+
+
+
+# 🎨 Creating Flows
Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://langchain.readthedocs.io/en/latest/reference.html) to choose from, including LLMs, prompt serializers, agents, and chains.
@@ -239,7 +249,7 @@ flow("Hey, have you heard of Langflow?")
```
-## 👋 Contributing
+# 👋 Contributing
We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our [contributing guidelines](./CONTRIBUTING.md) and help make Langflow more accessible.
@@ -252,6 +262,6 @@ Join our [Discord](https://discord.com/invite/EqksyE2EX9) server to ask question
[](https://star-history.com/#logspace-ai/langflow&Date)
-## 📄 License
+# 📄 License
Langflow is released under the MIT License. See the LICENSE file for details.
diff --git a/poetry.lock b/poetry.lock
index cac5bb037..c8e43837c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -739,13 +739,13 @@ sqlalchemy = ["sqlalchemy (>1.3.21,<2.0)"]
[[package]]
name = "cohere"
-version = "4.12.1"
+version = "4.13.0"
description = ""
optional = false
python-versions = ">=3.7,<4.0"
files = [
- {file = "cohere-4.12.1-py3-none-any.whl", hash = "sha256:80d17ae928873cdf63883a338618e477de5c71b3d510d7891af7dfdabc25186e"},
- {file = "cohere-4.12.1.tar.gz", hash = "sha256:2e93a094757576d6c8d42e76363aa7841eb4166c5b0de8e5ed7272783982d2a4"},
+ {file = "cohere-4.13.0-py3-none-any.whl", hash = "sha256:ac8faf352c9e8794dfd05002ce52b7d4f6da8f47a20172c8640ed58e8dfd3f10"},
+ {file = "cohere-4.13.0.tar.gz", hash = "sha256:7c8e65aa4fc50fe6a9e8fe19d64e18b77d4250ec70845d480c68644d5a903253"},
]
[package.dependencies]
@@ -1332,22 +1332,22 @@ importlib-resources = {version = ">=5.0", markers = "python_version < \"3.10\""}
[[package]]
name = "fastapi"
-version = "0.99.1"
+version = "0.100.0"
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
optional = false
python-versions = ">=3.7"
files = [
- {file = "fastapi-0.99.1-py3-none-any.whl", hash = "sha256:976df7bab51ac7beda9f68c4513b8c4490b5c1135c72aafd0a5ee4023ec5282e"},
- {file = "fastapi-0.99.1.tar.gz", hash = "sha256:ac78f717cd80d657bd183f94d33b9bda84aa376a46a9dab513586b8eef1dc6fc"},
+ {file = "fastapi-0.100.0-py3-none-any.whl", hash = "sha256:271662daf986da8fa98dc2b7c7f61c4abdfdccfb4786d79ed8b2878f172c6d5f"},
+ {file = "fastapi-0.100.0.tar.gz", hash = "sha256:acb5f941ea8215663283c10018323ba7ea737c571b67fc7e88e9469c7eb1d12e"},
]
[package.dependencies]
-pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0"
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<3.0.0"
starlette = ">=0.27.0,<0.28.0"
typing-extensions = ">=4.5.0"
[package.extras]
-all = ["email-validator (>=1.1.1)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
[[package]]
name = "filelock"
@@ -2826,20 +2826,20 @@ testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)",
[[package]]
name = "langchain"
-version = "0.0.219"
+version = "0.0.229"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
- {file = "langchain-0.0.219-py3-none-any.whl", hash = "sha256:1f08a00e622f1c75087d6013f34e82be3f8dd1859266eb583a0fd7bc045090cf"},
- {file = "langchain-0.0.219.tar.gz", hash = "sha256:842f8212939e5ac4005906d2215574ffb3e34d2fe28f5bc0f46eb3b28fb29c5d"},
+ {file = "langchain-0.0.229-py3-none-any.whl", hash = "sha256:a7ca79e4ab892756ede95d212bd42243303f91b172535cefd02b0b8965e4e7b7"},
+ {file = "langchain-0.0.229.tar.gz", hash = "sha256:ab1beac7f3fc1f06ab1a0b545ef0d47a3d5efef3b2b4c646aafaefc2eb3151d3"},
]
[package.dependencies]
aiohttp = ">=3.8.3,<4.0.0"
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
dataclasses-json = ">=0.5.7,<0.6.0"
-langchainplus-sdk = ">=0.0.17"
+langchainplus-sdk = ">=0.0.20,<0.0.21"
numexpr = ">=2.8.4,<3.0.0"
numpy = ">=1,<2"
openapi-schema-pydantic = ">=1.2,<2.0"
@@ -2850,27 +2850,27 @@ SQLAlchemy = ">=1.4,<3"
tenacity = ">=8.1.0,<9.0.0"
[package.extras]
-all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.2.6,<0.3.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.3,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (==9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=3,<4)", "deeplake (>=3.6.2,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.1.dev3,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "momento (>=1.5.0,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "octoai-sdk (>=0.1.1,<0.2.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.1.2,<2.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "spacy (>=3,<4)", "steamship (>=2.16.9,<3.0.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
+all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3,<0.4)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.3,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=3,<4)", "deeplake (>=3.6.2,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.1.dev3,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "marqo (>=0.11.0,<0.12.0)", "momento (>=1.5.0,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "octoai-sdk (>=0.1.1,<0.2.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.1.2,<2.0.0)", "rdflib (>=6.3.2,<7.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "spacy (>=3,<4)", "steamship (>=2.16.9,<3.0.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0a20230509004)", "openai (>=0,<1)"]
-clarifai = ["clarifai (==9.1.0)"]
+clarifai = ["clarifai (>=9.1.0)"]
cohere = ["cohere (>=3,<4)"]
docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
embeddings = ["sentence-transformers (>=2,<3)"]
-extended-testing = ["atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "chardet (>=5.1.0,<6.0.0)", "esprima (>=4.0.1,<5.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "openai (>=0,<1)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "streamlit (>=1.18.0,<2.0.0)", "telethon (>=1.28.5,<2.0.0)", "tqdm (>=4.48.0)", "zep-python (>=0.31)"]
+extended-testing = ["atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.0.7,<0.0.8)", "chardet (>=5.1.0,<6.0.0)", "esprima (>=4.0.1,<5.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "openai (>=0,<1)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "streamlit (>=1.18.0,<2.0.0)", "telethon (>=1.28.5,<2.0.0)", "tqdm (>=4.48.0)", "zep-python (>=0.32)"]
javascript = ["esprima (>=4.0.1,<5.0.0)"]
-llms = ["anthropic (>=0.2.6,<0.3.0)", "clarifai (==9.1.0)", "cohere (>=3,<4)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openllm (>=0.1.6)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
+llms = ["anthropic (>=0.3,<0.4)", "clarifai (>=9.1.0)", "cohere (>=3,<4)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openllm (>=0.1.19)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.4.0)"]
qdrant = ["qdrant-client (>=1.1.2,<2.0.0)"]
text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
[[package]]
name = "langchain-serve"
-version = "0.0.54"
+version = "0.0.56"
description = "Langchain Serve - serve your langchain apps on Jina AI Cloud."
optional = true
python-versions = "*"
files = [
- {file = "langchain-serve-0.0.54.tar.gz", hash = "sha256:5cbc980886c81f3bac7ed3337adeb0b94fc9f3645e4501dd7f0702f90766bbaa"},
+ {file = "langchain-serve-0.0.56.tar.gz", hash = "sha256:47c1e0290aec07c7e6366bb1b6b866d7d3f5446fb296160ca1bf2ef522c33fac"},
]
[package.dependencies]
@@ -7552,4 +7552,4 @@ deploy = ["langchain-serve"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.11"
-content-hash = "e25e43fde8f96f57beab702ac4c51cb3e569b81f85c540a7b4b5fb7b6388d04e"
+content-hash = "3ef18bc73e595f6aa8c3ee4b4c9666f3328c601933aef1bf225b865f39504e3c"
diff --git a/pyproject.toml b/pyproject.toml
index 08bdaad65..c82e532f8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,14 +23,14 @@ langflow = "langflow.__main__:main"
[tool.poetry.dependencies]
python = ">=3.9,<3.11"
-fastapi = "^0.99.0"
+fastapi = "^0.100.0"
uvicorn = "^0.22.0"
beautifulsoup4 = "^4.12.2"
google-search-results = "^2.4.1"
google-api-python-client = "^2.79.0"
typer = "^0.9.0"
gunicorn = "^20.1.0"
-langchain = "^0.0.219"
+langchain = "^0.0.229"
openai = "^0.27.8"
pandas = "^2.0.0"
chromadb = "^0.3.21"
@@ -78,7 +78,7 @@ black = "^23.1.0"
ipykernel = "^6.21.2"
mypy = "^1.1.1"
ruff = "^0.0.254"
-httpx = "^0.23.3"
+httpx = "*"
pytest = "^7.2.2"
types-requests = "^2.28.11"
requests = "^2.28.0"
diff --git a/render.yaml b/render.yaml
new file mode 100644
index 000000000..e67da9334
--- /dev/null
+++ b/render.yaml
@@ -0,0 +1,11 @@
+services:
+ # A Docker web service
+ - type: web
+ name: langflow
+ runtime: docker
+ plan: free
+ dockerfilePath: ./Dockerfile
+ repo: https://github.com/logspace-ai/langflow
+ branch: main
+ healthCheckPath: /health
+ autoDeploy: false
diff --git a/src/backend/langflow/api/v1/base.py b/src/backend/langflow/api/v1/base.py
index 420e1645f..71cac5412 100644
--- a/src/backend/langflow/api/v1/base.py
+++ b/src/backend/langflow/api/v1/base.py
@@ -60,12 +60,26 @@ INVALID_CHARACTERS = {
"}",
}
+INVALID_NAMES = {
+ "input_variables",
+ "output_parser",
+ "partial_variables",
+ "template",
+ "template_format",
+ "validate_template",
+}
+
def validate_prompt(template: str):
input_variables = extract_input_variables_from_prompt(template)
# Check if there are invalid characters in the input_variables
input_variables = check_input_variables(input_variables)
+ if any(var in INVALID_NAMES for var in input_variables):
+ raise ValueError(
+ f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. "
+ )
+
try:
PromptTemplate(template=template, input_variables=input_variables)
except Exception as exc:
diff --git a/src/backend/langflow/api/v1/callback.py b/src/backend/langflow/api/v1/callback.py
index 03f76543e..deddde47f 100644
--- a/src/backend/langflow/api/v1/callback.py
+++ b/src/backend/langflow/api/v1/callback.py
@@ -10,6 +10,7 @@ from fastapi import WebSocket
from langchain.schema import AgentAction, LLMResult, AgentFinish
+from langflow.utils.logger import logger
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py
@@ -62,12 +63,36 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
async def on_tool_end(self, output: str, **kwargs: Any) -> Any:
"""Run when tool ends running."""
+ observation_prefix = kwargs.get("observation_prefix", "Tool output: ")
+ split_output = output.split()
+ first_word = split_output[0]
+ rest_of_output = split_output[1:]
+ # Create a formatted message.
+ intermediate_steps = f"{observation_prefix}{first_word}"
+
+ # Create a ChatResponse instance.
resp = ChatResponse(
message="",
type="stream",
- intermediate_steps=f"Tool output: {output}",
+ intermediate_steps=intermediate_steps,
)
- await self.websocket.send_json(resp.dict())
+ rest_of_resps = [
+ ChatResponse(
+ message="",
+ type="stream",
+ intermediate_steps=f"{word}",
+ )
+ for word in rest_of_output
+ ]
+ resps = [resp] + rest_of_resps
+ # Try to send the response, handle potential errors.
+
+ try:
+ # This is to emulate the stream of tokens
+ for resp in resps:
+ await self.websocket.send_json(resp.dict())
+ except Exception as e:
+ logger.error(e)
async def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py
index 164dd2dd5..937eb2cf6 100644
--- a/src/backend/langflow/api/v1/chat.py
+++ b/src/backend/langflow/api/v1/chat.py
@@ -115,13 +115,6 @@ async def stream_build(flow_id: str):
number_of_nodes = len(graph.nodes)
flow_data_store[flow_id]["status"] = BuildStatus.IN_PROGRESS
- # To deal with the ZeroShotAgent case
- # we need to build the root node first
- # and then the rest of the graph
- # This is a big problem because certain nodes require
- # params that are not connected to it.
- # We should consider connecting the tools to the ZeroShotPrompt
- graph.build()
for i, vertex in enumerate(graph.generator_build(), 1):
try:
@@ -133,7 +126,7 @@ async def stream_build(flow_id: str):
params = vertex._built_object_repr()
valid = True
logger.debug(
- f"Building node {params[:50]}{'...' if len(params) > 50 else ''}"
+ f"Building node {str(params)[:50]}{'...' if len(str(params)) > 50 else ''}"
)
if vertex.artifacts:
# The artifacts will be prompt variables
diff --git a/src/backend/langflow/chat/manager.py b/src/backend/langflow/chat/manager.py
index 856ee226e..33de784b5 100644
--- a/src/backend/langflow/chat/manager.py
+++ b/src/backend/langflow/chat/manager.py
@@ -104,8 +104,14 @@ class ChatManager:
async def close_connection(self, client_id: str, code: int, reason: str):
if websocket := self.active_connections[client_id]:
- await websocket.close(code=code, reason=reason)
- self.disconnect(client_id)
+ try:
+ await websocket.close(code=code, reason=reason)
+ self.disconnect(client_id)
+ except RuntimeError as exc:
+ # This is to catch the following error:
+ # Unexpected ASGI message 'websocket.close', after sending 'websocket.close'
+ if "after sending" in str(exc):
+ logger.error(exc)
async def process_message(
self, client_id: str, payload: Dict, langchain_object: Any
diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml
index dfe16531c..8934ff92f 100644
--- a/src/backend/langflow/config.yaml
+++ b/src/backend/langflow/config.yaml
@@ -277,9 +277,14 @@ vectorstores:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
MongoDBAtlasVectorSearch:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/mongodb_atlas"
+ # Requires docarray >=0.32.0 but langchain-serve requires jina 3.15.2 which doesn't support docarray >=0.32.0
+ # DocArrayInMemorySearch:
+ # documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/docarray_in_memory"
wrappers:
RequestsWrapper:
documentation: ""
+ SQLDatabase:
+ documentation: ""
output_parsers:
StructuredOutputParser:
documentation: "https://python.langchain.com/docs/modules/model_io/output_parsers/structured"
diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py
index 65fff3239..86a2f98a9 100644
--- a/src/backend/langflow/graph/graph/base.py
+++ b/src/backend/langflow/graph/graph/base.py
@@ -214,3 +214,10 @@ class Graph:
if node_type in node_types:
children.append(node)
return children
+
+ def __repr__(self):
+ node_ids = [node.id for node in self.nodes]
+ edges_repr = "\n".join(
+ [f"{edge.source.id} --> {edge.target.id}" for edge in self.edges]
+ )
+ return f"Graph:\nNodes: {node_ids}\nConnections:\n{edges_repr}"
diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py
index c7aef883a..4eb39e8e8 100644
--- a/src/backend/langflow/graph/vertex/base.py
+++ b/src/backend/langflow/graph/vertex/base.py
@@ -123,89 +123,119 @@ class Vertex:
self.params = params
def _build(self):
- # The params dict is used to build the module
- # it contains values and keys that point to nodes which
- # have their own params dict
- # When build is called, we iterate through the params dict
- # and if the value is a node, we call build on that node
- # and use the output of that build as the value for the param
- # if the value is not a node, then we use the value as the param
- # and continue
- # Another aspect is that the node_type is the class that we need to import
- # and instantiate with these built params
+ """
+ Initiate the build process.
+ """
logger.debug(f"Building {self.vertex_type}")
- # Build each node in the params dict
+ self._build_each_node_in_params_dict()
+ self._get_and_instantiate_class()
+ self._validate_built_object()
+
+ self._built = True
+
+ def _build_each_node_in_params_dict(self):
+ """
+ Iterates over each node in the params dictionary and builds it.
+ """
for key, value in self.params.copy().items():
- # Check if Node or list of Nodes and not self
- # to avoid recursion
- if isinstance(value, Vertex):
+ if self._is_node(value):
if value == self:
del self.params[key]
continue
- result = value.build()
- # If the key is "func", then we need to use the run method
- if key == "func":
- if not isinstance(result, types.FunctionType):
- # func can be
- # PythonFunction(code='\ndef upper_case(text: str) -> str:\n return text.upper()\n')
- # so we need to check if there is an attribute called run
- if hasattr(result, "run"):
- result = result.run # type: ignore
- elif hasattr(result, "get_function"):
- result = result.get_function() # type: ignore
- elif inspect.iscoroutinefunction(result):
- self.params["coroutine"] = result
- else:
- # turn result which is a function into a coroutine
- # so that it can be awaited
- self.params["coroutine"] = sync_to_async(result)
- if isinstance(result, list):
- # If the result is a list, then we need to extend the list
- # with the result but first check if the key exists
- # if it doesn't, then we need to create a new list
- if isinstance(self.params[key], list):
- self.params[key].extend(result)
+ self._build_node_and_update_params(key, value)
+ elif isinstance(value, list) and self._is_list_of_nodes(value):
+ self._build_list_of_nodes_and_update_params(key, value)
- self.params[key] = result
- elif isinstance(value, list) and all(
- isinstance(node, Vertex) for node in value
- ):
- self.params[key] = []
- for node in value:
- built = node.build()
- if isinstance(built, list):
- self.params[key].extend(built)
- else:
- self.params[key].append(built)
+ def _is_node(self, value):
+ """
+ Checks if the provided value is an instance of Vertex.
+ """
+ return isinstance(value, Vertex)
- # Get the class from LANGCHAIN_TYPES_DICT
- # and instantiate it with the params
- # and return the instance
+ def _is_list_of_nodes(self, value):
+ """
+ Checks if the provided value is a list of Vertex instances.
+ """
+ return all(self._is_node(node) for node in value)
+ def _build_node_and_update_params(self, key, node):
+ """
+ Builds a given node and updates the params dictionary accordingly.
+ """
+ result = node.build()
+ self._handle_func(key, result)
+ if isinstance(result, list):
+ self._extend_params_list_with_result(key, result)
+ self.params[key] = result
+
+ def _build_list_of_nodes_and_update_params(self, key, nodes):
+ """
+ Iterates over a list of nodes, builds each and updates the params dictionary.
+ """
+ self.params[key] = []
+ for node in nodes:
+ built = node.build()
+ if isinstance(built, list):
+ self.params[key].extend(built)
+ else:
+ self.params[key].append(built)
+
+ def _handle_func(self, key, result):
+ """
+ Handles 'func' key by checking if the result is a function and setting it as coroutine.
+ """
+ if key == "func":
+ if not isinstance(result, types.FunctionType):
+ if hasattr(result, "run"):
+ result = result.run # type: ignore
+ elif hasattr(result, "get_function"):
+ result = result.get_function() # type: ignore
+ elif inspect.iscoroutinefunction(result):
+ self.params["coroutine"] = result
+ else:
+ self.params["coroutine"] = sync_to_async(result)
+
+ def _extend_params_list_with_result(self, key, result):
+ """
+ Extends a list in the params dictionary with the given result if it exists.
+ """
+ if isinstance(self.params[key], list):
+ self.params[key].extend(result)
+
+ def _get_and_instantiate_class(self):
+ """
+ Gets the class from a dictionary and instantiates it with the params.
+ """
+ if self.base_type is None:
+ raise ValueError(f"Base type for node {self.vertex_type} not found")
try:
- if self.base_type is None:
- raise ValueError(f"Base type for node {self.vertex_type} not found")
result = loading.instantiate_class(
node_type=self.vertex_type,
base_type=self.base_type,
params=self.params,
)
- # Result could be the _built_object or
- # (_built_object, dict) tuple
- if isinstance(result, tuple):
- self._built_object, self.artifacts = result
- else:
- self._built_object = result
+ self._update_built_object_and_artifacts(result)
except Exception as exc:
raise ValueError(
f"Error building node {self.vertex_type}: {str(exc)}"
) from exc
+ def _update_built_object_and_artifacts(self, result):
+ """
+ Updates the built object and its artifacts.
+ """
+ if isinstance(result, tuple):
+ self._built_object, self.artifacts = result
+ else:
+ self._built_object = result
+
+ def _validate_built_object(self):
+ """
+ Checks if the built object is None and raises a ValueError if so.
+ """
if self._built_object is None:
raise ValueError(f"Node type {self.vertex_type} not found")
- self._built = True
-
def build(self, force: bool = False) -> Any:
if not self._built or force:
self._build()
diff --git a/src/backend/langflow/graph/vertex/types.py b/src/backend/langflow/graph/vertex/types.py
index e6a1868db..0f09b0b20 100644
--- a/src/backend/langflow/graph/vertex/types.py
+++ b/src/backend/langflow/graph/vertex/types.py
@@ -1,3 +1,4 @@
+import ast
from typing import Any, Dict, List, Optional, Union
from langflow.graph.vertex.base import Vertex
@@ -79,7 +80,7 @@ class WrapperVertex(Vertex):
def build(self, force: bool = False) -> Any:
if not self._built or force:
if "headers" in self.params:
- self.params["headers"] = eval(self.params["headers"])
+ self.params["headers"] = ast.literal_eval(self.params["headers"])
self._build()
return self._built_object
@@ -93,7 +94,11 @@ class DocumentLoaderVertex(Vertex):
# show how many documents are in the list?
if self._built_object:
+ avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(
+ self._built_object
+ )
return f"""{self.vertex_type}({len(self._built_object)} documents)
+ \nAvg. Document Length (characters): {avg_length}
Documents: {self._built_object[:3]}..."""
return f"{self.vertex_type}()"
@@ -125,8 +130,13 @@ class TextSplitterVertex(Vertex):
def _built_object_repr(self):
# This built_object is a list of documents. Maybe we should
# show how many documents are in the list?
+
if self._built_object:
+ avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(
+ self._built_object
+ )
return f"""{self.vertex_type}({len(self._built_object)} documents)
+ \nAvg. Document Length (characters): {avg_length}
\nDocuments: {self._built_object[:3]}..."""
return f"{self.vertex_type}()"
@@ -202,13 +212,28 @@ class PromptVertex(Vertex):
return self._built_object
def _built_object_repr(self):
- if self.artifacts and hasattr(self._built_object, "format"):
- # We'll build the prompt with the artifacts
- # to show the user what the prompt looks like
- # with the variables filled in
- return self._built_object.format(**self.artifacts)
- else:
+ if (
+ not self.artifacts
+ or self._built_object is None
+ or not hasattr(self._built_object, "format")
+ ):
return super()._built_object_repr()
+ # We'll build the prompt with the artifacts
+ # to show the user what the prompt looks like
+ # with the variables filled in
+ artifacts = self.artifacts.copy()
+ # Remove the handle_keys from the artifacts
+ # so the prompt format doesn't break
+ artifacts.pop("handle_keys", None)
+ try:
+ template = self._built_object.format(**artifacts)
+ return (
+ template
+ if isinstance(template, str)
+ else f"{self.vertex_type}({template})"
+ )
+ except KeyError:
+ return str(self._built_object)
class OutputParserVertex(Vertex):
diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py
index aa0cfb5db..b4e2b9bac 100644
--- a/src/backend/langflow/interface/agents/custom.py
+++ b/src/backend/langflow/interface/agents/custom.py
@@ -157,7 +157,7 @@ class VectorStoreAgent(CustomAgentExecutor):
llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore
)
return AgentExecutor.from_agent_and_tools(
- agent=agent, tools=tools, verbose=True
+ agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
)
def run(self, *args, **kwargs):
@@ -232,6 +232,7 @@ class SQLAgent(CustomAgentExecutor):
verbose=True,
max_iterations=15,
early_stopping_method="force",
+ handle_parsing_errors=True,
)
def run(self, *args, **kwargs):
@@ -276,7 +277,7 @@ class VectorStoreRouterAgent(CustomAgentExecutor):
llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore
)
return AgentExecutor.from_agent_and_tools(
- agent=agent, tools=tools, verbose=True
+ agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
)
def run(self, *args, **kwargs):
@@ -308,6 +309,7 @@ class InitializeAgent(CustomAgentExecutor):
agent=agent, # type: ignore
memory=memory,
return_intermediate_steps=True,
+ handle_parsing_errors=True,
)
def __init__(self, *args, **kwargs):
diff --git a/src/backend/langflow/interface/chains/base.py b/src/backend/langflow/interface/chains/base.py
index ff7e1ee33..67d31308f 100644
--- a/src/backend/langflow/interface/chains/base.py
+++ b/src/backend/langflow/interface/chains/base.py
@@ -23,6 +23,7 @@ class ChainCreator(LangChainTypeCreator):
from_method_nodes = {
"ConversationalRetrievalChain": "from_llm",
"LLMCheckerChain": "from_llm",
+ "SQLDatabaseChain": "from_llm",
}
@property
diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py
index bfcd18caa..04ee8aba1 100644
--- a/src/backend/langflow/interface/importing/utils.py
+++ b/src/backend/langflow/interface/importing/utils.py
@@ -11,6 +11,7 @@ from langchain.chat_models.base import BaseChatModel
from langchain.tools import BaseTool
from langflow.interface.custom.custom import CustomComponent
from langflow.utils import validate
+from langflow.interface.wrappers.base import wrapper_creator
def import_module(module_path: str) -> Any:
@@ -103,7 +104,11 @@ def import_prompt(prompt: str) -> Type[PromptTemplate]:
def import_wrapper(wrapper: str) -> Any:
"""Import wrapper from wrapper name"""
- return import_module(f"from langchain.requests import {wrapper}")
+ if (
+ isinstance(wrapper_creator.type_dict, dict)
+ and wrapper in wrapper_creator.type_dict
+ ):
+ return wrapper_creator.type_dict.get(wrapper)
def import_toolkit(toolkit: str) -> Any:
diff --git a/src/backend/langflow/interface/initialize/loading.py b/src/backend/langflow/interface/initialize/loading.py
index 2ec59e3b5..9ec149ec9 100644
--- a/src/backend/langflow/interface/initialize/loading.py
+++ b/src/backend/langflow/interface/initialize/loading.py
@@ -1,3 +1,4 @@
+import contextlib
import json
from typing import Any, Callable, Dict, List, Sequence, Type
@@ -24,6 +25,7 @@ from langflow.interface.toolkits.base import toolkits_creator
from langflow.interface.chains.base import chain_creator
from langflow.interface.output_parsers.base import output_parser_creator
from langflow.interface.retrievers.base import retriever_creator
+from langflow.interface.wrappers.base import wrapper_creator
from langflow.interface.utils import load_file_into_dict
from langflow.utils import validate
from langchain.chains.base import Chain
@@ -70,7 +72,11 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
elif base_type == "prompts":
return instantiate_prompt(node_type, class_object, params)
elif base_type == "tools":
- return instantiate_tool(node_type, class_object, params)
+ tool = instantiate_tool(node_type, class_object, params)
+ if hasattr(tool, "name") and isinstance(tool, BaseTool):
+ # tool name shouldn't contain spaces
+ tool.name = tool.name.replace(" ", "_")
+ return tool
elif base_type == "toolkits":
return instantiate_toolkit(node_type, class_object, params)
elif base_type == "embeddings":
@@ -95,6 +101,8 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
return instantiate_memory(node_type, class_object, params)
elif base_type == "custom_components":
return instantiate_custom_component(node_type, class_object, params)
+ elif base_type == "wrappers":
+ return instantiate_wrapper(node_type, class_object, params)
else:
return class_object(**params)
@@ -104,6 +112,15 @@ def instantiate_custom_component(node_type, class_object, params):
return class_object().build(**params)
+def instantiate_wrapper(node_type, class_object, params):
+ if node_type in wrapper_creator.from_method_nodes:
+ method = wrapper_creator.from_method_nodes[node_type]
+ if class_method := getattr(class_object, method, None):
+ return class_method(**params)
+ raise ValueError(f"Method {method} not found in {class_object}")
+ return class_object(**params)
+
+
def instantiate_output_parser(node_type, class_object, params):
if node_type in output_parser_creator.from_method_nodes:
method = output_parser_creator.from_method_nodes[node_type]
@@ -119,12 +136,21 @@ def instantiate_llm(node_type, class_object, params: Dict):
# False if condition is True
if node_type == "VertexAI":
return initialize_vertexai(class_object=class_object, params=params)
+ # max_tokens sometimes is a string and should be an int
+ if "max_tokens" in params:
+ if isinstance(params["max_tokens"], str) and params["max_tokens"].isdigit():
+ params["max_tokens"] = int(params["max_tokens"])
+ elif not isinstance(params.get("max_tokens"), int):
+ params.pop("max_tokens", None)
return class_object(**params)
def instantiate_memory(node_type, class_object, params):
# process input_key and output_key to remove them if
# they are empty strings
+ if node_type == "ConversationEntityMemory":
+ params.pop("memory_key", None)
+
for key in ["input_key", "output_key"]:
if key in params and (params[key] == "" or not params[key]):
params.pop(key)
@@ -178,8 +204,7 @@ def instantiate_agent(node_type, class_object: Type[agent_module.Agent], params:
agent = class_method(**params)
tools = params.get("tools", [])
return AgentExecutor.from_agent_and_tools(
- agent=agent,
- tools=tools,
+ agent=agent, tools=tools, handle_parsing_errors=True
)
return load_agent_executor(class_object, params)
@@ -189,7 +214,7 @@ def instantiate_prompt(node_type, class_object, params: Dict):
if "tools" not in params:
params["tools"] = []
return ZeroShotAgent.create_prompt(**params)
- if "MessagePromptTemplate" in node_type:
+ elif "MessagePromptTemplate" in node_type:
# Then we only need the template
from_template_params = {
"template": params.pop("prompt", params.pop("template", ""))
@@ -197,12 +222,12 @@ def instantiate_prompt(node_type, class_object, params: Dict):
if not from_template_params.get("template"):
raise ValueError("Prompt template is required")
- return class_object.from_template(**from_template_params)
+ prompt = class_object.from_template(**from_template_params)
- if node_type == "ChatPromptTemplate":
- return class_object.from_messages(**params)
-
- prompt = class_object(**params)
+ elif node_type == "ChatPromptTemplate":
+ prompt = class_object.from_messages(**params)
+ else:
+ prompt = class_object(**params)
format_kwargs: Dict[str, Any] = {}
for input_variable in prompt.input_variables:
@@ -214,18 +239,23 @@ def instantiate_prompt(node_type, class_object, params: Dict):
variable, "get_format_instructions"
):
format_kwargs[input_variable] = variable.get_format_instructions()
- # check if is a list of Document
elif isinstance(variable, List) and all(
isinstance(item, Document) for item in variable
):
# Format document to contain page_content and metadata
# as one string separated by a newline
- format_kwargs[input_variable] = "\n".join(
- [
- f"Document:{item.page_content}\nMetadata:{item.metadata}"
- for item in variable
- ]
- )
+ if len(variable) > 1:
+ content = "\n".join(
+ [item.page_content for item in variable if item.page_content]
+ )
+ else:
+ content = variable[0].page_content
+ # content could be a json list of strings
+ with contextlib.suppress(json.JSONDecodeError):
+ content = json.loads(content)
+ if isinstance(content, list):
+ content = ",".join([str(item) for item in content])
+ format_kwargs[input_variable] = content
# handle_keys will be a list but it does not exist yet
# so we need to create it
@@ -247,12 +277,14 @@ def instantiate_prompt(node_type, class_object, params: Dict):
def instantiate_tool(node_type, class_object: Type[BaseTool], params: Dict):
if node_type == "JsonSpec":
- params["dict_"] = load_file_into_dict(params.pop("path"))
+ if file_dict := load_file_into_dict(params.pop("path")):
+ params["dict_"] = file_dict
+ else:
+ raise ValueError("Invalid file")
return class_object(**params)
elif node_type == "PythonFunctionTool":
params["func"] = get_function(params.get("code"))
return class_object(**params)
- # For backward compatibility
elif node_type == "PythonFunction":
function_string = params["code"]
if isinstance(function_string, str):
@@ -354,6 +386,12 @@ def instantiate_textsplitter(
"separator_type" in params and params["separator_type"] == "Text"
) or "separator_type" not in params:
params.pop("separator_type", None)
+ # separators might come in as an escaped string like \\n
+ # so we need to convert it to a string
+ if "separators" in params:
+ params["separators"] = (
+ params["separators"].encode().decode("unicode-escape")
+ )
text_splitter = class_object(**params)
else:
from langchain.text_splitter import Language
@@ -406,6 +444,7 @@ def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=allowed_tools,
+ handle_parsing_errors=True,
# memory=memory,
**kwargs,
)
diff --git a/src/backend/langflow/interface/utils.py b/src/backend/langflow/interface/utils.py
index 1ab2b4ce5..9203915cf 100644
--- a/src/backend/langflow/interface/utils.py
+++ b/src/backend/langflow/interface/utils.py
@@ -16,17 +16,15 @@ def load_file_into_dict(file_path: str) -> dict:
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
- file_extension = os.path.splitext(file_path)[1].lower()
-
- if file_extension == ".json":
- with open(file_path, "r") as json_file:
- data = json.load(json_file)
- elif file_extension in [".yaml", ".yml"]:
- with open(file_path, "r") as yaml_file:
- data = yaml.safe_load(yaml_file)
- else:
- raise ValueError("Unsupported file type. Please provide a JSON or YAML file.")
-
+ # Files names are UUID, so we can't find the extension
+ with open(file_path, "r") as file:
+ try:
+ data = json.load(file)
+ except json.JSONDecodeError:
+ file.seek(0)
+ data = yaml.safe_load(file)
+ except ValueError as exc:
+ raise ValueError("Invalid file type. Expected .json or .yaml.") from exc
return data
diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py
index f5773d07a..77e38f921 100644
--- a/src/backend/langflow/interface/wrappers/base.py
+++ b/src/backend/langflow/interface/wrappers/base.py
@@ -1,25 +1,36 @@
from typing import Dict, List, Optional
-from langchain import requests
+from langchain import requests, sql_database
from langflow.interface.base import LangChainTypeCreator
from langflow.utils.logger import logger
-from langflow.utils.util import build_template_from_class
+from langflow.utils.util import build_template_from_class, build_template_from_method
class WrapperCreator(LangChainTypeCreator):
type_name: str = "wrappers"
+ from_method_nodes = {"SQLDatabase": "from_uri"}
+
@property
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
self.type_dict = {
- wrapper.__name__: wrapper for wrapper in [requests.TextRequestsWrapper]
+ wrapper.__name__: wrapper
+ for wrapper in [requests.TextRequestsWrapper, sql_database.SQLDatabase]
}
return self.type_dict
def get_signature(self, name: str) -> Optional[Dict]:
try:
+ if name in self.from_method_nodes:
+ return build_template_from_method(
+ name,
+ type_to_cls_dict=self.type_to_loader_dict,
+ add_function=True,
+ method_name=self.from_method_nodes[name],
+ )
+
return build_template_from_class(name, self.type_to_loader_dict)
except ValueError as exc:
raise ValueError("Wrapper not found") from exc
diff --git a/src/backend/langflow/processing/base.py b/src/backend/langflow/processing/base.py
index 478b98816..f8690bbdf 100644
--- a/src/backend/langflow/processing/base.py
+++ b/src/backend/langflow/processing/base.py
@@ -19,8 +19,11 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
# Deactivating until we have a frontend solution
# to display intermediate steps
langchain_object.return_intermediate_steps = True
+ try:
+ fix_memory_inputs(langchain_object)
+ except Exception as exc:
+ logger.error(exc)
- fix_memory_inputs(langchain_object)
try:
async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)]
output = await langchain_object.acall(inputs, callbacks=async_callbacks)
@@ -39,7 +42,11 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
if isinstance(output, dict)
else output
)
- thought = format_actions(intermediate_steps) if intermediate_steps else ""
+ try:
+ thought = format_actions(intermediate_steps) if intermediate_steps else ""
+ except Exception as exc:
+ logger.exception(exc)
+ thought = ""
except Exception as exc:
logger.exception(exc)
raise ValueError(f"Error: {str(exc)}") from exc
diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py
index 1b219fe11..03e6e4c35 100644
--- a/src/backend/langflow/processing/process.py
+++ b/src/backend/langflow/processing/process.py
@@ -22,7 +22,10 @@ def fix_memory_inputs(langchain_object):
if not hasattr(langchain_object, "memory") or langchain_object.memory is None:
return
try:
- if langchain_object.memory.memory_key in langchain_object.input_variables:
+ if (
+ hasattr(langchain_object.memory, "memory_key")
+ and langchain_object.memory.memory_key in langchain_object.input_variables
+ ):
return
except AttributeError:
input_variables = (
@@ -92,6 +95,10 @@ def process_graph_cached(data_graph: Dict[str, Any], inputs: Optional[dict] = No
logger.debug("Loaded LangChain object")
if inputs is None:
inputs = {}
+
+ # Add artifacts to inputs
+ # artifacts can be documents loaded when building
+ # the flow
for (
key,
value,
@@ -113,8 +120,7 @@ def process_graph_cached(data_graph: Dict[str, Any], inputs: Optional[dict] = No
result = get_result_and_thought(langchain_object, inputs)
logger.debug("Generated result and thought")
elif isinstance(langchain_object, VectorStore):
- class_name = langchain_object.__class__.__name__
- result = {"message": f"Processed {class_name} successfully"}
+ result = langchain_object.search(**inputs)
else:
raise ValueError(
f"Unknown langchain_object type: {type(langchain_object).__name__}"
@@ -123,23 +129,23 @@ def process_graph_cached(data_graph: Dict[str, Any], inputs: Optional[dict] = No
def load_flow_from_json(
- input: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True
+ flow: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True
):
"""
Load flow from a JSON file or a JSON object.
- :param input: JSON file path or JSON object
+ :param flow: JSON file path or JSON object
:param tweaks: Optional tweaks to be processed
:param build: If True, build the graph, otherwise return the graph object
:return: Langchain object or Graph object depending on the build parameter
"""
# If input is a file path, load JSON from the file
- if isinstance(input, (str, Path)):
- with open(input, "r", encoding="utf-8") as f:
+ if isinstance(flow, (str, Path)):
+ with open(flow, "r", encoding="utf-8") as f:
flow_graph = json.load(f)
# If input is a dictionary, assume it's a JSON object
- elif isinstance(input, dict):
- flow_graph = input
+ elif isinstance(flow, dict):
+ flow_graph = flow
else:
raise TypeError(
"Input must be either a file path (str) or a JSON object (dict)"
diff --git a/src/backend/langflow/template/frontend_node/chains.py b/src/backend/langflow/template/frontend_node/chains.py
index ce8c1c62c..b678dec3b 100644
--- a/src/backend/langflow/template/frontend_node/chains.py
+++ b/src/backend/langflow/template/frontend_node/chains.py
@@ -81,7 +81,7 @@ class ChainFrontendNode(FrontendNode):
field.advanced = False
if field.name == "verbose":
field.required = False
- field.show = True
+ field.show = False
field.advanced = True
if field.name == "llm":
field.required = True
diff --git a/src/backend/langflow/template/frontend_node/constants.py b/src/backend/langflow/template/frontend_node/constants.py
index 90cdbf280..295995586 100644
--- a/src/backend/langflow/template/frontend_node/constants.py
+++ b/src/backend/langflow/template/frontend_node/constants.py
@@ -58,3 +58,7 @@ The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.
You can change this to use other APIs like JinaChat, LocalAI and Prem.
"""
+
+
+INPUT_KEY_INFO = """The variable to be used as Chat Input when more than one variable is available."""
+OUTPUT_KEY_INFO = """The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)"""
diff --git a/src/backend/langflow/template/frontend_node/memories.py b/src/backend/langflow/template/frontend_node/memories.py
index d98a322ff..374d36ff0 100644
--- a/src/backend/langflow/template/frontend_node/memories.py
+++ b/src/backend/langflow/template/frontend_node/memories.py
@@ -2,6 +2,7 @@ from typing import Optional
from langflow.template.field.base import TemplateField
from langflow.template.frontend_node.base import FrontendNode
+from langflow.template.frontend_node.constants import INPUT_KEY_INFO, OUTPUT_KEY_INFO
from langflow.template.template.base import Template
from langchain.memory.chat_message_histories.postgres import DEFAULT_CONNECTION_STRING
from langchain.memory.chat_message_histories.mongodb import (
@@ -70,11 +71,15 @@ class MemoryFrontendNode(FrontendNode):
field.required = False
field.show = True
field.advanced = False
- if field.name in ["input_key", "output_key"]:
+ if field.name in {"input_key", "output_key"}:
field.required = False
field.show = True
field.advanced = False
field.value = ""
+ field.info = (
+ INPUT_KEY_INFO if field.name == "input_key" else OUTPUT_KEY_INFO
+ )
+
if field.name == "memory_key":
field.value = "chat_history"
if field.name == "chat_memory":
@@ -84,9 +89,10 @@ class MemoryFrontendNode(FrontendNode):
if field.name == "url":
field.show = True
if field.name == "entity_store":
- field.show = True
- if name == "SQLiteEntityStore":
- field.show = True
+ field.show = False
+ if name == "ConversationEntityMemory" and field.name == "memory_key":
+ field.show = False
+ field.required = False
class PostgresChatMessageHistoryFrontendNode(MemoryFrontendNode):
diff --git a/src/backend/langflow/template/frontend_node/textsplitters.py b/src/backend/langflow/template/frontend_node/textsplitters.py
index 04700f1c4..1d5549042 100644
--- a/src/backend/langflow/template/frontend_node/textsplitters.py
+++ b/src/backend/langflow/template/frontend_node/textsplitters.py
@@ -45,7 +45,7 @@ class TextSplittersFrontendNode(FrontendNode):
field_type="str",
required=True,
show=True,
- value=".",
+ value="\\n",
name=name,
display_name="Separator",
)
diff --git a/src/backend/langflow/template/frontend_node/tools.py b/src/backend/langflow/template/frontend_node/tools.py
index 16e6955aa..d23033b35 100644
--- a/src/backend/langflow/template/frontend_node/tools.py
+++ b/src/backend/langflow/template/frontend_node/tools.py
@@ -112,7 +112,7 @@ class PythonFunctionToolNode(FrontendNode):
],
)
description: str = "Python function to be executed."
- base_classes: list[str] = ["Tool"]
+ base_classes: list[str] = ["BaseTool", "Tool"]
def to_dict(self):
return super().to_dict()
diff --git a/src/frontend/index.html b/src/frontend/index.html
index 426983565..8c21f0124 100644
--- a/src/frontend/index.html
+++ b/src/frontend/index.html
@@ -5,7 +5,7 @@
-
+
- {children}
-
- );
- },
- }}
- >
- {chat.message.toString()}
-
+ {children}
+
+ );
+ },
+ }}
+ >
+ {chat.message.toString()}
+ {parts}
; - }) - : chat.message[chat.chatKey]} - + // Update last index + lastIndex = regex.lastIndex; + } + // Push text after the last match + if (lastIndex !== line.length) { + parts.push(line.substring(lastIndex)); + } + return{parts}
; + }) + : chat.message[chat.chatKey]} + + + ) : ( + {chat.message[chat.chatKey]} + )}