id stringlengths 14 16 | text stringlengths 31 2.73k | source stringlengths 56 166 |
|---|---|---|
8d4cff8ae975-2 | cache_path = f'{file_prefix}_{i}.txt'
cache_obj.init(
pre_embedding_func=get_prompt,
data_manager=get_data_manager(data_path=cache_path),
)
i += 1
langchain.llm_cache = GPTCache(init_gptcache_map)
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
8d4cff8ae975-3 | onnx = Onnx()
cache_base = CacheBase('sqlite')
vector_base = VectorBase('faiss', dimension=onnx.dimension)
data_manager = get_data_manager(cache_base, vector_base, max_size=10, clean_size=2)
cache_obj.init(
pre_embedding_func=get_prompt,
embedding_func=onnx.to_embeddings,
data_ma... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
8d4cff8ae975-4 | # from langchain.cache import SQLAlchemyCache
# from sqlalchemy import create_engine
# engine = create_engine("postgresql://postgres:postgres@localhost:5432/postgres")
# langchain.llm_cache = SQLAlchemyCache(engine)
Custom SQLAlchemy Schemas#
# You can define your own declarative SQLAlchemyCache child class to customiz... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
8d4cff8ae975-5 | llm = OpenAI(model_name="text-davinci-002", n=2, best_of=2, cache=False)
%%time
llm("Tell me a joke")
CPU times: user 5.8 ms, sys: 2.71 ms, total: 8.51 ms
Wall time: 745 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
%%time
llm("Tell me a joke")
CPU times: user 4.91 ms, sys: 2.64 ms, total: 7... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
8d4cff8ae975-6 | from langchain.chains.summarize import load_summarize_chain
chain = load_summarize_chain(llm, chain_type="map_reduce", reduce_llm=no_cache_llm)
%%time
chain.run(docs)
CPU times: user 452 ms, sys: 60.3 ms, total: 512 ms
Wall time: 5.09 s
'\n\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infr... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
8d4cff8ae975-7 | Redis Cache
GPTCache
SQLAlchemy Cache
Custom SQLAlchemy Schemas
Optional Caching
Optional Caching in Chains
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
417481e5efea-0 | .ipynb
.pdf
如何使用 LLM 的异步 API
如何使用 LLM 的异步 API#
LangChain 利用 asyncio 库为 LLM 提供了异步支持。
异步支持特别适用于同时调用多个 LLM，因为这些调用都是网络绑定的。目前，支持 OpenAI, PromptLayerOpenAI, ChatOpenAI 和 Anthropic，但将在未来实现其他 LLM 的异步支持。
您可以使用 agenerate 方法异步调用 OpenAI 的 LLM。
import time
import asyncio
from langchain.llms import OpenAI
def generate_serially():
... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/async_llm.html |
417481e5efea-1 | generate_serially()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Serial executed in {elapsed:0.2f} seconds." + '\033[0m')
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about you?
I'm doing well, how about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you. How a... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/async_llm.html |
417481e5efea-2 | 接着，定义了一个异步函数 async_generate，该函数使用了 await 关键字，表示函数调用将异步执行。
该函数调用了 OpenAI 的 agenerate 方法来生成文本，生成的文本通过 print() 输出。
最后，定义了一个并发生成的 generate_concurrently 函数，它与 async_generate 函数结合起来使用，用于并发地调用 LLM 来生成文本。
在主函数运行时，
首先调用了异步函数 await generate_concurrently()，并计算了执行该函数所需的时间，以便与后续序列执行函数的时间进行比较。
然后，调用了序列执行函数 generate_serially()，并计算了它的... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/async_llm.html |
417481e5efea-3 | 在 asyncio.gather(*tasks) 中，星号运算符 * 用于解包 tasks 列表，将其作为单独的参数列表传递给 asyncio.gather() 函数。这样，每个 async_generate(llm) 函数就会以并发方式执行，从而加速整个过程。最终结果是，由 async_generate(llm) 函数生成的文本数据会返回一个包含在 gather() 函数返回值的列表中，该列表包含了所有并发执行的结果。
previous
通用功能
next
如何写一个自定义的LLM包装器
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/async_llm.html |
cf7d97ec50e0-0 | .ipynb
.pdf
如何写一个自定义的LLM包装器
如何写一个自定义的LLM包装器#
这个notebook将介绍如何创建一个自定义的LLM包装器，以便您可以使用自己的LLM或与LangChain支持的不同包装器。自定义LLM只需要实现一个必需的方法：
_call 方法，它接受一个字符串输入和一个可选的停用词列表，并返回一个字符串结果。
也可以实现一个可选的方法：
_identifying_params 属性，用于帮助打印该类。应返回一个字典。
我们来实现一个非常简单的自定义LLM，它只返回输入文本的前N个字符。
from langchain.llms.base import LLM
from typing import Opti... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/custom_llm.html |
cf7d97ec50e0-1 | llm("This is a foobar thing")
'This is a '
我们还可以打印这个LLM并查看自定义的打印效果。
print(llm)
CustomLLM
Params: {'n': 10}
previous
如何使用 LLM 的异步 API
next
How (and why) to use the fake LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/custom_llm.html |
4cc53d0d0584-0 | .ipynb
.pdf
How to serialize LLM classes
Contents
Loading
Saving
How to serialize LLM classes#
This notebook walks through how to write and read an LLM Configuration to and from disk. This is useful if you want to save the configuration for a given LLM (e.g., the provider, the temperature, etc).
from langchain.llms i... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_serialization.html |
4cc53d0d0584-1 | llm.save("llm.json")
llm.save("llm.yaml")
previous
How to cache LLM calls
next
如何实现 LLM 和 Chat Model 的流式响应
Contents
Loading
Saving
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/llm_serialization.html |
26d853c0e0b9-0 | .ipynb
.pdf
如何实现 LLM 和 Chat Model 的流式响应
如何实现 LLM 和 Chat Model 的流式响应#
LangChain为LLMs提供了流式支持。目前，我们支持使用OpenAI，ChatOpenAI和Anthropic实现进行流式传输，在路线图中也计划为其他LLM实现提供流式支持。要使用流式传输，需要实现on_llm_new_token的CallbackHandler 。在这个例子中，我们使用StreamingStdOutCallbackHandler。
from langchain.llms import OpenAI, Anthropic
from langchain.chat_models ... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/streaming_llm.html |
26d853c0e0b9-1 | It's the perfect way to stay refreshed.
Verse 3
I'm sippin' on sparkling water,
It's so light and so clear,
It's the perfect way to keep me cool
On a hot summer night.
Chorus
Sparkling water, sparkling water,
It's the best way to stay hydrated,
It's so crisp and so clean,
It's the perfect way to stay refreshed.
使用 gene... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/streaming_llm.html |
26d853c0e0b9-2 | Verse 2:
No sugar, no calories, just H2O
A drink that's good for me, don't you know
With lemon or lime, you're even better
Sparkling water, you're my forever
Chorus:
Sparkling water, oh how you shine
A taste so clean, it's simply divine
You quench my thirst, you make me feel alive
Sparkling water, you're my favorite vi... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/streaming_llm.html |
26d853c0e0b9-3 | Sparkling water, a drink quite all right,
Bubbles sparkling in the light.
'\nSparkling water, bubbles so bright,\n\nFizzing and popping in the light.\n\nNo sugar or calories, a healthy delight,\n\nSparkling water, refreshing and light.\n\nCarbonation that tickles the tongue,\n\nIn flavors of lemon and lime unsung.\n\nS... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/streaming_llm.html |
1af42b0ef692-0 | .ipynb
.pdf
How (and why) to use the fake LLM
How (and why) to use the fake LLM#
We expose a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.
In this notebook we go over how to use this.
We start this with usi... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/fake_llm.html |
eb989c3c76dd-0 | .ipynb
.pdf
How to track token usage
How to track token usage#
This notebook goes over how to track your token usage for specific calls. It is currently only implemented for the OpenAI API.
Let’s first look at an extremely simple example of tracking token usage for a single LLM call.
from langchain.llms import OpenAI
f... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/token_usage_tracking.html |
eb989c3c76dd-1 | print(f"Total Tokens: {cb.total_tokens}")
print(f"Prompt Tokens: {cb.prompt_tokens}")
print(f"Completion Tokens: {cb.completion_tokens}")
print(f"Total Cost (USD): ${cb.total_cost}")
> Entering new AgentExecutor chain...
I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised t... | https://langchain-cn.readthedocs.io/en/latest/modules/models/llms/examples/token_usage_tracking.html |
a2c912b44a1b-0 | .ipynb
.pdf
Getting Started
Contents
PromptTemplates
LLMChain
Streaming
Getting Started#
This notebook covers how to get started with chat models. The interface is based around messages rather than raw text.
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.pro... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/getting_started.html |
a2c912b44a1b-1 | [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
],
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
Hum... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/getting_started.html |
a2c912b44a1b-2 | system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted mes... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/getting_started.html |
a2c912b44a1b-3 | Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Verse 2:
No sugar, no calories, just pure bliss
A drink that's hard to resist
It's the perfect way to quench my thirst
A drink that always comes first
Chorus:
Sparkling water, oh so fine
A d... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/getting_started.html |
61992c4282d9-0 | .rst
.pdf
How-To Guides
How-To Guides#
The examples here all address certain “how-to” guides for working with chat models.
How to use few shot examples
How to stream responses
previous
Getting Started
next
How to use few shot examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated ... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/how_to_guides.html |
7a3ddb430991-0 | .rst
.pdf
Integrations
Integrations#
The examples here all highlight how to integrate with different chat models.
Azure
OpenAI
PromptLayer ChatOpenAI
previous
How to stream responses
next
Azure
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/integrations.html |
1f5379131e0d-0 | .ipynb
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PromptLayer ChatOpenAI
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
PromptLayer ChatOpenAI#
This example showcases how to connect to PromptLayer to start recording your ChatOpenAI requests.
Install PromptLayer#
The promp... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/integrations/promptlayer_chatopenai.html |
1f5379131e0d-1 | chat = PromptLayerChatOpenAI(return_pl_id=True)
chat_results = chat.generate([[HumanMessage(content="I am a cat and I want")]])
for res in chat_results.generations:
pl_request_id = res[0].generation_info["pl_request_id"]
promptlayer.track.score(request_id=pl_request_id, score=100)
Using this allows you to track... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/integrations/promptlayer_chatopenai.html |
bfaae92cef96-0 | .ipynb
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Azure
Azure#
This notebook goes over how to connect to an Azure hosted OpenAI endpoint
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
BASE_URL = "https://${TODO}.openai.azure.com"
API_KEY = "..."
DEPLOYMENT_NAME = "chat"
model = AzureChatOpenAI(
openai_api_ba... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/integrations/azure_chat_openai.html |
181c19d7be1b-0 | .ipynb
.pdf
OpenAI
OpenAI#
This notebook covers how to get started with OpenAI chat models.
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema impo... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/integrations/openai.html |
181c19d7be1b-1 | AIMessage(content="J'adore la programmation.", additional_kwargs={})
previous
Azure
next
PromptLayer ChatOpenAI
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/integrations/openai.html |
0276f03ad7fd-0 | .ipynb
.pdf
How to stream responses
How to stream responses#
This notebook goes over how to use streaming with a chat model.
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
HumanMessage,
)
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout impo... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/examples/streaming.html |
0276f03ad7fd-1 | Sparkling
previous
How to use few shot examples
next
Integrations
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/examples/streaming.html |
a509a08ba421-0 | .ipynb
.pdf
How to use few shot examples
Contents
Alternating Human/AI messages
System Messages
How to use few shot examples#
This notebook covers how to use few shot examples in chat models.
There does not appear to be solid consensus on how best to do few shot prompting. As a result, we are not solidifying any abst... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/examples/few_shot_examples.html |
a509a08ba421-1 | template="You are a helpful assistant that translates english to pirate."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = SystemMessagePromptTemplate.from_template("Hi", additional_kwargs={"name": "example_user"})
example_ai = SystemMessagePromptTemplate.from_template("Argh m... | https://langchain-cn.readthedocs.io/en/latest/modules/models/chat/examples/few_shot_examples.html |
9e7e1cfeb140-0 | .ipynb
.pdf
Jina
Jina#
Let’s load the Jina Embedding class.
from langchain.embeddings import JinaEmbeddings
embeddings = JinaEmbeddings(jina_auth_token=jina_auth_token, model_name="ViT-B-32::openai")
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([t... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/jina.html |
23f2cf33ab52-0 | .ipynb
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Self Hosted Embeddings
Self Hosted Embeddings#
Let’s load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.
from langchain.embeddings import (
SelfHostedEmbeddings,
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddi... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/self-hosted.html |
23f2cf33ab52-1 | tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
def inference_fn(pipeline, prompt):
# Return last hidden state of the model
if isinstance(prompt, list):
return [emb[... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/self-hosted.html |
6d27e00374ee-0 | .ipynb
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Hugging Face Hub
Hugging Face Hub#
Let’s load the Hugging Face Embedding class.
from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
previous
F... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/huggingfacehub.html |
9153e439e89b-0 | .ipynb
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Fake Embeddings
Fake Embeddings#
LangChain also provides a fake embedding class. You can use this to test your pipelines.
from langchain.embeddings import FakeEmbeddings
embeddings = FakeEmbeddings(size=1352)
query_result = embeddings.embed_query("foo")
doc_results = embeddings.embed_documents(["foo"])
prev... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/fake.html |
d4257e7d3061-0 | .ipynb
.pdf
TensorflowHub
TensorflowHub#
Let’s load the TensorflowHub Embedding class.
from langchain.embeddings import TensorflowHubEmbeddings
embeddings = TensorflowHubEmbeddings()
2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neu... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/tensorflowhub.html |
d531c77a22c2-0 | .ipynb
.pdf
Cohere
Cohere#
Let’s load the Cohere Embedding class.
from langchain.embeddings import CohereEmbeddings
embeddings = CohereEmbeddings(cohere_api_key=cohere_api_key)
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
previous
AzureOpe... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/cohere.html |
661f456353f7-0 | .ipynb
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InstructEmbeddings
InstructEmbeddings#
Let’s load the HuggingFace instruct Embeddings class.
from langchain.embeddings import HuggingFaceInstructEmbeddings
embeddings = HuggingFaceInstructEmbeddings(
query_instruction="Represent the query for retrieval: "
)
load INSTRUCTOR_Transformer
max_seq_length 51... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/instruct_embeddings.html |
1775a4684b7a-0 | .ipynb
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Llama-cpp
Llama-cpp#
This notebook goes over how to use Llama-cpp embeddings within LangChain
!pip install llama-cpp-python
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model/ggml-model-q4_0.bin")
text = "This is a test document."
query_result = llama.e... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/llamacpp.html |
369b1a931b55-0 | .ipynb
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AzureOpenAI
AzureOpenAI#
Let’s load the OpenAI Embedding class with environment variables set to indicate to use Azure endpoints.
# set the environment variables needed for openai package to know to reach out to azure
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https:/... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/azureopenai.html |
4cb55ed07378-0 | .ipynb
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Aleph Alpha
Contents
Asymmetric
Symmetric
Aleph Alpha#
There are two possible ways to use Aleph Alpha’s semantic embeddings. If you have texts with a dissimilar structure (e.g. a Document and a Query) you would want to use asymmetric embeddings. Conversely, for texts with comparable structures, symmetric ... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/aleph_alpha.html |
99dcdb5f59d2-0 | .ipynb
.pdf
OpenAI
OpenAI#
Let’s load the OpenAI Embedding class.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
Let’s load the OpenAI Embedding class with fir... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/openai.html |
9d8cb453110f-0 | .ipynb
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SageMaker Endpoint Embeddings
SageMaker Endpoint Embeddings#
Let’s load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.
For instrucstions on how to do this, please see here
!pip3 install langchain boto3
from typing import Dict
from ... | https://langchain-cn.readthedocs.io/en/latest/modules/models/text_embedding/examples/sagemaker-endpoint.html |
96724e09f3c8-0 | .ipynb
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Chat Prompt Template
Chat Prompt Template#
Chat Models takes a list of chat messages as input - this list commonly referred to as a prompt.
Typically this is not simply a hardcoded list of messages but rather a combination of a template, some examples, and user input.
LangChain provides several classes and ... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/chat_prompt_template.html |
96724e09f3c8-1 | HumanMessage(content='I love programming.', additional_kwargs={})]
If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:
prompt=PromptTemplate(
template="You are a helpful assistant that translates {input_language} to {output_language}... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/chat_prompt_template.html |
d2d7529cc0db-0 | .rst
.pdf
Prompt Templates
Prompt Templates#
Note
Conceptual Guide
Language models take text as input - that text is commonly referred to as a prompt.
Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
LangChain provides several classes and functions t... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates.html |
9bf9a4a8bbdd-0 | .rst
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Output Parsers
Output Parsers#
Note
Conceptual Guide
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an out... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers.html |
efc01cb34af0-0 | .rst
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Example Selectors
Example Selectors#
Note
Conceptual Guide
If you have a large number of examples, you may need to select which ones to include in the prompt. The ExampleSelector is the class responsible for doing so.
The base interface is defined as below:
class BaseExampleSelector(ABC):
"""Interface for... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors.html |
f9c36b300d20-0 | .md
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Getting Started
Contents
What is a prompt template?
Create a prompt template
Load a prompt template from LangChainHub
Pass few shot examples to a prompt template
Select examples for a prompt template
Getting Started#
In this tutorial, we will learn about:
what a prompt template is, and why it is needed,
how ... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
f9c36b300d20-1 | no_input_prompt.format()
# -> "Tell me a joke."
# An example prompt with one input variable
one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")
one_input_prompt.format(adjective="funny")
# -> "Tell me a funny joke."
# An example prompt with multiple input variables
m... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
f9c36b300d20-2 | In this example, we’ll create a prompt to generate word antonyms.
from langchain import PromptTemplate, FewShotPromptTemplate
# First, create the list of few shot examples.
examples = [
{"word": "happy", "antonym": "sad"},
{"word": "tall", "antonym": "short"},
]
# Next, we specify the template to format the exa... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
f9c36b300d20-3 | print(few_shot_prompt.format(input="big"))
# -> Give the antonym of every input
# ->
# -> Word: happy
# -> Antonym: sad
# ->
# -> Word: tall
# -> Antonym: short
# ->
# -> Word: big
# -> Antonym:
Select examples for a prompt template#
If you have a large number of examples, you can use the ExampleSelector to select a s... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
f9c36b300d20-4 | examples=examples,
# This is the PromptTemplate being used to format the examples.
example_prompt=example_prompt,
# This is the maximum length that the formatted examples should be.
# Length is measured by the get_text_length function below.
max_length=25,
)
# We can now use the `example_selector`... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
f9c36b300d20-5 | # -> Word: happy
# -> Antonym: sad
# ->
# -> Word: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else
# -> Antonym:
LangChain comes with a few example selectors that you can use. For more details on how to use them, see Example Selectors.
You can create cus... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
6375c2accb33-0 | .rst
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How-To Guides
How-To Guides#
If you’re new to the library, you may want to start with the Quickstart.
The user guide here shows more advanced workflows and how to use the library in different ways.
How to create a custom prompt template
How to create a prompt template that uses few shot examples
How to work w... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/how_to_guides.html |
fa22aeae1f0c-0 | .ipynb
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How to work with partial Prompt Templates
Contents
Partial With Strings
Partial With Functions
How to work with partial Prompt Templates#
A prompt template is a class with a .format method which takes in a key-value map and returns a string (a prompt) to pass to the language model. Like other methods, it ... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/partial.html |
fa22aeae1f0c-1 | print(prompt.format(bar="baz"))
foobaz
Partial With Functions#
The other common use is to partial with a function. The use case for this is when you have a variable you know that you always want to fetch in a common way. A prime example of this is with date or time. Imagine you have a prompt which you always want to ha... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/partial.html |
fa22aeae1f0c-2 | Contents
Partial With Strings
Partial With Functions
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/partial.html |
41d4f2384273-0 | .ipynb
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How to create a prompt template that uses few shot examples
Contents
Use Case
Using an example set
Create the example set
Create a formatter for the few shot examples
Feed examples and formatter to FewShotPromptTemplate
Using an example selector
Feed examples into ExampleSelector
Feed example selector int... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
41d4f2384273-1 | "answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
"""
},
... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
41d4f2384273-2 | print(example_prompt.format(**examples[0]))
Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate ... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
41d4f2384273-3 | Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
41d4f2384273-4 | # This is the list of examples available to select from.
examples,
# This is the embedding class used to produce embeddings which are used to measure semantic similarity.
OpenAIEmbeddings(),
# This is the VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma,... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
41d4f2384273-5 | suffix="Question: {input}",
input_variables=["input"]
)
print(prompt.format(input="Who was the father of Mary Ball Washington?"))
Question: Who was the maternal grandfather of George Washington?
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The m... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
ff4517bb6660-0 | .ipynb
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How to create a custom prompt template
Contents
Why are custom prompt templates needed?
Creating a Custom Prompt Template
Use the custom prompt template
How to create a custom prompt template#
Let’s suppose we want the LLM to generate English language explanations of a function given its name. To achieve ... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
ff4517bb6660-1 | def get_source_code(function_name):
# Get the source code of the function
return inspect.getsource(function_name)
Next, we’ll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.
from langchain.prompts import String... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
ff4517bb6660-2 | prompt = fn_explainer.format(function_name=get_source_code)
print(prompt)
Given the function name and source code, generate an English language explanation of the function.
Function Name: get_source_code
Source Code:
def get_source_code(function_name):
# Get the source code of the fu... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
e43397fa456f-0 | .ipynb
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How to serialize prompts
Contents
PromptTemplate
Loading from YAML
Loading from JSON
Loading Template from a File
FewShotPromptTemplate
Examples
Loading from YAML
Loading from JSON
Examples in the Config
Example Prompt from a File
How to serialize prompts#
It is often preferrable to store prompts not as p... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
e43397fa456f-1 | prompt = load_prompt("simple_prompt.yaml")
print(prompt.format(adjective="funny", content="chickens"))
Tell me a funny joke about chickens.
Loading from JSON#
This shows an example of loading a PromptTemplate from JSON.
!cat simple_prompt.json
{
"_type": "prompt",
"input_variables": ["adjective", "content"],
... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
e43397fa456f-2 | output: sad
- input: tall
output: short
Loading from YAML#
This shows an example of loading a few shot example from YAML.
!cat few_shot_prompt.yaml
_type: few_shot
input_variables:
["adjective"]
prefix:
Write antonyms for the following words.
example_prompt:
_type: prompt
input_variables:
["i... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
e43397fa456f-3 | !cat few_shot_prompt.json
{
"_type": "few_shot",
"input_variables": ["adjective"],
"prefix": "Write antonyms for the following words.",
"example_prompt": {
"_type": "prompt",
"input_variables": ["input", "output"],
"template": "Input: {input}\nOutput: {output}"
},
"exampl... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
e43397fa456f-4 | Output: short
Input: funny
Output:
Example Prompt from a File#
This shows an example of loading the PromptTemplate that is used to format the examples from a separate file. Note that the key changes from example_prompt to example_prompt_path.
!cat example_prompt.json
{
"_type": "prompt",
"input_variables": ["in... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
ac00c0f894dc-0 | .ipynb
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Maximal Marginal Relevance ExampleSelector
Maximal Marginal Relevance ExampleSelector#
The MaxMarginalRelevanceExampleSelector selects examples based on a combination of which examples are most similar to the inputs, while also optimizing for diversity. It does this by finding the examples with the embeddin... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/mmr.html |
ac00c0f894dc-1 | k=2
)
mmr_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Input: {adjective}\nOutput:",
input_variables=["adjective"],
)
# Input is a feeling,... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/mmr.html |
d4db0976013f-0 | .ipynb
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Similarity ExampleSelector
Similarity ExampleSelector#
The SemanticSimilarityExampleSelector selects examples based on which examples are most similar to the inputs. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs.
from langchain.prompts.exam... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/similarity.html |
d4db0976013f-1 | example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Input: {adjective}\nOutput:",
input_variables=["adjective"],
)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
# Input is a feeling, so should select the happy/sad example
pr... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/similarity.html |
ea36b81674e7-0 | .ipynb
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NGram Overlap ExampleSelector
NGram Overlap ExampleSelector#
The NGramOverlapExampleSelector selects and orders examples based on which examples are most similar to the input, according to an ngram overlap score. The ngram overlap score is a float between 0.0 and 1.0, inclusive.
The selector allows for a th... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
ea36b81674e7-1 | {"input": "Spot can run.", "output": "Spot puede correr."},
]
example_prompt = PromptTemplate(
input_variables=["input", "output"],
template="Input: {input}\nOutput: {output}",
)
example_selector = NGramOverlapExampleSelector(
# These are the examples it has available to choose from.
examples=examples, ... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
ea36b81674e7-2 | Output: Ver correr a Spot.
Input: My dog barks.
Output: Mi perro ladra.
Input: Spot can run fast.
Output:
# You can add examples to NGramOverlapExampleSelector as well.
new_example = {"input": "Spot plays fetch.", "output": "Spot juega a buscar."}
example_selector.add_example(new_example)
print(dynamic_prompt.format(se... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
ea36b81674e7-3 | Input: Spot plays fetch.
Output: Spot juega a buscar.
Input: Spot can play fetch.
Output:
# Setting threshold greater than 1.0
example_selector.threshold=1.0+1e-9
print(dynamic_prompt.format(sentence="Spot can play fetch."))
Give the Spanish translation of every input
Input: Spot can play fetch.
Output:
previous
Maxima... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
026962e7e9d1-0 | .ipynb
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LengthBased ExampleSelector
LengthBased ExampleSelector#
This ExampleSelector selects which examples to use based on length. This is useful when you are worried about constructing a prompt that will go over the length of the context window. For longer inputs, it will select fewer examples to include, while ... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/length_based.html |
026962e7e9d1-1 | # it is provided as a default value if none is specified.
# get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
)
dynamic_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
pref... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/length_based.html |
026962e7e9d1-2 | Input: sunny
Output: gloomy
Input: windy
Output: calm
Input: big
Output: small
Input: enthusiastic
Output:
previous
How to create a custom example selector
next
Maximal Marginal Relevance ExampleSelector
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/length_based.html |
9f84e87fa6dd-0 | .md
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How to create a custom example selector
Contents
Implement custom example selector
Use custom example selector
How to create a custom example selector#
In this tutorial, we’ll create a custom example selector that selects every alternate example from a given list of examples.
An ExampleSelector must implemen... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html |
9f84e87fa6dd-1 | # Add new example to the set of examples
example_selector.add_example({"foo": "4"})
example_selector.examples
# -> [{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]
# Select examples
example_selector.select_examples({"foo": "foo"})
# -> array([{'foo': '1'}, {'foo': '4'}], dtype=object)
previous
Example Selectors... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html |
b10a0bd6da21-0 | .ipynb
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Output Parsers
Output Parsers#
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an output parser must impl... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/getting_started.html |
b10a0bd6da21-1 | punchline: str = Field(description="answer to resolve the joke")
# You can add custom validation logic easily with Pydantic.
@validator('setup')
def question_ends_with_question_mark(cls, field):
if field[-1] != '?':
raise ValueError("Badly formed question!")
return field
# S... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/getting_started.html |
868a600c5950-0 | .ipynb
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Structured Output Parser
Structured Output Parser#
While the Pydantic/JSON parser is more powerful, we initially experimented data structures having text fields only.
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate, ChatPromptTemplate,... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/structured.html |
868a600c5950-1 | ],
input_variables=["question"],
partial_variables={"format_instructions": format_instructions}
)
_input = prompt.format_prompt(question="what's the capital of france")
output = chat_model(_input.to_messages())
output_parser.parse(output.content)
{'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Pari... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/structured.html |
f37758d206cd-0 | .ipynb
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OutputFixingParser
OutputFixingParser#
This output parser wraps another output parser and tries to fix any mmistakes
The Pydantic guardrail simply tries to parse the LLM response. If it does not parse correctly, then it errors.
But we can do other things besides throw errors. Specifically, we can pass the m... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
f37758d206cd-1 | 24 return self.pydantic_object.parse_obj(json_object)
File ~/.pyenv/versions/3.9.1/lib/python3.9/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
343 if (cls is None and object_hook is None and
344 parse_int is None and parse_float is N... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
f37758d206cd-2 | Cell In[6], line 1
----> 1 parser.parse(misformatted)
File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)
27 name = self.pydantic_object.__name__
28 msg = f"Failed to parse {name} from completion {text}. Got: {e}"
---> 29 raise OutputParserException(ms... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
88709ace2e22-0 | .ipynb
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RetryOutputParser
RetryOutputParser#
While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can’t. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example.
from langchain.prompts... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/retry.html |
88709ace2e22-1 | 23 json_object = json.loads(json_str)
---> 24 return self.pydantic_object.parse_obj(json_object)
26 except (json.JSONDecodeError, ValidationError) as e:
File ~/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pydantic/main.py:527, in pydantic.main.BaseModel.parse_obj()
File ~/.pyenv/version... | https://langchain-cn.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/retry.html |
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