Merge branch 'dev' into docs-basic-prompting

This commit is contained in:
Mendon Kissling 2024-04-16 18:29:34 -04:00
commit 16f379c50a
16 changed files with 853 additions and 342 deletions

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@ -6,7 +6,7 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat
## Standard VM ## Standard VM
[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) [![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial.md)
This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment. This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment.
@ -14,7 +14,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the
## Spot/Preemptible Instance ## Spot/Preemptible Instance
[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md) [![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts. When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts.

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@ -8,7 +8,7 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat
## Standard VM ## Standard VM
[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) [![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial.md)
This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment. This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment.
@ -16,7 +16,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the
## Spot/Preemptible Instance ## Spot/Preemptible Instance
[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md) [![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts. When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts.

606
poetry.lock generated
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@ -167,13 +167,13 @@ files = [
[[package]] [[package]]
name = "anthropic" name = "anthropic"
version = "0.25.1" version = "0.25.2"
description = "The official Python library for the anthropic API" description = "The official Python library for the anthropic API"
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.7"
files = [ files = [
{file = "anthropic-0.25.1-py3-none-any.whl", hash = "sha256:95d0cedc2a4b5beae3a78f9030aea4001caea5f46c6d263cce377c891c594e71"}, {file = "anthropic-0.25.2-py3-none-any.whl", hash = "sha256:f854030b11052f7cbb5257be6134c8a8f25aa538f73013260e12238ff94234a3"},
{file = "anthropic-0.25.1.tar.gz", hash = "sha256:0c01b30b77d041a8d07c532737bae69da58086031217150008e4541f52a64bd9"}, {file = "anthropic-0.25.2.tar.gz", hash = "sha256:cdf30ac234e3c0b305307399a6bb5dba45881adcb188d88fdf59802f90f15d6d"},
] ]
[package.dependencies] [package.dependencies]
@ -1149,13 +1149,13 @@ types = ["chardet (>=5.1.0)", "mypy", "pytest", "pytest-cov", "pytest-dependency
[[package]] [[package]]
name = "cohere" name = "cohere"
version = "5.2.5" version = "5.3.0"
description = "" description = ""
optional = false optional = false
python-versions = "<4.0,>=3.8" python-versions = "<4.0,>=3.8"
files = [ files = [
{file = "cohere-5.2.5-py3-none-any.whl", hash = "sha256:c29691eadd29c1253a5d9168c5a15a23583b982b0ffcfb9eace5dc17fddacb1a"}, {file = "cohere-5.3.0-py3-none-any.whl", hash = "sha256:9fe117cab7a0101e954f8f425eab1fc7b7c1eb2ce0da338eb6c4ce8737255616"},
{file = "cohere-5.2.5.tar.gz", hash = "sha256:5909083a9b00630108c009193fa02405dea176acc4262c590002a49ca8f7bdde"}, {file = "cohere-5.3.0.tar.gz", hash = "sha256:9f28910bd7eb897a8cde3abd66bbdee6051846909a431d2398089157ccb40c3c"},
] ]
[package.dependencies] [package.dependencies]
@ -2516,13 +2516,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
[[package]] [[package]]
name = "google-api-python-client" name = "google-api-python-client"
version = "2.125.0" version = "2.126.0"
description = "Google API Client Library for Python" description = "Google API Client Library for Python"
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.7"
files = [ files = [
{file = "google-api-python-client-2.125.0.tar.gz", hash = "sha256:51a0385cff65ec135106e8be60ee7112557396dde5f44113ae23912baddda143"}, {file = "google-api-python-client-2.126.0.tar.gz", hash = "sha256:97c0410630e2bebd194d99e91bd620dab5bc6b6ec0bf033f9a9109b700b83acb"},
{file = "google_api_python_client-2.125.0-py2.py3-none-any.whl", hash = "sha256:0a62b60fbd61b61a455f15d925264b3301099b67cafd2d33cf8bf151f1fca4f4"}, {file = "google_api_python_client-2.126.0-py2.py3-none-any.whl", hash = "sha256:299255fdb8dddf4eb96ab99e8358991160900b4109a9e0d3e3ac127c04b1e2ee"},
] ]
[package.dependencies] [package.dependencies]
@ -3735,19 +3735,19 @@ langchain-core = ">=0.1.32,<0.2.0"
[[package]] [[package]]
name = "langchain-community" name = "langchain-community"
version = "0.0.32" version = "0.0.33"
description = "Community contributed LangChain integrations." description = "Community contributed LangChain integrations."
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langchain_community-0.0.32-py3-none-any.whl", hash = "sha256:406977009999952d0705de3806de2b4867e9bb8eda8ca154a59c7a8ed58da38d"}, {file = "langchain_community-0.0.33-py3-none-any.whl", hash = "sha256:830f0d5f4ff9638b99ca01820c26abfa4b65fa705ef89b5ce55ac9aa3a7d83af"},
{file = "langchain_community-0.0.32.tar.gz", hash = "sha256:1510217d646c8380f54e9850351f6d2a0b0dd73c501b666c6f4b40baa8160b29"}, {file = "langchain_community-0.0.33.tar.gz", hash = "sha256:bb56dbc1ef11ca09f258468e11368781adda9219e144073e30cda69496d342b2"},
] ]
[package.dependencies] [package.dependencies]
aiohttp = ">=3.8.3,<4.0.0" aiohttp = ">=3.8.3,<4.0.0"
dataclasses-json = ">=0.5.7,<0.7" dataclasses-json = ">=0.5.7,<0.7"
langchain-core = ">=0.1.41,<0.2.0" langchain-core = ">=0.1.43,<0.2.0"
langsmith = ">=0.1.0,<0.2.0" langsmith = ">=0.1.0,<0.2.0"
numpy = ">=1,<2" numpy = ">=1,<2"
PyYAML = ">=5.3" PyYAML = ">=5.3"
@ -3761,13 +3761,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
[[package]] [[package]]
name = "langchain-core" name = "langchain-core"
version = "0.1.42" version = "0.1.43"
description = "Building applications with LLMs through composability" description = "Building applications with LLMs through composability"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langchain_core-0.1.42-py3-none-any.whl", hash = "sha256:c5653ffa08a44f740295c157a24c0def4a753333f6a2c41f76bf431cd00be8b5"}, {file = "langchain_core-0.1.43-py3-none-any.whl", hash = "sha256:9b601916602c17cb7588e8089302e30872cbd049b583a27debf5566018af6405"},
{file = "langchain_core-0.1.42.tar.gz", hash = "sha256:40751bf60ea5d8e2b2efe65290db434717ee3834870c002e40e2811f09d814e6"}, {file = "langchain_core-0.1.43.tar.gz", hash = "sha256:499133fadc28efcf7d24306236521518080bb10fd8bf6f7426de4a2bbf2aebb5"},
] ]
[package.dependencies] [package.dependencies]
@ -3850,6 +3850,21 @@ langchain-core = ">=0.1.28,<0.2.0"
[package.extras] [package.extras]
extended-testing = ["lxml (>=5.1.0,<6.0.0)"] extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
[[package]]
name = "langchainhub"
version = "0.1.15"
description = "The LangChain Hub API client"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
{file = "langchainhub-0.1.15-py3-none-any.whl", hash = "sha256:89a0951abd1db255e91c6d545d092a598fc255aa865d1ffc3ce8f93bbeae60e7"},
{file = "langchainhub-0.1.15.tar.gz", hash = "sha256:fa3ff81a31946860f84c119f1e2f6b7c7707e2bd7ed2394a7313b286d59f3bda"},
]
[package.dependencies]
requests = ">=2,<3"
types-requests = ">=2.31.0.2,<3.0.0.0"
[[package]] [[package]]
name = "langdetect" name = "langdetect"
version = "1.0.9" version = "1.0.9"
@ -3866,7 +3881,7 @@ six = "*"
[[package]] [[package]]
name = "langflow-base" name = "langflow-base"
version = "0.0.34" version = "0.0.35"
description = "A Python package with a built-in web application" description = "A Python package with a built-in web application"
optional = false optional = false
python-versions = ">=3.10,<3.12" python-versions = ">=3.10,<3.12"
@ -3888,6 +3903,7 @@ httpx = "*"
jq = {version = "^1.7.0", markers = "sys_platform != \"win32\""} jq = {version = "^1.7.0", markers = "sys_platform != \"win32\""}
langchain = "~0.1.16" langchain = "~0.1.16"
langchain-experimental = "*" langchain-experimental = "*"
langchainhub = "~0.1.15"
loguru = "^0.7.1" loguru = "^0.7.1"
multiprocess = "^0.70.14" multiprocess = "^0.70.14"
nest-asyncio = "^1.6.0" nest-asyncio = "^1.6.0"
@ -3920,13 +3936,13 @@ url = "src/backend/base"
[[package]] [[package]]
name = "langfuse" name = "langfuse"
version = "2.26.0" version = "2.26.1"
description = "A client library for accessing langfuse" description = "A client library for accessing langfuse"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langfuse-2.26.0-py3-none-any.whl", hash = "sha256:083e7d120d18bbe70b9d4d06f33483b96795110d2ebb2aed2e78d65c079a3ced"}, {file = "langfuse-2.26.1-py3-none-any.whl", hash = "sha256:cbfb03028df42d5e1894860d889a5cdf7cbfbf56ef9deb974c6691a08c689692"},
{file = "langfuse-2.26.0.tar.gz", hash = "sha256:c872a2cd5874bfad9780a6fcc7baa16c40de40cd50ce78d8fce59b01233d0464"}, {file = "langfuse-2.26.1.tar.gz", hash = "sha256:a95aab75d483614eb7ad8fd9f6e54078479c805e39098980caa676d9d42db54f"},
] ]
[package.dependencies] [package.dependencies]
@ -3943,13 +3959,13 @@ openai = ["openai (>=0.27.8)"]
[[package]] [[package]]
name = "langsmith" name = "langsmith"
version = "0.1.47" version = "0.1.48"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langsmith-0.1.47-py3-none-any.whl", hash = "sha256:17b0a908b8d39b6da3ecff658c8c00304b0b62f59945a5e16c2da5a254ea21a6"}, {file = "langsmith-0.1.48-py3-none-any.whl", hash = "sha256:2f8967e2aaaed8881efe6f346590681243b315af8ba8a037d969c299d42071d3"},
{file = "langsmith-0.1.47.tar.gz", hash = "sha256:f5ddd17628baa03a775525c5547a543a559313e425cdb2bf23579ffcf6056a76"}, {file = "langsmith-0.1.48.tar.gz", hash = "sha256:9cd21cd0928123b2bd2363f03515cb1f6a833d9a9f00420240d5132861d15fcc"},
] ]
[package.dependencies] [package.dependencies]
@ -3976,13 +3992,13 @@ regex = ["regex"]
[[package]] [[package]]
name = "litellm" name = "litellm"
version = "1.35.4" version = "1.35.8"
description = "Library to easily interface with LLM API providers" description = "Library to easily interface with LLM API providers"
optional = false optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8" python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
files = [ files = [
{file = "litellm-1.35.4-py3-none-any.whl", hash = "sha256:d07f6ca96c275cb286ba5d0e8b2df9d02ea3919dcb45fcd6c3e4257d51911776"}, {file = "litellm-1.35.8-py3-none-any.whl", hash = "sha256:0b328b37b6c40e5ece82f701bcb92e4d1d76ffb070e03f6c3abfbc6030bf07b8"},
{file = "litellm-1.35.4.tar.gz", hash = "sha256:760175fe4fb8a8e71b2d4b739a93fbb9f2ef8813b1ead98862cdf30dc6bb77be"}, {file = "litellm-1.35.8.tar.gz", hash = "sha256:0a8c031a01c19f2cfd9d873ff89bebc120917ec88f5656c1464a64d38e7c782d"},
] ]
[package.dependencies] [package.dependencies]
@ -4024,19 +4040,19 @@ test = ["httpx (>=0.24.1)", "pytest (>=7.4.0)", "scipy (>=1.10)"]
[[package]] [[package]]
name = "llama-index" name = "llama-index"
version = "0.10.28" version = "0.10.29"
description = "Interface between LLMs and your data" description = "Interface between LLMs and your data"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "llama_index-0.10.28-py3-none-any.whl", hash = "sha256:2fe2857af6c2d9cefdd978931ebd09f78265a57df2d9d094d0c78869dd0715b8"}, {file = "llama_index-0.10.29-py3-none-any.whl", hash = "sha256:df61c04c05ddf0488aa87085c464e52ff215338933de771de46e2e4965719eaa"},
{file = "llama_index-0.10.28.tar.gz", hash = "sha256:2ecf9d6da489cc3f99b1adaea50b1549efa55e04cd050f332da784a87c2db846"}, {file = "llama_index-0.10.29.tar.gz", hash = "sha256:f83b826007a8b4570a165f5db8ce770cf186adf7686ec0ddbeccf1887fcd5394"},
] ]
[package.dependencies] [package.dependencies]
llama-index-agent-openai = ">=0.1.4,<0.3.0" llama-index-agent-openai = ">=0.1.4,<0.3.0"
llama-index-cli = ">=0.1.2,<0.2.0" llama-index-cli = ">=0.1.2,<0.2.0"
llama-index-core = ">=0.10.28,<0.11.0" llama-index-core = ">=0.10.29,<0.11.0"
llama-index-embeddings-openai = ">=0.1.5,<0.2.0" llama-index-embeddings-openai = ">=0.1.5,<0.2.0"
llama-index-indices-managed-llama-cloud = ">=0.1.2,<0.2.0" llama-index-indices-managed-llama-cloud = ">=0.1.2,<0.2.0"
llama-index-legacy = ">=0.9.48,<0.10.0" llama-index-legacy = ">=0.9.48,<0.10.0"
@ -4081,13 +4097,13 @@ llama-index-llms-openai = ">=0.1.1,<0.2.0"
[[package]] [[package]]
name = "llama-index-core" name = "llama-index-core"
version = "0.10.28" version = "0.10.29"
description = "Interface between LLMs and your data" description = "Interface between LLMs and your data"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "llama_index_core-0.10.28-py3-none-any.whl", hash = "sha256:73e2ec86ea42a0efdc73dcfe46e03ba39ab7725699dcd791464c160d24a02cb1"}, {file = "llama_index_core-0.10.29-py3-none-any.whl", hash = "sha256:105eace45d877f31ff330e9afe7e86bd212b776b96d594862f24c60e03ab99b9"},
{file = "llama_index_core-0.10.28.tar.gz", hash = "sha256:b454640396daa2678b638299e2a5e2539fa3548afd280d45d224fd3b303c20f4"}, {file = "llama_index_core-0.10.29.tar.gz", hash = "sha256:65188c9917ec0847005ebd4343d7eaf9ddc80cea50d876a02eccbb16f1684371"},
] ]
[package.dependencies] [package.dependencies]
@ -4097,7 +4113,7 @@ deprecated = ">=1.2.9.3"
dirtyjson = ">=1.0.8,<2.0.0" dirtyjson = ">=1.0.8,<2.0.0"
fsspec = ">=2023.5.0" fsspec = ">=2023.5.0"
httpx = "*" httpx = "*"
llamaindex-py-client = ">=0.1.16,<0.2.0" llamaindex-py-client = ">=0.1.18,<0.2.0"
nest-asyncio = ">=1.5.8,<2.0.0" nest-asyncio = ">=1.5.8,<2.0.0"
networkx = ">=3.0" networkx = ">=3.0"
nltk = ">=3.8.1,<4.0.0" nltk = ">=3.8.1,<4.0.0"
@ -4254,13 +4270,13 @@ llama-index-program-openai = ">=0.1.1,<0.2.0"
[[package]] [[package]]
name = "llama-index-readers-file" name = "llama-index-readers-file"
version = "0.1.17" version = "0.1.19"
description = "llama-index readers file integration" description = "llama-index readers file integration"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "llama_index_readers_file-0.1.17-py3-none-any.whl", hash = "sha256:4c14d489716704246bd65c08e0f8ea6005cf63cca5b94df38667598b1d3d1937"}, {file = "llama_index_readers_file-0.1.19-py3-none-any.whl", hash = "sha256:25eb6d066dc38753de435e876ef8511c68d84102302c053b7dcb0776db68fced"},
{file = "llama_index_readers_file-0.1.17.tar.gz", hash = "sha256:240660ca93c04383fc4aebfa9c9bdaeb43790a533e6886b4631a5fc7f00190ca"}, {file = "llama_index_readers_file-0.1.19.tar.gz", hash = "sha256:194c1b9b85c265159b7302c7d80adba937aab06f05c170af7fd95c4e7a8aec35"},
] ]
[package.dependencies] [package.dependencies]
@ -4289,17 +4305,17 @@ llama-parse = ">=0.4.0,<0.5.0"
[[package]] [[package]]
name = "llama-parse" name = "llama-parse"
version = "0.4.0" version = "0.4.1"
description = "Parse files into RAG-Optimized formats." description = "Parse files into RAG-Optimized formats."
optional = false optional = false
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{file = "safetensors-0.4.2.tar.gz", hash = "sha256:acc85dcb09ec5e8aa787f588d7ad4d55c103f31e4ff060e17d92cc0e8b8cac73"},
] ]
[package.extras] [package.extras]
@ -8122,7 +8124,7 @@ paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"] pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"]
quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"] quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"]
tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"] tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"] testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
torch = ["safetensors[numpy]", "torch (>=1.10)"] torch = ["safetensors[numpy]", "torch (>=1.10)"]
[[package]] [[package]]
@ -8232,13 +8234,13 @@ transformers = ">=4.32.0,<5.0.0"
[[package]] [[package]]
name = "setuptools" name = "setuptools"
version = "69.5.0" version = "69.5.1"
description = "Easily download, build, install, upgrade, and uninstall Python packages" description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "setuptools-69.5.0-py3-none-any.whl", hash = "sha256:3b2dbd8f63dcc6b7c327d0243c2d7dc8c96cc507c016f09221f3787e6e528719"}, {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
{file = "setuptools-69.5.0.tar.gz", hash = "sha256:8d881f842bfc0e29e93bc98a2e650e8845609adff4d2989ba6c748e67b09d5be"}, {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
] ]
[package.extras] [package.extras]
@ -8490,19 +8492,19 @@ files = [
[[package]] [[package]]
name = "supabase" name = "supabase"
version = "2.4.1" version = "2.4.2"
description = "Supabase client for Python." description = "Supabase client for Python."
optional = false optional = false
python-versions = "<4.0,>=3.8" python-versions = "<4.0,>=3.8"
files = [ files = [
{file = "supabase-2.4.1-py3-none-any.whl", hash = "sha256:8b95744ce4ad24245ec23c090f273dfc9c2d9a53e3a80186959903947dbe1ed6"}, {file = "supabase-2.4.2-py3-none-any.whl", hash = "sha256:ce5de33e56eeccd92caa529dc29f484f0d5de073fd543b7dfd969e8cc57f5f62"},
{file = "supabase-2.4.1.tar.gz", hash = "sha256:a7dec0586f8931f378a45b2ffb28d8e37b3719f979c17f541b0156019144e645"}, {file = "supabase-2.4.2.tar.gz", hash = "sha256:04e7095c3458e94a83f2b77493f788e730d148b2bc0c5d899de6f0bfb43d1ea6"},
] ]
[package.dependencies] [package.dependencies]
gotrue = ">=1.3,<3.0" gotrue = ">=1.3,<3.0"
httpx = ">=0.24,<0.28" httpx = ">=0.24,<0.28"
postgrest = ">=0.10.8,<0.17.0" postgrest = ">=0.14,<0.17.0"
realtime = ">=1.0.0,<2.0.0" realtime = ">=1.0.0,<2.0.0"
storage3 = ">=0.5.3,<0.8.0" storage3 = ">=0.5.3,<0.8.0"
supafunc = ">=0.3.1,<0.5.0" supafunc = ">=0.3.1,<0.5.0"
@ -9045,13 +9047,13 @@ files = [
[[package]] [[package]]
name = "types-pillow" name = "types-pillow"
version = "10.2.0.20240406" version = "10.2.0.20240415"
description = "Typing stubs for Pillow" description = "Typing stubs for Pillow"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "types-Pillow-10.2.0.20240406.tar.gz", hash = "sha256:62e0cc1f17caba40e72e7154a483f4c7f3bea0e1c34c0ebba9de3c7745bc306d"}, {file = "types-Pillow-10.2.0.20240415.tar.gz", hash = "sha256:dd6058027639bcdc66ba78b228cc25fdae42524c2150c78c804da427e7e76e70"},
{file = "types_Pillow-10.2.0.20240406-py3-none-any.whl", hash = "sha256:5ac182e8afce53de30abca2fdf9cbec7b2500e549d0be84da035a729a84c7c47"}, {file = "types_Pillow-10.2.0.20240415-py3-none-any.whl", hash = "sha256:f933332b7e96010bae9b9cf82a4c9979ff0c270d63f5c5bbffb2d789b85cd00b"},
] ]
[[package]] [[package]]

View file

@ -1,6 +1,6 @@
[tool.poetry] [tool.poetry]
name = "langflow" name = "langflow"
version = "1.0.0a22" version = "1.0.0a23"
description = "A Python package with a built-in web application" description = "A Python package with a built-in web application"
authors = ["Logspace <contact@logspace.ai>"] authors = ["Logspace <contact@logspace.ai>"]
maintainers = [ maintainers = [

View file

@ -1,13 +1,19 @@
from typing import List, Optional, Union, cast from typing import List, Optional, Union, cast
from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
from langchain_core.messages import BaseMessage
from langchain_core.runnables import Runnable from langchain_core.runnables import Runnable
from langflow.base.agents.utils import get_agents_list, records_to_messages
from langflow.custom import CustomComponent from langflow.custom import CustomComponent
from langflow.field_typing import BaseMemory, Text, Tool from langflow.field_typing import Text, Tool
from langflow.schema.schema import Record
class LCAgentComponent(CustomComponent): class LCAgentComponent(CustomComponent):
def get_agents_list(self):
return get_agents_list()
def build_config(self): def build_config(self):
return { return {
"lc": { "lc": {
@ -42,9 +48,8 @@ class LCAgentComponent(CustomComponent):
self, self,
agent: Union[Runnable, BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor], agent: Union[Runnable, BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
inputs: str, inputs: str,
input_variables: list[str],
tools: List[Tool], tools: List[Tool],
memory: Optional[BaseMemory] = None, message_history: Optional[List[Record]] = None,
handle_parsing_errors: bool = True, handle_parsing_errors: bool = True,
output_key: str = "output", output_key: str = "output",
) -> Text: ) -> Text:
@ -55,13 +60,11 @@ class LCAgentComponent(CustomComponent):
agent=agent, # type: ignore agent=agent, # type: ignore
tools=tools, tools=tools,
verbose=True, verbose=True,
memory=memory,
handle_parsing_errors=handle_parsing_errors, handle_parsing_errors=handle_parsing_errors,
) )
input_dict = {"input": inputs} input_dict: dict[str, str | list[BaseMessage]] = {"input": inputs}
for var in input_variables: if message_history:
if var not in ["agent_scratchpad", "input"]: input_dict["chat_history"] = records_to_messages(message_history)
input_dict[var] = ""
result = await runnable.ainvoke(input_dict) result = await runnable.ainvoke(input_dict)
self.status = result self.status = result
if output_key in result: if output_key in result:

View file

@ -0,0 +1,23 @@
XML_AGENT_PROMPT = """You are a helpful assistant. Help the user answer any questions.
You have access to the following tools:
{tools}
In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
<tool>search</tool><tool_input>weather in SF</tool_input>
<observation>64 degrees</observation>
When you are done, respond with a final answer between <final_answer></final_answer>. For example:
<final_answer>The weather in SF is 64 degrees</final_answer>
Begin!
Previous Conversation:
{chat_history}
Question: {input}
{agent_scratchpad}"""

View file

@ -0,0 +1,143 @@
from typing import Any, Callable, Dict, List, Optional, Sequence, Union
from langchain.agents import (
create_json_chat_agent,
create_openai_tools_agent,
create_tool_calling_agent,
create_xml_agent,
)
from langchain.agents.xml.base import render_text_description
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
from langchain_core.tools import BaseTool
from pydantic import BaseModel
from langflow.schema.schema import Record
from .default_prompts import XML_AGENT_PROMPT
class AgentSpec(BaseModel):
func: Callable[
[
BaseLanguageModel,
Sequence[BaseTool],
BasePromptTemplate | ChatPromptTemplate,
Optional[Callable[[List[BaseTool]], str]],
Optional[Union[bool, List[str]]],
],
Any,
]
prompt: Optional[Any] = None
fields: List[str]
hub_repo: Optional[str] = None
def records_to_messages(records: List[Record]) -> List[BaseMessage]:
"""
Convert a list of records to a list of messages.
Args:
records (List[Record]): The records to convert.
Returns:
List[Message]: The records as messages.
"""
return [record.to_lc_message() for record in records]
def validate_and_create_xml_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: BasePromptTemplate,
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
*,
stop_sequence: Union[bool, List[str]] = True,
):
return create_xml_agent(
llm=llm,
tools=tools,
prompt=prompt,
tools_renderer=tools_renderer,
stop_sequence=stop_sequence,
)
def validate_and_create_openai_tools_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
*,
stop_sequence: Union[bool, List[str]] = True,
):
return create_openai_tools_agent(
llm=llm,
tools=tools,
prompt=prompt,
)
def validate_and_create_tool_calling_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
*,
stop_sequence: Union[bool, List[str]] = True,
):
return create_tool_calling_agent(
llm=llm,
tools=tools,
prompt=prompt,
)
def validate_and_create_json_chat_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
*,
stop_sequence: Union[bool, List[str]] = True,
):
return create_json_chat_agent(
llm=llm,
tools=tools,
prompt=prompt,
tools_renderer=tools_renderer,
stop_sequence=stop_sequence,
)
AGENTS: Dict[str, AgentSpec] = {
"Tool Calling Agent": AgentSpec(
func=validate_and_create_tool_calling_agent,
prompt=None,
fields=["llm", "tools", "prompt"],
hub_repo=None,
),
"XML Agent": AgentSpec(
func=validate_and_create_xml_agent,
prompt=XML_AGENT_PROMPT, # Ensure XML_AGENT_PROMPT is properly defined and typed.
fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"],
hub_repo="hwchase17/xml-agent-convo",
),
"OpenAI Tools Agent": AgentSpec(
func=validate_and_create_openai_tools_agent,
prompt=None,
fields=["llm", "tools", "prompt"],
hub_repo=None,
),
"JSON Chat Agent": AgentSpec(
func=validate_and_create_json_chat_agent,
prompt=None,
fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"],
hub_repo="hwchase17/react-chat-json",
),
}
def get_agents_list():
return list(AGENTS.keys())

View file

@ -0,0 +1,64 @@
from typing import List, Optional
from langchain.agents.tool_calling_agent.base import create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
from langflow.base.agents.agent import LCAgentComponent
from langflow.field_typing import BaseLanguageModel, Text, Tool
from langflow.schema.schema import Record
class ToolCallingAgentComponent(LCAgentComponent):
display_name: str = "Tool Calling Agent"
description: str = "Agent that uses tools. Only models that are compatible with function calling are supported."
def build_config(self):
return {
"llm": {"display_name": "LLM"},
"tools": {"display_name": "Tools"},
"user_prompt": {
"display_name": "Prompt",
"multiline": True,
"info": "This prompt must contain 'input' key.",
},
"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,
},
"memory": {
"display_name": "Memory",
"info": "Memory to use for the agent.",
},
"input_value": {
"display_name": "Inputs",
"info": "Input text to pass to the agent.",
},
}
async def build(
self,
input_value: str,
llm: BaseLanguageModel,
tools: List[Tool],
user_prompt: str = "{input}",
message_history: Optional[List[Record]] = None,
system_message: str = "You are a helpful assistant",
handle_parsing_errors: bool = True,
) -> Text:
if "input" not in user_prompt:
raise ValueError("Prompt must contain 'input' key.")
messages = [
("system", system_message),
(
"placeholder",
"{chat_history}",
),
("human", user_prompt),
("placeholder", "{agent_scratchpad}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
agent = create_tool_calling_agent(llm, tools, prompt)
result = await self.run_agent(agent, input_value, tools, message_history, handle_parsing_errors)
self.status = result
return result

View file

@ -1,10 +1,12 @@
from typing import List, Optional from typing import List, Optional
from langchain.agents import create_xml_agent from langchain.agents import create_xml_agent
from langchain_core.prompts import PromptTemplate from langchain_core.prompts import ChatPromptTemplate
from langflow.base.agents.agent import LCAgentComponent from langflow.base.agents.agent import LCAgentComponent
from langflow.field_typing import BaseLanguageModel, BaseMemory, Text, Tool from langflow.field_typing import BaseLanguageModel, Text, Tool
from langflow.schema.schema import Record
class XMLAgentComponent(LCAgentComponent): class XMLAgentComponent(LCAgentComponent):
@ -15,7 +17,7 @@ class XMLAgentComponent(LCAgentComponent):
return { return {
"llm": {"display_name": "LLM"}, "llm": {"display_name": "LLM"},
"tools": {"display_name": "Tools"}, "tools": {"display_name": "Tools"},
"prompt": { "user_prompt": {
"display_name": "Prompt", "display_name": "Prompt",
"multiline": True, "multiline": True,
"info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.", "info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.",
@ -43,6 +45,11 @@ class XMLAgentComponent(LCAgentComponent):
Question: {input} Question: {input}
{agent_scratchpad}""", {agent_scratchpad}""",
}, },
"system_message": {
"display_name": "System Message",
"info": "System message to be passed to the LLM.",
"advanced": True,
},
"tool_template": { "tool_template": {
"display_name": "Tool Template", "display_name": "Tool Template",
"info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.", "info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.",
@ -53,9 +60,9 @@ class XMLAgentComponent(LCAgentComponent):
"info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.", "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
"advanced": True, "advanced": True,
}, },
"memory": { "message_history": {
"display_name": "Memory", "display_name": "Message History",
"info": "Memory to use for the agent.", "info": "Message history to pass to the agent.",
}, },
"input_value": { "input_value": {
"display_name": "Inputs", "display_name": "Inputs",
@ -68,12 +75,13 @@ class XMLAgentComponent(LCAgentComponent):
input_value: str, input_value: str,
llm: BaseLanguageModel, llm: BaseLanguageModel,
tools: List[Tool], tools: List[Tool],
prompt: str, user_prompt: str = "{input}",
memory: Optional[BaseMemory] = None, system_message: str = "You are a helpful assistant",
message_history: Optional[List[Record]] = None,
tool_template: str = "{name}: {description}", tool_template: str = "{name}: {description}",
handle_parsing_errors: bool = True, handle_parsing_errors: bool = True,
) -> Text: ) -> Text:
if "input" not in prompt: if "input" not in user_prompt:
raise ValueError("Prompt must contain 'input' key.") raise ValueError("Prompt must contain 'input' key.")
def render_tool_description(tools): def render_tool_description(tools):
@ -81,9 +89,23 @@ class XMLAgentComponent(LCAgentComponent):
[tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools] [tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools]
) )
prompt_template = PromptTemplate.from_template(prompt) messages = [
input_variables = prompt_template.input_variables ("system", system_message),
agent = create_xml_agent(llm, tools, prompt_template, tools_renderer=render_tool_description) (
result = await self.run_agent(agent, input_value, input_variables, tools, memory, handle_parsing_errors) "placeholder",
"{chat_history}",
),
("human", user_prompt),
("placeholder", "{agent_scratchpad}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
agent = create_xml_agent(llm, tools, prompt, tools_renderer=render_tool_description)
result = await self.run_agent(
agent=agent,
inputs=input_value,
tools=tools,
message_history=message_history,
handle_parsing_errors=handle_parsing_errors,
)
self.status = result self.status = result
return result return result

View file

@ -0,0 +1,185 @@
from typing import Any, List, Optional, cast
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessagePromptTemplate
from langflow.base.agents.agent import LCAgentComponent
from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list
from langflow.field_typing import BaseLanguageModel, Text, Tool
from langflow.schema.dotdict import dotdict
from langflow.schema.schema import Record
class AgentComponent(LCAgentComponent):
display_name = "Agent"
description = "Run any LangChain agent using a simplified interface."
field_order = [
"agent_name",
"llm",
"tools",
"prompt",
"tool_template",
"handle_parsing_errors",
"memory",
"input_value",
]
def build_config(self):
return {
"agent_name": {
"display_name": "Agent",
"info": "The agent to use.",
"refresh_button": True,
"real_time_refresh": True,
"options": get_agents_list(),
},
"llm": {"display_name": "LLM"},
"tools": {"display_name": "Tools"},
"user_prompt": {
"display_name": "Prompt",
"multiline": True,
"info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.",
},
"system_message": {
"display_name": "System Message",
"info": "System message to be passed to the LLM.",
"advanced": True,
},
"tool_template": {
"display_name": "Tool Template",
"info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.",
"advanced": True,
},
"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,
},
"message_history": {
"display_name": "Message History",
"info": "Message history to pass to the agent.",
},
"input_value": {
"display_name": "Input",
"info": "Input text to pass to the agent.",
},
"langchain_hub_api_key": {
"display_name": "LangChain Hub API Key",
"info": "API key to use for LangChain Hub. If provided, prompts will be fetched from LangChain Hub.",
"advanced": True,
},
}
def get_system_and_user_message_from_prompt(self, prompt: Any):
"""
Extracts the system message and user prompt from a given prompt object.
Args:
prompt (Any): The prompt object from which to extract the system message and user prompt.
Returns:
Tuple[Optional[str], Optional[str]]: A tuple containing the system message and user prompt.
If the prompt object does not have any messages, both values will be None.
"""
if hasattr(prompt, "messages"):
system_message = None
user_prompt = None
for message in prompt.messages:
if isinstance(message, SystemMessagePromptTemplate):
s_prompt = message.prompt
if isinstance(s_prompt, list):
s_template = " ".join([cast(str, s.template) for s in s_prompt if hasattr(s, "template")])
elif hasattr(s_prompt, "template"):
s_template = s_prompt.template
system_message = s_template
elif isinstance(message, HumanMessagePromptTemplate):
h_prompt = message.prompt
if isinstance(h_prompt, list):
h_template = " ".join([cast(str, h.template) for h in h_prompt if hasattr(h, "template")])
elif hasattr(h_prompt, "template"):
h_template = h_prompt.template
user_prompt = h_template
return system_message, user_prompt
return None, None
def update_build_config(self, build_config: dotdict, field_value: Any, field_name: Text | None = None):
"""
Updates the build configuration based on the provided field value and field name.
Args:
build_config (dotdict): The build configuration to be updated.
field_value (Any): The value of the field being updated.
field_name (Text | None, optional): The name of the field being updated. Defaults to None.
Returns:
dotdict: The updated build configuration.
"""
if field_name == "agent":
build_config["agent"]["options"] = get_agents_list()
if field_value in AGENTS:
# if langchain_hub_api_key is provided, fetch the prompt from LangChain Hub
if build_config["langchain_hub_api_key"]["value"] and AGENTS[field_value].hub_repo:
from langchain import hub
hub_repo: str | None = AGENTS[field_value].hub_repo
if hub_repo:
hub_api_key: str = build_config["langchain_hub_api_key"]["value"]
prompt = hub.pull(hub_repo, api_key=hub_api_key)
system_message, user_prompt = self.get_system_and_user_message_from_prompt(prompt)
if system_message:
build_config["system_message"]["value"] = system_message
if user_prompt:
build_config["user_prompt"]["value"] = user_prompt
if AGENTS[field_value].prompt:
build_config["user_prompt"]["value"] = AGENTS[field_value].prompt
else:
build_config["user_prompt"]["value"] = "{input}"
fields = AGENTS[field_value].fields
for field in ["llm", "tools", "prompt", "tools_renderer"]:
if field not in fields:
build_config[field]["show"] = False
return build_config
async def build(
self,
agent_name: str,
input_value: str,
llm: BaseLanguageModel,
tools: List[Tool],
system_message: str = "You are a helpful assistant. Help the user answer any questions.",
user_prompt: str = "{input}",
message_history: Optional[List[Record]] = None,
tool_template: str = "{name}: {description}",
handle_parsing_errors: bool = True,
) -> Text:
agent_spec: Optional[AgentSpec] = AGENTS.get(agent_name)
if agent_spec is None:
raise ValueError(f"{agent_name} not found.")
def render_tool_description(tools):
return "\n".join(
[tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools]
)
messages = [
("system", system_message),
(
"placeholder",
"{chat_history}",
),
("human", user_prompt),
("placeholder", "{agent_scratchpad}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
agent_func = agent_spec.func
agent = agent_func(llm, tools, prompt, render_tool_description, True)
result = await self.run_agent(
agent=agent,
inputs=input_value,
tools=tools,
message_history=message_history,
handle_parsing_errors=handle_parsing_errors,
)
self.status = result
return result

View file

@ -10,8 +10,10 @@ from .RunFlow import RunFlowComponent
from .RunnableExecutor import RunnableExecComponent from .RunnableExecutor import RunnableExecComponent
from .SQLExecutor import SQLExecutorComponent from .SQLExecutor import SQLExecutorComponent
from .SubFlow import SubFlowComponent from .SubFlow import SubFlowComponent
from .AgentComponent import AgentComponent
__all__ = [ __all__ = [
"AgentComponent",
"ClearMessageHistoryComponent", "ClearMessageHistoryComponent",
"ExtractKeyFromRecordComponent", "ExtractKeyFromRecordComponent",
"FlowToolComponent", "FlowToolComponent",

View file

@ -105,7 +105,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
system_message: Optional[str] = None, system_message: Optional[str] = None,
max_tokens: Optional[int] = 1000, max_tokens: Optional[int] = 1000,
stream: bool = False, stream: bool = False,
) -> BaseLanguageModel: ) -> Text:
if api_key: if api_key:
secret_api_key = SecretStr(api_key) secret_api_key = SecretStr(api_key)
else: else:

View file

@ -142,7 +142,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
max_retries: int = 6, max_retries: int = 6,
verbose: bool = False, verbose: bool = False,
system_message: Optional[str] = None, system_message: Optional[str] = None,
) -> BaseLanguageModel: ) -> Text:
try: try:
import litellm # type: ignore import litellm # type: ignore

View file

@ -2,7 +2,9 @@ import copy
from typing import Literal, Optional from typing import Literal, Optional
from langchain_core.documents import Document from langchain_core.documents import Document
from langchain_core.messages import BaseMessage
from pydantic import BaseModel, model_validator from pydantic import BaseModel, model_validator
from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
class Record(BaseModel): class Record(BaseModel):
@ -54,6 +56,21 @@ class Record(BaseModel):
data["text"] = document.page_content data["text"] = document.page_content
return cls(data=data, text_key="text") return cls(data=data, text_key="text")
@classmethod
def from_lc_message(cls, message: BaseMessage) -> "Record":
"""
Converts a BaseMessage to a Record.
Args:
message (BaseMessage): The BaseMessage to convert.
Returns:
Record: The converted Record.
"""
data = {"text": message.content}
data["metadata"] = message.to_json()
return cls(data=data, text_key="text")
def __add__(self, other: "Record") -> "Record": def __add__(self, other: "Record") -> "Record":
""" """
Combines the data of two records by attempting to add values for overlapping keys Combines the data of two records by attempting to add values for overlapping keys
@ -85,6 +102,26 @@ class Record(BaseModel):
text = self.data.pop(self.text_key, self.default_value) text = self.data.pop(self.text_key, self.default_value)
return Document(page_content=text, metadata=self.data) return Document(page_content=text, metadata=self.data)
def to_lc_message(self) -> BaseMessage:
"""
Converts the Record to a BaseMessage.
Returns:
BaseMessage: The converted BaseMessage.
"""
# The idea of this function is to be a helper to convert a Record to a BaseMessage
# It will use the "sender" key to determine if the message is Human or AI
# If the key is not present, it will default to AI
# But first we check if all required keys are present in the data dictionary
# they are: "text", "sender"
if not all(key in self.data for key in ["text", "sender"]):
raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}")
sender = self.data.get("sender", "Machine")
text = self.data.get("text", "")
if sender == "User":
return HumanMessage(content=text)
return AIMessage(content=text)
def __getattr__(self, key): def __getattr__(self, key):
""" """
Allows attribute-like access to the data dictionary. Allows attribute-like access to the data dictionary.

View file

@ -1063,19 +1063,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
[[package]] [[package]]
name = "langchain-community" name = "langchain-community"
version = "0.0.32" version = "0.0.33"
description = "Community contributed LangChain integrations." description = "Community contributed LangChain integrations."
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langchain_community-0.0.32-py3-none-any.whl", hash = "sha256:406977009999952d0705de3806de2b4867e9bb8eda8ca154a59c7a8ed58da38d"}, {file = "langchain_community-0.0.33-py3-none-any.whl", hash = "sha256:830f0d5f4ff9638b99ca01820c26abfa4b65fa705ef89b5ce55ac9aa3a7d83af"},
{file = "langchain_community-0.0.32.tar.gz", hash = "sha256:1510217d646c8380f54e9850351f6d2a0b0dd73c501b666c6f4b40baa8160b29"}, {file = "langchain_community-0.0.33.tar.gz", hash = "sha256:bb56dbc1ef11ca09f258468e11368781adda9219e144073e30cda69496d342b2"},
] ]
[package.dependencies] [package.dependencies]
aiohttp = ">=3.8.3,<4.0.0" aiohttp = ">=3.8.3,<4.0.0"
dataclasses-json = ">=0.5.7,<0.7" dataclasses-json = ">=0.5.7,<0.7"
langchain-core = ">=0.1.41,<0.2.0" langchain-core = ">=0.1.43,<0.2.0"
langsmith = ">=0.1.0,<0.2.0" langsmith = ">=0.1.0,<0.2.0"
numpy = ">=1,<2" numpy = ">=1,<2"
PyYAML = ">=5.3" PyYAML = ">=5.3"
@ -1089,13 +1089,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
[[package]] [[package]]
name = "langchain-core" name = "langchain-core"
version = "0.1.42" version = "0.1.43"
description = "Building applications with LLMs through composability" description = "Building applications with LLMs through composability"
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langchain_core-0.1.42-py3-none-any.whl", hash = "sha256:c5653ffa08a44f740295c157a24c0def4a753333f6a2c41f76bf431cd00be8b5"}, {file = "langchain_core-0.1.43-py3-none-any.whl", hash = "sha256:9b601916602c17cb7588e8089302e30872cbd049b583a27debf5566018af6405"},
{file = "langchain_core-0.1.42.tar.gz", hash = "sha256:40751bf60ea5d8e2b2efe65290db434717ee3834870c002e40e2811f09d814e6"}, {file = "langchain_core-0.1.43.tar.gz", hash = "sha256:499133fadc28efcf7d24306236521518080bb10fd8bf6f7426de4a2bbf2aebb5"},
] ]
[package.dependencies] [package.dependencies]
@ -1144,15 +1144,30 @@ langchain-core = ">=0.1.28,<0.2.0"
[package.extras] [package.extras]
extended-testing = ["lxml (>=5.1.0,<6.0.0)"] extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
[[package]]
name = "langchainhub"
version = "0.1.15"
description = "The LangChain Hub API client"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
{file = "langchainhub-0.1.15-py3-none-any.whl", hash = "sha256:89a0951abd1db255e91c6d545d092a598fc255aa865d1ffc3ce8f93bbeae60e7"},
{file = "langchainhub-0.1.15.tar.gz", hash = "sha256:fa3ff81a31946860f84c119f1e2f6b7c7707e2bd7ed2394a7313b286d59f3bda"},
]
[package.dependencies]
requests = ">=2,<3"
types-requests = ">=2.31.0.2,<3.0.0.0"
[[package]] [[package]]
name = "langsmith" name = "langsmith"
version = "0.1.47" version = "0.1.48"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langsmith-0.1.47-py3-none-any.whl", hash = "sha256:17b0a908b8d39b6da3ecff658c8c00304b0b62f59945a5e16c2da5a254ea21a6"}, {file = "langsmith-0.1.48-py3-none-any.whl", hash = "sha256:2f8967e2aaaed8881efe6f346590681243b315af8ba8a037d969c299d42071d3"},
{file = "langsmith-0.1.47.tar.gz", hash = "sha256:f5ddd17628baa03a775525c5547a543a559313e425cdb2bf23579ffcf6056a76"}, {file = "langsmith-0.1.48.tar.gz", hash = "sha256:9cd21cd0928123b2bd2363f03515cb1f6a833d9a9f00420240d5132861d15fcc"},
] ]
[package.dependencies] [package.dependencies]
@ -2568,6 +2583,20 @@ rich = ">=10.11.0"
shellingham = ">=1.3.0" shellingham = ">=1.3.0"
typing-extensions = ">=3.7.4.3" typing-extensions = ">=3.7.4.3"
[[package]]
name = "types-requests"
version = "2.31.0.20240406"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
{file = "types-requests-2.31.0.20240406.tar.gz", hash = "sha256:4428df33c5503945c74b3f42e82b181e86ec7b724620419a2966e2de604ce1a1"},
{file = "types_requests-2.31.0.20240406-py3-none-any.whl", hash = "sha256:6216cdac377c6b9a040ac1c0404f7284bd13199c0e1bb235f4324627e8898cf5"},
]
[package.dependencies]
urllib3 = ">=2"
[[package]] [[package]]
name = "typing-extensions" name = "typing-extensions"
version = "4.11.0" version = "4.11.0"
@ -2861,4 +2890,4 @@ local = []
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.10,<3.12" python-versions = ">=3.10,<3.12"
content-hash = "11b7861d29ff2ca23defcb03faf670c409c639c7b4ee81455c0c1fea50ea54e9" content-hash = "4baea3d7c34ad33205fbb00a9da20b4c696b9487a2d62dada9babf1d21ed2dba"

View file

@ -1,6 +1,6 @@
[tool.poetry] [tool.poetry]
name = "langflow-base" name = "langflow-base"
version = "0.0.34" version = "0.0.35"
description = "A Python package with a built-in web application" description = "A Python package with a built-in web application"
authors = ["Logspace <contact@logspace.ai>"] authors = ["Logspace <contact@logspace.ai>"]
maintainers = [ maintainers = [
@ -31,6 +31,7 @@ httpx = "*"
uvicorn = "^0.29.0" uvicorn = "^0.29.0"
gunicorn = "^21.2.0" gunicorn = "^21.2.0"
langchain = "~0.1.16" langchain = "~0.1.16"
langchainhub = "~0.1.15"
sqlmodel = "^0.0.16" sqlmodel = "^0.0.16"
loguru = "^0.7.1" loguru = "^0.7.1"
rich = "^13.7.0" rich = "^13.7.0"