diff --git a/docs/docs_skeleton/vercel.json b/docs/docs_skeleton/vercel.json
index d197260f928..e2db7fda389 100644
--- a/docs/docs_skeleton/vercel.json
+++ b/docs/docs_skeleton/vercel.json
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+ "destination": "/docs/use_cases/agents/wikibase_agent"
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+ },
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+ "destination": "/"
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+ },
+ {
+ "source": "/en/latest/modules/indexes/retrievers/examples/:path*",
+ "destination": "/docs/modules/data_connection/retrievers/integrations:path*"
+ },
+ {
+ "source": "/en/latest/modules/indexes/:path*",
+ "destination": "/docs/modules/data_connection/:path*"
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+ "destination": "/docs/modules/memory/how_to/:path*"
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+ "destination": "/docs/modules/model_io/models/"
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+ "destination": "/docs/modules/model_io/models/:path*"
+ },
+ {
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+ "destination": "/docs/modules/model_io/prompts/prompt_templates/:path*"
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+ "destination": "/docs/modules/model_io/prompts/:path1*/:path*"
+ },
+ {
+ "source": "/en/latest/reference.html",
+ "destination": "https://api.python.langchain.com"
+ },
+ {
+ "source": "/en/latest/reference/:path*",
+ "destination": "https://api.python.langchain.com/en/latest/:path*"
+ },
+ {
+ "source": "/en/latest/:path*",
+ "destination": "/docs/:path*"
+ }
]
}
\ No newline at end of file
diff --git a/docs/extras/modules/agents/tools/integrations/zapier.ipynb b/docs/extras/modules/agents/tools/integrations/zapier.ipynb
index 06079315f5d..3114ad3bee0 100644
--- a/docs/extras/modules/agents/tools/integrations/zapier.ipynb
+++ b/docs/extras/modules/agents/tools/integrations/zapier.ipynb
@@ -21,7 +21,7 @@
"\n",
"2. User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.com\n",
"\n",
- "This quick start will focus on the server-side use case for brevity. Review [full docs](https://nla.zapier.com/start/) for user-facing oauth developer support.\n",
+ "This quick start will focus mostly on the server-side use case for brevity. Jump to [Example Using OAuth Access Token](#oauth) to see a short example how to set up Zapier for user-facing situations. Review [full docs](https://nla.zapier.com/start/) for full user-facing oauth developer support.\n",
"\n",
"This example goes over how to use the Zapier integration with a `SimpleSequentialChain`, then an `Agent`.\n",
"In code, below:"
@@ -149,7 +149,7 @@
"id": "bcdea831",
"metadata": {},
"source": [
- "# Example with SimpleSequentialChain\n",
+ "## Example with SimpleSequentialChain\n",
"If you need more explicit control, use a chain, like below."
]
},
@@ -323,12 +323,34 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"id": "09ff954e-45f2-4595-92ea-91627abde4a0",
"metadata": {},
+ "source": [
+ "## Example Using OAuth Access Token\n",
+ "The below snippet shows how to initialize the wrapper with a procured OAuth access token. Note the argument being passed in as opposed to setting an environment variable. Review the [authentication docs](https://nla.zapier.com/docs/authentication/#oauth-credentials) for full user-facing oauth developer support.\n",
+ "\n",
+ "The developer is tasked with handling the OAuth handshaking to procure and refresh the access token."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7c6835c8",
+ "metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "llm = OpenAI(temperature=0)\n",
+ "zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token='')\n",
+ "toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
+ "agent = initialize_agent(\n",
+ " toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+ ")\n",
+ "\n",
+ "agent.run(\n",
+ " \"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\"\n",
+ ")"
+ ]
}
],
"metadata": {
diff --git a/langchain/retrievers/multi_query.py b/langchain/retrievers/multi_query.py
index 78a2624b673..925a7d0a9a7 100644
--- a/langchain/retrievers/multi_query.py
+++ b/langchain/retrievers/multi_query.py
@@ -9,7 +9,7 @@ from langchain.output_parsers.pydantic import PydanticOutputParser
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseRetriever, Document
-logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
class LineList(BaseModel):
@@ -120,7 +120,7 @@ class MultiQueryRetriever(BaseRetriever):
response = self.llm_chain({"question": question})
lines = getattr(response["text"], self.parser_key, [])
if self.verbose:
- logging.info(f"Generated queries: {lines}")
+ logger.info(f"Generated queries: {lines}")
return lines
def retrieve_documents(self, queries: List[str]) -> List[Document]:
diff --git a/pyproject.toml b/pyproject.toml
index b4dc8f24efd..43ee54847b5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langchain"
-version = "0.0.217"
+version = "0.0.218"
description = "Building applications with LLMs through composability"
authors = []
license = "MIT"