"The [Hugging Face Model Hub](https://huggingface.co/models) hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
"\n",
"These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through the HuggingFaceHub class. For more information on the hosted pipelines, see the [HuggingFaceHub](./huggingface_hub) notebook."
"These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through the HuggingFaceHub class."
"We have a built-in tool in LangChain to easily use Passio NutritionAI to find food nutrition facts.\n",
"Note that this requires an API key - they have a free tier.\n",
@ -2098,7 +2098,7 @@
"source": [
"## Create the agent\n",
"\n",
"Now that we have defined the tools, we can create the agent. We will be using an OpenAI Functions agent - for more information on this type of agent, as well as other options, see [this guide](./agent_types)\n",
"Now that we have defined the tools, we can create the agent. We will be using an OpenAI Functions agent - for more information on this type of agent, as well as other options, see [this guide](/docs/modules/agents/agent_types/)\n",
"\n",
"First, we choose the LLM we want to be guiding the agent."
]
@ -2156,7 +2156,7 @@
"id": "f8014c9d",
"metadata": {},
"source": [
"Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](./concepts)"
"Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/modules/agents/concepts)"
]
},
{
@ -2176,7 +2176,7 @@
"id": "1a58c9f8",
"metadata": {},
"source": [
"Finally, we combine the agent (the brains) with the tools inside the AgentExecutor (which will repeatedly call the agent and execute tools). For more information about how to think about these components, see our [conceptual guide](./concepts)"
"Finally, we combine the agent (the brains) with the tools inside the AgentExecutor (which will repeatedly call the agent and execute tools). For more information about how to think about these components, see our [conceptual guide](/docs/modules/agents/concepts)"
"Besides raw text data, you may wish to extract information from other file types such as PowerPoint presentations or PDFs.\n",
"\n",
"You can use LangChain [document loaders](/modules/data_connection/document_loaders/) to parse files into a text format that can be fed into LLMs.\n",
"You can use LangChain [document loaders](/docs/modules/data_connection/document_loaders/) to parse files into a text format that can be fed into LLMs.\n",
"\n",
"LangChain features a large number of [document loader integrations](/docs/integrations/document_loaders).\n",
"\n",
"## MIME type based parsing\n",
"\n",
"For basic parsing exmaples take a look [at document loaders](/modules/data_connection/document_loaders/).\n",
"For basic parsing exmaples take a look [at document loaders](/docs/modules/data_connection/document_loaders/).\n",
"\n",
"Here, we'll be looking at mime-type based parsing which is often useful for extraction based applications if you're writing server code that accepts user uploaded files.\n",
"* The [output parser](/docs/modules/model_io/output_parsers/) documentation includes various parser examples for specific types (e.g., lists, datetime, enum, etc).\n",
"* LangChain [document loaders](/modules/data_connection/document_loaders/) to load content from files. Please see list of [integrations](/docs/integrations/document_loaders).\n",
"* LangChain [document loaders](/docs/modules/data_connection/document_loaders/) to load content from files. Please see list of [integrations](/docs/integrations/document_loaders).\n",
"* The experimental [Anthropic function calling](https://python.langchain.com/docs/integrations/chat/anthropic_functions) support provides similar functionality to Anthropic chat models.\n",
"* [LlamaCPP](https://python.langchain.com/docs/integrations/llms/llamacpp#grammars) natively supports constrained decoding using custom grammars, making it easy to output structured content using local LLMs \n",
"* [JSONFormer](/docs/integrations/llms/jsonformer_experimental) offers another way for structured decoding of a subset of the JSON Schema.\n",
@ -40,7 +40,7 @@ Apart from having `pgvector` extension enabled, you will need to do some setup b
In order to run RAG over your postgreSQL database you will need to generate the embeddings for the specific columns you want.
This process is covered in the [RAG empowered SQL cookbook](cookbook/retrieval_in_sql.ipynb), but the overall approach consist of:
This process is covered in the [RAG empowered SQL cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/retrieval_in_sql.ipynb), but the overall approach consist of:
1. Querying for unique values in the column
2. Generating embeddings for those values
3. Store the embeddings in a separate column or in an auxiliary table.
@ -102,4 +102,4 @@ We can access the template from code with:
Bagatur
2 months ago
committed by
GitHub