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https://api.github.com/repos/langchain-ai/langchain/issues/7068
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| 1,785,574,682 |
PR_kwDOIPDwls5UfnMm
| 7,068 |
Adding Marqo to vectorstore ecosystem
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CONTRIBUTOR
| null |
This PR brings in a vectorstore interface for [Marqo](https://www.marqo.ai/).
The Marqo vectorstore exposes some of Marqo's functionality in addition the the VectorStore base class. The Marqo vectorstore also makes the embedding parameter optional because inference for embeddings is an inherent part of Marqo.
Docs, notebook examples and integration tests included.
Related PR:
https://github.com/hwchase17/langchain/pull/2807
|
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I_kwDOIPDwls5qbPgX
| 7,067 |
Issue:IndexError: list index out of range
|
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open
| false | null |
[] | null | 7 | 2023-07-03T07:33:27 | 2023-11-21T00:32:16 | null |
NONE
| null |
### System Info
langchain-0.0.221
python-3.10.11
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.schema import Document
from langchain.vectorstores import FAISS
list_of_documents = [
Document(page_content="foo", metadata=dict(page=1)),
Document(page_content="bar", metadata=dict(page=1)),
Document(page_content="foo", metadata=dict(page=2)),
Document(page_content="barbar", metadata=dict(page=2)),
Document(page_content="foo", metadata=dict(page=3)),
Document(page_content="bar burr", metadata=dict(page=3)),
Document(page_content="foo", metadata=dict(page=4)),
Document(page_content="bar bruh", metadata=dict(page=4)),
]
db = FAISS.from_documents(list_of_documents, embeddings)
### Expected behavior
Now i'm following the FAISS Similarity Search with filtering code example on official Website,which is on https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss,an error occur:
IndexError Traceback (most recent call last)
Cell In[56], line 13
2 from langchain.vectorstores import FAISS
3 list_of_documents = [
4 Document(page_content="foo", metadata=dict(page=1)),
5 Document(page_content="bar", metadata=dict(page=1)),
(...)
11 Document(page_content="bar bruh", metadata=dict(page=4)),
12 ]
---> 13 db = FAISS.from_documents(list_of_documents, embeddings)
File [d:\Python310\lib\site-packages\langchain\vectorstores\base.py:332](file:///D:/Python310/lib/site-packages/langchain/vectorstores/base.py:332), in VectorStore.from_documents(cls, documents, embedding, **kwargs)
330 texts = [d.page_content for d in documents]
331 metadatas = [d.metadata for d in documents]
--> 332 return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File [d:\Python310\lib\site-packages\langchain\vectorstores\faiss.py:551](file:///D:/Python310/lib/site-packages/langchain/vectorstores/faiss.py:551), in FAISS.from_texts(cls, texts, embedding, metadatas, ids, **kwargs)
525 @classmethod
526 def from_texts(
527 cls,
(...)
532 **kwargs: Any,
533 ) -> FAISS:
534 """Construct FAISS wrapper from raw documents.
...
--> 314 results[indices[i]].append(batched_embeddings[i])
315 num_tokens_in_batch[indices[i]].append(len(tokens[i]))
317 for i in range(len(texts)):
IndexError: list index out of range
How's that happened and how can i fix it?
|
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I_kwDOIPDwls5qaxWL
| 7,066 |
Conversationalbuffermemory to maintain chathistory
|
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[] | null | 1 | 2023-07-03T06:05:30 | 2023-10-09T16:05:40 | 2023-10-09T16:05:40 |
NONE
| null |
### Issue you'd like to raise.
I am using Conversationalbuffermemory to maintain chathistory here is my code i dont know why my chathistory not working properly if any update required in my code please let me know I am using this code in my api
class Chatbot:
def __init__(self):
self.model = "gpt-3.5-turbo"
async def generate_response(self, user, question):
index = faiss.read_index("faiss_index/faisss_audien_new.index")
print(index)
with open("faiss_index/faisss_audien_new.pkl", "rb") as f:
store = pickle.load(f)
store.index = index
retriever = store.as_retriever(search_kwargs=dict(k=1))
memory = ConversationBufferMemory(retriever=retriever)
llm = OpenAI(temperature=0, max_tokens=1500, model_name='gpt-3.5-turbo') # Can be any valid LLM
_DEFAULT_TEMPLATE = """
You are an AI girlfriend having a conversation with a human. Respond to all inputs in 50 words or less. Do not say you are an AI language model.
Relevant pieces of previous conversation:
{history}
(You do not need to use these pieces of information if not relevant)
Current conversation:
User: {input}
Kira AI:
"""
PROMPT = PromptTemplate(
input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
)
conversation_with_summary = ConversationChain(
llm=llm,
prompt=PROMPT,
memory=memory,
verbose=True
)
result = conversation_with_summary.predict(input=question)
gpt_response = result
print(gpt_response)
return gpt_response
### Suggestion:
_No response_
|
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| 1,785,310,309 |
I_kwDOIPDwls5qaaxl
| 7,065 |
Use JSON format prompt
|
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[] | null | 5 | 2023-07-03T04:47:47 | 2023-10-10T01:06:44 | 2023-10-10T01:06:34 |
NONE
| null |
### Feature request
I find it is quite useful when using JSON format prompt.
Unfortunately, I cannot pass JSON format prompt to langchain.
### Motivation
Here is prompt example, which gives detail instruction on how the GPT should response to the student.
Could you please support the JSON format prompt in the feature release!
prompt：'''
{
"ai_tutor": {
"Author": "JushBJJ",
"name": "Mr. Ranedeer",
"version": "2.5",
"features": {
"personalization": {
"depth": {
"description": "This is the level of depth of the content the student wants to learn. The lowest depth level is 1, and the highest is 10.",
"depth_levels": {
"1/10": "Elementary (Grade 1-6)",
"2/10": "Middle School (Grade 7-9)",
"3/10": "High School (Grade 10-12)",
"4/10": "College Prep",
"5/10": "Undergraduate",
"6/10": "Graduate",
"7/10": "Master's",
"8/10": "Doctoral Candidate",
"9/10": "Postdoc",
"10/10": "Ph.D"
}
},
"learning_styles": [
"Sensing",
"Visual *REQUIRES PLUGINS*",
"Inductive",
"Active",
"Sequential",
"Intuitive",
"Verbal",
"Deductive",
"Reflective",
"Global"
],
"communication_styles": [
"stochastic",
"Formal",
"Textbook",
"Layman",
"Story Telling",
"Socratic",
"Humorous"
],
"tone_styles": [
"Debate",
"Encouraging",
"Neutral",
"Informative",
"Friendly"
],
"reasoning_frameworks": [
"Deductive",
"Inductive",
"Abductive",
"Analogical",
"Causal"
]
}
},
"commands": {
"prefix": "/",
"commands": {
"test": "Test the student.",
"config": "Prompt the user through the configuration process, incl. asking for the preferred language.",
"plan": "Create a lesson plan based on the student's preferences.",
"search": "Search based on what the student specifies. *REQUIRES PLUGINS*",
"start": "Start the lesson plan.",
"continue": "Continue where you left off.",
"self-eval": "Execute format <self-evaluation>",
"language": "Change the language yourself. Usage: /language [lang]. E.g: /language Chinese",
"visualize": "Use plugins to visualize the content. *REQUIRES PLUGINS*"
}
},
"rules": [
"1. Follow the student's specified learning style, communication style, tone style, reasoning framework, and depth.",
"2. Be able to create a lesson plan based on the student's preferences.",
"3. Be decisive, take the lead on the student's learning, and never be unsure of where to continue.",
"4. Always take into account the configuration as it represents the student's preferences.",
"5. Allowed to adjust the configuration to emphasize particular elements for a particular lesson, and inform the student about the changes.",
"6. Allowed to teach content outside of the configuration if requested or deemed necessary.",
"7. Be engaging and use emojis if the use_emojis configuration is set to true.",
"8. Obey the student's commands.",
"9. Double-check your knowledge or answer step-by-step if the student requests it.",
"10. Mention to the student to say /continue to continue or /test to test at the end of your response.",
"11. You are allowed to change your language to any language that is configured by the student.",
"12. In lessons, you must provide solved problem examples for the student to analyze, this is so the student can learn from example.",
"13. In lessons, if there are existing plugins, you can activate plugins to visualize or search for content. Else, continue."
],
"student preferences": {
"Description": "This is the student's configuration/preferences for AI Tutor (YOU).",
"depth": 0,
"learning_style": [],
"communication_style": [],
"tone_style": [],
"reasoning_framework": [],
"use_emojis": true,
"language": "Chinese (Default)"
},
"formats": {
"Description": "These are strictly the specific formats you should follow in order. Ignore Desc as they are contextual information.",
"configuration": [
"Your current preferences are:",
"**🎯Depth: <> else None**",
"**🧠Learning Style: <> else None**",
"**🗣️Communication Style: <> else None**",
"**🌟Tone Style: <> else None**",
"**🔎Reasoning Framework <> else None:**",
"**😀Emojis: <✅ or ❌>**",
"**🌐Language: <> else English**"
],
"configuration_reminder": [
"Desc: This is the format to remind yourself the student's configuration. Do not execute <configuration> in this format.",
"Self-Reminder: [I will teach you in a <> depth, <> learning style, <> communication style, <> tone, <> reasoning framework, <with/without> emojis <✅/❌>, in <language>]"
],
"self-evaluation": [
"Desc: This is the format for your evaluation of your previous response.",
"<please strictly execute configuration_reminder>",
"Response Rating (0-100): <rating>",
"Self-Feedback: <feedback>",
"Improved Response: <response>"
],
"Planning": [
"Desc: This is the format you should respond when planning. Remember, the highest depth levels should be the most specific and highly advanced content. And vice versa.",
"<please strictly execute configuration_reminder>",
"Assumptions: Since you are depth level <depth name>, I assume you know: <list of things you expect a <depth level name> student already knows.>",
"Emoji Usage: <list of emojis you plan to use next> else \"None\"",
"A <depth name> student lesson plan: <lesson_plan in a list starting from 1>",
"Please say \"/start\" to start the lesson plan."
],
"Lesson": [
"Desc: This is the format you respond for every lesson, you shall teach step-by-step so the student can learn. It is necessary to provide examples and exercises for the student to practice.",
"Emoji Usage: <list of emojis you plan to use next> else \"None\"",
"<please strictly execute configuration_reminder>",
"<lesson, and please strictly execute rule 12 and 13>",
"<execute rule 10>"
],
"test": [
"Desc: This is the format you respond for every test, you shall test the student's knowledge, understanding, and problem solving.",
"Example Problem: <create and solve the problem step-by-step so the student can understand the next questions>",
"Now solve the following problems: <problems>"
]
}
},
"init": "As an AI tutor, greet + 👋 + version + author + execute format <configuration> + ask for student's preferences + mention /language"
}
### Your contribution
I am not sure
|
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| 1,785,284,378 |
I_kwDOIPDwls5qaUca
| 7,064 |
DOC: Multiple callback handlers
|
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[] | null | 1 | 2023-07-03T04:21:12 | 2023-10-09T16:05:51 | 2023-10-09T16:05:50 |
NONE
| null |
### Issue with current documentation:
Example no longer working (key not found in dict) due to api changes: https://python.langchain.com/docs/modules/callbacks/how_to/multiple_callbacks
### Idea or request for content:
Corrected Serialization in several places:
from typing import Dict, Union, Any, List
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import tracing_enabled
from langchain.llms import OpenAI
# First, define custom callback handler implementations
class MyCustomHandlerOne(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> Any:
print(f"on_llm_start {serialized['id'][-1]}")
def on_llm_new_token(self, token: str, **kwargs: Any) -> Any:
print(f"on_new_token {token}")
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> Any:
"""Run when LLM errors."""
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> Any:
print(f"on_chain_start {serialized['id'][-1]}")
def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> Any:
print(f"on_tool_start {serialized['name']}")
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
print(f"on_agent_action {action}")
class MyCustomHandlerTwo(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> Any:
print(f"on_llm_start (I'm the second handler!!) {serialized['id'][-1]}")
# Instantiate the handlers
handler1 = MyCustomHandlerOne()
handler2 = MyCustomHandlerTwo()
# Setup the agent. Only the `llm` will issue callbacks for handler2
llm = OpenAI(temperature=0, streaming=True, callbacks=[handler2])
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
# Callbacks for handler1 will be issued by every object involved in the
# Agent execution (llm, llmchain, tool, agent executor)
agent.run("What is 2 raised to the 0.235 power?", callbacks=[handler1])
|
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| 1,785,264,664 |
I_kwDOIPDwls5qaPoY
| 7,063 |
MultiQueryRetriever broken on master and needs tests
|
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[] | null | 0 | 2023-07-03T04:00:32 | 2023-07-04T05:09:35 | 2023-07-04T05:09:35 |
COLLABORATOR
| null |
### System Info
v0.0.221
### Who can help?
@vowelparrot
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
### Reproduction
[MultiQueryRetriever](https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever) is broken due to [5962](https://github.com/hwchase17/langchain/pull/5962) introducing a new method `_get_relevant_documents`.
Caught this b/c I no longer see expected logging when running example in [docs](https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever).
```
unique_docs = retriever_from_llm.get_relevant_documents(query="What does the course say about regression?")
len(unique_docs)
```
For MultiQueryRetriever, get_relevant_documents does a few things (PR [here](https://github.com/hwchase17/langchain/pull/6833/files#diff-73c5e56610f556ddc4710db1b9da4c6277b8ffaef6d535e3cf1e1ceb4b22b186)).
E.g., it will run `queries = self.generate_queries(query, run_manager)` and log the queries.
It appears the method name has been changed to `_get_relevant_documents`?
And it now requires some additional args (e.g., `run_manager`)?
AFAICT, `get_relevant_documents` currently will just do retrieval w/o any of the `MultiQueryRetriever` logic.
If this is expected, then documentation will need to be updated for MultiQueryRetriever [here](https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever) to use _get_relevant_documents and supply run_manager and explain what run_manager is.
Also this exposes that fact that we need a test for MultiQueryRetriever.
### Expected behavior
We should actually run the logic in `MultiQueryRetriever` as noted in the [PR](https://github.com/hwchase17/langchain/pull/6833/files#diff-73c5e56610f556ddc4710db1b9da4c6277b8ffaef6d535e3cf1e1ceb4b22b186).
```
INFO:root:Generated queries: ["1. What is the course's perspective on regression?", '2. How does the course discuss regression?', '3. What information does the course provide about regression?']
```
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| 1,785,264,380 |
I_kwDOIPDwls5qaPj8
| 7,062 |
OpenAIChat(streaming=True) donot work
|
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[] | null | 2 | 2023-07-03T04:00:09 | 2023-11-16T16:07:11 | 2023-11-16T16:07:10 |
NONE
| null |
### System Info
index = VectorstoreIndexCreator(vectorstore_cls=DocArrayInMemorySearch, embedding=embeddings)
index = index.from_loaders([PyMuPDFLoader('孙子兵法.pdf')])
query = '孙子兵法的战略'
index.query(llm=OpenAIChat(streaming=True), question=query, chain_type="stuff")
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
### Reproduction
```
index = VectorstoreIndexCreator(vectorstore_cls=DocArrayInMemorySearch, embedding=embeddings)
index = index.from_loaders([PyMuPDFLoader('孙子兵法.pdf')])
query = '孙子兵法的战略'
index.query(llm=OpenAIChat(streaming=True), question=query, chain_type="stuff")
```
streaming ...
### Expected behavior
```
index = VectorstoreIndexCreator(vectorstore_cls=DocArrayInMemorySearch, embedding=embeddings)
index = index.from_loaders([PyMuPDFLoader('孙子兵法.pdf')])
query = '孙子兵法的战略'
index.query(llm=OpenAIChat(streaming=True), question=query, chain_type="stuff")
```
streaming ...
|
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| 1,785,201,120 |
I_kwDOIPDwls5qaAHg
| 7,061 |
OPENAI_FUNCTIONS Agent Memory won't work inside of Streamlit st.chat_input element
|
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[] | null | 10 | 2023-07-03T03:11:16 | 2023-12-20T16:07:28 | 2023-12-20T16:07:27 |
NONE
| null |
### System Info
langchain = 0.0.218
python = 3.11.4
### Who can help?
@hwchase17 , @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce:
1. Follow [Add Memory Open Ai Functions](https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions)
2. Import Streamlit
3. Create Chat Element using st.chat_input
4. Try running agent through Streamlit
### Expected behavior
The agent memory works correctly when the Agent is called via ``` agent_chain.run("Insert prompt") ``` but when this is being done via a Streamlit Chat element like:
```python
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
st.chat_message("user").write(prompt)
with st.spinner('Thinking...'):
st_callback = StreamlitCallbackHandler(st.container())
agent_chain.run(prompt, callbacks=[st_callback])
st.chat_message("assistant").write(response)
st.session_state.messages.append({"role": "assistant", "content": response})
```
It no longer works.
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| 1,785,200,414 |
I_kwDOIPDwls5qZ_8e
| 7,060 |
The memory did not record the full input message, it only record the input parameter values
|
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[] | null | 1 | 2023-07-03T03:10:39 | 2023-10-09T16:05:55 | 2023-10-09T16:05:55 |
NONE
| null |
### System Info
Langchina: 0.0.221,
OS: WSL
Python 3.11
### Who can help?
The memory did not record the full input message, it only record the input parameter values
Below is a simple code to use the default ConversationBufferMemory.
++++++++++++++++++++++++++
llm = conn.connecting.get_model_azureopenai()
mem = langchain.memory.ConversationBufferMemory()
msg1 = langchain.prompts.HumanMessagePromptTemplate.from_template("Hi, I am {name}. How do you pronounce my name?")
prompt1 = langchain.prompts.ChatPromptTemplate.from_messages([msg1])
print("Input message=", prompt1.format(name="Cao"))
chain1 = langchain.chains.LLMChain(
llm=llm,
prompt=prompt1,
memory=mem,
)
resp1 = chain1.run({"name": "Cao"})
print(resp1)
print("+"*100)
print(mem)
+++++++++++++++++++++++++++++++++++
But the real memory only recorded like below:
chat_memory=ChatMessageHistory(messages=[HumanMessage(content='Cao', additional_kwargs={}, example=False), AIMessage(content='Hello Cao! Your name is pronounced as "Ts-ow" with the "Ts" sound similar to the beginning of "tsunami" and "ow" rhyming with "cow."', additional_kwargs={}, example=False)]) output_key=None input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='history'
It should record the full input, not only the parameter itself:
Input message= Human: Hi, I am Cao. How do you pronounce my name?
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Below is a simple code to use the default ConversationBufferMemory.
++++++++++++++++++++++++++
llm = conn.connecting.get_model_azureopenai()
mem = langchain.memory.ConversationBufferMemory()
msg1 = langchain.prompts.HumanMessagePromptTemplate.from_template("Hi, I am {name}. How do you pronounce my name?")
prompt1 = langchain.prompts.ChatPromptTemplate.from_messages([msg1])
print("Input message=", prompt1.format(name="Cao"))
chain1 = langchain.chains.LLMChain(
llm=llm,
prompt=prompt1,
memory=mem,
)
resp1 = chain1.run({"name": "Cao"})
print(resp1)
print("+"*100)
print(mem)
+++++++++++++++++++++++++++++++++++
But the real memory only recorded like below:
chat_memory=ChatMessageHistory(messages=[HumanMessage(content='Cao', additional_kwargs={}, example=False), AIMessage(content='Hello Cao! Your name is pronounced as "Ts-ow" with the "Ts" sound similar to the beginning of "tsunami" and "ow" rhyming with "cow."', additional_kwargs={}, example=False)]) output_key=None input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='history'
It should record the full input, not only the parameter itself:
Input message= Human: Hi, I am Cao. How do you pronounce my name?
### Expected behavior
It should record the full input, not only the parameter itself:
Input message= Human: Hi, I am Cao. How do you pronounce my name?
|
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| 1,785,176,797 |
I_kwDOIPDwls5qZ6Ld
| 7,059 |
Chain.run() without any param will cause error
|
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closed
| false | null |
[] | null | 3 | 2023-07-03T02:43:11 | 2023-11-29T16:09:09 | 2023-11-29T16:09:09 |
NONE
| null |
### System Info
Langchain 0.0.221
WSL
Python 3.11
###
If design a very simple chain which do not need any input parameters. Then run it, it will throw error. I think this without any input params may be useful.
llm = conn.connecting.get_model_azureopenai()
mem = langchain.memory.ConversationBufferMemory()
msg1 = langchain.prompts.HumanMessagePromptTemplate.from_template("Hi there!")
prompt1 = langchain.prompts.ChatPromptTemplate.from_messages([msg1])
print(prompt1.input_variables)
chain1 = langchain.chains.LLMChain(
llm=llm,
prompt=prompt1,
memory=mem,
)
resp1 = chain1.run()
print(resp1)
==================
Error:
/usr/local/miniconda3/bin/python /mnt/c/TechBuilder/AIML/LangChain/Dev20230701/chains/runchain1.py
Traceback (most recent call last):
File "/mnt/c/TechBuilder/AIML/LangChain/Dev20230701/chains/runchain1.py", line 87, in <module>
run2()
File "/mnt/c/TechBuilder/AIML/LangChain/Dev20230701/chains/runchain1.py", line 81, in run2
resp1 = chain1.run()
^^^^^^^^^^^^
File "/usr/local/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py", line 296, in run
raise ValueError(
ValueError: `run` supported with either positional arguments or keyword arguments, but none were provided.
[]
Process finished with exit code 1
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### 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
### Reproduction
Step1, develop a simple chain
/usr/local/miniconda3/bin/python /mnt/c/TechBuilder/AIML/LangChain/Dev20230701/chains/runchain1.py
Traceback (most recent call last):
File "/mnt/c/TechBuilder/AIML/LangChain/Dev20230701/chains/runchain1.py", line 87, in <module>
run2()
File "/mnt/c/TechBuilder/AIML/LangChain/Dev20230701/chains/runchain1.py", line 81, in run2
resp1 = chain1.run()
^^^^^^^^^^^^
File "/usr/local/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py", line 296, in run
raise ValueError(
ValueError: `run` supported with either positional arguments or keyword arguments, but none were provided.
[]
Process finished with exit code 1
Step 2, run it without input params, because it does not need any input params.
### Expected behavior
Should run without error
|
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| 1,785,011,372 |
I_kwDOIPDwls5qZRys
| 7,058 |
get_openapi_chain fails to produce proper Openapi scheme when preparing url.
|
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closed
| false | null |
[] | null | 3 | 2023-07-02T23:22:34 | 2023-10-08T16:05:10 | 2023-10-08T16:05:09 |
NONE
| null |
### System Info
langchain==0.0.221
langchainplus-sdk==0.0.19
Windows 10
### Who can help?
@dev2049 @homanp
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. install `langchain` with the aformentioned version
2. install `openai`
3. run the following code:
```py
import os
from langchain.chains.openai_functions.openapi import get_openapi_chain
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
chain = get_openapi_chain("https://gptshop.bohita.com/openapi.yaml")
res = chain.run("I want a tight fitting red shirt for a man")
res
```
or this, as it will produce a similar error:
```py
import os
from langchain.chains.openai_functions.openapi import get_openapi_chain
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
chain = get_openapi_chain("https://nani.ooo/openapi.json")
res = chain.run("Show me the amount of nodes on the ethereum blockchain")
res
```
### Expected behavior
The response dict. (not sure what that would look like as I have not gotten a successful response)
|
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| 1,784,793,173 |
PR_kwDOIPDwls5Uc6mi
| 7,057 |
Buffer method of ConversationTokenBufferMemory should be able to return messages as string
|
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[] | null | 2 | 2023-07-02T19:21:18 | 2023-08-11T01:17:23 | 2023-08-11T01:17:23 |
CONTRIBUTOR
| null |
### Description:
`ConversationBufferTokenMemory` should have a simple way of returning the conversation messages as a string.
Previously to complete this, you would only have the option to return memory as an array through the buffer method and call `get_buffer_string` by importing it from `langchain.schema`, or use the `load_memory_variables` method and key into `self.memory_key`.
### Maintainer
@hwchase17
|
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| 1,784,742,819 |
I_kwDOIPDwls5qYQOj
| 7,056 |
Anthropic Upgrade issue
|
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[] | null | 2 | 2023-07-02T17:44:23 | 2023-10-08T16:05:15 | 2023-10-08T16:05:14 |
NONE
| null |
### System Info
Langchain version 0.0.221
Anthropic versions 0.3.1 and 0.3.2
Whenever using ChatAnthropic, this now appears. Version 0.2.10 works, but the below error appears since the Anthropic update a couple of days ago
`Anthropic.__init__() got an unexpected keyword argument 'api_url' (type=type_error)`
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
### Reproduction
Use ChatAnthropic in any form with the latest version of Anthropic and you will encounter: "Anthropic.__init__() got an unexpected keyword argument 'api_url' (type=type_error)"
### Expected behavior
It should work if the API key is provided. You should not have to specify a URL.
|
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| 1,784,738,959 |
PR_kwDOIPDwls5UcvAW
| 7,055 |
Fix typo in google_places_api.py
|
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[] | null | 2 | 2023-07-02T17:32:54 | 2023-07-03T08:14:19 | 2023-07-03T08:14:18 |
CONTRIBUTOR
| null |
enviroment -> environment
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5qYOBG
| 7,054 |
InvalidRequestError: This model's maximum context length
|
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[] | null | 2 | 2023-07-02T17:18:17 | 2023-08-09T01:43:27 | 2023-07-04T03:22:55 |
NONE
| null |
### System Info
langchain: 0.0.221
python:3.10.2
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader("my_file.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=10, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
### Expected behavior
I‘m follow the smae code on official wbsite "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss",but replace the txt file content with another (http://novel.tingroom.com/jingdian/5370/135425.html),and the CharacterTextSplitter chunk_size set to10 ,or 100 whatever,then the error kept on raising up:InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 10137 tokens (10137 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.
is this “from_documents” method has some kind of character restriction,or it just dont support Long text？
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| 1,784,729,131 |
I_kwDOIPDwls5qYM4r
| 7,053 |
Chroma, Deeplake, Faiss indexing error ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10379,) + inhomogeneous part.
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[] | null | 3 | 2023-07-02T17:04:55 | 2023-10-09T16:06:01 | 2023-10-09T16:06:00 |
NONE
| null |
### System Info
langchain Version: 0.0.220
python 3.9.16
Windows 11/ ubuntu 22.04
anaconda
### Who can help?
@agola11 When ingesting the attched file (I'm attaching a zip as .pkl cannot be updated) in a normal document splitting loop
```python
embeddings = OpenAIEmbeddings()
chunk_sizes = [512, 1024, 1536]
overlap= 100
docs_n_code = "both"
split_documents = []
base_dir ="..."
for chunk_size in chunk_sizes:
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap)
docs = text_splitter.split_documents(documents)
faiss_dir = "..."
faiss_vectorstore = FAISS.from_documents(docs, embeddings)
faiss_vectorstore.save_local(faiss_dir)
```
I get the following error:
```
setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10379,) + inhomogeneous part.
```
similar errors occur with chroma and deeplake, locally. With chroma i can get the error more frequently even when ingesting other documents.
[api_docs.zip](https://github.com/hwchase17/langchain/files/11930126/api_docs.zip)
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### 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
### Reproduction
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
with open('api_docs.pkl', 'rb') as file:
documents = pickle.load(file)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=100)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)
```
### Expected behavior
Create the index
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PR_kwDOIPDwls5Ucryz
| 7,052 |
Pass 'data' argument to async requests
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[] | null | 4 | 2023-07-02T16:52:59 | 2023-07-05T19:42:59 | 2023-07-05T19:42:59 |
NONE
| null |
Description: Pass 'data' argument to async requests to solve the missing required positional argument error.
Issue: [#6034](https://github.com/hwchase17/langchain/issues/6034)
Dependencies: aioresponses is added for test.
Tag maintainer: Async: @agola11
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| 1,784,720,520 |
PR_kwDOIPDwls5UcrRo
| 7,051 |
Fix for KeyError in MlflowCallbackHandler
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[] | null | 1 | 2023-07-02T16:44:34 | 2023-07-07T20:08:07 | 2023-07-07T20:08:07 |
CONTRIBUTOR
| null |
- Description: `MlflowCallbackHandler` fails with `KeyError: "['name'] not in index"`. See https://github.com/hwchase17/langchain/issues/5770 for more details. Root cause is that LangChain does not pass "name" as a part of `serialized` argument to `on_llm_start()` callback method. The commit where this change was made is probably this: https://github.com/hwchase17/langchain/commit/18af149e91e62b3ac7728ddea420688d41043734. My bug fix derives "name" from "id" field.
- Issue: https://github.com/hwchase17/langchain/issues/5770
- Dependencies: None
- Tag maintainer: @agola11
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PR_kwDOIPDwls5UcrKr
| 7,050 |
Fix sample in FAISS section
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[] | null | 2 | 2023-07-02T16:43:31 | 2023-07-03T08:29:00 | 2023-07-03T08:18:32 |
CONTRIBUTOR
| null |
This PR fixes a sample in the FAISS section in the reference docs.
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PR_kwDOIPDwls5UcgyU
| 7,048 |
Fix error when save chain to file and reload(#6889)
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NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Fix error when save chain to file and reload,
- Issue: SQLDatabaseChain cannot be loaded(#6889),
- Dependencies: none,
- Tag maintainer: @dev2049 ,
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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PR_kwDOIPDwls5Ucf8K
| 7,047 |
bump 221
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[] | null | 1 | 2023-07-02T14:20:24 | 2023-07-02T19:14:32 | 2023-07-02T14:32:15 |
COLLABORATOR
| null | null |
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| 1,784,658,716 |
PR_kwDOIPDwls5UcezU
| 7,046 |
Fixed Office365 tool __init__.py files, tests, and get_tools() function
|
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[] | null | 4 | 2023-07-02T14:04:03 | 2023-07-05T19:46:23 | 2023-07-05T19:46:22 |
CONTRIBUTOR
| null |
## Description
Added Office365 tool modules to `__init__.py` files
## Issue
As described in Issue https://github.com/hwchase17/langchain/issues/6936, the Office365 toolkit can't be loaded easily because it is not included in the `__init__.py` files.
## Reviewer
@dev2049
|
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PR_kwDOIPDwls5UcWSU
| 7,045 |
Polish reference docs
|
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[] | null | 2 | 2023-07-02T12:31:39 | 2023-07-02T14:13:50 | 2023-07-02T14:08:51 |
CONTRIBUTOR
| null |
This PR fixes broken links in the reference docs.
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I_kwDOIPDwls5qXwhR
| 7,044 |
Chain support missing variable
|
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[] | null | 8 | 2023-07-02T12:16:49 | 2023-10-19T16:06:48 | 2023-10-19T16:06:47 |
NONE
| null |
### Feature request
The LLM chain strictly validates all input variables against placeholders in the template. It will throw an error if they don't match. I wish it could handle missing variables, thus making the prompt template more flexible.
```python
def _validate_inputs(self, inputs: Dict[str, Any]) -> None:
"""Check that all inputs are present."""
missing_keys = set(self.input_keys).difference(inputs)
if missing_keys:
raise ValueError(f"Missing some input keys: {missing_keys}")
```
### Motivation
I'm using jinja2 to build a generic prompt template, but the chain's enforcement on variable validation forces me to take some extra steps, such as pre-rendering the prompt before running it through the chain.
I hope the official could support the handling of missing variables.
### Your contribution
I think I could add a variable to determine whether to check the consistency between input variables and template variables, and then submit a PR.
if you have better ideas, such as complex and complete solutions like custom detection rule callback functions.
I look forward to the official update. Please, I really need this, and I believe others do too.
This will make the design of the Prompt more flexible.
|
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I_kwDOIPDwls5qXsFZ
| 7,043 |
Map_reduce not adapted to the 16k model 😲😥
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[] | null | 2 | 2023-07-02T11:30:01 | 2023-10-08T16:05:26 | 2023-10-08T16:05:25 |
NONE
| null |
### System Info
LangChain==0.0.177
Python==3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the **map_reduce** technique with a **GPT 16k** model, you receive an error message: '**A single document was longer than the context length, we cannot handle this.'** I believe this is due to the token_max limit mentioned in the following context. Is it possible to address this issue? It no longer makes sense to use a 16k model if we cannot send documents exceeding the hardcoded limit of 3000 tokens. _Could you please modify this hardcoded value from the 4098 token models to accommodate the 16k models?_ Thank you.
<img width="1260" alt="Capture d’écran 2023-07-02 à 13 26 21" src="https://github.com/hwchase17/langchain/assets/20300624/5f09b7e6-0a7a-49ec-9585-a20524332682">
### Expected behavior
I would like be able to give a Document object that would be more than 3000 tokens.
|
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I_kwDOIPDwls5qXVjs
| 7,042 |
ClientSession._request() got an unexpected keyword argument 'verify'
|
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closed
| false | null |
[] | null | 4 | 2023-07-02T07:29:01 | 2023-09-17T18:22:23 | 2023-07-02T10:41:24 |
NONE
| null |
### System Info
PYTHON: 3.11.4
LANGCHAIN: 0.0.220
FLATFORM: WINDOWS
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**My code:**
```
import nest_asyncio
import gc
nest_asyncio.apply()
from langchain.document_loaders import WebBaseLoader
urls=['','']
loader = WebBaseLoader(urls)
loader.requests_per_second = 1
scrape_data = loader.aload()
```
**ERROR i got:**
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[4], line 13
10 loader = WebBaseLoader(urls)
11 loader.requests_per_second = 1
---> 13 scrape_data = loader.aload()
File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\document_loaders\web_base.py:227, in WebBaseLoader.aload(self)
224 def aload(self) -> List[Document]:
225 """Load text from the urls in web_path async into Documents."""
--> 227 results = self.scrape_all(self.web_paths)
228 docs = []
229 for i in range(len(results)):
File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\document_loaders\web_base.py:173, in WebBaseLoader.scrape_all(self, urls, parser)
170 """Fetch all urls, then return soups for all results."""
171 from bs4 import BeautifulSoup
--> 173 results = asyncio.run(self.fetch_all(urls))
174 final_results = []
175 for i, result in enumerate(results):
File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\nest_asyncio.py:35, in _patch_asyncio..run(main, debug)
33 task = asyncio.ensure_future(main)
34 try:
---> 35 return loop.run_until_complete(task)
...
921 return _RequestContextManager(
--> 922 self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs)
923 )
TypeError: ClientSession._request() got an unexpected keyword argument 'verify'
```
### Expected behavior
_**i want to get web content like basic guide you provide.
I remember I was initially using version 0.0.20, and it worked fine.
But today, after updating to the latest version: 0.0.220, it got this error**_
|
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I_kwDOIPDwls5qXTKD
| 7,041 |
Issue: Conditioning planner with custom instructions in Plan-And-Execute models
|
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[] | null | 4 | 2023-07-02T06:57:36 | 2023-12-18T23:49:38 | 2023-12-18T23:49:37 |
NONE
| null |
### Issue you'd like to raise.
Can Plan-And-Execute models be conditioned to plan in particular directions instead of doing the planning alone? Say I'd like the planning to take place differently and the LLM always decides on an exhaustive manner
### Suggestion:
_No response_
|
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| 1,784,492,025 |
I_kwDOIPDwls5qXS_5
| 7,040 |
Issue: Nested agents
|
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[] | null | 3 | 2023-07-02T06:55:45 | 2024-01-15T17:33:23 | 2023-10-08T16:05:29 |
NONE
| null |
### Issue you'd like to raise.
Can we nest agents in one another? I would like an agent to use a set of tools + an LLM to extract entities, and then another agent after this should use some other tools + LLM to refine them.
### Suggestion:
_No response_
|
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| 1,784,491,423 |
I_kwDOIPDwls5qXS2f
| 7,039 |
Issue: Doubt on STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION
|
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[] | null | 2 | 2023-07-02T06:54:27 | 2023-11-29T16:09:14 | 2023-11-29T16:09:13 |
NONE
| null |
### Issue you'd like to raise.
Can we reuse intermediate steps for an agent defined using STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION?
This is my definition:
```
agent_executor = initialize_agent(
tools,
llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
return_intermediate_steps=True,
agent_kwargs={
"prefix": PREFIX,
"suffix": SUFFIX,
"input_variables": [
"input",
"agent_scratchpad",
"intermediate_steps",
"tools",
"tool_names",
],
"stop": ["\nObservation:"],
},
)
```
But I'm getting this error:
```
Got mismatched input_variables. Expected: {'tools', 'agent_scratchpad', 'input', 'tool_names'}. Got: ['input', 'agent_scratchpad', 'intermediate_steps', 'tools', 'tool_names'] (type=value_error)
```
### Suggestion:
_No response_
|
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| 1,784,490,903 |
I_kwDOIPDwls5qXSuX
| 7,038 |
Issue: LLMSingleActionAgent vs STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION
|
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[] | null | 2 | 2023-07-02T06:52:29 | 2023-10-17T16:06:14 | 2023-10-17T16:06:13 |
NONE
| null |
### Issue you'd like to raise.
Is there a difference between these two agent types? Where should one use either of them?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5qXOUG
| 7,037 |
TypeError: issubclass() arg 1 must be a class (importing AsyncCallbackHandler)
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[] | null | 2 | 2023-07-02T06:04:32 | 2023-10-09T16:06:06 | 2023-10-09T16:06:05 |
NONE
| null |
### System Info
Using the latest version of langchain (0.0.220).
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### 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
- [X] Callbacks/Tracing
- [X] Async
### Reproduction
Steps to reproduce the behavior:
1. `pip install langchain==0.0.220`
2. Run `from langchain.callbacks.base import AsyncCallbackHandler` in a Python script
Running this simple import gives the following traceback on my system:
```
Traceback (most recent call last):
File "C:\Users\jblak\OneDrive\Documents\language-tutor\test.py", line 1, in <module>
from langchain.callbacks.base import AsyncCallbackHandler
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\agents\__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\agents\agent.py", line 16, in <module>
from langchain.agents.tools import InvalidTool
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\agents\tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\tools\__init__.py", line 3, in <module>
from langchain.tools.arxiv.tool import ArxivQueryRun
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\tools\arxiv\tool.py", line 12, in <module>
from langchain.utilities.arxiv import ArxivAPIWrapper
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\utilities\__init__.py", line 3, in <module>
from langchain.utilities.apify import ApifyWrapper
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\utilities\apify.py", line 5, in <module>
from langchain.document_loaders import ApifyDatasetLoader
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\document_loaders\__init__.py", line 52, in <module>
from langchain.document_loaders.github import GitHubIssuesLoader
File "C:\Users\jblak\anaconda3\lib\site-packages\langchain\document_loaders\github.py", line 37, in <module>
class GitHubIssuesLoader(BaseGitHubLoader):
File "pydantic\main.py", line 197, in pydantic.main.ModelMetaclass.__new__
File "pydantic\fields.py", line 506, in pydantic.fields.ModelField.infer
File "pydantic\fields.py", line 436, in pydantic.fields.ModelField.__init__
File "pydantic\fields.py", line 552, in pydantic.fields.ModelField.prepare
File "pydantic\fields.py", line 663, in pydantic.fields.ModelField._type_analysis
File "pydantic\fields.py", line 808, in pydantic.fields.ModelField._create_sub_type
File "pydantic\fields.py", line 436, in pydantic.fields.ModelField.__init__
File "pydantic\fields.py", line 552, in pydantic.fields.ModelField.prepare
File "pydantic\fields.py", line 668, in pydantic.fields.ModelField._type_analysis
File "C:\Users\jblak\anaconda3\lib\typing.py", line 852, in __subclasscheck__
return issubclass(cls, self.__origin__)
TypeError: issubclass() arg 1 must be a class
```
### Expected behavior
Running this line should import the `AsyncCallbackHandler` without error.
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I_kwDOIPDwls5qXM6j
| 7,036 |
Agent for MongoDB
|
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[] | null | 17 | 2023-07-02T05:49:29 | 2024-01-05T07:29:57 | null |
NONE
| null |
### Feature request
I was skimming through the repository for the MongoDB Agent and I discovered that it does not exist. Is it feasible to develop a MongoDB agent that establishes a connection with MongoDB, generates MongoDB queries based on given questions, and retrieves the corresponding data?
### Motivation
Within my organization, a significant portion of our data is stored in MongoDB. I intend to utilize this agent to establish connections with our databases and develop a practical application around it.
### Your contribution
If it is indeed possible, I am willing to work on implementing this feature. However, I would greatly appreciate some initial guidance or assistance from someone knowledgeable in this area.
|
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I_kwDOIPDwls5qXKLx
| 7,035 |
SelfQueryRetriever not working in async call
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[] | null | 1 | 2023-07-02T05:17:42 | 2023-10-08T16:05:40 | 2023-10-08T16:05:39 |
NONE
| null |
### Issue you'd like to raise.
I want to chain the ```SelfQueryRetriever``` with ```ConversationalRetrievalChain``` and stream the output. I tested it in synchronous function it's working. However, when I convert it to async function, it pauses there and the execution never ended, no output as well. I went through the source code and found that ```aget_relevant_documents()``` for ```SelfQueryRetriever``` is not implemented.
```python
async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError
```
Not sure whether the issue came from this, because I am using ```acall()``` for ```ConversationalRetrievalChain```. But I do not receive any error message from the execution. Any temporary workaround for me to use ```SelfQueryRetriever``` in async function or I should wait for the release?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5qWnQh
| 7,034 |
Loading online PDFs gives temporary file path as source in metadata
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[] | null | 4 | 2023-07-01T23:24:53 | 2023-11-29T20:07:47 | 2023-11-29T20:07:47 |
NONE
| null |
Hi,
first up, thank you for making langchain! I was playing around a little and found a minor issue with loading online PDFs, and would like to start contributing to langchain maybe by fixing this.
### System Info
langchain 0.0.220, google collab, python 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.document_loaders import PyMuPDFLoader
loader = PyMuPDFLoader('https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf')
pages = loader.load()
pages[0].metadata
```
<img width="977" alt="image" src="https://github.com/hwchase17/langchain/assets/21276922/4ededc60-bb03-4502-a8c8-3c221ab109c4">
### Expected behavior
Instead of giving the temporary file path, which is not useful and deleted shortly after, it could be more helpful if the source is set to be the URL passed to it. This would require some fixes in the `langchain/document_loaders/pdf.py` file.
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PR_kwDOIPDwls5Ua0pw
| 7,033 |
Add Better Errors for Comparison Chain
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[] | null | 3 | 2023-07-01T19:11:08 | 2023-07-06T13:37:06 | 2023-07-06T13:37:04 |
COLLABORATOR
| null |
+ change to ABC - this lets us add things like the evaluation name for loading
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PR_kwDOIPDwls5UazR1
| 7,032 |
Self hosted llm api
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[] | null | 2 | 2023-07-01T18:57:44 | 2023-08-08T00:58:25 | 2023-08-08T00:58:24 |
NONE
| null |
Description: An additional LLM subclass called SelfHostedApi. This subclass was designed to be used by developers using self-hosted LLMs in a VPS environment, but who have exposed the model's functionality to an API endpoint. The goal is to allow more flexibility and performance for those using a single llm for varying inference tasks.
Unit tests added (all tests use mocked services)
In-code documentation added
Linting in progress (would love some help from someone more familiar with it. I've never done it before)
No demo in docs/modules (didn't see that explicit path in the repo). Point me to the correct directory and I'll do it.
Issue: N/A
Dependencies: Requests
Maintainer: @hwchase17
Thank y'all for making this package!
|
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| 7,031 |
Log Errors in Evaluator Callback
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PR_kwDOIPDwls5Uax0Y
| 7,030 |
Accept no 'reasoning' response in qa evaluator
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| 7,028 |
Fix docs
|
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[] | null | 2 | 2023-07-01T18:26:50 | 2023-07-02T07:16:41 | 2023-07-02T07:16:40 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,784,135,218 |
I_kwDOIPDwls5qV74y
| 7,027 |
Probabilistic Language of Thought (PLoT) Agent Implementation
|
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[] | null | 2 | 2023-07-01T18:19:31 | 2023-10-07T16:04:57 | 2023-10-07T16:04:57 |
CONTRIBUTOR
| null |
### Feature request
## Summary
Implement a new type of Langchain agent that reasons using a PLoT architecture as described in this paper:
https://arxiv.org/abs/2306.12672
## Notes
* Such an agent would need to use a probabilistic programming language (the paper above used Church) to generate code that describes its world model. This means that it would only work for LLMs that can generate code, and it would also need access to a Church interpreter (if that is the language that is chosen)
### Motivation
An agent which uses probabilistic language of thought to construct a coherent world model would be better at reasoning tasks involving uncertainty and ambiguity. Basically, it would think more like a human, using an understanding of the world that is external to the weights & biases on which it was trained.
### Your contribution
I've already started work on this and I intend to submit a PR when I have time to finish it! I'm pretty busy so any help would be appreciated! Feel free to reach out on discord @jasondotparse or twitter https://twitter.com/jasondotparse if you are down to help!
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PR_kwDOIPDwls5UapoE
| 7,026 |
Fixes incorrect docstore creation in faiss.py
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[] | null | 2 | 2023-07-01T17:30:32 | 2023-07-12T07:35:50 | 2023-07-12T07:35:49 |
CONTRIBUTOR
| null |
- **Description**: Current implementation assumes that the length of `texts` and `ids` should be same but if the passed `ids` length is not equal to the passed length of `texts`, current code `dict(zip(index_to_id.values(), documents))` is not failing or giving any warning and silently creating docstores only for the passed `ids` i.e. if `ids = ['A']` and `texts=["I love Open Source","I love langchain"]` then only one `docstore` will be created. But either two docstores should be created assuming same id value for all the elements of `texts` or an error should be raised.
- **Issue**: My change fixes this by using dictionary comprehension instead of `zip`. This was if lengths of `ids` and `texts` mismatches an explicit `IndexError` will be raised.
@rlancemartin, @eyurtsev
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| 1,784,079,160 |
PR_kwDOIPDwls5Uaixh
| 7,025 |
Harrison/split schema dir
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[] | null | 1 | 2023-07-01T16:47:39 | 2023-07-01T17:39:20 | 2023-07-01T17:39:19 |
COLLABORATOR
| null |
should be no functional changes
also keep __init__ exposing a lot for backwards compat
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I_kwDOIPDwls5qVtOz
| 7,024 |
OutputParserException: Could not parse LLM output: `df = [] for i in range(len(df)):`
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[] | null | 1 | 2023-07-01T16:41:37 | 2023-10-07T16:05:02 | 2023-10-07T16:05:02 |
NONE
| null |
### System Info
OutputParserException: Could not parse LLM output: `df = [] for i in range(len(df)):`
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### 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
### Reproduction
I used open source LLM, HuggingFaceHub(repo_id = "google/flan-t5-xxl"), but not OpenAI LLM, to create agent below:
agent = create_csv_agent using HuggingFaceHub(repo_id = "google/flan-t5-xxl"), r"D:\ML\titanic\titanic.csv", verbose=True)
the agent created successfully:
but when I used the agent below, I got error above:
agent.run("Plot a bar chart comparing Survived vs Dead for Embarked")
agent.run("Find the count of missing values in each column")
agent.run("Fix the issues with missing values for column Embarked with mode")
agent.run("Drop cabin column")
### Expected behavior
I should get plot or other .
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PR_kwDOIPDwls5UahQV
| 7,023 |
Fix typo
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[] | null | 2 | 2023-07-01T16:38:01 | 2023-07-02T08:22:42 | 2023-07-02T07:17:31 |
CONTRIBUTOR
| null |
This PR fixes a typo.
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PR_kwDOIPDwls5UaePc
| 7,022 |
Rename Databerry to Chaindesk
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[] | null | 2 | 2023-07-01T16:21:47 | 2023-07-07T21:28:05 | 2023-07-07T21:28:04 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
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| 1,784,042,187 |
PR_kwDOIPDwls5UaaiS
| 7,021 |
fix(pipeline prompt): support PipelinePrompt format missing varable
|
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[] | null | 3 | 2023-07-01T15:57:39 | 2023-11-07T03:48:53 | 2023-11-07T03:48:52 |
NONE
| null |
Replace this comment with:
- Description: fixed the bug where PipelinePromptTemplate throws an error when input variable is missing,
- Issue: the issue #7016 it fixes (if applicable),
- Dependencies: none
- Tag maintainer: @dev2049
- Twitter handle:
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,784,032,576 |
PR_kwDOIPDwls5UaYUC
| 7,020 |
added customizable pipeline tool
|
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[] | null | 3 | 2023-07-01T15:50:35 | 2023-07-06T15:41:48 | 2023-07-06T15:41:48 |
CONTRIBUTOR
| null |
## Description
added a pipeline tool consisting multiple steps that makes tool/chain reuse easier.
with more generic tools reused by domain task tools, metadata-driven agent-tool chain loading will be easier.
## Dependencies
None
## Maintainer
- Agents / Tools / Toolkits: @vowelparrot
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| 1,784,012,562 |
I_kwDOIPDwls5qVd8S
| 7,019 |
Could not import sentence_transformers python package.
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[] | null | 5 | 2023-07-01T15:22:37 | 2023-08-10T08:22:31 | 2023-07-03T03:57:14 |
NONE
| null |
### System Info
```
langchain: latest as of yesterday
M2 Pro
16 GB
Macosx
Darwin UAVALOS-M-NR30 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:23 PDT 2023; root:xnu-8796.121.3~7/RELEASE_ARM64_T6020 arm64
Python 3.10.10
GNU Make 3.81
Apple clang version 14.0.3 (clang-1403.0.22.14.1)
```
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### 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
### Reproduction
Steps to repo.
1. Install latest requirement
2. Try to load the `gtr-t5-xxl` embedding model from file.
```
!pip uninstall llama-cpp-python -y
!CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir
!pip install 'llama-cpp-python[server]'
!pip install sentence_transformers
!pip install git+https://github.com/huggingface/peft.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install -v datasets loralib sentencepiece
!pip -v install bitsandbytes accelerate
!pip -v install langchain
!pip install scipy
!pip install xformers
!pip install langchain faiss-cpu
# needed to load git repo
!pip install GitPython
!pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
// not this is the latest repo cloned locally
from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model_name='/Users/uavalos/Documents/gtr-t5-xxl')
```
### Expected behavior
* that it loads the embeddings just fine
* in fact, it was working a few days ago
* it stopped working after creating a new conda environment and reinstalling everything fresh
* if you look at the logs, it's looking for a module that's already installed. It's also failing to import the `google` module... i get the same error if i explicitly install that as well
Failure logs
```
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/langchain/embeddings/huggingface.py:51, in HuggingFaceEmbeddings.__init__(self, **kwargs)
50 try:
---> 51 import sentence_transformers
53 except ImportError as exc:
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/sentence_transformers/__init__.py:3
2 __MODEL_HUB_ORGANIZATION__ = 'sentence-transformers'
----> 3 from .datasets import SentencesDataset, ParallelSentencesDataset
4 from .LoggingHandler import LoggingHandler
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/sentence_transformers/datasets/__init__.py:3
2 from .NoDuplicatesDataLoader import NoDuplicatesDataLoader
----> 3 from .ParallelSentencesDataset import ParallelSentencesDataset
4 from .SentencesDataset import SentencesDataset
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/sentence_transformers/datasets/ParallelSentencesDataset.py:4
3 import gzip
----> 4 from .. import SentenceTransformer
5 from ..readers import InputExample
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/sentence_transformers/SentenceTransformer.py:11
10 from numpy import ndarray
---> 11 import transformers
12 from huggingface_hub import HfApi, HfFolder, Repository, hf_hub_url, cached_download
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/transformers/__init__.py:26
25 # Check the dependencies satisfy the minimal versions required.
---> 26 from . import dependency_versions_check
27 from .utils import (
28 OptionalDependencyNotAvailable,
29 _LazyModule,
(...)
42 logging,
43 )
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/transformers/dependency_versions_check.py:16
15 from .dependency_versions_table import deps
---> 16 from .utils.versions import require_version, require_version_core
19 # define which module versions we always want to check at run time
20 # (usually the ones defined in `install_requires` in setup.py)
21 #
22 # order specific notes:
23 # - tqdm must be checked before tokenizers
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/transformers/utils/__init__.py:188
186 else:
187 # just to get the expected `No module named 'google.protobuf'` error
--> 188 from . import sentencepiece_model_pb2
191 WEIGHTS_NAME = "pytorch_model.bin"
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/transformers/utils/sentencepiece_model_pb2.py:17
1 # Generated by the protocol buffer compiler. DO NOT EDIT!
2 # source: sentencepiece_model.proto
3
(...)
15 # See the License for the specific language governing permissions and
16 # limitations under the License.
---> 17 from google.protobuf import descriptor as _descriptor
18 from google.protobuf import message as _message
ModuleNotFoundError: No module named 'google'
The above exception was the direct cause of the following exception:
ImportError Traceback (most recent call last)
Cell In[8], line 2
1 from langchain.embeddings import HuggingFaceEmbeddings
----> 2 embeddings = HuggingFaceEmbeddings(model_name='/Users/uavalos/Documents/gtr-t5-xxl')
File ~/miniconda3/envs/llama3/lib/python3.11/site-packages/langchain/embeddings/huggingface.py:54, in HuggingFaceEmbeddings.__init__(self, **kwargs)
51 import sentence_transformers
53 except ImportError as exc:
---> 54 raise ImportError(
55 "Could not import sentence_transformers python package. "
56 "Please install it with `pip install sentence_transformers`."
57 ) from exc
59 self.client = sentence_transformers.SentenceTransformer(
60 self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
61 )
ImportError: Could not import sentence_transformers python package. Please install it with `pip install sentence_transformers`.
```
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| 1,783,981,699 |
I_kwDOIPDwls5qVWaD
| 7,018 |
jinja2 formate suppot trim_blocks mode
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[] | null | 1 | 2023-07-01T14:43:22 | 2023-10-07T16:05:07 | 2023-10-07T16:05:06 |
NONE
| null |
### Feature request
when i use jinja2 template， i get the following the problem
template:
```
{% if a %}\n
{{a}}\n
{% endif %}\n
{{b}}
```
and i pass b variable to template render,then the output like this
```
\n
b
```
Each variable placeholder is followed by a '\n'.
Since a is not passed in, and '\n' is left after rendering
i wand the right output is this:
```
b
```
in jinja2 ,use enviroment create template ,and enable trim_blocks ,the jinja2 can ignore '\n'.
this is origin code
```python
def jinja2_formatter(template: str, **kwargs: Any) -> str:
"""Format a template using jinja2."""
try:
from jinja2 import Template
except ImportError:
raise ImportError(
"jinja2 not installed, which is needed to use the jinja2_formatter. "
"Please install it with `pip install jinja2`."
)
return Template(template).render(**kwargs)
```
I tried modifying it a bit
```python
def jinja2_formatter(template: str, **kwargs: Any) -> str:
"""Format a template using jinja2."""
try:
from jinja2 import Environment
except ImportError:
raise ImportError(
"jinja2 not installed, which is needed to use the jinja2_formatter. "
"Please install it with `pip install jinja2`."
)
env = Environment(trim_blocks=True)
jinja_template = env.from_string(template)
return jinja_template.render(**kwargs)
```
### Motivation
I often use the control semantics of Jinja2 to construct prompt templates, creating several different prompts by passing in different variables.
However, since the Jinja2 render in Langchain does not support trim_blocks, it always results in unnecessary blank lines.
Perhaps LLM doesn't care about these extra lines, but as a human being, my OCD makes me feel uncomfortable with them.
### Your contribution
The jinja2_formatter function is too low-level within the Langchain code.
Although the modification is simple, I fear that the PR I submit could potentially affect other parts of the code.
So, if you have time, perhaps you could personally implement this feature.
|
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Second Attempt - Add concurrent insertion of vector rows in the Cassandra Vector Store
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CONTRIBUTOR
| null |
Retrying with the same improvements as in #6772, this time trying not to mess up with branches.
@rlancemartin doing a fresh new PR from a branch with a new name. This should do. Thank you for your help!
|
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PipelinePromptTemplate format_prompt should support missing keyword
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[] | null | 6 | 2023-07-01T13:16:11 | 2023-10-12T16:07:57 | 2023-10-12T16:07:56 |
NONE
| null |
### Feature request
the following is part of the format code for PipelinePromptTemplate
```python
def _get_inputs(inputs: dict, input_variables: List[str]) -> dict:
return {k: inputs[k] for k in input_variables}
class PipelinePromptTemplate(BasePromptTemplate):
....
def format_prompt(self, **kwargs: Any) -> PromptValue:
for k, prompt in self.pipeline_prompts:
_inputs = _get_inputs(kwargs, prompt.input_variables)
if isinstance(prompt, BaseChatPromptTemplate):
kwargs[k] = prompt.format_messages(**_inputs)
else:
kwargs[k] = prompt.format(**_inputs)
_inputs = _get_inputs(kwargs, self.final_prompt.input_variables)
return self.final_prompt.format_prompt(**_inputs)
```
the function _get_inputs should support miss keyword process，like:
```python
def _get_inputs(inputs: dict, input_variables: List[str]) -> dict:
return {k: inputs.get(k) for k in input_variables}
```
### Motivation
I primarily use the Jinja2 template format and frequently use control statements. Consequently, there are times when I choose not to pass in certain variables. Thus, I hope that PipelinePromptTemplate could support handling missing variables.
### Your contribution
Perhaps, I can submit a PR to add this feature or fix this bug . If you don't have the time to address it, that is.
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The Fellowship of the Vectors: New Embeddings Filter using clustering.
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CONTRIBUTOR
| null |
Continuing with Tolkien inspired series of langchain tools. I bring to you:
**The Fellowship of the Vectors**, AKA EmbeddingsClusteringFilter.
This document filter uses embeddings to group vectors together into clusters, then allows you to pick an arbitrary number of documents vector based on proximity to the cluster centers. That's a representative sample of the cluster.
The original idea is from [Greg Kamradt](https://github.com/gkamradt) from this video (Level4):
https://www.youtube.com/watch?v=qaPMdcCqtWk&t=365s
I added few tricks to make it a bit more versatile, so you can parametrize what to do with duplicate documents in case of cluster overlap: replace the duplicates with the next closest document or remove it. This allow you to use it as an special kind of redundant filter too.
Additionally you can choose 2 diff orders: grouped by cluster or respecting the original retriever scores.
In my use case I was using the docs grouped by cluster to run refine chains per cluster to generate summarization over a large corpus of documents.
Let me know if you want to change anything!
@rlancemartin, @eyurtsev, @hwchase17,
|
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PR_kwDOIPDwls5UZg-2
| 7,014 |
Support params on GoogleSearchApiWrapper (#6810)
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[] | null | 1 | 2023-07-01T10:23:48 | 2023-07-02T07:18:39 | 2023-07-02T07:18:38 |
CONTRIBUTOR
| null |
## Description
Support search params in GoogleSearchApiWrapper's result call, for the extra filtering on search,
to support extra query parameters that google cse provides:
https://developers.google.com/custom-search/v1/reference/rest/v1/cse/list?hl=ko
## Issue
#6810
## Dependencies
None
## Maintainer
@dev2049
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Rm retriever kwargs
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[] | null | 1 | 2023-07-01T08:58:08 | 2023-07-02T17:24:12 | 2023-07-02T14:22:25 |
COLLABORATOR
| null |
Doesn't actually limit the Retriever interface but hopefully in practice it does
|
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PR_kwDOIPDwls5UZPUh
| 7,012 |
openapi chain nit
|
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[] | null | 2 | 2023-07-01T08:52:48 | 2023-07-03T08:22:54 | 2023-07-03T08:22:53 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,783,681,691 |
I_kwDOIPDwls5qUNKb
| 7,011 |
How to save embeddings when using Agent for local document Q&A to avoid unnecessary waste by rereading API calls to generate embeddings
|
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[] | null | 1 | 2023-07-01T08:31:43 | 2023-10-07T16:05:17 | 2023-10-07T16:05:17 |
NONE
| null |
Attach my code
```
llm = OpenAI(model_name='gpt-4', temperature=0)
embeddings = OpenAIEmbeddings(model='text-embedding-ada-002')
docs = TextLoader("state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
state_of_union_store = Chroma.from_documents(
texts,
embeddings,
collection_name="state-of-union"
)
vectorstore_info = VectorStoreInfo(
name="state_of_union_address",
description="the most recent state of the Union adress",
vectorstore=state_of_union_store,
)
toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)
agent_executor = create_vectorstore_agent(llm=llm, toolkit=toolkit, verbose=True)
agent_executor("What did biden say about ketanji brown jackson in the state of the union address?")
```
|
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| 1,783,670,404 |
I_kwDOIPDwls5qUKaE
| 7,010 |
How to add memory with` create_vectorstore_agent`
|
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[] | null | 2 | 2023-07-01T08:18:12 | 2023-10-07T16:05:22 | 2023-10-07T16:05:22 |
NONE
| null |
I want to add memory context for Agent, so I referred to the document [here](https://python.langchain.com/docs/modules/memory/how_to/agent_with_memory)
There is such a step in it
I want to implement the same step in the `create_vectorstore_agent `function,
but I found that `create_vectorstore_agent `is a pre-defined,
it has no way to pass some parameters, such as suffix(into `ZeroShotAgent.create_prompt`),
which means, if I want to use the `agent_with_memory `function ,
I must build the agent from scratch, and cannot use `create_vectorstore_agent`
|
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| 1,783,632,265 |
I_kwDOIPDwls5qUBGJ
| 7,009 |
python tool issue: NameError: name 're' is not defined
|
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[] | null | 2 | 2023-07-01T07:39:21 | 2023-10-07T16:05:28 | 2023-10-07T16:05:28 |
NONE
| null |
### System Info
I am using a create_pandas_dataframe_agent like so:
```
create_pandas_dataframe_agent(ChatOpenAI(model_name='gpt-4', temperature=0), df, verbose=True,
max_iterations=2,
early_stopping_method="generate",
handle_parsing_errors="I'm sorry, but the question you have asked is not available within the dataset. Is there anything else I can help you with?")
```
When I try to prompt it to do anything that requires a regex the output i get:
```
Thought: The client wants to know the top 5 hashtags used in the tweets. To find this, I need to extract all the hashtags from the 'full_text' column of the dataframe, count the frequency of each hashtag, and then return the top 5. However, the dataframe does not contain a column for hashtags. Therefore, I need to create a new column 'hashtags' in the dataframe where each entry is a list of hashtags found in the corresponding 'full_text'. Then, I can count the frequency of each hashtag in this new column and return the top 5.
Action: python_repl_ast
Action Input:
```python
import re
import pandas as pd
# Function to extract hashtags from a text
def extract_hashtags(text):
return re.findall(r'\#\w+', text)
# Create a new column 'hashtags' in the dataframe
df['hashtags'] = df['full_text'].apply(extract_hashtags)
# Flatten the list of hashtags and count the frequency of each hashtag
hashtag_counts = pd.Series([hashtag for hashtags in df['hashtags'] for hashtag in hashtags]).value_counts()
# Get the top 5 hashtags
top_5_hashtags = hashtag_counts.head(5)
top_5_hashtags
```
**Observation: NameError: name 're' is not defined**
```
Can anyone help?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
### Reproduction
use this agent:
create_pandas_dataframe_agent(ChatOpenAI(model_name='gpt-4', temperature=0), df, verbose=True,
max_iterations=2,
early_stopping_method="generate",
handle_parsing_errors="I'm sorry, but the question you have asked is not available within the dataset. Is there anything else I can help you with?")
then prompt it with
top 5 hashtags used
### Expected behavior
the regex should work and return the answer
|
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PR_kwDOIPDwls5UY7PD
| 7,008 |
docs: Fix wrong usage code
|
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[] | null | 1 | 2023-07-01T07:14:48 | 2023-07-01T07:21:06 | 2023-07-01T07:20:57 |
NONE
| null |
# Description
In the latest langchain version, the `predict()` method seems to be deprecated. Now we just call LLM object again to send some prompt or content as the changes shown.
|
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| 1,783,593,724 |
PR_kwDOIPDwls5UY2aY
| 7,006 |
Remove duplicate mongodb integration doc
|
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[] | null | 2 | 2023-07-01T06:44:13 | 2023-07-03T20:29:50 | 2023-07-03T08:23:34 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Removes MongoDB Atlas duplicate entry in the docs
- Twitter handle: @adam91holt
Maintainer responsibilities:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
|
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| 1,783,565,603 |
PR_kwDOIPDwls5UYwBj
| 7,005 |
Update get_started.mdx
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[] | null | 2 | 2023-07-01T06:11:27 | 2023-07-03T08:23:13 | 2023-07-03T08:23:13 |
CONTRIBUTOR
| null |
typo in chat = ChatOpenAI(open_api_key="...") should be openai_api_key
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,783,463,930 |
I_kwDOIPDwls5qTX_6
| 7,004 |
ElasticSearch embedding need a function to add single text string as AzureOpenAI only support single text input now
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[] | null | 1 | 2023-07-01T03:54:40 | 2023-10-07T16:05:33 | 2023-10-07T16:05:32 |
NONE
| null |
### Feature request
ElasticSearch embedding need a function to add single text string as Azure OpenAI only support single text input now.
Message from Azure OpenAI Embedding:
embed_documents(texts)
# openai.error.InvalidRequestError: Too many inputs for model None.
# The max number of inputs is 1. We hope to increase the number of inputs per request soon.
# Please contact us through an Azure support request at: https://go.microsoft.com/fwlink/?linkid=2213926 for further questions.
### Motivation
To support to use Azure OpenAI embedding API
### Your contribution
I can do more test after if this fixed.
|
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| 1,783,422,507 |
PR_kwDOIPDwls5UYPP7
| 7,003 |
Persist chroma db
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CONTRIBUTOR
| null |
Description:
I have made changes to the Chroma DB Vector Store Wrapper to accept a persistent directory from client settings.
Issue:
https://github.com/hwchase17/langchain/issues/6938
Dependencies:
None
Tag maintainer: @rlancemartin, @eyurtsev
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| 1,783,366,350 |
I_kwDOIPDwls5qTALO
| 7,002 |
FAISS Support for filter while using the as_retriever()
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[] | null | 1 | 2023-07-01T02:31:45 | 2023-10-07T16:05:38 | 2023-10-07T16:05:37 |
NONE
| null |
### Feature request
[https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss)
There's a section that allows you to filter the documents stored in FAISS
It will be cool to allow
results = db.similarity_search("foo", filter=dict(page=1), k=1, fetch_k=4)
when you are doing
vectorstore.as_retriever(<<Include the filter here>>)
This can allow you to select a subset of your vector documents to chat with them.
This is if I have multiple documents from multiple sources loaded in the vectorstroe.
### Motivation
When I do a conversational
qa = ConversationalRetrievalChain(
retriever=vector..as_retriever(), ...
If allow to use only a portion of the document
qa = ConversationalRetrievalChain(
retriever=vector.as_retriever(<<Filter goes here>>>), ...
### Your contribution
I can test it :-)
|
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I_kwDOIPDwls5qSz0-
| 7,001 |
Bug of csv agent, basically all query failed with json error
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[] | null | 12 | 2023-07-01T01:11:10 | 2023-11-06T12:40:01 | null |
NONE
| null |
### System Info
Python version: Python 3.10.6
Langchain version: 0.0.219
OS: Ubuntu 22.04
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
### Reproduction
I've simple code like this
```
from langchain.agents import create_csv_agent
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
import os
import sys
directory = './test'
f = []
for filename in os.listdir(directory):
if filename.endswith(".csv"):
f.append(directory + "/" +filename)
agent = create_csv_agent(
ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),
f,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
)
qry ="how many rows are there?"
while True:
if not qry:
qry = input("Q: ")
if qry in ['quit', 'q', 'exit']:
sys.exit()
agent.run(qry)
qry = None
```
I'm using titanic dataset
```
https://github.com/datasciencedojo/datasets/blob/master/titanic.csv
```
Error as below
```
$ python3 langchain-csv.py
> Entering new chain...
Traceback (most recent call last):
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py", line 112, in _parse_ai_message
_tool_input = json.loads(function_call["arguments"])
File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/data/mytuition/langchain-csv.py", line 32, in <module>
agent.run(qry)
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 290, in run
return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 166, in __call__
raise e
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/agents/agent.py", line 987, in _call
next_step_output = self._take_next_step(
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/agents/agent.py", line 803, in _take_next_step
raise e
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/agents/agent.py", line 792, in _take_next_step
output = self.agent.plan(
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py", line 212, in plan
agent_decision = _parse_ai_message(predicted_message)
File "/home/yockgenm/.local/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py", line 114, in _parse_ai_message
raise OutputParserException(
langchain.schema.OutputParserException: Could not parse tool input: {'name': 'python', 'arguments': 'len(df)'} because the `arguments` is not valid JSON.
```
### Expected behavior
Langchain provided answer of total row
|
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PR_kwDOIPDwls5UX0Xm
| 7,000 |
Create arize_llm_observability.ipynb
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[] | null | 2 | 2023-07-01T00:50:40 | 2023-07-05T19:55:48 | 2023-07-05T19:55:48 |
CONTRIBUTOR
| null |
Adding documentation and notebook for Arize callback handler.
- @dev2049
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
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I_kwDOIPDwls5qSc9h
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Unable to load OPENAI Embeddings
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[] | null | 1 | 2023-06-30T22:26:08 | 2023-10-06T16:05:33 | 2023-10-06T16:05:32 |
NONE
| null |
### System Info
Most recent versions of all
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings.openai import OpenAIEmbeddings
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\agents\__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\agents\agent.py", line 30, in <module>
from langchain.prompts.few_shot import FewShotPromptTemplate
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\prompts\__init__.py", line 12, in <module>
from langchain.prompts.example_selector import (
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\prompts\example_selector\__init__.py", line 4, in <module>
from langchain.prompts.example_selector.semantic_similarity import (
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\prompts\example_selector\semantic_similarity.py", line 10, in <module>
from langchain.vectorstores.base import VectorStore
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\vectorstores\__init__.py", line 2, in <module>
from langchain.vectorstores.alibabacloud_opensearch import (
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\vectorstores\alibabacloud_opensearch.py", line 9, in <module>
from langchain.vectorstores.base import VectorStore
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\vectorstores\base.py", line 372, in <module>
class VectorStoreRetriever(BaseRetriever, BaseModel):
File "C:\Users\hasaa\anaconda3\lib\site-packages\langchain\vectorstores\base.py", line 388, in VectorStoreRetriever
def validate_search_type(cls, values: Dict) -> Dict:
File "pydantic\class_validators.py", line 126, in pydantic.class_validators.root_validator.dec
File "pydantic\class_validators.py", line 144, in pydantic.class_validators._prepare_validator
pydantic.errors.ConfigError: duplicate validator function "langchain.vectorstores.base.VectorStoreRetriever.validate_search_type"; if this is intended, set `allow_reuse=True`
### Expected behavior
The error I am facing is due to a duplicate validator function in the VectorStoreRetriever
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DOC: <Please write a comprehensive title after the 'DOC: ' prefix>
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[] | null | 0 | 2023-06-30T20:58:25 | 2023-06-30T21:02:55 | 2023-06-30T21:02:55 |
NONE
| null |
### Issue with current documentation:
You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage -- ChatMessage takes in an arbitrary role parameter. Most of the time, you'll just be dealing with HumanMessage, AIMessage, and SystemMessage.
```
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatOpenAI(temperature=0)
chat.predict_messages([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
# >> AIMessage(content="J'aime programmer.", additional_kwargs={})
```
It is useful to understand how chat models are different from a normal LLM, but it can often be handy to just be able to treat them the same. LangChain makes that easy by also exposing an interface through which you can interact with a chat model as you would a normal LLM. You can access this through the predict interface.
```
chat.predict("Translate this sentence from English to French. I love programming.")
# >> J'aime programmer
```
### Idea or request for content:
This code gives me the error:
```
AttributeError: 'ChatOpenAI' object has no attribute 'predict'
```
Is there really an exposed interface for predict?
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GoogleDriveLoader: AttributeError: 'Credentials' object has no attribute 'with_scopes'
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[] | null | 2 | 2023-06-30T20:25:55 | 2023-07-05T18:00:02 | 2023-07-05T18:00:01 |
NONE
| null |
### System Info
Macbook OSX
Python 3.11.4 (main, Jun 25 2023, 18:18:14) [Clang 14.0.3 (clang-1403.0.22.14.1)] on darwin
LangChain Environment:
sdk_version:0.0.17
library:langchainplus_sdk
platform:macOS-13.3.1-arm64-arm-64bit
runtime:python
runtime_version:3.11.4
I'm getting this error, and no matter what configurations I try, I cannot get past it.
```
AttributeError: 'Credentials' object has no attribute 'with_scopes'
```
My abbreviated code snippet:
```
from langchain.document_loaders import GoogleDriveLoader
loader = GoogleDriveLoader(
folder_id="xxxx", recursive=True,
)
docs = loader.load()
```
```
cat ~/.credentials/credentials.json
{
"installed": {
"client_id": "xxxxxxxxx.apps.googleusercontent.com",
"project_id": "xxxxxxxxx",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_secret": "xxxxxxxxx",
"redirect_uris": [
"http://localhost"
]
}
}
```
```
#gcloud info
Google Cloud SDK [437.0.1]
Platform: [Mac OS X, arm] uname_result(system='Darwin', node='xxxxs-MBP-3', release='22.4.0', version='Darwin Kernel Version 22.4.0: Mon Mar 6 20:59:28 PST 2023; root:xnu-8796.101.5~3/RELEASE_ARM64_T6000', machine='arm64')
Locale: (None, 'UTF-8')
Python Version: [3.11.4 (main, Jun 25 2023, 18:18:14) [Clang 14.0.3 (clang-1403.0.22.14.1)]]
Python Location: [/Users/xxxx/.asdf/installs/python/3.11.4/bin/python3]
OpenSSL: [OpenSSL 1.1.1u 30 May 2023]
Requests Version: [2.25.1]
urllib3 Version: [1.26.9]
Default CA certs file: [/Users/xxxx/google-cloud-sdk/lib/third_party/certifi/cacert.pem]
Site Packages: [Disabled]
Installation Root: [/Users/xxxx/google-cloud-sdk]
Installed Components:
gsutil: [5.24]
core: [2023.06.30]
bq: [2.0.93]
gcloud-crc32c: [1.0.0]
System PATH: [/Users/xxxx/.asdf/plugins/python/shims:/Users/xxxx/.asdf/installs/python/3.11.4/bin:/Users/xxxx/.rvm/gems/ruby-3.0.3/bin:/Users/xxxx/.rvm/gems/ruby-3.0.3@global/bin:/Users/xxxx/.rvm/rubies/ruby-3.0.3/bin:/Users/xxxx/.rvm/bin:/Users/xxxx/google-cloud-sdk/bin:/Users/xxxx/.asdf/shims:/opt/homebrew/opt/asdf/libexec/bin:/Users/xxxx/.sdkman/candidates/gradle/current/bin:/Users/xxxx/.jenv/shims:/Users/xxxx/.jenv/bin:/Users/xxxx/.nvm/versions/node/v17.6.0/bin:/opt/homebrew/opt/[email protected]/bin:/opt/homebrew/opt/mysql-client/bin:/opt/homebrew/bin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin:/Applications/Postgres.app/Contents/Versions/latest/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/local/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/appleinternal/bin:/Users/xxxx/Library/Android/sdk/emulator:/Users/xxxx/Library/Android/sdk/platform-tools]
Python PATH: [/Users/xxxx/google-cloud-sdk/lib/third_party:/Users/xxxx/google-cloud-sdk/lib:/Users/xxxx/.asdf/installs/python/3.11.4/lib/python311.zip:/Users/xxxx/.asdf/installs/python/3.11.4/lib/python3.11:/Users/xxxx/.asdf/installs/python/3.11.4/lib/python3.11/lib-dynload]
Cloud SDK on PATH: [True]
Kubectl on PATH: [/usr/local/bin/kubectl]
Installation Properties: [/Users/xxxx/google-cloud-sdk/properties]
User Config Directory: [/Users/xxx/.config/gcloud]
Active Configuration Name: [default]
Active Configuration Path: [/Users/xxxx/.config/gcloud/configurations/config_default]
Account: [[email protected]]
Project: [project-name-replaced]
Current Properties:
[compute]
zone: [europe-west1-b] (property file)
region: [europe-west1] (property file)
[core]
account: [[email protected]] (property file)
disable_usage_reporting: [True] (property file)
project: [project-name-replaced] (property file)
Logs Directory: [/Users/xxx/.config/gcloud/logs]
Last Log File: [/Users/xxx/.config/gcloud/logs/xxxx.log]
git: [git version 2.39.2]
ssh: [OpenSSH_9.0p1, LibreSSL 3.3.6]
```
### Who can help?
@eyurtsev I think?
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's happening with a base app, nothing special. Using this template:
https://github.com/hwchase17/langchain-streamlit-template
### Expected behavior
Expected to get the oAuth window?
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| 1,783,082,367 |
PR_kwDOIPDwls5UXEBB
| 6,996 |
Remove Promptlayer Notebook
|
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[] | null | 1 | 2023-06-30T19:51:57 | 2023-06-30T21:39:06 | 2023-06-30T21:30:24 |
COLLABORATOR
| null |
It's breaking our docs build
|
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PR_kwDOIPDwls5UW40_
| 6,995 |
move base prompt to schema
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PR_kwDOIPDwls5UW2BE
| 6,994 |
Harrison/docs combine document chain
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I_kwDOIPDwls5qRqSl
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HuggingFaceTextGenInference does not accept 0 temperature
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[] | null | 4 | 2023-06-30T18:51:33 | 2023-09-29T16:20:59 | 2023-09-29T16:20:39 |
NONE
| null |
### System Info
running google/flan-tf-xxl on ghcr.io/huggingface/text-generation-inference:0.8
langchain==0.0.220
text-generation==0.6.0
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### 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
### Reproduction
`HuggingFaceTextGenInference` does not accept a temperature parameter of 0 or None.
```
llm = HuggingFaceTextGenInference(
inference_server_url="http://localhost:8010/",
temperature=0.
)
llm("What did foo say about bar?")
```
ValidationError: `temperature` must be strictly positive
```
llm = HuggingFaceTextGenInference(
inference_server_url="http://localhost:8010/",
temperature=None
)
llm("What did foo say about bar?")
```
ValidationError: 1 validation error for HuggingFaceTextGenInference
temperature none is not an allowed value (type=type_error.none.not_allowed)
### Expected behavior
I expect to be able to pass a parameter to `HuggingFaceTextGenInference` that instructs the model to do greedy decoding without getting an error.
It seems like the issue is that LangChain enforces that temperature be an integer while the text-generation [client requires temperature](https://github.com/huggingface/text-generation-inference/blob/main/clients/python/text_generation/types.py#L74C4-L74C4) be None for greedy decoding or >0 for sampling.
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PR_kwDOIPDwls5UWvhd
| 6,992 |
Replace JIRA Arbitrary Code Execution vulnerability with finer grain API wrapper
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[] | null | 2 | 2023-06-30T18:28:53 | 2023-11-17T16:40:22 | 2023-07-05T19:56:01 |
CONTRIBUTOR
| null |
This fixes #4833 and the critical vulnerability https://nvd.nist.gov/vuln/detail/CVE-2023-34540
Previously, the JIRA API Wrapper had a mode that simply pipelined user input into an `exec()` function.
[The intended use of the 'other' mode is to cover any of Atlassian's API that don't have an existing interface](https://github.com/hwchase17/langchain/blob/cc33bde74ff2e050a400e4451e04ff5b32c4a7bd/langchain/tools/jira/prompt.py#L24)
Fortunately all of the [Atlassian JIRA API methods are subfunctions of their `Jira` class](https://atlassian-python-api.readthedocs.io/jira.html), so this implementation calls these subfunctions directly.
As well as passing a string representation of the function to call, the implementation flexibly allows for optionally passing args and/or keyword-args. These are given as part of the dictionary input. Example:
```
{
"function": "update_issue_field", #function to execute
"args": [ #list of ordered args similar to other examples in this JiraAPIWrapper
"key",
{"summary": "New summary"}
],
"kwargs": {} #dict of key value keyword-args pairs
}
```
the above is equivalent to `self.jira.update_issue_field("key", {"summary": "New summary"})`
Alternate query schema designs are welcome to make querying easier without passing and evaluating arbitrary python code. I considered parsing (without evaluating) input python code and extracting the function, args, and kwargs from there and then pipelining them into the callable function via `*f(args, **kwargs)` - but this seemed more direct.
@vowelparrot @dev2049
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I_kwDOIPDwls5qRgnO
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langchain takes much time to reload
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[] | null | 3 | 2023-06-30T18:17:21 | 2023-10-07T16:05:43 | 2023-10-07T16:05:42 |
NONE
| null |
### System Info
Macos system
### Who can help?
_No response_
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### Reproduction
loading malfunction
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PR_kwDOIPDwls5UWmQD
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Pack of small fixes and refactorings that don't affect functionality
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[] | null | 1 | 2023-06-30T18:02:41 | 2023-07-07T07:40:50 | 2023-07-07T07:40:50 |
CONTRIBUTOR
| null |
Description: Pack of small fixes and refactorings that don't affect functionality, just making code prettier & fixing some misspelling (hand-filtered improvements proposed by SeniorAi.online, prototype of code improving tool based on gpt4), agents and callbacks folders was covered.
Dependencies: Nothing changed
Twitter: https://twitter.com/nayjest
@dev2049
@vowelparrot
@agola11
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added `Brave Search` document_loader
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COLLABORATOR
| null |
- Added `Brave Search` document loader.
- Refactored BraveSearch wrapper
- Added a Jupyter Notebook example
- Added `Ecosystem/Integrations` BraveSearch page
Please review:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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Fix: Correct typo
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CONTRIBUTOR
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Description: Correct a minor typo in the docs. @dev2049
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PR_kwDOIPDwls5UWZUh
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Handle kwargs in FAISS.load_local()
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CONTRIBUTOR
| null |
- Description: This allows parameters such as `relevance_score_fn` to be passed to the `FAISS` constructor via the `load_local()` class method.
- Tag maintainer: @rlancemartin @eyurtsev
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PR_kwDOIPDwls5UWWt9
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Add Flyte Callback Handler (#6139)
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PR_kwDOIPDwls5UWTVq
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Fix typo in quickstart.mdx
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CONTRIBUTOR
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Removed an extra "to" from a sentence. @dev2049 very minor documentation fix.
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VectaraRetriever does not use the parameters passed in by `Vectara.as_retriever()`
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[] | null | 4 | 2023-06-30T16:42:27 | 2023-10-08T16:05:51 | 2023-10-08T16:05:50 |
NONE
| null |
`class VectaraRetriever` does not use the parameters passed in by `Vectara.as_retriever()`. The class definition sets up `vectorstore` and `search_kwargs` but then there is no check for values coming in from `Vectara.as_retriever()`.
https://github.com/hwchase17/langchain/blob/e3b7effc8f39333076c2bedd7306de81ce988de6/langchain/vectorstores/vectara.py#L307
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| 1,782,823,112 |
I_kwDOIPDwls5qQ7jI
| 6,983 |
DOC: "Edit this page" returns 404
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[] | null | 1 | 2023-06-30T16:33:59 | 2023-10-06T16:05:48 | 2023-10-06T16:05:47 |
CONTRIBUTOR
| null |
### Issue with current documentation:
The links in the documentation to make contributions to pages point to the wrong file location within the repository. For example, the link on the "Quickstart" page points to the filepath `docs/docs/get_started/quickstart.mdx`, when it should point to `docs/docs_skeleton/docs/get_started/quickstart.mdx`. Presumably this is a result of #6300 and the links not getting updated.
### Idea or request for content:
If someone more familiar with the structure of the docs could tell me where to find the base template where the link templates are specified, and preferably a bit of guidance on how the documentation is structured now to make it easier to figure out the correct links, then I can make the updates.
|
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I_kwDOIPDwls5qQ4Kf
| 6,982 |
RetrievalQAWithSourcesChain object default prompt is problematic
|
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[] | null | 2 | 2023-06-30T16:22:47 | 2023-10-06T16:05:53 | 2023-10-06T16:05:52 |
NONE
| null |
### System Info
Here is my code to initialize the Chain, it should come with the default prompt template:
qa_source_chain = RetrievalQAWithSourcesChain.from_chain_type(
llm=llm_chat,
chain_type="stuff",
retriever=db_test.as_retriever()
)
Here is the source chain object, I guess default template for this chain is hacked?
RetrievalQAWithSourcesChain(memory=None, callbacks=None, callback_manager=None, verbose=False, combine_documents_chain=StuffDocumentsChain(memory=None, callbacks=None, callback_manager=None, verbose=False, input_key='input_documents', output_key='output_text', llm_chain=LLMChain(memory=None, callbacks=None, callback_manager=None, verbose=False, prompt=PromptTemplate(input_variables=['summaries', 'question'], output_parser=None, partial_variables={}, template='Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES"). \nIf you don\'t know the answer, just say that you don\'t know. Don\'t try to make up an answer.\nALWAYS return a "SOURCES" part in your answer.\n\nQUESTION: Which state/country\'s law governs the interpretation of the contract?\n=========\nContent: This Agreement is governed by English law and the parties submit to the exclusive jurisdiction of the English courts in relation to any dispute (contractual or non-contractual) concerning this Agreement save that either party may apply to any court for an injunction or other relief to protect its Intellectual Property Rights.\nSource: 28-pl\nContent: No Waiver. Failure or delay in exercising any right or remedy under this Agreement shall not constitute a waiver of such (or any other) right or remedy.\n\n11.7 Severability. The invalidity, illegality or unenforceability of any term (or part of a term) of this Agreement shall not affect the continuation in force of the remainder of the term (if any) and this Agreement.\n\n11.8 No Agency. Except as expressly stated otherwise, nothing in this Agreement shall create an agency, partnership or joint venture of any kind between the parties.\n\n11.9 No Third-Party Beneficiaries.\nSource: 30-pl\nContent: (b) if Google believes, in good faith, that the Distributor has violated or caused Google to violate any Anti-Bribery Laws (as defined in Clause 8.5) or that such a violation is reasonably likely to occur,\nSource: 4-pl\n=========\nFINAL ANSWER: This Agreement is governed by English law.\nSOURCES: 28-pl\n\nQUESTION: What did the president say about Michael Jackson?\n=========\nContent: Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n\nGroups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.\nSource: 0-pl\nContent: And we won’t stop. \n\nWe have lost so much to COVID-19. Time with one another. And worst of all, so much loss of life. \n\nLet’s use this moment to reset. Let’s stop looking at COVID-19 as a partisan dividing line and see it for what it is: A God-awful disease. \n\nLet’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans. \n\nWe can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n\nOfficer Mora was 27 years old. \n\nOfficer Rivera was 22. \n\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.\nSource: 24-pl\nContent: And a proud Ukrainian people, who have known 30 years of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards. \n\nTo all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. \n\nAnd I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n\nTonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world. \n\nAmerica will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies. \n\nThese steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n\nBut I want you to know that we are going to be okay.\nSource: 5-pl\nContent: More support for patients and families. \n\nTo get there, I call on Congress to fund ARPA-H, the Advanced Research Projects Agency for Health. \n\nIt’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more. \n\nARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more. \n\nA unity agenda for the nation. \n\nWe can do this. \n\nMy fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy. \n\nIn this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \n\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \n\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \n\nNow is the hour. \n\nOur moment of responsibility. \n\nOur test of resolve and conscience, of history itself. \n\nIt is in this moment that our character is formed. Our purpose is found. Our future is forged. \n\nWell I know this nation.\nSource: 34-pl\n=========\nFINAL ANSWER: The president did not mention Michael Jackson.\nSOURCES:\n\nQUESTION: {question}\n=========\n{summaries}\n=========\nFINAL ANSWER:', template_format='f-string', validate_template=True), llm=ChatOpenAI(verbose=False, callbacks=None, callback_manager=None, client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, model_name='gpt-3.5-turbo', temperature=0.0, model_kwargs={}, openai_api_key='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', openai_api_base=None, openai_organization=None, openai_proxy=None, request_timeout=None, max_retries=6, streaming=False, n=1, max_tokens=2000), output_key='text'), document_prompt=PromptTemplate(input_variables=['page_content', 'source'], output_parser=None, partial_variables={}, template='Content: {page_content}\nSource: {source}', template_format='f-string', validate_template=True), document_variable_name='summaries', document_separator='\n\n'), question_key='question', input_docs_key='docs', answer_key='answer', sources_answer_key='sources', return_source_documents=False, retriever=VectorStoreRetriever(vectorstore=<langchain.vectorstores.chroma.Chroma object at 0x14fa7cb50>, search_type='similarity', search_kwargs={}), reduce_k_below_max_tokens=False, max_tokens_limit=3375)
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. import and initialize a RetrievalQAWithSourcesChain, using RetrievalQAWithSourcesChain.from_chain_type() method
2. check the default prompt template of the RetrievalQAWithSourcesChain object
### Expected behavior
The default template should be very similar to a RetrievalQA chain object
|
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I_kwDOIPDwls5qQ4C4
| 6,981 |
Issue: 401 error when using ConversationalRetrievalQAChain
|
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[] | null | 3 | 2023-06-30T16:22:20 | 2023-10-06T16:05:58 | 2023-10-06T16:05:57 |
NONE
| null |
### Issue you'd like to raise.
Everything was working fine 10 min ago, but now when I use the ConversationalRetrievalQAChain I always get a 401 error, I tried 2 difrent API keys and the error persists, don't really know what the issue mght be, any ideas?
This is the error log:
[chain/error] [1:chain:conversational_retrieval_chain] [315ms] Chain run errored with error: "Request failed with status code 401"
error Error: Request failed with status code 401
at createError (EDITED\node_modules\axios\lib\core\createError.js:16:15)
at settle (EDITED\node_modules\axios\lib\core\settle.js:17:12)
at IncomingMessage.handleStreamEnd (EDITED\node_modules\axios\lib\adapters\http.js:322:11)
at IncomingMessage.emit (node:events:525:35)
at IncomingMessage.emit (node:domain:489:12)
at endReadableNT (node:internal/streams/readable:1359:12)
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| 1,782,808,752 |
I_kwDOIPDwls5qQ4Cw
| 6,980 |
llma Embeddings error
|
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[] | null | 3 | 2023-06-30T16:22:20 | 2023-12-01T16:09:18 | 2023-12-01T16:09:17 |
NONE
| null |
### System Info
Traceback (most recent call last):
File "/Users/apple/Desktop/LLM/gpt4all_langchain_chatbots/teacher/lib/python3.10/site-packages/langchain/embeddings/llamacpp.py", line 85, in validate_environment
from llama_cpp import Llama
ModuleNotFoundError: No module named 'llama_cpp'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/apple/Desktop/LLM/gpt4all_langchain_chatbots/mine.py", line 3, in <module>
llama = LlamaCppEmbeddings(model_path="./models/ggml-gpt4all-j.bin")
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1102, in pydantic.main.validate_model
File "/Users/apple/Desktop/LLM/gpt4all_langchain_chatbots/teacher/lib/python3.10/site-packages/langchain/embeddings/llamacpp.py", line 89, in validate_environment
raise ModuleNotFoundError(
ModuleNotFoundError: Could not import llama-cpp-python library. Please install the llama-cpp-python library to use this embedding model: pip install llama-cpp-python
### Who can help?
@sirrrik
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
### Reproduction
pip install the latest langChain package from pypy on mac
### Expected behavior
Traceback (most recent call last):
File "/Users/apple/Desktop/LLM/gpt4all_langchain_chatbots/teacher/lib/python3.10/site-packages/langchain/embeddings/llamacpp.py", line 85, in validate_environment
from llama_cpp import Llama
ModuleNotFoundError: No module named 'llama_cpp'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/apple/Desktop/LLM/gpt4all_langchain_chatbots/mine.py", line 3, in <module>
llama = LlamaCppEmbeddings(model_path="./models/ggml-gpt4all-j.bin")
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1102, in pydantic.main.validate_model
File "/Users/apple/Desktop/LLM/gpt4all_langchain_chatbots/teacher/lib/python3.10/site-packages/langchain/embeddings/llamacpp.py", line 89, in validate_environment
raise ModuleNotFoundError(
ModuleNotFoundError: Could not import llama-cpp-python library. Please install the llama-cpp-python library to use this embedding model: pip install llama-cpp-python
|
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PR_kwDOIPDwls5UWLEy
| 6,979 |
Beef up import test
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[] | null | 1 | 2023-06-30T16:19:04 | 2023-06-30T16:26:07 | 2023-06-30T16:26:06 |
COLLABORATOR
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| 1,782,799,836 |
PR_kwDOIPDwls5UWKBP
| 6,978 |
Rm pytz dep
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[] | null | 1 | 2023-06-30T16:15:51 | 2023-06-30T16:24:02 | 2023-06-30T16:24:01 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- 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!
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5UWApr
| 6,977 |
feat: enable `UnstructuredEmailLoader` to process attachments
|
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[] | null | 1 | 2023-06-30T15:47:47 | 2023-07-01T13:09:27 | 2023-07-01T13:09:26 |
CONTRIBUTOR
| null |
### Summary
Updates `UnstructuredEmailLoader` so that it can process attachments in addition to the e-mail content. The loader will process attachments if the `process_attachments` kwarg is passed when the loader is instantiated.
### Testing
```python
file_path = "fake-email-attachment.eml"
loader = UnstructuredEmailLoader(
file_path, mode="elements", process_attachments=True
)
docs = loader.load()
docs[-1]
```
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
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PR_kwDOIPDwls5UV8NA
| 6,976 |
Enable InMemoryDocstore to be constructed without providing a dict
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| false | null |
[] | null | 1 | 2023-06-30T15:32:51 | 2023-08-23T16:00:38 | 2023-07-05T20:56:31 |
CONTRIBUTOR
| null |
- Description: Allow `InMemoryDocstore` to be created without passing a dict to the constructor; the constructor can create a dict at runtime if one isn't provided.
- Tag maintainer: @dev2049
|
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Promptlayer Callback
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[] | null | 1 | 2023-06-30T15:19:04 | 2023-06-30T15:32:44 | 2023-06-30T15:32:43 |
COLLABORATOR
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I_kwDOIPDwls5qQeUR
| 6,974 |
Error 422 when using BraveSearch
|
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[] | null | 1 | 2023-06-30T15:12:25 | 2023-10-06T16:06:03 | 2023-10-06T16:06:03 |
CONTRIBUTOR
| null |
### System Info
langchain v0.0.220
Python 3.11.3
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Using `BraveSearch` with an agent always returns an error 422
[I've created a collab notebook with an example displaying this error.](https://colab.research.google.com/drive/1mUr6KWND4ZYmvFnPywbJevZLzssJMvBR?usp=sharing)
I've tried many times with both `ZERO_SHOT_REACT_DESCRIPTION` and `OPENAI_FUNCTIONS` agents
### Expected behavior
Agents should be able to use this tool
|
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| 1,782,539,266 |
I_kwDOIPDwls5qP2QC
| 6,973 |
Using JSONLinesLoader in Python
|
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[] | null | 6 | 2023-06-30T13:31:03 | 2023-07-05T13:33:14 | 2023-07-05T13:33:13 |
NONE
| null |
### Feature request
It would be great if the [JSONLinesLoader](https://js.langchain.com/docs/modules/indexes/document_loaders/examples/file_loaders/jsonlines) that's available in the JS version of Langchain could be ported to the Python version.
### Motivation
I find working with jsonl files to be frequently easier than json files.
### Your contribution
Not sure---I'm quite new to Python and so don't how to implement this.
|
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Arthur Callback
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[] | null | 1 | 2023-06-30T13:24:43 | 2023-06-30T14:48:03 | 2023-06-30T14:48:02 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5qPhRE
| 6,971 |
Cannot pass search_distance key to ConversationalRetrievalChain
|
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[] | null | 2 | 2023-06-30T12:34:14 | 2023-07-03T04:37:06 | 2023-07-03T04:37:06 |
NONE
| null |
### System Info
Traceback (most recent call last):
File "/tmp/pycharm_project_93/test_qa_generation.py", line 50, in <module>
result = qa.run({"question": query, "vectordbkwargs": vectordbkwargs })
File "/root/.cache/pypoetry/virtualenvs/irc-llm-0Bb4gJSe-py3.10/lib/python3.10/site-packages/langchain/chains/base.py", line 273, in run
return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
File "/root/.cache/pypoetry/virtualenvs/irc-llm-0Bb4gJSe-py3.10/lib/python3.10/site-packages/langchain/chains/base.py", line 151, in __call__
final_outputs: Dict[str, Any] = self.prep_outputs(
File "/root/.cache/pypoetry/virtualenvs/irc-llm-0Bb4gJSe-py3.10/lib/python3.10/site-packages/langchain/chains/base.py", line 216, in prep_outputs
self.memory.save_context(inputs, outputs)
File "/root/.cache/pypoetry/virtualenvs/irc-llm-0Bb4gJSe-py3.10/lib/python3.10/site-packages/langchain/memory/chat_memory.py", line 34, in save_context
input_str, output_str = self._get_input_output(inputs, outputs)
File "/root/.cache/pypoetry/virtualenvs/irc-llm-0Bb4gJSe-py3.10/lib/python3.10/site-packages/langchain/memory/chat_memory.py", line 21, in _get_input_output
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
File "/root/.cache/pypoetry/virtualenvs/irc-llm-0Bb4gJSe-py3.10/lib/python3.10/site-packages/langchain/memory/utils.py", line 11, in get_prompt_input_key
raise ValueError(f"One input key expected got {prompt_input_keys}")
ValueError: One input key expected got ['question', 'vectordbkwargs']
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### 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
### Reproduction
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Milvus
from langchain.text_splitter import CharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain.document_loaders import TextLoader
from langchain.memory import ConversationBufferWindowMemory
from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility
MILVUS_HOST = "_milvus.XXXX.com"
MILVUS_PORT = 19530
connections.connect(
alias="default",
host=MILVUS_HOST,
port=MILVUS_PORT
)
utility.drop_collection('LangChainCollection')
import os
os.environ['OPENAI_API_KEY'] = "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"
loader = TextLoader("/tmp/qa_chain1.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vectorstore = Milvus.from_documents(documents, embeddings,
connection_args={"host": "_milvus.XXXX.com", "port": "19530"}, search_params={"metric_type": "IP", "params": {"nprobe": 200}, "offset": 0})
memory = ConversationBufferWindowMemory(k=2 , memory_key="chat_history", return_messages=True)
vectordbkwargs = {"search_distance": 0.9}
retriver = vectorstore.as_retriever()
retriver.search_kwargs = search_kwargs={"k":5 }
qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0, model='gpt-3.5-turbo-16k'), retriver , memory = memory, condense_question_prompt = CONDENSE_QUESTION_PROMPT, verbose=True)
query = "Introduce Microsoft"
result = qa.run({"question": query, "vectordbkwargs": vectordbkwargs })
print(result)
### Expected behavior
return the relevant document with a score bigger than 0.9 when matching vectors by IP metric.
|
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HuggingFacePipeline not working
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[] | null | 2 | 2023-06-30T11:37:16 | 2023-06-30T11:59:52 | 2023-06-30T11:59:52 |
NONE
| null |
### System Info
langchain==0.0.219
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### 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
### Reproduction
>>> from langchain import HuggingFacePipeline
>>> mod = HuggingFacePipeline(model_id="tiiuae/falcon-7b-instruct", task="text-generation")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/guillem.garcia/miniconda3/envs/rene/lib/python3.10/site-packages/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for HuggingFacePipeline
task
extra fields not permitted (type=value_error.extra)
### Expected behavior
I expect it to work. When I instance a Pipeline with falcon using hugginface it works.
Also, Is there a way to deactivate pydantic? It makes it impossible to modify anything or make langchain compatible with our code
|
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| 1,782,326,661 |
I_kwDOIPDwls5qPCWF
| 6,968 |
langchain.chat_models.ChatOpenAI does not returns a response while langchain.OpeAI does rteturn results
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[] | null | 3 | 2023-06-30T10:55:01 | 2023-10-23T16:07:22 | 2023-10-23T16:07:21 |
NONE
| null |
### System Info
langchain==0.0.208
penai==0.27.8
python==3.10.6
### Who can help?
@hwchase17 Could you please help me in resolving the error whenver I run an gaent with the LLM as ChatOpenAI() i get the response as **""Do I need to use a tool? No.""**
While the agent returns the response perfectly fine with llm= OpenAI().
The irony is two days prior the ChatOpenAI() function was working fine and returned the results perfectly fine.
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### 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
### Reproduction
`import langchain
from langchain.prompts.base import StringPromptTemplate
from langchain.prompts import PromptTemplate,StringPromptTemplate
from langchain.agents import Tool, AgentExecutor, AgentOutputParser,LLMSingleActionAgent,initialize_agent
from langchain import OpenAI,LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.tools import DuckDuckGoSearchRun
from langchain.schema import AgentAction,AgentFinish
import re
from typing import List,Union
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
#
search = DuckDuckGoSearchRun()
#3640 def duck_wrapper(input_text):
search_results = search.run(f"site:webmd.com {input_text}")
return search_results
tools = [
Tool(
name = "Search WebMD",
func=duck_wrapper,
description="useful for when you need to answer medical and pharmalogical questions"
)
]
# Set up the base template
template = """Answer the following questions as best you can, but speaking as compasionate medical professional. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to answer as a compansionate medical professional when giving your final answer.
Previous conversation history:
{history}
Question: {input}
"""
tool_names = [tool.name for tool in tools]
history = None
prompt = PromptTemplate(template=template,input_variables=["tools","tool_names","history","input"])
from langchain.agents.agent_types import AgentType
agent = initialize_agent(tools=tools,
llm=ChatOpenAI(),
agent = AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
verbose=False,
return_intermediate_steps=True
)
try:
response = agent({"input":"how to treat acid reflux?",
"tools":tools,
"tool_names":tool_names,
"history":None,
"chat_history":None},
return_only_outputs=True)
except ValueError as e:
response = str(e)
if not response.startswith("Could not parse LLM output: `"):
raise e
response = response.removeprefix("Could not parse LLM output: `").removesuffix("`")
print(response)
`
### Expected behavior
{'output': 'There are a few lifestyle changes and home remedies that can help treat acid reflux, such as avoiding food triggers, eating smaller and more frequent meals, elevating the head of the bed while sleeping, and avoiding lying down after eating. Over-the-counter medications such as antacids and proton pump inhibitors can also be effective in treating acid reflux. However, it is always best to consult with a doctor if symptoms persist or worsen.', 'intermediate_steps': [(AgentAction(tool='Search WebMD', tool_input='how to treat acid reflux', log='Thought: Do I need to use a tool? Yes\nAction: Search WebMD\nAction Input: how to treat acid reflux'), 'Eat Earlier. Going to bed on a full stomach makes nighttime heartburn more likely. A full stomach puts pressure on the valve at the top of the stomach, which is supposed to keep stomach acid out ... Trim the fat off of meat and poultry, and cut the skin off chicken. Tweaks like these might be enough to tame your heartburn. Tomatoes (including foods like salsa and marinara sauce) and citrus ... Abdominal bloating. Abdominal pain. Vomiting. Indigestion. Burning or gnawing feeling in the stomach between meals or at night. Hiccups. Loss of appetite. Vomiting blood or coffee ground-like ... Esophagitis Symptoms. Symptoms of esophagitis include: Difficult or painful swallowing. Acid reflux. Heartburn. A feeling of something of being stuck in the throat. Chest pain. Nausea. Vomiting. This backflow (acid reflux) can irritate the lining of the esophagus. People with GERD may have heartburn, a burning sensation in the back of the throat, chronic cough, laryngitis, and nausea.')]}
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| 1,782,210,449 |
PR_kwDOIPDwls5UUJCs
| 6,967 |
Add SpacyEmbeddings class
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[] | null | 4 | 2023-06-30T09:30:48 | 2023-07-03T15:38:32 | 2023-07-03T15:38:31 |
CONTRIBUTOR
| null |
- Description: Added a new SpacyEmbeddings class for generating embeddings using the Spacy library.
- Issue: Sentencebert/Bert/Spacy/Doc2vec embedding support #6952
- Dependencies: This change requires the Spacy library and the 'en_core_web_sm' Spacy model.
- Tag maintainer: @dev2049
- Twitter handle: N/A
This change includes a new SpacyEmbeddings class, but does not include a test or an example notebook.
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| 1,782,206,965 |
PR_kwDOIPDwls5UUISM
| 6,966 |
Fixed a similarity score calculation bug in Chroma module
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[] | null | 7 | 2023-06-30T09:28:27 | 2023-07-14T01:12:53 | 2023-07-14T01:11:57 |
CONTRIBUTOR
| null |
The `_results_to_docs_and_scores()` function should have returned the similarity score (higher values indicating more similarity), but erroneously returned the distance (higher values indicating less similarity).
@rlancemartin, @eyurtsev
|
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| 1,782,126,816 |
I_kwDOIPDwls5qORjg
| 6,965 |
langchain agent took a lot of time
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[] | null | 5 | 2023-06-30T08:34:20 | 2023-10-06T16:06:08 | 2023-10-06T16:06:08 |
NONE
| null |
### Issue you'd like to raise.
run langchain agent takes a lot of time,about 20s。the question is simple. Is there any good way reduce time?
### Suggestion:
_No response_
|
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