Update docs (#1567)
* Add new documentation files and update package dependencies * Refactor tweak application logic in process_tweaks function * Add dynamic function creation and execution helpers * Refactor build method to be asynchronous * Add FlowToolComponent to handle flows as tools * Update RunFlowComponent to include a method for updating build config * Fix duplicated first layer results * Refactor vertex building and streaming endpoints * Add base_name attribute to Vertex class * Refactor flow.py to generate dynamic flow functions and build schemas * Refactor FlowToolComponent in FlowTool.py * Add JSONInputComponent to load JSON object as input * Update render_tool_description method in XMLAgent.py * Refactor XMLAgentComponent.render_tool_description() method * Refactor SearchApi.py to include typing and handle empty records * Refactor SearchApi class to simplify code * Add SearchApi and SearchApiTool components * Refactor ServiceFactory and Dependencies (#1560) * Update dependencies for OpenTelemetry * Update service dependency logic and add first version of telemetry service * Remove telemetry service and related code * Update cache service references * Refactor imports in env.py * Refactor code for initializing services and socketio server * Refactor parameterComponent to use inline button_text * Refactor build_vertex method and add RunnableVerticesManager class * Add import statement and update build_vertex function * Add import statement for SettingsService in MonitorServiceFactory.create() method * Refactor build_schema_from_inputs to use display_name and description for field names and descriptions respectively * Refactor graph building and running logic * Update input type mappings and function arguments * Update default values for input types in flow.py * Remove console.log statement in flowStore.ts * Add vertices_to_run field to VerticesOrderResponse * Add input_value parameter to chain components * Refactor CSVAgent build method to include handle_parse_errors parameter * Add agent_type parameter to CSVAgent build method * Update model imports in component files * Add LCAgentComponent and XMLAgentComponent * Add "agents" category to NATIVE_CATEGORIES * Refactor model.py to support chat models * Add system_message parameter to model components * Update CSVAgent.py: handle_parsing_errors and agent_type options * Add ping animation to update button * Fix encryption and decryption of API keys * Update CSVAgentComponent constructor * Refactor inputs parameter to inputs_dict in build_vertex function * Removes "component" table and drops "flowstyle" table * Delete component model and init files * Removes "flowstyle" table and drops "user" table index * Add typing import to CohereModel.py * Fix ShareModal rendering issue * Update models docs * Changed vector-stores docs * Update component documentation * Add AstraDB and AstraDBSearch components for AstraDB Vector Store docs * Rename GetNotified to Listen * Update GetNotifiedComponent import * Remove unused imports in flow-runner.mdx and features.mdx * Add new documentation files and update existing files * Update package versions in package-lock.json * Remove unused files * Delete run-flow.mdx file * Update topics * Add new file run-flow.mdx --------- Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai> Co-authored-by: anovazzi1 <otavio2204@gmail.com>
This commit is contained in:
Carlos Coelho
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import Admonition from '@theme/Admonition';
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# Models
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<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
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<p>
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We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
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</p>
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</Admonition>
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### AmazonBedrock
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This component facilitates the generation of text using the LLM (Large Language Model) model from Amazon Bedrock.
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**Params**
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- **Input Value:** Specifies the input text for text generation.
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- **System Message (Optional):** A system message to pass to the model.
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- **Model ID (Optional):** Specifies the model ID to be used for text generation. Defaults to _`"anthropic.claude-instant-v1"`_. Available options include:
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- _`"ai21.j2-grande-instruct"`_
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- _`"ai21.j2-jumbo-instruct"`_
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- _`"ai21.j2-mid"`_
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- _`"ai21.j2-mid-v1"`_
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- _`"ai21.j2-ultra"`_
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- _`"ai21.j2-ultra-v1"`_
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- _`"anthropic.claude-instant-v1"`_
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- _`"anthropic.claude-v1"`_
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- _`"anthropic.claude-v2"`_
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- _`"cohere.command-text-v14"`_
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- **Credentials Profile Name (Optional):** Specifies the name of the credentials profile.
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- **Region Name (Optional):** Specifies the region name.
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- **Model Kwargs (Optional):** Additional keyword arguments for the model.
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- **Endpoint URL (Optional):** Specifies the endpoint URL.
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- **Streaming (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **Cache (Optional):** Specifies whether to cache the response.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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<Admonition type="note" title="Note">
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<p>
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Ensure that necessary credentials are provided to connect to the Amazon Bedrock API. If connection fails, a ValueError will be raised.
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</p>
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</Admonition>
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---
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### AnthropicLLM
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This component allows the generation of text using Anthropic Chat&Completion large language models.
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**Params**
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- **Model Name:** Specifies the name of the Anthropic model to be used for text generation. Available options include:
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- _`"claude-2.1"`_
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- _`"claude-2.0"`_
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- _`"claude-instant-1.2"`_
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- _`"claude-instant-1"`_
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- **Anthropic API Key:** Your Anthropic API key.
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- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to _`256`_.
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- **Temperature (Optional):** Specifies the sampling temperature. Defaults to _`0.7`_.
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- **API Endpoint (Optional):** Specifies the endpoint of the Anthropic API. Defaults to _`"https://api.anthropic.com"`_ if not specified.
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- **Input Value:** Specifies the input text for text generation.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **System Message (Optional):** A system message to pass to the model.
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For detailed documentation and integration guides, please refer to the [Anthropic Component Documentation](https://python.langchain.com/docs/integrations/chat/anthropic).
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---
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### AzureChatOpenAI
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This component allows the generation of text using the LLM (Large Language Model) model from Azure OpenAI.
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**Params**
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- **Model Name:** Specifies the name of the Azure OpenAI model to be used for text generation. Available options include:
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- _`"gpt-35-turbo"`_
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- _`"gpt-35-turbo-16k"`_
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- _`"gpt-35-turbo-instruct"`_
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- _`"gpt-4"`_
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- _`"gpt-4-32k"`_
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- _`"gpt-4-vision"`_
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- **Azure Endpoint:** Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`.
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- **Deployment Name:** Specifies the name of the deployment.
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- **API Version:** Specifies the version of the Azure OpenAI API to be used. Available options include:
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- _`"2023-03-15-preview"`_
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- _`"2023-05-15"`_
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- _`"2023-06-01-preview"`_
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- _`"2023-07-01-preview"`_
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- _`"2023-08-01-preview"`_
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- _`"2023-09-01-preview"`_
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- _`"2023-12-01-preview"`_
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- **API Key:** Your Azure OpenAI API key.
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- **Temperature (Optional):** Specifies the sampling temperature. Defaults to _`0.7`_.
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- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to _`1000`_.
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- **Input Value:** Specifies the input text for text generation.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **System Message (Optional):** A system message to pass to the model.
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For detailed documentation and integration guides, please refer to the [Azure OpenAI Component Documentation](https://python.langchain.com/docs/integrations/llms/azure_openai).
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---
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### QianfanChatEndpoint
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This component facilitates the generation of text using Baidu Qianfan chat models.
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**Params**
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- **Model Name:** Specifies the name of the Qianfan chat model to be used for text generation. Available options include:
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- _`"ERNIE-Bot"`_
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- _`"ERNIE-Bot-turbo"`_
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- _`"BLOOMZ-7B"`_
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- _`"Llama-2-7b-chat"`_
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- _`"Llama-2-13b-chat"`_
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- _`"Llama-2-70b-chat"`_
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- _`"Qianfan-BLOOMZ-7B-compressed"`_
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- _`"Qianfan-Chinese-Llama-2-7B"`_
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- _`"ChatGLM2-6B-32K"`_
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- _`"AquilaChat-7B"`_
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- **Qianfan Ak:** Your Baidu Qianfan access key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop).
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- **Qianfan Sk:** Your Baidu Qianfan secret key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop).
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- **Top p (Optional):** Model parameter. Specifies the top-p value. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.8`_.
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- **Temperature (Optional):** Model parameter. Specifies the sampling temperature. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.95`_.
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- **Penalty Score (Optional):** Model parameter. Specifies the penalty score. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`1.0`_.
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- **Endpoint (Optional):** Endpoint of the Qianfan LLM, required if custom model is used.
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- **Input Value:** Specifies the input text for text generation.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **System Message (Optional):** A system message to pass to the model.
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---
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### Cohere
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This component enables text generation using Cohere large language models.
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**Params**
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- **Cohere API Key:** Your Cohere API key.
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- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to _`256`_.
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- **Temperature (Optional):** Specifies the sampling temperature. Defaults to _`0.75`_.
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- **Input Value:** Specifies the input text for text generation.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **System Message (Optional):** A system message to pass to the model.
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---
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### CTransformers
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This component allows the generation of text using CTransformers large language models.
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**Params**
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- **Model:** Specifies the CTransformers model to be used for text generation.
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- **Model File (Optional):** Path to the model file if using a custom model. Should be a _.bin_ file.
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- **Model Type:** Specifies the type of the CTransformers model.
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- **Config (Optional):** Additional configuration parameters for the model. It should be provided as a JSON object.
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Defaults to:
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`{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}`.
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- **Input Value:** Specifies the input text for text generation.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **System Message (Optional):** A system message to pass to the model.
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---
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### Google Generative AI
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This component enables text generation using Google Generative AI.
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**Params**
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- **Google API Key:** Your Google API key to use for the Google Generative AI.
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- **Model:** The name of the model to use. Supported examples are _`"gemini-pro"`_ and _`"gemini-pro-vision"`_.
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- **Max Output Tokens (Optional):** The maximum number of tokens to generate.
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- **Temperature:** Run inference with this temperature. Must be in the closed interval [0.0, 1.0].
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- **Top K (Optional):** Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.
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- **Top P (Optional):** The maximum cumulative probability of tokens to consider when sampling.
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- **N (Optional):** Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.
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- **Input Value:** The input to the model.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **System Message (Optional):** A system message to pass to the model.
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---
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### Hugging Face API
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This component facilitates text generation using LLM models from the Hugging Face Inference API.
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**Params**
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- **Endpoint URL:** The URL of the Hugging Face Inference API endpoint. Should be provided along with necessary authentication credentials.
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- **Task:** Specifies the task for text generation. Options include _`"text2text-generation"`_, _`"text-generation"`_, and _`"summarization"`_.
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- **API Token:** The API token required for authentication with the Hugging Face Hub.
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- **Model Keyword Arguments (Optional):** Additional keyword arguments for the model. Should be provided as a Python dictionary.
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- **Input Value:** The input text for text generation.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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- **System Message (Optional):** A system message to pass to the model.
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---
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### LlamaCpp
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The `LlamaCpp` is a component for generating text using the llama.cpp model.
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**Params**
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- **Model Path:** The path to the llama.cpp model file. This should be provided as a file type input.
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- **Input Value:** The input text for text generation.
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- **Grammar (Optional):** The grammar for text generation.
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- **Cache (Optional):** Specifies whether to cache the generated text.
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- **Client (Optional):** The client to use for text generation.
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- **Echo (Optional):** Specifies whether to echo the generated text. Defaults to _`False`_.
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- **F16 KV:** Specifies whether to use F16 key-value pairs. Defaults to _`True`_.
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- **Grammar Path (Optional):** The path to the grammar file.
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- **Last N Tokens Size (Optional):** The size of the last N tokens. Defaults to _`64`_.
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- **Logits All:** Specifies whether to include logits for all tokens. Defaults to _`False`_.
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- **Logprobs (Optional):** The log probabilities for text generation.
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- **Lora Base (Optional):** The base URL for Lora.
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- **Lora Path (Optional):** The path for Lora.
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- **Max Tokens (Optional):** The maximum number of tokens to generate. Defaults to _`256`_.
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- **Metadata (Optional):** Additional metadata for the model.
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- **Model Kwargs:** Additional keyword arguments for the model. Should be provided as a Python dictionary.
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- **N Batch (Optional):** The batch size. Defaults to _`8`_.
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- **N Ctx:** The context size. Defaults to _`512`_.
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- **N GPU Layers (Optional):** The number of GPU layers.
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- **N Parts:** The number of parts.
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- **N Threads (Optional):** The number of threads. Defaults to _`1`_.
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- **Repeat Penalty (Optional):** The repeat penalty for text generation. Defaults to _`1.1`_.
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- **Rope Freq Base:** The base frequency for rope.
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- **Rope Freq Scale:** The scale frequency for rope.
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- **Seed:** The seed for random generation.
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- **Stop (Optional):** The stop words for text generation.
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- **Streaming:** Specifies whether to stream the response from the model. Defaults to _`True`_.
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- **Suffix (Optional):** The suffix for text generation.
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- **Tags (Optional):** The tags for text generation.
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- **Temperature (Optional):** The temperature for text generation. Defaults to _`0.8`_.
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- **Top K (Optional):** The top K tokens to consider for text generation. Defaults to _`40`_.
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- **Top P (Optional):** The top P probability threshold for text generation. Defaults to _`0.95`_.
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- **Use Mlock:** Specifies whether to use Mlock. Defaults to _`False`_.
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- **Use Mmap (Optional):** Specifies whether to use Mmap. Defaults to _`True`_.
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- **Verbose:** Specifies whether to enable verbose mode. Defaults to _`True`_.
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- **Vocab Only:** Specifies whether to include vocabulary only.
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- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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For more information, please refer to the [documentation](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp).
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---
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### ChatOllama
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This component facilitates text generation using the Local LLM model for chat with Ollama.
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**Params**
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- **Base URL:** The endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.
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- **Model Name:** The name of the model to use. Refer to [https://ollama.ai/library](https://ollama.ai/library) for more models.
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- **Input Value:** The input text for text generation.
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- **Mirostat:** Enable/disable Mirostat sampling for controlling perplexity.
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- **Mirostat Eta (Optional):** The learning rate for the Mirostat algorithm. (Default: 0.1)
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- **Mirostat Tau (Optional):** Controls the balance between coherence and diversity of the output. (Default: 5.0)
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- **Repeat Last N (Optional):** How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)
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- **Verbose (Optional):** Whether to print out response text.
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- **Cache (Optional):** Enable or disable caching. Defaults to _`False`_.
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- **Context Window Size (Optional):** Size of the context window for generating tokens. (Default: 2048)
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- **Number of GPUs (Optional):** Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)
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- **Format (Optional):** Specify the format of the output (e.g., json).
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- **Metadata (Optional):** Metadata to add to the run trace.
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- **Number of Threads (Optional):** Number of threads to use during computation. (Default: detected for optimal performance)
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- **Repeat Penalty (Optional):** Penalty for repetitions in generated text. (Default: 1.1)
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- **Stop Tokens (Optional):** List of tokens to signal the model to stop generating text.
|
||||
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||||
- **System (Optional):** System to use for generating text.
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||||
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- **Tags (Optional):** Tags to add to the run trace.
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||||
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- **Temperature (Optional):** Controls the creativity of model responses. Defaults to _`0.8`_.
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||||
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- **Template (Optional):** Template to use for generating text.
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||||
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- **TFS Z (Optional):** Tail free sampling value. (Default: 1)
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- **Timeout (Optional):** Timeout for the request stream.
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||||
|
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- **Top K (Optional):** Limits token selection to top K. (Default: 40)
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||||
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||||
- **Top P (Optional):** Works together with top-k. (Default: 0.9)
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||||
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||||
- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
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||||
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||||
- **System Message (Optional):** System message to pass to the model.
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||||
|
||||
---
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||||
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### OpenAIModel
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|
||||
This component facilitates text generation using OpenAI's models.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input Value:** The input text for text generation.
|
||||
|
||||
- **Max Tokens (Optional):** The maximum number of tokens to generate. Defaults to _`256`_.
|
||||
|
||||
- **Model Kwargs (Optional):** Additional keyword arguments for the model. Should be provided as a nested dictionary.
|
||||
|
||||
- **Model Name (Optional):** The name of the model to use. Defaults to _`gpt-4-1106-preview`_. Supported options include: _`gpt-4-turbo-preview`_, _`gpt-4-0125-preview`_, _`gpt-4-1106-preview`_, _`gpt-4-vision-preview`_, _`gpt-3.5-turbo-0125`_, _`gpt-3.5-turbo-1106`_.
|
||||
|
||||
- **OpenAI API Base (Optional):** The base URL of the OpenAI API. Defaults to _`https://api.openai.com/v1`_.
|
||||
|
||||
- **OpenAI API Key (Optional):** The API key for accessing the OpenAI API.
|
||||
|
||||
- **Temperature:** Controls the creativity of model responses. Defaults to _`0.7`_.
|
||||
|
||||
- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
|
||||
|
||||
- **System Message (Optional):** System message to pass to the model.
|
||||
|
||||
---
|
||||
|
||||
### ChatVertexAI
|
||||
|
||||
The `ChatVertexAI` is a component for generating text using Vertex AI Chat large language models API.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input Value:** The input text for text generation.
|
||||
|
||||
- **Credentials:** The JSON file containing the credentials for accessing the Vertex AI Chat API.
|
||||
|
||||
- **Project:** The name of the project associated with the Vertex AI Chat API.
|
||||
|
||||
- **Examples (Optional):** List of examples to provide context for text generation.
|
||||
|
||||
- **Location:** The location of the Vertex AI Chat API service. Defaults to _`us-central1`_.
|
||||
|
||||
- **Max Output Tokens:** The maximum number of tokens to generate. Defaults to _`128`_.
|
||||
|
||||
- **Model Name:** The name of the model to use. Defaults to _`chat-bison`_.
|
||||
|
||||
- **Temperature:** Controls the creativity of model responses. Defaults to _`0.0`_.
|
||||
|
||||
- **Top K:** Limits token selection to top K. Defaults to _`40`_.
|
||||
|
||||
- **Top P:** Works together with top-k. Defaults to _`0.95`_.
|
||||
|
||||
- **Verbose:** Whether to print out response text. Defaults to _`False`_.
|
||||
|
||||
- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_.
|
||||
|
||||
- **System Message (Optional):** System message to pass to the model.
|
||||
|
|
@ -21,7 +21,7 @@ The `PromptTemplate` component allows users to create prompts and define variabl
|
|||
<Admonition type="info">
|
||||
Once a variable is defined in the prompt template, it becomes a component
|
||||
input of its own. Check out [Prompt
|
||||
Customization](../docs/guidelines/prompt-customization.mdx) to learn more.
|
||||
Customization](../guidelines/prompt-customization) to learn more.
|
||||
</Admonition>
|
||||
|
||||
- **template:** Template used to format an individual request.
|
||||
|
|
|
|||
|
|
@ -6,4 +6,640 @@ import Admonition from '@theme/Admonition';
|
|||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
</Admonition>
|
||||
|
||||
|
||||
### AstraDB
|
||||
|
||||
The `AstraDB` is a component for initializing an AstraDB Vector Store from Records. It facilitates the creation of AstraDB-based vector indexes for efficient document storage and retrieval.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input:** The input documents or records.
|
||||
|
||||
- **Embedding:** The embedding model used by AstraDB.
|
||||
|
||||
- **Collection Name:** The name of the collection in AstraDB.
|
||||
|
||||
- **Token:** The token for AstraDB.
|
||||
|
||||
- **API Endpoint:** The API endpoint for AstraDB.
|
||||
|
||||
- **Namespace:** The namespace in AstraDB.
|
||||
|
||||
- **Metric:** The metric to use in AstraDB.
|
||||
|
||||
- **Batch Size:** The batch size for AstraDB.
|
||||
|
||||
- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for AstraDB.
|
||||
|
||||
- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for AstraDB.
|
||||
|
||||
- **Bulk Delete Concurrency:** The bulk delete concurrency for AstraDB.
|
||||
|
||||
- **Setup Mode:** The setup mode for the vector store.
|
||||
|
||||
- **Pre Delete Collection:** Pre delete collection.
|
||||
|
||||
- **Metadata Indexing Include:** Metadata indexing include.
|
||||
|
||||
- **Metadata Indexing Exclude:** Metadata indexing exclude.
|
||||
|
||||
- **Collection Indexing Policy:** Collection indexing policy.
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
Ensure that the required AstraDB token and API endpoint are properly configured.
|
||||
</p>
|
||||
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### AstraDB Search
|
||||
|
||||
The `AstraDBSearch` is a component for searching an existing AstraDB Vector Store for similar documents. It extends the functionality of the `AstraDB` component to provide efficient document retrieval based on similarity metrics.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
|
||||
|
||||
- **Input Value:** The input value to search for.
|
||||
|
||||
- **Embedding:** The embedding model used by AstraDB.
|
||||
|
||||
- **Collection Name:** The name of the collection in AstraDB.
|
||||
|
||||
- **Token:** The token for AstraDB.
|
||||
|
||||
- **API Endpoint:** The API endpoint for AstraDB.
|
||||
|
||||
- **Namespace:** The namespace in AstraDB.
|
||||
|
||||
- **Metric:** The metric to use in AstraDB.
|
||||
|
||||
- **Batch Size:** The batch size for AstraDB.
|
||||
|
||||
- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for AstraDB.
|
||||
|
||||
- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for AstraDB.
|
||||
|
||||
- **Bulk Delete Concurrency:** The bulk delete concurrency for AstraDB.
|
||||
|
||||
- **Setup Mode:** The setup mode for the vector store.
|
||||
|
||||
- **Pre Delete Collection:** Pre delete collection.
|
||||
|
||||
- **Metadata Indexing Include:** Metadata indexing include.
|
||||
|
||||
- **Metadata Indexing Exclude:** Metadata indexing exclude.
|
||||
|
||||
- **Collection Indexing Policy:** Collection indexing policy.
|
||||
|
||||
---
|
||||
|
||||
### Chroma
|
||||
|
||||
The `Chroma` is a component designed for implementing a Vector Store using Chroma. This component allows users to utilize Chroma for efficient vector storage and retrieval within their language processing workflows.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Collection Name:** The name of the collection.
|
||||
|
||||
- **Persist Directory:** The directory to persist the Vector Store to.
|
||||
|
||||
- **Server CORS Allow Origins (Optional):** The CORS allow origins for the Chroma server.
|
||||
|
||||
- **Server Host (Optional):** The host for the Chroma server.
|
||||
|
||||
- **Server Port (Optional):** The port for the Chroma server.
|
||||
|
||||
- **Server gRPC Port (Optional):** The gRPC port for the Chroma server.
|
||||
|
||||
- **Server SSL Enabled (Optional):** Whether to enable SSL for the Chroma server.
|
||||
|
||||
|
||||
- **Input:** Input data for creating the Vector Store.
|
||||
|
||||
- **Embedding:** The embeddings to use for the Vector Store.
|
||||
|
||||
For detailed documentation and integration guides, please refer to the [Chroma Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/chroma).
|
||||
|
||||
---
|
||||
|
||||
### Chroma Search
|
||||
|
||||
|
||||
The `ChromaSearch` is a component designed for searching a Chroma collection for similar documents. This component integrates with Chroma to facilitate efficient document retrieval based on similarity metrics.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input:** The input text to search for similar documents.
|
||||
|
||||
- **Search Type:** The type of search to perform ("Similarity" or "MMR").
|
||||
|
||||
- **Collection Name:** The name of the Chroma collection.
|
||||
|
||||
- **Index Directory:** The directory where the Chroma index is stored.
|
||||
|
||||
- **Embedding:** The embedding model used to vectorize inputs (make sure to use the same as the index).
|
||||
|
||||
- **Server CORS Allow Origins (Optional):** The CORS allow origins for the Chroma server.
|
||||
|
||||
- **Server Host (Optional):** The host for the Chroma server.
|
||||
|
||||
- **Server Port (Optional):** The port for the Chroma server.
|
||||
|
||||
- **Server gRPC Port (Optional):** The gRPC port for the Chroma server.
|
||||
|
||||
- **Server SSL Enabled (Optional):** Whether SSL is enabled for the Chroma server.
|
||||
|
||||
|
||||
---
|
||||
|
||||
### FAISS
|
||||
|
||||
The `FAISS` is a component designed for ingesting documents into a FAISS Vector Store. It facilitates efficient document indexing and retrieval using the FAISS library.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Embedding:** The embedding model used to vectorize inputs.
|
||||
|
||||
- **Input:** The input documents to ingest into the FAISS Vector Store.
|
||||
|
||||
- **Folder Path:** The path to save the FAISS index. It will be relative to where Langflow is running.
|
||||
|
||||
- **Index Name:** The name of the FAISS index.
|
||||
|
||||
|
||||
For detailed documentation and integration guides, please refer to the [FAISS Component Documentation](https://faiss.ai/index.html).
|
||||
|
||||
---
|
||||
|
||||
### FAISS Search
|
||||
|
||||
The `FAISSSearch` is a component for searching a FAISS Vector Store for similar documents. It enables efficient document retrieval based on similarity metrics using FAISS.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Embedding:** The embedding model used by the FAISS Vector Store.
|
||||
|
||||
- **Folder Path:** The path from which to load the FAISS index. It will be relative to where Langflow is running.
|
||||
|
||||
- **Input:** The input value to search for similar documents.
|
||||
|
||||
- **Index Name:** The name of the FAISS index.
|
||||
|
||||
|
||||
---
|
||||
|
||||
|
||||
### MongoDB Atlas
|
||||
|
||||
The `MongoDBAtlas` is a component used to construct a MongoDB Atlas Vector Search vector store from Records. It facilitates the creation of MongoDB Atlas-based vector stores for efficient document storage and retrieval.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Embedding:** The embedding model used by the MongoDB Atlas Vector Search.
|
||||
|
||||
- **Input:** The input documents or records.
|
||||
|
||||
- **Collection Name:** The name of the collection in the MongoDB Atlas database.
|
||||
|
||||
- **Database Name:** The name of the database in MongoDB Atlas.
|
||||
|
||||
- **Index Name:** The name of the index in MongoDB Atlas.
|
||||
|
||||
- **MongoDB Atlas Cluster URI:** The URI of the MongoDB Atlas cluster.
|
||||
|
||||
- **Search Kwargs:** Additional search arguments for MongoDB Atlas.
|
||||
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
Ensure that pymongo is installed to use MongoDB Atlas Vector Store.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### MongoDB Atlas Search
|
||||
|
||||
The `MongoDBAtlasSearch` is a component for searching a MongoDB Atlas Vector Store for similar documents. It extends the functionality of the MongoDBAtlasComponent to provide efficient document retrieval based on similarity metrics.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform. Options: "Similarity", "MMR".
|
||||
|
||||
- **Input:** The input value to search for.
|
||||
|
||||
- **Embedding:** The embedding model used by the MongoDB Atlas Vector Store.
|
||||
|
||||
- **Collection Name:** The name of the collection in the MongoDB Atlas database.
|
||||
|
||||
- **Database Name:** The name of the database in MongoDB Atlas.
|
||||
|
||||
- **Index Name:** The name of the index in MongoDB Atlas.
|
||||
|
||||
- **MongoDB Atlas Cluster URI:** The URI of the MongoDB Atlas cluster.
|
||||
|
||||
- **Search Kwargs:** Additional search arguments for MongoDB Atlas.
|
||||
|
||||
|
||||
---
|
||||
|
||||
### PGVector
|
||||
|
||||
The `PGVector` is a component for implementing a Vector Store using PostgreSQL. It allows users to store and retrieve vectors efficiently within a PostgreSQL database.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input:** The input value to use for the Vector Store.
|
||||
|
||||
- **Embedding:** The embedding model used by the Vector Store.
|
||||
|
||||
- **PostgreSQL Server Connection String:** The URL for the PostgreSQL server.
|
||||
|
||||
- **Table:** The name of the table in the PostgreSQL database.
|
||||
|
||||
|
||||
For detailed documentation and integration guides, please refer to the [PGVector Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/pgvector).
|
||||
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
Ensure that the required PostgreSQL server is accessible and properly configured.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### PGVector Search
|
||||
|
||||
The `PGVectorSearch` is a component for searching a PGVector Store for similar documents. It extends the functionality of the PGVectorComponent to provide efficient document retrieval based on similarity metrics.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input:** The input value to search for.
|
||||
|
||||
- **Embedding:** The embedding model used by the Vector Store.
|
||||
|
||||
- **PostgreSQL Server Connection String:** The URL for the PostgreSQL server.
|
||||
|
||||
- **Table:** The name of the table in the PostgreSQL database.
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., "Similarity", "MMR").
|
||||
|
||||
|
||||
---
|
||||
|
||||
### Pinecone
|
||||
|
||||
The `Pinecone` is a component used to construct a Pinecone wrapper from Records. It facilitates the creation of Pinecone-based vector indexes for efficient document storage and retrieval.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input:** The input documents or records.
|
||||
|
||||
- **Embedding:** The embedding model used by Pinecone.
|
||||
|
||||
- **Index Name:** The name of the index in Pinecone.
|
||||
|
||||
- **Namespace:** The namespace in Pinecone.
|
||||
|
||||
- **Pinecone API Key:** The API key for Pinecone.
|
||||
|
||||
- **Pinecone Environment:** The environment for Pinecone.
|
||||
|
||||
- **Search Kwargs:** Additional search keyword arguments for Pinecone.
|
||||
|
||||
- **Pool Threads:** The number of threads to use for Pinecone.
|
||||
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
Ensure that the required Pinecone API key and environment are properly configured.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### Pinecone Search
|
||||
|
||||
The `PineconeSearch` is a component used to search a Pinecone Vector Store for similar documents. It extends the functionality of the `PineconeComponent` to provide efficient document retrieval based on similarity metrics.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
|
||||
|
||||
- **Input Value:** The input value to search for.
|
||||
|
||||
- **Embedding:** The embedding model used by Pinecone.
|
||||
|
||||
- **Index Name:** The name of the index in Pinecone.
|
||||
|
||||
- **Namespace:** The namespace in Pinecone.
|
||||
|
||||
- **Pinecone API Key:** The API key for Pinecone.
|
||||
|
||||
- **Pinecone Environment:** The environment for Pinecone.
|
||||
|
||||
- **Search Kwargs:** Additional search keyword arguments for Pinecone.
|
||||
|
||||
- **Pool Threads:** The number of threads to use for Pinecone.
|
||||
|
||||
|
||||
---
|
||||
|
||||
### Qdrant
|
||||
|
||||
The `Qdrant` is a component used to construct a Qdrant wrapper from a list of texts. It allows for efficient similarity search and retrieval operations based on the provided embeddings.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input:** The input documents or records.
|
||||
|
||||
- **Embedding:** The embedding model used by Qdrant.
|
||||
|
||||
- **API Key:** The API key for Qdrant (password field).
|
||||
|
||||
- **Collection Name:** The name of the collection in Qdrant.
|
||||
|
||||
- **Content Payload Key:** The key for the content payload in the documents (advanced).
|
||||
|
||||
- **Distance Function:** The distance function to use in Qdrant (advanced).
|
||||
|
||||
- **gRPC Port:** The gRPC port for Qdrant (advanced).
|
||||
|
||||
- **Host:** The host for Qdrant (advanced).
|
||||
|
||||
- **HTTPS:** Enable HTTPS for Qdrant (advanced).
|
||||
|
||||
- **Location:** The location for Qdrant (advanced).
|
||||
|
||||
- **Metadata Payload Key:** The key for the metadata payload in the documents (advanced).
|
||||
|
||||
- **Path:** The path for Qdrant (advanced).
|
||||
|
||||
- **Port:** The port for Qdrant (advanced).
|
||||
|
||||
- **Prefer gRPC:** Prefer gRPC for Qdrant (advanced).
|
||||
|
||||
- **Prefix:** The prefix for Qdrant (advanced).
|
||||
|
||||
- **Search Kwargs:** Additional search keyword arguments for Qdrant (advanced).
|
||||
|
||||
- **Timeout:** The timeout for Qdrant (advanced).
|
||||
|
||||
- **URL:** The URL for Qdrant (advanced).
|
||||
|
||||
---
|
||||
|
||||
### Qdrant Search
|
||||
|
||||
The `QdrantSearch` is a component used to search a Qdrant Vector Store for similar documents. It extends the functionality of the `QdrantComponent` to provide efficient document retrieval based on similarity metrics.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
|
||||
|
||||
- **Input Value:** The input value to search for.
|
||||
|
||||
- **Embedding:** The embedding model used by Qdrant.
|
||||
|
||||
- **API Key:** The API key for Qdrant (password field).
|
||||
|
||||
- **Collection Name:** The name of the collection in Qdrant.
|
||||
|
||||
- **Content Payload Key:** The key for the content payload in the documents (advanced).
|
||||
|
||||
- **Distance Function:** The distance function to use in Qdrant (advanced).
|
||||
|
||||
- **gRPC Port:** The gRPC port for Qdrant (advanced).
|
||||
|
||||
- **Host:** The host for Qdrant (advanced).
|
||||
|
||||
- **HTTPS:** Enable HTTPS for Qdrant (advanced).
|
||||
|
||||
- **Location:** The location for Qdrant (advanced).
|
||||
|
||||
- **Metadata Payload Key:** The key for the metadata payload in the documents (advanced).
|
||||
|
||||
- **Path:** The path for Qdrant (advanced).
|
||||
|
||||
- **Port:** The port for Qdrant (advanced).
|
||||
|
||||
- **Prefer gRPC:** Prefer gRPC for Qdrant (advanced).
|
||||
|
||||
- **Prefix:** The prefix for Qdrant (advanced).
|
||||
|
||||
- **Search Kwargs:** Additional search keyword arguments for Qdrant (advanced).
|
||||
|
||||
- **Timeout:** The timeout for Qdrant (advanced).
|
||||
|
||||
- **URL:** The URL for Qdrant (advanced).
|
||||
|
||||
---
|
||||
|
||||
### Redis
|
||||
|
||||
The `Redis` is a component for implementing a Vector Store using Redis. It provides functionality to store and retrieve vectors efficiently from a Redis database.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Index Name:** The name of the index in Redis (default: your_index).
|
||||
|
||||
- **Input:** The input data to build the Redis Vector Store (input types: Document, Record).
|
||||
|
||||
- **Embedding:** The embedding model used by Redis.
|
||||
|
||||
- **Schema:** The schema file (.yaml) to define the structure of the documents (optional).
|
||||
|
||||
- **Redis Server Connection String:** The connection string for the Redis server.
|
||||
|
||||
- **Redis Index:** The name of the Redis index (optional).
|
||||
|
||||
For detailed documentation, please refer to the [Redis Documentation](https://python.langchain.com/docs/integrations/vectorstores/redis).
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
Ensure that the required Redis server connection URL and index name are properly configured. If no documents are provided, a schema must be provided.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### Redis Search
|
||||
|
||||
The `RedisSearch` is a component for searching a Redis Vector Store for similar documents.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
|
||||
|
||||
- **Input Value:** The input value to search for.
|
||||
|
||||
- **Index Name:** The name of the index in Redis (default: your_index).
|
||||
|
||||
- **Embedding:** The embedding model used by Redis.
|
||||
|
||||
- **Schema:** The schema file (.yaml) to define the structure of the documents (optional).
|
||||
|
||||
- **Redis Server Connection String:** The connection string for the Redis server.
|
||||
|
||||
- **Redis Index:** The name of the Redis index (optional).
|
||||
|
||||
---
|
||||
|
||||
### Supabase
|
||||
|
||||
The `Supabase` is a component for initializing a Supabase Vector Store from texts and embeddings.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Input:** The input documents or records.
|
||||
|
||||
- **Embedding:** The embedding model used by Supabase.
|
||||
|
||||
- **Query Name:** The name of the query (optional).
|
||||
|
||||
- **Search Kwargs:** Additional search keyword arguments for Supabase (advanced).
|
||||
|
||||
- **Supabase Service Key:** The service key for Supabase.
|
||||
|
||||
- **Supabase URL:** The URL for the Supabase instance.
|
||||
|
||||
- **Table Name:** The name of the table in Supabase (advanced).
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
Ensure that the required Supabase service key, Supabase URL, and table name are properly configured.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### Supabase Search
|
||||
|
||||
The `SupabaseSearch` is a component for searching a Supabase Vector Store for similar documents.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
|
||||
|
||||
- **Input Value:** The input value to search for.
|
||||
|
||||
- **Embedding:** The embedding model used by Supabase.
|
||||
|
||||
- **Query Name:** The name of the query (optional).
|
||||
|
||||
- **Search Kwargs:** Additional search keyword arguments for Supabase (advanced).
|
||||
|
||||
- **Supabase Service Key:** The service key for Supabase.
|
||||
|
||||
- **Supabase URL:** The URL for the Supabase instance.
|
||||
|
||||
- **Table Name:** The name of the table in Supabase (advanced).
|
||||
|
||||
---
|
||||
|
||||
### Vectara
|
||||
|
||||
The `Vectara` is a component for implementing a Vector Store using Vectara.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Vectara Customer ID:** The customer ID for Vectara.
|
||||
|
||||
- **Vectara Corpus ID:** The corpus ID for Vectara.
|
||||
|
||||
- **Vectara API Key:** The API key for Vectara.
|
||||
|
||||
- **Files Url:** The URL(s) of the file(s) to be used for initializing the Vectara Vector Store (optional).
|
||||
|
||||
- **Input:** The input data to be upserted to the corpus (optional).
|
||||
|
||||
For detailed documentation and integration guides, please refer to the [Vectara Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/vectara).
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
If `inputs` are provided, they will be upserted to the corpus. If `files_url` are provided, Vectara will process the files from the URLs.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### Vectara Search
|
||||
|
||||
The `VectaraSearch` is a component for searching a Vectara Vector Store for similar documents.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
|
||||
|
||||
- **Input Value:** The input value to search for.
|
||||
|
||||
- **Vectara Customer ID:** The customer ID for Vectara.
|
||||
|
||||
- **Vectara Corpus ID:** The corpus ID for Vectara.
|
||||
|
||||
- **Vectara API Key:** The API key for Vectara.
|
||||
|
||||
- **Files Url:** The URL(s) of the file(s) to be used for initializing the Vectara Vector Store (optional).
|
||||
---
|
||||
|
||||
### Weaviate
|
||||
|
||||
The `Weaviate` is a component for implementing a Vector Store using Weaviate.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Weaviate URL:** The URL of the Weaviate instance (default: http://localhost:8080).
|
||||
|
||||
- **Search By Text:** Boolean indicating whether to search by text (default: False).
|
||||
|
||||
- **API Key:** The API key for authentication (optional).
|
||||
|
||||
- **Index name:** The name of the index in Weaviate (optional).
|
||||
|
||||
- **Text Key:** The key used to extract text from documents (default: "text").
|
||||
|
||||
- **Input:** The input document or record.
|
||||
|
||||
- **Embedding:** The embedding model used by Weaviate.
|
||||
|
||||
- **Attributes:** Additional attributes to consider during indexing (optional).
|
||||
|
||||
For detailed documentation and integration guides, please refer to the [Weaviate Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/weaviate).
|
||||
|
||||
<Admonition type="note" title="Note">
|
||||
<p>
|
||||
Before using the Weaviate Vector Store component, ensure that you have a Weaviate instance running and accessible at the specified URL. Additionally, make sure to provide the correct API key for authentication if required. Adjust the index name, text key, and attributes according to your dataset and indexing requirements. Finally, ensure that the provided embeddings are compatible with Weaviate's requirements.
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
---
|
||||
|
||||
### Weaviate Search
|
||||
|
||||
The `WeaviateSearch` component facilitates searching a Weaviate Vector Store for similar documents.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
|
||||
|
||||
- **Input Value:** The input value to search for.
|
||||
|
||||
- **Weaviate URL:** The URL of the Weaviate instance (default: http://localhost:8080).
|
||||
|
||||
- **Search By Text:** Boolean indicating whether to search by text (default: False).
|
||||
|
||||
- **API Key:** The API key for authentication (optional).
|
||||
|
||||
- **Index name:** The name of the index in Weaviate (optional).
|
||||
|
||||
- **Text Key:** The key used to extract text from documents (default: "text").
|
||||
|
||||
- **Embedding:** The embedding model used by Weaviate.
|
||||
|
||||
- **Attributes:** Additional attributes to consider during indexing (optional).
|
||||
Loading…
Add table
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Reference in a new issue