.ChatOpenAI instead.\n",
" warn_deprecated(\n"
]
}
],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts import ChatPromptTemplate\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"# Prompt template\n",
"template = \"\"\"Here is a set of questions input to LangChain QA system. \\n\n",
"\n",
"They are related to LCEL, LangChain Expression Language. \\n\n",
"\n",
"Reason about the questions, first. \\n\n",
"\n",
"Then, give me a list of the top 10 question themes.\n",
"\n",
"Give me one reprentitive question per theme.\n",
"\n",
"Questions:\n",
"{context}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4-1106-preview\")\n",
"chain = prompt | model | StrOutputParser()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "128dec15-054c-4f9b-b8bb-a3baebbd4655",
"metadata": {},
"outputs": [],
"source": [
"answers = []\n",
"for c in cluster_context:\n",
" answers.append(chain.invoke({\"context\":c}))"
]
},
{
"cell_type": "markdown",
"id": "5b14a647-9d0f-4442-9963-566558c45ed8",
"metadata": {},
"source": [
"## Themes\n",
"\n",
"* https://smith.langchain.com/public/69d0b729-cd8c-4d4b-859d-6e5ee683fc7a/r\n",
"\n",
"```\n",
"1. **Basic Understanding of LCEL**\n",
" - What is LCEL?\n",
"\n",
"2. **LCEL Integration with Agents**\n",
" - Can I use agents with LCEL?\n",
"\n",
"3. **LCEL Coding and Implementation Examples**\n",
" - Code me a question answering example with LCEL.\n",
"\n",
"4. **LCEL with Memory and Storage**\n",
" - How to use VectorStoreRetrieverMemory in LCEL?\n",
"\n",
"5. **LCEL Configuration and Settings**\n",
" - How to set verbose true for LCEL?\n",
"\n",
"6. **LCEL with Retrieval-Augmented Generation (RAG)**\n",
" - Can you give me an example to run a simple RAG using LCEL in Python?\n",
"\n",
"7. **LCEL Asynchronous Operations**\n",
" - LCEL 异步invoke (LCEL asynchronous invoke)\n",
"\n",
"8. **LCEL Error Handling and Debugging**\n",
" - How can I get the finish reason using LCEL?\n",
"\n",
"9. **LCEL with Multiple Inputs and Variables**\n",
" - How to use multiple partial variables in LCEL?\n",
"\n",
"10. **LCEL Advanced Features and Customization**\n",
" - Can LCEL execute custom python functions?\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "42c0186a-3336-4297-b5d7-812f4be2c5fb",
"metadata": {},
"source": [
"* https://smith.langchain.com/public/b3aba7b6-e877-4d99-bede-e607138fe171/r\n",
"\n",
"```\n",
"1. **Parallel and Asynchronous Execution**: Questions about running multiple chains in parallel or asynchronously.\n",
" - Representative question: \"I want to run three chains in parallel. They share the same input variables, but produce different output objects. How do I do this with LCEL?\"\n",
"\n",
"2. **Custom Functions and Configurations**: How to include custom functions or add configurable fields to a chain.\n",
" - Representative question: \"How to include a custom function as part of an LCEL chain?\"\n",
"\n",
"3. **Memory Management**: Questions about how memory is handled within chains, including buffer memory and conversation memory.\n",
" - Representative question: \"I have a LCEL chain with e.g. buffer memory, and I serve it via Langserve. When is the memory reset? Do all API calls use the same memory under the hood?\"\n",
"\n",
"4. **Chain Composition and Modularity**: How to compose chains from multiple components or steps, and how to pass data between them.\n",
" - Representative question: \"How can I connect several chains, i.e. the output of the former chain is the input of the latter chain? Can I achieve this through LCEL?\"\n",
"\n",
"5. **Error Handling and Retries**: How to handle errors and implement retries within a chain.\n",
" - Representative question: \"How can I use a RetryWithErrorOutputParser in a LCEL chain?\"\n",
"\n",
"6. **Retrieval and Querying**: Questions about setting up retrieval chains, including those with specific querying capabilities.\n",
" - Representative question: \"How to create a Retrieval QA chain with streaming, using LCEL?\"\n",
"\n",
"7. **Verbose and Debugging**: How to enable verbose output or debugging within a chain.\n",
" - Representative question: \"How to set verbose True in LangChain Expression Language (LCEL)?\"\n",
"\n",
"8. **Integration with External Services**: Questions about integrating LCEL chains with external services or databases.\n",
" - Representative question: \"I need a LCEL chain that takes a YouTube link and transcribes it with Whisper.\"\n",
"\n",
"9. **Chain Customization and Enhancement**: How to enhance chains with additional features like callbacks, custom parsers, or specific output formatting.\n",
" - Representative question: \"How do I pass a pre-written history variable into my LCEL chain?\"\n",
"\n",
"10. **Understanding LCEL Fundamentals**: Basic questions about what LCEL is and how to use it effectively.\n",
" - Representative question: \"What is LangChain Expression Language (LCEL)?\"\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "2ec53ff1-40fc-4986-b799-bf1bdd90dbaa",
"metadata": {},
"source": [
"* https://smith.langchain.com/public/3346bc94-146e-451a-9af4-7b7fe07d9a84/r\n",
"\n",
"```\n",
"1. **LCEL Chain Construction**: How to build and structure chains using LCEL components.\n",
" - Representative question: \"Give an example of an LCEL chain with LLMSingleActionAgent and AgentExecutor.\"\n",
"\n",
"2. **Output Parsing and Formatting**: How to parse and format the output from LCEL chains.\n",
" - Representative question: \"What LangChain tool can I use to parse this output into a single message?\"\n",
"\n",
"3. **Component Ordering and Interaction**: Understanding the order and interaction between components in an LCEL chain.\n",
" - Representative question: \"When using LCEL, is the order of the chained components arbitrary?\"\n",
"\n",
"4. **Custom Agents and Tools Integration**: How to integrate custom agents and tools within an LCEL chain.\n",
" - Representative question: \"I would like to use my own custom agent in an LCEL chain. How do I build this chain?\"\n",
"\n",
"5. **Conditional Logic and Prompts**: Implementing conditional logic and handling prompts in LCEL.\n",
" - Representative question: \"How to conditionally choose between prompts in LCEL.\"\n",
"\n",
"6. **Memory and Conversation History**: Utilizing memory and conversation history within LCEL chains.\n",
" - Representative question: \"Conversation chain with memory using LCEL.\"\n",
"\n",
"7. **Runnable and Agent Configuration**: Configuring and using Runnables and agents in LCEL.\n",
" - Representative question: \"How do I configure ReAct agent 'Thought' with custom OutputParser and Custom Agent, using LCEL?\"\n",
"\n",
"8. **LCEL Syntax and Expressions**: Understanding and using the syntax and expressions specific to LCEL.\n",
" - Representative question: \"Can I create an LCEL chain with prompt templates having no input variables?\"\n",
"\n",
"9. **LCEL with Specific Models and Tools**: Using LCEL with specific models like GPT-4 and tools like vectorstore retrievers.\n",
" - Representative question: \"Can you show me an example of an agent using gpt4 with a web search tool and memory? All using LCEL.\"\n",
"\n",
"10. **LCEL in Different Environments and Applications**: Applying LCEL in various environments and for different types of applications.\n",
" - Representative question: \"Provide LCEL code for a simple chat app using Azure OpenAI.\"\n",
"```"
]
},
{
"cell_type": "markdown",
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"* https://smith.langchain.com/public/47648f18-b543-477c-91cf-84d612eb6810/r\n",
"\n",
"```\n",
"1. **Error Handling in LCEL Chains**\n",
" - Representative Question: \"This code gives me this error TypeError: Expected a Runnable, callable or dict. Instead got an unsupported type: <class 'str'>\"\n",
"\n",
"2. **Integration of LangChain with AI Models**\n",
" - Representative Question: \"Create the LCEL chain using ChatOpenAI with a specific model and temperature settings.\"\n",
"\n",
"3. **PDF Processing with PyMuPDF**\n",
" - Representative Question: \"Convert a PDF page to a pixmap using the PyMuPDF library.\"\n",
"\n",
"4. **Base64 Encoding of Images**\n",
" - Representative Question: \"Encode a pixmap to a base64 string for image processing.\"\n",
"\n",
"5. **Template Formatting and Data Injection**\n",
" - Representative Question: \"Define the prompt templates and format them with dynamic data for the AI model.\"\n",
"\n",
"6. **AI-Assisted Data Interpretation**\n",
" - Representative Question: \"Use the AI model to assist in marking images using a provided mark scheme.\"\n",
"\n",
"7. **File I/O Operations**\n",
" - Representative Question: \"Write the results of the LCEL chain to a file.\"\n",
"\n",
"8. **Debugging Lambda Functions in LCEL**\n",
" - Representative Question: \"Change the RunnableLambda to RunnablePassthrough from the start of the template.\"\n",
"\n",
"9. **Understanding LCEL Chain Outputs**\n",
" - Representative Question: \"What will be the type of marking_output and how to make it a string?\"\n",
"\n",
"10. **Correct Usage of LCEL Components**\n",
" - Representative Question: \"The output of the LCEL chain isn't a string; find a way to fix it.\"\n",
"```"
]
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eval/eval.csv
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@@ -2,10 +2,20 @@ question,answer
"How do I set up a retrieval-augmented generation chain using LCEL?","Here is an example RAG chain using LCEL: \n\nfrom operator import itemgetter\n\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.prompts import ChatPromptTemplate\nfrom langchain.vectorstores import FAISS\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langchain_core.runnables import RunnableLambda, RunnablePassthrough\n\nvectorstore = FAISS.from_texts(\n [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n)\nretriever = vectorstore.as_retriever()\n\ntemplate = \"\"\"Answer the question based only on the following context:\n{context}\n\nQuestion: {question}\n\"\"\"\nprompt = ChatPromptTemplate.from_template(template)\n\nmodel = ChatOpenAI()\n\nchain = (\n {\"context\": retriever, \"question\": RunnablePassthrough()}\n | prompt\n | model\n | StrOutputParser()\n)\n\nchain.invoke(\"where did harrison work?\")"
"How can I use a prompt and model to create a chain in LCEL?","Here is an example of a prompt + LLM chain using LCEL: \nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_template(\"tell me a joke about {foo}\")\nmodel = ChatOpenAI()\nchain = prompt | model\n\nchain.invoke({\"foo\": \"bears\"})"
"How can I add memory to an arbitrary chain using LCEL?","Here is an example adding memory to a chain: \nfrom operator import itemgetter\n\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.memory import ConversationBufferMemory\nfrom langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\nfrom langchain_core.runnables import RunnableLambda, RunnablePassthrough\n\nmodel = ChatOpenAI()\nprompt = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are a helpful chatbot\"),\n MessagesPlaceholder(variable_name=\"history\"),\n (\"human\", \"{input}\"),\n ]\n)\n\nmemory = ConversationBufferMemory(return_messages=True)\n\nchain = (\n RunnablePassthrough.assign(\n history=RunnableLambda(memory.load_memory_variables) | itemgetter(\"history\")\n )\n | prompt\n | model\n)\n\ninputs = {\"input\": \"hi im bob\"}\nresponse = chain.invoke(inputs)"
"I've defined a runnable chain = prompt | model. How can I look at the input schema?","All runnables expose input and output schemas to inspect the inputs and outputs. input_schema is an input Pydantic model auto-generated from the structure of the Runnable. You can call .schema() on it to obtain a JSONSchema representation of any runnable: # The input schema of the chain is the input schema of its first part, the prompt. chain.input_schema.schema()"
"I have a runnable, chain, and am passing in a map w/ {'question' 'where did harrison work', 'language': 'italian'}. How can I extract the value of 'language' to pass to my prompt?","For this example, we can use itemgetter to extract specific values from the map. Here is an example: from operator import itemgetter from langchain.chat_models import ChatOpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.prompts import ChatPromptTemplate from langchain.vectorstores import FAISS from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough vectorstore = FAISS.from_texts(['harrison worked at kensho'], embedding=OpenAIEmbeddings()) retriever = vectorstore.as_retriever() template = 'Answer the question based only on the following context: {context} Question: {question} Answer in the following language: {language}' prompt = ChatPromptTemplate.from_template(template) chain = ( { 'context': itemgetter('question') | retriever, 'question': itemgetter('question'), 'language': itemgetter('language'), } | prompt | model | StrOutputParser() ) chain.invoke({'question': 'where did harrison work', 'language': 'italian'})"
"I've defined a LCEL runnable chain = prompt | model. How can I look at the input schema?","All runnables expose input and output schemas to inspect the inputs and outputs. input_schema is an input Pydantic model auto-generated from the structure of the Runnable. You can call .schema() on it to obtain a JSONSchema representation of any runnable: # The input schema of the chain is the input schema of its first part, the prompt. chain.input_schema.schema()"
"I have a LCEL runnable, chain, and am passing in a map w/ {'question' 'where did harrison work', 'language': 'italian'}. How can I extract the value of 'language' to pass to my prompt?","For this example, we can use itemgetter to extract specific values from the map. Here is an example: from operator import itemgetter from langchain.chat_models import ChatOpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.prompts import ChatPromptTemplate from langchain.vectorstores import FAISS from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough vectorstore = FAISS.from_texts(['harrison worked at kensho'], embedding=OpenAIEmbeddings()) retriever = vectorstore.as_retriever() template = 'Answer the question based only on the following context: {context} Question: {question} Answer in the following language: {language}' prompt = ChatPromptTemplate.from_template(template) chain = ( { 'context': itemgetter('question') | retriever, 'question': itemgetter('question'), 'language': itemgetter('language'), } | prompt | model | StrOutputParser() ) chain.invoke({'question': 'where did harrison work', 'language': 'italian'})"
"I am passing text key 'foo' to my prompt and want to process it with a function, process_text(...), prior to the prompt. How can I do this using LCEL?","You can use a RunnableLambda to apply a function to the value of foo: chain = ( { 'a': itemgetter('foo') | RunnableLambda(process_text), | RunnableLambda(multiple_length_function), } | prompt | model )"
"I have two chains, conversation_chain and retriever. I want to use conversation_chain if the input has 'chat_history' and I want to use retriever if the input has 'question'. How can I do this in LCEL using RunnableBranch","Using RunnableBranch: RunnableBranch( ( RunnableLambda(lambda x: bool(x.get('chat_history')) ), conversation_chain ), ( RunnableLambda(itemgetter('question')) | retriever ) )"
"My map contains the key 'question'. What is the difference between using itemgetter('question'), lambda x: x['question'], and x.get('question')?","Itemgetter can be used as shorthand to extract specific keys from the map. In the context of a map operation, the lambda function is applied to each element in the input map and the function returns the value associated with the key 'question'. (get) is safer for accessing values in a dictionary because it handles the case where the key might not exist."
"I'm invoking a chain with a map that contain {'question': 'how do I use Anthropic?'}. The full chain definition is full_chain = {'question': lambda x: x['question']} | sub_chain. Why is a lambda used?","The lambda function is an anonymous function that takes one argument, x, and returns x['question']. In the context of a map operation, this function is applied to each element in the input iterable. If the input is a dictionary (map), as in this case, x would be this map, and the function returns the value associated with the key 'question'."
"How can I create the self-query retriever query-construction chain in LCEL?","To create a self-query retriever query-construction chain in LCEL: from langchain.chains.query_constructor.base import ( StructuredQueryOutputParser, get_query_constructor_prompt, ) prompt = get_query_constructor_prompt(document_content_description, metadata_field_info,) output_parser = StructuredQueryOutputParser.from_components() query_constructor = prompt | llm | output_parser query_constructor.invoke({ 'query': 'What are some sci-fi movies from the 90's directed by Luc Besson about taxi drivers' }) StructuredQuery(query='taxi driver', filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2000)]), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Luc Besson')]), limit=None)"
"My LCEL map contains the key 'question'. What is the difference between using itemgetter('question'), lambda x: x['question'], and x.get('question')?","Itemgetter can be used as shorthand to extract specific keys from the map. In the context of a map operation, the lambda function is applied to each element in the input map and the function returns the value associated with the key 'question'. (get) is safer for accessing values in a dictionary because it handles the case where the key might not exist."
"I'm invoking a LCEL chain with a map that contain {'question': 'how do I use Anthropic?'}. The full chain definition is full_chain = {'question': lambda x: x['question']} | sub_chain. Why is a lambda used?","The lambda function is an anonymous function that takes one argument, x, and returns x['question']. In the context of a map operation, this function is applied to each element in the input iterable. If the input is a dictionary (map), as in this case, x would be this map, and the function returns the value associated with the key 'question'."
"How can I create the self-query retriever query-construction chain in LCEL?","To create a self-query retriever query-construction chain in LCEL: from langchain.chains.query_constructor.base import ( StructuredQueryOutputParser, get_query_constructor_prompt, ) prompt = get_query_constructor_prompt(document_content_description, metadata_field_info,) output_parser = StructuredQueryOutputParser.from_components() query_constructor = prompt | llm | output_parser query_constructor.invoke({ 'query': 'What are some sci-fi movies from the 90's directed by Luc Besson about taxi drivers' }) StructuredQuery(query='taxi driver', filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2000)]), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Luc Besson')]), limit=None)"
"I’m passing {'a':1} and want to create an output map of {'a':1,'b':2, 'c':3}. How can I do this in LCEL?","Use RunnablePassthrough: Use RunnableParallel with lambdas: \n from langchain_core.runnables import RunnableParallel, RunnablePassthrough; runnable = RunnableParallel(a=lambda x: x['a'], b=lambda x: x['a']+1, c=lambda x: x['a']+2); runnable.invoke({'num': 1}). Also you can use RunnablePassthrough: from langchain_core.runnables import RunnablePassthrough; original_input = {'a': 1}; chain = RunnablePassthrough.assign(b=lambda x: 2, c=lambda x: 3); output = chain.invoke(original_input); print(output)"
"How can I make the output of my LCEL chain a string?","Use StrOutputParser. from langchain_openai import ChatOpenAI; from langchain_core.prompts import ChatPromptTemplate; from langchain_core.output_parsers import StrOutputParser; prompt = ChatPromptTemplate.from_template('Tell me a short joke about {topic}'); model = ChatOpenAI(model='gpt-3.5-turbo'); output_parser = StrOutputParser(); chain = prompt | model | output_parser"
"How can I apply a custom function to one of the inputs of an LCEL chain?","Use RunnableLambda with itemgetter to extract the relevant key. from operator import itemgetter; from langchain_core.prompts import ChatPromptTemplate; from langchain_core.runnables import RunnableLambda; from langchain_openai import ChatOpenAI; def length_function(text): return len(text); chain = ({'prompt_input': itemgetter('foo') | RunnableLambda(length_function),} | prompt | model); chain.invoke({'foo':'hello world'})"
"I have a LCEL chain that applies a function using RunnableLambda to an input and write it out an output key before I pass it to a prompt. How can I pass through the input to the output key?","Use RunnablePassthrough. from langchain_core.runnables import RunnableParallel, RunnablePassthrough; runnable = ({'output' : RunnablePassthrough() | prompt | model}); runnable.invoke(1)"
"With a RAG chain in LCEL, why are documents retrieved automatically when we construct the prompt like {'context': retriever, 'question': RunnablePassthrough()} and invoke it using chain.invoke('where did harrison work?')","When we create the chain, get_relevant_documents is invoked automatically. vectorstore = FAISS.from_texts(['harrison worked at kensho'], embedding=OpenAIEmbeddings()); retriever = vectorstore.as_retriever(); chain = ({'context': retriever, 'question': RunnablePassthrough()} | prompt | model | StrOutputParser()); chain.invoke('where did harrison work?')"
"How to structure output of an LCEL chain as a Pydantic objecgt with prefix and code_block?","We can use PydanticOutputParser. from langchain.output_parsers import PydanticOutputParser; from langchain.prompts import PromptTemplate; from langchain.pydantic_v1 import BaseModel, Field; class FunctionOutput(BaseModel): prefix: str = Field(description='The prefix of the output'); code_block: str = Field(description='The code block of the output'); parser = PydanticOutputParser(pydantic_object=FunctionOutput); format_instructions = parser.get_format_instructions(); prompt = PromptTemplate(template='Output format instructions:\n{format_instructions}\n\nQuery: {query}\n', input_variables=['query'], partial_variables={'format_instructions': format_instructions}); model = ChatOpenAI(model_name='gpt-4'); prompt_and_model = prompt | model; output = prompt_and_model.invoke({'query': 'Give me a function that will add two numbers:'}); parsed_output = parser.invoke(output); prefix = parsed_output.prefix; code_block = parsed_output.code_block"
"I am passing a map with {'num': 1} to a LCEL chain. How can I add an extra key num2 to this map that adds 1 to the value of num. Then I want to assign this new map to a new key named output?","We can use RunnablePassthrough with a lambda function. from langchain_core.runnables import RunnableParallel, RunnablePassthrough; runnable = RunnableParallel(output=RunnablePassthrough.assign(num2=lambda x: x['num'] + 1),); runnable.invoke({'num': 1})"
"How can I configure the temperature of an LLM when invoking the LCEL chain?","Use configuration fields. from langchain.prompts import PromptTemplate; from langchain_core.runnables import ConfigurableField; from langchain_openai import ChatOpenAI; model = ChatOpenAI(temperature=0).configurable_fields(temperature=ConfigurableField(id='llm_temperature', name='LLM Temperature', description='The temperature of the LLM', )); model.with_config(configurable={'llm_temperature': 0.9}).invoke('pick a random number')"
"How can we apply a function call to an LLM in an LCEL chain?","We can attach a function call to the model using bind: functions = [{'name': 'joke', 'description': 'A joke', 'parameters': {'type': 'object', 'properties': {'setup': {'type': 'string', 'description': 'The setup for the joke'}, 'punchline': {'type': 'string', 'description': 'The punchline for the joke'}}, 'required': ['setup', 'punchline']}]; chain = prompt | model.bind(function_call={'name': 'joke'}, functions=functions)"
"How can I run two LCEL chains in parallel and write their output to a map?","We can use RunnableParallel: from langchain_core.prompts import ChatPromptTemplate; from langchain_core.runnables import RunnableParallel; from langchain_openai import ChatOpenAI; model = ChatOpenAI(); joke_chain = ChatPromptTemplate.from_template('tell me a joke about {topic}') | model; poem_chain = (ChatPromptTemplate.from_template('write a 2-line poem about {topic}') | model); map_chain = RunnableParallel(joke=joke_chain, poem=poem_chain); map_chain.invoke({'topic': 'bear'})"
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eval/eval_code_langchain.ipynb
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poetry.lock
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langsmith = ">=0.0.83,<0.1"
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tiktoken = ">=0.5.2,<0.6.0"
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typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
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@@ -3647,4 +3955,4 @@ multidict = ">=4.0"
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pyproject.toml
+4
View File
@@ -14,6 +14,10 @@ langchainhub = "^0.1.14"
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openai = "^1.6.0"
langchain = "~0.1.0"
pandas = "^2.1.4"
tiktoken = "^0.5.2"
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langchain-openai = "^0.0.4"
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langchain-cli = ">=0.0.15"