id stringlengths 14 16 | text stringlengths 45 2.73k | source stringlengths 49 114 |
|---|---|---|
251d5d6cfb51-0 | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
251d5d6cfb51-1 | prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
verbose=verbose,
max_... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
64d1175aa8a7-0 | Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types impo... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
64d1175aa8a7-1 | if tool_names != {"Lookup", "Search"}:
raise ValueError(
f"Tool names should be Lookup and Search, got {tool_names}"
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def ... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
64d1175aa8a7-2 | if len(lookups) == 0:
return "No Results"
elif self.lookup_index >= len(lookups):
return "No More Results"
else:
result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})"
return f"{result_prefix} {lookups[self.lookup_index]}"
@property
def... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
64d1175aa8a7-3 | """Initialize with the LLM and a docstore."""
docstore_explorer = DocstoreExplorer(docstore)
tools = [
Tool(
name="Search",
func=docstore_explorer.search,
description="Search for a term in the docstore.",
),
Tool(
... | https://python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
7784ec1ac67f-0 | Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self ask with search."""
from typing import Any, Sequence, Union
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.se... | https://python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
7784ec1ac67f-1 | if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Intermediate Answer, got {tool_names}"
)
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Intermediate answer: "
@prope... | https://python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
537b9d5bd45d-0 | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
537b9d5bd45d-1 | cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str]] = None,
) -> PromptTemplate:
"""Create ... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
537b9d5bd45d-2 | callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variabl... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
9a856bb07311-0 | Source code for langchain.agents.mrkl.base
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, A... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
9a856bb07311-1 | """Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFI... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
9a856bb07311-2 | callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agen... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
9a856bb07311-3 | llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
"""
[docs] @classmethod
def from_chains(
cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any
) -> AgentExecutor:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
9a856bb07311-4 | func=c.action,
description=c.action_description,
)
for c in chains
]
agent = ZeroShotAgent.from_llm_and_tools(llm, tools)
return cls(agent=agent, tools=tools, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on... | https://python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
d61e75eb8888-0 | Source code for langchain.embeddings.llamacpp
"""Wrapper around llama.cpp embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
"""Wrapper ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
d61e75eb8888-1 | use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
d61e75eb8888-2 | raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception:
raise NameError(f"Could not load Llama m... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
64289af6ade9-0 | Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
64289af6ade9-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
64289af6ade9-2 | texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
responses = self.client(inputs=texts, params=_model_kwargs)
return responses
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to HuggingFaceHub's embedding endpoint for embed... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
34d7967ae065-0 | Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_M... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
34d7967ae065-1 | self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
except ImportError:
raise ValueError(
"Could not import sentence_transformers python package. "
"Please install it with `pip install sentence_transformers`."
)
class Con... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
34d7967ae065-2 | model_name=model_name, model_kwargs=model_kwargs
)
"""
client: Any #: :meta private:
model_name: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable."... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
34d7967ae065-3 | [docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace instruct model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedd... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
416d0c13477b-0 | Source code for langchain.embeddings.aleph_alpha
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
"""... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
416d0c13477b-1 | """Attention control parameters only apply to those tokens that have
explicitly been set in the request."""
control_log_additive: Optional[bool] = True
"""Apply controls on prompt items by adding the log(control_factor)
to attention scores."""
@root_validator()
def validate_environment(cls, va... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
416d0c13477b-2 | "representation": SemanticRepresentation.Document,
"compress_to_size": self.compress_to_size,
"normalize": self.normalize,
"contextual_control_threshold": self.contextual_control_threshold,
"control_log_additive": self.control_log_additive,
}
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
416d0c13477b-3 | """The symmetric version of the Aleph Alpha's semantic embeddings.
The main difference is that here, both the documents and
queries are embedded with a SemanticRepresentation.Symmetric
Example:
.. code-block:: python
from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
416d0c13477b-4 | """
document_embeddings = []
for text in texts:
document_embeddings.append(self._embed(text))
return document_embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to Aleph Alpha's asymmetric, query embedding endpoint
Args:
text... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
99db303956b4-0 | Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import BaseModel, Extra... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
99db303956b4-1 | """Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
return embeddings.client.create(**kwargs)
return _embed_with_retry(**kwargs)
[docs]class OpenAIEmbeddings(BaseModel, Embeddings):
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
99db303956b4-2 | text = "This is a test query."
query_result = embeddings.embed_query(text)
"""
client: Any #: :meta private:
model: str = "text-embedding-ada-002"
deployment: str = model # to support Azure OpenAI Service custom deployment names
embedding_ctx_length: int = 8191
openai_api_key: Opti... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
99db303956b4-3 | "Please install it with `pip install openai`."
)
return values
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
def _get_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
)... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
99db303956b4-4 | results[indices[i]].append(batched_embeddings[i])
lens[indices[i]].append(len(batched_embeddings[i]))
for i in range(len(texts)):
average = np.average(results[i], axis=0, weights=lens[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist()
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
99db303956b4-5 | # handle batches of large input text
if self.embedding_ctx_length > 0:
return self._get_len_safe_embeddings(texts, engine=self.deployment)
else:
results = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(texts), _chunk_size):
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
8471585f7f68-0 | Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "senten... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
8471585f7f68-1 | if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated wi... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
8471585f7f68-2 | model_load_fn: Callable = load_embedding_model
"""Function to load the model remotely on the server."""
load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
inference_fn: Callable = _embed_documents
"""Inference function to extract the embeddings."""
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
8471585f7f68-3 | model_name=model_name, hardware=gpu)
"""
model_id: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
8471585f7f68-4 | text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
return embedding.tolist()
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
63d6df08bb66-0 | Source code for langchain.embeddings.fake
from typing import List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
size: int
def _get_embedding(self) -> List[float]:
return list(np.random.normal(size=sel... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html |
5c42471df863-0 | Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(Base... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
5c42471df863-1 | raise ValueError(
"Could not import cohere python package. "
"Please install it with `pip install cohere`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Cohere's embedding endpoint.
Args:
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
893a04d9778e-0 | Source code for langchain.embeddings.self_hosted
"""Running custom embedding models on self-hosted remote hardware."""
from typing import Any, Callable, List
from pydantic import Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline: Any, *arg... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
893a04d9778e-1 | model_load_fn=get_pipeline,
hardware=gpu
model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:
.. code-block:: python
from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
893a04d9778e-2 | [docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embeddings = self.clie... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
84f22e0feb26-0 | Source code for langchain.embeddings.tensorflow_hub
"""Wrapper around TensorflowHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]clas... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
84f22e0feb26-1 | Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.embed(texts).numpy()
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
0326e829580b-0 | Source code for langchain.embeddings.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0326e829580b-1 | """The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credential... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0326e829580b-2 | endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
"""
class Config:
"""Configuration for this pydantic object."""
extr... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0326e829580b-3 | _endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(texts, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# send request
try:
response = self.client.invoke_endpoint(
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0326e829580b-4 | """
return self._embedding_func([text])
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
61c12327fb85-0 | .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... | https://python.langchain.com/en/latest/modules/chains.html |
41654bb70720-0 | .rst
.pdf
Agents
Contents
Go Deeper
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 ... | https://python.langchain.com/en/latest/modules/agents.html |
82e63ea6351b-0 | .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 sh... | https://python.langchain.com/en/latest/modules/memory.html |
52f160f8f47d-0 | .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... | https://python.langchain.com/en/latest/modules/indexes.html |
52f160f8f47d-1 | previous
Structured Output Parser
next
Getting Started
Contents
Go Deeper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/modules/indexes.html |
51d361bdc35f-0 | .rst
.pdf
Models
Contents
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” ... | https://python.langchain.com/en/latest/modules/models.html |
ce9c50b0133a-0 | .rst
.pdf
Prompts
Contents
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... | https://python.langchain.com/en/latest/modules/prompts.html |
0f0dd40120dc-0 | .rst
.pdf
Prompt Templates
Prompt Templates#
Note
Conceptual Guide
Language models take text as input - that text is commonly referred to as a prompt.
Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
LangChain provides several classes and functions t... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates.html |
1d71a6898950-0 | .rst
.pdf
Output Parsers
Output Parsers#
Note
Conceptual Guide
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an out... | https://python.langchain.com/en/latest/modules/prompts/output_parsers.html |
601199d56b87-0 | .ipynb
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Chat Prompt Template
Chat Prompt Template#
Chat Models takes a list of chat messages as input - this list commonly referred to as a prompt.
Typically this is not simply a hardcoded list of messages but rather a combination of a template, some examples, and user input.
LangChain provides several classes and ... | https://python.langchain.com/en/latest/modules/prompts/chat_prompt_template.html |
601199d56b87-1 | HumanMessage(content='I love programming.', additional_kwargs={})]
If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:
prompt=PromptTemplate(
template="You are a helpful assistant that translates {input_language} to {output_language}... | https://python.langchain.com/en/latest/modules/prompts/chat_prompt_template.html |
05658f408b34-0 | .rst
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Example Selectors
Example Selectors#
Note
Conceptual Guide
If you have a large number of examples, you may need to select which ones to include in the prompt. The ExampleSelector is the class responsible for doing so.
The base interface is defined as below:
class BaseExampleSelector(ABC):
"""Interface for... | https://python.langchain.com/en/latest/modules/prompts/example_selectors.html |
3c82f533e90d-0 | .rst
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How-To Guides
How-To Guides#
If you’re new to the library, you may want to start with the Quickstart.
The user guide here shows more advanced workflows and how to use the library in different ways.
How to create a custom prompt template
How to create a prompt template that uses few shot examples
How to work w... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/how_to_guides.html |
0592885aabb8-0 | .md
.pdf
Getting Started
Contents
What is a prompt template?
Create a prompt template
Load a prompt template from LangChainHub
Pass few shot examples to a prompt template
Select examples for a prompt template
Getting Started#
In this tutorial, we will learn about:
what a prompt template is, and why it is needed,
how ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
0592885aabb8-1 | no_input_prompt.format()
# -> "Tell me a joke."
# An example prompt with one input variable
one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")
one_input_prompt.format(adjective="funny")
# -> "Tell me a funny joke."
# An example prompt with multiple input variables
m... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
0592885aabb8-2 | In this example, we’ll create a prompt to generate word antonyms.
from langchain import PromptTemplate, FewShotPromptTemplate
# First, create the list of few shot examples.
examples = [
{"word": "happy", "antonym": "sad"},
{"word": "tall", "antonym": "short"},
]
# Next, we specify the template to format the exa... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
0592885aabb8-3 | print(few_shot_prompt.format(input="big"))
# -> Give the antonym of every input
# ->
# -> Word: happy
# -> Antonym: sad
# ->
# -> Word: tall
# -> Antonym: short
# ->
# -> Word: big
# -> Antonym:
Select examples for a prompt template#
If you have a large number of examples, you can use the ExampleSelector to select a s... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
0592885aabb8-4 | # These are the examples is has available to choose from.
examples=examples,
# This is the PromptTemplate being used to format the examples.
example_prompt=example_prompt,
# This is the maximum length that the formatted examples should be.
# Length is measured by the get_text_length function below... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
0592885aabb8-5 | # -> Word: happy
# -> Antonym: sad
# ->
# -> Word: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else
# -> Antonym:
LangChain comes with a few example selectors that you can use. For more details on how to use them, see Example Selectors.
You can create cus... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
fca02fc776e1-0 | .ipynb
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How to work with partial Prompt Templates
Contents
Partial With Strings
Partial With Functions
How to work with partial Prompt Templates#
A prompt template is a class with a .format method which takes in a key-value map and returns a string (a prompt) to pass to the language model. Like other methods, it ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
fca02fc776e1-1 | print(prompt.format(bar="baz"))
foobaz
Partial With Functions#
The other common use is to partial with a function. The use case for this is when you have a variable you know that you always want to fetch in a common way. A prime example of this is with date or time. Imagine you have a prompt which you always want to ha... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
fca02fc776e1-2 | Contents
Partial With Strings
Partial With Functions
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
924a3434a165-0 | .ipynb
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How to create a custom prompt template
Contents
Why are custom prompt templates needed?
Creating a Custom Prompt Template
Use the custom prompt template
How to create a custom prompt template#
Let’s suppose we want the LLM to generate English language explanations of a function given its name. To achieve ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
924a3434a165-1 | import inspect
def get_source_code(function_name):
# Get the source code of the function
return inspect.getsource(function_name)
Next, we’ll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.
from langchain.prompt... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
924a3434a165-2 | prompt = fn_explainer.format(function_name=get_source_code)
print(prompt)
Given the function name and source code, generate an English language explanation of the function.
Function Name: get_source_code
Source Code:
def get_source_code(function_name):
# Get the source code of the fu... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
0065c6218ff1-0 | .ipynb
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How to create a prompt template that uses few shot examples
Contents
Use Case
Using an example set
Create the example set
Create a formatter for the few shot examples
Feed examples and formatter to FewShotPromptTemplate
Using an example selector
Feed examples into ExampleSelector
Feed example selector int... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
0065c6218ff1-1 | "answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
"""
},
... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
0065c6218ff1-2 | print(example_prompt.format(**examples[0]))
Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
0065c6218ff1-3 | Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
0065c6218ff1-4 | # This is the list of examples available to select from.
examples,
# This is the embedding class used to produce embeddings which are used to measure semantic similarity.
OpenAIEmbeddings(),
# This is the VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma,... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
0065c6218ff1-5 | suffix="Question: {input}",
input_variables=["input"]
)
print(prompt.format(input="Who was the father of Mary Ball Washington?"))
Question: Who was the maternal grandfather of George Washington?
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The m... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
ea6302b58a6b-0 | .ipynb
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How to serialize prompts
Contents
PromptTemplate
Loading from YAML
Loading from JSON
Loading Template from a File
FewShotPromptTemplate
Examples
Loading from YAML
Loading from JSON
Examples in the Config
Example Prompt from a File
How to serialize prompts#
It is often preferrable to store prompts not as p... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
ea6302b58a6b-1 | prompt = load_prompt("simple_prompt.yaml")
print(prompt.format(adjective="funny", content="chickens"))
Tell me a funny joke about chickens.
Loading from JSON#
This shows an example of loading a PromptTemplate from JSON.
!cat simple_prompt.json
{
"_type": "prompt",
"input_variables": ["adjective", "content"],
... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
ea6302b58a6b-2 | output: sad
- input: tall
output: short
Loading from YAML#
This shows an example of loading a few shot example from YAML.
!cat few_shot_prompt.yaml
_type: few_shot
input_variables:
["adjective"]
prefix:
Write antonyms for the following words.
example_prompt:
_type: prompt
input_variables:
["i... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
ea6302b58a6b-3 | !cat few_shot_prompt.json
{
"_type": "few_shot",
"input_variables": ["adjective"],
"prefix": "Write antonyms for the following words.",
"example_prompt": {
"_type": "prompt",
"input_variables": ["input", "output"],
"template": "Input: {input}\nOutput: {output}"
},
"exampl... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
ea6302b58a6b-4 | Output: short
Input: funny
Output:
Example Prompt from a File#
This shows an example of loading the PromptTemplate that is used to format the examples from a separate file. Note that the key changes from example_prompt to example_prompt_path.
!cat example_prompt.json
{
"_type": "prompt",
"input_variables": ["in... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
3d772ce2cec8-0 | .ipynb
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Output Parsers
Output Parsers#
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an output parser must impl... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/getting_started.html |
3d772ce2cec8-1 | punchline: str = Field(description="answer to resolve the joke")
# You can add custom validation logic easily with Pydantic.
@validator('setup')
def question_ends_with_question_mark(cls, field):
if field[-1] != '?':
raise ValueError("Badly formed question!")
return field
# S... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/getting_started.html |
25e59f64b1ca-0 | .ipynb
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OutputFixingParser
OutputFixingParser#
This output parser wraps another output parser and tries to fix any mmistakes
The Pydantic guardrail simply tries to parse the LLM response. If it does not parse correctly, then it errors.
But we can do other things besides throw errors. Specifically, we can pass the m... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
25e59f64b1ca-1 | 24 return self.pydantic_object.parse_obj(json_object)
File ~/.pyenv/versions/3.9.1/lib/python3.9/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
343 if (cls is None and object_hook is None and
344 parse_int is None and parse_float is N... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
25e59f64b1ca-2 | Cell In[6], line 1
----> 1 parser.parse(misformatted)
File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)
27 name = self.pydantic_object.__name__
28 msg = f"Failed to parse {name} from completion {text}. Got: {e}"
---> 29 raise OutputParserException(ms... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
8bb335f9bfa7-0 | .ipynb
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RetryOutputParser
RetryOutputParser#
While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can’t. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example.
from langchain.prompts... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
8bb335f9bfa7-1 | 23 json_object = json.loads(json_str)
---> 24 return self.pydantic_object.parse_obj(json_object)
26 except (json.JSONDecodeError, ValidationError) as e:
File ~/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pydantic/main.py:527, in pydantic.main.BaseModel.parse_obj()
File ~/.pyenv/version... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
8bb335f9bfa7-2 | fix_parser.parse(bad_response)
Action(action='search', action_input='')
Instead, we can use the RetryOutputParser, which passes in the prompt (as well as the original output) to try again to get a better response.
from langchain.output_parsers import RetryWithErrorOutputParser
retry_parser = RetryWithErrorOutputParser.... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
20444c3240ea-0 | .ipynb
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PydanticOutputParser
PydanticOutputParser#
This output parser allows users to specify an arbitrary JSON schema and query LLMs for JSON outputs that conform to that schema.
Keep in mind that large language models are leaky abstractions! You’ll have to use an LLM with sufficient capacity to generate well-form... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/pydantic.html |
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