id stringlengths 14 16 | text stringlengths 31 2.73k | source stringlengths 56 166 |
|---|---|---|
cb658ba15ae5-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.agent_types import AgentType
from langchain.agents.chat.base impo... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/loading.html |
cb658ba15ae5-1 | ) -> BaseSingleActionAgent:
config_type = config.pop("_type")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
agent_cls = AGENT_TO_CLA... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/loading.html |
cb658ba15ae5-2 | if "llm_chain" in config:
config["llm_chain"] = load_chain_from_config(config.pop("llm_chain"))
elif "llm_chain_path" in config:
config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
combi... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/loading.html |
cb658ba15ae5-3 | return load_agent_from_config(config, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/loading.html |
220a2e419e74-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.callbacks.base import BaseCallbackMa... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/initialize.html |
220a2e419e74-1 | "but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
agent_cls = AGENT_TO_CLASS[agent]
ag... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/initialize.html |
bac13fbb3425-0 | Source code for langchain.agents.agent_types
from enum import Enum
[docs]class AgentType(str, Enum):
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
REACT_DOCSTORE = "react-docstore"
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
CONVERSATIONAL_REACT_DESCRIPTION = "conversational-react-descri... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_types.html |
c745f204e074-0 | Source code for langchain.agents.tools
"""Interface for tools."""
from inspect import signature
from typing import Any, Awaitable, Callable, Optional, Union
from langchain.tools.base import BaseTool
[docs]class Tool(BaseTool):
"""Tool that takes in function or coroutine directly."""
description: str = ""
fu... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/tools.html |
c745f204e074-1 | return f"{tool_name} is not a valid tool, try another one."
[docs]def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
"""Make tools out of functions, can be used with or without arguments.
Requires:
- Function must be of type (str) -> str
- Function must have a docstr... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/tools.html |
c745f204e074-2 | elif len(args) == 1 and callable(args[0]):
# if the argument is a function, then we use the function name as the tool name
# Example usage: @tool
return _make_with_name(args[0].__name__)(args[0])
elif len(args) == 0:
# if there are no arguments, then we use the function name as the t... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/tools.html |
fd265ceb60a1-0 | Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
import re
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent, AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.a... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
fd265ceb60a1-1 | return text + "\nAction:"
def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
action_prefix = "Action: "
if not text.strip().split("\n")[-1].startswith(action_prefix):
return None
action_block = text.strip().split("\n")[-1]
action_str = action_block... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
fd265ceb60a1-2 | """Search for a term in the docstore, and if found save."""
result = self.docstore.search(term)
if isinstance(result, Document):
self.document = result
return self._summary
else:
self.document = None
return result
def lookup(self, term: str) ->... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
fd265ceb60a1-3 | """Return default prompt."""
return TEXTWORLD_PROMPT
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
if len(tools) != 1:
raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if to... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
ae5fd683be19-0 | Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self ask with search."""
from typing import Any, Optional, Sequence, Tuple, Union
from langchain.agents.agent import Agent, AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.self_ask_with_search.prompt imp... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
ae5fd683be19-1 | if followup not in last_line:
finish_string = "So the final answer is: "
if finish_string not in last_line:
return None
return "Final Answer", last_line[len(finish_string) :]
after_colon = text.split(":")[-1]
if " " == after_colon[0]:
after... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
ae5fd683be19-2 | search_tool = Tool(
name="Intermediate Answer", func=search_chain.run, description="Search"
)
agent = SelfAskWithSearchAgent.from_llm_and_tools(llm, [search_tool])
super().__init__(agent=agent, tools=[search_tool], **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Cha... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
25089bc177d0-0 | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
import re
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent
from langchain.agents.agent_types import AgentType... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/conversational/base.html |
25089bc177d0-1 | """Create prompt in the style of the zero shot agent.
Args:
tools: List of tools the agent will have access to, used to format the
prompt.
prefix: String to put before the list of tools.
suffix: String to put after the list of tools.
ai_prefix: Str... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/conversational/base.html |
25089bc177d0-2 | match = re.search(regex, llm_output)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1)
action_input = match.group(2)
return action.strip(), action_input.strip(" ").strip('"')
[docs] @classmethod
def from_llm_and_tools... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/conversational/base.html |
fcff29dc735f-0 | Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.llms.base import BaseLLM
[docs]def create_csv... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
fe228885c6a9-0 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit import... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
fe228885c6a9-1 | prefix: str = ROUTER_PREFIX,
verbose: bool = False,
**kwargs: Any,
) -> AgentExecutor:
"""Construct a vectorstore router agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=pr... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
9e373f461f2d-0 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
from l... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
9e373f461f2d-1 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
a3bf6d4ba558-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits.opena... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
a3bf6d4ba558-1 | 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=toolkit.get_tools(),
verbose=verbose... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
44809b44d5df-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://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
44809b44d5df-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://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
1c66e299621b-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAgent
f... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
1c66e299621b-1 | llm_chain = LLMChain(
llm=llm,
prompt=partial_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,
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
903aaf058574-0 | Source code for langchain.agents.conversational_chat.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
import json
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent
from langchain.agents.conversational_chat.p... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/conversational_chat/base.html |
903aaf058574-1 | cleaned_output = cleaned_output.strip()
response = json.loads(cleaned_output)
return {"action": response["action"], "action_input": response["action_input"]}
[docs]class ConversationalChatAgent(Agent):
"""An agent designed to hold a conversation in addition to using tools."""
output_parser: Base... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/conversational_chat/base.html |
903aaf058574-2 | MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template(final_prompt),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
return ChatPromptTemplate(input_variables=input_variables, messages=messages)
def _extract_tool_and_input(self, ... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/conversational_chat/base.html |
903aaf058574-3 | prompt = cls.create_prompt(
tools,
system_message=system_message,
human_message=human_message,
input_variables=input_variables,
output_parser=_output_parser,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
ca... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/conversational_chat/base.html |
5a15a01fafa4-0 | 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
import re
from typing import Any, Callable, List, NamedTuple, Optional, Sequence, Tuple
from langchain.agents.agent import Agent, AgentExecutor
from langcha... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
5a15a01fafa4-1 | # \s matches against tab/newline/whitespace
regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
retu... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
5a15a01fafa4-2 | input_variables: List of input variables the final prompt will expect.
Returns:
A PromptTemplate with the template assembled from the pieces here.
"""
tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
tool_names = ", ".join([tool.name for tool i... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
5a15a01fafa4-3 | @classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
for tool in tools:
if tool.description is None:
raise ValueError(
f"Got a tool {tool.name} without a description. For this agent, "
f"a description must always be pro... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
5a15a01fafa4-4 | from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm)
chains = [
ChainConfig(
action_name = "Search",
... | https://langchain-cn.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
75a649497bfc-0 | .md
.pdf
Jina
Contents
Installation and Setup
Wrappers
Embeddings
Jina#
This page covers how to use the Jina ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
Installation and Setup#
Install the Python SDK with pip install jina
Get a Jina A... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/jina.html |
e6bd362eb2fd-0 | .md
.pdf
Runhouse
Contents
Installation and Setup
Self-hosted LLMs
Self-hosted Embeddings
Runhouse#
This page covers how to use the Runhouse ecosystem within LangChain.
It is broken into three parts: installation and setup, LLMs, and Embeddings.
Installation and Setup#
Install the Python SDK with pip install runhouse... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/runhouse.html |
7cfc541d4cdd-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://langchain-cn.readthedocs.io/en/latest/ecosystem/comet_tracking.html |
7cfc541d4cdd-1 | print("LLM result", llm_result)
comet_callback.flush_tracker(llm, finish=True)
Scenario 2: Using an LLM in a Chain#
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain.callbacks.base import CallbackManager
from langchain.chains import LLMChain
from langchain.llms import OpenAI
fro... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/comet_tracking.html |
7cfc541d4cdd-2 | )
manager = CallbackManager([StdOutCallbackHandler(), comet_callback])
llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)
tools = load_tools(["serpapi", "llm-math"], llm=llm, callback_manager=manager)
agent = initialize_agent(
tools,
llm,
agent="zero-shot-react-description",
callback_... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/comet_tracking.html |
7cfc541d4cdd-3 | "reference": self.reference,
}
reference = """
The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.
It was the first structure to reach a height of 300 metres.
It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)
Excluding transmitters, the Eiffe... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/comet_tracking.html |
7cfc541d4cdd-4 | a title it held for 41 years until the Chrysler Building
in New York City was finished in 1930.
It was the first structure to reach a height of 300 metres.
Due to the addition of a broadcasting aerial at the top of the tower in 1957,
it is now taller t... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/comet_tracking.html |
413d3985411c-0 | .md
.pdf
OpenSearch
Contents
Installation and Setup
Wrappers
VectorStore
OpenSearch#
This page covers how to use the OpenSearch ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers.
Installation and Setup#
Install the Python package with ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/opensearch.html |
16d515200f1e-0 | .md
.pdf
Replicate
Contents
Installation and Setup
Calling a model
Replicate#
This page covers how to run models on Replicate within LangChain.
Installation and Setup#
Create a Replicate account. Get your API key and set it as an environment variable (REPLICATE_API_TOKEN)
Install the Replicate python client with pip ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/replicate.html |
16d515200f1e-1 | prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
llm(prompt)
We can call any Replicate model (not just LLMs) using this syntax. For example, we can call Stable Diffusion:
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6d... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/replicate.html |
9a32479656d5-0 | .md
.pdf
StochasticAI
Contents
Installation and Setup
Wrappers
LLM
StochasticAI#
This page covers how to use the StochasticAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific StochasticAI wrappers.
Installation and Setup#
Install with pip install stochas... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/stochasticai.html |
1a68b6969219-0 | .md
.pdf
ForefrontAI
Contents
Installation and Setup
Wrappers
LLM
ForefrontAI#
This page covers how to use the ForefrontAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers.
Installation and Setup#
Get an ForefrontAI api key and set i... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/forefrontai.html |
959b7b42738a-0 | .md
.pdf
Databerry
Contents
What is Databerry?
Quick start
Databerry#
This page covers how to use the Databerry within LangChain.
What is Databerry?#
Databerry is an open source document retrievial platform that helps to connect your personal data with Large Language Models.
Quick start#
Retrieving documents stored i... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/databerry.html |
a0df2b001041-0 | .md
.pdf
PGVector
Contents
Installation
Setup
Wrappers
VectorStore
Usage
PGVector#
This page covers how to use the Postgres PGVector ecosystem within LangChain
It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
Installation#
Install the Python package with pip inst... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/pgvector.html |
005bd99bd987-0 | .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://langchain-cn.readthedocs.io/en/latest/ecosystem/zilliz.html |
fccb0df00974-0 | .md
.pdf
RWKV-4
Contents
Installation and Setup
Usage
RWKV
Model File
Rwkv-4 models -> recommended VRAM
RWKV-4#
This page covers how to use the RWKV-4 wrapper within LangChain.
It is broken into two parts: installation and setup, and then usage with an example.
Installation and Setup#
Install the Python package with ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/rwkv.html |
fccb0df00974-1 | RWKV VRAM
Model | 8bit | bf16/fp16 | fp32
14B | 16GB | 28GB | >50GB
7B | 8GB | 14GB | 28GB
3B | 2.8GB| 6GB | 12GB
1b5 | 1.3GB| 3GB | 6GB
See the rwkv pip page for more information about strategies, including streaming and cuda support.
previous
Runhouse
next
SearxNG Search API
Contents... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/rwkv.html |
87a8b027fd8c-0 | .md
.pdf
SearxNG Search API
Contents
Installation and Setup
Self Hosted Instance:
Wrappers
Utility
Tool
SearxNG Search API#
This page covers how to use the SearxNG search API within LangChain.
It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper.
Installation an... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/searx.html |
87a8b027fd8c-1 | s.run("what is a large language model?")
Tool#
You can also load this wrapper as a Tool (to use with an Agent).
You can do this with:
from langchain.agents import load_tools
tools = load_tools(["searx-search"],
searx_host="http://localhost:8888",
engines=["github"])
Note that we ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/searx.html |
51fbf4d91d19-0 | .md
.pdf
AtlasDB
Contents
Installation and Setup
Wrappers
VectorStore
AtlasDB#
This page covers how to use Nomic’s Atlas ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
Installation and Setup#
Install the Python package with pip install ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/atlas.html |
6f9be6b6f126-0 | .md
.pdf
Wolfram Alpha Wrapper
Contents
Installation and Setup
Wrappers
Utility
Tool
Wolfram Alpha Wrapper#
This page covers how to use the Wolfram Alpha API within LangChain.
It is broken into two parts: installation and setup, and then references to specific Wolfram Alpha wrappers.
Installation and Setup#
Install r... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/wolfram_alpha.html |
b7b8977e67f3-0 | .md
.pdf
Google Search Wrapper
Contents
Installation and Setup
Wrappers
Utility
Tool
Google Search Wrapper#
This page covers how to use the Google Search API within LangChain.
It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper.
Installation and Setup#
Instal... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/google_search.html |
758c1e59c73f-0 | .md
.pdf
CerebriumAI
Contents
Installation and Setup
Wrappers
LLM
CerebriumAI#
This page covers how to use the CerebriumAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
Installation and Setup#
Install with pip install cerebrium
G... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/cerebriumai.html |
7490c13c368e-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://langchain-cn.readthedocs.io/en/latest/ecosystem/huggingface.html |
7490c13c368e-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://langchain-cn.readthedocs.io/en/latest/ecosystem/huggingface.html |
46ccb5fecae3-0 | .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://langchain-cn.readthedocs.io/en/latest/ecosystem/google_serper.html |
46ccb5fecae3-1 | 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://langchain-cn.readthedocs.io/en/latest/ecosystem/google_serper.html |
0b2f648b0f65-0 | .ipynb
.pdf
Aim
Aim#
Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents.
With Aim, you can easily debug and examine an individual execution:
Additionally, you have the option to compare multiple executions side by side:
Aim ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/aim_tracking.html |
0b2f648b0f65-1 | aim_callback = AimCallbackHandler(
repo=".",
experiment_name="scenario 1: OpenAI LLM",
)
manager = CallbackManager([StdOutCallbackHandler(), aim_callback])
llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)
The flush_tracker function is used to record LangChain assets on Aim. By default, the se... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/aim_tracking.html |
0b2f648b0f65-2 | ]
synopsis_chain.apply(test_prompts)
aim_callback.flush_tracker(
langchain_asset=synopsis_chain, experiment_name="scenario 3: Agent with Tools"
)
Scenario 3 The third scenario involves an agent with tools.
from langchain.agents import initialize_agent, load_tools
from langchain.agents import AgentType
# scenario 3 ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/aim_tracking.html |
0b2f648b0f65-3 | Thought: I now know the final answer
Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.
> Finished chain.
previous
AI21 Labs
next
Apify
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/ecosystem/aim_tracking.html |
a22d5a056b2c-0 | .md
.pdf
Hazy Research
Contents
Installation and Setup
Wrappers
LLM
Hazy Research#
This page covers how to use the Hazy Research ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.
Installation and Setup#
To use the manifest, install... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/hazy_research.html |
ce8a5d87f76c-0 | .md
.pdf
Banana
Contents
Installation and Setup
Define your Banana Template
Build the Banana app
Wrappers
LLM
Banana#
This page covers how to use the Banana ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
Installation and Setup#
Install... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/bananadev.html |
ce8a5d87f76c-1 | )
result = tokenizer.decode(output[0], skip_special_tokens=True)
# Return the results as a dictionary
result = {'output': result}
return result
You can find a full example of a Banana app here.
Wrappers#
LLM#
There exists an Banana LLM wrapper, which you can access with
from langchain.llms import Banana... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/bananadev.html |
c21b2972326e-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://langchain-cn.readthedocs.io/en/latest/ecosystem/pinecone.html |
9d57dbd9bf81-0 | .md
.pdf
Weaviate
Contents
Installation and Setup
Wrappers
VectorStore
Weaviate#
This page covers how to use the Weaviate ecosystem within LangChain.
What is Weaviate?
Weaviate in a nutshell:
Weaviate is an open-source database of the type vector search engine.
Weaviate allows you to store JSON documents in a class... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/weaviate.html |
9d57dbd9bf81-1 | To import this vectorstore:
from langchain.vectorstores import Weaviate
For a more detailed walkthrough of the Weaviate wrapper, see this notebook
previous
Weights & Biases
next
Wolfram Alpha Wrapper
Contents
Installation and Setup
Wrappers
VectorStore
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/weaviate.html |
9853a9ef8b1a-0 | .md
.pdf
Modal
Contents
Installation and Setup
Define your Modal Functions and Webhooks
Wrappers
LLM
Modal#
This page covers how to use the Modal ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
Installation and Setup#
Install with pip in... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/modal.html |
9853a9ef8b1a-1 | @stub.webhook(method="POST")
def get_text(item: Item):
return {"prompt": run_gpt2.call(item.prompt)}
Wrappers#
LLM#
There exists an Modal LLM wrapper, which you can access with
from langchain.llms import Modal
previous
Milvus
next
NLPCloud
Contents
Installation and Setup
Define your Modal Functions and Webhooks... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/modal.html |
cc645cabfb5d-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://langchain-cn.readthedocs.io/en/latest/ecosystem/writer.html |
a0083c5fe832-0 | .md
.pdf
Cohere
Contents
Installation and Setup
Wrappers
LLM
Embeddings
Cohere#
This page covers how to use the Cohere ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Cohere wrappers.
Installation and Setup#
Install the Python SDK with pip install coher... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/cohere.html |
ea766e149253-0 | .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://langchain-cn.readthedocs.io/en/latest/ecosystem/qdrant.html |
74aa9bbb2e47-0 | .md
.pdf
Petals
Contents
Installation and Setup
Wrappers
LLM
Petals#
This page covers how to use the Petals ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Petals wrappers.
Installation and Setup#
Install with pip install petals
Get a Hugging Face api k... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/petals.html |
f7037acc2caa-0 | .md
.pdf
Helicone
Contents
What is Helicone?
Quick start
How to enable Helicone caching
How to use Helicone custom properties
Helicone#
This page covers how to use the Helicone ecosystem within LangChain.
What is Helicone?#
Helicone is an open source observability platform that proxies your OpenAI traffic and provide... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/helicone.html |
f7037acc2caa-1 | Quick start
How to enable Helicone caching
How to use Helicone custom properties
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 18, 2023. | https://langchain-cn.readthedocs.io/en/latest/ecosystem/helicone.html |
293511a6e466-0 | .md
.pdf
Milvus
Contents
Installation and Setup
Wrappers
VectorStore
Milvus#
This page covers how to use the Milvus ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
Installation and Setup#
Install the Python SDK with pip install pymilvus... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/milvus.html |
37f8e48c93e4-0 | .md
.pdf
Unstructured
Contents
Installation and Setup
Wrappers
Data Loaders
Unstructured#
This page covers how to use the unstructured
ecosystem within LangChain. The unstructured package from
Unstructured.IO extracts clean text from raw source documents like
PDFs and Word documents.
This page is broken into two part... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/unstructured.html |
37f8e48c93e4-1 | loader = UnstructuredFileLoader("state_of_the_union.txt")
loader.load()
If you instantiate the loader with UnstructuredFileLoader(mode="elements"), the loader
will track additional metadata like the page number and text type (i.e. title, narrative text)
when that information is available.
previous
StochasticAI
next
Wei... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/unstructured.html |
cc4a360fb36b-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://langchain-cn.readthedocs.io/en/latest/ecosystem/chroma.html |
91ad4bdcacd9-0 | .md
.pdf
SerpAPI
Contents
Installation and Setup
Wrappers
Utility
Tool
SerpAPI#
This page covers how to use the SerpAPI search APIs within LangChain.
It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.
Installation and Setup#
Install requirements with pip install ... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/serpapi.html |
e554f2dba1c9-0 | .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://langchain-cn.readthedocs.io/en/latest/ecosystem/llamacpp.html |
5d5c171ee09e-0 | .ipynb
.pdf
ClearML Integration
Contents
Getting API Credentials
Setting Up
Scenario 1: Just an LLM
Scenario 2: Creating an agent with tools
Tips and Next Steps
ClearML Integration#
In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. ClearML is an e... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-1 | # Get the OpenAI model ready to go
llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)
The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.
Scenario 1: Just an... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-2 | {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}
{'action': 'on_llm_start', 'name': 'OpenAI', '... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-3 | {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}
{'action': 'on_llm_start', 'name': 'OpenAI', '... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-4 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-5 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-6 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-7 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
5d5c171ee09e-8 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://langchain-cn.readthedocs.io/en/latest/ecosystem/clearml_tracking.html |
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