1.0 Alpha (#1599)
* Update model kwargs and temperature values
* Update keyboard shortcuts for advanced editing
* make Message field have no handles
* Update OpenAI API Key handling in OpenAIEmbeddingsComponent
* Remove unnecessary field_type key from CustomComponent class
* Update required field behavior in CustomComponent class
* Refactor AzureOpenAIModel.py: Removed unnecessary "required" attribute from input parameters
* Update BaiduQianfanChatModel and OpenAIModel configurations
* Fix range_spec step type validation
* Update RangeSpec step_type default value to "float"
* Fix Save debounce
* Update parameterUtils to use debounce instead of throttle
* Update input type options in schemas and graph base classes
* Refactor run_flow_with_caching endpoint to include simplified and experimental versions
* Add PythonFunctionComponent and test case for it
* Add nest_asyncio to fix event loop issue
* Refactor test_initial_setup.py to use RunOutputs instead of ResultData
* Remove unused code in test_endpoints.py
* Add asyncio loop to uvicorn command
* Refactor load_session method to handle coroutine result
* Fixed saving
* Fixed debouncing
* Add InputType and OutputType literals to schema.py
* Update input type in Graph class
* Add new schema for simplified API request
* Add delete_messages function and update test_successful_run assertions
* Add STREAM_INFO_TEXT constant to model components
* Add session_id to simplified_run_flow_with_caching endpoint
* Add field_typing import to OpenAIModel.py
* update starter projects
* Add constants for Langflow base module
* Update setup.py to include latest component versions
* Update Starter Examples
* sets starter_project fixture to Basic Prompting
* Refactor test_endpoints.py: Update test names and add new tests for different output types
* Update HuggingFace Spaces link and add image for dark mode
* Remove filepath reference
* Update Vertex params in base.py
* Add tests for different input types
* Add type annotations and improve test coverage
* Add duplicate space link to README
* Update HuggingFace Spaces badge in README
* Add Python 3.10 installation requirement to README
* Refactor flow running endpoints
* Refactor SimplifiedAPIRequest and add documentation for Tweaks
* Refactor input_request parameter in simplified_run_flow function
* Add support for retrieving specific component output
* Add custom Uvicorn worker for Langflow application
* Add asyncio loop to LangflowApplication initialization
* Update Makefile with new variables and start command
* Fix indentation in Makefile
* Refactor run_graph function to add support for running a JSON flow
* Refactor getChatInputField function and update API code
* Update HuggingFace Spaces documentation with duplication process
* Add asyncio event loop to uvicorn command
* Add installation of backend in start target
* udpate some starter projects
* Fix formatting in hugging-face-spaces.mdx
* Update installation instructions for Langflow
* set examples order
* Update start command in Makefile
* Add installation and usage instructions for Langflow
* Update Langflow installation and usage instructions
* Fix langflow command in README.md
* Fix broken link to HuggingFace Spaces guide
* Add new SVG assets for blog post, chat bot, and cloud docs
* Refactor example rendering in NewFlowModal
* Add new SVG file for short bio section
* Remove unused import and add new component
* Update title in usage.mdx
* Update HuggingFace Spaces heading in usage.mdx
* Update usage instructions in getting-started/usage.mdx
* Update cache option in usage documentation
* Remove 'advanced' flag from 'n_messages' parameter in MemoryComponent.py
* Refactor code to improve performance and readability
* Update project names and flow examples
* fix document qa example
* Remove commented out code in sidebars.js
* Delete unused documentation files
* Fix bug in login functionality
* Remove global variables from components
* Fix bug in login functionality
* fix modal returning to input
* Update max-width of chat message sender name
* Update styling for chat message component
* Refactor OpenAIEmbeddingsComponent signature
* Update usage.mdx file
* Update path in Makefile
* Add new migration and what's new documentation files
* Add new chapters and migration guides
* Update version to 0.0.13 in pyproject.toml
* new locks
* Update dependencies in pyproject.toml
* general fixes
* Update dependencies in pyproject.toml and poetry.lock files
* add padding to modal
* ✨ (undrawCards/index.tsx): update the SVG used for BasicPrompt component to undraw_short_bio_re_fmx0.svg to match the desired design
♻️ (undrawCards/index.tsx): adjust the width and height of the BasicPrompt SVG to 65% to improve the visual appearance
* Commented out components/data in sidebars.js
* Refactor component names in outputs.mdx
* Update embedded chat script URL
* Add data component and fix formatting in outputs component
* Update dependencies in poetry.lock and pyproject.toml
* Update dependencies in poetry.lock and pyproject.toml
* Refactor code to improve performance and readability
* Update dependencies in poetry.lock and pyproject.toml
* Fixed IO Modal updates
* Remove dead code at API Modal
* Fixed overflow at CodeTabsComponent tweaks page
* ✨ (NewFlowModal/index.tsx): update the name of the example from "Blog Writter" to "Blog Writer" for better consistency and clarity
* Update dependencies versions
* Update langflow-base to version 0.0.15 and fix setup_env script
* Update dependencies in pyproject.toml
* Lock dependencies in parallel
* Add logging statement to setup_app function
* Fix Ace not having type="module" and breaking build
* Update authentication settings for access token cookie
* Update package versions in package-lock.json
* Add scripts directory to Dockerfile
* Add setup_env command to build_and_run target
* Remove unnecessary make command in setup_env
* Remove unnecessary installation step in build_and_run
* Add debug configuration for CLI
* 🔧 chore(Makefile): refactor build_langflow target to use a separate script for updating dependencies and building
✨ feat(update_dependencies.py): add script to update pyproject.toml dependency version based on langflow-base version in src/backend/base/pyproject.toml
* Add number_of_results parameter to AstraDBSearchComponent
* Update HuggingFace Spaces links
* Remove duplicate imports in hugging-face-spaces.mdx
* Add number_of_results parameter to vector search components
* Fixed supabase not commited
* Revert "Fixed supabase not commited"
This reverts commit afb10a6262.
* Update duplicate-space.png image
* Delete unused files and components
* Add/update script to update dependencies
* Add .bak files to .gitignore
* Update version numbers and remove unnecessary dependencies
* Update langflow-base dependency path
* Add Text import to VertexAiModel.py
* Update langflow-base version to 0.0.16 and update dependencies
* Delete start projects and commit session in delete_start_projects function
* Refactor backend startup script to handle autologin option
* Update poetry installation script to include pipx update check
* Update pipx installation script for different operating systems
* Update Makefile to improve setup process
* Add error handling on streaming and fix streaming bug on error
* Added description to Blog Writer
* Sort base classes alphabetically
* Update duplicate-space.png image
* update position on langflow prompt chaining
* Add Langflow CLI and first steps documentation
* Add exception handling for missing 'content' field in search_with_vector_store method
* Remove unused import and update type hinting
* fix bug on egdes after creating group component
* Refactor APIRequest class and update model imports
* Remove unused imports and fix formatting issues
* Refactor reactflowUtils and styleUtils
* Add CLI documentation to getting-started/cli.mdx
* Add CLI usage instructions
* Add ZoomableImage component to first-steps.mdx
* Update CLI and first steps documentation
* Remove duplicate import and add new imports for ThemedImage and useBaseUrl
* Update Langflow CLI documentation link
* Remove first-steps.mdx and update index.mdx and sidebars.js
* Update Docusaurus dependencies
* Add AstraDB RAG Flow guide
* Remove unused imports
* Remove unnecessary import statement
* Refactor guide for better readability
* Add data component documentation
* Update component headings and add prompt template
* Fix logging level and version display
* Add datetime import and buffer for alembic log
* Update flow names in NewFlowModal and documentation
* Add starter projects to sidebars.js
* Fix error handling in DirectoryReader class
* Handle exception when loading components in setup.py
* Update version numbers in pyproject.toml files
* Update build_langflow_base and build_langflow_backup in Makefile
* Added docs
* Update dependencies and build process
* Add Admonition component for API Key documentation
* Update API endpoint in async-api.mdx
* Remove async-api guidelines
* Fix UnicodeDecodeError in DirectoryReader
* Update dependency version and fix encoding issues
* Add conditional build and publish for base and main projects
* Update version to 1.0.0a2 in pyproject.toml
* Remove duplicate imports and unnecessary code in custom-component.mdx
* Fix poetry lock command in Makefile
* Update package versions in pyproject.toml
* Remove unused components and update imports
* 📦 chore(pre-release-base.yml): add pre-release workflow for base project
📦 chore(pre-release-langflow.yml): add pre-release workflow for langflow project
* Add ChatLiteLLMModelComponent to models package
* Add frontend installation and build steps
* Add Dockerfile for building and pushing base image
* Add emoji package and nest-asyncio dependency
* 📝 (components.mdx): update margin style of ZoomableImage to improve spacing
📝 (features.mdx): update margin style of ZoomableImage to improve spacing
📝 (login.mdx): update margin style of ZoomableImage to improve spacing
* Fix module import error in validate.py
* Fix error message in directory_reader.py
* Update version import and handle ImportError
* Add cryptography and langchain-openai dependencies
* Update poetry installation and remove poetry-monorepo-dependency-plugin
* Update workflow and Dockerfile for Langflow base pre-release
* Update display names and descriptions for AstraDB components
* Update installation instructions for Langflow
* Update Astra DB links and remove unnecessary imports
* Rename AstraDB
* Add new components and images
* Update HuggingFace Spaces URLs
* Update Langflow documentation and add new starter projects
* Update flow name to "Basic Prompting (Hello, world!)" in relevant files
* Update Basic Prompting flow name to "Ahoy World!"
* Remove HuggingFace Spaces documentation
* Add new files and update sidebars.js
* Remove async-tasks.mdx and update sidebars.js
* Update starter project URLs
* Enable migration of global variables
* Update OpenAIEmbeddings deployment and model
* 📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (inputs.mdx): add margin to image style to improve spacing and center alignment
📝 (rag-with-astradb.mdx): add margin to image styles to improve spacing and readability
* Update welcome message in index.mdx
* Add global variable feature to Langflow documentation
* Reorganized sidebar categories
* Update migration documentation
* Refactor SplitTextComponent class to accept inputs of type Record and Text
* Adjust embeddings docs
* ✨ (cardComponent/index.tsx): add a minimum height to the card component to ensure consistent layout and prevent content from overlapping when the card is empty or has minimal content
* Update flow name from "Ahoy World!" to "Hello, world!"
* Update documentation for embeddings, models, and vector stores
* Update CreateRecordComponent and parameterUtils.ts
* Add documentation for Text and Record types
* Remove commented lines in sidebars.js
* Add run_flow_from_json function to load.py
* Update Langflow package to run flow from JSON file
* Fix type annotations and import errors
* Refactor tests and fix test data
---------
Co-authored-by: Rodrigo Nader <rodrigosilvanader@gmail.com>
Co-authored-by: anovazzi1 <otavio2204@gmail.com>
Co-authored-by: Lucas Oliveira <lucas.edu.oli@hotmail.com>
Co-authored-by: carlosrcoelho <carlosrodrigo.coelho@gmail.com>
Co-authored-by: cristhianzl <cristhian.lousa@gmail.com>
Co-authored-by: Matheus <jacquesmats@gmail.com>
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
parent
20f640c4ce
commit
05cd6e4fd7
853 changed files with 59936 additions and 15456 deletions
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import Admonition from '@theme/Admonition';
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import Admonition from "@theme/Admonition";
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# Vector Stores
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<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
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### Astra DB
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The `Astra DB` is a component for initializing an Astra DB Vector Store from Records. It facilitates the creation of Astra DB-based vector indexes for efficient document storage and retrieval.
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**Params**
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- **Input:** The input documents or records.
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- **Embedding:** The embedding model used by Astra DB.
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- **Collection Name:** The name of the collection in Astra DB.
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- **Token:** The token for Astra DB.
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- **API Endpoint:** The API endpoint for Astra DB.
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- **Namespace:** The namespace in Astra DB.
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- **Metric:** The metric to use in Astra DB.
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- **Batch Size:** The batch size for Astra DB.
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- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for Astra DB.
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- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for Astra DB.
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- **Bulk Delete Concurrency:** The bulk delete concurrency for Astra DB.
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- **Setup Mode:** The setup mode for the vector store.
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- **Pre Delete Collection:** Pre delete collection.
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- **Metadata Indexing Include:** Metadata indexing include.
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- **Metadata Indexing Exclude:** Metadata indexing exclude.
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- **Collection Indexing Policy:** Collection indexing policy.
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<Admonition type="note" title="Note">
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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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Ensure that the required Astra DB token and API endpoint are properly configured.
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</p>
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</Admonition>
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</Admonition>
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---
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### Astra DB Search
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The `Astra DBSearch` is a component for searching an existing Astra DB Vector Store for similar documents. It extends the functionality of the `Astra DB` component to provide efficient document retrieval based on similarity metrics.
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**Params**
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- **Search Type:** The type of search to perform (e.g., Similarity, MMR).
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- **Input Value:** The input value to search for.
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- **Embedding:** The embedding model used by Astra DB.
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- **Collection Name:** The name of the collection in Astra DB.
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- **Token:** The token for Astra DB.
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- **API Endpoint:** The API endpoint for Astra DB.
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- **Namespace:** The namespace in Astra DB.
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- **Metric:** The metric to use in Astra DB.
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- **Batch Size:** The batch size for Astra DB.
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- **Bulk Insert Batch Concurrency:** The bulk insert batch concurrency for Astra DB.
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- **Bulk Insert Overwrite Concurrency:** The bulk insert overwrite concurrency for Astra DB.
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- **Bulk Delete Concurrency:** The bulk delete concurrency for Astra DB.
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- **Setup Mode:** The setup mode for the vector store.
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- **Pre Delete Collection:** Pre delete collection.
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- **Metadata Indexing Include:** Metadata indexing include.
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- **Metadata Indexing Exclude:** Metadata indexing exclude.
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- **Collection Indexing Policy:** Collection indexing policy.
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---
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### Chroma
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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.
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**Params**
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- **Collection Name:** The name of the collection.
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- **Persist Directory:** The directory to persist the Vector Store to.
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- **Server CORS Allow Origins (Optional):** The CORS allow origins for the Chroma server.
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- **Server Host (Optional):** The host for the Chroma server.
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- **Server Port (Optional):** The port for the Chroma server.
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- **Server gRPC Port (Optional):** The gRPC port for the Chroma server.
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- **Server SSL Enabled (Optional):** Whether to enable SSL for the Chroma server.
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- **Input:** Input data for creating the Vector Store.
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- **Embedding:** The embeddings to use for the Vector Store.
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For detailed documentation and integration guides, please refer to the [Chroma Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/chroma).
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---
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### Chroma Search
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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.
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**Params**
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- **Input:** The input text to search for similar documents.
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- **Search Type:** The type of search to perform ("Similarity" or "MMR").
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- **Collection Name:** The name of the Chroma collection.
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- **Index Directory:** The directory where the Chroma index is stored.
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- **Embedding:** The embedding model used to vectorize inputs (make sure to use the same as the index).
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- **Server CORS Allow Origins (Optional):** The CORS allow origins for the Chroma server.
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- **Server Host (Optional):** The host for the Chroma server.
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- **Server Port (Optional):** The port for the Chroma server.
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- **Server gRPC Port (Optional):** The gRPC port for the Chroma server.
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- **Server SSL Enabled (Optional):** Whether SSL is enabled for the Chroma server.
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---
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### FAISS
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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.
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**Params**
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- **Embedding:** The embedding model used to vectorize inputs.
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- **Input:** The input documents to ingest into the FAISS Vector Store.
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- **Folder Path:** The path to save the FAISS index. It will be relative to where Langflow is running.
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- **Index Name:** The name of the FAISS index.
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For detailed documentation and integration guides, please refer to the [FAISS Component Documentation](https://faiss.ai/index.html).
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---
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### FAISS Search
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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.
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**Params**
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- **Embedding:** The embedding model used by the FAISS Vector Store.
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- **Folder Path:** The path from which to load the FAISS index. It will be relative to where Langflow is running.
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- **Input:** The input value to search for similar documents.
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- **Index Name:** The name of the FAISS index.
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---
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### MongoDB Atlas
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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.
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**Params**
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- **Embedding:** The embedding model used by the MongoDB Atlas Vector Search.
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- **Input:** The input documents or records.
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- **Collection Name:** The name of the collection in the MongoDB Atlas database.
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- **Database Name:** The name of the database in MongoDB Atlas.
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- **Index Name:** The name of the index in MongoDB Atlas.
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- **MongoDB Atlas Cluster URI:** The URI of the MongoDB Atlas cluster.
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- **Search Kwargs:** Additional search arguments for MongoDB Atlas.
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<Admonition type="note" title="Note">
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<p>Ensure that pymongo is installed to use MongoDB Atlas Vector Store.</p>
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</Admonition>
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---
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### MongoDB Atlas Search
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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.
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**Params**
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- **Search Type:** The type of search to perform. Options: "Similarity", "MMR".
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- **Input:** The input value to search for.
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- **Embedding:** The embedding model used by the MongoDB Atlas Vector Store.
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- **Collection Name:** The name of the collection in the MongoDB Atlas database.
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- **Database Name:** The name of the database in MongoDB Atlas.
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- **Index Name:** The name of the index in MongoDB Atlas.
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- **MongoDB Atlas Cluster URI:** The URI of the MongoDB Atlas cluster.
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- **Search Kwargs:** Additional search arguments for MongoDB Atlas.
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---
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### PGVector
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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.
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**Params**
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||||
|
||||
- **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
Add a link
Reference in a new issue