id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 114 |
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1d550228d9cb-53 | similarity_search_by_vector_with_relevance_scores(query: List[float], k: int) → List[Tuple[langchain.schema.Document, float]][source]#
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs and relevance scores in the range [0,... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
1d550228d9cb-54 | Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
async classmethod afrom_documents(documents: List[langchain.schema.Document], embed... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
1d550228d9cb-55 | Return docs most similar to embedding vector.
classmethod from_documents(documents: List[langchain.schema.Document], embedding: langchain.embeddings.base.Embeddings, **kwargs: Any) → langchain.vectorstores.base.VST[source]#
Return VectorStore initialized from documents and embeddings.
abstract classmethod from_texts(te... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
1d550228d9cb-56 | among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 correspondi... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
1d550228d9cb-57 | Example
import weaviate
from langchain.vectorstores import Weaviate
client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
weaviate = Weaviate(client, index_name, text_key)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Upload texts with metadata (prop... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
1d550228d9cb-58 | k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documen... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
1d550228d9cb-59 | Look up similar documents by embedding vector in Weaviate.
class langchain.vectorstores.Zilliz(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
1d550228d9cb-60 | that name if it exists. Defaults to False.
Returns
Zilliz Vector Store
Return type
Zilliz
previous
Document Loaders
next
Retrievers
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
344d4fcd0c02-0 | .rst
.pdf
Document Transformers
Document Transformers#
Transform documents
pydantic model langchain.document_transformers.EmbeddingsRedundantFilter[source]#
Filter that drops redundant documents by comparing their embeddings.
field embeddings: langchain.embeddings.base.Embeddings [Required]#
Embeddings to use for embed... | https://python.langchain.com/en/latest/reference/modules/document_transformers.html |
a7ec43df6555-0 | .rst
.pdf
Python REPL
Python REPL#
For backwards compatibility.
pydantic model langchain.python.PythonREPL[source]#
Simulates a standalone Python REPL.
field globals: Optional[Dict] [Optional] (alias '_globals')#
field locals: Optional[Dict] [Optional] (alias '_locals')#
run(command: str) → str[source]#
Run command wit... | https://python.langchain.com/en/latest/reference/modules/python.html |
1cdbd44aab38-0 | .rst
.pdf
SerpAPI
SerpAPI#
For backwards compatiblity.
pydantic model langchain.serpapi.SerpAPIWrapper[source]#
Wrapper around SerpAPI.
To use, you should have the google-search-results python package installed,
and the environment variable SERPAPI_API_KEY set with your API key, or pass
serpapi_api_key as a named param... | https://python.langchain.com/en/latest/reference/modules/serpapi.html |
1bfa6302a19e-0 | .rst
.pdf
Docstore
Docstore#
Wrappers on top of docstores.
class langchain.docstore.InMemoryDocstore(_dict: Dict[str, langchain.schema.Document])[source]#
Simple in memory docstore in the form of a dict.
add(texts: Dict[str, langchain.schema.Document]) → None[source]#
Add texts to in memory dictionary.
search(search: s... | https://python.langchain.com/en/latest/reference/modules/docstore.html |
90c140cc379b-0 | .rst
.pdf
Document Compressors
Document Compressors#
pydantic model langchain.retrievers.document_compressors.DocumentCompressorPipeline[source]#
Document compressor that uses a pipeline of transformers.
field transformers: List[Union[langchain.schema.BaseDocumentTransformer, langchain.retrievers.document_compressors.b... | https://python.langchain.com/en/latest/reference/modules/document_compressors.html |
90c140cc379b-1 | Filter down documents.
compress_documents(documents: Sequence[langchain.schema.Document], query: str) → Sequence[langchain.schema.Document][source]#
Filter documents based on similarity of their embeddings to the query.
pydantic model langchain.retrievers.document_compressors.LLMChainExtractor[source]#
field get_input:... | https://python.langchain.com/en/latest/reference/modules/document_compressors.html |
90c140cc379b-2 | The chain prompt is expected to have a BooleanOutputParser.
async acompress_documents(documents: Sequence[langchain.schema.Document], query: str) → Sequence[langchain.schema.Document][source]#
Filter down documents.
compress_documents(documents: Sequence[langchain.schema.Document], query: str) → Sequence[langchain.sche... | https://python.langchain.com/en/latest/reference/modules/document_compressors.html |
cb353a23b337-0 | .rst
.pdf
Output Parsers
Output Parsers#
pydantic model langchain.output_parsers.CommaSeparatedListOutputParser[source]#
Parse out comma separated lists.
get_format_instructions() → str[source]#
Instructions on how the LLM output should be formatted.
parse(text: str) → List[str][source]#
Parse the output of an LLM call... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
cb353a23b337-1 | field retry_chain: langchain.chains.llm.LLMChain [Required]#
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, parser: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T], prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'instructions']... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
cb353a23b337-2 | and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
pydantic model langchain.output_parsers.RegexDictParser[source]#
Class to parse the output into a dictionary.
field no_update_value: Optional[str] = None#
field output_key_to_format: Dict[str, str] [Required]#
field ... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
cb353a23b337-3 | field retry_chain: langchain.chains.llm.LLMChain [Required]#
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T], prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['completion', 'prompt'], output_pars... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
cb353a23b337-4 | that was raised to another language and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
field parser: langchain.schema... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
cb353a23b337-5 | The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
pydantic model langchain.output_parsers.StructuredOutputParser[sourc... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
8e1552b28254-0 | .rst
.pdf
Text Splitter
Text Splitter#
Functionality for splitting text.
class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class... | https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
8e1552b28254-1 | Split incoming text and return chunks.
class langchain.text_splitter.TextSplitter(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: typing.Callable[[str], int] = <built-in function len>)[source]#
Interface for splitting text into chunks.
async atransform_documents(documents: Sequence[langchain.schema.D... | https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
8e1552b28254-2 | Transform sequence of documents by splitting them.
class langchain.text_splitter.TokenTextSplitter(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any)[source]#
Imp... | https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
830fb548d03c-0 | .rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of doc... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-1 | Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Aleph Alpha’s asymmetric, query embedding endpoint
:param text: The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.AlephAlphaSymmet... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-2 | embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Cohere’s embedding endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Cohere’s embedding endpoint.
Parameters
text – The text to embed... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-3 | embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceHubEmbeddings[source]#
Wrapper around HuggingFaceHub embedding models.
To use, you should hav... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-4 | Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers
and InstructorEmbedding python package installed.
Example
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
hf = HuggingFaceInstru... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-5 | Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool = False#
Use half-precision for key/value cache.
field logits_all: bool = False#
Return logits for all tokens, not just the last token.
field n_batch: Optional[int] = 8#
Number of t... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-6 | environment variable OPENAI_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, ... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-7 | embed_query(text: str) → List[float][source]#
Call out to OpenAI’s embedding endpoint for embedding query text.
Parameters
text – The text to embed.
Returns
Embedding for the text.
pydantic model langchain.embeddings.SagemakerEndpointEmbeddings[source]#
Wrapper around custom Sagemaker Inference Endpoints.
To use, you m... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-8 | function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
field endpoint_name: str = ''#
The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
field model_kwargs: Optional[Dict] = None#
Key word arguments to pas... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-9 | from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
def get_pipeline():
model_id = "facebook/bart-large"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-10 | Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedHuggingFaceEmbeddings[source]#
Runs sentence_tr... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-11 | Requirements to install on hardware to inference the model.
pydantic model langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings[source]#
Runs InstructorEmbedding embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as server... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
830fb548d03c-12 | Compute query embeddings using a HuggingFace instruct model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.TensorflowHubEmbeddings[source]#
Wrapper around tensorflow_hub embedding models.
To use, you should have the tensorflow_text python package installed.
Ex... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
36dcd3af717a-0 | .rst
.pdf
Chat Models
Chat Models#
pydantic model langchain.chat_models.AzureChatOpenAI[source]#
Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use deployment_name in the
constructor to refer to the “Model deployment name” in the Azure portal.
In addit... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
36dcd3af717a-1 | field callback_manager: langchain.callbacks.base.BaseCallbackManager [Optional]#
field verbose: bool [Optional]#
Whether to print out response text.
pydantic model langchain.chat_models.ChatOpenAI[source]#
Wrapper around OpenAI Chat large language models.
To use, you should have the openai python package installed, and... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
36dcd3af717a-2 | get_num_tokens(text: str) → int[source]#
Calculate num tokens with tiktoken package.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int[source]#
Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: openai/openai-cookbook
main/examples/How_to_forma... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
0cf8b0630207-0 | .rst
.pdf
Agent Toolkits
Agent Toolkits#
Agent toolkits.
pydantic model langchain.agents.agent_toolkits.JiraToolkit[source]#
Jira Toolkit.
field tools: List[langchain.tools.base.BaseTool] = []#
classmethod from_jira_api_wrapper(jira_api_wrapper: langchain.utilities.jira.JiraAPIWrapper) → langchain.agents.agent_toolkits... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-1 | Instantiate the toolkit from an OpenAPI Spec URL
classmethod from_llm_and_spec(llm: langchain.llms.base.BaseLLM, spec: langchain.tools.openapi.utils.openapi_utils.OpenAPISpec, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAT... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-2 | Toolkit for interacting with PowerBI dataset.
field callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None#
field examples: Optional[str] = None#
field llm: langchain.schema.BaseLanguageModel [Required]#
field powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]#
get_tools() → List[la... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-3 | field llm: langchain.llms.base.BaseLLM [Optional]#
field vectorstore_info: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.ZapierToolkit[source]#
Zapier... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-4 | langchain.agents.agent_toolkits.create_json_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON.\nYour goal is to return ... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-5 | you cannot use it.\nYou should only add one key at a time to the path. You cannot add multiple keys at once.\nIf you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.\n\nIf the question does not seem to be related to the JSON, just return "I don\'t know" as the answer.\nAlw... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-6 | str = 'Begin!"\n\nQuestion: {input}\nThought: I should look at the keys that exist in data to see what I have access to\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to ta... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-7 | Construct a json agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-8 | langchain.agents.agent_toolkits.create_openapi_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You are an agent designed to answer questions by making web requ... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-9 | by checking which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you are using ... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-10 | = None, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, return_intermediate_steps: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-11 | Construct a json agent from an LLM and tools.
langchain.agents.agent_toolkits.create_pandas_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a pandas dataframe in Python. The name of the data... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-12 | langchain.agents.agent_toolkits.create_pbi_agent(llm: langchain.llms.base.BaseLLM, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, pre... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-13 | Usually I should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I should ask the query tool to execute it, finally create a nice sentence that answers the question. If you receive an error back that mentions that the query was wron... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-14 | always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the ori... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-15 | Construct a pbi agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-16 | langchain.agents.agent_toolkits.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackMa... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-17 | wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics. \n\nGiven an input question, create a syntactically correct DAX query to run, then look at the resul... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-18 | to query tool to create a query for me and then I should ask the query tool to execute it, finally create a complete sentence that answers the question. If you receive an error back that mentions that the query was wrong try to phrase the question differently and get a new query from the question to query tool.\n', suf... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-19 | Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
langchain.agents.agent_toolkits.create_python_agent(llm: langchain.llms.base.BaseLLM, tool: langchain.tools.python.tool.PythonREPLTool, callback_manager: Optional[langchain.callbacks.bas... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-20 | langchain.agents.agent_toolkits.create_sql_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a SQL database.\nGiven an... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-21 | a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should look at the tables in th... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-22 | early_stopping_method: str = 'force', verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-23 | Construct a sql agent from an LLM and tools.
langchain.agents.agent_toolkits.create_vectorstore_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are... | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
0cf8b0630207-24 | Utilities
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
3ef26d49b528-0 | .rst
.pdf
PromptTemplates
PromptTemplates#
Prompt template classes.
pydantic model langchain.prompts.BaseChatPromptTemplate[source]#
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
3ef26d49b528-1 | file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
pydantic model langchain.prompts.ChatPromptTemplate[source]#
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt tem... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
3ef26d49b528-2 | A list of the names of the variables the prompt template expects.
field prefix: str = ''#
A prompt template string to put before the examples.
field suffix: str [Required]#
A prompt template string to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: ‘f-str... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
3ef26d49b528-3 | field suffix: langchain.prompts.base.StringPromptTemplate [Required]#
A PromptTemplate to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
field validate_template: bool = True#
Whether or not to try validating the template.
dict(**kwa... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
3ef26d49b528-4 | Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
classmethod from_examples(examples: List[str], suffix: str, input_variables: List[str], example_separator: str = '\n\n', prefix: str = '', **kwarg... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
3ef26d49b528-5 | Create Chat Messages.
langchain.prompts.load_prompt(path: Union[str, pathlib.Path]) → langchain.prompts.base.BasePromptTemplate[source]#
Unified method for loading a prompt from LangChainHub or local fs.
previous
Prompts
next
Example Selector
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last ... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
864c1cc0f6bd-0 | .rst
.pdf
Experimental Modules
Contents
Autonomous Agents
Generative Agents
Experimental Modules#
This module contains experimental modules and reproductions of existing work using LangChain primitives.
Autonomous Agents#
Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
class ... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
864c1cc0f6bd-1 | property output_keys: List[str]#
Output keys this chain expects.
prioritize_tasks(this_task_id: int, objective: str) → List[Dict][source]#
Prioritize tasks.
class langchain.experimental.AutoGPT(ai_name: str, memory: langchain.vectorstores.base.VectorStoreRetriever, chain: langchain.chains.llm.LLMChain, output_parser: l... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
864c1cc0f6bd-2 | React to a given observation.
get_full_header(force_refresh: bool = False) → str[source]#
Return a full header of the agent’s status, summary, and current time.
get_summary(force_refresh: bool = False) → str[source]#
Return a descriptive summary of the agent.
field last_refreshed: datetime.datetime [Optional]#
The last... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
864c1cc0f6bd-3 | field traits: str = 'N/A'#
Permanent traits to ascribe to the character.
class langchain.experimental.GenerativeAgentMemory(*, llm: langchain.schema.BaseLanguageModel, memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever, verbose: bool = False, reflection_threshold: Optional[f... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
864c1cc0f6bd-4 | Return key-value pairs given the text input to the chain.
field memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]#
The retriever to fetch related memories.
property memory_variables: List[str]#
Input keys this memory class will load dynamically.
pause_to_reflect(... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
50d30b3886c4-0 | .rst
.pdf
Retrievers
Retrievers#
pydantic model langchain.retrievers.ChatGPTPluginRetriever[source]#
field aiosession: Optional[aiohttp.client.ClientSession] = None#
field bearer_token: str [Required]#
field filter: Optional[dict] = None#
field top_k: int = 3#
field url: str [Required]#
async aget_relevant_documents(qu... | https://python.langchain.com/en/latest/reference/modules/retrievers.html |
50d30b3886c4-1 | Parameters
query – string to find relevant documents for
Returns
Sequence of relevant documents
class langchain.retrievers.DataberryRetriever(datastore_url: str, top_k: Optional[int] = None, api_key: Optional[str] = None)[source]#
async aget_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get ... | https://python.langchain.com/en/latest/reference/modules/retrievers.html |
50d30b3886c4-2 | Click “Reset password”
Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
add_texts(texts: Iterable[str], refresh_indices: bool = True) → List[str][source]#
Run more texts through the embeddings and add to the retriver.
Para... | https://python.langchain.com/en/latest/reference/modules/retrievers.html |
50d30b3886c4-3 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.PineconeHybridSearchRetriever[source]#
field alpha: float = 0.5#
field embeddings: langchain.embeddings.base.Embeddings [Required]#
field index: Any = None#
field sparse_encoder: Any = None#
f... | https://python.langchain.com/en/latest/reference/modules/retrievers.html |
50d30b3886c4-4 | Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.SVMRetriever[source]#
field embeddings: langchain.embeddings.base.Embeddings [Required]#
field index: Any = None#
field k: int = 4#
field relevancy_threshold: Optional[float] = None#
field tex... | https://python.langchain.com/en/latest/reference/modules/retrievers.html |
50d30b3886c4-5 | get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.TimeWeightedVectorStoreRetriever[source]#
Retriever combining embededing simil... | https://python.langchain.com/en/latest/reference/modules/retrievers.html |
50d30b3886c4-6 | get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Return documents that are relevant to the query.
get_salient_docs(query: str) → Dict[int, Tuple[langchain.schema.Document, float]][source]#
Return documents that are salient to the query.
class langchain.retrievers.WeaviateHybridSearchRetriev... | https://python.langchain.com/en/latest/reference/modules/retrievers.html |
f088712952a0-0 | .md
.pdf
Llama.cpp
Contents
Installation and Setup
Wrappers
LLM
Embeddings
Llama.cpp#
This page covers how to use llama.cpp within LangChain.
It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.
Installation and Setup#
Install the Python package with pip install lla... | https://python.langchain.com/en/latest/ecosystem/llamacpp.html |
5fe84fdb4788-0 | .md
.pdf
Pinecone
Contents
Installation and Setup
Wrappers
VectorStore
Pinecone#
This page covers how to use the Pinecone ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
Installation and Setup#
Install the Python SDK with pip install ... | https://python.langchain.com/en/latest/ecosystem/pinecone.html |
8c473ad83dc0-0 | .md
.pdf
Hugging Face
Contents
Installation and Setup
Wrappers
LLM
Embeddings
Tokenizer
Datasets
Hugging Face#
This page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) within LangChain.
It is broken into two parts: installation and setup, and then references to specific Hugging Face wra... | https://python.langchain.com/en/latest/ecosystem/huggingface.html |
8c473ad83dc0-1 | from langchain.embeddings import HuggingFaceHubEmbeddings
For a more detailed walkthrough of this, see this notebook
Tokenizer#
There are several places you can use tokenizers available through the transformers package.
By default, it is used to count tokens for all LLMs.
You can also use it to count tokens when splitt... | https://python.langchain.com/en/latest/ecosystem/huggingface.html |
c75e431f90d9-0 | .ipynb
.pdf
Comet
Contents
Install Comet and Dependencies
Initialize Comet and Set your Credentials
Set OpenAI and SerpAPI credentials
Scenario 1: Using just an LLM
Scenario 2: Using an LLM in a Chain
Scenario 3: Using An Agent with Tools
Scenario 4: Using Custom Evaluation Metrics
Comet#
In this guide we will demons... | https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
c75e431f90d9-1 | )
manager = CallbackManager([StdOutCallbackHandler(), comet_callback])
llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)
llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3)
print("LLM result", llm_result)
comet_callback.flush_tracker(llm, finish=True)
Scenario 2: Us... | https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
c75e431f90d9-2 | from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain.callbacks.base import CallbackManager
from langchain.llms import OpenAI
comet_callback = CometCallbackHandler(
project_name="comet-example-langchain",
complexity_metrics=True,
stream_logs=True,
tags=["agent"],
)
m... | https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
c75e431f90d9-3 | def compute_metric(self, generation, prompt_idx, gen_idx):
prediction = generation.text
results = self.scorer.score(target=self.reference, prediction=prediction)
return {
"rougeLsum_score": results["rougeLsum"].fmeasure,
"reference": self.reference,
}
reference = ... | https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
c75e431f90d9-4 | an 81-storey building, and the tallest structure in Paris. Its base is square,
measuring 125 metres (410 ft) on each side.
During its construction, the Eiffel Tower surpassed the
Washington Monument to become the tallest man-made structure in the world,
... | https://python.langchain.com/en/latest/ecosystem/comet_tracking.html |
45faa7c3229d-0 | .md
.pdf
Zilliz
Contents
Installation and Setup
Wrappers
VectorStore
Zilliz#
This page covers how to use the Zilliz Cloud ecosystem within LangChain.
Zilliz uses the Milvus integration.
It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
Installation and Setup#
Instal... | https://python.langchain.com/en/latest/ecosystem/zilliz.html |
53c4565636da-0 | .md
.pdf
Google Serper Wrapper
Contents
Setup
Wrappers
Utility
Output
Tool
Google Serper Wrapper#
This page covers how to use the Serper Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search.
It is br... | https://python.langchain.com/en/latest/ecosystem/google_serper.html |
53c4565636da-1 | Yes.
Follow up: Who is the reigning men's U.S. Open champion?
Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion.
Follow up: Where is Carlos Alcaraz from?
Intermediate answer: El Palmar, Spain
So the final answer is: El Palmar, Spain
> Finished chain.
'El Palmar, Spain'
For a more detail... | https://python.langchain.com/en/latest/ecosystem/google_serper.html |
4cde1a477f2e-0 | .md
.pdf
Qdrant
Contents
Installation and Setup
Wrappers
VectorStore
Qdrant#
This page covers how to use the Qdrant ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
Installation and Setup#
Install the Python SDK with pip install qdrant-c... | https://python.langchain.com/en/latest/ecosystem/qdrant.html |
fa26aab6488a-0 | .md
.pdf
Prediction Guard
Contents
Installation and Setup
LLM Wrapper
Example usage
Prediction Guard#
This page covers how to use the Prediction Guard ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
Installation and Setup#
Ins... | https://python.langchain.com/en/latest/ecosystem/predictionguard.html |
fa26aab6488a-1 | llm_chain.predict(question=question)
previous
Pinecone
next
PromptLayer
Contents
Installation and Setup
LLM Wrapper
Example usage
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | https://python.langchain.com/en/latest/ecosystem/predictionguard.html |
b01aa6548ae2-0 | .md
.pdf
Writer
Contents
Installation and Setup
Wrappers
LLM
Writer#
This page covers how to use the Writer ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
Installation and Setup#
Get an Writer api key and set it as an environment varia... | https://python.langchain.com/en/latest/ecosystem/writer.html |
488b33dc9c33-0 | .md
.pdf
Chroma
Contents
Installation and Setup
Wrappers
VectorStore
Chroma#
This page covers how to use the Chroma ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.
Installation and Setup#
Install the Python package with pip install chro... | https://python.langchain.com/en/latest/ecosystem/chroma.html |
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