id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
57f836b58ab0-1 | 'This is a '
We can also print the LLM and see its custom print.
print(llm)
CustomLLM
Params: {'n': 10}
previous
How to use the async API for LLMs
next
How (and why) to use the fake LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/models/llms/examples/custom_llm.html |
65b9a2cae31a-0 | .ipynb
.pdf
How to use the async API for LLMs
How to use the async API for LLMs#
LangChain provides async support for LLMs by leveraging the asyncio library.
Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, OpenAI, PromptLayerOpenAI, ChatOpenAI an... | https://python.langchain.com/en/latest/modules/models/llms/examples/async_llm.html |
65b9a2cae31a-1 | 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 about you?
I'm doing well, thank you. How about yourself?
I'm doing well, thank you! How about you?
I'm doing well, thank you. How a... | https://python.langchain.com/en/latest/modules/models/llms/examples/async_llm.html |
6c736b8147b7-0 | .ipynb
.pdf
How (and why) to use the human input LLM
How (and why) to use the human input LLM#
Similar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they rece... | https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html |
6c736b8147b7-1 | Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: What is 'Bocchi the Rock!'?
Thought:
=====END OF PROMPT===... | https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html |
6c736b8147b7-2 | Page: Manga Time Kirara Max
Summary: Manga Time Kirara Max (まんがタイムきららMAX) is a Japanese four-panel seinen manga magazine published by Houbunsha. It is the third magazine of the "Kirara" series, after "Manga Time Kirara" and "Manga Time Kirara Carat". The first issue was released on September 29, 2004. Currently the mag... | https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html |
6c736b8147b7-3 | Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōb... | https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html |
6c736b8147b7-4 | Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōb... | https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html |
6c736b8147b7-5 | Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōb... | https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html |
6c736b8147b7-6 | Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōb... | https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html |
16ce7e6d0819-0 | .ipynb
.pdf
How to stream LLM and Chat Model responses
How to stream LLM and Chat Model responses#
LangChain provides streaming support for LLMs. Currently, we support streaming for the OpenAI, ChatOpenAI, and ChatAnthropic implementations, but streaming support for other LLM implementations is on the roadmap. To utili... | https://python.langchain.com/en/latest/modules/models/llms/examples/streaming_llm.html |
16ce7e6d0819-1 | 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.
We still have access to the end LLMResult if using generate. However, token_usage is not currently supported for streaming.
llm.generate(["Tell me a jok... | https://python.langchain.com/en/latest/modules/models/llms/examples/streaming_llm.html |
16ce7e6d0819-2 | Sparkling water, you're my favorite vibe
Bridge:
You're my go-to drink, day or night
You make me feel so light
I'll never give you up, you're my true love
Sparkling water, you're sent from above
Chorus:
Sparkling water, oh how you shine
A taste so clean, it's simply divine
You quench my thirst, you make me feel alive
S... | https://python.langchain.com/en/latest/modules/models/llms/examples/streaming_llm.html |
919f5be47177-0 | .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://python.langchain.com/en/latest/modules/models/llms/examples/token_usage_tracking.html |
919f5be47177-1 | 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://python.langchain.com/en/latest/modules/models/llms/examples/token_usage_tracking.html |
19f1f4521d58-0 | .ipynb
.pdf
How to cache LLM calls
Contents
In Memory Cache
SQLite Cache
Redis Cache
Standard Cache
Semantic Cache
GPTCache
Momento Cache
SQLAlchemy Cache
Custom SQLAlchemy Schemas
Optional Caching
Optional Caching in Chains
How to cache LLM calls#
This notebook covers how to cache results of individual LLM calls.
im... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-1 | llm("Tell me a joke")
CPU times: user 17 ms, sys: 9.76 ms, total: 26.7 ms
Wall time: 825 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
%%time
# The second time it is, so it goes faster
llm("Tell me a joke")
CPU times: user 2.46 ms, sys: 1.23 ms, total: 3.7 ms
Wall time: 2.67 ms
'\n\nWhy did ... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-2 | Semantic Cache#
Use Redis to cache prompts and responses and evaluate hits based on semantic similarity.
from langchain.embeddings import OpenAIEmbeddings
from langchain.cache import RedisSemanticCache
langchain.llm_cache = RedisSemanticCache(
redis_url="redis://localhost:6379",
embedding=OpenAIEmbeddings()
)
%... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-3 | cache_obj.init(
pre_embedding_func=get_prompt,
data_manager=manager_factory(manager="map", data_dir=f"map_cache_{hashed_llm}"),
)
langchain.llm_cache = GPTCache(init_gptcache)
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
CPU times: user 21.5 ms, sys... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-4 | Wall time: 8.44 s
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
%%time
# This is an exact match, so it finds it in the cache
llm("Tell me a joke")
CPU times: user 866 ms, sys: 20 ms, total: 886 ms
Wall time: 226 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
%%time
# ... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-5 | Wall time: 1.73 s
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
%%time
# The second time it is, so it goes faster
# When run in the same region as the cache, latencies are single digit ms
llm("Tell me a joke")
CPU times: user 3.16 ms, sys: 2.98 ms, total: 6.14 ms
Wall time: 57.9 ms
'\n\nWhy did... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-6 | idx = Column(Integer)
response = Column(String)
prompt_tsv = Column(TSVectorType(), Computed("to_tsvector('english', llm || ' ' || prompt)", persisted=True))
__table_args__ = (
Index("idx_fulltext_prompt_tsv", prompt_tsv, postgresql_using="gin"),
)
engine = create_engine("postgresql://postgres:p... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-7 | llm = OpenAI(model_name="text-davinci-002")
no_cache_llm = OpenAI(model_name="text-davinci-002", cache=False)
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
text_splitter = CharacterTextSplitter()
with open('../../../state_of_the_union.txt') as f:
sta... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
19f1f4521d58-8 | %%time
chain.run(docs)
CPU times: user 11.5 ms, sys: 4.33 ms, total: 15.8 ms
Wall time: 1.04 s
'\n\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education a... | https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html |
3729254f4be4-0 | .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://python.langchain.com/en/latest/modules/models/llms/examples/fake_llm.html |
4ff8991f59b2-0 | .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://python.langchain.com/en/latest/modules/models/chat/getting_started.html |
4ff8991f59b2-1 | [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="I love programming.")
],
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="I love artificial intelligenc... | https://python.langchain.com/en/latest/modules/models/chat/getting_started.html |
4ff8991f59b2-2 | 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 messages
chat(chat_prompt.format_prompt(input_language="English", output_langua... | https://python.langchain.com/en/latest/modules/models/chat/getting_started.html |
4ff8991f59b2-3 | 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 drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Bridge:... | https://python.langchain.com/en/latest/modules/models/chat/getting_started.html |
4f37b4447973-0 | .rst
.pdf
Integrations
Integrations#
The examples here all highlight how to integrate with different chat models.
Anthropic
Azure
Google Cloud Platform Vertex AI PaLM
OpenAI
PromptLayer ChatOpenAI
previous
How to stream responses
next
Anthropic
By Harrison Chase
© Copyright 2023, Harrison Chase.
Las... | https://python.langchain.com/en/latest/modules/models/chat/integrations.html |
10c389e816cc-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://python.langchain.com/en/latest/modules/models/chat/how_to_guides.html |
2e7483b75118-0 | .ipynb
.pdf
Google Cloud Platform Vertex AI PaLM
Google Cloud Platform Vertex AI PaLM#
Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.
PaLM API on Vertex AI is a Preview offering, su... | https://python.langchain.com/en/latest/modules/models/chat/integrations/google_vertex_ai_palm.html |
2e7483b75118-1 | HumanMessage,
SystemMessage
)
chat = ChatVertexAI()
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
chat(messages)
AIMessage(content='Sure, here is the translat... | https://python.langchain.com/en/latest/modules/models/chat/integrations/google_vertex_ai_palm.html |
c040d126709b-0 | .ipynb
.pdf
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://python.langchain.com/en/latest/modules/models/chat/integrations/azure_chat_openai.html |
6260a82f7b0e-0 | .ipynb
.pdf
Anthropic
Contents
ChatAnthropic also supports async and streaming functionality:
Anthropic#
This notebook covers how to get started with Anthropic chat models.
from langchain.chat_models import ChatAnthropic
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
... | https://python.langchain.com/en/latest/modules/models/chat/integrations/anthropic.html |
20f07f138817-0 | .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://python.langchain.com/en/latest/modules/models/chat/integrations/openai.html |
20f07f138817-1 | AIMessage(content="J'adore la programmation.", additional_kwargs={})
previous
Google Cloud Platform Vertex AI PaLM
next
PromptLayer ChatOpenAI
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/models/chat/integrations/openai.html |
4c7e49e242b9-0 | .ipynb
.pdf
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://python.langchain.com/en/latest/modules/models/chat/integrations/promptlayer_chatopenai.html |
4c7e49e242b9-1 | 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://python.langchain.com/en/latest/modules/models/chat/integrations/promptlayer_chatopenai.html |
05c9f02b921c-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.streaming_stdout import StreamingStdOutCallbackHandler
chat = ChatOpenAI(s... | https://python.langchain.com/en/latest/modules/models/chat/examples/streaming.html |
05c9f02b921c-1 | previous
How to use few shot examples
next
Integrations
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/models/chat/examples/streaming.html |
2d441156cdd0-0 | .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://python.langchain.com/en/latest/modules/models/chat/examples/few_shot_examples.html |
2d441156cdd0-1 | 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://python.langchain.com/en/latest/modules/models/chat/examples/few_shot_examples.html |
88d28e900446-0 | .ipynb
.pdf
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
G... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/huggingfacehub.html |
49e107b1728c-0 | .ipynb
.pdf
Sentence Transformers Embeddings
Sentence Transformers Embeddings#
SentenceTransformers embeddings are called using the HuggingFaceEmbeddings integration. We have also added an alias for SentenceTransformerEmbeddings for users who are more familiar with directly using that package.
SentenceTransformers is a... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/sentence_transformers.html |
e5b0d009adbc-0 | .ipynb
.pdf
Contents
!pip -q install elasticsearch langchain
import elasticsearch
from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings
# Define the model ID
model_id = 'your_model_id'
# Instantiate ElasticsearchEmbeddings using credentials
embeddings = ElasticsearchEmbeddings.from_credentials(
m... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/elasticsearch.html |
e1faa4c8276b-0 | .ipynb
.pdf
MosaicML embeddings
MosaicML embeddings#
MosaicML offers a managed inference service. You can either use a variety of open source models, or deploy your own.
This example goes over how to use LangChain to interact with MosaicML Inference for text embedding.
# sign up for an account: https://forms.mosaicml.c... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/mosaicml.html |
d4955ae3be9e-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://python.langchain.com/en/latest/modules/models/text_embedding/examples/cohere.html |
0f6dda265801-0 | .ipynb
.pdf
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 instructions on how to do this, please see here. Note: In order to handle batched requests, you will need to... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/sagemaker-endpoint.html |
0f6dda265801-1 | query_result = embeddings.embed_query("foo")
doc_results = embeddings.embed_documents(["foo"])
doc_results
previous
OpenAI
next
Self Hosted Embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/sagemaker-endpoint.html |
c96872fa55e9-0 | .ipynb
.pdf
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://python.langchain.com/en/latest/modules/models/text_embedding/examples/fake.html |
34bd5766da13-0 | .ipynb
.pdf
Self Hosted Embeddings
Self Hosted Embeddings#
Let’s load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.
from langchain.embeddings import (
SelfHostedEmbeddings,
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddi... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/self-hosted.html |
34bd5766da13-1 | 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://python.langchain.com/en/latest/modules/models/text_embedding/examples/self-hosted.html |
34db98d3078f-0 | .ipynb
.pdf
ModelScope
ModelScope#
Let’s load the ModelScope Embedding class.
from langchain.embeddings import ModelScopeEmbeddings
model_id = "damo/nlp_corom_sentence-embedding_english-base"
embeddings = ModelScopeEmbeddings(model_id=model_id)
text = "This is a test document."
query_result = embeddings.embed_query(tex... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/modelscope_hub.html |
a69b1f6d3b4d-0 | .ipynb
.pdf
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://python.langchain.com/en/latest/modules/models/text_embedding/examples/llamacpp.html |
fdd28944a286-0 | .ipynb
.pdf
Google Cloud Platform Vertex AI PaLM
Google Cloud Platform Vertex AI PaLM#
Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.
PaLM API on Vertex AI is a Preview offering, su... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/google_vertex_ai_palm.html |
fdd28944a286-1 | previous
Fake Embeddings
next
Hugging Face Hub
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/google_vertex_ai_palm.html |
9c7f55dc621f-0 | .ipynb
.pdf
MiniMax
MiniMax#
MiniMax offers an embeddings service.
This example goes over how to use LangChain to interact with MiniMax Inference for text embedding.
import os
os.environ["MINIMAX_GROUP_ID"] = "MINIMAX_GROUP_ID"
os.environ["MINIMAX_API_KEY"] = "MINIMAX_API_KEY"
from langchain.embeddings import MiniMaxEm... | https://python.langchain.com/en/latest/modules/models/text_embedding/examples/minimax.html |
3f7f7690c26d-0 | .ipynb
.pdf
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://python.langchain.com/en/latest/modules/models/text_embedding/examples/azureopenai.html |
16fcebc80d06-0 | .ipynb
.pdf
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://python.langchain.com/en/latest/modules/models/text_embedding/examples/instruct_embeddings.html |
e3ecd363e666-0 | .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://python.langchain.com/en/latest/modules/models/text_embedding/examples/jina.html |
4baa6d9881a5-0 | .ipynb
.pdf
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://python.langchain.com/en/latest/modules/models/text_embedding/examples/aleph_alpha.html |
efe046c37789-0 | .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://python.langchain.com/en/latest/modules/models/text_embedding/examples/openai.html |
83b0eec13a0f-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://python.langchain.com/en/latest/modules/models/text_embedding/examples/tensorflowhub.html |
ccf461e2324e-0 | .ipynb
.pdf
Getting Started
Contents
ChatMessageHistory
ConversationBufferMemory
Using in a chain
Saving Message History
Getting Started#
This notebook walks through how LangChain thinks about memory.
Memory involves keeping a concept of state around throughout a user’s interactions with an language model. A user’s i... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
ccf461e2324e-1 | history.messages
[HumanMessage(content='hi!', additional_kwargs={}),
AIMessage(content='whats up?', additional_kwargs={})]
ConversationBufferMemory#
We now show how to use this simple concept in a chain. We first showcase ConversationBufferMemory which is just a wrapper around ChatMessageHistory that extracts the mess... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
ccf461e2324e-2 | Current conversation:
Human: Hi there!
AI:
> Finished chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation betw... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
ccf461e2324e-3 | Human: Tell me about yourself.
AI:
> Finished chain.
" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
Saving ... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
7743b9381cfe-0 | .rst
.pdf
How-To Guides
Contents
Types
Usage
How-To Guides#
Types#
The first set of examples all highlight different types of memory.
ConversationBufferMemory
ConversationBufferWindowMemory
Entity Memory
Conversation Knowledge Graph Memory
ConversationSummaryMemory
ConversationSummaryBufferMemory
ConversationTokenBuf... | https://python.langchain.com/en/latest/modules/memory/how_to_guides.html |
8633e206a361-0 | .ipynb
.pdf
VectorStore-Backed Memory
Contents
Initialize your VectorStore
Create your the VectorStoreRetrieverMemory
Using in a chain
VectorStore-Backed Memory#
VectorStoreRetrieverMemory stores memories in a VectorDB and queries the top-K most “salient” docs every time it is called.
This differs from most of the ot... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
8633e206a361-1 | memory = VectorStoreRetrieverMemory(retriever=retriever)
# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"})
memory.save_context({"input": "My favorite sport is soccer"},... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
8633e206a361-2 | memory=memory,
verbose=True
)
conversation_with_summary.predict(input="Hi, my name is Perry, what's up?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
8633e206a361-3 | conversation_with_summary.predict(input="Whats my favorite food")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a que... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
8633e206a361-4 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
09cb305a543a-0 | .ipynb
.pdf
ConversationTokenBufferMemory
Contents
Using in a chain
ConversationTokenBufferMemory#
ConversationTokenBufferMemory keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.
Let’s first walk through how to use the ut... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
09cb305a543a-1 | > Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
09cb305a543a-2 | AI: Sounds like a productive day! What kind of documentation are you writing?
Human: For LangChain! Have you heard of it?
AI:
> Finished chain.
" Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're wr... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
514090a24bee-0 | .ipynb
.pdf
ConversationSummaryMemory
Contents
Initializing with messages
Using in a chain
ConversationSummaryMemory#
Now let’s take a look at using a slightly more complex type of memory - ConversationSummaryMemory. This type of memory creates a summary of the conversation over time. This can be useful for condensin... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
514090a24bee-1 | history.add_user_message("hi")
history.add_ai_message("hi there!")
memory = ConversationSummaryMemory.from_messages(llm=OpenAI(temperature=0), chat_memory=history, return_messages=True)
memory.buffer
'\nThe human greets the AI, to which the AI responds with a friendly greeting.'
Using in a chain#
Let’s walk through an ... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
514090a24bee-2 | Human: Tell me more about it!
AI:
> Finished chain.
" Sure! The customer is having trouble with their computer not connecting to the internet. I'm helping them troubleshoot the issue and figure out what the problem is. So far, we've tried resetting the router and checking the network settings, but the issue still persi... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
31a937147253-0 | .ipynb
.pdf
Entity Memory
Contents
Using in a chain
Inspecting the memory store
Entity Memory#
This notebook shows how to work with a memory module that remembers things about specific entities. It extracts information on entities (using LLMs) and builds up its knowledge about that entity over time (also using LLMs).... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-1 | 'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}
Using in a chain#
Let’s now use it in a chain!
from langchain.chains import ConversationChain
from langchain.memory import ConversationEntityMemory
from langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from pydantic import BaseM... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-2 | Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.
Context:
{'Deven': 'Deven is wor... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-3 | You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the ... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-4 | You are an assistant to a human, powered by a large language model trained by OpenAI.
You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-5 | AI: That sounds like a great project! What kind of project are they working on?
Human: They are trying to add more complex memory structures to Langchain
AI: That sounds like an interesting project! What kind of memory structures are they trying to add?
Last line:
Human: They are adding in a key-value store for entit... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-6 | Context:
{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.', 'Sam': 'Sam is working on a hackathon project with Deven, t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-7 | {'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.',
'Deven': 'Deven is working on a hackathon project with Sam, which they are '
'entering into a hackathon. They are trying to add more complex '
'memory structures to Langchain, including a key-value store for '
'e... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-8 | You are an assistant to a human, powered by a large language model trained by OpenAI.
You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-9 | Human: What do you know about Deven & Sam?
AI: Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how ... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-10 | 'memory structures, including a key-value store for entities '
'mentioned so far in the conversation.',
'Sam': 'Sam is working on a hackathon project with Deven, trying to add more '
'complex memory structures to Langchain, including a key-value store '
'for entities mentioned so far in t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-11 | Context:
{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation, and seem to be working hard on this project with a great idea... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
31a937147253-12 | Last line:
Human: What do you know about Sam?
You:
> Finished chain.
' Sam is the founder of a successful company called Daimon. He is also working on a hackathon project with Deven to add more complex memory structures to Langchain. They seem to have a great idea for how the key-value store can help.'
previous
Convers... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
bd3ecff4dc75-0 | .ipynb
.pdf
ConversationSummaryBufferMemory
Contents
Using in a chain
ConversationSummaryBufferMemory#
ConversationSummaryBufferMemory combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses bot... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
bd3ecff4dc75-1 | from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
llm=llm,
# We set a very low max_token_limit for the purposes of testing.
memory=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),
verbose=True
)
conversation_with_summary.predict(input="Hi, w... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
bd3ecff4dc75-2 | > Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
bd3ecff4dc75-3 | AI:
> Finished chain.
' Oh, okay. What is LangChain?'
previous
ConversationSummaryMemory
next
ConversationTokenBufferMemory
Contents
Using in a chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
a4f727e04f31-0 | .ipynb
.pdf
ConversationBufferMemory
Contents
Using in a chain
ConversationBufferMemory#
This notebook shows how to use ConversationBufferMemory. This memory allows for storing of messages and then extracts the messages in a variable.
We can first extract it as a string.
from langchain.memory import ConversationBuffe... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
a4f727e04f31-1 | Current conversation:
Human: Hi there!
AI:
> Finished chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation betw... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
a4f727e04f31-2 | Human: Tell me about yourself.
AI:
> Finished chain.
" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
And tha... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
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