(langchain.document_loaders.RedditPostsLoader method)
(langchain.document_loaders.RoamLoader method)
(langchain.document_loaders.S3DirectoryLoader method)
(langchain.document_loaders.S3FileLoader method)
(langchain.document_loaders.SeleniumURLLoader method)
(langchain.document_loaders.SitemapLoader method)
(langchain.document_loaders.SlackDirectoryLoader method)
(langchain.document_loaders.SpreedlyLoader method)
(langchain.document_loaders.SRTLoader method)
(langchain.document_loaders.StripeLoader method)
(langchain.document_loaders.TelegramChatApiLoader method)
(langchain.document_loaders.TelegramChatFileLoader method)
(langchain.document_loaders.TextLoader method)
(langchain.document_loaders.ToMarkdownLoader method)
(langchain.document_loaders.TomlLoader method)
(langchain.document_loaders.TwitterTweetLoader method)
(langchain.document_loaders.UnstructuredURLLoader method)
(langchain.document_loaders.WeatherDataLoader method)
(langchain.document_loaders.WebBaseLoader method)
(langchain.document_loaders.WhatsAppChatLoader method)
(langchain.document_loaders.WikipediaLoader method)
(langchain.document_loaders.YoutubeLoader method)
(langchain.utilities.ArxivAPIWrapper method)
(langchain.utilities.WikipediaAPIWrapper method)
load_agent() (in module langchain.agents)
load_all_available_meta (langchain.utilities.ArxivAPIWrapper attribute)
(langchain.utilities.WikipediaAPIWrapper attribute)
load_all_recursively (langchain.document_loaders.BlackboardLoader attribute)
load_chain() (in module langchain.chains)
load_comments() (langchain.document_loaders.HNLoader method)
load_device() (langchain.document_loaders.IFixitLoader method)
load_file() (langchain.document_loaders.DirectoryLoader method)


load_file() (langchain.document_loaders.DirectoryLoader method)
load_fn_kwargs (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
load_guide() (langchain.document_loaders.IFixitLoader method)
load_huggingface_tool() (in module langchain.agents)
load_local() (langchain.vectorstores.Annoy class method)
(langchain.vectorstores.FAISS class method)
load_max_docs (langchain.utilities.ArxivAPIWrapper attribute)
load_memory_variables() (langchain.experimental.GenerativeAgentMemory method)
(langchain.memory.CombinedMemory method)
(langchain.memory.ConversationBufferMemory method)
(langchain.memory.ConversationBufferWindowMemory method)
(langchain.memory.ConversationEntityMemory method)
(langchain.memory.ConversationKGMemory method)
(langchain.memory.ConversationStringBufferMemory method)
(langchain.memory.ConversationSummaryBufferMemory method)
(langchain.memory.ConversationSummaryMemory method)
(langchain.memory.ConversationTokenBufferMemory method)
(langchain.memory.ReadOnlySharedMemory method)
(langchain.memory.SimpleMemory method)
(langchain.memory.VectorStoreRetrieverMemory method)
load_messages() (langchain.memory.CosmosDBChatMessageHistory method)
load_page() (langchain.document_loaders.NotionDBLoader method)
load_prompt() (in module langchain.prompts)
load_questions_and_answers() (langchain.document_loaders.IFixitLoader method)
load_results() (langchain.document_loaders.HNLoader method)
load_suggestions() (langchain.document_loaders.IFixitLoader static method)
load_tools() (in module langchain.agents)
load_trashed_files (langchain.document_loaders.GoogleDriveLoader attribute)


load_trashed_files (langchain.document_loaders.GoogleDriveLoader attribute)
locals (langchain.python.PythonREPL attribute)
(langchain.utilities.PythonREPL attribute)
location (langchain.llms.VertexAI attribute)
log_probs (langchain.llms.AlephAlpha attribute)
logit_bias (langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
logitBias (langchain.llms.AI21 attribute)
logits_all (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
logprobs (langchain.llms.LlamaCpp attribute)
(langchain.llms.Writer attribute)
lookup_tool() (langchain.agents.AgentExecutor method)
lora_base (langchain.llms.LlamaCpp attribute)
lora_path (langchain.llms.LlamaCpp attribute)
M
MarkdownTextSplitter (class in langchain.text_splitter)
MastodonTootsLoader (class in langchain.document_loaders)
MathpixPDFLoader (class in langchain.document_loaders)
max_checks (langchain.chains.LLMSummarizationCheckerChain attribute)
max_execution_time (langchain.agents.AgentExecutor attribute)
max_iter (langchain.chains.FlareChain attribute)
max_iterations (langchain.agents.agent_toolkits.PowerBIToolkit attribute)
(langchain.agents.AgentExecutor attribute)
(langchain.tools.QueryPowerBITool attribute)
max_length (langchain.llms.NLPCloud attribute)
(langchain.llms.Petals attribute)
(langchain.prompts.example_selector.LengthBasedExampleSelector attribute)


(langchain.prompts.example_selector.LengthBasedExampleSelector attribute)
max_marginal_relevance_search() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
max_marginal_relevance_search_by_vector() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
max_new_tokens (langchain.llms.Petals attribute)
max_output_tokens (langchain.llms.GooglePalm attribute)
(langchain.llms.VertexAI attribute)
max_results (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
max_retries (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.OpenAIEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
max_token_limit (langchain.memory.ConversationSummaryBufferMemory attribute)
(langchain.memory.ConversationTokenBufferMemory attribute)
max_tokens (langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)


(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.Writer attribute)
max_tokens_for_prompt() (langchain.llms.AzureOpenAI method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
max_tokens_limit (langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(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)
memories (langchain.memory.CombinedMemory attribute)
(langchain.memory.SimpleMemory attribute)
memory (langchain.chains.ConversationChain attribute)
(langchain.experimental.GenerativeAgent attribute)
(langchain.memory.ReadOnlySharedMemory attribute)
memory_key (langchain.memory.ConversationSummaryBufferMemory attribute)
(langchain.memory.ConversationTokenBufferMemory attribute)
(langchain.memory.VectorStoreRetrieverMemory attribute)
memory_retriever (langchain.experimental.GenerativeAgentMemory attribute)
memory_stream (langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
memory_variables (langchain.experimental.GenerativeAgentMemory property)
(langchain.memory.CombinedMemory property)
(langchain.memory.ConversationStringBufferMemory property)
(langchain.memory.ReadOnlySharedMemory property)
(langchain.memory.SimpleMemory property)


(langchain.memory.ReadOnlySharedMemory property)
(langchain.memory.SimpleMemory property)
(langchain.memory.VectorStoreRetrieverMemory property)
merge_from() (langchain.vectorstores.FAISS method)
messages (langchain.memory.CassandraChatMessageHistory property)
(langchain.memory.ChatMessageHistory attribute)
(langchain.memory.DynamoDBChatMessageHistory property)
(langchain.memory.FileChatMessageHistory property)
(langchain.memory.MomentoChatMessageHistory property)
(langchain.memory.MongoDBChatMessageHistory property)
(langchain.memory.PostgresChatMessageHistory property)
(langchain.memory.RedisChatMessageHistory property)
metadata_column (langchain.vectorstores.MyScale property)
metadata_key (langchain.retrievers.RemoteLangChainRetriever attribute)
METADATA_KEY (langchain.vectorstores.Qdrant attribute)
MetalRetriever (class in langchain.retrievers)
metaphor_api_key (langchain.utilities.MetaphorSearchAPIWrapper attribute)
method (langchain.tools.APIOperation attribute)
metric (langchain.vectorstores.MyScaleSettings attribute)
Milvus (class in langchain.vectorstores)
min_chunk_size (langchain.document_loaders.DocugamiLoader attribute)
min_length (langchain.llms.NLPCloud attribute)
min_prob (langchain.chains.FlareChain attribute)
min_token_gap (langchain.chains.FlareChain attribute)
min_tokens (langchain.llms.GooseAI attribute)
(langchain.llms.Writer attribute)
minimax_api_key (langchain.embeddings.MiniMaxEmbeddings attribute)
minimax_group_id (langchain.embeddings.MiniMaxEmbeddings attribute)
minimum_tokens (langchain.llms.AlephAlpha attribute)
minTokens (langchain.llms.AI21 attribute)
model (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)


model (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)
(langchain.embeddings.CohereEmbeddings attribute)
(langchain.embeddings.MiniMaxEmbeddings attribute)
(langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.CTransformers attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.RWKV attribute)
(langchain.retrievers.document_compressors.CohereRerank attribute)
model_file (langchain.llms.CTransformers attribute)
model_id (langchain.embeddings.ModelScopeEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.Writer attribute)
model_key (langchain.llms.Banana attribute)
model_kwargs (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.HuggingFaceEmbeddings attribute)
(langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.embeddings.HuggingFaceInstructEmbeddings attribute)
(langchain.embeddings.SagemakerEndpointEmbeddings attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Beam attribute)
(langchain.llms.CerebriumAI attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)


(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.Modal attribute)
(langchain.llms.MosaicML attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.StochasticAI attribute)
model_load_fn (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
model_name (langchain.chains.OpenAIModerationChain attribute)
(langchain.chat_models.ChatGooglePalm attribute)
(langchain.chat_models.ChatOpenAI attribute)
(langchain.chat_models.ChatVertexAI attribute)
(langchain.embeddings.HuggingFaceEmbeddings attribute)
(langchain.embeddings.HuggingFaceInstructEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
(langchain.tools.SteamshipImageGenerationTool attribute)
model_path (langchain.llms.LlamaCpp attribute)
model_reqs (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)


model_reqs (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
model_type (langchain.llms.CTransformers attribute)
model_url (langchain.embeddings.TensorflowHubEmbeddings attribute)
modelname_to_contextsize() (langchain.llms.AzureOpenAI method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
ModernTreasuryLoader (class in langchain.document_loaders)
    module

langchain.agents
langchain.agents.agent_toolkits
langchain.chains
langchain.chat_models
langchain.docstore
langchain.document_loaders
langchain.document_transformers
langchain.embeddings
langchain.llms
langchain.memory
langchain.output_parsers
langchain.prompts
langchain.prompts.example_selector
langchain.python
langchain.retrievers
langchain.retrievers.document_compressors
langchain.serpapi
langchain.text_splitter
langchain.tools
langchain.utilities
langchain.utilities.searx_search
langchain.vectorstores
MomentoChatMessageHistory (class in langchain.memory)
MongoDBChatMessageHistory (class in langchain.memory)
moving_summary_buffer (langchain.memory.ConversationSummaryBufferMemory attribute)
MWDumpLoader (class in langchain.document_loaders)
MyScale (class in langchain.vectorstores)
N
n (langchain.chat_models.ChatGooglePalm attribute)
(langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)


(langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Writer attribute)
n_batch (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
n_ctx (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
n_gpu_layers (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.LlamaCpp attribute)
n_parts (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
n_predict (langchain.llms.GPT4All attribute)
n_threads (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
name (langchain.agents.agent_toolkits.VectorStoreInfo attribute)
(langchain.experimental.GenerativeAgent attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.output_parsers.ResponseSchema attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.ClickTool attribute)
(langchain.tools.CopyFileTool attribute)
(langchain.tools.CurrentWebPageTool attribute)
(langchain.tools.DeleteFileTool attribute)
(langchain.tools.ExtractHyperlinksTool attribute)
(langchain.tools.ExtractTextTool attribute)
(langchain.tools.FileSearchTool attribute)
(langchain.tools.GetElementsTool attribute)
(langchain.tools.GmailCreateDraft attribute)
(langchain.tools.GmailGetMessage attribute)


(langchain.tools.GmailCreateDraft attribute)
(langchain.tools.GmailGetMessage attribute)
(langchain.tools.GmailGetThread attribute)
(langchain.tools.GmailSearch attribute)
(langchain.tools.GmailSendMessage attribute)
(langchain.tools.ListDirectoryTool attribute)
(langchain.tools.MoveFileTool attribute)
(langchain.tools.NavigateBackTool attribute)
(langchain.tools.NavigateTool attribute)
(langchain.tools.ReadFileTool attribute)
(langchain.tools.ShellTool attribute)
(langchain.tools.Tool attribute)
(langchain.tools.WriteFileTool attribute)
nla_tools (langchain.agents.agent_toolkits.NLAToolkit attribute)
NLTKTextSplitter (class in langchain.text_splitter)
no_update_value (langchain.output_parsers.RegexDictParser attribute)
normalize (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)
NotebookLoader (class in langchain.document_loaders)
NotionDBLoader (class in langchain.document_loaders)
NotionDirectoryLoader (class in langchain.document_loaders)
num_beams (langchain.llms.NLPCloud attribute)
num_pad_tokens (langchain.chains.FlareChain attribute)
num_results (langchain.tools.BingSearchResults attribute)
(langchain.tools.DuckDuckGoSearchResults attribute)
(langchain.tools.GoogleSearchResults attribute)
num_return_sequences (langchain.llms.NLPCloud attribute)
numResults (langchain.llms.AI21 attribute)
O
object_ids (langchain.document_loaders.OneDriveLoader attribute)
observation_prefix (langchain.agents.Agent property)
(langchain.agents.ConversationalAgent property)
(langchain.agents.ConversationalChatAgent property)
(langchain.agents.StructuredChatAgent property)
(langchain.agents.ZeroShotAgent property)
ObsidianLoader (class in langchain.document_loaders)


ObsidianLoader (class in langchain.document_loaders)
OnlinePDFLoader (class in langchain.document_loaders)
openai_api_base (langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
openai_api_key (langchain.chains.OpenAIModerationChain attribute)
(langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
openai_api_type (langchain.chat_models.AzureChatOpenAI attribute)
openai_api_version (langchain.chat_models.AzureChatOpenAI attribute)
openai_organization (langchain.chains.OpenAIModerationChain attribute)
(langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
openai_proxy (langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
OpenSearchVectorSearch (class in langchain.vectorstores)
openweathermap_api_key (langchain.utilities.OpenWeatherMapAPIWrapper attribute)
operation_id (langchain.tools.APIOperation attribute)
other_score_keys (langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
OutlookMessageLoader (class in langchain.document_loaders)
output_key (langchain.chains.QAGenerationChain attribute)
(langchain.memory.ConversationStringBufferMemory attribute)
output_key_to_format (langchain.output_parsers.RegexDictParser attribute)
output_keys (langchain.chains.ConstitutionalChain property)
(langchain.chains.FlareChain property)
(langchain.chains.HypotheticalDocumentEmbedder property)
(langchain.chains.QAGenerationChain property)
(langchain.experimental.BabyAGI property)
(langchain.output_parsers.RegexParser attribute)
output_parser (langchain.agents.Agent attribute)
(langchain.agents.ConversationalAgent attribute)


(langchain.agents.ConversationalAgent attribute)
(langchain.agents.ConversationalChatAgent attribute)
(langchain.agents.LLMSingleActionAgent attribute)
(langchain.agents.StructuredChatAgent attribute)
(langchain.agents.ZeroShotAgent attribute)
(langchain.chains.FlareChain attribute)
(langchain.prompts.BasePromptTemplate attribute)
output_variables (langchain.chains.TransformChain attribute)
owm (langchain.utilities.OpenWeatherMapAPIWrapper attribute)
P
p (langchain.llms.Cohere attribute)
page_content_key (langchain.retrievers.RemoteLangChainRetriever attribute)
PagedPDFSplitter (in module langchain.document_loaders)
paginate_request() (langchain.document_loaders.ConfluenceLoader method)
param_mapping (langchain.chains.OpenAPIEndpointChain attribute)
params (langchain.serpapi.SerpAPIWrapper attribute)
(langchain.tools.ZapierNLARunAction attribute)
(langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
(langchain.utilities.SerpAPIWrapper attribute)
params_schema (langchain.tools.ZapierNLARunAction attribute)
parse() (langchain.agents.AgentOutputParser method)
(langchain.output_parsers.CommaSeparatedListOutputParser method)
(langchain.output_parsers.GuardrailsOutputParser method)
(langchain.output_parsers.ListOutputParser method)
(langchain.output_parsers.OutputFixingParser method)
(langchain.output_parsers.PydanticOutputParser method)
(langchain.output_parsers.RegexDictParser method)
(langchain.output_parsers.RegexParser method)
(langchain.output_parsers.RetryOutputParser method)
(langchain.output_parsers.RetryWithErrorOutputParser method)
(langchain.output_parsers.StructuredOutputParser method)


(langchain.output_parsers.StructuredOutputParser method)
parse_filename() (langchain.document_loaders.BlackboardLoader method)
parse_obj() (langchain.tools.OpenAPISpec class method)
parse_sitemap() (langchain.document_loaders.SitemapLoader method)
parse_with_prompt() (langchain.output_parsers.RetryOutputParser method)
(langchain.output_parsers.RetryWithErrorOutputParser method)
parser (langchain.output_parsers.OutputFixingParser attribute)
(langchain.output_parsers.RetryOutputParser attribute)
(langchain.output_parsers.RetryWithErrorOutputParser attribute)
partial() (langchain.prompts.BasePromptTemplate method)
(langchain.prompts.ChatPromptTemplate method)
password (langchain.vectorstores.MyScaleSettings attribute)
patch() (langchain.utilities.TextRequestsWrapper method)
path (langchain.tools.APIOperation attribute)
path_params (langchain.tools.APIOperation property)
pause_to_reflect() (langchain.experimental.GenerativeAgentMemory method)
PDFMinerLoader (class in langchain.document_loaders)
PDFMinerPDFasHTMLLoader (class in langchain.document_loaders)
PDFPlumberLoader (class in langchain.document_loaders)
penalty_alpha_frequency (langchain.llms.RWKV attribute)
penalty_alpha_presence (langchain.llms.RWKV attribute)
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)
pipeline_key (langchain.llms.PipelineAI attribute)
pipeline_kwargs (langchain.llms.HuggingFacePipeline attribute)


pipeline_kwargs (langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.PipelineAI attribute)
pl_tags (langchain.chat_models.PromptLayerChatOpenAI attribute)
plan() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.agents.LLMSingleActionAgent method)
playwright_strict (langchain.tools.ClickTool attribute)
playwright_timeout (langchain.tools.ClickTool attribute)
PlaywrightURLLoader (class in langchain.document_loaders)
plugin (langchain.tools.AIPluginTool attribute)
port (langchain.vectorstores.MyScaleSettings attribute)
post() (langchain.utilities.TextRequestsWrapper method)
PostgresChatMessageHistory (class in langchain.memory)
powerbi (langchain.agents.agent_toolkits.PowerBIToolkit attribute)
(langchain.tools.InfoPowerBITool attribute)
(langchain.tools.ListPowerBITool attribute)
(langchain.tools.QueryPowerBITool attribute)
predict() (langchain.chains.LLMChain method)
(langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.Anyscale method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.CTransformers method)
(langchain.llms.Databricks method)
(langchain.llms.DeepInfra method)
(langchain.llms.FakeListLLM method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)


(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)
(langchain.llms.HuggingFaceTextGenInference method)
(langchain.llms.HumanInputLLM method)
(langchain.llms.LlamaCpp method)
(langchain.llms.Modal method)
(langchain.llms.MosaicML method)
(langchain.llms.NLPCloud method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.OpenLM method)
(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)
(langchain.llms.PredictionGuard method)
(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)
(langchain.llms.VertexAI method)
(langchain.llms.Writer method)
predict_and_parse() (langchain.chains.LLMChain method)
predict_messages() (langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.Anyscale method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam method)
(langchain.llms.CerebriumAI method)


(langchain.llms.Beam method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.CTransformers method)
(langchain.llms.Databricks method)
(langchain.llms.DeepInfra method)
(langchain.llms.FakeListLLM method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)
(langchain.llms.HuggingFaceTextGenInference method)
(langchain.llms.HumanInputLLM method)
(langchain.llms.LlamaCpp method)
(langchain.llms.Modal method)
(langchain.llms.MosaicML method)
(langchain.llms.NLPCloud method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.OpenLM method)
(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)
(langchain.llms.PredictionGuard method)
(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)
(langchain.llms.VertexAI method)
(langchain.llms.Writer method)
prefix (langchain.prompts.FewShotPromptTemplate attribute)


prefix (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
prefix_messages (langchain.llms.OpenAIChat attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
prep_prompts() (langchain.chains.LLMChain method)
prep_streaming_params() (langchain.llms.AzureOpenAI method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
prepare_cosmos() (langchain.memory.CosmosDBChatMessageHistory method)
presence_penalty (langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Writer attribute)
presencePenalty (langchain.llms.AI21 attribute)
prioritize_tasks() (langchain.experimental.BabyAGI method)
process (langchain.tools.ShellTool attribute)
process_attachment() (langchain.document_loaders.ConfluenceLoader method)
process_doc() (langchain.document_loaders.ConfluenceLoader method)
process_image() (langchain.document_loaders.ConfluenceLoader method)
process_index_results() (langchain.vectorstores.Annoy method)
process_output() (langchain.utilities.BashProcess method)
process_page() (langchain.document_loaders.ConfluenceLoader method)
process_pages() (langchain.document_loaders.ConfluenceLoader method)
process_pdf() (langchain.document_loaders.ConfluenceLoader method)
process_svg() (langchain.document_loaders.ConfluenceLoader method)
process_xls() (langchain.document_loaders.ConfluenceLoader method)
project (langchain.llms.VertexAI attribute)


project (langchain.llms.VertexAI attribute)
Prompt (in module langchain.prompts)
prompt (langchain.chains.ConversationChain attribute)
(langchain.chains.LLMBashChain attribute)
(langchain.chains.LLMChain attribute)
(langchain.chains.LLMMathChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
prompt_func (langchain.tools.HumanInputRun attribute)
properties (langchain.tools.APIOperation attribute)
prune() (langchain.memory.ConversationSummaryBufferMemory method)
PsychicLoader (class in langchain.document_loaders)
put() (langchain.utilities.TextRequestsWrapper method)
pydantic_object (langchain.output_parsers.PydanticOutputParser attribute)
PyMuPDFLoader (class in langchain.document_loaders)
PyPDFDirectoryLoader (class in langchain.document_loaders)
PyPDFium2Loader (class in langchain.document_loaders)
PyPDFLoader (class in langchain.document_loaders)
python_globals (langchain.chains.PALChain attribute)
python_locals (langchain.chains.PALChain attribute)
PythonCodeTextSplitter (class in langchain.text_splitter)
PythonLoader (class in langchain.document_loaders)
Q
qa_chain (langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.GraphQAChain attribute)
Qdrant (class in langchain.vectorstores)
query_checker_prompt (langchain.chains.SQLDatabaseChain attribute)
query_instruction (langchain.embeddings.HuggingFaceInstructEmbeddings attribute)
(langchain.embeddings.MosaicMLInstructorEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
query_name (langchain.vectorstores.SupabaseVectorStore attribute)


query_name (langchain.vectorstores.SupabaseVectorStore attribute)
query_params (langchain.tools.APIOperation property)
query_suffix (langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
question_generator_chain (langchain.chains.FlareChain attribute)
question_to_checked_assertions_chain (langchain.chains.LLMCheckerChain attribute)
R
raw_completion (langchain.llms.AlephAlpha attribute)
REACT_DOCSTORE (langchain.agents.AgentType attribute)
ReadTheDocsLoader (class in langchain.document_loaders)
recall_ttl (langchain.memory.RedisEntityStore attribute)
recursive (langchain.document_loaders.GoogleDriveLoader attribute)
RecursiveCharacterTextSplitter (class in langchain.text_splitter)
RedditPostsLoader (class in langchain.document_loaders)
Redis (class in langchain.vectorstores)
redis_client (langchain.memory.RedisEntityStore attribute)
RedisChatMessageHistory (class in langchain.memory)
RedisEntityStore (class in langchain.memory)
reduce_k_below_max_tokens (langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
reflection_threshold (langchain.experimental.GenerativeAgentMemory attribute)
regex (langchain.output_parsers.RegexParser attribute)
regex_pattern (langchain.output_parsers.RegexDictParser attribute)
region (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
region_name (langchain.embeddings.SagemakerEndpointEmbeddings attribute)
(langchain.llms.SagemakerEndpoint attribute)
relevancy_threshold (langchain.retrievers.KNNRetriever attribute)
(langchain.retrievers.SVMRetriever attribute)
remove_end_sequence (langchain.llms.NLPCloud attribute)


remove_end_sequence (langchain.llms.NLPCloud attribute)
remove_input (langchain.llms.NLPCloud attribute)
repeat_last_n (langchain.llms.GPT4All attribute)
repeat_penalty (langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
repetition_penalties_include_completion (langchain.llms.AlephAlpha attribute)
repetition_penalties_include_prompt (langchain.llms.AlephAlpha attribute)
repetition_penalty (langchain.llms.ForefrontAI attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.Writer attribute)
repo_id (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.llms.HuggingFaceHub attribute)
request_body (langchain.tools.APIOperation attribute)
request_timeout (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.OpenAIEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
request_url (langchain.utilities.PowerBIDataset property)
requests (langchain.chains.OpenAPIEndpointChain attribute)
(langchain.utilities.TextRequestsWrapper property)
requests_per_second (langchain.document_loaders.WebBaseLoader attribute)
requests_wrapper (langchain.agents.agent_toolkits.OpenAPIToolkit attribute)
(langchain.chains.APIChain attribute)
(langchain.chains.LLMRequestsChain attribute)
response_chain (langchain.chains.FlareChain attribute)
response_key (langchain.retrievers.RemoteLangChainRetriever attribute)
response_schemas (langchain.output_parsers.StructuredOutputParser attribute)
results() (langchain.serpapi.SerpAPIWrapper method)
(langchain.utilities.BingSearchAPIWrapper method)
(langchain.utilities.DuckDuckGoSearchAPIWrapper method)


(langchain.utilities.DuckDuckGoSearchAPIWrapper method)
(langchain.utilities.GoogleSearchAPIWrapper method)
(langchain.utilities.GoogleSerperAPIWrapper method)
(langchain.utilities.MetaphorSearchAPIWrapper method)
(langchain.utilities.searx_search.SearxSearchWrapper method)
(langchain.utilities.SearxSearchWrapper method)
(langchain.utilities.SerpAPIWrapper method)
results_async() (langchain.utilities.MetaphorSearchAPIWrapper method)
retriever (langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.FlareChain attribute)
(langchain.chains.RetrievalQA attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.memory.VectorStoreRetrieverMemory attribute)
retry_chain (langchain.output_parsers.OutputFixingParser attribute)
(langchain.output_parsers.RetryOutputParser attribute)
(langchain.output_parsers.RetryWithErrorOutputParser attribute)
retry_sleep (langchain.embeddings.MosaicMLInstructorEmbeddings attribute)
(langchain.llms.MosaicML attribute)
return_all (langchain.chains.SequentialChain attribute)
return_direct (langchain.chains.SQLDatabaseChain attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.Tool attribute)
return_docs (langchain.memory.VectorStoreRetrieverMemory attribute)
return_intermediate_steps (langchain.agents.AgentExecutor attribute)
(langchain.chains.ConstitutionalChain attribute)
(langchain.chains.OpenAPIEndpointChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
(langchain.chains.SQLDatabaseSequentialChain attribute)
return_pl_id (langchain.chat_models.PromptLayerChatOpenAI attribute)
return_stopped_response() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)


(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
return_urls (langchain.tools.SteamshipImageGenerationTool attribute)
return_values (langchain.agents.Agent property)
(langchain.agents.BaseMultiActionAgent property)
(langchain.agents.BaseSingleActionAgent property)
revised_answer_prompt (langchain.chains.LLMCheckerChain attribute)
revised_summary_prompt (langchain.chains.LLMSummarizationCheckerChain attribute)
revision_chain (langchain.chains.ConstitutionalChain attribute)
RoamLoader (class in langchain.document_loaders)
root_dir (langchain.agents.agent_toolkits.FileManagementToolkit attribute)
run() (langchain.python.PythonREPL method)
(langchain.serpapi.SerpAPIWrapper method)
(langchain.tools.BaseTool method)
(langchain.utilities.ArxivAPIWrapper method)
(langchain.utilities.BashProcess method)
(langchain.utilities.BingSearchAPIWrapper method)
(langchain.utilities.DuckDuckGoSearchAPIWrapper method)
(langchain.utilities.GooglePlacesAPIWrapper method)
(langchain.utilities.GoogleSearchAPIWrapper method)
(langchain.utilities.GoogleSerperAPIWrapper method)
(langchain.utilities.GraphQLAPIWrapper method)
(langchain.utilities.LambdaWrapper method)
(langchain.utilities.OpenWeatherMapAPIWrapper method)
(langchain.utilities.PowerBIDataset method)
(langchain.utilities.PythonREPL method)
(langchain.utilities.searx_search.SearxSearchWrapper method)
(langchain.utilities.SearxSearchWrapper method)
(langchain.utilities.SerpAPIWrapper method)
(langchain.utilities.SparkSQL method)
(langchain.utilities.TwilioAPIWrapper method)
(langchain.utilities.WikipediaAPIWrapper method)
(langchain.utilities.WolframAlphaAPIWrapper method)
run_creation() (langchain.llms.Beam method)


run_creation() (langchain.llms.Beam method)
run_no_throw() (langchain.utilities.SparkSQL method)
rwkv_verbose (langchain.llms.RWKV attribute)
S
S3DirectoryLoader (class in langchain.document_loaders)
S3FileLoader (class in langchain.document_loaders)
safesearch (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
sample_rows_in_table_info (langchain.utilities.PowerBIDataset attribute)
save() (langchain.agents.AgentExecutor method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.Anyscale method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.CTransformers method)
(langchain.llms.Databricks method)
(langchain.llms.DeepInfra method)
(langchain.llms.FakeListLLM method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)
(langchain.llms.HuggingFaceTextGenInference method)
(langchain.llms.HumanInputLLM method)
(langchain.llms.LlamaCpp method)
(langchain.llms.Modal method)
(langchain.llms.MosaicML method)


(langchain.llms.Modal method)
(langchain.llms.MosaicML method)
(langchain.llms.NLPCloud method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.OpenLM method)
(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)
(langchain.llms.PredictionGuard method)
(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)
(langchain.llms.VertexAI method)
(langchain.llms.Writer method)
(langchain.prompts.BasePromptTemplate method)
(langchain.prompts.ChatPromptTemplate method)
save_agent() (langchain.agents.AgentExecutor method)
save_context() (langchain.experimental.GenerativeAgentMemory method)
(langchain.memory.CombinedMemory method)
(langchain.memory.ConversationEntityMemory method)
(langchain.memory.ConversationKGMemory method)
(langchain.memory.ConversationStringBufferMemory method)
(langchain.memory.ConversationSummaryBufferMemory method)
(langchain.memory.ConversationSummaryMemory method)
(langchain.memory.ConversationTokenBufferMemory method)
(langchain.memory.ReadOnlySharedMemory method)
(langchain.memory.SimpleMemory method)
(langchain.memory.VectorStoreRetrieverMemory method)
save_local() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.FAISS method)
schemas (langchain.utilities.PowerBIDataset attribute)
scrape() (langchain.document_loaders.WebBaseLoader method)


scrape() (langchain.document_loaders.WebBaseLoader method)
scrape_all() (langchain.document_loaders.WebBaseLoader method)
scrape_page() (langchain.tools.ExtractHyperlinksTool static method)
search() (langchain.docstore.InMemoryDocstore method)
(langchain.docstore.Wikipedia method)
(langchain.vectorstores.VectorStore method)
search_kwargs (langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
search_type (langchain.chains.VectorDBQA attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
searx_host (langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
SearxResults (class in langchain.utilities.searx_search)
seed (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
select_examples() (langchain.prompts.example_selector.LengthBasedExampleSelector method)
(langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector method)
(langchain.prompts.example_selector.SemanticSimilarityExampleSelector method)
selected_tools (langchain.agents.agent_toolkits.FileManagementToolkit attribute)
SeleniumURLLoader (class in langchain.document_loaders)
SELF_ASK_WITH_SEARCH (langchain.agents.AgentType attribute)
send_pdf() (langchain.document_loaders.MathpixPDFLoader method)
SentenceTransformerEmbeddings (in module langchain.embeddings)
sequential_chain (langchain.chains.LLMSummarizationCheckerChain attribute)


sequential_chain (langchain.chains.LLMSummarizationCheckerChain attribute)
serpapi_api_key (langchain.serpapi.SerpAPIWrapper attribute)
(langchain.utilities.SerpAPIWrapper attribute)
serper_api_key (langchain.utilities.GoogleSerperAPIWrapper attribute)
service_account_key (langchain.document_loaders.GoogleDriveLoader attribute)
service_account_path (langchain.document_loaders.GoogleApiClient attribute)
service_name (langchain.retrievers.AzureCognitiveSearchRetriever attribute)
session_cache (langchain.tools.QueryPowerBITool attribute)
session_id (langchain.memory.RedisEntityStore attribute)
set() (langchain.memory.InMemoryEntityStore method)
(langchain.memory.RedisEntityStore method)
settings (langchain.document_loaders.OneDriveLoader attribute)
similarity_fn (langchain.document_transformers.EmbeddingsRedundantFilter attribute)
(langchain.retrievers.document_compressors.EmbeddingsFilter attribute)
similarity_search() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.AtlasDB method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.LanceDB method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.Tair method)
(langchain.vectorstores.Typesense method)
(langchain.vectorstores.Vectara method)
(langchain.vectorstores.VectorStore method)


(langchain.vectorstores.Vectara method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
similarity_search_by_index() (langchain.vectorstores.Annoy method)
similarity_search_by_text() (langchain.vectorstores.Weaviate method)
similarity_search_by_vector() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
similarity_search_by_vector_returning_embeddings() (langchain.vectorstores.SupabaseVectorStore method)
similarity_search_by_vector_with_relevance_scores() (langchain.vectorstores.SupabaseVectorStore method)
similarity_search_limit_score() (langchain.vectorstores.Redis method)
similarity_search_with_relevance_scores() (langchain.vectorstores.MyScale method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
similarity_search_with_score() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.Typesense method)


(langchain.vectorstores.Redis method)
(langchain.vectorstores.Typesense method)
(langchain.vectorstores.Vectara method)
(langchain.vectorstores.Weaviate method)
similarity_search_with_score_by_index() (langchain.vectorstores.Annoy method)
similarity_search_with_score_by_vector() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
similarity_threshold (langchain.document_transformers.EmbeddingsRedundantFilter attribute)
(langchain.retrievers.document_compressors.EmbeddingsFilter attribute)
SitemapLoader (class in langchain.document_loaders)
siterestrict (langchain.utilities.GoogleSearchAPIWrapper attribute)
size (langchain.tools.SteamshipImageGenerationTool attribute)
SlackDirectoryLoader (class in langchain.document_loaders)
SpacyTextSplitter (class in langchain.text_splitter)
SparkSQL (class in langchain.utilities)
sparse_encoder (langchain.retrievers.PineconeHybridSearchRetriever attribute)
spec (langchain.agents.agent_toolkits.JsonToolkit attribute)
split_documents() (langchain.text_splitter.TextSplitter method)
split_text() (langchain.text_splitter.CharacterTextSplitter method)
(langchain.text_splitter.NLTKTextSplitter method)
(langchain.text_splitter.RecursiveCharacterTextSplitter method)
(langchain.text_splitter.SpacyTextSplitter method)
(langchain.text_splitter.TextSplitter method)
(langchain.text_splitter.TokenTextSplitter method)
SpreedlyLoader (class in langchain.document_loaders)
sql_chain (langchain.chains.SQLDatabaseSequentialChain attribute)
SRTLoader (class in langchain.document_loaders)


SRTLoader (class in langchain.document_loaders)
start_with_retrieval (langchain.chains.FlareChain attribute)
status (langchain.experimental.GenerativeAgent attribute)
steamship (langchain.tools.SteamshipImageGenerationTool attribute)
stop (langchain.agents.LLMSingleActionAgent attribute)
(langchain.chains.PALChain attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.Writer attribute)
stop_sequences (langchain.llms.AlephAlpha attribute)
store (langchain.memory.InMemoryEntityStore attribute)
strategy (langchain.llms.RWKV attribute)
stream() (langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.LlamaCpp method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
streaming (langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
strip_outputs (langchain.chains.SimpleSequentialChain attribute)
StripeLoader (class in langchain.document_loaders)
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
structured_query_translator (langchain.retrievers.SelfQueryRetriever attribute)
suffix (langchain.llms.LlamaCpp attribute)
(langchain.prompts.FewShotPromptTemplate attribute)


(langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
summarize_related_memories() (langchain.experimental.GenerativeAgent method)
summary (langchain.experimental.GenerativeAgent attribute)
summary_message_cls (langchain.memory.ConversationKGMemory attribute)
summary_refresh_seconds (langchain.experimental.GenerativeAgent attribute)
SupabaseVectorStore (class in langchain.vectorstores)
sync_browser (langchain.agents.agent_toolkits.PlayWrightBrowserToolkit attribute)
T
table (langchain.vectorstores.MyScaleSettings attribute)
table_info (langchain.utilities.PowerBIDataset property)
table_name (langchain.vectorstores.SupabaseVectorStore attribute)
table_names (langchain.utilities.PowerBIDataset attribute)
Tair (class in langchain.vectorstores)
task (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
tbs (langchain.utilities.GoogleSerperAPIWrapper attribute)
TelegramChatApiLoader (class in langchain.document_loaders)
TelegramChatFileLoader (class in langchain.document_loaders)
TelegramChatLoader (in module langchain.document_loaders)
temp (langchain.llms.GPT4All attribute)
temperature (langchain.chat_models.ChatGooglePalm attribute)
(langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)


(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
template (langchain.prompts.PromptTemplate attribute)
(langchain.tools.QueryPowerBITool attribute)
template_format (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
template_tool_response (langchain.agents.ConversationalChatAgent attribute)
text_length (langchain.chains.LLMRequestsChain attribute)
text_splitter (langchain.chains.AnalyzeDocumentChain attribute)
(langchain.chains.MapReduceChain attribute)
(langchain.chains.QAGenerationChain attribute)
TextLoader (class in langchain.document_loaders)
texts (langchain.retrievers.KNNRetriever attribute)
(langchain.retrievers.SVMRetriever attribute)
TextSplitter (class in langchain.text_splitter)
tfidf_array (langchain.retrievers.TFIDFRetriever attribute)
time (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
to_typescript() (langchain.tools.APIOperation method)
token (langchain.utilities.PowerBIDataset attribute)
token_path (langchain.document_loaders.GoogleApiClient attribute)
(langchain.document_loaders.GoogleDriveLoader attribute)
tokenizer (langchain.llms.Petals attribute)


tokenizer (langchain.llms.Petals attribute)
tokens (langchain.llms.AlephAlpha attribute)
tokens_path (langchain.llms.RWKV attribute)
TokenTextSplitter (class in langchain.text_splitter)
ToMarkdownLoader (class in langchain.document_loaders)
TomlLoader (class in langchain.document_loaders)
tool() (in module langchain.agents)
(in module langchain.tools)
tool_run_logging_kwargs() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.agents.LLMSingleActionAgent method)
tools (langchain.agents.agent_toolkits.JiraToolkit attribute)
(langchain.agents.agent_toolkits.ZapierToolkit attribute)
(langchain.agents.AgentExecutor attribute)
top_k (langchain.chains.SQLDatabaseChain attribute)
(langchain.chat_models.ChatGooglePalm attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.Petals attribute)
(langchain.llms.VertexAI attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.DataberryRetriever attribute)
(langchain.retrievers.PineconeHybridSearchRetriever attribute)
top_k_docs_for_context (langchain.chains.ChatVectorDBChain attribute)
top_k_results (langchain.utilities.ArxivAPIWrapper attribute)
(langchain.utilities.GooglePlacesAPIWrapper attribute)
(langchain.utilities.WikipediaAPIWrapper attribute)


(langchain.utilities.GooglePlacesAPIWrapper attribute)
(langchain.utilities.WikipediaAPIWrapper attribute)
top_n (langchain.retrievers.document_compressors.CohereRerank attribute)
top_p (langchain.chat_models.ChatGooglePalm attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
topP (langchain.llms.AI21 attribute)
traits (langchain.experimental.GenerativeAgent attribute)
transform (langchain.chains.TransformChain attribute)
transform_documents() (langchain.document_transformers.EmbeddingsRedundantFilter method)
(langchain.text_splitter.TextSplitter method)
transform_input_fn (langchain.llms.Databricks attribute)
transform_output_fn (langchain.llms.Databricks attribute)
transformers (langchain.retrievers.document_compressors.DocumentCompressorPipeline attribute)
truncate (langchain.embeddings.CohereEmbeddings attribute)
(langchain.llms.Cohere attribute)
ts_type_from_python() (langchain.tools.APIOperation static method)
ttl (langchain.memory.RedisEntityStore attribute)
tuned_model_name (langchain.llms.VertexAI attribute)
TwitterTweetLoader (class in langchain.document_loaders)
type (langchain.utilities.GoogleSerperAPIWrapper attribute)


type (langchain.utilities.GoogleSerperAPIWrapper attribute)
Typesense (class in langchain.vectorstores)
U
unsecure (langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
UnstructuredAPIFileIOLoader (class in langchain.document_loaders)
UnstructuredAPIFileLoader (class in langchain.document_loaders)
UnstructuredEmailLoader (class in langchain.document_loaders)
UnstructuredEPubLoader (class in langchain.document_loaders)
UnstructuredFileIOLoader (class in langchain.document_loaders)
UnstructuredFileLoader (class in langchain.document_loaders)
UnstructuredHTMLLoader (class in langchain.document_loaders)
UnstructuredImageLoader (class in langchain.document_loaders)
UnstructuredMarkdownLoader (class in langchain.document_loaders)
UnstructuredODTLoader (class in langchain.document_loaders)
UnstructuredPDFLoader (class in langchain.document_loaders)
UnstructuredPowerPointLoader (class in langchain.document_loaders)
UnstructuredRTFLoader (class in langchain.document_loaders)
UnstructuredURLLoader (class in langchain.document_loaders)
UnstructuredWordDocumentLoader (class in langchain.document_loaders)
update_document() (langchain.vectorstores.Chroma method)
update_forward_refs() (langchain.llms.AI21 class method)
(langchain.llms.AlephAlpha class method)
(langchain.llms.Anthropic class method)
(langchain.llms.Anyscale class method)
(langchain.llms.AzureOpenAI class method)
(langchain.llms.Banana class method)
(langchain.llms.Beam class method)
(langchain.llms.CerebriumAI class method)
(langchain.llms.Cohere class method)


(langchain.llms.Cohere class method)
(langchain.llms.CTransformers class method)
(langchain.llms.Databricks class method)
(langchain.llms.DeepInfra class method)
(langchain.llms.FakeListLLM class method)
(langchain.llms.ForefrontAI class method)
(langchain.llms.GooglePalm class method)
(langchain.llms.GooseAI class method)
(langchain.llms.GPT4All class method)
(langchain.llms.HuggingFaceEndpoint class method)
(langchain.llms.HuggingFaceHub class method)
(langchain.llms.HuggingFacePipeline class method)
(langchain.llms.HuggingFaceTextGenInference class method)
(langchain.llms.HumanInputLLM class method)
(langchain.llms.LlamaCpp class method)
(langchain.llms.Modal class method)
(langchain.llms.MosaicML class method)
(langchain.llms.NLPCloud class method)
(langchain.llms.OpenAI class method)
(langchain.llms.OpenAIChat class method)
(langchain.llms.OpenLM class method)
(langchain.llms.Petals class method)
(langchain.llms.PipelineAI class method)
(langchain.llms.PredictionGuard class method)
(langchain.llms.PromptLayerOpenAI class method)
(langchain.llms.PromptLayerOpenAIChat class method)
(langchain.llms.Replicate class method)
(langchain.llms.RWKV class method)
(langchain.llms.SagemakerEndpoint class method)
(langchain.llms.SelfHostedHuggingFaceLLM class method)
(langchain.llms.SelfHostedPipeline class method)
(langchain.llms.StochasticAI class method)
(langchain.llms.VertexAI class method)
(langchain.llms.Writer class method)


(langchain.llms.VertexAI class method)
(langchain.llms.Writer class method)
upsert_messages() (langchain.memory.CosmosDBChatMessageHistory method)
url (langchain.document_loaders.MathpixPDFLoader property)
(langchain.llms.Beam attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.RemoteLangChainRetriever attribute)
(langchain.tools.IFTTTWebhook attribute)
urls (langchain.document_loaders.PlaywrightURLLoader attribute)
(langchain.document_loaders.SeleniumURLLoader attribute)
use_mlock (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
use_mmap (langchain.llms.LlamaCpp attribute)
use_multiplicative_presence_penalty (langchain.llms.AlephAlpha attribute)
use_query_checker (langchain.chains.SQLDatabaseChain attribute)
username (langchain.vectorstores.MyScaleSettings attribute)
V
validate_channel_or_videoIds_is_set() (langchain.document_loaders.GoogleApiClient class method)
(langchain.document_loaders.GoogleApiYoutubeLoader class method)
validate_init_args() (langchain.document_loaders.ConfluenceLoader static method)
validate_template (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
Vectara (class in langchain.vectorstores)
vectorizer (langchain.retrievers.TFIDFRetriever attribute)
VectorStore (class in langchain.vectorstores)
vectorstore (langchain.agents.agent_toolkits.VectorStoreInfo attribute)
(langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)


(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
vectorstore_info (langchain.agents.agent_toolkits.VectorStoreToolkit attribute)
vectorstores (langchain.agents.agent_toolkits.VectorStoreRouterToolkit attribute)
verbose (langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Beam attribute)
(langchain.llms.CerebriumAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.CTransformers attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.DeepInfra attribute)
(langchain.llms.FakeListLLM attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.HuggingFaceTextGenInference attribute)
(langchain.llms.HumanInputLLM attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.Modal attribute)
(langchain.llms.MosaicML attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)


(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PipelineAI attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.Replicate attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
(langchain.llms.StochasticAI attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.Tool attribute)
VespaRetriever (class in langchain.retrievers)
video_ids (langchain.document_loaders.GoogleApiYoutubeLoader attribute)
visible_only (langchain.tools.ClickTool attribute)
vocab_only (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
W
wait_for_processing() (langchain.document_loaders.MathpixPDFLoader method)
WeatherDataLoader (class in langchain.document_loaders)
Weaviate (class in langchain.vectorstores)
WeaviateHybridSearchRetriever (class in langchain.retrievers)
WeaviateHybridSearchRetriever.Config (class in langchain.retrievers)
web_path (langchain.document_loaders.WebBaseLoader property)
web_paths (langchain.document_loaders.WebBaseLoader attribute)
WebBaseLoader (class in langchain.document_loaders)
WhatsAppChatLoader (class in langchain.document_loaders)
Wikipedia (class in langchain.docstore)


Wikipedia (class in langchain.docstore)
WikipediaLoader (class in langchain.document_loaders)
wolfram_alpha_appid (langchain.utilities.WolframAlphaAPIWrapper attribute)
writer_api_key (langchain.llms.Writer attribute)
writer_org_id (langchain.llms.Writer attribute)
Y
YoutubeLoader (class in langchain.document_loaders)
Z
zapier_description (langchain.tools.ZapierNLARunAction attribute)
ZepRetriever (class in langchain.retrievers)
ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
Zilliz (class in langchain.vectorstores)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Memory
Memory#
Note
Conceptual Guide
By default, Chains and Agents are stateless,
meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
In some applications (chatbots being a GREAT example) it is highly important
to remember previous interactions, both at a short term but also at a long term level.
The concept of “Memory” exists to do exactly that.
LangChain provides memory components in two forms.
First, LangChain provides helper utilities for managing and manipulating previous chat messages.
These are designed to be modular and useful regardless of how they are used.
Secondly, LangChain provides easy ways to incorporate these utilities into chains.
The following sections of documentation are provided:
Getting Started: An overview of how to get started with different types of memory.
How-To Guides: A collection of how-to guides. These highlight different types of memory, as well as how to use memory in chains.
Memory
Getting Started
How-To Guides
previous
Structured Output Parser
next
Getting Started
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Indexes
 Contents
Go Deeper
Indexes#
Note
Conceptual Guide
Indexes refer to ways to structure documents so that LLMs can best interact with them.
This module contains utility functions for working with documents, different types of indexes, and then examples for using those indexes in chains.
The most common way that indexes are used in chains is in a “retrieval” step.
This step refers to taking a user’s query and returning the most relevant documents.
We draw this distinction because (1) an index can be used for other things besides retrieval, and (2) retrieval can use other logic besides an index to find relevant documents.
We therefore have a concept of a “Retriever” interface - this is the interface that most chains work with.
Most of the time when we talk about indexes and retrieval we are talking about indexing and retrieving unstructured data (like text documents).
For interacting with structured data (SQL tables, etc) or APIs, please see the corresponding use case sections for links to relevant functionality.
The primary index and retrieval types supported by LangChain are currently centered around vector databases, and therefore
a lot of the functionality we dive deep on those topics.
For an overview of everything related to this, please see the below notebook for getting started:
Getting Started
We then provide a deep dive on the four main components.
Document Loaders
How to load documents from a variety of sources.
Text Splitters
An overview of the abstractions and implementions around splitting text.
VectorStores
An overview of VectorStores and the many integrations LangChain provides.
Retrievers
An overview of Retrievers and the implementations LangChain provides.
Go Deeper#
Document Loaders
Text Splitters
Vectorstores
Retrievers
previous
Zep Memory
next
Getting Started
 Contents

Go Deeper


previous
Zep Memory
next
Getting Started
 Contents

Go Deeper
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Prompts
 Contents
Getting Started
Go Deeper
Prompts#
Note
Conceptual Guide
The new way of programming models is through prompts.
A “prompt” refers to the input to the model.
This input is rarely hard coded, but rather is often constructed from multiple components.
A PromptTemplate is responsible for the construction of this input.
LangChain provides several classes and functions to make constructing and working with prompts easy.
This section of documentation is split into four sections:
LLM Prompt Templates
How to use PromptTemplates to prompt Language Models.
Chat Prompt Templates
How to use PromptTemplates to prompt Chat Models.
Example Selectors
Often times it is useful to include examples in prompts.
These examples can be hardcoded, but it is often more powerful if they are dynamically selected.
This section goes over example selection.
Output Parsers
Language models (and Chat 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 responsible for (1) instructing the model how output should be formatted,
(2) parsing output into the desired formatting (including retrying if necessary).
Getting Started#
Getting Started
Go Deeper#
Prompt Templates
Chat Prompt Template
Example Selectors
Output Parsers
previous
TensorflowHub
next
Getting Started
 Contents

Getting Started
Go Deeper
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Models
 Contents
Getting Started
Go Deeper
Models#
Note
Conceptual Guide
This section of the documentation deals with different types of models that are used in LangChain.
On this page we will go over the model types at a high level,
but we have individual pages for each model type.
The pages contain more detailed “how-to” guides for working with that model,
as well as a list of different model providers.
LLMs
Large Language Models (LLMs) are the first type of models we cover.
These models take a text string as input, and return a text string as output.
Chat Models
Chat Models are the second type of models we cover.
These models are usually backed by a language model, but their APIs are more structured.
Specifically, these models take a list of Chat Messages as input, and return a Chat Message.
Text Embedding Models
The third type of models we cover are text embedding models.
These models take text as input and return a list of floats.
Getting Started#
Getting Started
Go Deeper#
LLMs
Chat Models
Text Embedding Models
previous
Tutorials
next
Getting Started
 Contents

Getting Started
Go Deeper
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Chains
Chains#
Note
Conceptual Guide
Using an LLM in isolation is fine for some simple applications,
but many more complex ones require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of use.
The following sections of documentation are provided:
Getting Started: A getting started guide for chains, to get you up and running quickly.
How-To Guides: A collection of how-to guides. These highlight how to use various types of chains.
Reference: API reference documentation for all Chain classes.
previous
Zep Memory
next
Getting Started
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Agents
 Contents
Action Agents
Plan-and-Execute Agents
Agents#
Note
Conceptual Guide
Some applications will require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the user’s input.
In these types of chains, there is a “agent” which has access to a suite of tools.
Depending on the user input, the agent can then decide which, if any, of these tools to call.
At the moment, there are two main types of agents:
“Action Agents”: these agents decide an action to take and take that action one step at a time
“Plan-and-Execute Agents”: these agents first decide a plan of actions to take, and then execute those actions one at a time.
When should you use each one? Action Agents are more conventional, and good for small tasks.
For more complex or long running tasks, the initial planning step helps to maintain long term objectives and focus. However, that comes at the expense of generally more calls and higher latency.
These two agents are also not mutually exclusive - in fact, it is often best to have an Action Agent be in charge of the execution for the Plan and Execute agent.
Action Agents#
High level pseudocode of agents looks something like:
Some user input is received
The agent decides which tool - if any - to use, and what the input to that tool should be
That tool is then called with that tool input, and an observation is recorded (this is just the output of calling that tool with that tool input)
That history of tool, tool input, and observation is passed back into the agent, and it decides what step to take next
This is repeated until the agent decides it no longer needs to use a tool, and then it responds directly to the user.
The different abstractions involved in agents are as follows:


The different abstractions involved in agents are as follows:
Agent: this is where the logic of the application lives. Agents expose an interface that takes in user input along with a list of previous steps the agent has taken, and returns either an AgentAction or AgentFinish
AgentAction corresponds to the tool to use and the input to that tool
AgentFinish means the agent is done, and has information around what to return to the user
Tools: these are the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
Toolkits: these are groups of tools designed for a specific use case. For example, in order for an agent to interact with a SQL database in the best way it may need access to one tool to execute queries and another tool to inspect tables.
Agent Executor: this wraps an agent and a list of tools. This is responsible for the loop of running the agent iteratively until the stopping criteria is met.
The most important abstraction of the four above to understand is that of the agent.
Although an agent can be defined in whatever way one chooses, the typical way to construct an agent is with:
PromptTemplate: this is responsible for taking the user input and previous steps and constructing a prompt to send to the language model
Language Model: this takes the prompt constructed by the PromptTemplate and returns some output
Output Parser: this takes the output of the Language Model and parses it into an AgentAction or AgentFinish object.
In this section of documentation, we first start with a Getting Started notebook to cover how to use all things related to agents in an end-to-end manner.
We then split the documentation into the following sections:
Tools
In this section we cover the different types of tools LangChain supports natively.
We then cover how to add your own tools.
Agents
In this section we cover the different types of agents LangChain supports natively.


Agents
In this section we cover the different types of agents LangChain supports natively.
We then cover how to modify and create your own agents.
Toolkits
In this section we go over the various toolkits that LangChain supports out of the box,
and how to create an agent from them.
Agent Executor
In this section we go over the Agent Executor class, which is responsible for calling
the agent and tools in a loop. We go over different ways to customize this, and options you
can use for more control.
Go Deeper
Tools
Agents
Toolkits
Agent Executors
Plan-and-Execute Agents#
High level pseudocode of agents looks something like:
Some user input is received
The planner lists out the steps to take
The executor goes through the list of steps, executing them
The most typical implementation is to have the planner be a language model,
and the executor be an action agent.
Go Deeper
Plan and Execute
Imports
Tools
Planner, Executor, and Agent
Run Example
previous
Chains
next
Getting Started
 Contents

Action Agents
Plan-and-Execute Agents
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.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 interactions with a language model are captured in the concept of ChatMessages, so this boils down to ingesting, capturing, transforming and extracting knowledge from a sequence of chat messages. There are many different ways to do this, each of which exists as its own memory type.
In general, for each type of memory there are two ways to understanding using memory. These are the standalone functions which extract information from a sequence of messages, and then there is the way you can use this type of memory in a chain.
Memory can return multiple pieces of information (for example, the most recent N messages and a summary of all previous messages). The returned information can either be a string or a list of messages.
In this notebook, we will walk through the simplest form of memory: “buffer” memory, which just involves keeping a buffer of all prior messages. We will show how to use the modular utility functions here, then show how it can be used in a chain (both returning a string as well as a list of messages).
ChatMessageHistory#
One of the core utility classes underpinning most (if not all) memory modules is the ChatMessageHistory class. This is a super lightweight wrapper which exposes convenience methods for saving Human messages, AI messages, and then fetching them all.
You may want to use this class directly if you are managing memory outside of a chain.
from langchain.memory import ChatMessageHistory
history = ChatMessageHistory()
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
[HumanMessage(content='hi!', additional_kwargs={}),


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 messages in a variable.
We can first extract it as a string.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")
memory.load_memory_variables({})
{'history': 'Human: hi!\nAI: whats up?'}
We can also get the history as a list of messages
memory = ConversationBufferMemory(return_messages=True)
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")
memory.load_memory_variables({})
{'history': [HumanMessage(content='hi!', additional_kwargs={}),
  AIMessage(content='whats up?', additional_kwargs={})]}
Using in a chain#
Finally, let’s take a look at using this in a chain (setting verbose=True so we can see the prompt).
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
llm = OpenAI(temperature=0)
conversation = ConversationChain(
    llm=llm,
    verbose=True,
    memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")
> 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:
Human: Hi there!
AI:


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 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:
Human: Hi there!
AI:  Hi there! It's nice to meet you. How can I help you today?
Human: I'm doing well! Just having a conversation with an AI.
AI:
> Finished chain.
" That's great! It's always nice to have a conversation with someone new. What would you like to talk about?"
conversation.predict(input="Tell me about yourself.")
> 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:
Human: Hi there!
AI:  Hi there! It's nice to meet you. How can I help you today?
Human: I'm doing well! Just having a conversation with an AI.
AI:  That's great! It's always nice to have a conversation with someone new. What would you like to talk about?
Human: Tell me about yourself.
AI:
> Finished chain.


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 Message History#
You may often have to save messages, and then load them to use again. This can be done easily by first converting the messages to normal python dictionaries, saving those (as json or something) and then loading those. Here is an example of doing that.
import json
from langchain.memory import ChatMessageHistory
from langchain.schema import messages_from_dict, messages_to_dict
history = ChatMessageHistory()
history.add_user_message("hi!")
history.add_ai_message("whats up?")
dicts = messages_to_dict(history.messages)
dicts
[{'type': 'human', 'data': {'content': 'hi!', 'additional_kwargs': {}}},
 {'type': 'ai', 'data': {'content': 'whats up?', 'additional_kwargs': {}}}]
new_messages = messages_from_dict(dicts)
new_messages
[HumanMessage(content='hi!', additional_kwargs={}),
 AIMessage(content='whats up?', additional_kwargs={})]
And that’s it for the getting started! There are plenty of different types of memory, check out our examples to see them all
previous
Memory
next
How-To Guides
 Contents

ChatMessageHistory
ConversationBufferMemory
Using in a chain
Saving Message History
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.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
ConversationTokenBufferMemory
VectorStore-Backed Memory
Usage#
The examples here all highlight how to use memory in different ways.
How to add Memory to an LLMChain
How to add memory to a Multi-Input Chain
How to add Memory to an Agent
Adding Message Memory backed by a database to an Agent
Cassandra Chat Message History
How to customize conversational memory
How to create a custom Memory class
Dynamodb Chat Message History
Momento
Mongodb Chat Message History
Motörhead Memory
How to use multiple memory classes in the same chain
Postgres Chat Message History
Redis Chat Message History
Zep Memory
previous
Getting Started
next
ConversationBufferMemory
 Contents

Types
Usage
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.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).
Let’s first walk through using this functionality.
from langchain.llms import OpenAI
from langchain.memory import ConversationEntityMemory
llm = OpenAI(temperature=0)
memory = ConversationEntityMemory(llm=llm)
_input = {"input": "Deven & Sam are working on a hackathon project"}
memory.load_memory_variables(_input)
memory.save_context(
    _input,
    {"output": " That sounds like a great project! What kind of project are they working on?"}
)
memory.load_memory_variables({"input": 'who is Sam'})
{'history': 'Human: Deven & Sam are working on a hackathon project\nAI:  That sounds like a great project! What kind of project are they working on?',
 'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}
memory = ConversationEntityMemory(llm=llm, return_messages=True)
_input = {"input": "Deven & Sam are working on a hackathon project"}
memory.load_memory_variables(_input)
memory.save_context(
    _input,
    {"output": " That sounds like a great project! What kind of project are they working on?"}
)
memory.load_memory_variables({"input": 'who is Sam'})
{'history': [HumanMessage(content='Deven & Sam are working on a hackathon project', additional_kwargs={}),
  AIMessage(content=' That sounds like a great project! What kind of project are they working on?', additional_kwargs={})],


'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 BaseModel
from typing import List, Dict, Any
conversation = ConversationChain(
    llm=llm,
    verbose=True,
    prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
    memory=ConversationEntityMemory(llm=llm)
)
conversation.predict(input="Deven & Sam are working on a hackathon project")
> Entering new ConversationChain chain...
Prompt after formatting:
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 text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
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 human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.


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 working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.'}
Current conversation:
Last line:
Human: Deven & Sam are working on a hackathon project
You:
> Finished chain.
' That sounds like a great project! What kind of project are they working on?'
conversation.memory.entity_store.store
{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon.',
 'Sam': 'Sam is working on a hackathon project with Deven.'}
conversation.predict(input="They are trying to add more complex memory structures to Langchain")
> Entering new ConversationChain chain...
Prompt after formatting:
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 text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.


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 human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.
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 working on a hackathon project with Sam, which they are entering into a hackathon.', 'Sam': 'Sam is working on a hackathon project with Deven.', 'Langchain': ''}
Current conversation:
Human: Deven & Sam are working on a hackathon project
AI:  That sounds like a great project! What kind of project are they working on?
Last line:
Human: They are trying to add more complex memory structures to Langchain
You:
> Finished chain.
' That sounds like an interesting project! What kind of memory structures are they trying to add?'
conversation.predict(input="They are adding in a key-value store for entities mentioned so far in the conversation.")
> Entering new ConversationChain chain...
Prompt after formatting:
You are an assistant to a human, powered by a large language model trained by OpenAI.


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 text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
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 human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.
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 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.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain.', 'Langchain': 'Langchain is a project that is trying to add more complex memory structures.', 'Key-Value Store': ''}
Current conversation:
Human: Deven & Sam are working on a hackathon project
AI:  That sounds like a great project! What kind of project are they working on?


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 entities mentioned so far in the conversation.
You:
> Finished chain.
' That sounds like a great idea! How will the key-value store help with the project?'
conversation.predict(input="What do you know about Deven & Sam?")
> Entering new ConversationChain chain...
Prompt after formatting:
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 text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
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 human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.
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:


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, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}
Current conversation:
Human: Deven & Sam are working on a hackathon project
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?
Human: They are adding in a key-value store for entities mentioned so far in the conversation.
AI:  That sounds like a great idea! How will the key-value store help with the project?
Last line:
Human: What do you know about Deven & Sam?
You:
> Finished chain.
' 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 the key-value store can help.'
Inspecting the memory store#
We can also inspect the memory store directly. In the following examaples, we look at it directly, and then go through some examples of adding information and watch how it changes.
from pprint import pprint
pprint(conversation.memory.entity_store.store)
{'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.',


{'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 '
          'entities mentioned so far in the conversation, and seem to be '
          'working hard on this project with a great idea for how the '
          'key-value store can help.',
 'Key-Value Store': 'A key-value store is being added to the project to store '
                    'entities mentioned in the conversation.',
 'Langchain': 'Langchain is a project that is trying to add more complex '
              '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 the conversation. They seem to have '
        'a great idea for how the key-value store can help, and Sam is also '
        'the founder of a company called Daimon.'}
conversation.predict(input="Sam is the founder of a company called Daimon.")
> Entering new ConversationChain chain...
Prompt after formatting:
You are an assistant to a human, powered by a large language model trained by OpenAI.


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 text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
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 human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.
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:
{'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.', '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 the conversation. They seem to have a great idea for how the key-value store can help, and Sam is also the founder of a company called Daimon.'}
Current conversation:
Human: They are adding in a key-value store for entities mentioned so far in the conversation.
AI:  That sounds like a great idea! How will the key-value store help with the project?


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 the key-value store can help.
Human: Sam is the founder of a company called Daimon.
AI:
That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?
Last line:
Human: Sam is the founder of a company called Daimon.
You:
> Finished chain.
" That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?"
from pprint import pprint
pprint(conversation.memory.entity_store.store)
{'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur, who '
           'is working on a hackathon project with Deven to add more complex '
           'memory structures to Langchain.',
 '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 for how the '
          'key-value store can help.',
 'Key-Value Store': 'A key-value store is being added to the project to store '
                    'entities mentioned in the conversation.',
 'Langchain': 'Langchain is a project that is trying to add more complex '
              'memory structures, including a key-value store for entities '