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Code Issues Packages Projects Releases Wiki Activity

Harrison/stop importing from init (#10690)

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This commit is contained in:
Harrison Chase
2023-09-16 17:22:48 -07:00
committed by GitHub
parent 9749f8ebae
commit 5442d2b1fa
202 changed files with 321 additions and 327 deletions
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2
docs/snippets/modules/agents/agent_types/conversational_agent.mdx
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@@ -4,7 +4,7 @@ This is accomplished with a specific type of agent (`conversational-react-descri
from langchain.agents import Tool
from langchain.agents import AgentType
from langchain.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.llms import OpenAI
from langchain.utilities import SerpAPIWrapper
from langchain.agents import initialize_agent
```

4
docs/snippets/modules/agents/agent_types/plan_and_execute.mdx
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@@ -5,9 +5,9 @@
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
from langchain.llms import OpenAI
from langchain import SerpAPIWrapper
from langchain.utilities import SerpAPIWrapper
from langchain.agents.tools import Tool
from langchain import LLMMathChain
from langchain.chains import LLMMathChain
```
## Tools

2
docs/snippets/modules/agents/how_to/custom_llm_agent.mdx
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@@ -20,7 +20,7 @@ Do necessary imports, etc.
```python
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, SerpAPIWrapper, LLMChain
from langchain.llms import OpenAI\nfrom langchain.utilities import SerpAPIWrapper\nfrom langchain.chains import LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish, OutputParserException
import re

3
docs/snippets/modules/agents/how_to/custom_llm_chat_agent.mdx
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@@ -27,7 +27,8 @@ pip install openai
```python
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import BaseChatPromptTemplate
from langchain import SerpAPIWrapper, LLMChain
from langchain.utilities import SerpAPIWrapper
from langchain.chains.llm import LLMChain
from langchain.chat_models import ChatOpenAI
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish, HumanMessage

2
docs/snippets/modules/agents/how_to/mrkl.mdx
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@@ -1,5 +1,5 @@
```python
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain
from langchain.chains import LLMMathChain\nfrom langchain.llms import OpenAI\nfrom langchain.utilities import SerpAPIWrapper\nfrom langchain.utilities import SQLDatabase\nfrom langchain_experimental.sql import SQLDatabaseChain
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
```

2
docs/snippets/modules/chains/additional/analyze_document.mdx
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@@ -8,7 +8,7 @@ Let's take a look at it in action below, using it to summarize a long document.
```python
from langchain import OpenAI
from langchain.llms import OpenAI
from langchain.chains.summarize import load_summarize_chain
llm = OpenAI(temperature=0)

2
docs/snippets/modules/chains/foundational/llm_chain.mdx
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@@ -1,5 +1,5 @@
```python
from langchain import PromptTemplate, OpenAI, LLMChain
from langchain.prompts import PromptTemplate\nfrom langchain.llms import OpenAI\nfrom langchain.chains import LLMChain
prompt_template = "What is a good name for a company that makes {product}?"

4
docs/snippets/modules/chains/popular/sqlite.mdx
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@@ -537,7 +537,7 @@ Sometimes you may not have the luxury of using OpenAI or other service-hosted la
import logging
import torch
from transformers import AutoTokenizer, GPT2TokenizerFast, pipeline, AutoModelForSeq2SeqLM, AutoModelForCausalLM
from langchain import HuggingFacePipeline
from langchain.llms import HuggingFacePipeline
# Note: This model requires a large GPU, e.g. an 80GB A100. See documentation for other ways to run private non-OpenAI models.
model_id = "google/flan-ul2"
@@ -882,7 +882,7 @@ Now that you have some examples (with manually corrected output SQL), you can do
```python
from langchain import FewShotPromptTemplate, PromptTemplate
from langchain.prompts import FewShotPromptTemplate, PromptTemplate
from langchain.chains.sql_database.prompt import _sqlite_prompt, PROMPT_SUFFIX
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelector

2
docs/snippets/modules/chains/popular/vector_db_qa_with_sources.mdx
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@@ -49,7 +49,7 @@ docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": f"{i}-pl"
```python
from langchain.chains import RetrievalQAWithSourcesChain
from langchain import OpenAI
from langchain.llms import OpenAI
chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())
```

2
docs/snippets/modules/model_io/models/chat/how_to/prompts.mdx
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@@ -4,7 +4,7 @@ For convenience, there is a `from_template` method defined on the template. If y
```python
from langchain import PromptTemplate
from langchain.prompts import PromptTemplate
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,

6
docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx
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@@ -8,7 +8,7 @@ By default, `PromptTemplate` uses [Python's str.format](https://docs.python.org/
syntax for templating; however other templating syntax is available (e.g., `jinja2`).
```python
from langchain import PromptTemplate
from langchain.prompts import PromptTemplate
prompt_template = PromptTemplate.from_template(
"Tell me a {adjective} joke about {content}."
@@ -27,7 +27,7 @@ prompt_template.format(adjective="funny", content="chickens")
The template supports any number of variables, including no variables:
```python
from langchain import PromptTemplate
from langchain.prompts import PromptTemplate
prompt_template = PromptTemplate.from_template(
"Tell me a joke"
@@ -40,7 +40,7 @@ will be compared against the variables present in the template string during ins
there is a mismatch; for example,
```python
from langchain import PromptTemplate
from langchain.prompts import PromptTemplate
invalid_prompt = PromptTemplate(
input_variables=["adjective"],