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] | open | false | null | [] | null | 3 | 2024-01-15T17:04:57 | 2024-01-15T18:17:33 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
# No code provided
# LangSmith trace link in Description
```
### Description
I test a sample code of CrewAI, which is based on Langchain and I found, again :( that tool_input is incorrectly set, which causes incorrect answers.
I track it in LangSmith and here is the RunnableSequence's input:
```
input: Investigate which AI / LLM assisted IDE assistant can assist coding related questions not only related to the selected code or the opened file, but all the project code files, the console and terminal messages. Check also the most recent IDEs, not only the most popular ones. Your final answer MUST be a full analysis report
tool_names: Google Search
tools: "Google Search: Search Google for recent results."
chat_history: ""
intermediate_steps: []
```
and output:
```
output:
tool: Google Search
tool_input: AI / LLM assisted IDE assistant for coding related questions
log: |-
Thought: Do I need to use a tool? Yes
Action: Google Search
Action Input: "AI / LLM assisted IDE assistant for coding related questions"
type: AgentAction
```
so we can see the original task of:
_**"Investigate which AI / LLM assisted IDE assistant can assist coding related questions not only related to the selected code or the opened file, but all the project code files, the console and terminal messages. Check also the most recent IDEs, not only the most popular ones. Your final answer MUST be a full analysis report"**_
was transformed to a Google search for:
_**"AI / LLM assisted IDE assistant for coding related questions"**_
which obviously doesn't provide the search results relevant to original task!
The trace with Zephyr-7B via Hugging Face: https://smith.langchain.com/public/d4076fff-78d7-48ab-bdfe-d463bd1ffcb3/r
The same with gpt-4: https://smith.langchain.com/public/1fa8da61-6cc9-448c-8aa3-fd75d930e623/r
**So what and where should we finetune, modify to keep search term or any other tool_input correct, without huge distortion?**
### System Info
Windows 10
Python 3.11.6
langchain-core: 0.1.10
langchain-community: 0.0.12
crewai: 0.1.32
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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- **Issue:**
- **Dependencies:** | {
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Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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] | closed | false | null | [] | null | 1 | 2024-01-15T15:43:11 | 2024-01-15T16:36:55 | 2024-01-15T16:36:55 | CONTRIBUTOR | null | **Description:** Added the new gpt-3.5-turbo-1106 for **finetuned** cost calculation,
**Issue:** no issue found open
By the information in OpenAI the pricing is the same as the older model (0613) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16038 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16038/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16038/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16038/events | https://github.com/langchain-ai/langchain/issues/16038 | 2,082,253,988 | I_kwDOIPDwls58HKyk | 16,038 | RetrievalQAWithSourcesChain result sources is not deterministic | {
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] | open | false | null | [] | null | 1 | 2024-01-15T15:26:14 | 2024-01-15T15:38:43 | null | CONTRIBUTOR | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
I implemented a custom retriever like this:
```
class CustomUserRetriever(BaseRetriever):
vectorstore: VectorStore
username: str # New attribute to store the filename
def clean_metadata(self, doc):
metadata = doc.metadata
return { # Add a button to clear chat history
"Title": metadata["title"],
"source": metadata["title"],
"chunk_id": metadata["chunk_id"]
}
def get_relevant_documents(self, query):
docs = []
for doc in self.vectorstore.similarity_search(query, search_type="hybrid", k=5, filters=f"search.ismatch('{self.username.split('@')[0]}', 'usernames_result') "):
cleaned_metadata = self.clean_metadata(doc)
docs.append(Document(
page_content=doc.page_content,
metadata=cleaned_metadata))
return docs
```
And then I created a custom functionality which should return the answer, and the source of the answer. (document name with extension)
```
retriever = CustomUserRetriever(vectorstore=vector_store, username=user_name)
chain = RetrievalQAWithSourcesChain.from_chain_type(llm,
chain_type="stuff",
memory=memory,
retriever=retriever,
return_source_documents=True
)
#result = chain({"question": user_question})
result = chain({"question":user_question}, return_only_outputs=True)
```
THen I ask a question which should get a response from 2 document chunks, and the answer is generated correctly.
However asking the same question multiple times, sometimes I get an empty source and sometimes I get a list of the source documents,
### Description
I want to get answers from internal documents, but the document retriever returns by similarity chunks that are not relevant to the generated answer, so those ones should be removed, sometimes it works! I asked a question and the chunks are returned, sometimes I get an empty source list.
### System Info
python 3.11
langchain 0.0.353
openai 1.6.1
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16037 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16037/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16037/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16037/events | https://github.com/langchain-ai/langchain/issues/16037 | 2,082,196,329 | I_kwDOIPDwls58G8tp | 16,037 | langchain 0.0.27 should NOT be installed on python 3.6.5 | {
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] | closed | false | null | [] | null | 3 | 2024-01-15T14:54:25 | 2024-01-15T23:26:19 | 2024-01-15T20:37:32 | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
This is a minor issue with metadata for pip. I opened a ticket against pip.
https://github.com/pypa/pip/issues/12476
But I was told that I should report the issue to this project.
[langchain_0.0.27.log](https://github.com/langchain-ai/langchain/files/13940154/langchain_0.0.27.log)
If there existed a langchain version which doesn't use future feature annotations, install it.
Otherwise, prompt the user that no suitable version.
### Description
I installed langchain 0.0.27 on python 3.6.5 on my Windows 10 successfully. But I run into an issue when I tried import the library.
from langchain.llms import Ollama
SyntaxError: future feature annotations is not defined
[PEP-563](https://www.python.org/dev/peps/pep-0563/#enabling-the-future-behavior-in-python-3-7)future annotations are introduced in Python 3.7. That means langchain 0.0.27 which depends on future feature annotations should NOT be installed on Python 3.6.5.
### System Info
pip 21.3.1
python 3.6.5/Windows 10
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16036 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16036/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16036/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16036/events | https://github.com/langchain-ai/langchain/issues/16036 | 2,082,177,846 | I_kwDOIPDwls58G4M2 | 16,036 | Using faiss.from_documents require tokenizer pad_token | {
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] | open | false | null | [] | null | 2 | 2024-01-15T14:44:21 | 2024-01-15T23:32:19 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
embeddings = HuggingFaceEmbeddings(
model_name="mistralai/Mistral-7B-v0.1", # Provide the pre-trained model's path
)
cache_embeddings = CacheBackedEmbeddings.from_bytes_store(embeddings, cached_dir)
vectorstore = FAISS.from_documents(docs, cache_embeddings)
```
I use this code.
I met this error.
Why occur this occur? I look around all. I can't fix ti. so Sad ㅠㅠㅠ
Could you explain me how to get fix it?
```
ValueError Traceback (most recent call last)
Cell In[8], [line 1](vscode-notebook-cell:?execution_count=8&line=1)
----> [1](vscode-notebook-cell:?execution_count=8&line=1) vectorstore = FAISS.from_documents(docs, cache_embeddings)
[3](vscode-notebook-cell:?execution_count=8&line=3) retriver = vectorstore.as_retriever(reduce_k_below_max_tokens=True)
[4](vscode-notebook-cell:?execution_count=8&line=4) # retriver = vectorstore.as_retriever()
File [~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:508](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:508), in VectorStore.from_documents(cls, documents, embedding, **kwargs)
[506](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:506) texts = [d.page_content for d in documents]
[507](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:507) metadatas = [d.metadata for d in documents]
--> [508](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:508) return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File [~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:913](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:913), in FAISS.from_texts(cls, texts, embedding, metadatas, ids, **kwargs)
[886](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:886) @classmethod
[887](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:887) def from_texts(
[888](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:888) cls,
(...)
[893](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:893) **kwargs: Any,
[894](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:894) ) -> FAISS:
[895](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:895) """Construct FAISS wrapper from raw documents.
[896](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:896)
[897](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:897) This is a user friendly interface that:
(...)
[911](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:911) faiss = FAISS.from_texts(texts, embeddings)
[912](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:912) """
...
(...)
[2719](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:2719) and (max_length % pad_to_multiple_of != 0)
[2720](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:2720) ):
ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`
```
### Description
I use this code.
I met this error.
Why occur this occur? I look around all. I can't fix ti. so Sad ㅠㅠㅠ
Could you explain me how to get fix it?
### System Info
langchain 0.1.0
langchain-community 0.0.12
langchain-core 0.1.10
langsmith 0.0.80
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16035 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16035/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16035/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16035/events | https://github.com/langchain-ai/langchain/pull/16035 | 2,082,154,517 | PR_kwDOIPDwls5kGRSV | 16,035 | community: Ollama - Parameter structure to follow official documentation | {
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] | closed | false | null | [] | null | 1 | 2024-01-15T14:31:58 | 2024-01-15T18:17:58 | 2024-01-15T18:17:58 | CONTRIBUTOR | null | ## Feature
- Follow parameter structure as per official documentation
- top level parameters (e.g. model, system, template) will be passed as top level parameters
- other parameters will be sent in options unless options is provided
## Tests
- Test if top level parameters handled properly
- Test if parameters that are not top level parameters are handled as options
- Test if options is provided, it will be passed as is | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16034 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16034/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16034/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16034/events | https://github.com/langchain-ai/langchain/issues/16034 | 2,082,115,236 | I_kwDOIPDwls58Go6k | 16,034 | AttributeError: 'list' object has no attribute 'embed_query' | {
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] | open | false | null | [] | null | 1 | 2024-01-15T14:11:22 | 2024-01-15T14:59:47 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
``` Python
# Chunking the sentence with fixed size
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
all_splits = text_splitter.split_documents(documents)
```
``` Python
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-base-en"
model_kwargs = {"device": "cpu"}
encode_kwargs = {"normalize_embeddings": True}
embeddings = HuggingFaceBgeEmbeddings(
model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
)
```
``` Python
# Creating New Index:
!CREATE VECTOR INDEX KG_QuestionAnswering IF NOT EXISTS FOR (n:LabelName) ON (n.propertyName) OPTIONS {dimension: 1024};
```
``` Python
index_name="KG_QuestionAnswering"
# Instantiate Neo4j vector from documents
store = Neo4jVector.from_existing_index(
documents,
HuggingFaceBgeEmbeddings(),
url=os.environ["NEO4J_URI"],
username=os.environ["NEO4J_USERNAME"],
password=os.environ["NEO4J_PASSWORD"]
)
```
### Description
```
AttributeError Traceback (most recent call last)
[<ipython-input-24-4e3c558558b3>](https://localhost:8080/#) in <cell line: 3>()
1 index_name="KG_QuestionAnswering"
2 # Instantiate Neo4j vector from documents
----> 3 store = Neo4jVector.from_existing_index(
4 documents,
5 HuggingFaceBgeEmbeddings(),
1 frames
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in __init__(self, embedding, search_type, username, password, url, keyword_index_name, database, index_name, node_label, embedding_node_property, text_node_property, distance_strategy, logger, pre_delete_collection, retrieval_query, relevance_score_fn)
236 self.search_type = search_type
237 # Calculate embedding dimension
--> 238 self.embedding_dimension = len(embedding.embed_query("foo"))
239
240 # Delete existing data if flagged
AttributeError: 'list' object has no attribute 'embed_query'
```
### System Info
``` YAML
Windows: 11
pip == 23.3.1
python == 3.10.10
langchain == 0.1.0
transformers == 4.36.2
sentence_transformers == 2.2.2
Neo4j == 5
```
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16032 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16032/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16032/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16032/events | https://github.com/langchain-ai/langchain/issues/16032 | 2,081,995,442 | I_kwDOIPDwls58GLqy | 16,032 | ElasticVectorSearch Error similarity_search openai.NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}} | {
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] | open | false | null | [] | null | 3 | 2024-01-15T13:03:17 | 2024-01-15T14:51:16 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
My code was fully working till i updated from 0.0.259 to 0.1.0 langchain
```
embeddings = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"],
model=os.environ["AZUREOPENAI_EMBEDDINGS_MODEL"],
deployment=os.environ["AZUREOPENAI_EMBEDDINGS_DEPLOYMENT"])
db = ElasticVectorSearch(
embedding=embeddings,
elasticsearch_url=url,
index_name=os.environ["AZUREOPENAI_INDEX_NAME"],
ssl_verify={"ca_certs": os.environ["APP_CERT_PATH"]}
)
docs = db.similarity_search(query=query, # new
k=10)
# Here is when it crashes
```
### Description
The code is crashing giving a weird error and It looks it is because of similarity_search doesnt work in the new version
### System Info
aiohttp==3.9.1
aiosignal==1.3.1
annotated-types==0.6.0
anyio==4.2.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2023.11.17
charset-normalizer==3.3.2
click==8.1.7
dataclasses-json==0.6.3
distro==1.9.0
elastic-transport==8.11.0
elasticsearch==8.11.1
exceptiongroup==1.2.0
fastapi==0.109.0
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.2
httpx==0.26.0
idna==3.6
jsonpatch==1.33
jsonpointer==2.4
langchain==0.1.0
langchain-community==0.0.12
langchain-core==0.1.10
langsmith==0.0.80
marshmallow==3.20.2
multidict==6.0.4
mypy-extensions==1.0.0
numpy==1.26.3
openai==1.7.2
packaging==23.2
pydantic==2.5.3
pydantic_core==2.14.6
PyYAML==6.0.1
regex==2023.12.25
requests==2.31.0
sniffio==1.3.0
SQLAlchemy==2.0.25
starlette==0.35.1
tenacity==8.2.3
tiktoken==0.5.2
tqdm==4.66.1
typing-inspect==0.9.0
typing_extensions==4.9.0
urllib3==2.1.0
uvicorn==0.25.0
yarl==1.9.4
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16031 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16031/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16031/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16031/events | https://github.com/langchain-ai/langchain/pull/16031 | 2,081,988,502 | PR_kwDOIPDwls5kFtL6 | 16,031 | community: add StreamingAPIGatewayWebSocketCallbackHandler | {
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- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
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https://api.github.com/repos/langchain-ai/langchain/issues/16029 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16029/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16029/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16029/events | https://github.com/langchain-ai/langchain/issues/16029 | 2,081,867,005 | I_kwDOIPDwls58FsT9 | 16,029 | Unable to install langchain-google-genai | {
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] | open | false | null | [] | null | 1 | 2024-01-15T11:47:08 | 2024-01-15T11:54:15 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
pip install langchain-google-genai
### Description
I am trying to install langchain-google-genai package, however getting below error:
ERROR: Could not find a version that satisfies the requirement langchain-google-genai==0.0.5 (from versions: none)
ERROR: No matching distribution found for langchain-google-genai==0.0.5
### System Info
python 3.8.3
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16028 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16028/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16028/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16028/events | https://github.com/langchain-ai/langchain/pull/16028 | 2,081,848,898 | PR_kwDOIPDwls5kFO9j | 16,028 | docs: add milvus multitenancy doc | {
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] | closed | false | null | [] | null | 3 | 2024-01-15T11:35:48 | 2024-01-18T05:06:55 | 2024-01-18T05:06:55 | CONTRIBUTOR | null | - **Description:** add milvus multitenancy doc, it is an example for this [pr](https://github.com/langchain-ai/langchain/pull/15740) .
- **Issue:** no,
- **Dependencies:** no,
- **Twitter handle:** no
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https://api.github.com/repos/langchain-ai/langchain/issues/16027 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16027/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16027/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16027/events | https://github.com/langchain-ai/langchain/issues/16027 | 2,081,826,200 | I_kwDOIPDwls58FiWY | 16,027 | Multiple GPU - Degradation over time | {
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] | open | false | null | [] | null | 3 | 2024-01-15T11:21:31 | 2024-01-15T12:30:21 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```llm = LlamaCpp(model_path=model_loc, n_ctx=0, verbose=True, n_gpu_layers=-1)
chain = LLMChain(llm=llm, prompt=prompt)
result = chain(inputs={"context": context, "question": question})
answer = result["text"]
```
I do use the PromptTemplate, but this is not relevant to my issue.
### Description
I am using 2x A100 GPU. What I have noticed is that there is GPU degradation over time.
This is my spool up data:
```
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 2 CUDA devices:
Device 0: NVIDIA A100-PCIE-40GB, compute capability 8.0, VMM: no
Device 1: NVIDIA A100-PCIE-40GB, compute capability 8.0, VMM: no
```
In my code, the above is part of an API that takes in a request and then responds to it. I am making 5 calls to the API one after another, and collating the data for a final summarisation call.
I am not overly impressed with the speeds here in any event - so if anyone has any tips for improving, I don't think 17.25 tokens is very good for dual A100s.
```
llama_print_timings: eval time = 57.98 ms / 1 runs ( 57.98 ms per token, 17.25 tokens per second)
llama_print_timings: total time = 6722.12 ms
llama_print_timings: eval time = 4804.66 ms / 70 runs ( 68.64 ms per token, 14.57 tokens per second)
llama_print_timings: total time = 17722.48 ms
llama_print_timings: eval time = 5950.61 ms / 77 runs ( 77.28 ms per token, 12.94 tokens per second)
llama_print_timings: total time = 23459.58 ms
```
I would have expected the "overall" time to change but the actual tokens per second to remain somewhat consistent - but instead it just gets worse.
I also created a GPU monitor tool, and in that tool, the first call is utilising 25% and 20% for GPU 1 and 2 - but then it goes down to 20% and 17% and then 16% and 15%, and when making a LOT of calls, I have seen it as low as 9% on each GPU - at this point the response time is a minute plus.
Why am I writing a bug report here? Sounds like a you problem....because I ran it with Llama-cpp-python - without Langchain and this was not a problem, there was no degradation over time.
### System Info
Wrinkles....
I am using Window 10, inside a VMWare Virtual Machine. Python, Langchain, Llama-cpp-python is all up to date, as are the Nvidia drivers and CUDA toolkit (12.2).
As noted above - this only occurs when using langchain - not when doing the same workflow, without langchain.
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16026 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16026/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16026/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16026/events | https://github.com/langchain-ai/langchain/issues/16026 | 2,081,765,188 | I_kwDOIPDwls58FTdE | 16,026 | DOC: <Please write a comprehensive title after the 'DOC: ' prefix> | {
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] | open | false | null | [] | null | 1 | 2024-01-15T10:46:12 | 2024-01-15T10:48:34 | null | NONE | null | ### Issue with current documentation:
https://python.langchain.com/docs/integrations/text_embedding/together
import is incorrect in documentation
```python
from langchain_together.embeddings import TogetherEmbeddings
embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
```
### Idea or request for content:
update to:
```python
from langchain_together import TogetherEmbeddings <------------ this line changed
embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval")
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16024 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16024/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16024/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16024/events | https://github.com/langchain-ai/langchain/pull/16024 | 2,081,726,847 | PR_kwDOIPDwls5kE0vM | 16,024 | community: Fixing WARNING by providing embedding_function in collection creation | {
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] | open | false | null | [] | null | 3 | 2024-01-15T10:28:08 | 2024-01-16T15:27:39 | null | NONE | null | **Description:**
Resolved the WARNING message that occurred due to the absence of the embedding_function parameter during the creation of a collection. Updated the code to include the embedding_function parameter in the collection creation process.
**Issue:** #15400
**Dependencies:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/16023 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16023/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16023/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16023/events | https://github.com/langchain-ai/langchain/issues/16023 | 2,081,714,878 | I_kwDOIPDwls58FHK- | 16,023 | langchain_community: SQL Database - Don't assume "PostgreSQL" in the else | {
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] | open | false | null | [] | null | 2 | 2024-01-15T10:22:57 | 2024-01-15T19:08:15 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
Following code in https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/utilities/sql_database.py from line 386:
```
with self._engine.begin() as connection: # type: Connection
if self._schema is not None:
if self.dialect == "snowflake":
connection.exec_driver_sql(
"ALTER SESSION SET search_path = %s", (self._schema,)
)
elif self.dialect == "bigquery":
connection.exec_driver_sql("SET @@dataset_id=?", (self._schema,))
elif self.dialect == "mssql":
pass
elif self.dialect == "trino":
connection.exec_driver_sql("USE ?", (self._schema,))
elif self.dialect == "duckdb":
# Unclear which parameterized argument syntax duckdb supports.
# The docs for the duckdb client say they support multiple,
# but `duckdb_engine` seemed to struggle with all of them:
# https://github.com/Mause/duckdb_engine/issues/796
connection.exec_driver_sql(f"SET search_path TO {self._schema}")
elif self.dialect == "oracle":
connection.exec_driver_sql(
f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}"
)
elif self.dialect == "sqlany":
# If anybody using Sybase SQL anywhere database then it should not
# go to else condition. It should be same as mssql.
pass
else: # postgresql and other compatible dialects
connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
```
### Description
Can we change the code of line 412:
```
else: # postgresql and other compatible dialects
connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
```
to
```
elif self.dialect == "postgresql":
connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
else:
pass # Do nothing
```
It is in my opinion simply wrong to assume that any other DB will work like PostgreSQL. And it's not possible to cut that statement in the dialect easily. If we need more `elif`, so be it.
### System Info
Python 3.10 - it's independent of the Python version or environment.
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16022 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16022/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16022/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16022/events | https://github.com/langchain-ai/langchain/pull/16022 | 2,081,652,389 | PR_kwDOIPDwls5kEkVj | 16,022 | langchain: Allow OpenSearch Query Translator to correctly work with Date types | {
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Fixes an issue where the Date type in an OpenSearch Self Querying Retriever would fail to generate a valid query
**Issue:**
https://github.com/langchain-ai/langchain/issues/14225
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https://api.github.com/repos/langchain-ai/langchain/issues/16021 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16021/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16021/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16021/events | https://github.com/langchain-ai/langchain/issues/16021 | 2,081,650,878 | I_kwDOIPDwls58E3i- | 16,021 | SessionExpired: Failed to read from defunct connection IPv4Address(('ae3cd622.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.64.110', 7687))) | {
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] | closed | false | null | [] | null | 1 | 2024-01-15T09:50:22 | 2024-01-17T08:18:48 | 2024-01-17T08:18:48 | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
from langchain.embeddings import HuggingFaceEmbeddings
neo4j_db = Neo4jVector.from_documents(
documents,
HuggingFaceEmbeddings(),
url=os.environ["NEO4J_URI"],
username=os.environ["NEO4J_USERNAME"],
password=os.environ["NEO4J_PASSWORD"],
database="neo4j",
index_name="graphdb",
node_label="huggingface_dataset",
text_node_property="info",
embedding_node_property="vector",
create_id_index=True,
)
```
### Description
```
ERROR:neo4j.io:Failed to read from defunct connection IPv4Address(('ae3cd622.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.64.110', 7687)))
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in _buffer_one_chunk(self)
50 # Determine the chunk size and skip noop
---> 51 receive_into_buffer(self._socket, self._buffer, 2)
52 chunk_size = self._buffer.pop_u16()
16 frames
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in receive_into_buffer(sock, buffer, n_bytes)
327 if n == 0:
--> 328 raise OSError("No data")
329 buffer.used += n
OSError: No data
The above exception was the direct cause of the following exception:
SessionExpired Traceback (most recent call last)
[<ipython-input-43-6c577b606d00>](https://localhost:8080/#) in <cell line: 1>()
----> 1 neo4j_db = Neo4jVector.from_documents(
2 documents,
3 HuggingFaceEmbeddings(),
4 url=os.environ["NEO4J_URI"],
5 username=os.environ["NEO4J_USERNAME"],
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in from_documents(cls, documents, embedding, distance_strategy, ids, **kwargs)
815 metadatas = [d.metadata for d in documents]
816
--> 817 return cls.from_texts(
818 texts=texts,
819 embedding=embedding,
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in from_texts(cls, texts, embedding, metadatas, distance_strategy, ids, **kwargs)
679 embeddings = embedding.embed_documents(list(texts))
680
--> 681 return cls.__from(
682 texts,
683 embeddings,
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in __from(cls, texts, embeddings, embedding, metadatas, ids, create_id_index, search_type, **kwargs)
471 )
472
--> 473 store.add_embeddings(
474 texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
475 )
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in add_embeddings(self, texts, embeddings, metadatas, ids, **kwargs)
520 }
521
--> 522 self.query(import_query, params=parameters)
523
524 return ids
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in query(self, query, params)
272 with self._driver.session(database=self._database) as session:
273 try:
--> 274 data = session.run(query, params)
275 return [r.data() for r in data]
276 except CypherSyntaxError as e:
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/work/session.py](https://localhost:8080/#) in run(self, query, parameters, **kwargs)
311 bookmarks = self._get_bookmarks()
312 parameters = dict(parameters or {}, **kwargs)
--> 313 self._auto_result._run(
314 query, parameters, self._config.database,
315 self._config.impersonated_user, self._config.default_access_mode,
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/work/result.py](https://localhost:8080/#) in _run(self, query, parameters, db, imp_user, access_mode, bookmarks, notifications_min_severity, notifications_disabled_categories)
179 self._pull()
180 self._connection.send_all()
--> 181 self._attach()
182
183 def _pull(self):
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/work/result.py](https://localhost:8080/#) in _attach(self)
296 if self._exhausted is False:
297 while self._attached is False:
--> 298 self._connection.fetch_message()
299
300 def _buffer(self, n=None):
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in inner(*args, **kwargs)
176 def inner(*args, **kwargs):
177 try:
--> 178 func(*args, **kwargs)
179 except (Neo4jError, ServiceUnavailable, SessionExpired) as exc:
180 assert not asyncio.iscoroutinefunction(self.__on_error)
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_bolt.py](https://localhost:8080/#) in fetch_message(self)
844
845 # Receive exactly one message
--> 846 tag, fields = self.inbox.pop(
847 hydration_hooks=self.responses[0].hydration_hooks
848 )
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in pop(self, hydration_hooks)
70
71 def pop(self, hydration_hooks):
---> 72 self._buffer_one_chunk()
73 try:
74 size, tag = self._unpacker.unpack_structure_header()
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in _buffer_one_chunk(self)
66 ) as error:
67 self._broken = True
---> 68 Util.callback(self.on_error, error)
69 raise
70
[/usr/local/lib/python3.10/dist-packages/neo4j/_async_compat/util.py](https://localhost:8080/#) in callback(cb, *args, **kwargs)
116 def callback(cb, *args, **kwargs):
117 if callable(cb):
--> 118 return cb(*args, **kwargs)
119
120 @staticmethod
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_bolt.py](https://localhost:8080/#) in _set_defunct_read(self, error, silent)
870 self.unresolved_address, self.server_info.address
871 )
--> 872 self._set_defunct(message, error=error, silent=silent)
873
874 def _set_defunct_write(self, error=None, silent=False):
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_bolt.py](https://localhost:8080/#) in _set_defunct(self, message, error, silent)
922 else:
923 if error:
--> 924 raise SessionExpired(message) from error
925 else:
926 raise SessionExpired(message)
SessionExpired: Failed to read from defunct connection IPv4Address(('ae3cd622.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.64.110', 7687)))
```
### System Info
```
Windows: 11
pip == 23.3.1
python == 3.10.10
langchain == 0.1.0
transformers == 4.36.2
sentence_transformers == 2.2.2
Neo4j == 5
```
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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**Issue**: I could not find a related issue.
**Dependencies**: this PR does not introduce or affect dependencies. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16019 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16019/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16019/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16019/events | https://github.com/langchain-ai/langchain/pull/16019 | 2,081,467,738 | PR_kwDOIPDwls5kD8dR | 16,019 | langchain, community: Implement GraphDB QA Chain | {
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This change also allows you to optionally provide a list of text splitters to `.add_documents` (being the same length as the list of documents), in case a single text splitter type for all docs that go through the receiver isn't appropriate. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16014 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16014/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16014/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16014/events | https://github.com/langchain-ai/langchain/issues/16014 | 2,081,104,570 | I_kwDOIPDwls58CyK6 | 16,014 | retriever=retriever has a yellow underline, unable to perform the retrieval of vector database function | {
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] | open | false | null | [] | null | 2 | 2024-01-15T01:53:38 | 2024-01-15T02:33:20 | null | NONE | null | ### Issue with current documentation:
import os
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.qdrant import Qdrant
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder, \
HumanMessagePromptTemplate
from qdrant_client import QdrantClient
os.environ['OPENAI_API_KEY'] = "key"
client = QdrantClient(host="192.168.0.312", port=6333)
COLLECTION_NAME = "embed"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Qdrant(
client=client,
collection_name=COLLECTION_NAME,
embeddings=embeddings
)
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"You are robot."
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 100
retriever = vectorstore.as_retriever()
memory = ConversationTokenBufferMemory(
llm=llm,
prompt=prompt,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True,
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
verbose=True,
)
chat_history = []
while True:
memory.load_memory_variables({"chat_history": chat_history})
question = input('提問：')
result = qa.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
if question.lower() == 'bye':
break
### Idea or request for content:
During each round of questioning, OpenAI will retrieve and reference similar conversation histories from a vector database before providing a response | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16013 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16013/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16013/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16013/events | https://github.com/langchain-ai/langchain/pull/16013 | 2,081,086,975 | PR_kwDOIPDwls5kCqRW | 16,013 | community: New documents loader for visio files (with extension .vsdx) | {
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] | closed | false | null | [] | null | 1 | 2024-01-15T01:34:46 | 2024-01-18T01:25:46 | 2024-01-18T01:25:46 | NONE | null | **Description** : New documents loader for visio files (with extension .vsdx)
A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with extension .vsdx) is associated with Microsoft Visio, a diagram creation software. It stores information about the structure, layout, and graphical elements of a diagram. This format facilitates the creation and sharing of visualizations in areas such as business, engineering, and computer science.
A Visio file can contain multiple pages. Some of them may serve as the background for others, and this can occur across multiple layers. This **loader** extracts the textual content from each page and its associated pages, enabling the extraction of all visible text from each page, similar to what an OCR algorithm would do.
**Dependencies** : xmltodict package | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16012 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16012/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16012/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16012/events | https://github.com/langchain-ai/langchain/pull/16012 | 2,080,929,873 | PR_kwDOIPDwls5kCGxL | 16,012 | fix output parsers navigation url | {
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] | closed | false | null | [] | null | 2 | 2024-01-14T22:44:19 | 2024-01-14T22:50:39 | 2024-01-14T22:48:07 | CONTRIBUTOR | null | Output parsers documentation have broken links.
[output parsers](https://python.langchain.com/docs/modules/model_io/output_parsers/)
Corrected the prefix for all links. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16010 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16010/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16010/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16010/events | https://github.com/langchain-ai/langchain/pull/16010 | 2,080,911,207 | PR_kwDOIPDwls5kCDD_ | 16,010 | fix(langchain): force ReAct agent to stop after final answer | {
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] | open | false | null | [] | null | 4 | 2024-01-14T21:51:02 | 2024-01-17T19:06:22 | null | CONTRIBUTOR | null | - **Description:** ReAct agents often fail when used with completion models because they don't stop after providing the final answer. Specifically, the chain continues because the LLM hallucinates newer questions. This PR makes "Question" a default stop sequence for a react agent.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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https://api.github.com/repos/langchain-ai/langchain/issues/16008 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16008/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16008/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16008/events | https://github.com/langchain-ai/langchain/issues/16008 | 2,080,881,498 | I_kwDOIPDwls58B7ta | 16,008 | WeaviateHybridSearchRetriever docs result in error | {
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] | open | false | null | [] | null | 2 | 2024-01-14T20:14:55 | 2024-01-14T20:44:25 | null | NONE | null | ### Issue with current documentation:
I'm basically running the same code as [Weaviate Hybrid Search](https://python.langchain.com/docs/integrations/retrievers/weaviate-hybrid):
```python
import os
import dotenv
import weaviate
from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
from langchain.schema import Document
dotenv.load_dotenv()
# using an existing weaviate database (definitely works with llama-index)
client = weaviate.Client(
url="http://34.170.94.90",
auth_client_secret=weaviate.AuthApiKey(os.environ["WEAVIATE_API_KEY"]),
additional_headers={
"X-Openai-Api-Key": os.getenv("OPENAI_API_KEY"),
},
)
retriever = WeaviateHybridSearchRetriever(
client=client,
index_name="LangChain",
text_key="text",
attributes=[],
create_schema_if_missing=True,
)
```
The error:
```
UnexpectedStatusCodeException Traceback (most recent call last)
Cell 4 line 1
----> [1] retriever = WeaviateHybridSearchRetriever(
[2] client=client,
[3] index_name="LangChain",
[4] text_key="text",
[5] attributes=[],
[6] create_schema_if_missing=True,
[7]
File usr/local/lib/python3.9/site-packages/langchain_core/load/serializable.py:107 /usr/local/lib/python3.9/site-packages/langchain_core/load/serializable.py:107), in Serializable.__init__(self, **kwargs)
106 def __init__(self, **kwargs: Any) -> None:
--> 107 super().__init__(**kwargs)
108 self._lc_kwargs = kwargs
File [usr/local/lib/python3.9/site-packages/pydantic/v1/main.py:339, in BaseModel.__init__(__pydantic_self__, **data)
333 """
334 Create a new model by parsing and validating input data from keyword arguments.
335
336 Raises ValidationError if the input data cannot be parsed to form a valid model.
337 """
338 # Uses something other than `self` the first arg to allow "self" as a settable attribute
--> 339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
...
814 raise RequestsConnectionError("Class may not have been created properly.") from conn_err
815 if response.status_code != 200:
--> 816 raise UnexpectedStatusCodeException("Create class", response)
UnexpectedStatusCodeException: Create class! Unexpected status code: 422, with response body: {'error': [{'message': 'vectorizer: no module with name "text2vec-openai" present'}]}.
```
I'm using:
```
python-dotenv==1.0.0
langchain==0.1.0
openai==1.7.2
langchain-openai==0.0.2.post1
weaviate-client==3.26.0
```
### Idea or request for content:
It appears that the docs are out-of-date, at least for:
```
langchain==0.1.0
openai==1.7.2
weaviate-client==3.26.0
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16007 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16007/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16007/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16007/events | https://github.com/langchain-ai/langchain/issues/16007 | 2,080,868,064 | I_kwDOIPDwls58B4bg | 16,007 | unclear docs for using existing Weaviate database | {
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] | open | false | null | [] | null | 1 | 2024-01-14T19:34:08 | 2024-01-14T19:40:39 | null | NONE | null | ### Issue with current documentation:
The [Weaviate docs](https://python.langchain.com/docs/integrations/vectorstores/weaviate) just show examples of using `Weaviate.from_documents()` or `Weaviate.from_texts()`, but do not describe how to use an existing database with embedded documents already stored in the database.
Moreover, the descriptions of the `index_name` or `text_key` that are required when calling `Weaviate()` are not described anywhere in the [Weaviate docs](https://python.langchain.com/docs/integrations/vectorstores/weaviate), and `index_name` seems to be very misleading, given that it should be the name of the Weaviate *class*.
A naive attempt of:
```python
import os
import dotenv
import weaviate
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_community.vectorstores import Weaviate
from langchain_core.runnables import RunnablePassthrough
dotenv.load_dotenv()
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
client = weaviate.Client(
url="http://34.170.94.90",
auth_client_secret=weaviate.AuthApiKey(os.environ["WEAVIATE_API_KEY"])
)
db = Weaviate(client, 'my_class_name', 'text')
retriever = db.as_retriever()
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| output_parser
)
chain.invoke({"input" : "My particular query"})
```
Results in the error:
```
ValueError: Error during query: [{'locations': [{'column': 46, 'line': 1}], 'message': 'Syntax Error GraphQL request (1:46) Expected Name, found String "input"\n\n1: {Get{my_class_name(limit: 4 nearText: {concepts: [{"input": "My particular query"}]} ){text}}}\n ^\n', 'path': None}]
```
...which isn't too helpful on understanding the issue.
Note: I did actually use the correct class name instead of `my_class_name`.
### Idea or request for content:
It would be very helpful to include an example of using an existing Weaviate database, which included inspecting the DB schema to determine the values to use for `index_name` and `text_key`. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16006 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16006/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16006/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16006/events | https://github.com/langchain-ai/langchain/issues/16006 | 2,080,840,998 | I_kwDOIPDwls58Bx0m | 16,006 | KeyError: 'intermediate_steps' when using Wikipedia tool within the create_react_agent function | {
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] | closed | false | null | [] | null | 3 | 2024-01-14T18:20:13 | 2024-01-16T20:35:08 | 2024-01-16T20:35:08 | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
from langchain import hub
from langchain.agents import create_react_agent
from langchain.tools import Tool
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
from langchain_community.llms.openai import OpenAI
instructions = """You are an agent designed to write and execute python code to answer questions.
You have access to Wikipedia, which you can use to search informations.
Be smart and select the correct wikipedia for a specific search when several are available.
"""
base_prompt = hub.pull("langchain-ai/react-agent-template")
prompt = base_prompt.partial(instructions=instructions)
wiki = WikipediaAPIWrapper()
wiki_tool = Tool(
name="wikipedia",
description="A wikipedia tool that allow to search in it",
func=wiki.run,
)
agent = create_react_agent(OpenAI(name="gpt-3.5-turbo", temperature=0), tools=[wiki_tool], prompt=prompt)
print(agent.invoke({"input":"What is Nostradamus know for"}) )
```
### Description
I was trying to use the Wikipedia tool in a ReAct agent. I thought it will run smoothly but a KeyError intermediate_step trigged
### System Info
langchain 0.1.0
langchain-community 0.0.12
langchain-core 0.1.10
langchain-experimental 0.0.49
langchainhub 0.1.14
langsmith 0.0.80
wikipedia 1.4.0
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16005 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16005/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16005/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16005/events | https://github.com/langchain-ai/langchain/pull/16005 | 2,080,788,096 | PR_kwDOIPDwls5kBqnJ | 16,005 | community: implement adelete from VectorStore in Qdrant | {
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] | closed | false | null | [] | null | 1 | 2024-01-14T16:07:08 | 2024-01-16T03:57:10 | 2024-01-16T03:57:10 | CONTRIBUTOR | null | **Description:**
Implement `adelete` function from `VectorStore` in `Qdrant` to support other asynchronous flows such as async indexing (`aindex`) which requires `adelete` to be implemented. Since `Qdrant` can be passed an async qdrant client, this can be supported easily.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
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- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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https://api.github.com/repos/langchain-ai/langchain/issues/16004 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16004/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16004/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16004/events | https://github.com/langchain-ai/langchain/issues/16004 | 2,080,785,194 | I_kwDOIPDwls58BkMq | 16,004 | Status is Pending Forever while using a websocket connection (py & FastAPI) | {
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] | open | false | null | [] | null | 1 | 2024-01-14T15:59:22 | 2024-01-14T16:04:57 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
using python and FastAPI
``` async def websocket_handler(websocket: WebSocket, chain, ws_name="Websocket"):
await websocket.accept()
try:
json_payload = await websocket.receive_json()
payload = Payload.parse_obj(json_payload)
async with atrace_as_chain_group(ws_name, run_id=uuid.uuid4(),
tags=[f"store_id:{payload.metadata.store_id}"]) as group_manager:
res = await chain.arun(dialog=payload.dialog, page_sku=payload.metadata.sku,
extracted_properties=payload.metadata.extracted_properties,
callbacks=group_manager, websocket=websocket)
await websocket.send_json(res.json())
await websocket.close()
except WebSocketDisconnect:
print(f"WebSocket connection - {ws_name} - Closed")
@app.websocket("/chat")
async def chat(websocket: WebSocket):
await websocket_handler(websocket, router_chain, ws_name="Chat")
@app.post("/quick_suggestions")
async def quick_suggestions(payload: Payload):
print(f"Quick Suggestions: {payload}")
async with atrace_as_chain_group("QuickSuggestions",
run_id=uuid.uuid4(),
tags=[f"store_id:{payload.metadata.store_id}"]) as group_manager:
return await quick_suggestions_chain.arun(dialog=payload.dialog,
page_sku=payload.metadata.sku,
callbacks=group_manager) ```
When calling to chat route that is using websocket connection it stays in a pending status forever no matter what.
When calling QuickSuggestions it behave as expected.
I guess its something related to the websocket connection.
Any ideas?
### Description
when using a websocket connection it stays in a pending status forever no matter what
### System Info
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
langchain = ">=0.0.257"
openai = "*"
pydantic = "==1.10.11"
requests = "*"
python-dotenv = "*"
pandas = "*"
pydantic-sqlalchemy = "*"
sentence-transformers = "*"
google-cloud-logging = "*"
asyncpg = "*"
fastapi = "*"
starlette = "*"
uvicorn = "*"
sqlalchemy = {extras = ["asyncio"], version = "*"}
asyncio = "*"
elasticsearch = {extras = ["async"], version = "*"}
faiss-cpu = "*"
nltk = "*"
async-lru = "*"
posthog = "*"
websockets = "*"
spacy = "*"
[dev-packages]
scikit-learn = "*"
gradio = "*"
[requires]
python_version = "3.10"
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/16003 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16003/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16003/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16003/events | https://github.com/langchain-ai/langchain/pull/16003 | 2,080,774,961 | PR_kwDOIPDwls5kBn_d | 16,003 | langchain: Add konlpy based text splitting for Korean | {
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] | open | false | null | [] | null | 2 | 2024-01-14T15:31:32 | 2024-01-18T03:46:30 | null | CONTRIBUTOR | null | - **Description:** Adds a text splitter based on [Konlpy](https://konlpy.org/en/latest/#start) which is a Python package for natural language processing (NLP) of the Korean language. (It is like Spacy or NLTK for Korean)
- **Dependencies:** Konlpy would have to be installed before this splitter is used,
- **Twitter handle:** @untilhamza
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https://api.github.com/repos/langchain-ai/langchain/issues/16000 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/16000/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/16000/comments | https://api.github.com/repos/langchain-ai/langchain/issues/16000/events | https://github.com/langchain-ai/langchain/issues/16000 | 2,080,696,735 | I_kwDOIPDwls58BOmf | 16,000 | LLM cache includes `azure_ad_token` in caching key which may frequently change across identical requests. | {
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] | open | false | null | [] | null | 1 | 2024-01-14T11:52:00 | 2024-01-14T11:56:13 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
None
### Description
* I am using the `AzureChatOpenAI` integration combined with the `SQLiteCache`.
* I noticed that the cache was working as intended within a single script invocation but not across multiple script invocations.
* I looked at what was being used as the cache key by inspecting what was being passed to the `SQLAlchemyCache.update()` method in the `llm_string` argument. Included is the `azure_ad_token` field used to initialise the `AzureChatOpenAI` class:
```
{"lc": 1, "type": "constructor", "id": ["langchain", "chat_models", "azure_openai", "AzureChatOpenAI"], "kwargs": {"azure_ad_token": "<removed>", "azure_endpoint": "<removed>", "openai_api_version": "2023-09-01-preview", "model": "gpt-4-0613", "temperature": 0.0, "max_tokens": 2000, "openai_api_type": "azure"}}---[('stop', **None)]**
```
* Our workflow involves rotating the `azure_ad_token` for every script invocation which is invalidating any cache matches.
The `azure_ad_token` field does not have any effect on the model output and is thus not needed and should not be included in the caching key.
### System Info
N/A
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | null | 2 | 2024-01-14T11:36:25 | 2024-01-17T19:26:49 | 2024-01-17T19:26:48 | NONE | null | - **Description:** This PR adds a license field to the pyproject.toml files of the langchain partner libraries (langchain-openai etc.).
- **Issue:** #15998
- **Dependencies:** Not applicable | {
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] | open | false | null | [] | null | 1 | 2024-01-14T11:27:54 | 2024-01-14T11:33:25 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
Not applicable
### Description
The pyproject.toml files of the langchain partner libraries (langchain-openai etc.) are missing a license field. This prevents acceptance with some automated package scanning software.
### System Info
Not applicable
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15997 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15997/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15997/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15997/events | https://github.com/langchain-ai/langchain/issues/15997 | 2,080,548,669 | I_kwDOIPDwls58Aqc9 | 15,997 | The `dict` method is deprecated; use `model_dump` instead [pydantic] | {
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] | open | false | null | [] | null | 1 | 2024-01-14T02:06:34 | 2024-01-14T02:12:37 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
The followiwng simple example will log the warning:
```python
from langchain_openai import AzureChatOpenAI
from langchain.schema import HumanMessage
chat_llm = AzureChatOpenAI()
chat_llm.invoke([HumanMessage(content='howdy!')])
```
### Description
I'm seeing a few warning logs saying that langchain-openai is using a deprecated method from pydantic: `dict`
```txt
.venv/Lib/site-packages/langchain_openai/chat_models/azure.py:199: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
response = response.dict()
tests/test_zllm_completion.py::test_prompt
.venv/Lib/site-packages/langchain_openai/llms/base.py:344: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
response = response.dict()
```
Here:
https://github.com/langchain-ai/langchain/blob/7b084b4cc768a8e5cddc29bfdfb6e5dc1003a5d9/libs/partners/openai/langchain_openai/chat_models/azure.py#L199
And here:
https://github.com/langchain-ai/langchain/blob/7b084b4cc768a8e5cddc29bfdfb6e5dc1003a5d9/libs/partners/openai/langchain_openai/llms/base.py#L344
(There might me other cases)
### System Info
langchain==0.1.0
Microsoft Windows 11 Pro
Python 3.11.3
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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This fix addresses that change.
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Support Cloudflare AI Gateway's endpoint `https://gateway.ai.cloudflare.com/v1/ACCOUNT_TAG/GATEWAY`
See https://developers.cloudflare.com/ai-gateway/providers/universal/
### Motivation
CF Workers AI (https://developers.cloudflare.com/workers-ai/get-started/rest-api/) is currently supported via the `CloudflareWorkersAI`,
Cloudflare AI Gateway is a different product with a different API (`CloudflareWorkersAI` with a custom `endpoint_url` won't work), and is currently unsupported.
### Your contribution
Yes I can make a PR. | {
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In this PR, I am adding a `PolygonLastQuote` Tool, which can be used to get the latest price quote for a given ticker / stock.
Additionally, I've added a Polygon Toolkit, which we can use to encapsulate future tools that we build for Polygon.
**Twitter handle:** [@virattt](https://twitter.com/virattt) | {
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Tested manually with `test_cassandra.py` vector store integration test.
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- **Description:** Added parenthesis in return statement of aembed_query() funtion to fix 'coroutine' object is not subscriptable error.
- **Dependencies:** NA
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Add the 'id' attribute to the Document class. Or add a method to get id.
### Motivation
Adding an 'id' attribute makes data manipulation easier.
When using the Weaviate database, I need to manually set additional parameters to query the 'id', and it's not convenient to use.
### Your contribution
At the moment, there isn't one. I'd like to discuss this issue first. | {
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] | open | false | null | [] | null | 2 | 2024-01-13T06:07:52 | 2024-01-13T15:47:42 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
qa_chain = (
RunnablePassthrough.assign(context=( lambda x: format_docs(x["context"])))
| rag_prompt
| local_llm_api()
| StrOutputParser()
)
### Description
I am trying to call my local api endpoint in the qa chain.
The return type of the endpoint is a dict of list.
The error i am getting is :
Expected a Runnable, callable or dict.Instead got an unsupported type: <class 'list'>
### System Info
python version - 3.11
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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Checklist:
- [x] Verify that each event has a good schema
- [x] Verify that each event does not do unintuitive type coercsions (inputs/outputs should not be automatically upgraded to dicts). This needs to be addressed `inputs=inputs if isinstance(inputs, dict) else {"input": inputs}`
- [ ] ~~Event `data` should not be dict if it doesn't need to be, but we don't want to get this wrong in case we need to dump extra info there!~~ `data` will always be a dict, with optional keys of `input` / `output` / `chunk` depending on event type. The reason is that `input` is not always available at event start
- [x] We need to make sure that the root run also generates event information
- [x] Propagate inputs when possible in Runnables
- [x] Tool inputs are encoded as json strings? Need to decode potentially
- [ ] Documentation
Changes downstream:
- `inputs` available in `logs` path of astream_log -- this could lead to breaking changes in langserve if langserve fails to serialize any of the messages. (If there are messages there that are well known but haven't been registered as well known messages).
- the `output` and `final_output` has been made more consistent, one could argue that this is a bug fix, but if it's widely used could cause user's code to break.
Potential Future Changes:
* StreamingEvent add `parent_id` key which has the id of the parent runnable
* [Internal] Update the BaseTracer or create a new version of it that stops doing type
coercions (needs to be backwards compatibility to avoid breaking existing functionality that relies on dictinoaries)
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- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** Konlpy would have to be installed before this splitter is used,
- **Twitter handle:** @untilhamza
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17
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- **Twitter handle:** [@virattt](https://twitter.com/virattt)
The Polygon.io Stocks API provides REST endpoints that let you query the latest market data from all US stock exchanges. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15970 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15970/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15970/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15970/events | https://github.com/langchain-ai/langchain/pull/15970 | 2,079,818,957 | PR_kwDOIPDwls5j-kdT | 15,970 | Fix bug in login functionality | {
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Replace this entire comment with:
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https://api.github.com/repos/langchain-ai/langchain/issues/15969 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15969/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15969/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15969/events | https://github.com/langchain-ai/langchain/issues/15969 | 2,079,773,966 | I_kwDOIPDwls579tUO | 15,969 | Bug in langchain_community/embeddings/huggingface_hub.py HuggingFaceHubEmbeddings.embed_query | {
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- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
from langchain_community.embeddings import HuggingFaceHubEmbeddings
url = "https://svvwc5yh51gt1pp3.us-east-1.aws.endpoints.huggingface.cloud"
text = "You do not need a weatherman to know which way the wind blows"
token = "<my_token>"
embeddings = HuggingFaceHubEmbeddings(
model = url,
huggingfacehub_api_token=token
)
qembed = embeddings.embed_query(text)
### Description
The code above should call my HuggingFace inference endpoint and generate an embedding. Instead, it throws:
Traceback (most recent call last):
File "/Users/jraby/python/hfhub/callhub2.py", line 29, in <module>
qembed = embeddings.embed_query(text)
File "/opt/homebrew/lib/python3.10/site-packages/langchain_community/embeddings/huggingface_hub.py", line 109, in embed_query
response = self.embed_documents([text])[0]
KeyError: 0
The "[0]" at the end of line 109 is incorrect. self.embed_documents returns JSON, -not a list. The first node in the JSON is 'embeddings', and it contains the list of embedding arrays.
I believe that this line:
response = self.embed_documents([text])[0]
Should be replaced with this:
response = self.embed_documents([text])['embeddings'][0]
This resolves the problem for me.
### System Info
Platform = MacOS
python version = 3.10
pip3
huggingface_hub (0.20.2)
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15966 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15966/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15966/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15966/events | https://github.com/langchain-ai/langchain/pull/15966 | 2,079,708,792 | PR_kwDOIPDwls5j-NoN | 15,966 | community: Added new Utility runnables for NVIDIA Riva. | {
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] | null | 2 | 2024-01-12T20:58:28 | 2024-01-17T21:45:35 | null | NONE | null | **Please tag this issue with `nvidia_genai`**
- **Description:** Added new Runnables for integration NVIDIA Riva into LCEL chains for Automatic Speech Recognition (ASR) and Text To Speech (TTS).
- **Issue:** N/A
- **Dependencies:** To use these runnables, the NVIDIA Riva client libraries are required. It they are not installed, an error will be raised instructing how to install them. The Runnables can be safely imported without the riva client libraries.
- **Twitter handle:** N/A
All of the Riva Runnables are inside a single folder in the Utilities module. In this folder are four files:
- common.py - Contains all code that is common to both TTS and ASR
- stream.py - Contains a class representing an audio stream that allows the end user to put data into the stream like a queue.
- asr.py - Contains the RivaASR runnable
- tts.py - Contains the RivaTTS runnable
The following Python function is an example of creating a chain that makes use of both of these Runnables:
```python
def create(
config: Configuration,
audio_encoding: RivaAudioEncoding,
sample_rate: int,
audio_channels: int = 1,
) -> Runnable[ASRInputType, TTSOutputType]:
"""Create a new instance of the chain."""
_LOGGER.info("Instantiating the chain.")
# create the riva asr client
riva_asr = RivaASR(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
encoding=audio_encoding,
audio_channel_count=audio_channels,
sample_rate_hertz=sample_rate,
profanity_filter=config.riva_asr.profanity_filter,
enable_automatic_punctuation=config.riva_asr.enable_automatic_punctuation,
language_code=config.riva_asr.language_code,
)
# create the prompt template
prompt = PromptTemplate.from_template("{user_input}")
# model = ChatOpenAI()
model = ChatNVIDIA(model="mixtral_8x7b") # type: ignore
# create the riva tts client
riva_tts = RivaTTS(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
output_directory=config.riva_tts.output_directory,
language_code=config.riva_tts.language_code,
voice_name=config.riva_tts.voice_name,
)
# construct and return the chain
return {"user_input": riva_asr} | prompt | model | riva_tts # type: ignore
```
The following code is an example of creating a new audio stream for Riva:
```python
input_stream = AudioStream(maxsize=1000)
# Send bytes into the stream
for chunk in audio_chunks:
await input_stream.aput(chunk)
input_stream.close()
```
The following code is an example of how to execute the chain with RivaASR and RivaTTS
```python
output_stream = asyncio.Queue()
while not input_stream.complete:
async for chunk in chain.astream(input_stream):
output_stream.put(chunk)
```
Everything should be async safe and thread safe. Audio data can be put into the input stream while the chain is running without interruptions. | {
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] | open | false | null | [] | null | 1 | 2024-01-12T19:27:42 | 2024-01-12T19:36:22 | null | NONE | null | ### Issue with current documentation:
I wasn't able to follow the [quick start guide](https://python.langchain.com/docs/get_started/quickstart) because of a dependency issue. Everything was going well until I tried to install Faiss.
### Idea or request for content:
My experience trying to follow langchain quick start guide:
# The background
I'm a experienced developer, but I'm new to python and notebooks. I've been studying langchain for a while now, and I'm trying to get started with the quick start guide. I'm using a mac (Apple M2) and I'm trying to follow the guide using the jupyter notebook on VS code.
# The beginning
Starting with the first step, I'm trying to install the dependencies. I'm using the following command:
```
pyenv install 3.12.0 # install python version
pyenv local 3.12.0 # set python version
python -m venv venv # create virtual environment
source ./venv/bin/activate # activate virtual environment
pip3 install -r requirements.txt # install dependencies
```
Where the requirements.txt file contains the following:
```
jupyterlab
langchain==0.1.0
langchain-openai
beautifulsoup4
```
So far so good. Actually, I was so surprised and happy that everything worked so well, that I kind of knew something was going to go wrong. Anyways... I was able to install everything without any problems, and with that run the notebook from beginning to the beginning of "Retrieval Chain".
# The problem
When I try to [install Faiss](https://python.langchain.com/docs/get_started/quickstart#retrieval-chain):
```
pip install faiss-cpu
```
I get the following a [error](https://github.com/facebookresearch/faiss/issues/2868#issuecomment-1889724871) (which apparently is not only me who is getting it). Not only that, but it seems Faiss is not [supposed to be installed using `pip`](https://github.com/facebookresearch/faiss/issues/2991#issuecomment-1667640023).
After trying `conda` and a bunch of other things, none of which worked, I decided to try a different local vector db.
# The kafkaesque journey
So the next step was trying to install any local vector db that could work and hopefully be compatible with the quick start guide.
**Chroma**:
Added `chromadb` to the requirements.txt file and tried to install it. Got the following error:
```bash
...
ERROR: Cannot install -r requirements.txt (line 1), -r requirements.txt (line 3) and -r requirements.txt (line 5) because these package versions have conflicting dependencies.
The conflict is caused by:
chromadb 0.4.22 depends on onnxruntime>=1.14.1
chromadb 0.4.21 depends on onnxruntime>=1.14.1
chromadb 0.4.20 depends on onnxruntime>=1.14.1
chromadb 0.4.19 depends on onnxruntime>=1.14.1
chromadb 0.4.18 depends on onnxruntime>=1.14.1
chromadb 0.4.17 depends on onnxruntime>=1.14.1
chromadb 0.4.16 depends on onnxruntime>=1.14.1
chromadb 0.4.15 depends on onnxruntime>=1.14.1
chromadb 0.4.14 depends on onnxruntime>=1.14.1
chromadb 0.4.13 depends on onnxruntime>=1.14.1
chromadb 0.4.12 depends on onnxruntime>=1.14.1
chromadb 0.4.11 depends on onnxruntime>=1.14.1
chromadb 0.4.10 depends on onnxruntime>=1.14.1
chromadb 0.4.9 depends on onnxruntime>=1.14.1
chromadb 0.4.8 depends on onnxruntime>=1.14.1
chromadb 0.4.7 depends on onnxruntime>=1.14.1
chromadb 0.4.6 depends on onnxruntime>=1.14.1
chromadb 0.4.5 depends on onnxruntime>=1.14.1
chromadb 0.4.4 depends on onnxruntime>=1.14.1
chromadb 0.4.3 depends on onnxruntime>=1.14.1
chromadb 0.4.2 depends on onnxruntime>=1.14.1
chromadb 0.4.1 depends on onnxruntime>=1.14.1
chromadb 0.4.0 depends on onnxruntime>=1.14.1
chromadb 0.3.29 depends on onnxruntime>=1.14.1
chromadb 0.3.27 depends on onnxruntime>=1.14.1
chromadb 0.3.26 depends on onnxruntime>=1.14.1
chromadb 0.3.25 depends on onnxruntime>=1.14.1
langchain 0.1.0 depends on numpy<2 and >=1
langchain-openai 0.0.2 depends on numpy<2 and >=1
chromadb 0.3.2 depends on numpy~=1.21.6
langchain 0.1.0 depends on numpy<2 and >=1
langchain-openai 0.0.2 depends on numpy<2 and >=1
chromadb 0.3.1 depends on numpy~=1.21.6
langchain 0.1.0 depends on numpy<2 and >=1
langchain-openai 0.0.2 depends on numpy<2 and >=1
chromadb 0.3.0 depends on numpy~=1.21.6
langchain 0.1.0 depends on numpy<2 and >=1
langchain-openai 0.0.2 depends on numpy<2 and >=1
chromadb 0.2.0 depends on numpy~=1.21.6
langchain 0.1.0 depends on numpy<2 and >=1
langchain-openai 0.0.2 depends on numpy<2 and >=1
chromadb 0.1.0 depends on numpy~=1.21.6
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
```
As far as I can tell, and after reading a few posts, it seems that there is not much I can do about this. So I tried the next one.
**Milvus**:
Added `pymilvus` to the requirements.txt file and tried to install it. Got the following error:
```
distutils.errors.DistutilsExecError: command '/usr/bin/clang' failed with exit code 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
...
...
File "/private/var/folders/tl/nm6kkwtd3nvdnxbqy2l70c5c0000gn/T/pip-install-e9hhztoq/grpcio_1166b67744e74d958d0c1fd853565676/src/python/grpcio/commands.py", line 293, in new_compile
return old_compile(
^^^^^^^^^^^^
File "/private/var/folders/tl/nm6kkwtd3nvdnxbqy2l70c5c0000gn/T/pip-build-env-3zwys1bz/overlay/lib/python3.12/site-packages/setuptools/_distutils/unixccompiler.py", line 187, in _compile
raise CompileError(msg)
distutils.errors.CompileError: command '/usr/bin/clang' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for grpcio
Failed to build grpcio
ERROR: Could not build wheels for grpcio, which is required to install pyproject.toml-based projects
```
I tried a few stackoverflow solutions, but none of them worked. Blocked again.
# The solution
I haven't found a solution yet, but I would like to share my experience so far. I will keep trying to find a solution, and if I do, I will update this post. However, I would like to enfatize how painful has been trying to start using langchain for someone who is new to python and the hole ecosystem. I know it's a new field and in a way I felt like configuring the video driver on my first linux distro in the 90s. Sharing this experience is not meant to be a rant, but to help the community to improve the experience for new users. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15960 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15960/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15960/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15960/events | https://github.com/langchain-ai/langchain/issues/15960 | 2,079,356,436 | I_kwDOIPDwls578HYU | 15,960 | Runnable error | {
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] | open | false | null | [] | null | 3 | 2024-01-12T17:35:06 | 2024-01-12T18:13:58 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
qa_chain = (
RunnablePassthrough.assign(context=( lambda x: format_docs(x["context"])))
| rag_prompt
| custom_model_api(query)
| StrOutputParser()
)
### Description
I am getting the below error
Expected a Runnable, callable or dict.Instead got an unsupported type: <class 'list'>
### System Info
python version 3.10
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15959 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15959/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15959/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15959/events | https://github.com/langchain-ai/langchain/issues/15959 | 2,079,265,947 | I_kwDOIPDwls577xSb | 15,959 | generate_queries results in list with empty inbetween generated questions. | {
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] | open | false | null | [] | null | 1 | 2024-01-12T16:46:16 | 2024-01-12T16:48:41 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
multiquery_llm_retriever = MultiQueryRetriever.from_llm(
retriever = vectordb.as_retriever(),
llm=llm,
include_original = top_k_params["mq_include_original"]
)
docs = multiquery_llm_retriever.get_relevant_documents(query=user_input)
Response of the underlying generate_queries function:
{'question': 'The actual question', 'text': LineList(lines=['Alternative Question 1: First generated question?', '', 'Alternative Question 2: Second generated question?', '', 'Alternative Question 3: Third generated question?'])}
Notice that in between are empty strings: ''
They later cause an error in similarity search since empty strings cannot be embedded. Consequently, the retrieve_documents function fails.
### Description
I am trying to use the MultiQueryRetriever. However, the generate queries function returns a list that contains empty strings in between the 3 (as in the default prompt) generated answers.
This will later cause the retrieve_documents to fail since the similarity search throws an exception.
### System Info
langchain==0.0.348
langchain-community==0.0.2
langchain-core==0.1.0
Python 3.11.6
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | closed | false | null | [] | null | 1 | 2024-01-12T16:45:40 | 2024-01-12T19:01:28 | 2024-01-12T19:01:28 | CONTRIBUTOR | null | **Description**: the "page" mode in the AzureAIDocumentIntelligenceParser is not accessible due to a wrong membership test. The mode argument can only be a string (also see the assertion in the `__init__`: `assert self.mode in ["single", "page", "object", "markdown"]`, so the check `elif self.mode == ["page"]:` always fails.
As a result, effectively the "object" mode is used when selecting the "page" mode, which may lead to errors.
The docstring of the `AzureAIDocumentIntelligenceLoader` also ommitted the `mode` parameter alltogether, so I added it.
**Issue**: I could not find a related issue (this class is only 3 weeks old anyways)
**Dependencies**: this PR does not introduce or affect dependencies.
The current demo notebook and examples are not affected because they all use the default markdown mode.
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https://api.github.com/repos/langchain-ai/langchain/issues/15954 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15954/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15954/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15954/events | https://github.com/langchain-ai/langchain/issues/15954 | 2,079,064,234 | I_kwDOIPDwls577ACq | 15,954 | Input or output validation errors are not reported to run manager | {
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] | open | false | null | [] | null | 1 | 2024-01-12T15:10:12 | 2024-01-12T15:12:36 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```python
prompt_template = ("Human: Say hello to the user using the given username."
"Username: {username}")
chain = LLMChain(
llm=llm,
prompt=PromptTemplate(template=prompt_template, input_variables=[]),
verbose=True,
)
def execute(args:dict):
with callbacks.collect_runs() as cb:
try:
output = chain.invoke(args)
print(f"output: {output}")
finally:
print(f"traced_runs: {cb.traced_runs}")
# executes successfully
execute({"username": "John"})
# failed with validation error, but traced runs is empty, while it should have one run with exception details
execute({"user_name": "John"})
``
### Description
Callback manager doesn't get invoked if there's an input or output validation error.
For logging purposes the callback manager should be invoked and any chain related error, even if it's a validation error.
The related logic exists in file `langchain/chains/base.py` in `acall` and `__call__` functions, as the `callback_manager` & `run_manager` gets initialized after validating input. To fix this, `run_manager` should be initialized first, then wrap preparing input, output and executing in try/except and update the `run_manager` accordingly
### System Info
langchain==0.0.354
langchain-community==0.0.11
langchain-core==0.1.9
Python 3.10.13
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15952 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15952/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15952/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15952/events | https://github.com/langchain-ai/langchain/pull/15952 | 2,078,981,458 | PR_kwDOIPDwls5j7q95 | 15,952 | Add documentation for AstraDBStore | {
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] | closed | false | null | [] | null | 1 | 2024-01-12T14:35:11 | 2024-01-12T15:01:32 | 2024-01-12T14:48:51 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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] | open | false | null | [] | null | 2 | 2024-01-12T14:18:25 | 2024-01-13T22:51:15 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
I run this in google colab and get error:
```
!pip3 install langchain==0.1.0
!CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip3 install llama-cpp-python==0.2.28
!pip3 install pypdf==3.17.4
!pip3 install gdown==4.7.0
!pip3 install sentence-transformers==2.2.2
!pip3 install chromadb==0.4.18
!wget https://huggingface.co/TheBloke/neural-chat-7B-v3-3-GGUF/resolve/main/neural-chat-7b-v3-3.Q4_K_M.gguf
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import LlamaCpp
n_gpu_layers = 2 # Metal set to 1 is enough.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
# Make sure the model path is correct for your system!
llm = LlamaCpp(
model_path='/content/neural-chat-7b-v3-3.Q4_K_M.gguf',
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
n_ctx=4096,
f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls
callback_manager=callback_manager,
verbose=False,
)
```
### Description
I am trying using LlamaCpp with `from langchain.llms import LlamaCpp`.
I run the previous code and I get this error:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
[<ipython-input-4-a01cc1428fc3>](https://localhost:8080/#) in <cell line: 6>()
4
5 # Make sure the model path is correct for your system!
----> 6 llm = LlamaCpp(
7 model_path='/content/Merak-7B-v4-model-q4_k_m.gguf',
8 n_gpu_layers=n_gpu_layers,
1 frames
/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for LlamaCpp
__root__
Could not load Llama model from path: '/content/neural-chat-7b-v3-3.Q4_K_M.gguf'. Received error fileno (type=value_error)
```
instead of it successfully loaded.
### System Info
```
!apt-get update
!apt-get install neofetch -y
!neofetch
```
This is the result
```
root@182e8520dc9b
-----------------
OS: Ubuntu 22.04.3 LTS x86_64
Host: Google Compute Engine
Kernel: 6.1.58+
Uptime: 14 mins
Packages: 1301 (dpkg)
Shell: bash 5.1.16
Terminal: jupyter-noteboo
CPU: Intel Xeon (2) @ 2.299GHz
GPU: NVIDIA Tesla T4
Memory: 827MiB / 12978MiB
```
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [x] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15948 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15948/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15948/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15948/events | https://github.com/langchain-ai/langchain/issues/15948 | 2,078,924,254 | I_kwDOIPDwls576d3e | 15,948 | OpenAIAssistantRunnable raise exception when pass client | {
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] | open | false | null | [] | null | 1 | 2024-01-12T14:08:14 | 2024-01-12T14:10:37 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
I want to create an OpenAI assistant using an existing ID, but I'm encountering an error.
1. assistant = OpenAIAssistantRunnable(assistant_id='assistant_id', as_agent=True)
`openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable`
2. assistant = OpenAIAssistantRunnable(assistant_id='assistant_id', as_agent=True, client=openai.OpenAI(api_key=self.model.openai_api_key))
`pydantic.v1.errors.ConfigError: field "client" not yet prepared, so the type is still a ForwardRef. You might need to call OpenAIAssistantRunnable.update_forward_refs()`
In both cases, an error is encountered. The only successful scenario is when the openai_api_key is set in the environment, but my requirement is for the user to provide it. Has anyone else faced a similar issue? Any assistance would be appreciated.
### Description
OpenAIAssistantRunnable raise exception when pass client. There should be a way to pass the key from outside
### System Info
langchain==0.1.0
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | open | false | null | [] | null | 1 | 2024-01-12T11:52:06 | 2024-01-12T12:08:27 | null | NONE | null | qa = RetrievalQA.from_chain_type(llm=llm,
chain_type='stuff',
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={'prompt': prompt},
memory=memory,
)
print(qa)
below is my result when I am printing qa , I am implementing RAG
memory=ConversationBufferMemory(output_key='result', input_key='query', return_messages=True, memory_key='chat_history') combine_documents_chain=StuffDocumentsChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['context', 'question'], template="Use the following pieces of information to answer the user's question.\n If you don't know the answer, just say that you don't know, don't try to make up an answer.\n Context: {context}\n Question: {question}\n Only return the helpful answer below and nothing else.\n Helpful answer:\n "), llm=ChatOpenAI(client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, model_name='gpt-3.5-turbo-16k', temperature=0.1, openai_api_key='sk-??', openai_proxy='')), document_variable_name='context') return_source_documents=True retriever=VectorStoreRetriever(tags=['Chroma', 'OpenAIEmbeddings'], vectorstore=<langchain_community.vectorstores.chroma.Chroma object at 0x7fbc6a8bfd60>, search_kwargs={'k': 2}) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15944 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15944/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15944/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15944/events | https://github.com/langchain-ai/langchain/issues/15944 | 2,078,621,814 | I_kwDOIPDwls575UB2 | 15,944 | Making a generalized method to ingest documents in any vector database. | {
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] | open | false | null | [] | null | 1 | 2024-01-12T11:22:20 | 2024-01-12T11:31:07 | null | NONE | null | ### Discussed in https://github.com/langchain-ai/langchain/discussions/15877
<div type='discussions-op-text'>
<sup>Originally posted by **arthurbrenno** January 11, 2024</sup>
I'm trying to make a **generalized method** that **works with any vector database**, so I can **change the implementation** later. The main focus is simply ingesting documents inside a vectorstore.
My main issue always has been **_data persistency_** and the _flexibility_ to change the vector database implementation, because I like to explore different alternatives and there are many of them. That's why I'm (trying) to make a method like this.
Here's a snippet of my implementation:
```py
from langchain_community.document_loaders.base import BaseLoader
from langchain_core.documents.base import Document
from langchain_core.embeddings import Embeddings
from langchain.vectorstores import VectorStore
from conversai.core.services import BaseService
from langchain.text_splitter import TextSplitter
from typing import List, Type, Dict
from typing import Any
class IngestionService(BaseService):
"""Class that handles the ingestion of documents inside a vector store."""
async def execute(url: str,
collection: str,
loader_class: Type[BaseLoader],
splitter: TextSplitter,
embeddings: Embeddings,
vector_store_cls: Type[VectorStore],
**vector_store_kwargs) -> Dict[str, Any]:
"""Ingests a document inside the desired vector store.
Args:
url (str): Url of the document to ingest.
collection (str): Collection of the vector store to ingest the document.
loader (BaseLoader): The loader of the document.
splitter (TextSplitter): The splitter of the document.
embeddings (Embeddings): The embedding model to apply on the splitted documents.
vector_store (VectorStore): The vector store to store the document.
Returns:
bool: True on success, False on failure.
"""
# Instantiating the loader
loader: BaseLoader = loader_class(url)
# Loading and splitting the document
documents: List[Document] = loader.load_and_split(text_splitter=splitter)
# Instantiating the vector store (assuming every implementation should support collection_name and embedding_func.)
vector_store: VectorStore = vector_store_cls(collection_name=collection,
embedding_func=embeddings,
**vector_store_kwargs)
# ids of the created texts
texts_ids: List[str] = await vector_store.aadd_documents(documents)
return {"success": True, "ingested_texts_ids": texts_ids}
```
The problem is that every vector database seems to have a different way of persisting itself (as I understood). For an example:
Chroma:
```py
db = Chroma(persist_directory=...)
```
(or using their separate client whitin Docker
```py
db = Chroma(client=client)
```
FAISS:
```py
# FAISS has
db.save_local("faiss_index")
```
I need to ingest documents **at runtime.** This is important. I also need to group them inside `collections` for better searching later and group similar documents. Creating collections is one of my future implementations.
My current vector store implementation is `Milvus`. And I think I'm generalising too much when I assume every vector db will have a "collection_name" and "embedding_func", like written in:
```py
vector_store: VectorStore = vector_store_cls(collection_name=collection,
embedding_func=embeddings,
**vector_store_kwargs)
```
Any support on making my method better is appreciated. I'm also reading the documentation and trying to solve this for myself, but I think community help would also be a great startpoint. One of my suggestions (I've seen some people talking about this) is making the documentation more clear about data persistency. The examples only show short-term usage of the vectorstores, which is not the case in many production enviroments</div> | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15943 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15943/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15943/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15943/events | https://github.com/langchain-ai/langchain/pull/15943 | 2,078,607,233 | PR_kwDOIPDwls5j6YF7 | 15,943 | docs: Update Robocorp Action Server installation instructions | {
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Remove section on how to install Action Server and direct the users t o the instructions on Robocorp repository.
**Reason:**
Robocorp Action Server has moved from a pip installation to a standalone cli application and is due for changes. Because of that, leaving only LangChain integration relevant part in the documentation. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15941 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15941/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15941/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15941/events | https://github.com/langchain-ai/langchain/pull/15941 | 2,078,445,750 | PR_kwDOIPDwls5j504c | 15,941 | Community: Adding asynchronous function implementation for Doctran | {
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In this update, I addressed the missing implementation for atransform_document, which is the asynchronous counterpart of transform_document in Doctran.
### Usage Example:
```py
# Instantiate DoctranPropertyExtractor with specified properties
property_extractor = DoctranPropertyExtractor(properties=properties)
# Asynchronously extract properties from a list of documents
extracted_document = await property_extractor.atransform_documents(
documents, properties=properties
)
# Display metadata of the first extracted document
print(json.dumps(extracted_document[0].metadata, indent=2))
```
## Issue
- Pull request #14525 has caused a break in the aforementioned code. Instead of removing an asynchronous implementation of a function, consider implementing a synchronous version alongside it. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15940 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15940/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15940/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15940/events | https://github.com/langchain-ai/langchain/pull/15940 | 2,078,425,379 | PR_kwDOIPDwls5j5wfV | 15,940 | docs: update baidu_qianfan_endpoint.ipynb doc | {
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- **Issue:** #15664
- **Dependencies:**N/A
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https://api.github.com/repos/langchain-ai/langchain/issues/15939 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15939/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15939/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15939/events | https://github.com/langchain-ai/langchain/pull/15939 | 2,078,416,070 | PR_kwDOIPDwls5j5uhw | 15,939 | community: jaguar vector store fix integer-element error when joining metadata values | {
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] | closed | false | null | [] | null | 1 | 2024-01-12T09:22:55 | 2024-01-15T19:13:46 | 2024-01-15T19:13:46 | CONTRIBUTOR | null |
- **Description:** some document loaders add integer-type metadata values which cause error
- **Issue:** 15937
- **Dependencies:** none | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15938 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15938/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15938/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15938/events | https://github.com/langchain-ai/langchain/issues/15938 | 2,078,415,718 | I_kwDOIPDwls574htm | 15,938 | getting error while implementing memory | {
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] | open | false | null | [] | null | 2 | 2024-01-12T09:22:41 | 2024-01-16T21:29:58 | null | NONE | null | <code>
def set_custom_prompt():
prompt_template = """Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Context: {context}
Question: {question}
Only return the helpful answer below and nothing else.
Helpful answer:
"""
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
return prompt
def retreival_qa_chain(chroma_db_path):
prompt = set_custom_prompt()
embedding = OpenAIEmbeddings()
vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding)
llm = ChatOpenAI(model="gpt-3.5-turbo-16k",temperature=0.1)
retriever = vectordb.as_retriever(search_kwargs={"k": 2})
memory = ConversationTokenBufferMemory(llm=llm, memory_key="chat_history", return_messages=True, input_key='question', output_key='answer')
qa = RetrievalQA.from_chain_type(llm=llm,
chain_type='stuff',
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={'prompt': prompt},
memory=memory,
)
# qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever,return_source_documents=True)
return qa
def create_global_qa_chain():
chroma_db_path = "chroma-databases"
folders = os.listdir(chroma_db_path)
qa_chains = {}
for index, folder in enumerate(folders):
folder_path = f"{chroma_db_path}/{folder}"
project = retreival_qa_chain(folder_path)
qa_chains[folder] = project
return qa_chains
def chat_langchain(new_project_qa, query):
bot_response = new_project_qa({"query": query})
result = bot_response['result']
return result
qa_chains = create_global_qa_chain()
</code>
I am getting below error
self.memory.save_context(inputs, outputs)
File "/home/hs/env/lib/python3.8/site-packages/langchain/memory/token_buffer.py", line 51, in save_context
super().save_context(inputs, outputs)
File "/home/hs/env/lib/python3.8/site-packages/langchain/memory/chat_memory.py", line 37, in save_context
input_str, output_str = self._get_input_output(inputs, outputs)
File "/home/hs/env/lib/python3.8/site-packages/langchain/memory/chat_memory.py", line 33, in _get_input_output
return inputs[prompt_input_key], outputs[output_key]
KeyError: 'question' | {
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] | open | false | null | [] | null | 1 | 2024-01-12T09:01:32 | 2024-01-12T09:03:58 | null | CONTRIBUTOR | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
In langchain_community/vectorstores/jaguar.py line 196:
values_comma = "'" + "','".join(vvec) + "'"
If any element in vvec is not string, runtime error is thrown:
TypeError: sequence item 0: expected str instance, int found
The vvec is a list of values in metadata dictionary.
### Description
* Some document loaders, for instance, PyPDFDirectoryLoader, add an integer page number to the metadata. In such cases, the list of values contain integer elements. The statement "','".join(vvec) will report an error.
### System Info
langchain==0.1.0
langchain-community==0.0.11
langchain-core==0.1.10
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | open | false | null | [] | null | 1 | 2024-01-12T08:45:09 | 2024-01-16T01:59:41 | null | CONTRIBUTOR | null | - **Description:** Fix an issue where the image path (uris) were inserted instead of the b64 texts when image metadata is present. In response to https://github.com/langchain-ai/langchain/issues/15935
- **Issue:** Returns b64 text instead of image uris when doing a similarity search with multimodal embeddings
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https://api.github.com/repos/langchain-ai/langchain/issues/15935 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15935/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15935/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15935/events | https://github.com/langchain-ai/langchain/issues/15935 | 2,078,326,124 | I_kwDOIPDwls574L1s | 15,935 | b64 strings not inserted for Chroma 'add_images' method when metadata is present | {
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] | open | false | null | [] | null | 1 | 2024-01-12T08:29:07 | 2024-01-12T08:31:31 | null | CONTRIBUTOR | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
``` if non_empty_ids:
metadatas = [metadatas[idx] for idx in non_empty_ids]
images_with_metadatas = [uris[idx] for idx in non_empty_ids]
embeddings_with_metadatas = (
[embeddings[idx] for idx in non_empty_ids] if embeddings else None
)
ids_with_metadata = [ids[idx] for idx in non_empty_ids]
try:
self._collection.upsert(
metadatas=metadatas,
embeddings=embeddings_with_metadatas,
documents=images_with_metadatas,
ids=ids_with_metadata,
```
should be
``` if non_empty_ids:
metadatas = [metadatas[idx] for idx in non_empty_ids]
images_with_metadatas = [b64_texts[idx] for idx in non_empty_ids]
embeddings_with_metadatas = (
[embeddings[idx] for idx in non_empty_ids] if embeddings else None
)
ids_with_metadata = [ids[idx] for idx in non_empty_ids]
try:
self._collection.upsert(
metadatas=metadatas,
embeddings=embeddings_with_metadatas,
documents=images_with_metadatas,
ids=ids_with_metadata,
```
### Description
When inserting images with metadata (Chroma), the 'add_images' function does not insert the b64 strings.
### System Info
Python = 3.10
Langchain = 0.1.0
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15934 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15934/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15934/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15934/events | https://github.com/langchain-ai/langchain/issues/15934 | 2,078,174,147 | I_kwDOIPDwls573mvD | 15,934 | adding special instruction to llm model. | {
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] | open | false | null | [] | null | 3 | 2024-01-12T06:43:17 | 2024-01-12T13:53:50 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
here is my code:
import sqlalchemy as sal
import os, sys, openai
import pandas as pd
import constants
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
from langchain.chat_models import ChatOpenAI
from typing import List, Optional
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain.sql_database import SQLDatabase
from langchain.text_splitter import TokenTextSplitter
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool
from secret_key import openapi_key
from langchain.prompts import PromptTemplate
os.environ['OPENAI_API_KEY'] = openapi_key
from urllib.parse import quote_plus
server_name = constants.server_name
database_name = constants.database_name
username = constants.username
password = constants.password
encoded_password = quote_plus(password)
connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server"
engine = create_engine(connection_uri)
def chat(question, sql_format):
greetings = ["hi", "hello", "hey"]
if question.lower() in greetings:
return "Hello! How can I assist you today?"
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
answer = None
if sql_format==False:
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
answer = db_chain.run(question)
else:
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True , return_sql =True)
sql_query = db_chain.run(question)
print("SQLQuery: "+str(sql_query))
# result = engine.execute(sql_query)
result_df = pd.read_sql(sql_query, engine)
if result_df.empty:
return "No results found"
answer = result_df.to_dict()
PROMPT = """
Given an input question, first create a syntactically correct mssql query to run,
then look at the results of the query and return the answer.Return the answer in a sentence form alsways.
The question: {db_chain.run}
"""
prompt_template = """
Use the following pieces of context to answer the question at the end. If you don't know the answer,
just say that you don't know, don't try to make up an answer. Return the answer in a sentence form alsways.
{context}
Question: {question}
Helpful Answer:"""
def handle_greetings(context, question):
greetings = ["hi", "hello", "hey"]
if any(greeting in question.lower() for greeting in greetings):
return "Hello! How can I assist you today?"
else:
return None
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"],
preprocessor=handle_greetings
)
def split_text(text, chunk_size, chunk_overlap=0):
text_splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
yield from text_splitter.split_text(text)
class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
result = self.db.run_no_throw(query)
return next(split_text(result, chunk_size=14_000))
class SQLDatabaseToolkit2(SQLDatabaseToolkit):
def get_tools(self) -> List[BaseTool]:
tools = super().get_tools()
original_query_tool_description = tools[0].description
new_query_tool = QuerySQLDatabaseTool2(
db=self.db, description=original_query_tool_description
)
tools[0] = new_query_tool
return tools
# return db_chain.run(question)
return answer
def chain1(question):
text = chat(question,False)
return text
def chain2(question):
query = chat(question,True)
return query
# answer=chat("what is Ganesh D desigation")
# print(answer)
# Define a function named chain1 that uses the chat function without SQL format
def chain1(question):
text = chat(question, False)
return text
# Define a function named chain2 that uses the chat function with SQL format
def chain2(question):
query = chat(question, True)
return query
chain1("give me aadhaar number of all Jayachitra R") #, give answer in sentence form")
print(chain1)
### Description
how to added instruction in the llm model to personalize the output request by modify the prompt here or add other way to tell the llm model to give the answer in sentence form or any other instructions.
here is my code:
import sqlalchemy as sal
import os, sys, openai
import pandas as pd
import constants
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
from langchain.chat_models import ChatOpenAI
from typing import List, Optional
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain.sql_database import SQLDatabase
from langchain.text_splitter import TokenTextSplitter
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool
from secret_key import openapi_key
from langchain.prompts import PromptTemplate
os.environ['OPENAI_API_KEY'] = openapi_key
from urllib.parse import quote_plus
server_name = constants.server_name
database_name = constants.database_name
username = constants.username
password = constants.password
encoded_password = quote_plus(password)
connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server"
engine = create_engine(connection_uri)
def chat(question, sql_format):
greetings = ["hi", "hello", "hey"]
if question.lower() in greetings:
return "Hello! How can I assist you today?"
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
answer = None
if sql_format==False:
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
answer = db_chain.run(question)
else:
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True , return_sql =True)
sql_query = db_chain.run(question)
print("SQLQuery: "+str(sql_query))
# result = engine.execute(sql_query)
result_df = pd.read_sql(sql_query, engine)
if result_df.empty:
return "No results found"
answer = result_df.to_dict()
PROMPT = """
Given an input question, first create a syntactically correct mssql query to run,
then look at the results of the query and return the answer.Return the answer in a sentence form alsways.
The question: {db_chain.run}
"""
prompt_template = """
Use the following pieces of context to answer the question at the end. If you don't know the answer,
just say that you don't know, don't try to make up an answer. Return the answer in a sentence form alsways.
{context}
Question: {question}
Helpful Answer:"""
def handle_greetings(context, question):
greetings = ["hi", "hello", "hey"]
if any(greeting in question.lower() for greeting in greetings):
return "Hello! How can I assist you today?"
else:
return None
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"],
preprocessor=handle_greetings
)
def split_text(text, chunk_size, chunk_overlap=0):
text_splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
yield from text_splitter.split_text(text)
class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
result = self.db.run_no_throw(query)
return next(split_text(result, chunk_size=14_000))
class SQLDatabaseToolkit2(SQLDatabaseToolkit):
def get_tools(self) -> List[BaseTool]:
tools = super().get_tools()
original_query_tool_description = tools[0].description
new_query_tool = QuerySQLDatabaseTool2(
db=self.db, description=original_query_tool_description
)
tools[0] = new_query_tool
return tools
# return db_chain.run(question)
return answer
def chain1(question):
text = chat(question,False)
return text
def chain2(question):
query = chat(question,True)
return query
# answer=chat("what is Ganesh D desigation")
# print(answer)
# Define a function named chain1 that uses the chat function without SQL format
def chain1(question):
text = chat(question, False)
return text
# Define a function named chain2 that uses the chat function with SQL format
def chain2(question):
query = chat(question, True)
return query
chain1("give me aadhaar number of all Jayachitra R") #, give answer in sentence form")
print(chain1)
### System Info
python = 3.11
langchain= latest
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15933 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15933/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15933/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15933/events | https://github.com/langchain-ai/langchain/pull/15933 | 2,078,171,152 | PR_kwDOIPDwls5j45Zq | 15,933 | langchain_google_vertexai: Add support for SystemMessage for Gemini chat model | {
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] | null | 7 | 2024-01-12T06:41:09 | 2024-01-17T22:41:26 | null | NONE | null | - **Description:** In Google Vertex AI, Gemini Chat models currently doesn't have a support for SystemMessage. This PR adds support for it only if a user provides additional convert_system_message_to_human flag during model initialization (in this case, SystemMessage would be prepended to the first HumanMessage). **NOTE:** The implementation is similar to #14824
- **Twitter handle:** rajesh_thallam | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15932 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15932/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15932/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15932/events | https://github.com/langchain-ai/langchain/pull/15932 | 2,078,094,135 | PR_kwDOIPDwls5j4o_G | 15,932 | Community: Basic Logging and Human input to ShellTool | {
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] | closed | false | null | [] | null | 5 | 2024-01-12T05:22:28 | 2024-01-17T20:57:52 | 2024-01-17T20:57:51 | CONTRIBUTOR | null | - **Description:** As Shell tool is very versatile, while integrating it into applications as openai functions, developers have no clue about what command is being executed using the ShellTool. All one can see is:
Summarising my feature request:
1. There's no visibility about what command was executed.
2. There's no mechanism to prevent a command to be executed using ShellTool, like a y/n human input which can be accepted from user to proceed with executing the command.,
- **Issue:** the issue #15931 it fixes if applicable,
- **Dependencies:** There isn't any dependancy,
- **Twitter handle:** @krishnashed
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] | open | false | null | [] | null | 4 | 2024-01-12T05:19:27 | 2024-01-12T11:39:53 | null | CONTRIBUTOR | null | ### Feature request
The tool I'm talking about is:
from langchain.tools import ShellTool
As Shell tool is very versatile, while integrating it into applications as openai functions, developers have no clue about what command is being executed using the ShellTool. All one can see is:
Which shouldn't be the case right ? Developers need to know what command is going to be executed by the ShellTool, and whether its the right command to execute, or you just prompt better for the LLM to generate a new command and let that execute.
Summarising my feature request:
1. There's no visibility about what command was executed.
2. There's no mechanism to prevent a command to be executed using ShellTool, like a y/n human input which can be accepted from user to proceed with executing the command.
I would like to implement this in a PR if the project maintainer feels this feature adds value.
### Motivation
I was using ShellTool as a OpenAI Function in Autogen, which is a multi-agent conversation framework. Where in one of my AI Agent is a programmer, and it has access to the ShellTool. When its executing any command using ShellTool, we have no clue about what command is being executed, and are thrown to mercy of the LLM that the generated command is what we need.
### Your contribution
I have raised a [PR](https://github.com/langchain-ai/langchain/pull/15932), would like you to review and provide feedback on it. Or if there's some other way around to implement the feature I'm proposing, please let me know. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15929 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15929/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15929/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15929/events | https://github.com/langchain-ai/langchain/issues/15929 | 2,077,988,964 | I_kwDOIPDwls5725hk | 15,929 | i am working on SQL Langchain to ask natural english questions and get answers from postgress sql but not getting any answers | {
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<div type='discussions-op-text'>
<sup>Originally posted by **BalajiPolisetty2207** January 11, 2024</sup>
i am using this langchain to query my postgres sql with natural language.
with sql server i am successful in asking and getting answers .
I am using azure open ai key and endpoint and gpt 4 model and deployment .
Can some one help what is missing here to query postgressql and how i can set the schema before querying.
Below is the code
# Importing necessary libraries
from sqlalchemy import create_engine, text
from langchain.chat_models import AzureChatOpenAI
from langchain.prompts.chat import ChatPromptTemplate
from langchain.agents import AgentType, create_sql_agent
from langchain.sql_database import SQLDatabase
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
import os
db_url = 'postgresql://asdas:[email protected]:5432/asdad' # PostgreSQL connection
openai_api_key = 'asdsadad' # Azure OpenAI key
openai_model = 'eqwewqe-4' # OpenAI model, like GPT-4
openai_deployment = 'eqwewqe' # OpenAI deployment name
os.environ["OPENAI_API_KEY"] = 'xxxx'
os.environ["OPENAI_API_BASE"]="https://asdasd.qweqw.azure.com/"
os.environ["OPENAI_API_TYPE"]="azure"
os.environ["OPENAI_API_VERSION"]="2023-07-01-preview"
# Creating the database engine
engine = create_engine(db_url)
# Setting up the Azure OpenAI for LangChain
llm = AzureChatOpenAI(
model=openai_model,
deployment_name=openai_deployment,
temperature=0
)
# Setting up the prompt template
final_prompt = ChatPromptTemplate.from_messages(
[
("system",
"""
You are an AI assistant expert in querying the PostgreSQL database.
you are going to respond the question on the students schema.
"""
),
("user", "{question}\n ai: "),
]
)
# Creating the SQL Database object for LangChain
db = SQLDatabase(engine)
# Setting up the SQL Toolkit for LangChain
sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm)
sql_toolkit.get_tools()
# Creating the SQL Agent for LangChain
sqldb_agent = create_sql_agent(
llm=llm,
toolkit=sql_toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
)
# Example usage of the agent to answer a question
question = "get me list of tables in the students schema ?"
final_prompt.messages[-1] = ("user", f"{question}\n ai: ")
response = sqldb_agent.run(final_prompt)
print(response)
</div> | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15926 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15926/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15926/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15926/events | https://github.com/langchain-ai/langchain/pull/15926 | 2,077,928,156 | PR_kwDOIPDwls5j4Fq9 | 15,926 | stop making copies of inputs | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15924 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15924/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15924/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15924/events | https://github.com/langchain-ai/langchain/issues/15924 | 2,077,910,530 | I_kwDOIPDwls572mYC | 15,924 | Please modify ChatOllama to allow the option to specify main_gpu | {
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] | closed | false | null | [] | null | 2 | 2024-01-12T01:38:21 | 2024-01-12T02:59:13 | 2024-01-12T02:59:13 | NONE | null | ### Feature request
When there are multiple GPUs available, the Ollama API provides the main_gpu option to specify which GPU to use as the main one. Please modify Langchain's ChatOllama to also include this feature.
### Motivation
When running multiple tasks simultaneously on a server, it is necessary to designate specific processes for Ollama.
### Your contribution
None | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15922 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15922/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15922/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15922/events | https://github.com/langchain-ai/langchain/pull/15922 | 2,077,873,518 | PR_kwDOIPDwls5j35ey | 15,922 | <langchain_community.vectorstores>:<Fix pinecone.py __init__ docsrting instruction> | {
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] | closed | false | null | [] | null | 1 | 2024-01-12T01:14:17 | 2024-01-12T05:26:33 | 2024-01-12T05:26:33 | CONTRIBUTOR | null | - **Description:** The pinecone docstring instructs to pass the embedding query text causing the warning below. It should be the embeddings object.
warning message: UserWarning: Passing in `embedding` as a Callable is deprecated. Please pass in an Embeddings object instead.
- **Issue:** NA
- **Dependencies:** None
@baskaryan
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https://api.github.com/repos/langchain-ai/langchain/issues/15921 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15921/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15921/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15921/events | https://github.com/langchain-ai/langchain/issues/15921 | 2,077,798,404 | I_kwDOIPDwls572LAE | 15,921 | DOC: stop params does not work with langchain_community.llms import VLLM but work in LLMChain | {
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] | open | false | null | [] | null | 1 | 2024-01-12T00:02:57 | 2024-01-12T00:08:57 | null | NONE | null | ### Issue with current documentation:
With langchain version == 0.1.0
This stop param of the Class VLLM does not work.
For instance, this code has no effect regarding stop word.
```
model = VLLM(
stop=["stop_word"],
model=model_name,
trust_remote_code=True, # mandatory for hf models
max_new_tokens=512,
top_k=10,
top_p=0.95,
temperature=0.2,
vllm_kwargs=vllm_kwargs,
)
```
But this code works:
```
prompt = PromptTemplate(
template=template, input_variables=["system_message", "question"]
)
llm_chain = LLMChain(prompt=prompt, llm=model)
llm_chain.run(
{"system_message": system_message, "question": question, "stop":["stop_word"]}
)
```
### Idea or request for content:
Clear the documentation or it is a bug. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15920 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15920/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15920/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15920/events | https://github.com/langchain-ai/langchain/pull/15920 | 2,077,763,912 | PR_kwDOIPDwls5j3h7y | 15,920 | Rm deprecated | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15919 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15919/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15919/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15919/events | https://github.com/langchain-ai/langchain/issues/15919 | 2,077,750,276 | I_kwDOIPDwls571_QE | 15,919 | Issues with SelfQueryRetriever and the "AND" operator failing in queries that search for multiple metadata flags | {
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] | open | false | null | [] | null | 7 | 2024-01-11T23:03:50 | 2024-01-15T11:19:56 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```python
metadata_field_info = [
AttributeInfo(name="source",description="The document this chunk is from.",type="string"),
AttributeInfo(name="origin",description="The origin the document came from. Comes from either scraped websites like TheKinection.org, Kinecta.org or database files like Bancworks. Bancworks is the higher priority.",type="string"),
AttributeInfo(name="date_day",description="The day the document was uploaded.",type="string"),
AttributeInfo(name="date_uploaded",description="The month year the document is current to.",type="string"),
AttributeInfo(name="date_month",description="The month the document was uploaded.",type="string"),
AttributeInfo(name="date_month_name",description="The month name the document was uploaded.",type="string"),
AttributeInfo(name="date_year_long",description="The full year the document was uploaded.",type="string"),
AttributeInfo(name="date_year_short",description="The short year the document was uploaded.",type="string"),
]
llm = ChatOpenAI(temperature=0)
vectorstore = Pinecone.from_existing_index(index_name="test", embedding=get_embedding_function())
# print("Load existing vector store")\
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
"Information about when the document was created and where it was grabbed from.",
metadata_field_info,
)
```
```python
question = "Give the minimum opening deposits for each accounts for the rate sheets in January"
retriever.get_relevant_documents(question)
```
### Description
When I ask to fetch relevant documents with the following query:
- "Give the minimum opening deposits for each accounts for the rate sheets in January"
There is no problem. However, if I make this query a little more robust...
- "Give the minimum opening deposits for each accounts for the rate sheets in January 2023"
I get a CNAME "and" error. This happens in both Pinecone and ChromaDB. Something is wrong with how the query translator is operating or I am missing some crucial step. We should be able to use multiple metadata flags at once.
### System Info
Python 3.11
Langchain 0.1.0
Chroma 0.4.22
Pinecone 2.2.4
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | closed | false | null | [] | null | 1 | 2024-01-11T22:27:25 | 2024-01-12T00:22:18 | 2024-01-12T00:22:17 | COLLABORATOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
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Fixes OutputParserException thrown by the output_parser when 'query' is 'Null'.
Replace this entire comment with:
- **Description:** Current implentation of output_parser throws OutputParserException if the response from the LLM contains `query: null`. This unfortunately happens for my use case. And since there is no way to modify the prompt used in SelfQueryRetriever, then we have to fix it here, so it doesn't crash.
- **Issue:** https://github.com/langchain-ai/langchain/issues/15914
Didn't run tests. `make test` is not working. There is no `test` rule in the `Makefile`. | {
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Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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] | open | false | null | [] | null | 1 | 2024-01-11T21:54:11 | 2024-01-11T22:03:08 | null | CONTRIBUTOR | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
See description.
### Description
I am using SelfQueryRetriever. For a response JSON that contains null query, for example:
```
json
{
"query": null,
"filter": ...
}
```
The output parser throws OutputParserException at [line 51](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/query_constructor/base.py#L51).
OutputParserException('Parsing text\n```json\n{\n "query": null,\n "filter": "eq(\\"kategorie\\", \\"Pravo\\")"\n}\n```\n raised following error:\nobject of type \'NoneType\' has no len()')Traceback (most recent call last):
File "/home/MetaExponential/.local/lib/python3.10/site-packages/langchain/chains/query_constructor/base.py", line 51, in parse
if len(parsed["query"]) == 0:
### System Info
absl-py==2.0.0
ai21==1.2.8
aioboto3==12.0.0
aiobotocore==2.7.0
aiohttp==3.8.4
aioitertools==0.11.0
aiosignal==1.3.1
altgraph @ file:///AppleInternal/Library/BuildRoots/9dd5efe2-7fad-11ee-b588-aa530c46a9ea/Library/Caches/com.apple.xbs/Sources/python3/altgraph-0.17.2-py2.py3-none-any.whl
annotated-types==0.6.0
annoy==1.17.3
antlr4-python3-runtime==4.9.3
anyio==3.7.1
argilla==1.7.0
astunparse==1.6.3
async-generator==1.10
async-timeout==4.0.2
attrs==23.1.0
Babel==2.12.1
backoff==2.2.1
bcrypt==4.0.1
beautifulsoup4==4.12.2
blinker==1.6.2
boto3==1.28.64
botocore==1.31.64
build==0.10.0
CacheControl==0.12.11
cachetools==5.3.1
camel-converter==3.1.0
certifi==2022.12.7
cffi==1.15.1
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chardet==5.2.0
charset-normalizer==3.1.0
Chroma==0.2.0
chroma-hnswlib==0.7.3
chromadb==0.4.13
cleo==2.0.1
click==8.1.7
clickhouse-connect==0.6.18
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cohere==4.31
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commonmark==0.9.1
contourpy==1.0.7
crashtest==0.4.1
cryptography==40.0.2
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cycler==0.11.0
dataclasses-json==0.5.7
datasets==2.12.0
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Deprecated==1.2.13
deprecation==2.1.0
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distro==1.8.0
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docutils==0.19
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dulwich==0.21.5
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et-xmlfile==1.1.0
exceptiongroup==1.1.1
facebook-sdk==3.1.0
facebooktoken==0.0.1
faiss-cpu==1.7.4
fastapi==0.103.2
fastavro==1.8.2
feedfinder2==0.0.4
feedparser==6.0.11
filelock==3.12.0
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Flask-Limiter==3.4.1
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flatbuffers==23.5.26
fonttools==4.39.4
frozenlist==1.3.3
fsspec==2023.6.0
future @ file:///AppleInternal/Library/BuildRoots/9dd5efe2-7fad-11ee-b588-aa530c46a9ea/Library/Caches/com.apple.xbs/Sources/python3/future-0.18.2-py3-none-any.whl
fuzzywuzzy==0.18.0
gast==0.5.4
google-ai-generativelanguage==0.4.0
google-api-core==2.12.0
google-auth==2.23.3
google-auth-oauthlib==1.0.0
google-cloud-aiplatform==1.38.1
google-cloud-bigquery==3.12.0
google-cloud-core==2.3.3
google-cloud-resource-manager==1.10.4
google-cloud-storage==2.12.0
google-crc32c==1.5.0
google-generativeai==0.3.2
google-pasta==0.2.0
google-resumable-media==2.6.0
googleapis-common-protos==1.56.4
grpc-gateway-protoc-gen-openapiv2==0.1.0
grpc-google-iam-v1==0.12.6
grpcio==1.59.0
grpcio-status==1.59.0
grpcio-tools==1.59.0
h11==0.14.0
h2==4.1.0
h5py==3.10.0
hnswlib==0.7.0
hpack==4.0.0
html5lib==1.1
httpcore==0.16.3
httptools==0.5.0
httpx==0.23.3
huggingface-hub==0.14.1
humanfriendly==10.0
humbug==0.3.2
hyperframe==6.0.1
identify==2.5.23
idna==3.4
importlib-metadata==6.6.0
importlib-resources==5.12.0
iniconfig==2.0.0
install==1.3.5
installer==0.7.0
iopath==0.1.10
itsdangerous==2.1.2
jaraco.classes==3.2.3
jieba3k==0.35.1
Jinja2==3.1.2
jmespath==1.0.1
joblib==1.2.0
jq==1.6.0
jsonpatch==1.33
jsonpointer==2.4
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jwt==1.3.1
keras==2.14.0
keyring==23.13.1
kiwisolver==1.4.4
lancedb==0.3.2
langchain==0.1.0
langchain-community==0.0.11
langchain-core==0.1.9
langchain-google-genai==0.0.5
langsmith==0.0.77
lark==1.1.7
layoutparser==0.3.4
Levenshtein==0.23.0
libclang==16.0.6
libdeeplake==0.0.84
limits==3.5.0
llama-cpp-python==0.1.39
lockfile==0.12.2
loguru==0.7.0
lxml==4.9.2
lz4==4.3.2
macholib @ file:///AppleInternal/Library/BuildRoots/9dd5efe2-7fad-11ee-b588-aa530c46a9ea/Library/Caches/com.apple.xbs/Sources/python3/macholib-1.15.2-py2.py3-none-any.whl
Mako==1.2.4
Markdown==3.4.3
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marshmallow==3.19.0
marshmallow-enum==1.5.1
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ml-dtypes==0.2.0
monotonic==1.6
more-itertools==9.1.0
mpmath==1.3.0
msg-parser==1.2.0
msgpack==1.0.5
multidict==6.0.4
multiprocess==0.70.15
mypy-extensions==1.0.0
nest-asyncio==1.5.8
networkx==3.1
newspaper3k==0.2.8
nltk==3.8.1
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numpy==1.26.1
oauthlib==3.2.2
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omegaconf==2.3.0
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openpyxl==3.1.2
opt-einsum==3.3.0
ordered-set==4.1.0
outcome==1.2.0
overrides==7.4.0
packaging==23.2
pandas==1.5.3
pathos==0.3.1
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pdfminer.six==20221105
pdfplumber==0.9.0
pexpect==4.8.0
Pillow==9.5.0
pinecone-client==2.2.4
pkginfo==1.9.6
platformdirs==2.6.2
Plim==1.0.0
pluggy==1.3.0
poetry==1.4.2
poetry-core==1.5.2
poetry-plugin-export==1.3.1
poppler-utils==0.1.0
portalocker==2.7.0
posthog==3.0.1
pox==0.3.3
ppft==1.7.6.7
pre-commit==3.2.2
proto-plus==1.22.3
protobuf==4.24.4
ptyprocess==0.7.0
pulsar-client==3.3.0
py==1.11.0
pyarrow==12.0.0
pyasn1==0.5.0
pyasn1-modules==0.3.0
pycocotools==2.0.6
pycparser==2.21
pydantic==2.4.2
pydantic_core==2.10.1
PyExecJS==1.5.1
pyfb==0.6.0
Pygments==2.15.1
PyJWT==2.7.0
pylance==0.8.7
PyMuPDF==1.22.3
pypandoc==1.11
pyparsing==3.0.9
pypdf==3.8.1
PyPDF2==3.0.1
PyPika==0.48.9
pyproject_hooks==1.0.0
pyrsistent==0.19.3
pyScss==1.4.0
PySocks==1.7.1
pytesseract==0.3.10
pytest==7.4.4
python-dateutil==2.8.2
python-docx==0.8.11
python-dotenv==1.0.0
python-Levenshtein==0.23.0
python-magic==0.4.27
python-multipart==0.0.6
python-pptx==0.6.21
pytz==2023.3
PyYAML==6.0
qdrant-client==1.6.4
rank-bm25==0.2.2
rapidfuzz==3.4.0
ratelimiter==1.2.0.post0
readability-lxml==0.8.1
redis==5.0.1
regex==2023.3.23
requests==2.31.0
requests-file==1.5.1
requests-oauthlib==1.3.1
requests-toolbelt==0.10.1
responses==0.18.0
retry==0.9.2
rfc3986==1.5.0
rich==13.0.1
rsa==4.9
s3transfer==0.7.0
safetensors==0.3.1
scikit-learn==1.2.2
scipy==1.10.1
selenium==4.9.1
semver==3.0.2
sentence-transformers==2.2.2
sentencepiece==0.1.98
sgmllib3k==1.0.0
shapely==2.0.2
shellingham==1.5.0.post1
simplejson==3.19.1
six @ file:///AppleInternal/Library/BuildRoots/9dd5efe2-7fad-11ee-b588-aa530c46a9ea/Library/Caches/com.apple.xbs/Sources/python3/six-1.15.0-py2.py3-none-any.whl
snakeviz==2.2.0
sniffio==1.3.0
sortedcontainers==2.4.0
soupsieve==2.5
SQLAlchemy==2.0.16
sqlean.py==0.21.8.4
starlette==0.27.0
stylus==0.1.2
sympy==1.11.1
tenacity==8.2.2
tensorboard==2.14.1
tensorboard-data-server==0.7.2
tensorflow==2.14.0
tensorflow-estimator==2.14.0
tensorflow-io-gcs-filesystem==0.34.0
tensorflow-macos==2.14.0
termcolor==2.3.0
threadpoolctl==3.1.0
tiktoken==0.4.0
timm==0.9.1
tinysegmenter==0.3
tldextract==5.1.1
tokenizers==0.13.3
tomli==2.0.1
tomlkit==0.11.8
torch==2.1.0
torchvision==0.15.1
tornado==6.2
tqdm==4.65.0
transformers==4.28.1
trio==0.22.0
trio-websocket==0.10.3
trove-classifiers==2023.5.2
typer==0.9.0
typing-inspect==0.8.0
typing_extensions==4.8.0
tzdata==2023.3
unstructured==0.6.6
unstructured-inference==0.4.4
urllib3==1.26.15
uvicorn==0.22.0
uvloop==0.17.0
virtualenv==20.21.1
Wand==0.6.11
watchfiles==0.19.0
webencodings==0.5.1
websockets==11.0.2
Werkzeug==2.3.6
wrapt==1.14.1
wsproto==1.2.0
xattr==0.10.1
XlsxWriter==3.1.0
xxhash==3.2.0
yarl==1.9.2
zipp==3.15.0
zstandard==0.21.0
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | closed | false | null | [] | null | 1 | 2024-01-11T21:52:50 | 2024-01-12T17:36:53 | 2024-01-12T17:36:52 | COLLABORATOR | null | <!-- Thank you for contributing to LangChain!
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