metadata dict | id stringlengths 14 16 | text stringlengths 31 2.73k |
|---|---|---|
{
"url": "https://langchain.readthedocs.io/en/latest/index.html"
} | 7fb9f2a39073-0 | .rst
.pdf
Welcome to LangChain
Contents
Getting Started
Modules
Use Cases
Reference Docs
LangChain Ecosystem
Additional Resources
Welcome to LangChain#
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call ... |
{
"url": "https://langchain.readthedocs.io/en/latest/index.html"
} | 7fb9f2a39073-1 | Agents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
Use Cases#
The above modules can be used in a... |
{
"url": "https://langchain.readthedocs.io/en/latest/index.html"
} | 7fb9f2a39073-2 | Guides for how other companies/products can be used with LangChain
LangChain Ecosystem
Additional Resources#
Additional collection of resources we think may be useful as you develop your application!
LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.
Glossary: A glossary o... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-0 | Index
_
| A
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| C
| D
| E
| F
| G
| H
| I
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| K
| L
| M
| N
| O
| P
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| R
| S
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| U
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| Z
_
__call__() (langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llm... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-1 | (langchain.llms.StochasticAI method)
(langchain.llms.Writer method)
A
aadd_documents() (langchain.vectorstores.VectorStore method)
aadd_texts() (langchain.vectorstores.VectorStore method)
aapply() (langchain.chains.LLMChain method)
aapply_and_parse() (langchain.chains.LLMChain method)
add() (langchain.docstore.InMemory... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-2 | (langchain.llms.Cohere method)
(langchain.llms.DeepInfra method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)
(langchain.llms.LlamaCpp met... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-3 | (langchain.llms.HuggingFacePipeline method)
(langchain.llms.LlamaCpp method)
(langchain.llms.Modal method)
(langchain.llms.NLPCloud method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.Petals method)
(langchain.llms.PromptLayerOpenAI method)
(langchain.llms.PromptLayerOpenAIChat met... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-4 | api_answer_chain (langchain.chains.APIChain attribute)
api_docs (langchain.chains.APIChain attribute)
api_operation (langchain.chains.OpenAPIEndpointChain attribute)
api_request_chain (langchain.chains.APIChain attribute)
(langchain.chains.OpenAPIEndpointChain attribute)
api_response_chain (langchain.chains.OpenAPIEndp... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-5 | (langchain.llms.ForefrontAI attribute)
(langchain.llms.Writer attribute)
batch_size (langchain.llms.AzureOpenAI attribute)
beam_search_diversity_rate (langchain.llms.Writer attribute)
beam_width (langchain.llms.Writer attribute)
best_of (langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)
C
call... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-6 | compress_to_size (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)
constitutional_principles (langchain.chains.ConstitutionalChain attribute)
construct() (langchain.llms.AI21 class method)
(langchain.llms.AlephAlpha class method)
(langchain.llms.Anthropic class method)
(langchain.llms.AzureOpenAI c... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-7 | content_handler (langchain.embeddings.SagemakerEndpointEmbeddings attribute)
(langchain.llms.SagemakerEndpoint attribute)
CONTENT_KEY (langchain.vectorstores.Qdrant attribute)
contextual_control_threshold (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)
(langchain.llms.AlephAlpha attribute)
contro... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-8 | (langchain.llms.SelfHostedHuggingFaceLLM method)
(langchain.llms.SelfHostedPipeline method)
(langchain.llms.StochasticAI method)
(langchain.llms.Writer method)
coroutine (langchain.agents.Tool attribute)
countPenalty (langchain.llms.AI21 attribute)
create_assertions_prompt (langchain.chains.LLMSummarizationCheckerChain... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-9 | critique_chain (langchain.chains.ConstitutionalChain attribute)
D
database (langchain.chains.SQLDatabaseChain attribute)
decider_chain (langchain.chains.SQLDatabaseSequentialChain attribute)
DeepLake (class in langchain.vectorstores)
delete() (langchain.vectorstores.DeepLake method)
delete_collection() (langchain.vecto... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-10 | (langchain.llms.PromptLayerOpenAI method)
(langchain.llms.PromptLayerOpenAIChat method)
(langchain.llms.Replicate method)
(langchain.llms.RWKV method)
(langchain.llms.SagemakerEndpoint method)
(langchain.llms.SelfHostedHuggingFaceLLM method)
(langchain.llms.SelfHostedPipeline method)
(langchain.llms.StochasticAI method... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-11 | (langchain.embeddings.LlamaCppEmbeddings method)
(langchain.embeddings.OpenAIEmbeddings method)
(langchain.embeddings.SagemakerEndpointEmbeddings method)
(langchain.embeddings.SelfHostedEmbeddings method)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings method)
(langchain.embeddings.TensorflowHubEmbeddings... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-12 | (langchain.llms.ForefrontAI attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.Modal attribute)
engines (langchain.utilities.searx_search.SearxSearchWrapper attribute)
entity_extraction_chain (langchain.chains.GraphQAChain attribute)
error (langchain.chains.OpenAIModerationChain attribute)
exampl... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-13 | (langchain.prompts.PromptTemplate method)
format_messages() (langchain.prompts.BaseChatPromptTemplate method)
(langchain.prompts.ChatPromptTemplate method)
(langchain.prompts.MessagesPlaceholder method)
format_prompt() (langchain.prompts.BaseChatPromptTemplate method)
(langchain.prompts.BasePromptTemplate method)
(lang... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-14 | (langchain.chains.ConstitutionalChain class method)
(langchain.chains.ConversationalRetrievalChain class method)
(langchain.chains.GraphQAChain class method)
(langchain.chains.HypotheticalDocumentEmbedder class method)
(langchain.chains.QAGenerationChain class method)
(langchain.chains.SQLDatabaseSequentialChain class ... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-15 | (langchain.vectorstores.VectorStore class method)
(langchain.vectorstores.Weaviate class method)
from_tiktoken_encoder() (langchain.text_splitter.TextSplitter class method)
from_url_and_method() (langchain.chains.OpenAPIEndpointChain class method)
func (langchain.agents.Tool attribute)
G
generate() (langchain.chains.LL... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-16 | (langchain.llms.StochasticAI method)
(langchain.llms.Writer method)
generate_prompt() (langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(l... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-17 | get_answer_expr (langchain.chains.PALChain attribute)
get_full_inputs() (langchain.agents.Agent method)
get_num_tokens() (langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.CerebriumAI method... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-18 | (langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.DeepInfra method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langcha... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-19 | graph (langchain.chains.GraphQAChain attribute)
H
hardware (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
headers (langchain.utilities.searx_search.SearxSearchWrapper attribute)
hosting (langchain.embeddi... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-20 | (langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.DeepInfra method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.Hu... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-21 | langchain.chains
module
langchain.docstore
module
langchain.embeddings
module
langchain.llms
module
langchain.prompts
module
langchain.prompts.example_selector
module
langchain.python
module
langchain.serpapi
module
langchain.text_s... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-22 | (langchain.chains.QAGenerationChain attribute)
llm_prefix (langchain.agents.Agent property)
(langchain.agents.ConversationalAgent property)
(langchain.agents.ConversationalChatAgent property)
(langchain.agents.ZeroShotAgent property)
load_agent() (in module langchain.agents)
load_chain() (in module langchain.chains)
lo... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-23 | (langchain.agents.SelfAskWithSearchChain attribute)
max_iterations (langchain.agents.AgentExecutor attribute)
(langchain.agents.MRKLChain attribute)
(langchain.agents.ReActChain attribute)
(langchain.agents.SelfAskWithSearchChain attribute)
max_length (langchain.llms.NLPCloud attribute)
(langchain.llms.Petals attribute... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-24 | (langchain.chains.VectorDBQAWithSourcesChain attribute)
max_tokens_per_generation (langchain.llms.RWKV attribute)
max_tokens_to_sample (langchain.llms.Anthropic attribute)
maximum_tokens (langchain.llms.AlephAlpha attribute)
maxTokens (langchain.llms.AI21 attribute)
memory (langchain.agents.MRKLChain attribute)
(langch... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-25 | model_kwargs (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.embeddings.SagemakerEndpointEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.CerebriumAI attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.HuggingFaceEndpoint attri... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-26 | (langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
model_url (langchain.embeddings.TensorflowHubEmbeddings attribute)
modelname_to_contextsize() (langchain.llms.AzureOpenAI method)
(langchain.llms.Ope... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-27 | normalize (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)
num_beams (langchain.llms.NLPCloud attribute)
num_return_sequences (langchain.llms.NLPCloud attribute)
numResults (langchain.llms.AI21 attribute)
O
observation_prefix (langchain.agents.Agent property)
(langchain.agents.ConversationalAgent ... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-28 | penalty_bias (langchain.llms.AlephAlpha attribute)
penalty_exceptions (langchain.llms.AlephAlpha attribute)
penalty_exceptions_include_stop_sequences (langchain.llms.AlephAlpha attribute)
persist() (langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
Pinecone (class in langchain.vectorstores)... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-29 | (langchain.chains.PALChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
python_globals (langchain.chains.PALChain attribute)
python_locals (langchain.chains.PALChain attribute)
PythonCodeTextSplitter (class in langchain.text_splitter)
Q
qa_chain (langchain.chains.GraphQAChain attribute)
Qdrant (class in lan... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-30 | (langchain.llms.NLPCloud attribute)
(langchain.llms.Writer attribute)
repo_id (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.llms.HuggingFaceHub attribute)
request_timeout (langchain.llms.AzureOpenAI attribute)
requests (langchain.chains.OpenAPIEndpointChain attribute)
requests_wrapper (langchain.... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-31 | revised_summary_prompt (langchain.chains.LLMSummarizationCheckerChain attribute)
revision_chain (langchain.chains.ConstitutionalChain attribute)
run() (langchain.python.PythonREPL method)
(langchain.serpapi.SerpAPIWrapper method)
(langchain.utilities.searx_search.SearxSearchWrapper method)
rwkv_verbose (langchain.llms.... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-32 | (langchain.llms.SelfHostedHuggingFaceLLM method)
(langchain.llms.SelfHostedPipeline method)
(langchain.llms.StochasticAI method)
(langchain.llms.Writer method)
(langchain.prompts.BasePromptTemplate method)
(langchain.prompts.ChatPromptTemplate method)
save_agent() (langchain.agents.AgentExecutor method)
save_local() (l... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-33 | (langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
similarity_search_by_ve... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-34 | (langchain.llms.LlamaCpp attribute)
(langchain.llms.Writer attribute)
stop_sequences (langchain.llms.AlephAlpha attribute)
strategy (langchain.llms.RWKV attribute)
stream() (langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.OpenAI method)
(langchain.llms.PromptLayerOpenAI method)
stre... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-35 | (langchain.llms.Writer attribute)
template (langchain.prompts.PromptTemplate attribute)
template_format (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
text_length (langchain.chains.LLMRequestsChain attribute)
text... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-36 | top_k_docs_for_context (langchain.chains.ChatVectorDBChain attribute)
top_p (langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.Llama... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-37 | (langchain.llms.NLPCloud class method)
(langchain.llms.OpenAI class method)
(langchain.llms.OpenAIChat class method)
(langchain.llms.Petals class method)
(langchain.llms.PromptLayerOpenAI class method)
(langchain.llms.PromptLayerOpenAIChat class method)
(langchain.llms.Replicate class method)
(langchain.llms.RWKV class... |
{
"url": "https://python.langchain.com/en/latest/genindex.html"
} | f445e36f9edb-38 | vocab_only (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
W
Weaviate (class in langchain.vectorstores)
Wikipedia (class in langchain.docstore)
Z
ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
By Harrison Chase
© C... |
{
"url": "https://python.langchain.com/en/latest/glossary.html"
} | e9f602475ea6-0 | .md
.pdf
Glossary
Contents
Chain of Thought Prompting
Action Plan Generation
ReAct Prompting
Self-ask
Prompt Chaining
Memetic Proxy
Self Consistency
Inception
MemPrompt
Glossary#
This is a collection of terminology commonly used when developing LLM applications.
It contains reference to external papers or sources whe... |
{
"url": "https://python.langchain.com/en/latest/glossary.html"
} | e9f602475ea6-1 | Language Model Cascades
ICE Primer Book
Socratic Models
Memetic Proxy#
Encouraging the LLM to respond in a certain way framing the discussion in a context that the model knows of and that will result in that type of response. For example, as a conversation between a student and a teacher.
Resources:
Paper
Self Consiste... |
{
"url": "https://python.langchain.com/en/latest/reference.html"
} | f60104d7b9b5-0 | .rst
.pdf
API References
API References#
All of LangChain’s reference documentation, in one place.
Full documentation on all methods, classes, and APIs in LangChain.
Prompts
Utilities
Chains
Agents
previous
Integrations
next
Utilities
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated ... |
{
"url": "https://python.langchain.com/en/latest/index.html"
} | 23852d460fa9-0 | .rst
.pdf
Welcome to LangChain
Contents
Getting Started
Modules
Use Cases
Reference Docs
LangChain Ecosystem
Additional Resources
Welcome to LangChain#
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call ... |
{
"url": "https://python.langchain.com/en/latest/index.html"
} | 23852d460fa9-1 | Agents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
Use Cases#
The above modules can be used in a... |
{
"url": "https://python.langchain.com/en/latest/index.html"
} | 23852d460fa9-2 | Guides for how other companies/products can be used with LangChain
LangChain Ecosystem
Additional Resources#
Additional collection of resources we think may be useful as you develop your application!
LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.
Glossary: A glossary o... |
{
"url": "https://python.langchain.com/en/latest/gallery.html"
} | 8147846d684e-0 | .rst
.pdf
LangChain Gallery
Contents
Open Source
Misc. Colab Notebooks
Proprietary
LangChain Gallery#
Lots of people have built some pretty awesome stuff with LangChain.
This is a collection of our favorites.
If you see any other demos that you think we should highlight, be sure to let us know!
Open Source#
HowDoI.ai... |
{
"url": "https://python.langchain.com/en/latest/gallery.html"
} | 8147846d684e-1 | Record sounds of anything (birds, wind, fire, train station) and chat with it.
ChatGPT LangChain
This simple application demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain. When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather.
GPT Mat... |
{
"url": "https://python.langchain.com/en/latest/gallery.html"
} | 8147846d684e-2 | Daimon
A chat-based AI personal assistant with long-term memory about you.
AI Assisted SQL Query Generator
An app to write SQL using natural language, and execute against real DB.
Clerkie
Stack Tracing QA Bot to help debug complex stack tracing (especially the ones that go multi-function/file deep).
Sales Email Writer
... |
{
"url": "https://python.langchain.com/en/latest/ecosystem.html"
} | 6bc875b46e8e-0 | .rst
.pdf
LangChain Ecosystem
LangChain Ecosystem#
Guides for how other companies/products can be used with LangChain
AI21 Labs
Aim
Apify
AtlasDB
Banana
CerebriumAI
Chroma
ClearML Integration
Getting API Credentials
Setting Up
Scenario 1: Just an LLM
Scenario 2: Creating a agent with tools
Tips and Next Steps
Cohere
De... |
{
"url": "https://python.langchain.com/en/latest/model_laboratory.html"
} | edf665b7054b-0 | .ipynb
.pdf
Model Comparison
Model Comparison#
Constructing your language model application will likely involved choosing between many different options of prompts, models, and even chains to use. When doing so, you will want to compare these different options on different inputs in an easy, flexible, and intuitive way... |
{
"url": "https://python.langchain.com/en/latest/model_laboratory.html"
} | edf665b7054b-1 | pink
prompt = PromptTemplate(template="What is the capital of {state}?", input_variables=["state"])
model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)
model_lab_with_prompt.compare("New York")
Input:
New York
OpenAI
Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p'... |
{
"url": "https://python.langchain.com/en/latest/model_laboratory.html"
} | edf665b7054b-2 | names = [str(open_ai_llm), str(cohere_llm)]
model_lab = ModelLaboratory(chains, names=names)
model_lab.compare("What is the hometown of the reigning men's U.S. Open champion?")
Input:
What is the hometown of the reigning men's U.S. Open champion?
OpenAI
Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tok... |
{
"url": "https://python.langchain.com/en/latest/model_laboratory.html"
} | edf665b7054b-3 | So the final answer is:
Carlos Alcaraz
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. |
{
"url": "https://python.langchain.com/en/latest/tracing.html"
} | 3b985276c6a0-0 | .md
.pdf
Tracing
Contents
Tracing Walkthrough
Changing Sessions
Tracing#
By enabling tracing in your LangChain runs, you’ll be able to more effectively visualize, step through, and debug your chains and agents.
First, you should install tracing and set up your environment properly.
You can use either a locally hosted... |
{
"url": "https://python.langchain.com/en/latest/tracing.html"
} | 3b985276c6a0-1 | Changing Sessions#
To initially record traces to a session other than "default", you can set the LANGCHAIN_SESSION environment variable to the name of the session you want to record to:
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
os.environ["LANGCHAIN_SESSION"] = "my_session" # Make sure this session actual... |
{
"url": "https://python.langchain.com/en/latest/deployments.html"
} | 166820098a9e-0 | .md
.pdf
Deployments
Contents
Streamlit
Gradio (on Hugging Face)
Beam
Vercel
SteamShip
Langchain-serve
Deployments#
So you’ve made a really cool chain - now what? How do you deploy it and make it easily sharable with the world?
This section covers several options for that.
Note that these are meant as quick deploymen... |
{
"url": "https://python.langchain.com/en/latest/deployments.html"
} | 166820098a9e-1 | This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.
Langchain-serve#
This repository allows users to serve local chains and agents as RESTful, gRPC, or Websocket APIs thanks to Jina. Deploy your chains & agents with ease and enj... |
{
"url": "https://python.langchain.com/en/latest/search.html"
} | 4bcfa78caad0-0 | Search
Error
Please activate JavaScript to enable the search functionality.
Ctrl+K
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. |
{
"url": "https://python.langchain.com/en/latest/reference/prompts.html"
} | 29448f94ed77-0 | .rst
.pdf
Prompts
Prompts#
The reference guides here all relate to objects for working with Prompts.
PromptTemplates
Example Selector
previous
How to serialize prompts
next
PromptTemplates
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. |
{
"url": "https://python.langchain.com/en/latest/reference/integrations.html"
} | 8e4645c1026b-0 | .md
.pdf
Integrations
Integrations#
Besides the installation of this python package, you will also need to install packages and set environment variables depending on which chains you want to use.
Note: the reason these packages are not included in the dependencies by default is that as we imagine scaling this package,... |
{
"url": "https://python.langchain.com/en/latest/reference/integrations.html"
} | 8e4645c1026b-1 | PromptLayer:
Install requirements with pip install promptlayer (be sure to be on version 0.1.62 or higher)
Get an API key from promptlayer.com and set it using promptlayer.api_key=<API KEY>
SerpAPI:
Install requirements with pip install google-search-results
Get a SerpAPI api key and either set it as an environment var... |
{
"url": "https://python.langchain.com/en/latest/reference/integrations.html"
} | 8e4645c1026b-2 | DeepLake:
Install requirements with pip install deeplake
LlamaCpp:
Install requirements with pip install llama-cpp-python
Download model and convert following llama.cpp instructions
If you are using the NLTKTextSplitter or the SpacyTextSplitter, you will also need to install the appropriate models. For example, if you ... |
{
"url": "https://python.langchain.com/en/latest/reference/utils.html"
} | fe06cc2bf5ae-0 | .rst
.pdf
Utilities
Utilities#
There are a lot of different utilities that LangChain provides integrations for
These guides go over how to use them.
These can largely be grouped into two categories: generic utilities, and then utilities for working with larger text documents.
Generic Utilities
Python REPL
SerpAPI
Searx... |
{
"url": "https://python.langchain.com/en/latest/reference/installation.html"
} | 9554fbc7ebe9-0 | .md
.pdf
Installation
Contents
Official Releases
Installing from source
Installation#
Official Releases#
LangChain is available on PyPi, so to it is easily installable with:
pip install langchain
That will install the bare minimum requirements of LangChain.
A lot of the value of LangChain comes when integrating it wi... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/python.html"
} | 6a22d5789610-0 | .rst
.pdf
Python REPL
Python REPL#
Mock Python REPL.
pydantic model langchain.python.PythonREPL[source]#
Simulates a standalone Python REPL.
field globals: Optional[Dict] [Optional] (alias '_globals')#
field locals: Optional[Dict] [Optional] (alias '_locals')#
run(command: str) → str[source]#
Run command with own globa... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/example_selector.html"
} | 682d8972ff00-0 | .rst
.pdf
Example Selector
Example Selector#
Logic for selecting examples to include in prompts.
pydantic model langchain.prompts.example_selector.LengthBasedExampleSelector[source]#
Select examples based on length.
Validators
calculate_example_text_lengths » example_text_lengths
field example_prompt: langchain.prompts... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/example_selector.html"
} | 682d8972ff00-1 | Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g.... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/example_selector.html"
} | 682d8972ff00-2 | Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g.... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-0 | .rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of doc... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-1 | Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Aleph Alpha’s asymmetric, query embedding endpoint
:param text: The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.AlephAlphaSymmet... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-2 | embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Cohere’s embedding endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Cohere’s embedding endpoint.
Parameters
text – The text to embed... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-3 | Wrapper around HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.embeddings import HuggingFaceHubEmbeddings
repo_... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-4 | hf = HuggingFaceInstructEmbeddings(model_name=model_name)
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embedding documents.
field model_name: str = 'hkunlp/instructor-large'#
Model name to use.
field query_instruction: str = 'Represent the question for retrieving suppo... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-5 | field n_parts: int = -1#
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
field n_threads: Optional[int] = None#
Number of threads to use. If None, the number
of threads is automatically determined.
field seed: int = -1#
Seed. If -1, a random seed is used.
field use_mlock... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-6 | In addition, the deployment name must be passed as the model parameter.
Example
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
from langchain.embeddings.openai import OpenAIEmbeddings
embe... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-7 | https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
S... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-8 | Compute doc embeddings using a SageMaker Inference Endpoint.
Parameters
texts – The list of texts to embed.
chunk_size – The chunk size defines how many input texts will
be grouped together as request. If None, will use the
chunk size specified by the class.
Returns
List of embeddings, one for each text.
embed_query(te... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-9 | import runhouse as rh
from transformers import pipeline
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
rh.blob(pickle.dumps(pipeline),
path="models/pipeline.pkl").save().to(gpu, path="models")
embeddings = SelfHostedHFEmbeddings.fro... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-10 | Example
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
import runhouse as rh
model_name = "sentence-transformers/all-mpnet-base-v2"
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
Validators
set_callback_manager » callb... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-11 | gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
hf = SelfHostedHuggingFaceInstructEmbeddings(
model_name=model_name, hardware=gpu)
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embeddi... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/embeddings.html"
} | f9d90154b39f-12 | embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a TensorflowHub embedding model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a TensorflowHub embeddin... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/text_splitter.html"
} | c7562d5f5ebd-0 | .rst
.pdf
Text Splitter
Text Splitter#
Functionality for splitting text.
class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/text_splitter.html"
} | c7562d5f5ebd-1 | Split incoming text and return chunks.
class langchain.text_splitter.TextSplitter(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: typing.Callable[[str], int] = <built-in function len>)[source]#
Interface for splitting text into chunks.
create_documents(texts: List[str], metadatas: Optional[List[dict]... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/text_splitter.html"
} | c7562d5f5ebd-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-0 | .rst
.pdf
VectorStores
VectorStores#
Wrappers on top of vector stores.
class langchain.vectorstores.AtlasDB(name: str, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, api_key: Optional[str] = None, description: str = 'A description for your project', is_public: bool = True, reset_project_if_e... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-1 | for full detail.
classmethod from_documents(documents: List[langchain.schema.Document], embedding: Optional[langchain.embeddings.base.Embeddings] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, persist_directory: Optional[str] = None, description: str = 'A description... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-2 | Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
classmethod from_texts(texts: List[str], embedding: Optional[langchain.embeddings.base.Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = No... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-3 | Run similarity search with AtlasDB
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
Returns
List of documents most similar to the query text.
Return type
List[Document]
class langchain.vectorstores.Chroma(collection_name: str = 'langchain', embedding_function: Opt... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-4 | Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Parameters
collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-5 | Returns
Chroma vectorstore.
Return type
Chroma
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, filter: Optional[Dict[str, str]] = None) → List[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to qu... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-6 | It will also be called automatically when the object is destroyed.
similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[langchain.schema.Document][source]#
Run similarity search with Chroma.
Parameters
query (str) – Query text to search for.
k (int) – Number of result... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-7 | Return type
List[Tuple[Document, float]]
class langchain.vectorstores.DeepLake(dataset_path: str = 'mem://langchain', token: Optional[str] = None, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, read_only: Optional[bool] = None)[source]#
Wrapper around Deep Lake, a data lake for deep learning... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-8 | Returns
List of IDs of the added texts.
Return type
List[str]
delete(ids: Any[List[str], None] = None, filter: Any[Dict[str, str], None] = None, delete_all: Any[bool, None] = None) → bool[source]#
Delete the entities in the dataset
Parameters
ids (Optional[List[str]], optional) – The document_ids to delete.
Defaults to... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-9 | in either the environment
Local file system path of the form ./path/to/dataset or~/path/to/dataset or path/to/dataset.
In-memory path of the form mem://path/to/dataset which doesn’tsave the dataset, but keeps it in memory instead.
Should be used only for testing as it does not persist.
documents (List[Document]) – List... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-10 | Returns
List of Documents selected by maximal marginal relevance.
persist() → None[source]#
Persist the collection.
search(query: Any[str, None] = None, embedding: Any[float, None] = None, k: int = 4, distance_metric: str = 'L2', use_maximal_marginal_relevance: Optional[bool] = False, fetch_k: Optional[int] = 20, filte... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-11 | k – Number of Documents to return.
Defaults to 4.
query – Text to look up documents similar to.
embedding – Embedding function to use.
Defaults to None.
k – Number of Documents to return.
Defaults to 4.
distance_metric – L2 for Euclidean, L1 for Nuclear, max
L-infinity distance, cos for cosine similarity, ‘dot’ for dot... |
{
"url": "https://python.langchain.com/en/latest/reference/modules/vectorstore.html"
} | 423a5ff855c1-12 | distance, cos for cosine similarity, ‘dot’ for dot product.
Defaults to L2.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of documents most similar to the querytext with distance in float.
Return type
List[Tuple[Document, flo... |
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