diff --git a/.gitignore b/.gitignore index fb5586bc5..0d74c2208 100644 --- a/.gitignore +++ b/.gitignore @@ -264,3 +264,4 @@ scratchpad* chroma*/* stuff/* src/frontend/playwright-report/index.html +*.bak \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json index 7e30dff76..962599449 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -24,6 +24,23 @@ }, "envFile": "${workspaceFolder}/.env" }, + { + "name": "Debug CLI", + "type": "python", + "request": "launch", + "module": "langflow", + "args": [ + "run", + "--path", + "${workspaceFolder}/src/backend/langflow/frontend" + ], + "jinja": true, + "justMyCode": false, + "env": { + "LANGFLOW_LOG_LEVEL": "debug" + }, + "envFile": "${workspaceFolder}/.env" + }, { "name": "Python: Remote Attach", "type": "python", diff --git a/Makefile b/Makefile index 96e70a50c..e1407c733 100644 --- a/Makefile +++ b/Makefile @@ -58,6 +58,9 @@ lint: install_frontend: cd src/frontend && npm install +install_frontendci: + cd src/frontend && npm ci + install_frontendc: cd src/frontend && rm -rf node_modules package-lock.json && npm install @@ -127,27 +130,29 @@ frontendc: make run_frontend install_backend: - @echo 'Installing backend dependencies' + @echo 'Setting up the environment' @make setup_env + @echo 'Installing backend dependencies' @poetry install --extras deploy backend: make install_backend @-kill -9 `lsof -t -i:7860` -ifeq ($(login),1) - @echo "Running backend without autologin"; - poetry run uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --env-file .env --loop asyncio +ifdef login + @echo "Running backend autologin is $(login)"; + LANGFLOW_AUTO_LOGIN=$(login) poetry run uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --env-file .env --loop asyncio else - @echo "Running backend with autologin"; - LANGFLOW_AUTO_LOGIN=True poetry run uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --env-file .env --loop asyncio + @echo "Running backend respecting the .env file"; + poetry run uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --env-file .env --loop asyncio endif build_and_run: @echo 'Removing dist folder' + @make setup_env rm -rf dist rm -rf src/backend/base/dist make build - poetry run pip install dist/*.tar.gz && pip install src/backend/base/dist/*.tar.gz + poetry run pip install dist/*.tar.gz poetry run langflow run build_and_install: @@ -166,15 +171,22 @@ build: make build_langflow_base make build_langflow -build_langflow: - poetry build-rewrite-path-deps --version-pinning-strategy=semver - build_langflow_base: - make install_frontend + make install_frontendci make build_frontend cd src/backend/base && poetry build-rewrite-path-deps --version-pinning-strategy=semver rm -rf src/backend/base/langflow/frontend +build_langflow_backup: + poetry lock && poetry build-rewrite-path-deps --version-pinning-strategy=semver + +build_langflow: + cd ./scripts && python update_dependencies.py + poetry lock + poetry build-rewrite-path-deps --version-pinning-strategy=semver + mv pyproject.toml.bak pyproject.toml + mv poetry.lock.bak poetry.lock + dev: make install_frontend ifeq ($(build),1) @@ -193,10 +205,9 @@ lock_langflow: lock: # Run both in parallel - # cd src/backend/base && poetry lock - # poetry lock @echo 'Locking dependencies' - @make -j2 lock_base lock_langflow + cd src/backend/base && poetry lock + poetry lock publish_base: make build_langflow_base cd src/backend/base && poetry publish diff --git a/README.md b/README.md index 5a42ecaa2..8d243a0dc 100644 --- a/README.md +++ b/README.md @@ -15,11 +15,9 @@ [![GitHub fork](https://img.shields.io/github/forks/logspace-ai/langflow?style=social)](https://github.com/logspace-ai/langflow/fork) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langflow_ai.svg?style=social&label=Follow%20%40langflow_ai)](https://twitter.com/langflow_ai) [![](https://dcbadge.vercel.app/api/server/EqksyE2EX9?compact=true&style=flat)](https://discord.com/invite/EqksyE2EX9) -[![HuggingFace Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md.svg)](https://huggingface.co/spaces/Logspace/Langflow?duplicate=true) +[![HuggingFace Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md.svg)](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/logspace-ai/langflow) -The easiest way to create and customize your flow - @@ -64,7 +62,7 @@ langflow run # or langflow --help ### HuggingFace Spaces -You can also check it out on HuggingFace Spaces and run it in your browser for free! [Click here to duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow?duplicate=true) +You can also check it out on HuggingFace Spaces and run it in your browser for free! [Click here to duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) # 🖥️ Command Line Interface (CLI) diff --git a/build_and_push.Dockerfile b/build_and_push.Dockerfile index 3cce7804b..136f34f9e 100644 --- a/build_and_push.Dockerfile +++ b/build_and_push.Dockerfile @@ -62,9 +62,10 @@ RUN apt-get update \ WORKDIR /app COPY pyproject.toml poetry.lock ./ COPY src ./src +COPY scripts ./scripts COPY Makefile ./ COPY README.md ./ -RUN curl -sSL https://install.python-poetry.org | python3 - && make build +RUN make build # Final stage for the application FROM python-base as final diff --git a/docs/docs/components/data.mdx b/docs/docs/components/data.mdx index e69de29bb..94620d047 100644 --- a/docs/docs/components/data.mdx +++ b/docs/docs/components/data.mdx @@ -0,0 +1,87 @@ +import Admonition from '@theme/Admonition'; + +# Data + +### API Request + +This component makes HTTP requests to the specified URLs. + +**Params** + +- **URLs:** URLs to make requests to. +- **Method:** The HTTP method to use. +- **Headers:** The headers to send with the request. +- **Body:** The body to send with the request (for POST, PATCH, PUT). +- **Timeout:** The timeout to use for the request. + + +

+ Use this component to make HTTP requests to external APIs or services and retrieve data. +

+

+ Ensure that you provide valid URLs and configure the method, headers, body, and timeout appropriately. +

+ + +--- + +### Directory + +This component recursively loads files from a directory. + +**Params** + +- **Path:** The path to the directory. +- **Types:** File types to load. Leave empty to load all types. +- **Depth:** Depth to search for files. +- **Max Concurrency:** The maximum number of concurrent file loading operations. +- **Load Hidden:** If true, hidden files will be loaded. +- **Recursive:** If true, the search will be recursive. +- **Silent Errors:** If true, errors will not raise an exception. +- **Use Multithreading:** If true, use multithreading for loading files. + + +

+ Use this component to load files from a directory, such as text files, JSON files, etc. +

+

+ Ensure that you provide the correct path to the directory and configure other parameters as needed. +

+ + + +--- + +### File + +This component loads a generic file. + +**Params** + +- **Path:** The path to the file. +- **Silent Errors:** If true, errors will not raise an exception. + + +

+ Use this component to load a generic file, such as a text file, JSON file, etc. +

+

+ Ensure that you provide the correct path to the file and configure other parameters as needed. +

+ + +--- + +### URL + +This component fetches content from one or more URLs. + +**Params** + +- **URLs:** The URLs from which content will be fetched. + + +

+ Ensure that you provide valid URLs and configure other parameters as needed. +

+ diff --git a/docs/docs/components/helpers.mdx b/docs/docs/components/helpers.mdx index 142ea8783..d965761aa 100644 --- a/docs/docs/components/helpers.mdx +++ b/docs/docs/components/helpers.mdx @@ -2,17 +2,57 @@ import Admonition from '@theme/Admonition'; # Helpers -### Custom Component +### Chat Memory -This component serves as a template for creating your own custom components. +This component retrieves stored chat messages given a specific Session ID. **Params** -- **Display Name:** Parameter +- **Sender Type:** Choose the sender type from options like "Machine", "User", or "Machine and User". +- **Sender Name:** (Optional) The name of the sender. +- **Number of Messages:** Number of messages to retrieve. +- **Session ID:** The Session ID of the chat history. +- **Order:** Choose the order of the messages, either "Ascending" or "Descending". +- **Record Template:** (Optional) Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key. -**Usage** +--- -To use this component, provide the required parameter as specified. +### Combine Text + +This component concatenates two text sources into a single text chunk using a specified delimiter. + +**Params** + +- **First Text:** The first text input to concatenate. +- **Second Text:** The second text input to concatenate. +- **Delimiter:** A string used to separate the two text inputs. Defaults to a whitespace. + +--- + +### Create Record + +This component dynamically creates a Record with a specified number of fields. + +**Params** + +- **Number of Fields:** Number of fields to be added to the record. +- **Text Key:** Key to be used as text. + +--- + +### Custom Component + +Use this component as a template to create your own custom component. + +**Params** + +- **Parameter:** Describe the purpose of this parameter. + + +

+ Customize the build_config and build methods according to your requirements. +

+ Learn more about [Custom Component](http://docs.langflow.org/components/custom). @@ -20,154 +60,68 @@ Learn more about [Custom Component](http://docs.langflow.org/components/custom). ### Documents to Records -This component converts documents to records. +Convert LangChain Documents into Records. -**Params** +**Parameters** -- **Documents:** - - **Display Name:** Documents - -**Usage** - -To use this component, provide a list of documents to be converted into records. +- **Documents:** Documents to be converted into Records. --- -### Unique ID Generator +### ID Generator -This component generates a unique ID. +Generates a unique ID. -**Params** +**Parameters** -- **Value:** - - **Display Name:** Value - - **Real Time Refresh:** True - -**Usage** - -To use this component, simply retrieve the generated unique ID from the provided value parameter. +- **Value:** Unique ID generated. --- ### Message History -This component is used to retrieve stored messages from the message history. +Retrieves stored chat messages given a specific Session ID. -**Params** +**Parameters** -- **Sender Type:** - - **Display Name:** Sender Type - - **Options:** Machine, User, Machine and User - -- **Sender Name:** - - **Display Name:** Sender Name - -- **Number of Messages:** - - **Display Name:** Number of Messages - - **Info:** Number of messages to retrieve. - -- **Session ID:** - - **Display Name:** Session ID - - **Info:** Session ID of the chat history. - - **Input Types:** Text - -**Usage** - -To use this component, configure the parameters as needed to retrieve messages from the message history. - ---- - -### Python Function - -**Params** - -- **Code:** - - **Display Name:** Code - - **Info:** The code for the function. - - **Show:** True - -**Usage** - -To use this component, provide the Python code for the function you want to define. +- **Sender Type:** Options for the sender type. +- **Sender Name:** Sender name. +- **Number of Messages:** Number of messages to retrieve. +- **Session ID:** Session ID of the chat history. +- **Order:** Order of the messages. --- ### Records to Text -This component converts records into a single piece of text using a template. +Convert Records into plain text following a specified template. -**Params** +**Parameters** -- **Records:** - - **Display Name:** Records - - **Info:** The records to convert to text. - -- **Template:** - - **Display Name:** Template - - **Info:** The template to use for formatting the records. It can contain the keys `{text}`, `{data}` or any other key in the Record. - -**Usage** - -To use this component, provide the records you want to convert to text along with a template for formatting. +- **Records:** The records to convert to text. +- **Template:** The template to use for formatting the records. It can contain the keys `{text}`, `{data}` or any other key in the Record. --- -### SearchApi +### Split Text -This component provides access to the real-time search engine results API. +Split text into chunks of a specified length. -**Params** +**Parameters** -- **Engine:** - - **Display Name:** Engine - - **Info:** The search engine to use. - -- **Parameters:** - - **Display Name:** Parameters - - **Info:** The parameters to send with the request. - -- **API Key:** - - **Display Name:** API Key - - **Info:** The API key to use SearchApi. - - **Required:** True - - **Password:** True - -Learn more about [SearchApi Documentation](https://www.searchapi.io/docs/google). - ---- - -### Text to Record - -This component enables the creation of a record from text data. - -**Params** - -- **Data:** - - **Display Name:** Data - - **Info:** The data to convert to a record. - - **Input Types:** Text - -**Usage** - -To use this component, provide the text data to convert into a record. +- **Texts:** Texts to split. +- **Separators:** The characters to split on. Defaults to [" "]. +- **Max Chunk Size:** The maximum length (in number of characters) of each chunk. +- **Chunk Overlap:** The amount of character overlap between chunks. +- **Recursive:** Whether to split recursively. --- ### Update Record -This component updates a record with new data. +Update Record with text-based key/value pairs, similar to updating a Python dictionary. -**Params** +**Parameters** -- **Record:** - - **Display Name:** Record - - **Info:** The record to update. - -- **New Data:** - - **Display Name:** New Data - - **Info:** The new data to update the record with. - - **Input Types:** Text - -**Usage** - -To use this component, provide the record to be updated along with the new data. +- **Record:** The record to update. +- **New Data:** The new data to update the record with. diff --git a/docs/docs/components/inputs.mdx b/docs/docs/components/inputs.mdx index 2e953d986..78ca6f0f6 100644 --- a/docs/docs/components/inputs.mdx +++ b/docs/docs/components/inputs.mdx @@ -22,6 +22,25 @@ This component is designed to get user input from the chat.

+--- + +### Prompt + +Create a prompt template with dynamic variables. + +**Parameters** + +- **Template:** the template for the prompt. + + +

+ Prompt variables can be created with any chosen name inside curly brackets, e.g. `{variable_name}` +

+ + +--- + + ### Text Input This component is designed for simple text input, allowing users to pass textual data to subsequent components in the workflow. It's particularly useful for scenarios where a brief user input is required to initiate or influence the flow. diff --git a/docs/docs/components/models.mdx b/docs/docs/components/models.mdx index 9d4f67257..8c47c8c7b 100644 --- a/docs/docs/components/models.mdx +++ b/docs/docs/components/models.mdx @@ -8,7 +8,7 @@ import Admonition from '@theme/Admonition';

-### AmazonBedrock +### Amazon Bedrock This component facilitates the generation of text using the LLM (Large Language Model) model from Amazon Bedrock. @@ -53,7 +53,7 @@ This component facilitates the generation of text using the LLM (Large Language --- -### AnthropicLLM +### Anthropic This component allows the generation of text using Anthropic Chat&Completion large language models. @@ -83,7 +83,7 @@ For detailed documentation and integration guides, please refer to the [Anthropi --- -### AzureChatOpenAI +### Azure OpenAI This component allows the generation of text using the LLM (Large Language Model) model from Azure OpenAI. @@ -125,44 +125,6 @@ This component allows the generation of text using the LLM (Large Language Model For detailed documentation and integration guides, please refer to the [Azure OpenAI Component Documentation](https://python.langchain.com/docs/integrations/llms/azure_openai). ---- - -### QianfanChatEndpoint - -This component facilitates the generation of text using Baidu Qianfan chat models. - -**Params** - -- **Model Name:** Specifies the name of the Qianfan chat model to be used for text generation. Available options include: - - _`"ERNIE-Bot"`_ - - _`"ERNIE-Bot-turbo"`_ - - _`"BLOOMZ-7B"`_ - - _`"Llama-2-7b-chat"`_ - - _`"Llama-2-13b-chat"`_ - - _`"Llama-2-70b-chat"`_ - - _`"Qianfan-BLOOMZ-7B-compressed"`_ - - _`"Qianfan-Chinese-Llama-2-7B"`_ - - _`"ChatGLM2-6B-32K"`_ - - _`"AquilaChat-7B"`_ - -- **Qianfan Ak:** Your Baidu Qianfan access key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). - -- **Qianfan Sk:** Your Baidu Qianfan secret key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). - -- **Top p (Optional):** Model parameter. Specifies the top-p value. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.8`_. - -- **Temperature (Optional):** Model parameter. Specifies the sampling temperature. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.95`_. - -- **Penalty Score (Optional):** Model parameter. Specifies the penalty score. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`1.0`_. - -- **Endpoint (Optional):** Endpoint of the Qianfan LLM, required if custom model is used. - -- **Input Value:** Specifies the input text for text generation. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - --- ### Cohere @@ -185,32 +147,6 @@ This component enables text generation using Cohere large language models. --- -### CTransformers - -This component allows the generation of text using CTransformers large language models. - -**Params** - -- **Model:** Specifies the CTransformers model to be used for text generation. - -- **Model File (Optional):** Path to the model file if using a custom model. Should be a _.bin_ file. - -- **Model Type:** Specifies the type of the CTransformers model. - -- **Config (Optional):** Additional configuration parameters for the model. It should be provided as a JSON object. - - Defaults to: - - `{"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}`. - -- **Input Value:** Specifies the input text for text generation. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - ---- - ### Google Generative AI This component enables text generation using Google Generative AI. @@ -261,151 +197,42 @@ This component facilitates text generation using LLM models from the Hugging Fac --- -### LlamaCpp +### Ollama -The `LlamaCpp` is a component for generating text using the llama.cpp model. +Generate text using Ollama Local LLMs. -**Params** +**Parameters** -- **Model Path:** The path to the llama.cpp model file. This should be provided as a file type input. - -- **Input Value:** The input text for text generation. - -- **Grammar (Optional):** The grammar for text generation. - -- **Cache (Optional):** Specifies whether to cache the generated text. - -- **Client (Optional):** The client to use for text generation. - -- **Echo (Optional):** Specifies whether to echo the generated text. Defaults to _`False`_. - -- **F16 KV:** Specifies whether to use F16 key-value pairs. Defaults to _`True`_. - -- **Grammar Path (Optional):** The path to the grammar file. - -- **Last N Tokens Size (Optional):** The size of the last N tokens. Defaults to _`64`_. - -- **Logits All:** Specifies whether to include logits for all tokens. Defaults to _`False`_. - -- **Logprobs (Optional):** The log probabilities for text generation. - -- **Lora Base (Optional):** The base URL for Lora. - -- **Lora Path (Optional):** The path for Lora. - -- **Max Tokens (Optional):** The maximum number of tokens to generate. Defaults to _`256`_. - -- **Metadata (Optional):** Additional metadata for the model. - -- **Model Kwargs:** Additional keyword arguments for the model. Should be provided as a Python dictionary. - -- **N Batch (Optional):** The batch size. Defaults to _`8`_. - -- **N Ctx:** The context size. Defaults to _`512`_. - -- **N GPU Layers (Optional):** The number of GPU layers. - -- **N Parts:** The number of parts. - -- **N Threads (Optional):** The number of threads. Defaults to _`1`_. - -- **Repeat Penalty (Optional):** The repeat penalty for text generation. Defaults to _`1.1`_. - -- **Rope Freq Base:** The base frequency for rope. - -- **Rope Freq Scale:** The scale frequency for rope. - -- **Seed:** The seed for random generation. - -- **Stop (Optional):** The stop words for text generation. - -- **Streaming:** Specifies whether to stream the response from the model. Defaults to _`True`_. - -- **Suffix (Optional):** The suffix for text generation. - -- **Tags (Optional):** The tags for text generation. - -- **Temperature (Optional):** The temperature for text generation. Defaults to _`0.8`_. - -- **Top K (Optional):** The top K tokens to consider for text generation. Defaults to _`40`_. - -- **Top P (Optional):** The top P probability threshold for text generation. Defaults to _`0.95`_. - -- **Use Mlock:** Specifies whether to use Mlock. Defaults to _`False`_. - -- **Use Mmap (Optional):** Specifies whether to use Mmap. Defaults to _`True`_. - -- **Verbose:** Specifies whether to enable verbose mode. Defaults to _`True`_. - -- **Vocab Only:** Specifies whether to include vocabulary only. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -For more information, please refer to the [documentation](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp). +- **Base URL:** Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified. +- **Model Name:** The model name to use. Refer to [Ollama Library](https://ollama.ai/library) for more models. +- **Temperature:** Controls the creativity of model responses. (Default: 0.8) +- **Cache:** Enable or disable caching. (Default: False) +- **Format:** Specify the format of the output (e.g., json). (Advanced) +- **Metadata:** Metadata to add to the run trace. (Advanced) +- **Mirostat:** Enable/disable Mirostat sampling for controlling perplexity. (Default: Disabled) +- **Mirostat Eta:** Learning rate for Mirostat algorithm. (Default: None) (Advanced) +- **Mirostat Tau:** Controls the balance between coherence and diversity of the output. (Default: None) (Advanced) +- **Context Window Size:** Size of the context window for generating tokens. (Default: None) (Advanced) +- **Number of GPUs:** Number of GPUs to use for computation. (Default: None) (Advanced) +- **Number of Threads:** Number of threads to use during computation. (Default: None) (Advanced) +- **Repeat Last N:** How far back the model looks to prevent repetition. (Default: None) (Advanced) +- **Repeat Penalty:** Penalty for repetitions in generated text. (Default: None) (Advanced) +- **TFS Z:** Tail free sampling value. (Default: None) (Advanced) +- **Timeout:** Timeout for the request stream. (Default: None) (Advanced) +- **Top K:** Limits token selection to top K. (Default: None) (Advanced) +- **Top P:** Works together with top-k. (Default: None) (Advanced) +- **Verbose:** Whether to print out response text. +- **Tags:** Tags to add to the run trace. (Advanced) +- **Stop Tokens:** List of tokens to signal the model to stop generating text. (Advanced) +- **System:** System to use for generating text. (Advanced) +- **Template:** Template to use for generating text. (Advanced) +- **Input:** The input text. +- **Stream:** Whether to stream the response. +- **System Message:** System message to pass to the model. (Advanced) --- -### ChatOllama - -This component facilitates text generation using the Local LLM model for chat with Ollama. - -**Params** - -- **Base URL:** The endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified. - -- **Model Name:** The name of the model to use. Refer to [https://ollama.ai/library](https://ollama.ai/library) for more models. - -- **Input Value:** The input text for text generation. - -- **Mirostat:** Enable/disable Mirostat sampling for controlling perplexity. - -- **Mirostat Eta (Optional):** The learning rate for the Mirostat algorithm. (Default: 0.1) - -- **Mirostat Tau (Optional):** Controls the balance between coherence and diversity of the output. (Default: 5.0) - -- **Repeat Last N (Optional):** How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) - -- **Verbose (Optional):** Whether to print out response text. - -- **Cache (Optional):** Enable or disable caching. Defaults to _`False`_. - -- **Context Window Size (Optional):** Size of the context window for generating tokens. (Default: 2048) - -- **Number of GPUs (Optional):** Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable) - -- **Format (Optional):** Specify the format of the output (e.g., json). - -- **Metadata (Optional):** Metadata to add to the run trace. - -- **Number of Threads (Optional):** Number of threads to use during computation. (Default: detected for optimal performance) - -- **Repeat Penalty (Optional):** Penalty for repetitions in generated text. (Default: 1.1) - -- **Stop Tokens (Optional):** List of tokens to signal the model to stop generating text. - -- **System (Optional):** System to use for generating text. - -- **Tags (Optional):** Tags to add to the run trace. - -- **Temperature (Optional):** Controls the creativity of model responses. Defaults to _`0.8`_. - -- **Template (Optional):** Template to use for generating text. - -- **TFS Z (Optional):** Tail free sampling value. (Default: 1) - -- **Timeout (Optional):** Timeout for the request stream. - -- **Top K (Optional):** Limits token selection to top K. (Default: 40) - -- **Top P (Optional):** Works together with top-k. (Default: 0.9) - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** System message to pass to the model. - ---- - -### OpenAIModel +### OpenAI This component facilitates text generation using OpenAI's models. @@ -431,14 +258,50 @@ This component facilitates text generation using OpenAI's models. --- -### ChatVertexAI +### Qianfan + +This component facilitates the generation of text using Baidu Qianfan chat models. + +**Params** + +- **Model Name:** Specifies the name of the Qianfan chat model to be used for text generation. Available options include: + - _`"ERNIE-Bot"`_ + - _`"ERNIE-Bot-turbo"`_ + - _`"BLOOMZ-7B"`_ + - _`"Llama-2-7b-chat"`_ + - _`"Llama-2-13b-chat"`_ + - _`"Llama-2-70b-chat"`_ + - _`"Qianfan-BLOOMZ-7B-compressed"`_ + - _`"Qianfan-Chinese-Llama-2-7B"`_ + - _`"ChatGLM2-6B-32K"`_ + - _`"AquilaChat-7B"`_ + +- **Qianfan Ak:** Your Baidu Qianfan access key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). + +- **Qianfan Sk:** Your Baidu Qianfan secret key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). + +- **Top p (Optional):** Model parameter. Specifies the top-p value. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.8`_. + +- **Temperature (Optional):** Model parameter. Specifies the sampling temperature. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.95`_. + +- **Penalty Score (Optional):** Model parameter. Specifies the penalty score. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`1.0`_. + +- **Endpoint (Optional):** Endpoint of the Qianfan LLM, required if custom model is used. + +- **Input Value:** Specifies the input text for text generation. + +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. + +- **System Message (Optional):** A system message to pass to the model. + +--- + +### Vertex AI 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. @@ -453,6 +316,8 @@ The `ChatVertexAI` is a component for generating text using Vertex AI Chat large - **Temperature:** Controls the creativity of model responses. Defaults to _`0.0`_. +- **Input Value:** The input text for text generation. + - **Top K:** Limits token selection to top K. Defaults to _`40`_. - **Top P:** Works together with top-k. Defaults to _`0.95`_. diff --git a/docs/docs/components/outputs.mdx b/docs/docs/components/outputs.mdx index a23255a58..6fe3f9d2a 100644 --- a/docs/docs/components/outputs.mdx +++ b/docs/docs/components/outputs.mdx @@ -2,7 +2,7 @@ import Admonition from '@theme/Admonition'; # Outputs -## Chat Output +### Chat Output This component is designed to send a message to the chat. @@ -22,7 +22,7 @@ This component is designed to send a message to the chat.

-## Text Output +### Text Output This component is designed to display text data to the user. It's particularly useful for scenarios where you don't want to send the text data to the chat, but still want to display it. diff --git a/docs/docs/getting-started/usage.mdx b/docs/docs/getting-started/cli.mdx similarity index 85% rename from docs/docs/getting-started/usage.mdx rename to docs/docs/getting-started/cli.mdx index a61e5cf04..050c4ab50 100644 --- a/docs/docs/getting-started/usage.mdx +++ b/docs/docs/getting-started/cli.mdx @@ -1,33 +1,16 @@ -# ⛓️ Running Langflow - -Langflow can be run in a variety of ways, including using the command-line interface (CLI) or HuggingFace Spaces. - -```bash -python -m langflow run -``` - -or - -```bash -langflow run # or langflow --help -``` - - -### 🤗 HuggingFace Spaces - -Check out our [guide](./hugging-face-spaces) on how to get your Langflow instance running on HuggingFace Spaces. - - # 🖥️ Command Line Interface (CLI) -Langflow provides a command-line interface (CLI) for easy management and configuration. -## Usage +## Overview -You can run the Langflow using the following command: +Langflow's Command Line Interface (CLI) is a powerful tool that allows you to interact with the Langflow server from the command line. The CLI provides a wide range of commands to help you shape Langflow to your needs. + +Running the CLI without any arguments will display a list of available commands and options. ```bash -langflow run [OPTIONS] +langflow --help +# or +langflow ``` Each option is detailed below: @@ -58,4 +41,4 @@ These parameters are important for users who need to customize the behavior of L You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a `.env` file and loaded using the `--env-file` option. -A sample `.env` file named `.env.example` is included with the project. Copy this file to a new file named `.env` and replace the example values with your actual settings. If you're setting values in both your OS and the `.env` file, the `.env` settings will take precedence. \ No newline at end of file +A sample `.env` file named `.env.example` is included with the project. Copy this file to a new file named `.env` and replace the example values with your actual settings. If you're setting values in both your OS and the `.env` file, the `.env` settings will take precedence. diff --git a/docs/docs/getting-started/hugging-face-spaces.mdx b/docs/docs/getting-started/hugging-face-spaces.mdx index ce2a8aa70..c61a99f32 100644 --- a/docs/docs/getting-started/hugging-face-spaces.mdx +++ b/docs/docs/getting-started/hugging-face-spaces.mdx @@ -2,7 +2,7 @@ ## TLDR; -A fully featured version of Langflow can be accessed via [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow?duplicate=true) with no installation required. All you gotta do is [duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow?duplicate=true) and you'll have your own copy to play around with! +A fully featured version of Langflow can be accessed via [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) with no installation required. All you gotta do is [duplicate the Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) and you'll have your own copy to play around with! --- @@ -10,7 +10,7 @@ A fully featured version of Langflow can be accessed via [HuggingFace Spaces](ht HuggingFace provides great support for running Langflow in their Spaces environment. This means you can run Langflow without any installation required. -The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Logspace/Langflow?duplicate=true). +The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true). You'll be greeted with the following screen: diff --git a/docs/docs/getting-started/installation.mdx b/docs/docs/getting-started/installation.mdx deleted file mode 100644 index 1ad62abf6..000000000 --- a/docs/docs/getting-started/installation.mdx +++ /dev/null @@ -1,17 +0,0 @@ -# 📦 How to install? - -## Installation - -Make sure you have **Python 3.10** installed on your system. - -You can install **Langflow** using pip: - -```bash -pip install langflow -U -``` - -Or you can install a pre-release version using: - -```bash -pip install langflow --pre -U -``` \ No newline at end of file diff --git a/docs/docs/guides/rag-with-astradb.mdx b/docs/docs/guides/rag-with-astradb.mdx new file mode 100644 index 000000000..310f9ea70 --- /dev/null +++ b/docs/docs/guides/rag-with-astradb.mdx @@ -0,0 +1,202 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import DownloadableJsonFile from "/src/theme/DownloadableJsonFile.js"; +import Admonition from "@theme/Admonition"; + +# 🌟 RAG with AstraDB + +This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **AstraDB** and **Langflow**. + +AstraDB is a cloud-native database built on Apache Cassandra that is optimized for the cloud. It is a fully managed database-as-a-service that simplifies operations and reduces costs. AstraDB is built on the same technology that powers the largest Cassandra deployments in the world. + +In this guide, we will use AstraDB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses. + + + This guide assumes that you have Langflow up and running. If you are new to + Langflow, you can check out the [Getting Started](/) guide. + + +TLDR; + +- Visit the [Astra](https://astra.datastax.com) website and create a free account +- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) +- Create a new database, get a **Token** and the **API Endpoint** +- +- Import the project into Langflow by dropping it on the Canvas or My Collection page +- Update the **Token** and **API Endpoint** in the **AstraDB** components +- Update the OpenAI API key in the **OpenAI** components +- Run the ingestion flow which is the one that uses the **AstraDB** component +- Click on the ⚡ _Run_ button and start interacting with your RAG application + +# First things first + +## Create an AstraDB Database + +To get started, you will need to create an AstraDB database. Visit the [Astra](https://astra.datastax.com) website and create a free account. + +Once you have created an account, you will be taken to the AstraDB dashboard. Click on the **Create Database** button. + + + +Now you will need to configure your database. Choose the **Serverless (Vector)** deployment type, and pick a Database name, provider and region. + +After you have configured your database, click on the **Create Database** button. + + + +Once your database is initialized, to the right of the page, you will see the _Database Details_ section which contains a button for you to copy the **API Endpoint** and another to generate a **Token**. + + + +Now we are all set to start building our RAG application using AstraDB and Langflow. + +## (Optional) Duplicate the Langflow 1.0 HuggingFace Space + +If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you. + +You'll still need to get the Project file and import it so, let's get to that. + +## Import AstraDB RAG Flows + +To get started, you will need to . + +Once you have downloaded the project file, you can import it into Langflow by dropping it on the Canvas or My Collection page. + + + +This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the AstraDB database. + +Your first step should be to understand what each flow does and how they interact with each other. + +The ingestion flow consists of: + +- **Files** component that uploads a text file to Langflow +- **Recursive Character Text Splitter** component that splits the text into smaller chunks +- **OpenAIEmbeddings** component that generates embeddings for the text chunks +- **AstraDB** component that stores the text chunks in the AstraDB database + + + +Now, let's update the **AstraDB** and **AstraDB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key. + + + +And run it! This will ingest the Text data from your file into the AstraDB database. + + + +Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries. + +The RAG flow is a bit more complex. It consists of: + +- **Chat Input** component that defines where to put the user input coming from the Interaction Panel +- **OpenAI Embeddings** component that generates embeddings from the user input +- **AstraDB Search** component that retrieves the most relevant Records from the AstraDB database +- **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Interaction Panel + - One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Interaction Panel +- **Prompt** component that takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model +- **OpenAI** component that generates a response to the prompt +- **Chat Output** component that displays the response in the Interaction Panel + + + +To run it all we have to do is click on the ⚡ _Run_ button and start interacting with your RAG application. + + + +This opens the Interaction Panel where you can chat your data. + +Because this flow has a **Chat Input** and a **Text Output** component, the Panel displays a chat input at the bottom and the Extracted Chunks section on the left. + + + +Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved records. + + + +And that's it! You have successfully built a RAG application using AstraDB and Langflow. + +# Conclusion + +In this guide, we have learned how to build a RAG application using AstraDB and Langflow. We have seen how to create an AstraDB database, import the AstraDB RAG Flows project into Langflow, and run the ingestion and RAG flows. diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx index 840f10f10..c2c15ec32 100644 --- a/docs/docs/index.mdx +++ b/docs/docs/index.mdx @@ -1,6 +1,6 @@ # 👋 Welcome to Langflow -Langflow is an easy way to create flows. The drag-and-drop feature allows quick and effortless experimentation, while the built-in chat interface facilitates real-time interaction. It provides options to edit prompt parameters, create chains and agents, track thought processes, and export flows. +Langflow is an easy way to build from simple to complex AI applications. It is a low-code platform that allows you to integrate AI into everything you do. import ThemedImage from "@theme/ThemedImage"; import useBaseUrl from "@docusaurus/useBaseUrl"; @@ -16,3 +16,79 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; }} style={{ width: "100%" }} /> + + +## 🚀 First steps + +## Installation + +Make sure you have **Python 3.10** installed on your system. + +You can install **Langflow** with [pipx](https://pipx.pypa.io/stable/installation/) or with pip. + +Pipx can fetch the missing Python version for you, but you can also install it manually. + +```bash +pipx install langflow --python python3.10 --fetch-missing-python +# or +pip install langflow -U +``` + +Or you can install a pre-release version using: + +```bash +pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre" +# or +pip install langflow --pre -U +``` + +### ⛓️ Running Langflow + +Langflow can be run in a variety of ways, including using the command-line interface (CLI) or HuggingFace Spaces. + +```bash +langflow run # or langflow --help +``` + +#### 🤗 HuggingFace Spaces + +Hugging Face provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow without any local installation required. + +The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Logspace/Langflow?duplicate=true). + +Remember to use a Chromium-based browser for the best experience. You'll be presented with the following screen: + + + + +From here, just name your Space, define the visibility (Public or Private), and click on `Duplicate Space` to start the installation process. When that is done, you'll be redirected to the Space's main page to start using Langflow right away! + +Once you get Langflow running, click on New Project in the top right corner of the screen. Langflow provides a range of example flows to help you get started. + +To quickly try one of them, open a starter example, set up your API keys and click ⚡ Run, on the bottom right corner of the canvas. This will open up Langflow's Interaction Panel with the chat console, text inputs, and outputs. + + +### 🖥️ Command Line Interface (CLI) + +Langflow provides a command-line interface (CLI) for easy management and configuration. + +#### Usage + +You can run the Langflow using the following command: + +```bash +langflow run [OPTIONS] +``` + +Find more information about the available options by running: + +```bash +langflow --help +``` \ No newline at end of file diff --git a/docs/docs/whats-new/migrating-to-one-point-zero.mdx b/docs/docs/whats-new/migrating-to-one-point-zero.mdx index 4619798eb..45bae6084 100644 --- a/docs/docs/whats-new/migrating-to-one-point-zero.mdx +++ b/docs/docs/whats-new/migrating-to-one-point-zero.mdx @@ -10,7 +10,7 @@ We have a special channel in our Discord server dedicated to Langflow 1.0 migrat ## TLDR; - Inputs and Outputs of Components have changed -- The composition model has been replaced with a flow of data +- We've surfaced steps that were previously run in the background - Continued support for LangChain and new support for multiple frameworks - Redesigned sidebar and customizable interaction panel - New Native Categories and Components @@ -32,11 +32,11 @@ Langflow 1.0 introduces adds the concept of Inputs and Outputs to flows, allowin [Learn more about Inputs and Outputs of Components](../migration/inputs-and-outputs) -## From Composition to Freedom +## To Compose or Not to Compose: the choice is yours -Even though composition is still possible in Langflow 1.0, the new standard is getting data moving through the flow. This allows for more flexibility and control over the data flow in your projects. Check out how to use this in new and existing projects. +Even though composition is still possible in Langflow 1.0, the new standard is getting data moving through the flow. This allows for more flexibility and control over the data flow in your projects. -[Learn more about the Flow of Data](../migration/flow-of-data) +We will create guides on how to interweave LangChain components with our Core components soon. ## Continued Support for LangChain and Multiple Frameworks diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index 430aebcb0..a9a8a06e0 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -14,6 +14,7 @@ module.exports = { organizationName: "logspace-ai", projectName: "langflow", trailingSlash: false, + staticDirectories: ["static"], customFields: { mendableAnonKey: process.env.MENDABLE_ANON_KEY, }, diff --git a/docs/package-lock.json b/docs/package-lock.json index f00d7c283..7e5d0e8b9 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -10,11 +10,11 @@ "dependencies": { "@babel/preset-react": "^7.22.3", "@code-hike/mdx": "^0.9.0", - "@docusaurus/core": "^3.1.1", - 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"integrity": "sha512-Z0DbgELS9/L/75wZbro8xAnT50pBVFQZ+hUEueGDU5FN51YSCYM+jdxsfCiHjwNP/4LCDD0i/graKpeBnOXKRA==" }, "node_modules/utility-types": { - "version": "3.10.0", - "resolved": "https://registry.npmjs.org/utility-types/-/utility-types-3.10.0.tgz", - "integrity": "sha512-O11mqxmi7wMKCo6HKFt5AhO4BwY3VV68YU07tgxfz8zJTIxr4BpsezN49Ffwy9j3ZpwwJp4fkRwjRzq3uWE6Rg==", + "version": "3.11.0", + "resolved": "https://registry.npmjs.org/utility-types/-/utility-types-3.11.0.tgz", + "integrity": "sha512-6Z7Ma2aVEWisaL6TvBCy7P8rm2LQoPv6dJ7ecIaIixHcwfbJ0x7mWdbcwlIM5IGQxPZSFYeqRCqlOOeKoJYMkw==", "engines": { "node": ">= 4" } diff --git a/docs/package.json b/docs/package.json index d03e8f4a7..ce14c7568 100644 --- a/docs/package.json +++ b/docs/package.json @@ -16,11 +16,11 @@ "dependencies": { "@babel/preset-react": "^7.22.3", "@code-hike/mdx": "^0.9.0", - "@docusaurus/core": "^3.1.1", - "@docusaurus/plugin-ideal-image": "^3.1.1", - "@docusaurus/preset-classic": "^3.1.1", - "@docusaurus/theme-classic": "^3.1.1", - "@docusaurus/theme-search-algolia": "^3.0.1", + "@docusaurus/core": "^3.2.0", + "@docusaurus/plugin-ideal-image": "^3.2.0", + "@docusaurus/preset-classic": "^3.2.0", + "@docusaurus/theme-classic": "^3.2.0", + "@docusaurus/theme-search-algolia": "^3.2.0", "@mdx-js/react": "^2.3.0", "@mendable/search": "^0.0.154", "@pbe/react-yandex-maps": "^1.2.4", @@ -47,7 +47,7 @@ "tailwindcss": "^3.3.2" }, "devDependencies": { - "@docusaurus/module-type-aliases": "2.4.1", + "@docusaurus/module-type-aliases": "^3.2.0", "css-loader": "^6.8.1", "docusaurus-node-polyfills": "^1.0.0", "node-sass": "^9.0.0", diff --git a/docs/sidebars.js b/docs/sidebars.js index fc6a1522b..6f8cd3411 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -2,26 +2,34 @@ module.exports = { docs: [ { type: "category", - label: "Getting Started", + label: " Getting Started", collapsed: false, items: [ "index", - "getting-started/installation", - "getting-started/usage", + "getting-started/cli", "getting-started/hugging-face-spaces", "getting-started/creating-flows", ], }, { type: "category", - label: "What's New", + label: " What's New", collapsed: false, items: [ "whats-new/a-new-chapter-langflow", "whats-new/migrating-to-one-point-zero", - "whats-new/customization-control", - "whats-new/debugging-reimagined", - "whats-new/simplification-standardization", + ], + }, + { + type: "category", + label: " Step-by-Step Guides", + collapsed: false, + items: [ + "guides/rag-with-astradb", + "guides/async-tasks", + "guides/loading_document", + "guides/chatprompttemplate_guide", + "guides/langfuse_integration", ], }, { @@ -29,22 +37,22 @@ module.exports = { label: "Migration Guides", collapsed: false, items: [ - "migration/flow-of-data", + // "migration/flow-of-data", "migration/inputs-and-outputs", - "migration/supported-frameworks", - "migration/sidebar-and-interaction-panel", - "migration/new-categories-and-components", - "migration/text-and-record", - "migration/custom-component", + // "migration/supported-frameworks", + // "migration/sidebar-and-interaction-panel", + // "migration/new-categories-and-components", + // "migration/text-and-record", + // "migration/custom-component", "migration/compatibility", - "migration/multiple-flows", - "migration/component-status-and-data-passing", - "migration/connecting-output-components", - "migration/renaming-and-editing-components", - "migration/passing-tweaks-and-inputs", - "migration/global-variables", - "migration/experimental-components", - "migration/state-management", + // "migration/multiple-flows", + // "migration/component-status-and-data-passing", + // "migration/connecting-output-components", + // "migration/renaming-and-editing-components", + // "migration/passing-tweaks-and-inputs", + // "migration/global-variables", + // "migration/experimental-components", + // "migration/state-management", ], }, { @@ -66,42 +74,36 @@ module.exports = { }, { type: "category", - label: "Component Reference", + label: "Core Components", collapsed: false, items: [ "components/inputs", "components/outputs", "components/data", - "components/prompts", "components/models", "components/helpers", - "components/experimental", - "components/agents", - "components/chains", - "components/custom", - "components/embeddings", - "components/model_specs", - "components/loaders", - "components/memories", - "components/prompts", - "components/retrievers", - "components/text-splitters", - "components/toolkits", - "components/tools", - "components/utilities", "components/vector-stores", - "components/wrappers", + "components/embeddings", ], }, { type: "category", - label: "Step-by-Step Guides", + label: "Extended Components", collapsed: false, items: [ - "guides/async-tasks", - "guides/loading_document", - "guides/chatprompttemplate_guide", - "guides/langfuse_integration", + "components/agents", + "components/chains", + "components/loaders", + "components/experimental", + "components/utilities", + "components/memories", + "components/model_specs", + "components/retrievers", + "components/text-splitters", + "components/toolkits", + "components/tools", + "components/wrappers", + // "components/prompts", ], }, { diff --git a/docs/src/theme/DownloadableJsonFile.js b/docs/src/theme/DownloadableJsonFile.js new file mode 100644 index 000000000..7b5466eac --- /dev/null +++ b/docs/src/theme/DownloadableJsonFile.js @@ -0,0 +1,29 @@ +const DownloadableJsonFile = ({ source, title }) => { + const handleDownload = (event) => { + event.preventDefault(); + fetch(source) + .then((response) => response.blob()) + .then((blob) => { + const url = window.URL.createObjectURL( + new Blob([blob], { type: "application/json" }) + ); + const link = document.createElement("a"); + link.href = url; + link.setAttribute("download", title); + document.body.appendChild(link); + link.click(); + link.parentNode.removeChild(link); + }) + .catch((error) => { + console.error("Error downloading file:", error); + }); + }; + + return ( + + {title} + + ); +}; + +export default DownloadableJsonFile; diff --git a/docs/static/data/AstraDB-RAG-Flows.json b/docs/static/data/AstraDB-RAG-Flows.json new file mode 100644 index 000000000..cf96c6841 --- /dev/null +++ b/docs/static/data/AstraDB-RAG-Flows.json @@ -0,0 +1,3407 @@ +{ + "id": "51e2b78a-199b-4054-9f32-e288eef6924c", + "data": { + "nodes": [ + { + "id": "ChatInput-yxMKE", + "type": "genericNode", + "position": { + "x": 1195.5276981160775, + "y": 209.421875 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "what is a line" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Get chat inputs from the Interaction Panel.", + "icon": "ChatInput", + "base_classes": [ + "Text", + "str", + "object", + "Record" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-yxMKE" + }, + "selected": false, + "width": 384, + "height": 383 + }, + { + "id": "TextOutput-BDknO", + "type": "genericNode", + "position": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Display a text output in the Interaction Panel.", + "icon": "type", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "Extracted Chunks", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-BDknO" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-ZlOk1", + "type": "genericNode", + "position": { + "x": 1183.667250865064, + "y": 687.3171828430261 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-3-small\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-3-small\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-3-small", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [ + "all" + ], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-3-small", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" + ], + "name": "model", + "display_name": "Model", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "openai_api_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_type", + "display_name": "OpenAI API Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_version": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_version", + "display_name": "OpenAI API Version", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_organization": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_organization", + "display_name": "OpenAI Organization", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_proxy": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_proxy", + "display_name": "OpenAI Proxy", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "request_timeout": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "request_timeout", + "display_name": "Request Timeout", + "advanced": true, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "show_progress_bar": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "show_progress_bar", + "display_name": "Show Progress Bar", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "skip_empty": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "skip_empty", + "display_name": "Skip Empty", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_enable": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_enable", + "display_name": "TikToken Enable", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_model_name", + "display_name": "TikToken Model Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Generate embeddings using OpenAI models.", + "base_classes": [ + "Embeddings" + ], + "display_name": "OpenAI Embeddings", + "documentation": "", + "custom_fields": { + "openai_api_key": null, + "default_headers": null, + "default_query": null, + "allowed_special": null, + "disallowed_special": null, + "chunk_size": null, + "client": null, + "deployment": null, + "embedding_ctx_length": null, + "max_retries": null, + "model": null, + "model_kwargs": null, + "openai_api_base": null, + "openai_api_type": null, + "openai_api_version": null, + "openai_organization": null, + "openai_proxy": null, + "request_timeout": null, + "show_progress_bar": null, + "skip_empty": null, + "tiktoken_enable": null, + "tiktoken_model_name": null + }, + "output_types": [ + "Embeddings" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "OpenAIEmbeddings-ZlOk1" + }, + "selected": false, + "width": 384, + "height": 383, + "dragging": false + }, + { + "id": "OpenAIModel-EjXlN", + "type": "genericNode", + "position": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-EjXlN" + }, + "selected": true, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "dragging": false + }, + { + "id": "Prompt-xeI6K", + "type": "genericNode", + "position": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "question", + "display_name": "question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } + }, + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "Text", + "str" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "context", + "question" + ] + }, + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-xeI6K", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "positionAbsolute": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "dragging": false + }, + { + "id": "ChatOutput-Q39I8", + "type": "genericNode", + "position": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Interaction Panel.", + "icon": "ChatOutput", + "base_classes": [ + "object", + "Text", + "Record", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-Q39I8" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "dragging": false + }, + { + "id": "File-t0a6a", + "type": "genericNode", + "position": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx" + ], + "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n \n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false, + "display_name": "code" + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "A generic file loader.", + "icon": "file-text", + "base_classes": [ + "Record" + ], + "display_name": "File", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null + }, + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-t0a6a" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "dragging": false + }, + { + "id": "RecursiveCharacterTextSplitter-tR9QM", + "type": "genericNode", + "position": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "data": { + "type": "RecursiveCharacterTextSplitter", + "node": { + "template": { + "inputs": { + "type": "Document", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Input", + "advanced": false, + "input_types": [ + "Document", + "Record" + ], + "dynamic": false, + "info": "The texts to split.", + "load_from_db": false, + "title_case": false + }, + "chunk_overlap": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 200, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_overlap", + "display_name": "Chunk Overlap", + "advanced": false, + "dynamic": false, + "info": "The amount of overlap between chunks.", + "load_from_db": false, + "title_case": false + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": false, + "dynamic": false, + "info": "The maximum length of each chunk.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false, + "display_name": "code" + }, + "separators": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "separators", + "display_name": "Separators", + "advanced": false, + "dynamic": false, + "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": [ + "" + ] + }, + "_type": "CustomComponent" + }, + "description": "Split text into chunks of a specified length.", + "base_classes": [ + "Record" + ], + "display_name": "Recursive Character Text Splitter", + "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", + "custom_fields": { + "inputs": null, + "separators": null, + "chunk_size": null, + "chunk_overlap": null + }, + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "RecursiveCharacterTextSplitter-tR9QM" + }, + "selected": false, + "width": 384, + "height": 501, + "positionAbsolute": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "dragging": false + }, + { + "id": "AstraDBSearch-41nRz", + "type": "genericNode", + "position": { + "x": 1723.976434815103, + "y": 277.03317407245913 + }, + "data": { + "type": "AstraDBSearch", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input Value", + "advanced": false, + "dynamic": false, + "info": "Input value to search", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the AstraDB service.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"AstraDB Search\"\n description = \"Searches an existing AstraDB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within AstraDB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing AstraDB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the AstraDB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within AstraDB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain the 'content' field.\"\n )\n else:\n raise e\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within AstraDB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within AstraDB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "number_of_results": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 4, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "number_of_results", + "display_name": "Number of Results", + "advanced": true, + "dynamic": false, + "info": "Number of results to return.", + "load_from_db": false, + "title_case": false + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "search_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Similarity", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Similarity", + "MMR" + ], + "name": "search_type", + "display_name": "Search Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Sync", + "Async", + "Off" + ], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing AstraDB.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "_type": "CustomComponent" + }, + "description": "Searches an existing AstraDB Vector Store.", + "icon": "AstraDB", + "base_classes": [ + "Record" + ], + "display_name": "AstraDB Search", + "documentation": "", + "custom_fields": { + "embedding": null, + "collection_name": null, + "input_value": null, + "token": null, + "api_endpoint": null, + "search_type": null, + "number_of_results": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null + }, + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "input_value", + "embedding" + ], + "beta": false + }, + "id": "AstraDBSearch-41nRz" + }, + "selected": false, + "width": 384, + "height": 713, + "dragging": false, + "positionAbsolute": { + "x": 1723.976434815103, + "y": 277.03317407245913 + } + }, + { + "id": "AstraDB-eUCSS", + "type": "genericNode", + "position": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "data": { + "type": "AstraDB", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "inputs": { + "type": "Record", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Inputs", + "advanced": false, + "dynamic": false, + "info": "Optional list of records to be processed and stored in the vector store.", + "load_from_db": false, + "title_case": false + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the AstraDB service.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"AstraDB\"\n description = \"Builds or loads an AstraDB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within AstraDB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing AstraDB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the AstraDB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within AstraDB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Async\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> VectorStore:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within AstraDB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within AstraDB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Async", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Sync", + "Async", + "Off" + ], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing AstraDB.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "_type": "CustomComponent" + }, + "description": "Builds or loads an AstraDB Vector Store.", + "icon": "AstraDB", + "base_classes": [ + "VectorStore" + ], + "display_name": "AstraDB", + "documentation": "", + "custom_fields": { + "embedding": null, + "token": null, + "api_endpoint": null, + "collection_name": null, + "inputs": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null + }, + "output_types": [ + "VectorStore" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "inputs", + "embedding" + ], + "beta": false + }, + "id": "AstraDB-eUCSS" + }, + "selected": false, + "width": 384, + "height": 573, + "positionAbsolute": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-9TPjc", + "type": "genericNode", + "position": { + "x": 2814.0402191223047, + "y": 1955.9268168273086 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-3-small\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-3-small\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-3-small", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [ + "all" + ], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "embedding_ctx_length": { + "type": 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(>=2.8.1)", "pytest-mypy (>=0.8.0)", "pytest-timeout (>=2.1.0)", "redis", "sphinx (>=6.0.0)", "types-redis"] +[[package]] +name = "postgrest" +version = "0.16.2" +description = "PostgREST client for Python. This library provides an ORM interface to PostgREST." +optional = false +python-versions = "<4.0,>=3.8" +files = [ + {file = "postgrest-0.16.2-py3-none-any.whl", hash = "sha256:cf89106d0877ac2c7b070ad136f78350eb89dbdd998cd83d6852010e0bcdb878"}, + {file = "postgrest-0.16.2.tar.gz", hash = "sha256:6c5c8e53cdcede8b6654ddfc7505e5af0c41ce56c6935f7b1d05545bb899d8b8"}, +] + +[package.dependencies] +deprecation = ">=2.1.0,<3.0.0" +httpx = ">=0.24,<0.28" +pydantic = ">=1.9,<3.0" +strenum = ">=0.4.9,<0.5.0" + [[package]] name = "posthog" version = "3.5.0" @@ -7723,6 +7730,22 @@ files = [ [package.extras] full = ["numpy"] +[[package]] +name = "realtime" +version = "1.0.3" +description = "" +optional = false +python-versions = "<4.0,>=3.8" +files = [ + {file = "realtime-1.0.3-py3-none-any.whl", hash = "sha256:809b99a1c09390a4580ca2d37d84c85dffacb1804f80c6f5a4491d312c20e6e3"}, + {file = "realtime-1.0.3.tar.gz", hash = "sha256:1a39b5dcdb345b4cc7fd43bc035feb38ca915c9248962f20d264625bc8eb2c4e"}, +] + +[package.dependencies] +python-dateutil = ">=2.8.1,<3.0.0" +typing-extensions = ">=4.2.0,<5.0.0" +websockets = ">=11,<13" + [[package]] name = "red-black-tree-mod" version = "1.20" @@ -8451,6 +8474,38 @@ anyio = ">=3.4.0,<5" [package.extras] full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"] +[[package]] +name = "storage3" +version = "0.7.4" +description = "Supabase Storage client for Python." +optional = false +python-versions = "<4.0,>=3.8" +files = [ + {file = "storage3-0.7.4-py3-none-any.whl", hash = "sha256:0b8e8839b10a64063796ce55a41462c7ffd6842e0ada74f25f5dcf37e1d1bade"}, + {file = "storage3-0.7.4.tar.gz", hash = "sha256:61fcbf836f566405981722abb7d56caa57025b261e7a316e73316701abf0c040"}, +] + +[package.dependencies] +httpx = ">=0.24,<0.28" +python-dateutil = ">=2.8.2,<3.0.0" +typing-extensions = ">=4.2.0,<5.0.0" + +[[package]] +name = "strenum" +version = "0.4.15" +description = "An Enum that inherits from str." +optional = false +python-versions = "*" +files = [ + {file = "StrEnum-0.4.15-py3-none-any.whl", hash = "sha256:a30cda4af7cc6b5bf52c8055bc4bf4b2b6b14a93b574626da33df53cf7740659"}, + {file = "StrEnum-0.4.15.tar.gz", hash = "sha256:878fb5ab705442070e4dd1929bb5e2249511c0bcf2b0eeacf3bcd80875c82eff"}, +] + +[package.extras] +docs = ["myst-parser[linkify]", "sphinx", "sphinx-rtd-theme"] +release = ["twine"] +test = ["pylint", "pytest", "pytest-black", "pytest-cov", "pytest-pylint"] + [[package]] name = "striprtf" version = "0.0.26" @@ -8462,6 +8517,39 @@ files = [ {file = "striprtf-0.0.26.tar.gz", hash = "sha256:fdb2bba7ac440072d1c41eab50d8d74ae88f60a8b6575c6e2c7805dc462093aa"}, ] +[[package]] +name = "supabase" +version = "2.4.1" +description = "Supabase client for Python." +optional = false +python-versions = "<4.0,>=3.8" +files = [ + {file = "supabase-2.4.1-py3-none-any.whl", hash = "sha256:8b95744ce4ad24245ec23c090f273dfc9c2d9a53e3a80186959903947dbe1ed6"}, + {file = "supabase-2.4.1.tar.gz", hash = "sha256:a7dec0586f8931f378a45b2ffb28d8e37b3719f979c17f541b0156019144e645"}, +] + +[package.dependencies] +gotrue = ">=1.3,<3.0" +httpx = ">=0.24,<0.28" +postgrest = ">=0.10.8,<0.17.0" +realtime = ">=1.0.0,<2.0.0" +storage3 = ">=0.5.3,<0.8.0" +supafunc = ">=0.3.1,<0.5.0" + +[[package]] +name = "supafunc" +version = "0.4.5" +description = "Library for Supabase Functions" +optional = false +python-versions = "<4.0,>=3.8" +files = [ + {file = "supafunc-0.4.5-py3-none-any.whl", hash = "sha256:2208045f8f5c797924666f6a332efad75ad368f8030b2e4ceb9d2bf63f329373"}, + {file = "supafunc-0.4.5.tar.gz", hash = "sha256:a6466d78bdcaa58b7f0303793643103baae8106a87acd5d01e196179a9d0d024"}, +] + +[package.dependencies] +httpx = ">=0.24,<0.28" + [[package]] name = "sympy" version = "1.12" @@ -8845,13 +8933,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0, [[package]] name = "transformers" -version = "4.39.2" +version = "4.39.3" description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow" optional = true python-versions = ">=3.8.0" files = [ - {file = "transformers-4.39.2-py3-none-any.whl", hash = "sha256:8388a4ae1d91ade935f5c5b36dc47aa1a352b092c30595e3337b49a5f7e71b4e"}, - {file = "transformers-4.39.2.tar.gz", hash = "sha256:be0c7392cb92ab48efab2656f1cfd1cbda33b2b8a2917a18bd1196707dbebe14"}, + {file = "transformers-4.39.3-py3-none-any.whl", hash = "sha256:7838034a12cca3168247f9d2d1dba6724c9de3ae0f73a108258c6b8fc5912601"}, + {file = "transformers-4.39.3.tar.gz", hash = "sha256:2586e5ff4150f122716fc40f5530e92871befc051848fbe82600969c535b762d"}, ] [package.dependencies] @@ -9001,13 +9089,13 @@ files = [ [[package]] name = "types-pyasn1" -version = "0.5.0.20240301" +version = "0.6.0.20240402" description = "Typing stubs for pyasn1" optional = false python-versions = ">=3.8" files = [ - {file = "types-pyasn1-0.5.0.20240301.tar.gz", hash = "sha256:da328f5771d54a2016863270b281047f9cc38e39f65a297ba9f987d5de3403f1"}, - {file = "types_pyasn1-0.5.0.20240301-py3-none-any.whl", hash = "sha256:d9989899184bbd6e2adf6f812c8f49c48197fceea251a6fb13666dae3203f80d"}, + {file = "types-pyasn1-0.6.0.20240402.tar.gz", hash = "sha256:5d54dcb33f69dd269071ca098e923ac20c5f03c814631fa7f3ed9ee035a5da3a"}, + {file = "types_pyasn1-0.6.0.20240402-py3-none-any.whl", hash = "sha256:848d01e7313c200acc035a8b3d377fe7b2aecbe77f2be49eb160a7f82835aaaf"}, ] [[package]] @@ -9088,13 +9176,13 @@ types-pyOpenSSL = "*" [[package]] name = "types-requests" -version = "2.31.0.20240311" +version = "2.31.0.20240402" description = "Typing stubs for requests" optional = false python-versions = ">=3.8" files = [ - {file = "types-requests-2.31.0.20240311.tar.gz", hash = "sha256:b1c1b66abfb7fa79aae09097a811c4aa97130eb8831c60e47aee4ca344731ca5"}, - {file = "types_requests-2.31.0.20240311-py3-none-any.whl", hash = "sha256:47872893d65a38e282ee9f277a4ee50d1b28bd592040df7d1fdaffdf3779937d"}, + {file = "types-requests-2.31.0.20240402.tar.gz", hash = "sha256:e5c09a202f8ae79cd6ffbbba2203b6c3775a83126283bb2a6abbc129abc02a12"}, + {file = "types_requests-2.31.0.20240402-py3-none-any.whl", hash = "sha256:bd7eb7102168d4b5b489f15cdd9842b63ab7fe56aa82a0589fa595b94195acf4"}, ] [package.dependencies] @@ -9452,15 +9540,26 @@ test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)" [[package]] name = "validators" -version = "0.24.0" +version = "0.22.0" description = "Python Data Validation for Humans™" optional = false python-versions = ">=3.8" files = [ - {file = "validators-0.24.0-py3-none-any.whl", hash = "sha256:4a99eb368747e60900bae947418eb21e230ff4ff5e7b7944b9308c456d86da32"}, - {file = "validators-0.24.0.tar.gz", hash = "sha256:cd23defb36de42d14e7559cf0757f761bb46b10d9de2998e6ef805f769d859e3"}, + {file = "validators-0.22.0-py3-none-any.whl", hash = "sha256:61cf7d4a62bbae559f2e54aed3b000cea9ff3e2fdbe463f51179b92c58c9585a"}, + {file = "validators-0.22.0.tar.gz", hash = "sha256:77b2689b172eeeb600d9605ab86194641670cdb73b60afd577142a9397873370"}, ] +[package.extras] +docs-offline = ["myst-parser (>=2.0.0)", "pypandoc-binary (>=1.11)", "sphinx (>=7.1.1)"] +docs-online = ["mkdocs (>=1.5.2)", "mkdocs-git-revision-date-localized-plugin (>=1.2.0)", "mkdocs-material (>=9.2.6)", "mkdocstrings[python] (>=0.22.0)", "pyaml (>=23.7.0)"] +hooks = ["pre-commit (>=3.3.3)"] +package = ["build (>=1.0.0)", "twine (>=4.0.2)"] +runner = ["tox (>=4.11.1)"] +sast = ["bandit[toml] (>=1.7.5)"] +testing = ["pytest (>=7.4.0)"] +tooling = ["black (>=23.7.0)", "pyright (>=1.1.325)", "ruff (>=0.0.287)"] +tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"] + [[package]] name = "vine" version = "5.1.0" @@ -9572,22 +9671,24 @@ files = [ [[package]] name = "weaviate-client" -version = "3.26.2" +version = "4.5.4" description = "A python native Weaviate client" optional = false python-versions = ">=3.8" files = [ - {file = "weaviate-client-3.26.2.tar.gz", hash = "sha256:63ec70839b64909810a64aa7b3e5b85088462e93c7e2ed3c32ebefb702f36723"}, - {file = "weaviate_client-3.26.2-py3-none-any.whl", hash = "sha256:ca43bfb9c06b8ae3fd938dc9158acd93d4cbf4622192e173333e1ff63cf97164"}, + {file = "weaviate-client-4.5.4.tar.gz", hash = "sha256:fc53dc73cd53df453c5e6dc758e49a6a1549212d6670ddd013392107120692f8"}, + {file = "weaviate_client-4.5.4-py3-none-any.whl", hash = "sha256:f6d3a6b759e5aa0d3350067490526ea38b9274ae4043b4a3ae0064c28d56883f"}, ] [package.dependencies] authlib = ">=1.2.1,<2.0.0" +grpcio = ">=1.57.0,<2.0.0" +grpcio-health-checking = ">=1.57.0,<2.0.0" +grpcio-tools = ">=1.57.0,<2.0.0" +httpx = "0.27.0" +pydantic = ">=2.5.0,<3.0.0" requests = ">=2.30.0,<3.0.0" -validators = ">=0.21.2,<1.0.0" - -[package.extras] -grpc = ["grpcio (>=1.57.0,<2.0.0)", "grpcio-tools (>=1.57.0,<2.0.0)"] +validators = "0.22.0" [[package]] name = "websocket-client" @@ -10054,21 +10155,6 @@ files = [ idna = ">=2.0" multidict = ">=4.0" -[[package]] -name = "zep-python" -version = "2.0.0rc5" -description = "Long-Term Memory for AI Assistants. This is the Python client for the Zep service." -optional = false -python-versions = "<4,>=3.9.0" -files = [ - {file = "zep_python-2.0.0rc5-py3-none-any.whl", hash = "sha256:8b1b5c22c9e1ef439c9ef3d785347abf89b1243c7149e32025dd065cc022af40"}, - {file = "zep_python-2.0.0rc5.tar.gz", hash = "sha256:e6ced8089760374dead948d6b4b88fceb09a356bf9a7fe182b4ceb6e828f0bb1"}, -] - -[package.dependencies] -httpx = ">=0.24.0,<0.29.0" -pydantic = ">=2.0.0" - [[package]] name = "zipp" version = "3.18.1" @@ -10163,4 +10249,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "b1b40cf39cc544faf5ca6ad04a2be009df7d8d343d86c1e9d7c02d21b2cad431" +content-hash = "3eb1181a83884c7ba52a7d1c98dcff13a307452eaf8f4a148fc0778f97499dfd" diff --git a/pyproject.toml b/pyproject.toml index 6c85de803..346767203 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,8 +28,7 @@ enable = true [tool.poetry.dependencies] python = ">=3.10,<3.12" -# langflow-base = { path = "./src/backend/base", develop = true } -langflow-base = "0.0.13" +langflow-base = { path = "./src/backend/base", develop = true } beautifulsoup4 = "^4.12.2" google-search-results = "^2.4.1" google-api-python-client = "^2.118.0" @@ -50,6 +49,7 @@ faiss-cpu = "^1.7.4" types-cachetools = "^5.3.0.5" pinecone-client = "^3.0.3" pymongo = "^4.6.0" +supabase = "^2.3.0" certifi = "^2023.11.17" psycopg = "^3.1.9" psycopg-binary = "^3.1.9" @@ -58,9 +58,8 @@ celery = { extras = ["redis"], version = "^5.3.6", optional = true } redis = { version = "^5.0.1", optional = true } flower = { version = "^2.0.0", optional = true } metaphor-python = "^0.1.11" -zep-python = "*" pywin32 = { version = "^306", markers = "sys_platform == 'win32'" } -langfuse = "*" +langfuse = "^2.9.0" metal-sdk = "^2.5.0" markupsafe = "^2.1.3" extract-msg = "^0.47.0" @@ -109,7 +108,7 @@ respx = "^0.20.2" [tool.poetry.extras] -deploy = ["langchain-serve", "celery", "redis", "flower"] +deploy = ["celery", "redis", "flower"] local = ["llama-cpp-python", "sentence-transformers", "ctransformers"] all = ["deploy", "local"] diff --git a/scripts/setup/update_poetry.sh b/scripts/setup/update_poetry.sh index f9b44f9f6..ecaf21356 100644 --- a/scripts/setup/update_poetry.sh +++ b/scripts/setup/update_poetry.sh @@ -7,6 +7,129 @@ then exit 1 fi +# Utility function to display an error message and exit +exit_with_message() { + echo "$1" >&2 + exit 1 +} + +# Check if version argument is provided +if [ -z "$1" ]; then + exit_with_message "No argument supplied. Please provide the Poetry version to check." +fi + +# Detect Operating System +OS="$(uname -s)" +case "$OS" in + Darwin) + OS="macOS" + ;; + Linux) + OS="Linux" + ;; + *) + exit_with_message "Unsupported operating system. This script supports macOS and Linux." + ;; +esac + +echo "Detected Operating System: $OS" + +# Installation of pipx based on the detected OS +install_pipx() { + case $1 in + macOS) + # macOS installation using Homebrew + command -v brew >/dev/null 2>&1 || exit_with_message "Homebrew is not installed. Please install Homebrew first." + echo "Installing pipx using Homebrew..." + brew install pipx + pipx ensurepath + ;; + Linux) + # Linux installation. Further checks are needed to distinguish between distributions + if grep -qEi "(ubuntu|debian)" /etc/*release; then + echo "Installing pipx on Ubuntu/Debian..." + sudo apt update + sudo apt install pipx -y + elif grep -qEi "fedora" /etc/*release; then + echo "Installing pipx on Fedora..." + sudo dnf install pipx -y + else + echo "Installing pipx using pip (other Linux distributions)..." + python3 -m pip install --user pipx + fi + pipx ensurepath + ;; + *) + exit_with_message "Unsupported operating system for pipx installation." + ;; + esac +} + +# Function to fetch the latest version of pipx from GitHub and compare with the installed version +check_for_pipx_update() { + echo "Checking for updates to pipx..." + # Fetch the latest version of pipx, ensuring only to capture the numeric version without 'v' prefix. + local latest_version=$(curl -s https://api.github.com/repos/pypa/pipx/releases/latest | grep '"tag_name":' | sed -E 's/.*"tag_name": "v?([^"]+)".*/\1/') + # Extract the current installed version of pipx. + local current_version=$(pipx --version | grep -oE '[0-9]+\.[0-9]+\.[0-9]+') + + if [[ "$latest_version" == "$current_version" ]]; then + echo "You have the latest version of pipx ($current_version)." + else + echo "A newer version of pipx ($latest_version) is available. You have $current_version. Do you want to update? (yes/no)" + read -r user_input + if [[ "$user_input" == "yes" ]]; then + echo "Updating pipx..." + case "$OS" in + macOS) + brew upgrade pipx + ;; + Linux) + if grep -qEi "(ubuntu|debian)" /etc/*release; then + sudo apt update + sudo apt install --only-upgrade pipx -y + elif grep -qEi "fedora" /etc/*release; then + sudo dnf upgrade pipx -y + else + python3 -m pip install --user --upgrade pipx + fi + ;; + *) + exit_with_message "Unsupported operating system for pipx update." + ;; + esac + pipx ensurepath + echo "pipx updated to version $latest_version" + else + echo "Not updating pipx at this time." + fi + fi +} + +# Now, modify the existing check to call check_for_pipx_update even if pipx is installed +if ! command -v pipx &> /dev/null; then + echo "Pipx is not installed. Installing..." + install_pipx "$OS" + echo "Pipx installed successfully." +else + echo "Pipx is already installed." + check_for_pipx_update +fi + + +echo "Checking Poetry installation..." + +# Check if Poetry is installed +if ! command -v poetry &> /dev/null +then + echo "Poetry is not installed. Installing..." + # Also install python 3.10 and use + pipx install poetry --python python3.10 --fetch-missing-python + echo "Poetry installed successfully." +else + echo "Poetry is already installed." +fi + echo "Checking Poetry version..." # Check Poetry version diff --git a/scripts/update_dependencies.py b/scripts/update_dependencies.py new file mode 100644 index 000000000..a46a95f8a --- /dev/null +++ b/scripts/update_dependencies.py @@ -0,0 +1,42 @@ +import re +from pathlib import Path + + +def read_version_from_pyproject(file_path): + with open(file_path, "r") as file: + for line in file: + match = re.search(r'version = "(.*)"', line) + if match: + return match.group(1) + return None + + +def update_pyproject_dependency(pyproject_path, version): + pattern = re.compile(r'langflow-base = \{ path = "\./src/backend/base", develop = true \}') + replacement = f'langflow-base = "^{version}"' + with open(pyproject_path, "r") as file: + content = file.read() + content = pattern.sub(replacement, content) + with open(pyproject_path, "w") as file: + file.write(content) + + +if __name__ == "__main__": + # Backing up files + pyproject_path = Path(__file__).resolve().parent / "../pyproject.toml" + pyproject_path = pyproject_path.resolve() + with open(pyproject_path, "r") as original, open(pyproject_path.with_name("pyproject.toml.bak"), "w") as backup: + backup.write(original.read()) + # Now backup poetry.lock + with open(pyproject_path.with_name("poetry.lock"), "r") as original, open( + pyproject_path.with_name("poetry.lock.bak"), "w" + ) as backup: + backup.write(original.read()) + + # Reading version and updating pyproject.toml + langflow_base_path = Path(__file__).resolve().parent / "../src/backend/base/pyproject.toml" + version = read_version_from_pyproject(langflow_base_path) + if version: + update_pyproject_dependency(pyproject_path, version) + else: + print("Error: Version not found.") diff --git a/src/backend/base/langflow/__main__.py b/src/backend/base/langflow/__main__.py index 6841ae1d1..34ed75f9b 100644 --- a/src/backend/base/langflow/__main__.py +++ b/src/backend/base/langflow/__main__.py @@ -149,13 +149,13 @@ def run( Run the Langflow. """ + configure(log_level=log_level, log_file=log_file) set_var_for_macos_issue() # override env variables with .env file if env_file: load_dotenv(env_file, override=True) - configure(log_level=log_level, log_file=log_file) update_settings( config, dev=dev, @@ -246,10 +246,10 @@ def get_free_port(port): def print_banner(host, port): - # console = Console() + from langflow.version import __version__ word = "Langflow" - colors = ["#3300cc"] + colors = ["#6e42f5"] styled_word = "" @@ -259,7 +259,7 @@ def print_banner(host, port): # Title with emojis and gradient text title = ( - f"[bold]Welcome to :chains: {styled_word} [/bold]\n\n" + f"[bold]Welcome to :chains: {styled_word} v{__version__}[/bold]\n" f"Access [link=http://{host}:{port}]http://{host}:{port}[/link]" ) info_text = ( @@ -307,7 +307,7 @@ def run_langflow(host, port, log_level, options, app): def superuser( username: str = typer.Option(..., prompt=True, help="Username for the superuser."), password: str = typer.Option(..., prompt=True, hide_input=True, help="Password for the superuser."), - log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"), + log_level: str = typer.Option("error", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"), ): """ Create a superuser. diff --git a/src/backend/base/langflow/alembic/env.py b/src/backend/base/langflow/alembic/env.py index 495cbb061..7400906c8 100644 --- a/src/backend/base/langflow/alembic/env.py +++ b/src/backend/base/langflow/alembic/env.py @@ -1,4 +1,5 @@ import os +import warnings from logging.config import fileConfig from alembic import context @@ -82,11 +83,12 @@ def run_migrations_online() -> None: prefix="sqlalchemy.", poolclass=pool.NullPool, ) - with connectable.connect() as connection: - context.configure(connection=connection, target_metadata=target_metadata, render_as_batch=True) - - with context.begin_transaction(): - context.run_migrations() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata, render_as_batch=True) + with context.begin_transaction(): + context.run_migrations() if context.is_offline_mode(): diff --git a/src/backend/base/langflow/components/data/APIRequest.py b/src/backend/base/langflow/components/data/APIRequest.py index 2b796dc32..762564aa8 100644 --- a/src/backend/base/langflow/components/data/APIRequest.py +++ b/src/backend/base/langflow/components/data/APIRequest.py @@ -58,9 +58,7 @@ class APIRequest(CustomComponent): data = body if body else None payload = json.dumps(data) try: - response = await client.request( - method, url, headers=headers, content=payload, timeout=timeout - ) + response = await client.request(method, url, headers=headers, content=payload, timeout=timeout) try: result = response.json() except Exception: @@ -117,10 +115,7 @@ class APIRequest(CustomComponent): bodies += [None] * (len(urls) - len(bodies)) # type: ignore async with httpx.AsyncClient() as client: results = await asyncio.gather( - *[ - self.make_request(client, method, u, headers, rec, timeout) - for u, rec in zip(urls, bodies) - ] + *[self.make_request(client, method, u, headers, rec, timeout) for u, rec in zip(urls, bodies)] ) self.status = results return results diff --git a/src/backend/base/langflow/components/helpers/SplitText.py b/src/backend/base/langflow/components/helpers/SplitText.py index 4d73f7f42..3c823c4aa 100644 --- a/src/backend/base/langflow/components/helpers/SplitText.py +++ b/src/backend/base/langflow/components/helpers/SplitText.py @@ -9,7 +9,7 @@ from langchain_core.documents import Document from langflow.interface.custom.custom_component import CustomComponent from langflow.schema import Record from langflow.field_typing import Text -from langflow.utils.util import build_loader_repr_from_records, unescape_string +from langflow.utils.util import unescape_string class SplitTextComponent(CustomComponent): @@ -54,7 +54,6 @@ class SplitTextComponent(CustomComponent): chunk_overlap: Optional[int] = 200, recursive: bool = False, ) -> list[Record]: - separators = [unescape_string(x) for x in separators] # Make sure chunk_size and chunk_overlap are ints diff --git a/src/backend/base/langflow/components/models/AnthropicModel.py b/src/backend/base/langflow/components/models/AnthropicModel.py index 29b2a1fe4..1e5a7aec5 100644 --- a/src/backend/base/langflow/components/models/AnthropicModel.py +++ b/src/backend/base/langflow/components/models/AnthropicModel.py @@ -5,6 +5,7 @@ from pydantic.v1 import SecretStr from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent +from langflow.field_typing import Text class AnthropicLLM(LCModelComponent): diff --git a/src/backend/base/langflow/components/models/AzureOpenAIModel.py b/src/backend/base/langflow/components/models/AzureOpenAIModel.py index 51117eb56..9e1d32bb7 100644 --- a/src/backend/base/langflow/components/models/AzureOpenAIModel.py +++ b/src/backend/base/langflow/components/models/AzureOpenAIModel.py @@ -5,6 +5,7 @@ from langchain_openai import AzureChatOpenAI from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent +from langflow.field_typing import Text class AzureChatOpenAIComponent(LCModelComponent): diff --git a/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py b/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py index f4a7415a4..f5e6497d0 100644 --- a/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py +++ b/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py @@ -5,6 +5,7 @@ from pydantic.v1 import SecretStr from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent +from langflow.field_typing import Text class QianfanChatEndpointComponent(LCModelComponent): diff --git a/src/backend/base/langflow/components/models/CohereModel.py b/src/backend/base/langflow/components/models/CohereModel.py index 21406f793..665aacc13 100644 --- a/src/backend/base/langflow/components/models/CohereModel.py +++ b/src/backend/base/langflow/components/models/CohereModel.py @@ -2,7 +2,7 @@ from typing import Optional from langchain_community.chat_models.cohere import ChatCohere from pydantic.v1 import SecretStr - +from langflow.field_typing import Text from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent diff --git a/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py b/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py index 412ff2d4e..0c2b8eef5 100644 --- a/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py +++ b/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py @@ -2,7 +2,7 @@ from typing import Optional from langchain_google_genai import ChatGoogleGenerativeAI from pydantic.v1 import SecretStr - +from langflow.field_typing import Text, RangeSpec from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent diff --git a/src/backend/base/langflow/components/models/HuggingFaceModel.py b/src/backend/base/langflow/components/models/HuggingFaceModel.py index 4be24ab13..19750ef9f 100644 --- a/src/backend/base/langflow/components/models/HuggingFaceModel.py +++ b/src/backend/base/langflow/components/models/HuggingFaceModel.py @@ -2,7 +2,7 @@ from typing import Optional from langchain_community.chat_models.huggingface import ChatHuggingFace from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint - +from langflow.field_typing import Text from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent diff --git a/src/backend/base/langflow/components/models/VertexAiModel.py b/src/backend/base/langflow/components/models/VertexAiModel.py index 69387c872..ff520e0d1 100644 --- a/src/backend/base/langflow/components/models/VertexAiModel.py +++ b/src/backend/base/langflow/components/models/VertexAiModel.py @@ -4,6 +4,7 @@ from langchain_core.messages.base import BaseMessage from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent +from langflow.field_typing import Text class ChatVertexAIComponent(LCModelComponent): diff --git a/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py b/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py index 1337bffb9..e2d8e61e9 100644 --- a/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py @@ -92,6 +92,11 @@ class AstraDBSearchComponent(LCVectorStoreComponent): "info": "Optional dictionary defining the indexing policy for the collection.", "advanced": True, }, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( @@ -102,6 +107,7 @@ class AstraDBSearchComponent(LCVectorStoreComponent): token: str, api_endpoint: str, search_type: str = "Similarity", + number_of_results: int = 4, namespace: Optional[str] = None, metric: Optional[str] = None, batch_size: Optional[int] = None, @@ -131,4 +137,12 @@ class AstraDBSearchComponent(LCVectorStoreComponent): metadata_indexing_exclude=metadata_indexing_exclude, collection_indexing_policy=collection_indexing_policy, ) - return self.search_with_vector_store(input_value, search_type, vector_store) + try: + return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results) + except KeyError as e: + if "content" in str(e): + raise ValueError( + "You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'." + ) + else: + raise e diff --git a/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py b/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py index edfb93d4e..3e4b7880d 100644 --- a/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py @@ -48,6 +48,11 @@ class ChromaSearchComponent(LCVectorStoreComponent): "display_name": "Server SSL Enabled", "advanced": True, }, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( @@ -57,6 +62,7 @@ class ChromaSearchComponent(LCVectorStoreComponent): collection_name: str, embedding: Embeddings, chroma_server_ssl_enabled: bool, + number_of_results: int = 4, index_directory: Optional[str] = None, chroma_server_cors_allow_origins: Optional[str] = None, chroma_server_host: Optional[str] = None, @@ -102,4 +108,4 @@ class ChromaSearchComponent(LCVectorStoreComponent): client_settings=chroma_settings, ) - return self.search_with_vector_store(input_value, search_type, vector_store) + return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results) diff --git a/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py b/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py index 27cdc606c..d68f455cc 100644 --- a/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py @@ -21,6 +21,11 @@ class FAISSSearchComponent(LCVectorStoreComponent): }, "input_value": {"display_name": "Input"}, "index_name": {"display_name": "Index Name"}, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( @@ -28,6 +33,7 @@ class FAISSSearchComponent(LCVectorStoreComponent): input_value: Text, embedding: Embeddings, folder_path: str, + number_of_results: int = 4, index_name: str = "langflow_index", ) -> List[Record]: if not folder_path: @@ -38,5 +44,5 @@ class FAISSSearchComponent(LCVectorStoreComponent): raise ValueError("Failed to load the FAISS index.") return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type="similarity" + vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py b/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py index cbb583fb3..0ecde1688 100644 --- a/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py @@ -23,6 +23,11 @@ class MongoDBAtlasSearchComponent(LCVectorStoreComponent): "index_name": {"display_name": "Index Name"}, "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"}, "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( # type: ignore[override] @@ -30,6 +35,7 @@ class MongoDBAtlasSearchComponent(LCVectorStoreComponent): input_value: Text, search_type: str, embedding: Embeddings, + number_of_results: int = 4, collection_name: str = "", db_name: str = "", index_name: str = "", @@ -47,5 +53,5 @@ class MongoDBAtlasSearchComponent(LCVectorStoreComponent): if not vector_store: raise ValueError("Failed to create MongoDB Atlas Vector Store") return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type=search_type + vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py b/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py index 56dd11196..96ba35743 100644 --- a/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py @@ -38,6 +38,11 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent): "default": 1, "advanced": True, }, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( # type: ignore[override] @@ -46,6 +51,7 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent): embedding: Embeddings, pinecone_env: str, text_key: str = "text", + number_of_results: int = 4, pool_threads: int = 4, index_name: Optional[str] = None, pinecone_api_key: Optional[str] = None, @@ -66,5 +72,5 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent): raise ValueError("Failed to load the Pinecone index.") return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type=search_type + vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py b/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py index 1562a9cd0..8fcc5d761 100644 --- a/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py @@ -41,6 +41,11 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent): "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, "timeout": {"display_name": "Timeout", "advanced": True}, "url": {"display_name": "URL", "advanced": True}, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( # type: ignore[override] @@ -48,6 +53,7 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent): input_value: Text, embedding: Embeddings, collection_name: str, + number_of_results: int = 4, search_type: str = "similarity", api_key: Optional[str] = None, content_payload_key: str = "page_content", @@ -88,5 +94,5 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent): raise ValueError("Failed to load the Qdrant index.") return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type=search_type + vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorsearch/RedisSearch.py b/src/backend/base/langflow/components/vectorsearch/RedisSearch.py index 17fb03cb3..25e71c64b 100644 --- a/src/backend/base/langflow/components/vectorsearch/RedisSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/RedisSearch.py @@ -39,6 +39,11 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent): "advanced": False, }, "redis_index_name": {"display_name": "Redis Index", "advanced": False}, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( # type: ignore[override] @@ -48,6 +53,7 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent): embedding: Embeddings, redis_server_url: str, redis_index_name: str, + number_of_results: int = 4, schema: Optional[str] = None, ) -> List[Record]: """ @@ -72,5 +78,5 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent): raise ValueError("Failed to load the Redis index.") return self.search_with_vector_store( - input_value=input_value, search_type=search_type, vector_store=vector_store + input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py b/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py index ca8113c56..aef1c13b7 100644 --- a/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py @@ -26,6 +26,11 @@ class SupabaseSearchComponent(LCVectorStoreComponent): "supabase_service_key": {"display_name": "Supabase Service Key"}, "supabase_url": {"display_name": "Supabase URL"}, "table_name": {"display_name": "Table Name", "advanced": True}, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( @@ -33,6 +38,7 @@ class SupabaseSearchComponent(LCVectorStoreComponent): input_value: Text, search_type: str, embedding: Embeddings, + number_of_results: int = 4, query_name: str = "", supabase_service_key: str = "", supabase_url: str = "", @@ -45,4 +51,4 @@ class SupabaseSearchComponent(LCVectorStoreComponent): table_name=table_name, query_name=query_name, ) - return self.search_with_vector_store(input_value, search_type, vector_store) + return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results) diff --git a/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py b/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py index 3ffbe50b5..459054f67 100644 --- a/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py @@ -34,6 +34,11 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent): "display_name": "Files Url", "info": "Make vectara object using url of files (optional)", }, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( # type: ignore[override] @@ -43,6 +48,7 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent): vectara_customer_id: str, vectara_corpus_id: str, vectara_api_key: str, + number_of_results: int = 4, ) -> List[Record]: source = "Langflow" vector_store = Vectara( @@ -56,5 +62,5 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent): raise ValueError("Failed to create Vectara Vector Store") return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type=search_type + vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py b/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py index fd90fe50b..f47538f2e 100644 --- a/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py @@ -49,7 +49,11 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo "field_type": "bool", "advanced": True, }, - "code": {"show": False}, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( # type: ignore[override] @@ -57,6 +61,7 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo input_value: Text, search_type: str, url: str, + number_of_results: int = 4, search_by_text: bool = False, api_key: Optional[str] = None, index_name: Optional[str] = None, @@ -77,5 +82,5 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo raise ValueError("Failed to load the Weaviate index.") return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type=search_type + vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py b/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py index 55a8ed077..9b074b5f6 100644 --- a/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py @@ -33,6 +33,11 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent): }, "collection_name": {"display_name": "Table", "advanced": False}, "input_value": {"display_name": "Input"}, + "number_of_results": { + "display_name": "Number of Results", + "info": "Number of results to return.", + "advanced": True, + }, } def build( # type: ignore[override] @@ -42,6 +47,7 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent): search_type: str, pg_server_url: str, collection_name: str, + number_of_results: int = 4, ) -> List[Record]: """ Builds the Vector Store or BaseRetriever object. @@ -64,5 +70,5 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent): except Exception as e: raise RuntimeError(f"Failed to build PGVector: {e}") return self.search_with_vector_store( - input_value=input_value, search_type=search_type, vector_store=vector_store + input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results ) diff --git a/src/backend/base/langflow/components/vectorstores/base/model.py b/src/backend/base/langflow/components/vectorstores/base/model.py index eef99ef21..668c5eff2 100644 --- a/src/backend/base/langflow/components/vectorstores/base/model.py +++ b/src/backend/base/langflow/components/vectorstores/base/model.py @@ -19,6 +19,8 @@ class LCVectorStoreComponent(CustomComponent): input_value: Text, search_type: str, vector_store: Union[VectorStore, BaseRetriever], + k=10, + **kwargs, ) -> List[Record]: """ Search for records in the vector store based on the input value and search type. @@ -37,7 +39,7 @@ class LCVectorStoreComponent(CustomComponent): docs: List[Document] = [] if input_value and isinstance(input_value, str) and hasattr(vector_store, "search"): - docs = vector_store.search(query=input_value, search_type=search_type.lower()) + docs = vector_store.search(query=input_value, search_type=search_type.lower(), k=k, **kwargs) else: raise ValueError("Invalid inputs provided.") records = docs_to_records(docs) diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py index fd6789a17..a94110f7e 100644 --- a/src/backend/base/langflow/graph/graph/base.py +++ b/src/backend/base/langflow/graph/graph/base.py @@ -1,7 +1,7 @@ import asyncio from collections import defaultdict, deque from itertools import chain -from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Literal, Optional, Type, Union +from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Optional, Type, Union from loguru import logger diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py index d6985c8f2..cf958d0c2 100644 --- a/src/backend/base/langflow/initial_setup/setup.py +++ b/src/backend/base/langflow/initial_setup/setup.py @@ -192,6 +192,7 @@ def delete_start_projects(session): ).all() for flow in flows: session.delete(flow) + session.commit() def create_or_update_starter_projects(): diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index f56b5603f..9663a78ef 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -982,7 +982,7 @@ "zoom": 0.47344308394045925 } }, - "description": "", + "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.", "name": "Blog Writer", "last_tested_version": "1.0.0a0", "is_component": false diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index f06b51074..43e8d729c 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -3,7 +3,7 @@ "data": { "nodes": [ { - "id": "Prompt-6qkyO", + "id": "Prompt-amqBu", "type": "genericNode", "position": { "x": 2191.5837146441663, @@ -109,7 +109,7 @@ "beta": false, "error": null }, - "id": "Prompt-6qkyO", + "id": "Prompt-amqBu", "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt" }, @@ -123,7 +123,7 @@ "dragging": false }, { - "id": "Prompt-8M7lZ", + "id": "Prompt-gTNiz", "type": "genericNode", "position": { "x": 3731.0813766902447, @@ -229,16 +229,17 @@ "beta": false, "error": null }, - "id": "Prompt-8M7lZ", + "id": "Prompt-gTNiz", "description": "Create a prompt template with dynamic variables.", "display_name": "Prompt" }, "selected": false, "width": 384, - "height": 385 + "height": 385, + "dragging": false }, { - "id": "ChatOutput-TKkOi", + "id": "ChatOutput-EJkG3", "type": "genericNode", "position": { "x": 3722.1747844849388, @@ -426,7 +427,7 @@ "field_order": [], "beta": false }, - "id": "ChatOutput-TKkOi" + "id": "ChatOutput-EJkG3" }, "selected": false, "width": 384, @@ -434,7 +435,7 @@ "dragging": false }, { - "id": "ChatOutput-WOZJj", + "id": "ChatOutput-DNmvg", "type": "genericNode", "position": { "x": 5077.71285886074, @@ -622,14 +623,14 @@ "field_order": [], "beta": false }, - "id": "ChatOutput-WOZJj" + "id": "ChatOutput-DNmvg" }, "selected": false, "width": 384, "height": 385 }, { - "id": "TextInput-fd38z", + "id": "TextInput-sptaH", "type": "genericNode", "position": { "x": 1700.5624822024752, @@ -725,9 +726,9 @@ "field_order": [], "beta": false }, - "id": "TextInput-fd38z" + "id": "TextInput-sptaH" }, - "selected": true, + "selected": false, "width": 384, "height": 290, "positionAbsolute": { @@ -737,11 +738,11 @@ "dragging": false }, { - "id": "TextOutput-LH9pW", + "id": "TextOutput-2MS4a", "type": "genericNode", "position": { - "x": 2429.4897030584134, - "y": 552.2482060219679 + "x": 2917.216113690115, + "y": 513.0058511435552 }, "data": { "type": "TextOutput", @@ -833,19 +834,19 @@ "field_order": [], "beta": false }, - "id": "TextOutput-LH9pW" + "id": "TextOutput-2MS4a" }, "selected": false, "width": 384, "height": 290, "positionAbsolute": { - "x": 2429.4897030584134, - "y": 552.2482060219679 + "x": 2917.216113690115, + "y": 513.0058511435552 }, "dragging": false }, { - "id": "OpenAIModel-WAiN2", + "id": "OpenAIModel-uYXZJ", "type": "genericNode", "position": { "x": 2925.784767523062, @@ -1111,7 +1112,7 @@ ], "beta": false }, - "id": "OpenAIModel-WAiN2" + "id": "OpenAIModel-uYXZJ" }, "selected": false, "width": 384, @@ -1123,7 +1124,7 @@ "dragging": false }, { - "id": "TextOutput-ZUN0s", + "id": "TextOutput-MUDOR", "type": "genericNode", "position": { "x": 4446.064323520379, @@ -1219,7 +1220,7 @@ "field_order": [], "beta": false }, - "id": "TextOutput-ZUN0s" + "id": "TextOutput-MUDOR" }, "selected": false, "width": 384, @@ -1231,7 +1232,7 @@ } }, { - "id": "OpenAIModel-4b8eN", + "id": "OpenAIModel-XawYB", "type": "genericNode", "position": { "x": 4500.152018344182, @@ -1497,7 +1498,7 @@ ], "beta": false }, - "id": "OpenAIModel-4b8eN" + "id": "OpenAIModel-XawYB" }, "selected": false, "width": 384, @@ -1511,14 +1512,14 @@ ], "edges": [ { - "source": "TextInput-fd38z", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-fd38zœ}", - "target": "Prompt-6qkyO", - "targetHandle": "{œfieldNameœ:œdocumentœ,œidœ:œPrompt-6qkyOœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "source": "TextInput-sptaH", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}", + "target": "Prompt-amqBu", + "targetHandle": "{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "document", - "id": "Prompt-6qkyO", + "id": "Prompt-amqBu", "inputTypes": [ "Document", "BaseOutputParser", @@ -1534,24 +1535,24 @@ "object" ], "dataType": "TextInput", - "id": "TextInput-fd38z" + "id": "TextInput-sptaH" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-TextInput-fd38z{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-fd38zœ}-Prompt-6qkyO{œfieldNameœ:œdocumentœ,œidœ:œPrompt-6qkyOœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-TextInput-sptaH{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}-Prompt-amqBu{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" }, { - "source": "Prompt-6qkyO", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-6qkyOœ}", - "target": "TextOutput-LH9pW", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-LH9pWœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "source": "Prompt-amqBu", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", + "target": "TextOutput-2MS4a", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", - "id": "TextOutput-LH9pW", + "id": "TextOutput-2MS4a", "inputTypes": [ "Record", "Text" @@ -1565,24 +1566,24 @@ "Text" ], "dataType": "Prompt", - "id": "Prompt-6qkyO" + "id": "Prompt-amqBu" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-Prompt-6qkyO{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-6qkyOœ}-TextOutput-LH9pW{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-LH9pWœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-TextOutput-2MS4a{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" }, { - "source": "Prompt-6qkyO", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-6qkyOœ}", - "target": "OpenAIModel-WAiN2", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-WAiN2œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "source": "Prompt-amqBu", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", + "target": "OpenAIModel-uYXZJ", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", - "id": "OpenAIModel-WAiN2", + "id": "OpenAIModel-uYXZJ", "inputTypes": [ "Text" ], @@ -1595,24 +1596,24 @@ "Text" ], "dataType": "Prompt", - "id": "Prompt-6qkyO" + "id": "Prompt-amqBu" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-Prompt-6qkyO{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-6qkyOœ}-OpenAIModel-WAiN2{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-WAiN2œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-OpenAIModel-uYXZJ{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" }, { - "source": "OpenAIModel-WAiN2", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-WAiN2œ}", - "target": "Prompt-8M7lZ", - "targetHandle": "{œfieldNameœ:œsummaryœ,œidœ:œPrompt-8M7lZœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "source": "OpenAIModel-uYXZJ", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", + "target": "Prompt-gTNiz", + "targetHandle": "{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "summary", - "id": "Prompt-8M7lZ", + "id": "Prompt-gTNiz", "inputTypes": [ "Document", "BaseOutputParser", @@ -1628,24 +1629,24 @@ "object" ], "dataType": "OpenAIModel", - "id": "OpenAIModel-WAiN2" + "id": "OpenAIModel-uYXZJ" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-OpenAIModel-WAiN2{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-WAiN2œ}-Prompt-8M7lZ{œfieldNameœ:œsummaryœ,œidœ:œPrompt-8M7lZœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-Prompt-gTNiz{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" }, { - "source": "OpenAIModel-WAiN2", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-WAiN2œ}", - "target": "ChatOutput-TKkOi", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-TKkOiœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "source": "OpenAIModel-uYXZJ", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", + "target": "ChatOutput-EJkG3", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", - "id": "ChatOutput-TKkOi", + "id": "ChatOutput-EJkG3", "inputTypes": [ "Text" ], @@ -1658,24 +1659,24 @@ "object" ], "dataType": "OpenAIModel", - "id": "OpenAIModel-WAiN2" + "id": "OpenAIModel-uYXZJ" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-OpenAIModel-WAiN2{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-WAiN2œ}-ChatOutput-TKkOi{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-TKkOiœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-ChatOutput-EJkG3{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" }, { - "source": "Prompt-8M7lZ", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-8M7lZœ}", - "target": "TextOutput-ZUN0s", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-ZUN0sœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "source": "Prompt-gTNiz", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "target": "TextOutput-MUDOR", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", - "id": "TextOutput-ZUN0s", + "id": "TextOutput-MUDOR", "inputTypes": [ "Record", "Text" @@ -1689,24 +1690,24 @@ "Text" ], "dataType": "Prompt", - "id": "Prompt-8M7lZ" + "id": "Prompt-gTNiz" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-Prompt-8M7lZ{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-8M7lZœ}-TextOutput-ZUN0s{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-ZUN0sœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-TextOutput-MUDOR{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" }, { - "source": "Prompt-8M7lZ", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-8M7lZœ}", - "target": "OpenAIModel-4b8eN", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-4b8eNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "source": "Prompt-gTNiz", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "target": "OpenAIModel-XawYB", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", - "id": "OpenAIModel-4b8eN", + "id": "OpenAIModel-XawYB", "inputTypes": [ "Text" ], @@ -1719,24 +1720,24 @@ "Text" ], "dataType": "Prompt", - "id": "Prompt-8M7lZ" + "id": "Prompt-gTNiz" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-Prompt-8M7lZ{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-8M7lZœ}-OpenAIModel-4b8eN{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-4b8eNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-OpenAIModel-XawYB{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" }, { - "source": "OpenAIModel-4b8eN", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-4b8eNœ}", - "target": "ChatOutput-WOZJj", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-WOZJjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "source": "OpenAIModel-XawYB", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}", + "target": "ChatOutput-DNmvg", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", - "id": "ChatOutput-WOZJj", + "id": "ChatOutput-DNmvg", "inputTypes": [ "Text" ], @@ -1749,20 +1750,20 @@ "object" ], "dataType": "OpenAIModel", - "id": "OpenAIModel-4b8eN" + "id": "OpenAIModel-XawYB" } }, "style": { "stroke": "#555" }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-OpenAIModel-4b8eN{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-4b8eNœ}-ChatOutput-WOZJj{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-WOZJjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-XawYB{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}-ChatOutput-DNmvg{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" } ], "viewport": { - "x": -269.16751160112597, - "y": 42.28236292358156, - "zoom": 0.30778611191960875 + "x": -383.7251879618552, + "y": 69.19813933800037, + "zoom": 0.3105753483695743 } }, "description": "The Prompt Chaining flow chains prompts with LLMs, refining outputs through iterative stages.", diff --git a/src/backend/base/langflow/interface/toolkits/base.py b/src/backend/base/langflow/interface/toolkits/base.py index 9ff3ad045..efcfefaa4 100644 --- a/src/backend/base/langflow/interface/toolkits/base.py +++ b/src/backend/base/langflow/interface/toolkits/base.py @@ -1,3 +1,4 @@ +import warnings from typing import Callable, Dict, List, Optional from langchain.agents import agent_toolkits @@ -29,13 +30,15 @@ class ToolkitCreator(LangChainTypeCreator): @property def type_to_loader_dict(self) -> Dict: if self.type_dict is None: - settings_service = get_settings_service() - self.type_dict = { - toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") - # if toolkit_name is not lower case it is a class - for toolkit_name in agent_toolkits.__all__ - if not toolkit_name.islower() and toolkit_name in settings_service.settings.TOOLKITS - } + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + settings_service = get_settings_service() + self.type_dict = { + toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") + # if toolkit_name is not lower case it is a class + for toolkit_name in agent_toolkits.__all__ + if not toolkit_name.islower() and toolkit_name in settings_service.settings.TOOLKITS + } return self.type_dict diff --git a/src/backend/base/langflow/interface/tools/base.py b/src/backend/base/langflow/interface/tools/base.py index ddaccb4bb..b4c933baa 100644 --- a/src/backend/base/langflow/interface/tools/base.py +++ b/src/backend/base/langflow/interface/tools/base.py @@ -1,6 +1,7 @@ from typing import Dict, List, Optional from langchain.agents.load_tools import _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS + from langflow.interface.base import LangChainTypeCreator from langflow.interface.tools.constants import ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS from langflow.interface.tools.util import get_tool_params diff --git a/src/backend/base/langflow/main.py b/src/backend/base/langflow/main.py index 0aa55b2d9..9ed6e2984 100644 --- a/src/backend/base/langflow/main.py +++ b/src/backend/base/langflow/main.py @@ -9,6 +9,7 @@ from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import FileResponse from fastapi.staticfiles import StaticFiles +from loguru import logger from langflow.api import router from langflow.initial_setup.setup import create_or_update_starter_projects @@ -106,6 +107,7 @@ def get_static_files_dir(): def setup_app(static_files_dir: Optional[Path] = None, backend_only: bool = False) -> FastAPI: """Setup the FastAPI app.""" # get the directory of the current file + logger.info(f"Setting up app with static files directory {static_files_dir}") if not static_files_dir: static_files_dir = get_static_files_dir() @@ -128,7 +130,7 @@ if __name__ == "__main__": host="127.0.0.1", port=7860, workers=get_number_of_workers(), - log_level="debug", + log_level="error", reload=True, loop="asyncio", ) diff --git a/src/backend/base/langflow/server.py b/src/backend/base/langflow/server.py index 8451a9d08..b7e86934d 100644 --- a/src/backend/base/langflow/server.py +++ b/src/backend/base/langflow/server.py @@ -1,3 +1,5 @@ +import os + from gunicorn.app.base import BaseApplication # type: ignore from uvicorn.workers import UvicornWorker @@ -11,6 +13,7 @@ class LangflowApplication(BaseApplication): self.options = options or {} self.options["worker_class"] = "langflow.server.LangflowUvicornWorker" + self.options["loglevel"] = os.getenv("LANGFLOW_LOG_LEVEL", "error").lower() self.application = app super().__init__() diff --git a/src/backend/base/langflow/services/database/service.py b/src/backend/base/langflow/services/database/service.py index ebaccf600..f3d77f6d7 100644 --- a/src/backend/base/langflow/services/database/service.py +++ b/src/backend/base/langflow/services/database/service.py @@ -1,3 +1,4 @@ +from datetime import datetime import time from pathlib import Path from typing import TYPE_CHECKING @@ -124,10 +125,16 @@ class DatabaseService(Service): # if not self.script_location.exists(): # this is not the correct way to check if alembic has been initialized # We need to check if the alembic_version table exists # if not, we need to initialize alembic - alembic_cfg = Config() + # stdout should be something like sys.stdout + # which is a buffer + # I don't want to output anything + # subprocess.DEVNULL is an int + buffer = open(self.script_location / "alembic.log", "w") + alembic_cfg = Config(stdout=buffer) # alembic_cfg.attributes["connection"] = session alembic_cfg.set_main_option("script_location", str(self.script_location)) alembic_cfg.set_main_option("sqlalchemy.url", self.database_url) + should_initialize_alembic = False with Session(self.engine) as session: # If the table does not exist it throws an error @@ -150,6 +157,7 @@ class DatabaseService(Service): logger.info(f"Running DB migrations in {self.script_location}") try: + buffer.write(f"{datetime.now().isoformat()}: Checking migrations\n") command.check(alembic_cfg) except Exception as exc: if isinstance(exc, (util.exc.CommandError, util.exc.AutogenerateDiffsDetected)): @@ -157,6 +165,7 @@ class DatabaseService(Service): time.sleep(3) try: + buffer.write(f"{datetime.now().isoformat()}: Checking migrations\n") command.check(alembic_cfg) except util.exc.AutogenerateDiffsDetected as exc: logger.error(f"AutogenerateDiffsDetected: {exc}") diff --git a/src/backend/base/langflow/services/settings/auth.py b/src/backend/base/langflow/services/settings/auth.py index a5bd7ed80..103c96a40 100644 --- a/src/backend/base/langflow/services/settings/auth.py +++ b/src/backend/base/langflow/services/settings/auth.py @@ -40,9 +40,9 @@ class AuthSettings(BaseSettings): """The Secure attribute of the refresh token cookie.""" REFRESH_HTTPONLY: bool = True """The HttpOnly attribute of the refresh token cookie.""" - ACCESS_SAME_SITE: Literal["lax", "strict", "none"] = "none" + ACCESS_SAME_SITE: Literal["lax", "strict", "none"] = "lax" """The SameSite attribute of the access token cookie.""" - ACCESS_SECURE: bool = True + ACCESS_SECURE: bool = False """The Secure attribute of the access token cookie.""" ACCESS_HTTPONLY: bool = False """The HttpOnly attribute of the access token cookie.""" diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 7bd68ddf9..8a051058e 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -94,7 +94,8 @@ class FrontendNode(BaseModel): def process_base_classes(self, base_classes: List[str]) -> List[str]: """Removes unwanted base classes from the list of base classes.""" - return list(set(base_classes)) + sorted_base_classes = sorted(list(set(base_classes)), key=lambda x: x.lower()) + return sorted_base_classes @field_serializer("display_name") def process_display_name(self, display_name: str) -> str: diff --git a/src/backend/base/langflow/utils/logger.py b/src/backend/base/langflow/utils/logger.py index 5d9ec8406..d92bdb0e1 100644 --- a/src/backend/base/langflow/utils/logger.py +++ b/src/backend/base/langflow/utils/logger.py @@ -28,7 +28,7 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None): if os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS and log_level is None: log_level = os.getenv("LANGFLOW_LOG_LEVEL") if log_level is None: - log_level = "INFO" + log_level = "ERROR" # Human-readable log_format = ( "{time:YYYY-MM-DD HH:mm:ss} - " diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock index 37d28f3f8..2d5c03cba 100644 --- a/src/backend/base/poetry.lock +++ b/src/backend/base/poetry.lock @@ -2050,13 +2050,13 @@ files = [ [[package]] name = "importlib-metadata" -version = "6.11.0" +version = "7.0.0" description = "Read metadata from Python packages" optional = false python-versions = ">=3.8" files = [ - {file = "importlib_metadata-6.11.0-py3-none-any.whl", hash = "sha256:f0afba6205ad8f8947c7d338b5342d5db2afbfd82f9cbef7879a9539cc12eb9b"}, - {file = "importlib_metadata-6.11.0.tar.gz", hash = "sha256:1231cf92d825c9e03cfc4da076a16de6422c863558229ea0b22b675657463443"}, + {file = "importlib_metadata-7.0.0-py3-none-any.whl", hash = "sha256:d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67"}, + {file = "importlib_metadata-7.0.0.tar.gz", hash = "sha256:7fc841f8b8332803464e5dc1c63a2e59121f46ca186c0e2e182e80bf8c1319f7"}, ] [package.dependencies] @@ -2487,13 +2487,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15. [[package]] name = "langchain-core" -version = "0.1.37" +version = "0.1.38" description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_core-0.1.37-py3-none-any.whl", hash = "sha256:63c6aecb0f2eb1a21f8e944da622748cfeafa2cc3d94c0182ffa8038bd00fe0b"}, - {file = "langchain_core-0.1.37.tar.gz", hash = "sha256:3db7008796e25aea90f98c2159dbf29bf1fd296bdcb78dc2d8183a92fdde4433"}, + {file = "langchain_core-0.1.38-py3-none-any.whl", hash = "sha256:d881b2754254cb4bdb0d5bb56e5c138d032b6e75e5cb21f151b01224b322e02b"}, + {file = "langchain_core-0.1.38.tar.gz", hash = "sha256:ee8da6d061c06cce7dc22fec224b6ecbc3a8de106d6dd9f409c7fe448ea41861"}, ] [package.dependencies] @@ -3071,7 +3071,6 @@ files = [ {file = "msgpack-1.0.8-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5fbb160554e319f7b22ecf530a80a3ff496d38e8e07ae763b9e82fadfe96f273"}, {file = "msgpack-1.0.8-cp39-cp39-win32.whl", hash = "sha256:f9af38a89b6a5c04b7d18c492c8ccf2aee7048aff1ce8437c4683bb5a1df893d"}, {file = "msgpack-1.0.8-cp39-cp39-win_amd64.whl", hash = "sha256:ed59dd52075f8fc91da6053b12e8c89e37aa043f8986efd89e61fae69dc1b011"}, - 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-from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent - -from langflow import CustomComponent -from langflow.field_typing import BaseMemory, Text, Tool - - -class LCAgentComponent(CustomComponent): - def build_config(self): - return { - "lc": { - "display_name": "LangChain", - "info": "The LangChain to interact with.", - }, - "handle_parsing_errors": { - "display_name": "Handle Parsing Errors", - "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.", - "advanced": True, - }, - "output_key": { - "display_name": "Output Key", - "info": "The key to use to get the output from the agent.", - "advanced": True, - }, - "memory": { - "display_name": "Memory", - "info": "Memory to use for the agent.", - }, - "tools": { - "display_name": "Tools", - "info": "Tools the agent can use.", - }, - "input_value": { - "display_name": "Input", - "info": "Input text to pass to the agent.", - }, - } - - async def run_agent( - self, - agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor], - inputs: str, - input_variables: list[str], - tools: List[Tool], - memory: BaseMemory = None, - handle_parsing_errors: bool = True, - output_key: str = "output", - ) -> Text: - if isinstance(agent, AgentExecutor): - runnable = agent - else: - runnable = AgentExecutor.from_agent_and_tools( - agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors - ) - input_dict = {"input": inputs} - for var in input_variables: - if var not in ["agent_scratchpad", "input"]: - input_dict[var] = "" - result = await runnable.ainvoke(input_dict) - self.status = result - if output_key in result: - return result.get(output_key) - elif "output" not in result: - if output_key != "output": - raise ValueError(f"Output key not found in result. Tried '{output_key}' and 'output'.") - else: - raise ValueError("Output key not found in result. Tried 'output'.") - - return result.get("output") diff --git a/src/backend/langflow/base/models/__init__.py b/src/backend/langflow/base/models/__init__.py deleted file mode 100644 index 921f10336..000000000 --- a/src/backend/langflow/base/models/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .model import LCModelComponent - -__all__ = ["LCModelComponent"] diff --git a/src/backend/langflow/base/models/model.py b/src/backend/langflow/base/models/model.py deleted file mode 100644 index e2ab4b6cf..000000000 --- a/src/backend/langflow/base/models/model.py +++ /dev/null @@ -1,48 +0,0 @@ -from typing import Optional - -from langchain_core.language_models.chat_models import BaseChatModel -from langchain_core.language_models.llms import LLM -from langchain_core.messages import HumanMessage, SystemMessage - -from langflow import CustomComponent - - -class LCModelComponent(CustomComponent): - display_name: str = "Model Name" - description: str = "Model Description" - - def get_result(self, runnable: LLM, stream: bool, input_value: str): - """ - Retrieves the result from the output of a Runnable object. - - Args: - output (Runnable): The output object to retrieve the result from. - stream (bool): Indicates whether to use streaming or invocation mode. - input_value (str): The input value to pass to the output object. - - Returns: - The result obtained from the output object. - """ - if stream: - result = runnable.stream(input_value) - else: - message = runnable.invoke(input_value) - result = message.content if hasattr(message, "content") else message - self.status = result - return result - - def get_chat_result( - self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None - ): - messages = [] - if input_value: - messages.append(HumanMessage(input_value)) - if system_message: - messages.append(SystemMessage(system_message)) - if stream: - result = runnable.stream(messages) - else: - message = runnable.invoke(messages) - result = message.content - self.status = result - return result diff --git a/src/backend/langflow/components/models/base/__init__.py b/src/backend/langflow/components/models/base/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/backend/langflow/components/tools/SearchAPITool.py b/src/backend/langflow/components/tools/SearchAPITool.py deleted file mode 100644 index 76c82ad9c..000000000 --- a/src/backend/langflow/components/tools/SearchAPITool.py +++ /dev/null @@ -1,37 +0,0 @@ -from langchain_community.tools.searchapi import SearchAPIRun -from langchain_community.utilities.searchapi import SearchApiAPIWrapper - -from langflow import CustomComponent -from langflow.field_typing import Tool - - -class SearchApiToolComponent(CustomComponent): - display_name: str = "SearchApi Tool" - description: str = "Real-time search engine results API." - documentation: str = "https://www.searchapi.io/docs/google" - field_config = { - "engine": { - "display_name": "Engine", - "field_type": "str", - "info": "The search engine to use.", - }, - "api_key": { - "display_name": "API Key", - "field_type": "str", - "required": True, - "password": True, - "info": "The API key to use SearchApi.", - }, - } - - def build( - self, - engine: str, - api_key: str, - ) -> Tool: - search_api_wrapper = SearchApiAPIWrapper(engine=engine, searchapi_api_key=api_key) - - tool = SearchAPIRun(api_wrapper=search_api_wrapper) - - self.status = tool - return tool diff --git a/src/backend/langflow/components/tools/SearchApi.py b/src/backend/langflow/components/tools/SearchApi.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/backend/langflow/services/deps.py b/src/backend/langflow/services/deps.py deleted file mode 100644 index ba990eb70..000000000 --- a/src/backend/langflow/services/deps.py +++ /dev/null @@ -1,103 +0,0 @@ -from contextlib import contextmanager -from typing import TYPE_CHECKING, Generator - -from langflow.services import ServiceType, service_manager - -if TYPE_CHECKING: - from sqlmodel import Session - - from langflow.services.cache.service import CacheService - from langflow.services.chat.service import ChatService - from langflow.services.credentials.service import CredentialService - from langflow.services.database.service import DatabaseService - from langflow.services.monitor.service import MonitorService - from langflow.services.plugins.service import PluginService - from langflow.services.session.service import SessionService - from langflow.services.settings.service import SettingsService - from langflow.services.socket.service import SocketIOService - from langflow.services.storage.service import StorageService - from langflow.services.store.service import StoreService - from langflow.services.task.service import TaskService - - -def get_socket_service() -> "SocketIOService": - return service_manager.get(ServiceType.SOCKETIO_SERVICE) # type: ignore - - -def get_storage_service() -> "StorageService": - return service_manager.get(ServiceType.STORAGE_SERVICE) # type: ignore - - -def get_credential_service() -> "CredentialService": - return service_manager.get(ServiceType.CREDENTIAL_SERVICE) # type: ignore - - -def get_plugins_service() -> "PluginService": - return service_manager.get(ServiceType.PLUGIN_SERVICE) # type: ignore - - -def get_settings_service() -> "SettingsService": - try: - return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore - except ValueError: - # initialize settings service - from langflow.services.manager import initialize_settings_service - - initialize_settings_service() - return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore - - -def get_db_service() -> "DatabaseService": - return service_manager.get(ServiceType.DATABASE_SERVICE) # type: ignore - - -def get_session() -> Generator["Session", None, None]: - db_service = get_db_service() - yield from db_service.get_session() - - -@contextmanager -def session_scope(): - """ - Context manager for managing a session scope. - - Yields: - session: The session object. - - Raises: - Exception: If an error occurs during the session scope. - - """ - session = next(get_session()) - try: - yield session - session.commit() - except: - session.rollback() - raise - finally: - session.close() - - -def get_cache_service() -> "CacheService": - return service_manager.get(ServiceType.CACHE_SERVICE) # type: ignore - - -def get_session_service() -> "SessionService": - return service_manager.get(ServiceType.SESSION_SERVICE) # type: ignore - - -def get_monitor_service() -> "MonitorService": - return service_manager.get(ServiceType.MONITOR_SERVICE) # type: ignore - - -def get_task_service() -> "TaskService": - return service_manager.get(ServiceType.TASK_SERVICE) # type: ignore - - -def get_chat_service() -> "ChatService": - return service_manager.get(ServiceType.CHAT_SERVICE) # type: ignore - - -def get_store_service() -> "StoreService": - return service_manager.get(ServiceType.STORE_SERVICE) # type: ignore diff --git a/src/backend/langflow/services/factory.py b/src/backend/langflow/services/factory.py deleted file mode 100644 index 4780b53c7..000000000 --- a/src/backend/langflow/services/factory.py +++ /dev/null @@ -1,83 +0,0 @@ -import importlib -import inspect -from typing import TYPE_CHECKING, Type, get_type_hints - -from cachetools import LRUCache, cached -from loguru import logger - -from langflow.services.schema import ServiceType - -if TYPE_CHECKING: - from langflow.services.base import Service - - -class ServiceFactory: - def __init__( - self, - service_class, - ): - self.service_class = service_class - self.dependencies = infer_service_types(self, import_all_services_into_a_dict()) - - def create(self, *args, **kwargs) -> "Service": - raise self.service_class(*args, **kwargs) - - -def hash_factory(factory: ServiceFactory) -> str: - return factory.service_class.__name__ - - -def hash_dict(d: dict) -> str: - return str(d) - - -def hash_infer_service_types_args(factory_class: Type[ServiceFactory], available_services=None) -> str: - factory_hash = hash_factory(factory_class) - services_hash = hash_dict(available_services) - return f"{factory_hash}_{services_hash}" - - -@cached(cache=LRUCache(maxsize=10), key=hash_infer_service_types_args) -def infer_service_types(factory_class: Type[ServiceFactory], available_services=None) -> "ServiceType": - create_method = factory_class.create - type_hints = get_type_hints(create_method, globalns=available_services) - service_types = [] - for param_name, param_type in type_hints.items(): - # Skip the return type if it's included in type hints - if param_name == "return": - continue - - # Convert the type to the expected enum format directly without appending "_SERVICE" - type_name = param_type.__name__.upper().replace("SERVICE", "_SERVICE") - - try: - # Attempt to find a matching enum value - service_type = ServiceType[type_name] - service_types.append(service_type) - except KeyError: - raise ValueError(f"No matching ServiceType for parameter type: {param_type.__name__}") - return service_types - - -@cached(cache=LRUCache(maxsize=1)) -def import_all_services_into_a_dict(): - # Services are all in langflow.services.{service_name}.service - # and are subclass of Service - # We want to import all of them and put them in a dict - # to use as globals - from langflow.services.base import Service - - services = {} - for service_type in ServiceType: - try: - service_name = ServiceType(service_type).value.replace("_service", "") - module_name = f"langflow.services.{service_name}.service" - module = importlib.import_module(module_name) - for name, obj in inspect.getmembers(module, inspect.isclass): - if issubclass(obj, Service) and obj is not Service: - services[name] = obj - break - except Exception as exc: - logger.exception(exc) - raise RuntimeError("Could not initialize services. Please check your settings.") from exc - return services diff --git a/src/frontend/index.html b/src/frontend/index.html index 8c21f0124..d4b03effb 100644 --- a/src/frontend/index.html +++ b/src/frontend/index.html @@ -1,16 +1,19 @@ - - - - + + + + - + Langflow - - + + -
+
- + diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index a32895097..971c9eee1 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -969,9 +969,9 @@ } }, "node_modules/@humanwhocodes/object-schema": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/@humanwhocodes/object-schema/-/object-schema-2.0.2.tgz", - "integrity": "sha512-6EwiSjwWYP7pTckG6I5eyFANjPhmPjUX9JRLUSfNPC7FX7zK9gyZAfUEaECL6ALTpGX5AjnBq3C9XmVWPitNpw==", + "version": "2.0.3", + "resolved": "https://registry.npmjs.org/@humanwhocodes/object-schema/-/object-schema-2.0.3.tgz", + "integrity": "sha512-93zYdMES/c1D69yZiKDBj0V24vqNzB/koF26KPaagAfd3P/4gUlh3Dys5ogAK+Exi9QyzlD8x/08Zt7wIKcDcA==", "dev": true }, "node_modules/@isaacs/cliui": { @@ -4888,9 +4888,9 @@ } }, "node_modules/caniuse-lite": { - "version": "1.0.30001600", - "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001600.tgz", - "integrity": "sha512-+2S9/2JFhYmYaDpZvo0lKkfvuKIglrx68MwOBqMGHhQsNkLjB5xtc/TGoEPs+MxjSyN/72qer2g97nzR641mOQ==", + "version": "1.0.30001603", + "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001603.tgz", + "integrity": "sha512-iL2iSS0eDILMb9n5yKQoTBim9jMZ0Yrk8g0N9K7UzYyWnfIKzXBZD5ngpM37ZcL/cv0Mli8XtVMRYMQAfFpi5Q==", "funding": [ { "type": "opencollective", @@ -5677,9 +5677,9 @@ "integrity": "sha512-I88TYZWc9XiYHRQ4/3c5rjjfgkjhLyW2luGIheGERbNQ6OY7yTybanSpDXZa8y7VUP9YmDcYa+eyq4ca7iLqWA==" }, "node_modules/electron-to-chromium": { - "version": "1.4.722", - "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.4.722.tgz", - "integrity": "sha512-5nLE0TWFFpZ80Crhtp4pIp8LXCztjYX41yUcV6b+bKR2PqzjskTMOOlBi1VjBHlvHwS+4gar7kNKOrsbsewEZQ==" + "version": "1.4.723", + "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.4.723.tgz", + "integrity": "sha512-rxFVtrMGMFROr4qqU6n95rUi9IlfIm+lIAt+hOToy/9r6CDv0XiEcQdC3VP71y1pE5CFTzKV0RvxOGYCPWWHPw==" }, "node_modules/emoji-regex": { "version": "9.2.2", diff --git a/src/frontend/src/components/codeTabsComponent/index.tsx b/src/frontend/src/components/codeTabsComponent/index.tsx index e53968254..78c344714 100644 --- a/src/frontend/src/components/codeTabsComponent/index.tsx +++ b/src/frontend/src/components/codeTabsComponent/index.tsx @@ -205,7 +205,7 @@ export default function CodeTabsComponent({
{data?.map((node: any, i) => ( diff --git a/src/frontend/src/icons/GoogleGenerativeAI/GoogleGemini.jsx b/src/frontend/src/icons/GoogleGenerativeAI/GoogleGemini.jsx index dcd531ebb..e88c9cf00 100644 --- a/src/frontend/src/icons/GoogleGenerativeAI/GoogleGemini.jsx +++ b/src/frontend/src/icons/GoogleGenerativeAI/GoogleGemini.jsx @@ -1,10 +1,28 @@ -const SvgGoogleGenerativeAI = (props) => ( - - - - +const SvgGoogleGenerativeAI = (props) => ( + + + + + + + - + ); export default SvgGoogleGenerativeAI; diff --git a/src/frontend/src/modals/EditNodeModal/index.tsx b/src/frontend/src/modals/EditNodeModal/index.tsx index 2ba561c2d..17da8c73b 100644 --- a/src/frontend/src/modals/EditNodeModal/index.tsx +++ b/src/frontend/src/modals/EditNodeModal/index.tsx @@ -28,9 +28,7 @@ import { LANGFLOW_SUPPORTED_TYPES, limitScrollFieldsModal, } from "../../constants/constants"; -import useAlertStore from "../../stores/alertStore"; import useFlowStore from "../../stores/flowStore"; -import { useGlobalVariablesStore } from "../../stores/globalVariables"; import { NodeDataType } from "../../types/flow"; import { convertObjToArray, diff --git a/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx b/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx index 55fbbf3cf..37c9461b5 100644 --- a/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx +++ b/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx @@ -23,7 +23,9 @@ export default function IOFieldView({ case "TextInput": return (