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https://api.github.com/repos/langchain-ai/langchain/issues/15909 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15909/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15909/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15909/events | https://github.com/langchain-ai/langchain/pull/15909 | 2,077,526,741 | PR_kwDOIPDwls5j2tWo | 15,909 | community: SQLStrStore/SQLDocStore provide an easy SQL alternative to `InMemoryStore` to persist data remotely in a SQL storage | {
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- Implement `SQLStrStore` and `SQLDocStore` classes that inherits from `BaseStore` to allow to persist data remotely on a SQL server.
- SQL is widely used and sometimes we do not want to install a caching solution like Redis.
- Multiple issues/comments complain that there is no easy remote and persistent solution that are not in memory (users want to replace InMemoryStore), e.g., https://github.com/langchain-ai/langchain/issues/14267, https://github.com/langchain-ai/langchain/issues/15633, https://github.com/langchain-ai/langchain/issues/14643, https://stackoverflow.com/questions/77385587/persist-parentdocumentretriever-of-langchain
- This is particularly painful when wanting to use `ParentDocumentRetriever `
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* it's expensive to construct an InMemoryDocstore/dict
* you want to retrieve documents from remote sources
* you just want to reuse existing objects
- This implementation integrates well with PGVector, indeed, when using PGVector, you already have a SQL instance running. `SQLDocStore` is a convenient way of using this instance to store documents associated to vectors. An integration example with ParentDocumentRetriever and PGVector is provided in docs/docs/integrations/stores/sql.ipynb or [here](https://github.com/gcheron/langchain/blob/sql-store/docs/docs/integrations/stores/sql.ipynb).
- It persists `str` and `Document` objects but can be easily extended.
**Issue:**
Provide an easy SQL alternative to `InMemoryStore`. | {
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This PR:
1. Add `metadata[_job_ib]` in Document returned by any similarity search
2. Add `explore_job_stats` to enable users to explore job statistics and better the debuggability
3. Set the minimum row limit for running create vector index.
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Make it easy to use `tokenizers` for HF tokenizers instead of `transformers`
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link to agent types.
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Please find the attached pdf, where everything is detailed described :D
[langchain_core.pdf](https://github.com/langchain-ai/langchain/files/13905129/langchain_core.pdf)
**Dependencies:**
In order to solve Generalized assignment problem, we need to add 'mip' solver.
**Twitter handle:** no need ;)
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15889 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15889/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15889/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15889/events | https://github.com/langchain-ai/langchain/pull/15889 | 2,076,945,053 | PR_kwDOIPDwls5j0soA | 15,889 | [docs] Integration Documentation updated run to invoke for llms/ai21.ipynb | {
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- **Description:** Updated Integration Documentation for [llms/ai21.ipynb](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/llms/ai21.ipynb)
- **Issue:** #15664,
- **Dependencies:** NA,
- **Twitter handle:** @BhadreshSavani | {
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- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()
llm.invoke("how can langsmith help with testing?")
### Description
site-packages\langchain_openai\chat_models\base.py", line 454, in _create_chat_result
response = response.dict()
AttributeError: 'str' object has no attribute 'dict'
### System Info
Python 3.10.12
langchain 0.1.0
### 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 | {
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- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
The following code:
```python
from arango import ArangoClient
from langchain_community.graphs import ArangoGraph
from langchain.chains import ArangoGraphQAChain
# Initialize the ArangoDB client.
client = ArangoClient(hosts='http://localhost:8529')
# Connect to Database
db = client.db('mydb', username='myuser', password='mypass')
# Instantiate the ArangoDB-LangChain Graph
graph = ArangoGraph(db)
```
Produces the following exception:
Traceback (most recent call last):
File "/Users/vgreen/working_dir/xpm/graph_qa_01.py", line 19, in <module>
graph = ArangoGraph(db)
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/langchain_community/graphs/arangodb_graph.py", line 23, in __init__
self.set_db(db)
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/langchain_community/graphs/arangodb_graph.py", line 42, in set_db
self.set_schema()
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/langchain_community/graphs/arangodb_graph.py", line 49, in set_schema
self.__schema = self.generate_schema() if schema is None else schema
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/langchain_community/graphs/arangodb_graph.py", line 96, in generate_schema
for doc in self.__db.aql.execute(aql):
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/arango/aql.py", line 453, in execute
return self._execute(request, response_handler)
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/arango/api.py", line 74, in _execute
return self._executor.execute(request, response_handler)
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/arango/executor.py", line 66, in execute
return response_handler(resp)
File "/Users/vgreen/Documents/2023/repos/Trancendence/pipeline/devenv/lib/python3.10/site-packages/arango/aql.py", line 450, in response_handler
raise AQLQueryExecuteError(resp, request)
arango.exceptions.AQLQueryExecuteError: [HTTP 400][ERR 1501] AQL: syntax error, unexpected FOR declaration near 'for
LIMIT 1
...' at position 2:37 (while parsing)
### Description
I'm trying to use Langchain to connect to an ArangoDB Graph database to perform question and answering and when attempting to instantiate an ArangoGraph object it throws an AQLQueryExecuteError.
### System Info
Langchain version: v0.1.0
Platform: Mac OS Sonoma
Python version: 3.10 (venv)
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15884 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15884/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15884/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15884/events | https://github.com/langchain-ai/langchain/issues/15884 | 2,076,818,792 | I_kwDOIPDwls57yb1o | 15,884 | argument needs to be of type (SquadExample, dict) | {
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] | open | false | null | [] | null | 6 | 2024-01-11T14:29:24 | 2024-01-13T03:05:14 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```python
model_name = "Intel/dynamic_tinybert"
tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True, truncation=True, max_length=512)
question_answerer = pipeline(
"question-answering",
model=model_name,
tokenizer=tokenizer,
return_tensors='pt'
)
llm = HuggingFacePipeline(
pipeline=question_answerer,
model_kwargs={"temperature": 0.7, "max_length": 50},
)
prompt_template = """
As literature critic answer me
question: {question}
context: {context}
"""
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
chain_type_kwargs = {"prompt": prompt})
question = "Who is Hamlet ?"
answer = chain.invoke({"query": question}) # issue here <--
print(answer)
```
### Description
I tried to implement simple RetrievalQA from with langchain_chain.faiss vector search but I've faced with such assert,
argument needs to be of type (SquadExample, dict)
Here, there is an issue.
answer = chain.invoke({"query": question})
Thank you in advance.
### System Info
Windows 10, python 3.11, langchain 0.1.0
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15882 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15882/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15882/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15882/events | https://github.com/langchain-ai/langchain/pull/15882 | 2,076,794,963 | PR_kwDOIPDwls5j0K9p | 15,882 | Add document loader section to Astra provider doc page | {
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] | closed | false | null | [] | null | 1 | 2024-01-11T14:19:33 | 2024-01-11T21:51:21 | 2024-01-11T15:52:29 | CONTRIBUTOR | null | See preview: https://langchain-git-fork-cbornet-provider-astra-doc-loader-langchain.vercel.app/docs/integrations/providers/astradb#document-loader | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15881 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15881/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15881/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15881/events | https://github.com/langchain-ai/langchain/pull/15881 | 2,076,743,239 | PR_kwDOIPDwls5jz_Nt | 15,881 | community: Ollama - Pass headers to post request | {
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] | closed | false | null | [] | null | 1 | 2024-01-11T14:02:26 | 2024-01-12T05:40:36 | 2024-01-12T05:40:36 | CONTRIBUTOR | null | ## Feature
- Set additional headers in constructor
- Headers will be sent in post request
This feature is useful if deploying Ollama on a cloud service such as hugging face, which requires authentication tokens to be passed in the request header.
## Tests
- Test if header is passed
- Test if header is not passed | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15880 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15880/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15880/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15880/events | https://github.com/langchain-ai/langchain/issues/15880 | 2,076,708,819 | I_kwDOIPDwls57yA_T | 15,880 | CustomLLM cannot be used to build `Chains` when using `LLM` or `Runnable` | {
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] | open | false | null | [] | null | 3 | 2024-01-11T13:49:44 | 2024-01-11T14:47:49 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
- With `LLM`:
```py
import os
from typing import Any, List
import requests
from langchain.callbacks.base import Callbacks
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain.prompts import PromptTemplate
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
required_envs = ["API_BASE", "API_KEY", "DEPLOYMENT_NAME"]
for env in required_envs:
if env not in os.environ:
raise ValueError(f"Missing required environment variable: {env}")
class CustomLLM(LLM):
@property
def _llm_type(self) -> str:
return "CustomLLM"
def _call(
self,
prompt: str,
stop: List[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any
) -> str:
"""Call the API with the given prompt and return the result."""
self._api_endpoint: str = str(os.getenv("API_BASE"))
self._api_key: str = str(os.getenv("API_KEY"))
self._deployment_name: str = str(os.getenv("DEPLOYMENT_NAME"))
result = requests.post(
f"{self._api_endpoint}/llm/deployments/{self._deployment_name}/chat/completions?api-version=2023-05-15",
headers={
"Content-Type": "application/json",
"api-key": self._api_key,
},
json={
"messages": prompt,
"temperature": 0,
"top_p": 0,
"model": "gpt-4-32k",
},
)
if result.status_code != 200:
raise RuntimeError(
f"Failed to call API: {result.status_code} {result.content}"
)
else:
return result.json()["choices"][0]["message"]
def get_chain(prompt: PromptTemplate, callbacks: Callbacks = []) -> Chain:
"""
This function initializes and returns an LLMChain with a given prompt and callbacks.
Args:
prompt (str): The prompt to initialize the LLMChain with.
callbacks (Callbacks): Langchain callbacks fo
Returns:
Chain: An instance of LLMChain.
"""
llm = CustomLLM()
chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks)
return chain
if __name__ == "__main__":
prompt_template = """
You are an insurance agent. You are provided with instructions, and you must provide an answer.
Question: {question}
"""
prompt = PromptTemplate(
template=prompt_template,
input_variables=["question"],
)
chain = get_chain(prompt)
result = chain.invoke({"question": "What is the best insurance policy for me?"})
print(result)
```
- With `Runnable`:
```py
import os
from typing import Any, List
import requests
from langchain.callbacks.base import Callbacks
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain.prompts import PromptTemplate
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain.schema.runnable import Runnable
from langchain.schema.language_model import LanguageModelInput
required_envs = ["API_BASE", "API_KEY", "DEPLOYMENT_NAME"]
for env in required_envs:
if env not in os.environ:
raise ValueError(f"Missing required environment variable: {env}")
class CustomLLM(LLM):
@property
def _llm_type(self) -> str:
return "CustomLLM"
def invoke(
self, input: LanguageModelInput, config: RunnableConfig | None = None
) -> str:
return super().invoke(input)
def _call(
self,
prompt: str,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
"""Call the API with the given prompt and return the result."""
self._api_endpoint: str = str(os.getenv("OPENAI_API_BASE"))
self._api_key: str = str(os.getenv("OPENAI_API_BASE"))
self._deployment_name: str = str(os.getenv("DEPLOYMENT_NAME"))
result = requests.post(
f"{self._api_endpoint}/llm/deployments/{self._deployment_name}/chat/completions?api-version=2023-05-15",
headers={
"Content-Type": "application/json",
"api-key": self._api_key,
},
json={
"messages": prompt,
"temperature": 0,
"top_p": 0,
"model": "gpt-4-32k",
},
)
if result.status_code != 200:
raise RuntimeError(
f"Failed to call API: {result.status_code} {result.content}"
)
else:
return result.json()["choices"][0]["message"]
def get_chain(prompt: PromptTemplate, callbacks: Callbacks = []) -> Chain:
"""
This function initializes and returns an LLMChain with a given prompt and callbacks.
Args:
prompt (str): The prompt to initialize the LLMChain with.
callbacks (Callbacks): Langchain callbacks fo
Returns:
Chain: An instance of LLMChain.
"""
llm = CustomLLM()
chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks)
return chain
if __name__ == "__main__":
prompt_template = """
You are an insurance agent. You are provided with instructions, and you must provide an answer.
Question: {question}
"""
prompt = PromptTemplate(
template=prompt_template,
input_variables=["question"],
)
chain = get_chain(prompt)
result = chain.invoke({"question": "What is the best insurance policy for me?"})
print(result)
```
### Description
Hi!
I'm not exactly whether this is a bug, or an expected behavior.
I'm in a situation where I cannot use the LLM directly, and instead need to use APIs that interact with the LLM itself.
I've hence decided to create a CustomLLM using the documentation [here](https://python.langchain.com/docs/modules/model_io/llms/custom_llm) to keep leveraging `Chain` features.
Here are the problems I've been facing:
- When using the `LLM` class as the Base class of my `CustomLLM` class, I run into the following error:
```
Traceback (most recent call last):
File "custom_llm.py", line 83, in <module>
chain = get_chain(prompt)
^^^^^^^^^^^^^^^^^
File "custom_llm.py", line 70, in get_chain
chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain/load/serializable.py", line 97, in __init__
super().__init__(**kwargs)
File ".venv/lib/python3.11/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 2 validation errors for LLMChain
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)
```
- Following this error, I've decided to modify the class, so it extends from `Runnable` (cf second code snippet in the example) but when running the new code I get this:
```
Traceback (most recent call last):
File "utils/custom_llm.py", line 90, in <module>
result = chain.invoke({"question": "What is the best insurance policy for me?"})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain/chains/base.py", line 87, in invoke
return self(
^^^^^
File ".venv/lib/python3.11/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File ".venv/lib/python3.11/site-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File ".venv/lib/python3.11/site-packages/langchain/chains/llm.py", line 108, in _call
response = self.generate([inputs], run_manager=run_manager)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain/chains/llm.py", line 127, in generate
results = self.llm.bind(stop=stop, **self.llm_kwargs).batch(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain/schema/runnable/base.py", line 2658, in batch
return self.bound.batch(
^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain/schema/runnable/base.py", line 321, in batch
return cast(List[Output], [invoke(inputs[0], configs[0])])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain/schema/runnable/base.py", line 317, in invoke
return self.invoke(input, config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: CustomLLM.invoke() got an unexpected keyword argument 'stop'
```
### System Info
langchain==0.0.329
langchain-core==0.1.9
Platform: MacOS 13.6.2
Python: 3.11
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15879 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15879/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15879/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15879/events | https://github.com/langchain-ai/langchain/pull/15879 | 2,076,700,844 | PR_kwDOIPDwls5jz1pR | 15,879 | feat(community): make Amadeus toolkit LLM-agnostic | {
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] | closed | false | null | [] | null | 1 | 2024-01-11T13:46:25 | 2024-01-12T05:32:04 | 2024-01-12T05:32:03 | CONTRIBUTOR | null | - **Description:** `AmadeusToolkit` and `AmadeusClosestAirport` contained a hardcoded call to `ChatOpenAI`. This PR makes it LLM-independent, while guaranteeing backward compatibility.
- **Issue:** #15847
- **Dependencies:** None
@baskaryan
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
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https://api.github.com/repos/langchain-ai/langchain/issues/15878 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15878/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15878/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15878/events | https://github.com/langchain-ai/langchain/issues/15878 | 2,076,601,164 | I_kwDOIPDwls57xmtM | 15,878 | Resource not found error trying to use chromadb with Azure Open AI | {
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] | open | false | null | [] | null | 6 | 2024-01-11T13:05:25 | 2024-01-16T12:24:23 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
import os
import openai
import sys
import panel as pn # GUI
pn.extension()
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
import datetime
current_date = datetime.datetime.now().date()
if current_date < datetime.date(2023, 9, 2):
llm_name = "gpt-3.5-turbo-0301"
else:
llm_name = "gpt-3.5-turbo"
print(llm_name)
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import PyPDFLoader
docs = PyPDFLoader("sameer_mahajan.pdf").load()
from langchain.text_splitter import TokenTextSplitter
text_splitter = TokenTextSplitter(chunk_size=1, chunk_overlap=0)
splits = text_splitter.split_documents(docs)
embedding = OpenAIEmbeddings(
deployment = "embeddings",
openai_api_key = os.environ['OPENAI_API_KEY'],
openai_api_base = os.environ['OPENAI_ENDPOINT'],
openai_api_version = os.environ['OPENAI_DEPLOYMENT_VERSION'],
openai_api_type = "azure",
chunk_size = 1)
vectordb = Chroma.from_documents(
documents=splits,
embedding=embedding,
persist_directory=persist_directory
)
### Description
I expect vectordb to persist for my further chatbot however I get an exception of
`NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}`
### System Info
python 3.10.2
embedding model text-embedding-ada-002
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15876 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15876/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15876/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15876/events | https://github.com/langchain-ai/langchain/issues/15876 | 2,076,546,582 | I_kwDOIPDwls57xZYW | 15,876 | langchain_google_genai._common.GoogleGenerativeAIError: Error embedding content: Deadline of 60.0s exceeded while calling target function | {
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] | open | false | null | [] | null | 1 | 2024-01-11T12:44:17 | 2024-01-11T12:54:05 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
from langchain_google_genai import GoogleGenerativeAIEmbeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
vector = embeddings.embed_query("hello, world!")
### Description
langchain_google_genai._common.GoogleGenerativeAIError: Error embedding content: Deadline of 60.0s exceeded while calling target function
### System Info
langchain 0.1.0
langchain-community 0.0.10
langchain-core 0.1.8
langchain-google-genai 0.0.5
langchain-openai 0.0.2
langchainhub 0.1.14
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15875 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15875/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15875/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15875/events | https://github.com/langchain-ai/langchain/pull/15875 | 2,076,405,434 | PR_kwDOIPDwls5jyzJo | 15,875 | feat: Implement `stream` interface | {
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] | closed | false | null | [] | null | 4 | 2024-01-11T11:48:28 | 2024-01-12T05:32:49 | 2024-01-12T05:32:48 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
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2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
Major changes:
- Rename `wasm_chat.py` to `llama_edge.py`
- Rename the `WasmChatService` class to `ChatService`
- Implement the `stream` interface for `ChatService`
- Add `test_chat_wasm_service_streaming` in the integration test
- Update `llama_edge.ipynb`
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https://api.github.com/repos/langchain-ai/langchain/issues/15873 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15873/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15873/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15873/events | https://github.com/langchain-ai/langchain/pull/15873 | 2,076,157,009 | PR_kwDOIPDwls5jx8HX | 15,873 | docs: Correct a typo on Quickstart page | {
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] | closed | false | null | [] | null | 2 | 2024-01-11T09:38:54 | 2024-01-12T05:34:24 | 2024-01-12T05:34:23 | NONE | null | The ChatOpenAI class does not have a parameter called **openai_api_key**. Instead, it should be renamed to **api_key** only.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
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- **Dependencies:** any dependencies required for this change,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
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2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
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https://api.github.com/repos/langchain-ai/langchain/issues/15872 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15872/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15872/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15872/events | https://github.com/langchain-ai/langchain/issues/15872 | 2,076,039,740 | I_kwDOIPDwls57vdo8 | 15,872 | agent_executor.invoke method: An error occurred: Invalid control character at: line y column xxx (char xxx) | {
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] | closed | false | null | [] | null | 2 | 2024-01-11T08:29:48 | 2024-01-11T09:11:37 | 2024-01-11T09:11:37 | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
Here is my custom parser code:
```
def parse(output):
# If no function was invoked, return to user
if "function_call" not in output.additional_kwargs:
return AgentFinish(return_values={"answer": output.content, "sources":""}, log=output.content)
# Parse out the function call
function_call = output.additional_kwargs["function_call"]
name = function_call["name"]
inputs = json.loads(function_call["arguments"])
# If the Response function was invoked, return to the user with the function inputs
if name == "Response":
return AgentFinish(return_values=inputs, log=str(function_call))
# Otherwise, return an agent action
else:
return AgentActionMessageLog(
tool=name, tool_input=inputs, log="", message_log=[output]
)
```
Here is my agent code:
```
agent = (
{
"input": itemgetter("input"),
# Format agent scratchpad from intermediate steps
"agent_scratchpad": lambda x: format_to_openai_functions(
x["intermediate_steps"]),
"history" : lambda x:x["history"]
}
| prompt
| condense_prompt
| llm_with_tools
| parse
)
agent_executor = AgentExecutor(tools=[retriever_tool],
agent=agent,
memory=st.session_state.agentmemory,
verbose=True,
handle_parsing_errors=True,
)
```
### Description
I get the following error when I call my agent_executor.invoke method - An error occurred: Invalid control character at: line 2 column 129 (char 130) - This is only when my retriever returns some special characters such as " • " - I'm assuming it is this - a bullet point dots.
I have been using a custom parser: How can I use the solution from below link for output parser solution to solve the problem with a custom parser? Or add "strict=False" in the json response?
Or is there any other solution?
https://github.com/langchain-ai/langchain/issues/9460
### System Info
langchain==0.0.315
pydantic==2.5.2
streamlit==1.29.0
openai==0.28
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | closed | true | null | [] | null | 2 | 2024-01-11T08:24:30 | 2024-01-11T18:50:01 | 2024-01-11T18:50:01 | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnableMap
from langchain.schema.messages import HumanMessage, SystemMessage
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.utils.openai_functions import convert_pydantic_to_openai_function
from typing import List
from pydantic import BaseModel, Field
class PopulationSearch(BaseModel):
"""Get the population size based on the given city"""
city: str = Field(description="city")
population_function = convert_pydantic_to_openai_function(PopulationSearch)
model = ChatOpenAI(
temperature=0,
model_name="gpt4-turbo"
)
response = model.invoke("What is the population of Wuhan?", functions=[population_function])
print(response.additional_kwargs)
### Description
The result is {}. Why is it empty value?
The screenshots are as follows:
### System Info
Python version is 3.11
LangChain version is 0.0.343
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15869 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15869/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15869/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15869/events | https://github.com/langchain-ai/langchain/issues/15869 | 2,075,891,242 | I_kwDOIPDwls57u5Yq | 15,869 | ConfluenceLoader.load giving AttributeError: 'str' object has no attribute 'get' while reading all documents from space | {
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] | open | false | null | [] | null | 9 | 2024-01-11T06:48:42 | 2024-01-16T04:38:36 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
from langchain.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(
url="<my confluence link>", username="<my user name>",
api_key="<my token>"
)
documents = loader.load(space_key="<my space>", include_attachments=True, limit=1, max_pages=1)
### Description
I am trying to load all confluence pages using ConflueceLoader. I expect to get all the pages but instead I get the AttributeError: 'str' object has no attribute 'get'
### System Info
python version 3.10.2
langchain version 0.0.345
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15868 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15868/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15868/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15868/events | https://github.com/langchain-ai/langchain/issues/15868 | 2,075,869,822 | I_kwDOIPDwls57u0J- | 15,868 | Index Creation | {
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"name": "auto:improvement",
"color": "FBCA04",
"default": false,
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] | open | false | null | [] | null | 1 | 2024-01-11T06:35:00 | 2024-01-11T06:37:24 | null | NONE | null | ### Feature request
I noticed that the MongoDBChatMessageHistory class object is attempting to create an index during connection, causing each request to take longer than usual. Index Creation is one time process, So no need create index everytime. By default, index creation is enabled. To address this, add an additional parameter, index_creation. If index_creation is set to False, this step should be ignored.
Current Code:
```
def __init__(
self,
connection_string: str,
session_id: str,
database_name: str = DEFAULT_DBNAME,
collection_name: str = DEFAULT_COLLECTION_NAME,
):
from pymongo import MongoClient, errors
self.connection_string = connection_string
self.session_id = session_id
self.database_name = database_name
self.collection_name = collection_name
try:
self.client: MongoClient = MongoClient(connection_string)
except errors.ConnectionFailure as error:
logger.error(error)
self.db = self.client[database_name]
self.collection = self.db[collection_name]
self.collection.create_index("SessionId")
```
Proposed modification:
```
def __init__(
self,
connection_string: str,
session_id: str,
database_name: str = DEFAULT_DBNAME,
collection_name: str = DEFAULT_COLLECTION_NAME,
index_creation:bool =True #New argument
):
from pymongo import MongoClient, errors
self.connection_string = connection_string
self.session_id = session_id
self.database_name = database_name
self.collection_name = collection_name
try:
self.client: MongoClient = MongoClient(connection_string)
except errors.ConnectionFailure as error:
logger.error(error)
self.db = self.client[database_name]
self.collection = self.db[collection_name]
if index_creation: #Conditional Index Creation
self.collection.create_index("SessionId")
```
### Motivation
Developer can do custom modification, But if you make this feature that feature comes package.
### Your contribution
yes, can do this
Proposed modification:
```
def __init__(
self,
connection_string: str,
session_id: str,
database_name: str = DEFAULT_DBNAME,
collection_name: str = DEFAULT_COLLECTION_NAME,
index_creation:bool =True #New argument
):
from pymongo import MongoClient, errors
self.connection_string = connection_string
self.session_id = session_id
self.database_name = database_name
self.collection_name = collection_name
try:
self.client: MongoClient = MongoClient(connection_string)
except errors.ConnectionFailure as error:
logger.error(error)
self.db = self.client[database_name]
self.collection = self.db[collection_name]
if index_creation: #Conditional Index Creation
self.collection.create_index("SessionId")
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15866 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15866/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15866/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15866/events | https://github.com/langchain-ai/langchain/issues/15866 | 2,075,774,096 | I_kwDOIPDwls57ucyQ | 15,866 | How can I store chat history in a database and retrieve results using cosine similarity when querying the database?" | {
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] | open | false | null | [] | null | 1 | 2024-01-11T05:20:34 | 2024-01-11T05:31:57 | null | NONE | null | ### Issue with current documentation:
import os
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.qdrant import Qdrant
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder, \
HumanMessagePromptTemplate
from qdrant_client import QdrantClient
os.environ['OPENAI_API_KEY'] = "mykey"
client = QdrantClient(host="192.168.0.313", port=6333)
COLLECTION_NAME = "embed"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Qdrant.from_documents(
client=client,
collection_name=COLLECTION_NAME,
embeddings=embeddings,
search_params={"metric_type": "cosine"},
)
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"you are robot."
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 100
retriever = vectorstore.as_retriever()
memory = ConversationTokenBufferMemory(
llm=llm,
prompt=prompt,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True,
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
verbose=True,
)
chat_history = []
while True:
memory.load_memory_variables({})
question = input('ask：')
result = qa.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history)
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
### Idea or request for content:
embeddings
cosine | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15865 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15865/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15865/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15865/events | https://github.com/langchain-ai/langchain/pull/15865 | 2,075,746,642 | PR_kwDOIPDwls5jwibM | 15,865 | PR community:Removing knn beta content in mongodb atlas vectorstore | {
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] | closed | false | null | [] | null | 1 | 2024-01-11T04:54:32 | 2024-01-12T05:40:54 | 2024-01-12T05:40:54 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
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https://api.github.com/repos/langchain-ai/langchain/issues/15864 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15864/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15864/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15864/events | https://github.com/langchain-ai/langchain/pull/15864 | 2,075,701,741 | PR_kwDOIPDwls5jwYdQ | 15,864 | community: Handling missing key in Google Trends API response. | {
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] | null | 1 | 2024-01-11T04:09:31 | 2024-01-15T18:51:19 | null | NONE | null | - **Description:** Handing response where _interest_over_time_ is missing.
- **Issue:** #15859
- **Dependencies:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/15863 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15863/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15863/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15863/events | https://github.com/langchain-ai/langchain/issues/15863 | 2,075,684,626 | I_kwDOIPDwls57uG8S | 15,863 | the agent example in the quickstart documentation failed to run | {
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] | open | false | null | [] | null | 2 | 2024-01-11T03:54:33 | 2024-01-11T07:48:07 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```python
import os
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain import hub
from langchain.agents import create_openai_functions_agent
from langchain.agents import AgentExecutor
os.environ["OPENAI_API_KEY"] = "*************************************"
os.environ["TAVILY_API_KEY"] = "*************************************"
search = TavilySearchResults()
tools = [search]
# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/openai-functions-agent")
llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0,
verbose=True,
)
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the weather in SF?"})
```
### Description
Running the example code above will cause an error for both `gpt-3-turbo` and `gpt-4-0613`. The error message is:
```
openai.NotFoundError: Error code: 404 - {'error': {'message': 'Unrecognized request argument supplied: functions (request id: XXXXX)', 'type': 'invalid_request_error', 'param': '', 'code': None}}
```
I searched for this error and found a solution, which involves adding the parameter `api-version="2023-07-01-preview"`. However, I couldn't find a place to input this parameter. After reading through some source code, I finally figured out how:
```python
function_obj = agent.middle[1]
if function_obj.kwargs:
function_obj.kwargs["extra_query"] = {"api-version": "2023-07-01-preview"}
else:
function_obj.kwargs = {"extra_query": {"api-version": "2023-07-01-preview"}}
```
This led to another error:
```
openai.BadRequestError: Error code: 400 - {'error': {'message': "Invalid value for 'content': expected a string, got null. (request id: XXXXX", 'type': 'invalid_request_error', 'param': 'messages.[2].content', 'code': None}}
```
After some debugging, I found the reason. Inside `langchain_openai`, there is a function `_convert_message_to_dict`:
```python
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
# If function call only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
```
This code turns the search result into an `AIMessage` and, for some reason, does not allow the content to be an empty string, so it makes it `None`. However, the OpenAI API does not accept this. To make it work, I had to rewrite the code:
```python
def _convert_message_to_dict(message: BaseMessage) -> dict:
...
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
# If function call only, content is None not empty string
# ATTENTION: CHANGE HERE
# if message_dict["content"] == "":
# message_dict["content"] = None
if "tool_calls" in message.additional_kwargs:
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
# If tool calls only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
...
return message_dict
def new_create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = self._default_params
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
llm.Config.extra = Extra.allow
llm._create_message_dicts = partial(new_create_message_dicts, llm)
```
I mean, really? I'm not sure what I did wrong, but it's certainly not easy to make it work. If it's not a bug, I hope to get a simpler and more elegant solution.
### System Info
langchain==0.1.0
langchain-community==0.0.10
langchain-core==0.1.8
langchain-openai==0.0.2
langchainhub==0.1.14
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15862 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15862/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15862/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15862/events | https://github.com/langchain-ai/langchain/pull/15862 | 2,075,678,584 | PR_kwDOIPDwls5jwTRd | 15,862 | community: deprecate ErnieBotChat and ErnieEmbeddings classes | {
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] | closed | false | null | [] | null | 5 | 2024-01-11T03:48:31 | 2024-01-16T00:30:17 | 2024-01-15T19:14:44 | CONTRIBUTOR | null | - **Description:** add deprecated warning for ErnieBotChat and ErnieEmbeddings.
- These two classes **lack maintenance** and do not use the sdk provided by qianfan, which means hard to implement some key feature like streaming.
- The alternative `langchain_community.chat_models.QianfanChatEndpoint` and `langchain_community.embeddings.QianfanEmbeddingsEndpoint` can completely replace these two classes, only need to change configuration items.
- **Issue:** None,
- **Dependencies:** None,
- **Twitter handle:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/15861 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15861/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15861/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15861/events | https://github.com/langchain-ai/langchain/issues/15861 | 2,075,674,679 | I_kwDOIPDwls57uEg3 | 15,861 | AttributeError: 'str' object has no attribute 'log' | {
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] | open | false | null | [] | null | 2 | 2024-01-11T03:44:47 | 2024-01-11T16:59:29 | null | NONE | null | I am creating a tool using _run and _arun versions for my FastAPI code to use the tool in AgentExecutor. When I test my agent, I am running into this AttributeError which I am unable to resolve even with a debugger. Am I missing anything here?
from fastapi import Request
from langchain.tools import tool, BaseTool
from pydantic import BaseModel, Field
from typing import Type, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from settings import app_settings as settings
from ai.tools import run_chain
import httpx
class PlaceHolderSchema(BaseModel):
dummy: Optional[str]
async def run_chain_tool(request: Request) :
class ChainTool(BaseTool):
name = "run_chain"
description = "This tool takes a user question as input and returns the answer using the Cube JSON extraction and Cube API response."
args_schema: Type[PlaceHolderSchema] = PlaceHolderSchema
def _run(self,
question: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
dummy: Optional[str] = None,
) -> str:
"""
This function "synchronously" runs the tool.
Args:
question (str): User question about the Cube data model.
Returns:
answer (str): Answer to the user question by running a chain of steps such as generation of Cube JSON, calling Cube API, and generating final answer.
"""
raise NotImplementedError("run_chain tool does not support synchronous execution")
async def _arun(self,
question: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
dummy: Optional[str] = None,
) -> str:
"""
This function "asynchronously" runs the tool.
Args:
question (str): User question about the Cube data model.
Returns:
answer (str): Answer to the user question by running a chain of steps such as generation of Cube JSON, calling Cube API, and generating final answer.
"""
try:
answer = await run_chain(question, request)
print(answer)
return answer
except Exception as e: print(f"Error: {e}")
return ChainTool() | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15860 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15860/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15860/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15860/events | https://github.com/langchain-ai/langchain/pull/15860 | 2,075,637,377 | PR_kwDOIPDwls5jwKVQ | 15,860 | [docs] add beautiful soup dependency | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15859 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15859/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15859/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15859/events | https://github.com/langchain-ai/langchain/issues/15859 | 2,075,635,675 | I_kwDOIPDwls57t6_b | 15,859 | Google Trend utility makes assumptions on keys from response | {
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] | open | false | null | [] | null | 4 | 2024-01-11T03:03:52 | 2024-01-11T04:23:31 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
The following code:
```
import os
from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
os.environ['SERPAPI_API_KEY'] = ''
tool = GoogleTrendsAPIWrapper()
tool.run("Something that will yield an error like totally")
```
will yield this:
```
Traceback (most recent call last):
File "/home/ubuntu/Work/luc/langchain-google-trends-issue-1/langchain_google_trends_issue_1/main.py", line 9, in <module>
tool.run("Something that will yield an error like totally")
File "/home/ubuntu/Work/luc/langchain-google-trends-issue-1/.venv/lib/python3.10/site-packages/langchain_community/utilities/google_trends.py", line 68, in run
total_results = client.get_dict()["interest_over_time"]["timeline_data"]
KeyError: 'interest_over_time'
```
### Description
* I'm trying to use the Google Trends tool with some AI agent.
* Now the (not so smart) agent ran a query with Google Trend that did NOT return what its implementation expected.
* I would expect the implementation to follow a more foolproof logic.
### System Info
# pyproject.toml
python = "^3.10"
langchain = "0.0.354"
pytest = "^7.4.4"
google-search-results = "^2.4.2"
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15858 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15858/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15858/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15858/events | https://github.com/langchain-ai/langchain/pull/15858 | 2,075,617,763 | PR_kwDOIPDwls5jwGHQ | 15,858 | Agent streaming notebooks | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15856 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15856/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15856/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15856/events | https://github.com/langchain-ai/langchain/issues/15856 | 2,075,587,256 | I_kwDOIPDwls57tvK4 | 15,856 | SelfQueryRetriever with Pinecone Automatically Converts String Metadata into DateTime | {
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] | open | false | null | [] | null | 5 | 2024-01-11T02:23:09 | 2024-01-12T03:16:28 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
Metadata
```python
doc.metadata["date_year_short"] = date_obj.strftime("%y") # 23
doc.metadata["date_year_long"] = date_obj.strftime("%Y") # 2023
doc.metadata["date_month"] = date_obj.strftime("%-m") # 12
doc.metadata["date_month_name"] = calendar.month_name[date_obj.month] # December
doc.metadata["date_day"] = date_obj.strftime("%-d") # 31
doc.metadata["date_uploaded"] = calendar.month_name[date_obj.month] + " " + date_obj.strftime("%Y") # January 2023
```
Self-Query Retriever + Pinecone DB Instatiation
```python
llm = ChatOpenAI(temperature=0)
vectorstore = Pinecone.from_existing_index(index_name="test", embedding=get_embedding_function())
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
"Information about when the document was created and where it was grabbed from.",
metadata_field_info,
)
# bancworks_docs[1359]
retriever.vectorstore.similarity_search_with_score(question)
```
### Description
Should be able to see my metadata instantiated in string form the way I created them instead of being converted to date time.
For example, my date_month_name field is "Feburary 2023". It should not be converted to 2/1/2000.
### System Info
Docker image container, Python v3.11, Langchain v0.1.0
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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] | open | false | null | [] | null | 1 | 2024-01-11T02:20:38 | 2024-01-11T02:22:30 | null | CONTRIBUTOR | null | ## Why
the `tool` decorator is convenient to specify parameter infomations, but the default values are not parsed by default:
```
from langchain.agents import tool
from langchain.pydantic_v1 import Field
@tool
def text2images(
prompt: str,
n: int = Field(1, description="image count"),
):
'''generate images'''
...
text2images("draw a house")
# will fail because BaseTool._parse_input returns {"prompt": "draw a house"}, then the tool is called with parameters {"prompt": "draw a house", "n": FieldInfo(...)}.
# it should return {"prompt": "draw a house", "n": 1}
```
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https://api.github.com/repos/langchain-ai/langchain/issues/15854 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15854/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15854/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15854/events | https://github.com/langchain-ai/langchain/issues/15854 | 2,075,537,332 | I_kwDOIPDwls57ti-0 | 15,854 | embedding router with conversation history | {
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] | closed | false | null | [] | null | 6 | 2024-01-11T01:33:54 | 2024-01-11T02:34:53 | 2024-01-11T02:34:53 | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
I wrote the code for categorization of prompt.
```python
def prompt_router(input, embeddings, prompt_templates, prompt_embeddings):
query_embedding = embeddings.embed_query(input["query"])
similarity = cosine_similarity([query_embedding], prompt_embeddings)[0]
most_similar = prompt_templates[similarity.argmax()]
print(most_similar)
return PromptTemplate.from_template(most_similar)
def main_categorizer(message):
global memory, entry
formatted_history = memory.get_history_as_string()
case1_template = """Description of what case1 does
Chat History:
{chat_history}
Here is a question:
{query}"""
case2_template = """Description of what case2 does
Chat History:
{chat_history}
Here is a question:
{query}"""
case3_template = """Description of what case3 does
Chat History:
{chat_history}
Here is a question:
{query}"""
case4_template = """Description of what case4 does
Chat History:
{chat_history}
Here is a question:
{query}"""
prompt_templates = [case1_template, case2_template, case3_template, case4_template]
prompt_embeddings = embeddings.embed_documents(prompt_templates)
chain = (
{"query": RunnablePassthrough()}
| RunnableLambda(prompt_router)
| llm
| StrOutputParser()
)
```
### Description
Based on the document in https://python.langchain.com/docs/expression_language/cookbook/embedding_router,
I've tried to implement embedding router.
What I would like to do is, adding conversation history to the case prompts so that they can use historical conversation as well to consider which category the user prompt is.
In here, I have no idea where to put {chat_history} value just like the query being inserted with ```"query": RunnablePassthrough()```
### System Info
langchian==0.0.352
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Description
`AmadeusClosestAirport` contains a "hardcoded" call to `ChatOpenAI` (see [here](https://github.com/langchain-ai/langchain/blob/a06db53c37344b5a9906fbf656173c3421109398/libs/community/langchain_community/tools/amadeus/closest_airport.py#L50)), while it would make sense to use the same `llm` passed to the chain/agent when initialized.
In addition, this implies that `AmadeusToolkit` implicitly depends on `openai`, which should not be the case.
Example (source code from the [docs](https://python.langchain.com/docs/integrations/toolkits/amadeus))
```py
from langchain_community.agent_toolkits.amadeus.toolkit import AmadeusToolkit
# Set environmental variables here
import os
os.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"
os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"
os.environ["OPENAI_API_KEY"] = "API_KEY"
# os.environ["AMADEUS_HOSTNAME"] = "production" or "test"
toolkit = AmadeusToolkit()
tools = toolkit.get_tools()
llm = OpenAI(temperature=0) # this can be any `BaseLLM`
agent = initialize_agent(
tools=tools,
llm=llm,
verbose=False,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
)
# ==> agent calls `ChatOpenAI` regardless of `llm` <===
agent.run("What is the name of the airport in Cali, Colombia?")
```
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15845 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15845/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15845/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15845/events | https://github.com/langchain-ai/langchain/issues/15845 | 2,075,269,126 | I_kwDOIPDwls57shgG | 15,845 | DOC: Missing dependency when going through the Quickstart section | {
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When running the `WebBaseLoader` it requires `bs4` installed which is not mentioned in the docs.
https://github.com/langchain-ai/langchain/blob/21a153894917e530cbe82a778be6f9cf10c9ae5f/docs/docs/get_started/quickstart.mdx#L185C1-L194C1
### Idea or request for content:
I think it should be mentioned just like `faiss` a few lines below. | {
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title: "community: Fix use_pod_based parameter in Pinecone class"
---
- Description: This PR adds an `use_pods_based` parameter which is optional and `True` by default. This is to support upcoming changes to the Pinecone client. | {
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- **Description:** added a base class for `OpenAIFunctionsAgentOutputParser` to avoid code duplication
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https://api.github.com/repos/langchain-ai/langchain/issues/15838 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15838/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15838/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15838/events | https://github.com/langchain-ai/langchain/pull/15838 | 2,074,989,514 | PR_kwDOIPDwls5jt71w | 15,838 | add raga reranker | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15837 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15837/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15837/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15837/events | https://github.com/langchain-ai/langchain/issues/15837 | 2,074,963,053 | I_kwDOIPDwls57rWxt | 15,837 | DOC: Page Not Found when clicking on different agent types in table | {
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Clicking on any of the `agent_types` [here](https://python.langchain.com/docs/modules/agents/agent_types) leads to faulty links with the following message:
Example from this link: https://python.langchain.com/docs/modules/agents/openai_tools
<img width="873" alt="image" src="https://github.com/langchain-ai/langchain/assets/8833114/d6d5268c-397a-4eda-9a07-e5ec4b4b2d13">
### Idea or request for content:
Update table to point to correct hyperlink i.e. https://python.langchain.com/docs/modules/agents/agent_types/openai_tools for the example above. | {
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trying to use the text area, which is super confusing.
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https://api.github.com/repos/langchain-ai/langchain/issues/15835 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15835/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15835/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15835/events | https://github.com/langchain-ai/langchain/issues/15835 | 2,074,909,445 | I_kwDOIPDwls57rJsF | 15,835 | Cannot combine an output parser and a conversation buffer memory | {
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] | open | false | null | [] | null | 3 | 2024-01-10T18:08:47 | 2024-01-11T13:31:35 | null | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation, with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
from langchain.memory import ConversationBufferMemory
from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
from langchain.chains import LLMChain
class TennisPlayer(BaseModel):
age: int = Field(description="Age of the player")
nb_victories: int = Field(description="Nb of victories in major tournaments")
llm = None # Instantiate the LLM here
parser = PydanticOutputParser(pydantic_object=TennisPlayer)
prompt = "You'll be asked information about a tennis player.\n" \
"You'll answer with the following format:\n" \
"{format_instructions}"
memory = ConversationBufferMemory(memory_key="chat_history", input_key="query", return_messages=True)
chat_prompt = ChatPromptTemplate.from_messages([SystemMessagePromptTemplate.from_template(prompt),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{query}")])
chain = LLMChain(llm=llm, prompt=chat_prompt, memory=memory, output_parser=parser)
chain.invoke(input={"query": "Rafael Nadal", "format_instructions": parser.get_format_instructions()})
```
### Description
The previous code triggers an error while converting the output from the LLM to an AIMessage to place in the ConversationBufferMemory object. The problem is that it passes the constructed object (the output of PydanticOutputParser.parse) instead of the output message as a plain string.
### System Info
langchain 0.1.0
python 3.10.13
Windows 10
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15834 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15834/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15834/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15834/events | https://github.com/langchain-ai/langchain/issues/15834 | 2,074,900,437 | I_kwDOIPDwls57rHfV | 15,834 | ValueError: Index with name vector already exists.The provided embedding function and vector index dimensions do not match. | {
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] | closed | false | null | [] | null | 5 | 2024-01-10T18:02:46 | 2024-01-12T12:40:02 | 2024-01-12T12:40:02 | NONE | null | ### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation, with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
### Example Code
```
# Chunking the sentence with fixed size
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
all_splits = text_splitter.split_documents(documents)
```
```
# Creating Embdeddings of the sentences and storing it into Graph DB
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import Neo4jVector
model_name = "BAAI/bge-small-en"
model_kwargs = {"device": "cuda"}
embeddings = HuggingFaceBgeEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
# storing embeddings in the vector store
vectorstore = Neo4jVector.from_documents(all_splits, embeddings)
```
```
# Instantiate Neo4j vector from documents
neo4j_new_index = Neo4jVector.from_documents(
documents,
HuggingFaceBgeEmbeddings(),
url=os.environ["NEO4J_URI"],
username=os.environ["NEO4J_USERNAME"],
password=os.environ["NEO4J_PASSWORD"]
)
```
```
ERROR:neo4j.io:Failed to write data to connection ResolvedIPv4Address(('34.126.171.25', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
ERROR:neo4j.io:Failed to write data to connection IPv4Address(('07e87ccd.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
ERROR:neo4j.io:Failed to write data to connection ResolvedIPv4Address(('34.126.171.25', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
ERROR:neo4j.io:Failed to write data to connection IPv4Address(('07e87ccd.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-25-7220f62f84c8>](https://localhost:8080/#) in <cell line: 2>()
1 # Instantiate Neo4j vector from documents
----> 2 neo4j_new_index = Neo4jVector.from_documents(
3 documents,
4 HuggingFaceBgeEmbeddings(),
5 url=os.environ["NEO4J_URI"],
2 frames
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in __from(cls, texts, embeddings, embedding, metadatas, ids, create_id_index, search_type, **kwargs)
445 # If the index already exists, check if embedding dimensions match
446 elif not store.embedding_dimension == embedding_dimension:
--> 447 raise ValueError(
448 f"Index with name {store.index_name} already exists."
449 "The provided embedding function and vector index "
ValueError: Index with name vector already exists.The provided embedding function and vector index dimensions do not match.
Embedding function dimension: 1024
Vector index dimension: 384
```
### Description
```
ERROR:neo4j.io:Failed to write data to connection ResolvedIPv4Address(('34.126.171.25', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
ERROR:neo4j.io:Failed to write data to connection IPv4Address(('07e87ccd.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
ERROR:neo4j.io:Failed to write data to connection ResolvedIPv4Address(('34.126.171.25', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
ERROR:neo4j.io:Failed to write data to connection IPv4Address(('07e87ccd.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.171.25', 7687)))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-25-7220f62f84c8>](https://localhost:8080/#) in <cell line: 2>()
1 # Instantiate Neo4j vector from documents
----> 2 neo4j_new_index = Neo4jVector.from_documents(
3 documents,
4 HuggingFaceBgeEmbeddings(),
5 url=os.environ["NEO4J_URI"],
2 frames
[/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in __from(cls, texts, embeddings, embedding, metadatas, ids, create_id_index, search_type, **kwargs)
445 # If the index already exists, check if embedding dimensions match
446 elif not store.embedding_dimension == embedding_dimension:
--> 447 raise ValueError(
448 f"Index with name {store.index_name} already exists."
449 "The provided embedding function and vector index "
ValueError: Index with name vector already exists.The provided embedding function and vector index dimensions do not match.
Embedding function dimension: 1024
Vector index dimension: 384
```
### System Info
Windows: `11`
pip == `23.3.1`
python == `3.10.10`
langchain ==` 0.1.0`
transformers == `4.36.2`
sentence_transformers == `2.2.2`
Neo4j == `5`
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15833 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15833/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15833/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15833/events | https://github.com/langchain-ai/langchain/pull/15833 | 2,074,889,166 | PR_kwDOIPDwls5jtmHF | 15,833 | More updates to issue template | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15831 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15831/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15831/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15831/events | https://github.com/langchain-ai/langchain/pull/15831 | 2,074,861,503 | PR_kwDOIPDwls5jtgeZ | 15,831 | Infra: Fix syntax error in BUG REPORT template | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15830 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15830/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15830/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15830/events | https://github.com/langchain-ai/langchain/issues/15830 | 2,074,843,378 | I_kwDOIPDwls57q5jy | 15,830 | Intercepting the output message in a callback handler before it is sent to the output parser | {
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] | null | 5 | 2024-01-10T17:27:36 | 2024-01-11T14:11:08 | null | NONE | null | ### System Info
langchain v0.1.0
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
from typing import Any, Dict
from langchain_core.outputs.llm_result import LLMResult
from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.base import BaseCallbackHandler
class CustomCallBack(BaseCallbackHandler):
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
print(f"on_llm_end => {response}")
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
print(f"on_chain_end => {outputs}")
def on_text(self, text: str, **kwargs: Any) -> Any:
print(f"on_text => {text}")
class TennisPlayer(BaseModel):
age: int = Field(description="Age of the player")
nb_victories: int = Field(description="Nb of victories in major tournaments")
# Instantiate the LLM here
llm = None
parser = PydanticOutputParser(pydantic_object=TennisPlayer)
prompt = "Give me some information about Rafael Nadal.\n" \
"You'll answer with the following format:\n" \
"{format_instructions}"
chat_prompt = ChatPromptTemplate.from_messages([HumanMessagePromptTemplate.from_template(prompt)])
chain = LLMChain(llm=llm, prompt=chat_prompt, callbacks=[CustomCallBack()], output_parser=parser)
chain.invoke(input={"format_instructions": parser.get_format_instructions()})
```
### Expected behavior
The custom callback handler makes it possible to intercept the prompt sent to the LLM (through _on_text_) and the output in _on_chain_end_. The problem is that when an output parser is involved, the _outputs_ dictionary of _on_chain_end_ associates the "text" key with the final constructed object and not the output message (containing the JSON data that has been marshalled to an object). And for an unknown reason, the _on_llm_end_ callback function isn't invoked...
When something goes wrong in the marshalling process, the analysis of the LLM's output message is mandatory. Well, it doesn't seem abnormal to get the final object produced by the chain in the _on_chain_end_ callback, but in that case I would expect the _on_llm_end_ callback to be called just before with the output message in parameter. But it is not. So, at this stage, it's not possible to intercept the raw LLM's output message for debugging purposes. | {
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] | null | 1 | 2024-01-10T17:22:08 | 2024-01-17T16:11:12 | 2024-01-17T16:11:11 | CONTRIBUTOR | null | - **Description:**
Currently the embed_documents() function of HuggingFaceInferenceAPIEmbeddings class posts a request to HuggingFaceInference API to generate text embeddings. The POST request may not always return successful response due to various technical glitches. This PR handles the event when the status code of the response returned by HuggingFaceInference API is not 200 and raises an exception for this event.
- **Dependencies:** None
- **Twitter handle:** None | {
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] | open | false | null | [] | null | 1 | 2024-01-10T17:15:20 | 2024-01-10T17:25:19 | null | NONE | null | ### System Info
Langchain 0.1.0
python 3.10
### Who can help?
@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
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
reproducing the error could be as follows
```python
...
#################################################################
## get multiple queries to be searched on the web
query_generation_chain = (
search_queries_prompt
| llm.bind(stop=TOGETHER_STOP_KEYWORDS)
| CommaSeparatedListOutputParser()
)
#################################################################
## scrape and summarize a webpages based on urls
summarize_chain = RunnablePassthrough.assign(
summary=RunnablePassthrough.assign(text=lambda x: scrape_webpage(x["url"])[:10_000])
| summarize_prompt
| llm
| StrOutputParser(),
) | (lambda x: f'URL: {x["url"]} \n\nSUMMARY: {x["summary"]}')
chain = (
RunnablePassthrough.assign(urls = query_generation_chain | fetch_links_from_web)
| RunnableLambda(lambda x: [{"question": x["question"], "url": url} for url in x["urls"]])
| summarize_chain.map() ## generate list of summarized article for each link
| (lambda reports: "\n\n".join(reports)) ## combine the summaries into a report
)
```
if i invoke `get_graph()` on the chain like this :
```python
chain.get_graph()
```
i get this error :
```console
Traceback (most recent call last):
File "/home/joede/dev/llm_playground/researcher/main.py", line 30, in <module>
report_writer_chain.get_graph().print_ascii()
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1690, in get_graph
step_graph = step.get_graph(config)
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/passthrough.py", line 379, in get_graph
graph = self.mapper.get_graph(config)
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2282, in get_graph
step_graph = step.get_graph()
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/passthrough.py", line 379, in get_graph
graph = self.mapper.get_graph(config)
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2282, in get_graph
step_graph = step.get_graph()
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1690, in get_graph
step_graph = step.get_graph(config)
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2906, in get_graph
graph = super().get_graph(config)
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 399, in get_graph
output_node = graph.add_node(self.get_output_schema(config))
File "/home/joede/.cache/pypoetry/virtualenvs/researcher-Jp5_VGHR-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 331, in get_output_schema
if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
File "/usr/lib/python3.10/abc.py", line 123, in __subclasscheck__
return _abc_subclasscheck(cls, subclass)
TypeError: issubclass() arg 1 must be a class
```
with further inspection i found it error when it gets here:
```python
def get_input_schema(...):
....
root_type = self.OutputType
if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
return root_type
```
in the `langchain_core/runnable/base.py`
### Expected behavior
the expected output should be a graph of ascii characters that should look like this :
```console
+---------------------------------+
| Parallel<research_summary>Input |
+---------------------------------+
**** *******
*** *******
** ******
+---------------------+ ****
| Parallel<urls>Input | *
+---------------------+ *
*** **** *
**** *** *
** **** *
+--------------------+ ** *
| ChatPromptTemplate | * *
+--------------------+ * *
* * *
* * *
* * *
+---------------+ * *
| WithFallbacks | * *
+---------------+ * *
* * *
* * *
* * *
+--------------------------------+ * *
| CommaSeparatedListOutputParser | * *
+--------------------------------+ * *
* * *
* * *
* * *
+------------------------------+ +-------------+ *
| Lambda(fetch_links_from_web) | | Passthrough | *
+------------------------------+ *+-------------+ *
*** **** *
**** **** *
** ** *
+----------------------+ +-------------+
| Parallel<urls>Output | **| Passthrough |
+----------------------+ ******* +-------------+
**** ******
*** *******
** ****
+----------------------------------+
| Parallel<research_summary>Output |
+----------------------------------+
*
*
*
+--------------------+
| ChatPromptTemplate |
+--------------------+
*
*
*
+---------------+
| WithFallbacks |
+---------------+
*
*
*
+-----------------+
| StrOutputParser |
+-----------------+
*
*
*
+-----------------------+
| StrOutputParserOutput |
+-----------------------+
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15827 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15827/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15827/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15827/events | https://github.com/langchain-ai/langchain/pull/15827 | 2,074,791,768 | PR_kwDOIPDwls5jtRMH | 15,827 | Enable customizing the output parser of `OpenAIFunctionsAgent` | {
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- **Issue:** Subclassing is currently impossible as the `OpenAIFunctionsAgentOutputParser` class is hard coded into the `plan` and `aplan` methods
- **Dependencies:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/15824 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15824/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15824/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15824/events | https://github.com/langchain-ai/langchain/pull/15824 | 2,074,739,427 | PR_kwDOIPDwls5jtFqS | 15,824 | infra: downgrade setup-python to v5 -> v4 | {
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] | null | 1 | 2024-01-10T15:59:26 | 2024-01-17T01:01:54 | 2024-01-17T01:01:27 | CONTRIBUTOR | null | Replace this entire comment with:
- **Description:** Description: added support for tools on VertexAI
- **Issue:** #15073
- **Twitter handle:** lkuligin
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https://api.github.com/repos/langchain-ai/langchain/issues/15820 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15820/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15820/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15820/events | https://github.com/langchain-ai/langchain/pull/15820 | 2,074,591,134 | PR_kwDOIPDwls5jslC- | 15,820 | community: Handle HuggingFaceInferenceAPI failure response of POST request | {
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Currently the _embed_documents()_ function of _HuggingFaceInferenceAPIEmbeddings_ class posts a request to HuggingFaceInference API to generate text embeddings. The POST request may not always return successful response due to various technical glitches. This PR handles the event when the status code of the response returned by HuggingFaceInference API is not 200 and raises an exception for this event.
- **Dependencies:** None
- **Twitter handle:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/15819 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15819/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15819/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15819/events | https://github.com/langchain-ai/langchain/issues/15819 | 2,074,391,510 | I_kwDOIPDwls57pLPW | 15,819 | KeyError: 'llm' in create_pandas_dataframe_agent | {
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python=3.11
langchain= latest
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
while running a code with create_pandas_dataframe_agent it throwing key error
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
import pandas as pd
from langchain_openai import OpenAI
df = pd.read_csv(r"C:\Users\rndbcpsoft\OneDrive\Desktop\test\chat_data_2024-01-05_13-20-11.csv")
# agent = create_pandas_dataframe_agent(
# ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
# df,
# verbose=True,
# agent_type=AgentType.OPENAI_FUNCTIONS,
# )
llm = ChatOpenAI(
temperature=0, model="gpt-3.5-turbo-0613", openai_api_key=openapi_key , streaming=True
)
pandas_df_agent = create_pandas_dataframe_agent(
llm,
df,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
handle_parsing_errors=True,
)
error:
KeyError Traceback (most recent call last)
Cell In[7], [line 12](vscode-notebook-cell:?execution_count=7&line=12)
[1](vscode-notebook-cell:?execution_count=7&line=1) # agent = create_pandas_dataframe_agent(
[2](vscode-notebook-cell:?execution_count=7&line=2) # ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
[3](vscode-notebook-cell:?execution_count=7&line=3) # df,
[4](vscode-notebook-cell:?execution_count=7&line=4) # verbose=True,
[5](vscode-notebook-cell:?execution_count=7&line=5) # agent_type=AgentType.OPENAI_FUNCTIONS,
[6](vscode-notebook-cell:?execution_count=7&line=6) # )
[8](vscode-notebook-cell:?execution_count=7&line=8) llm = ChatOpenAI(
[9](vscode-notebook-cell:?execution_count=7&line=9) temperature=0, model="gpt-3.5-turbo-0613", openai_api_key=openapi_key , streaming=True
[10](vscode-notebook-cell:?execution_count=7&line=10) )
---> [12](vscode-notebook-cell:?execution_count=7&line=12) pandas_df_agent = create_pandas_dataframe_agent(
[13](vscode-notebook-cell:?execution_count=7&line=13) llm,
[14](vscode-notebook-cell:?execution_count=7&line=14) df,
[15](vscode-notebook-cell:?execution_count=7&line=15) verbose=True,
[16](vscode-notebook-cell:?execution_count=7&line=16) agent_type=AgentType.OPENAI_FUNCTIONS,
[17](vscode-notebook-cell:?execution_count=7&line=17) handle_parsing_errors=True,
[18](vscode-notebook-cell:?execution_count=7&line=18) )
File [c:\Users\rndbcpsoft\OneDrive\Desktop\test\envtest\Lib\site-packages\langchain_experimental\agents\agent_toolkits\pandas\base.py:322](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:322), in create_pandas_dataframe_agent(llm, df, agent_type, callback_manager, prefix, suffix, input_variables, verbose, return_intermediate_steps, max_iterations, max_execution_time, early_stopping_method, agent_executor_kwargs, include_df_in_prompt, number_of_head_rows, extra_tools, **kwargs)
[313](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:313) _prompt, base_tools = _get_functions_prompt_and_tools(
[314](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:314) df,
[315](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:315) prefix=prefix,
(...)
...
---> [57](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain/agents/openai_functions_agent/base.py:57) if not isinstance(values["llm"], ChatOpenAI):
[58](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain/agents/openai_functions_agent/base.py:58) raise ValueError("Only supported with ChatOpenAI models.")
[59](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain/agents/openai_functions_agent/base.py:59) return values
KeyError: 'llm'
### Expected behavior
while running a code with create_pandas_dataframe_agent it throwing key error
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
import pandas as pd
from langchain_openai import OpenAI
df = pd.read_csv(r"C:\Users\rndbcpsoft\OneDrive\Desktop\test\chat_data_2024-01-05_13-20-11.csv")
# agent = create_pandas_dataframe_agent(
# ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
# df,
# verbose=True,
# agent_type=AgentType.OPENAI_FUNCTIONS,
# )
llm = ChatOpenAI(
temperature=0, model="gpt-3.5-turbo-0613", openai_api_key=openapi_key , streaming=True
)
pandas_df_agent = create_pandas_dataframe_agent(
llm,
df,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
handle_parsing_errors=True,
)
error:
KeyError Traceback (most recent call last)
Cell In[7], [line 12](vscode-notebook-cell:?execution_count=7&line=12)
[1](vscode-notebook-cell:?execution_count=7&line=1) # agent = create_pandas_dataframe_agent(
[2](vscode-notebook-cell:?execution_count=7&line=2) # ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
[3](vscode-notebook-cell:?execution_count=7&line=3) # df,
[4](vscode-notebook-cell:?execution_count=7&line=4) # verbose=True,
[5](vscode-notebook-cell:?execution_count=7&line=5) # agent_type=AgentType.OPENAI_FUNCTIONS,
[6](vscode-notebook-cell:?execution_count=7&line=6) # )
[8](vscode-notebook-cell:?execution_count=7&line=8) llm = ChatOpenAI(
[9](vscode-notebook-cell:?execution_count=7&line=9) temperature=0, model="gpt-3.5-turbo-0613", openai_api_key=openapi_key , streaming=True
[10](vscode-notebook-cell:?execution_count=7&line=10) )
---> [12](vscode-notebook-cell:?execution_count=7&line=12) pandas_df_agent = create_pandas_dataframe_agent(
[13](vscode-notebook-cell:?execution_count=7&line=13) llm,
[14](vscode-notebook-cell:?execution_count=7&line=14) df,
[15](vscode-notebook-cell:?execution_count=7&line=15) verbose=True,
[16](vscode-notebook-cell:?execution_count=7&line=16) agent_type=AgentType.OPENAI_FUNCTIONS,
[17](vscode-notebook-cell:?execution_count=7&line=17) handle_parsing_errors=True,
[18](vscode-notebook-cell:?execution_count=7&line=18) )
File [c:\Users\rndbcpsoft\OneDrive\Desktop\test\envtest\Lib\site-packages\langchain_experimental\agents\agent_toolkits\pandas\base.py:322](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:322), in create_pandas_dataframe_agent(llm, df, agent_type, callback_manager, prefix, suffix, input_variables, verbose, return_intermediate_steps, max_iterations, max_execution_time, early_stopping_method, agent_executor_kwargs, include_df_in_prompt, number_of_head_rows, extra_tools, **kwargs)
[313](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:313) _prompt, base_tools = _get_functions_prompt_and_tools(
[314](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:314) df,
[315](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain_experimental/agents/agent_toolkits/pandas/base.py:315) prefix=prefix,
(...)
...
---> [57](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain/agents/openai_functions_agent/base.py:57) if not isinstance(values["llm"], ChatOpenAI):
[58](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain/agents/openai_functions_agent/base.py:58) raise ValueError("Only supported with ChatOpenAI models.")
[59](file:///C:/Users/rndbcpsoft/OneDrive/Desktop/test/envtest/Lib/site-packages/langchain/agents/openai_functions_agent/base.py:59) return values
KeyError: 'llm' | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15818 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15818/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15818/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15818/events | https://github.com/langchain-ai/langchain/issues/15818 | 2,074,381,981 | I_kwDOIPDwls57pI6d | 15,818 | using LLamaCPP with conversational retrieval chain. | {
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] | open | false | null | [] | null | 1 | 2024-01-10T13:29:03 | 2024-01-10T13:45:52 | null | NONE | null | ### Issue you'd like to raise.
Can someone please help me pass llamaCPP instance into langchain's conversational retrieval chain that uses a retriever.
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15817 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15817/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15817/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15817/events | https://github.com/langchain-ai/langchain/pull/15817 | 2,074,331,178 | PR_kwDOIPDwls5jrr86 | 15,817 | langchain, community, core: Async Cache support introduced | {
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] | null | 8 | 2024-01-10T13:01:41 | 2024-01-17T08:49:47 | null | NONE | null | # Description
We're using `FastAPI` in our current project with async functions from LangChain. Recently we needed to add `Redis`-based cache. However we quickly learned that regardless of the LangChain way of usage - sync or async - the cache lookup/update/clear will happen always in synchronous manner. Naturally, this means blocking the event loop, especially with slower than Redis cache implementations.
So I took a similar approach to `agenerate` and `ainvoke` for LLMs, and created an `AsyncBaseCache` mixin interface with corresponding `alookup/aupdate/aclear` async variants:
```python
class AsyncBaseCache(ABC):
"""Base interface for async cache."""
@abstractmethod
async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
@abstractmethod
async def aupdate(
self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
) -> None:
"""Update cache based on prompt and llm_string."""
@abstractmethod
async def aclear(self, **kwargs: Any) -> None:
"""Clear cache that can take additional keyword arguments."""
```
The reference implementation is done for `InMemoryCache` and `RedisCache`.
The `InMemoryCache` does not really need async variant, since there is no real IO, however having that this is the most convenient way to unit test changes.
The change spans throughout `core`, `community` and `langchain`. Linter, formatters, tests are passing.
# Issue
Fixes inability to use IO-bound Redis cache w/o blocking event loop. Opens opportunity for other cache implementations to have async variants.
# Dependencies
None
# Twitter
[@Mind_Clash](https://twitter.com/Mind_Clash) | {
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Description:
1. Modified doc strings inside clarifai vectorstore class and embeddings.
2. Modified notebook examples. | {
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] | open | false | null | [] | null | 6 | 2024-01-10T12:36:25 | 2024-01-10T22:14:33 | null | NONE | null | ### Feature request
The current document_loaders accept file path to process. But most of the time, especially if application deployed to somewhere, file is uploaded by user and not exist on file system.
Writing that in-memory bytes to disk and re-read is a unnecessary step.
It would be good to take BytesIO or some abstraction to process in-memory files.
### Motivation
It will eliminate writing in-memory files to disk and re-reading them from disk while using document_loaders.
### Your contribution
I can create a PR for this. | {
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] | open | false | null | [] | null | 1 | 2024-01-10T12:30:52 | 2024-01-10T12:33:14 | null | NONE | null | ### System Info
LC version: 0.1.0
Platform: MacOS
Python version: 3.12.1
### 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
```
Python 3.12.1 (main, Jan 9 2024, 18:02:09) [Clang 15.0.0 (clang-1500.0.40.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from langchain_openai import AzureChatOpenAI
>>> from langchain_core.runnables import ConfigurableField
>>> ConfigurableAzureChatOpenAI = AzureChatOpenAI(
... openai_api_key = "asdfg",
... openai_api_version = "asdg",
... deployment_name='asdg',
... azure_endpoint="asdg",
... temperature=0.9
... ).configurable_fields(
... azure_endpoint=ConfigurableField(id="azure_endpoint"),
... openai_api_key=ConfigurableField(id="openai_api_key"),
... azure_deployment=ConfigurableField(id="deployment_name"),
... openai_api_version=ConfigurableField(id="openai_api_version"),
... )
Traceback (most recent call last):
File "<stdin>", line 7, in <module>
File "/Users/pramodh/.pyenv/versions/3.12.1/lib/python3.12/site-packages/langchain_core/runnables/base.py", line 1368, in configurable_fields
raise ValueError(
ValueError: Configuration key azure_deployment not found in client=<openai.resources.chat.completions.Completions object at 0x1079f4350> async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x1079f58e0> temperature=0.9 openai_api_key='asdfg' openai_proxy='' azure_endpoint='asdg' deployment_name='asdg' openai_api_version='asdg' openai_api_type='azure': available keys are {self.__fields__.keys()}
```
### Expected behavior
`azure_deployment` is an alias for `deployment_name` defined inside `AzureChatOpenAI`, but it cannot be set as a `ConfigurableField` - we instead have to set `deployment_name` as a ConfigurableField.
I would expect the above code to not throw an error, as they are just aliases. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15812 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15812/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15812/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15812/events | https://github.com/langchain-ai/langchain/pull/15812 | 2,074,224,760 | PR_kwDOIPDwls5jrUoC | 15,812 | Add a BaseStore backed by AstraDB | {
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] | closed | false | null | [] | null | 1 | 2024-01-10T12:01:44 | 2024-01-12T09:30:59 | 2024-01-12T05:41:24 | CONTRIBUTOR | null | - **Description:** this change adds a `BaseStore` backed by AstraDB
- **Twitter handle:** cbornet_
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https://api.github.com/repos/langchain-ai/langchain/issues/15811 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15811/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15811/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15811/events | https://github.com/langchain-ai/langchain/pull/15811 | 2,074,214,476 | PR_kwDOIPDwls5jrSXO | 15,811 | Luzhan docs update | {
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] | closed | false | null | [] | null | 1 | 2024-01-10T11:55:31 | 2024-01-10T12:06:25 | 2024-01-10T12:06:25 | NONE | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
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https://api.github.com/repos/langchain-ai/langchain/issues/15808 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15808/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15808/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15808/events | https://github.com/langchain-ai/langchain/issues/15808 | 2,073,927,465 | I_kwDOIPDwls57nZ8p | 15,808 | DOC: <https://python.langchain.com/docs/integrations/chat/ollama_functions 'DOC: ' prefix>ollamafunctions not working at all | {
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] | open | false | null | [] | null | 1 | 2024-01-10T09:17:02 | 2024-01-10T09:25:01 | null | NONE | null | ### Issue with current documentation:
i have tried with several tests, even with the most basic e.g in the doc, nothing. Dissapointed because it got me superexcited at first:
from langchain_experimental.llms.ollama_functions import OllamaFunctions
from langchain.schema import HumanMessage
model = OllamaFunctions(model="dolphinmodel",)
model = model.bind(
functions=[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, " "e.g. San Francisco, CA",
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["location"],
},
}
],
function_call={"name": "get_current_weather"},
)
model.invoke("what is the weather in Boston?")
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15807 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15807/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15807/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15807/events | https://github.com/langchain-ai/langchain/pull/15807 | 2,073,884,043 | PR_kwDOIPDwls5jqJwo | 15,807 | Docs: Fix a typo in elasticsearch vectorstore notebook | {
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] | closed | false | null | [] | null | 1 | 2024-01-10T08:51:25 | 2024-01-11T04:30:44 | 2024-01-11T04:30:44 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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- **Description:** a description of the change,
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1. a test for the integration, preferably unit tests that do not rely on network access,
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https://api.github.com/repos/langchain-ai/langchain/issues/15806 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15806/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15806/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15806/events | https://github.com/langchain-ai/langchain/issues/15806 | 2,073,877,371 | I_kwDOIPDwls57nNt7 | 15,806 | Why can't I store and retrieve vectors? Please help me fix it. | {
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] | open | false | null | [] | null | 1 | 2024-01-10T08:47:26 | 2024-01-10T09:02:30 | null | NONE | null | ### Issue you'd like to raise.
import os
from langchain.prompts.prompt import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.chains import (
ConversationalRetrievalChain,
LLMChain
)
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.callbacks import CallbackManager
from qdrant_client import QdrantClient
from langchain.vectorstores import Qdrant
os.environ['OPENAI_API_KEY'] = "mykey"
embeddings = HuggingFaceEmbeddings(
model_name="all-MiniLM-L6-v2"
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
streaming_llm = OpenAI(
streaming=True,
callback_manager=CallbackManager([
StreamingStdOutCallbackHandler()
]),
verbose=True,
max_tokens=150,
temperature=0.2
)
condense_question_prompt = PromptTemplate.from_template(
"在幹嘛"
)
qa_prompt = PromptTemplate.from_template("測")
question_generator = LLMChain(
llm=llm,
prompt=condense_question_prompt
)
doc_chain = load_qa_chain(
llm=streaming_llm,
chain_type="stuff",
prompt=qa_prompt
)
client = QdrantClient(host="192.168.0.31", port=6333)
collection_name = "test"
vectorstore = Qdrant(client, collection_name,
embedding_function=embeddings.embed_query)
chatbot = ConversationalRetrievalChain(
retriever=vectorstore.as_retriever(),
combine_docs_chain=doc_chain,
question_generator=question_generator
)
chat_history = []
question = input("Hi! What are you looking for today?")
while True:
result = chatbot(
{"question": question, "chat_history": chat_history}
)
print("\n")
chat_history.append((result["question"], result["answer"]))
question = input()
### Suggestion:
Why can't I store and retrieve vectors? Please help me fix it. | {
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] | null | 2 | 2024-01-10T07:44:43 | 2024-01-17T09:41:44 | null | CONTRIBUTOR | null | added value error for `DirectoryLoader` if the given path is a valid path but it does not contain any files .
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https://api.github.com/repos/langchain-ai/langchain/issues/15804 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15804/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15804/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15804/events | https://github.com/langchain-ai/langchain/issues/15804 | 2,073,772,510 | I_kwDOIPDwls57m0He | 15,804 | Why can't you search vectors? | {
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] | open | false | null | [] | null | 1 | 2024-01-10T07:35:35 | 2024-01-10T07:45:01 | null | NONE | null | ### Issue you'd like to raise.
from langchain.chains import ConversationalRetrievalChain, ConversationChain, LLMChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder, \
HumanMessagePromptTemplate
from qdrant_client import QdrantClient, models
import os
from qdrant_client.grpc import PointStruct
os.environ['OPENAI_API_KEY'] = "mykey"
COLLECTION_NAME = "teeeeee"
embeddings = HuggingFaceEmbeddings(
model_name="all-MiniLM-L6-v2"
)
print("已成功連線到Qdrant")
def connection():
client = QdrantClient(host="192.168.0.311", port=6333)
client.recreate_collection(
collection_name=COLLECTION_NAME,
vectors_config=models.VectorParams(
distance=models.Distance.COSINE,
size=384),
optimizers_config=models.OptimizersConfigDiff(memmap_threshold=20000),
hnsw_config=models.HnswConfigDiff(on_disk=True, m=16, ef_construct=100)
)
return client
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"你是耶米菈."
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 2000
memory = ConversationTokenBufferMemory(
llm=llm,
prompt=prompt,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True,
)
conversation = LLMChain(llm=llm, prompt=prompt, verbose=True, memory=memory)
chat_history = []
def upsert_vector(client, vectors, data):
for i, vector in enumerate(vectors):
client.upsert(
collection_name=COLLECTION_NAME,
points=[PointStruct(id=i,
vector=vectors[i],
payload=data[i])]
)
print("upsert finish")
def search_from_qdrant(client, vector, k=1):
search_result = client.search(
collection_name=COLLECTION_NAME,
query_vector=vector,
limit=k,
append_payload=True,
)
return search_result
def get_embedding(text, model_name):
while True:
memory.load_memory_variables({})
question = input('提問：')
result = conversation.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history)
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
return st_history
def main():
qclient = connection()
data_objs = [
{
"id": 1,
"teeeeee": f"我是阿狗。你叫做阿狗"
},
]
embedding_array = [get_embedding(text["teeeeee"], embeddings)
for text in data_objs]
upsert_vector(qclient, embedding_array, data_objs)
query_text = "請複誦我剛才所說的話"
query_embedding = get_embedding(query_text, embeddings)
results = search_from_qdrant(qclient, query_embedding, k=1)
print(f"尋找 {query_text}:", results)
if __name__ == '__main__':
main()
Execution result:
Traceback (most recent call last):
File "C:\Users\syz\Downloads\Chat-Bot-using-gpt-3.5-turbo-main\models\測.py", line 117, in <module>
main()
File "C:\Users\syz\Downloads\Chat-Bot-using-gpt-3.5-turbo-main\models\測.py", line 109, in main
upsert_vector(qclient, embedding_array, data_objs)
File "C:\Users\syz\Downloads\Chat-Bot-using-gpt-3.5-turbo-main\models\測.py", line 63, in upsert_vector
points=[PointStruct(id=i,
^^^^^^^^^^^^^^^^^
TypeError: Message must be initialized with a dict: qdrant.PointStruct
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/15803 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15803/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15803/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15803/events | https://github.com/langchain-ai/langchain/issues/15803 | 2,073,770,325 | I_kwDOIPDwls57mzlV | 15,803 | DOC: modules/model_io/concepts in documentation | {
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] | open | false | null | [] | null | 1 | 2024-01-10T07:33:55 | 2024-01-10T07:36:15 | null | NONE | null | ### System Info
Issue with current documentation:
I was reading the documentation and in the modules/model_io/concepts page noticed a minor issue with the pagination navigation. Both the "Previous" and "Next" links currently point to the same page ('model_io'), which may lead to confusion for users.
### Who can help?
@hwchase17
### 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
// The error is on the documentation website.
### Expected behavior
Upon reviewing the content, I believe that the "Next" link should navigate users to the 'prompts' page of the 'model_io' section, providing a seamless transition for readers.
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https://api.github.com/repos/langchain-ai/langchain/issues/15802 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15802/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15802/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15802/events | https://github.com/langchain-ai/langchain/issues/15802 | 2,073,696,335 | I_kwDOIPDwls57mhhP | 15,802 | Why can't I pass 'res' to embedding_array and perform vector search? Also, please help me find out where else I might be going wrong | {
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"name": "area: embeddings",
"color": "C5DEF5",
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"description": "Related to text embedding models module"
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] | open | false | null | [] | null | 1 | 2024-01-10T06:35:58 | 2024-01-10T06:43:28 | null | NONE | null | ### Issue you'd like to raise.
from langchain.chains import ConversationalRetrievalChain, ConversationChain, LLMChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder, \
HumanMessagePromptTemplate
from qdrant_client import QdrantClient, models
import os
from qdrant_client.grpc import PointStruct
os.environ['OPENAI_API_KEY'] = "mykey"
COLLECTION_NAME = "lyric"
embeddings = HuggingFaceEmbeddings(
model_name="all-MiniLM-L6-v2"
)
print("已成功連線到Qdrant")
def connection():
client = QdrantClient(host="192.168.0.28", port=6333)
client.recreate_collection(
collection_name=COLLECTION_NAME,
vectors_config=models.VectorParams(
distance=models.Distance.COSINE,
size=1536),
optimizers_config=models.OptimizersConfigDiff(memmap_threshold=20000),
hnsw_config=models.HnswConfigDiff(on_disk=True, m=16, ef_construct=100)
)
return client
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"you are robot."
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 2000
memory = ConversationTokenBufferMemory(
llm=llm,
prompt=prompt,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True,
)
conversation = LLMChain(llm=llm, prompt=prompt, verbose=True, memory=memory)
chat_history = []
def upsert_vector(client, vectors, data):
for i, vector in enumerate(vectors):
client.upsert(
collection_name=COLLECTION_NAME,
points=[PointStruct(id=i,
vector=vectors[i],
payload=data[i])]
)
print("upsert finish")
def search_from_qdrant(client, vector, k=1):
search_result = client.search(
collection_name=COLLECTION_NAME,
query_vector=vector,
limit=k,
append_payload=True,
)
return search_result
def main():
qclient = connection()
while True:
memory.load_memory_variables({})
question = input('提問：')
result = conversation.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history)
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
data_objs = [
{
"id": 1,
"lyric": f"{res}"
},
]
embedding_array = [res(text["lyric"], embeddings)
for text in data_objs]
upsert_vector(qclient, embedding_array, data_objs)
query_text = "Please repeat what I just said"
query_embedding = res(query_text, embeddings)
results = search_from_qdrant(qclient, query_embedding, k=1)
print(f"select {query_text}:", results)
if __name__ == '__main__':
main()
Why can't I pass 'res' to embedding_array and perform vector search? Also, please help me find out where else I might be going wrong
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15801 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15801/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15801/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15801/events | https://github.com/langchain-ai/langchain/issues/15801 | 2,073,649,435 | I_kwDOIPDwls57mWEb | 15,801 | Encounter Error (KeyError: {'format_instructions'})while using StructuredOutputParser | {
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"id": 5680700839,
"node_id": "LA_kwDOIPDwls8AAAABUpidpw",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/auto:bug",
"name": "auto:bug",
"color": "E99695",
"default": false,
"description": "Related to a bug, vulnerability, unexpected error with an existing feature"
},
{
"id": 5820539098,
"node_id": "LA_kwDOIPDwls8AAAABWu5g2g",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/area:%20models",
"name": "area: models",
"color": "bfdadc",
"default": false,
"description": "Related to LLMs or chat model modules"
}
] | open | false | null | [] | null | 1 | 2024-01-10T06:00:17 | 2024-01-10T06:08:15 | null | NONE | null | ### System Info
langchain==0.0.350
python==3.9.2rc1
### Who can help?
@agola11
Sample code
```
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain.prompts import PromptTemplate
response_schemas = [
ResponseSchema(name="result", description="answer to the user's question"),
ResponseSchema(
name="source_documents",
description="source used to answer the user's question",
),
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
#
format_instructions = output_parser.get_format_instructions()
#
llms = LlamaCpp(streaming=True,
model_path=r"C:\Users\PLNAYAK\Documents\Local_LLM_Inference\zephyr-7b-alpha.Q4_K_M.gguf",
max_tokens = 500,
temperature=0.75,
top_p=1,
model_kwargs={"gpu_layers":0,"stream":True},
verbose=True,n_threads = int(os.cpu_count()/2),
n_ctx=4096)
#
prompt = PromptTemplate(
template="Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}\n\nQuestion: {question}\nHelpful Answer:",
input_variables=["context","question"],
partial_variables={"format_instructions": format_instructions},
output_parser=output_parser
)
#
chain = prompt | llms | output_parser
chain.invoke({"question":query,"context":complete_context})
```
Error Log
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[41], [line 1](vscode-notebook-cell:?execution_count=41&line=1)
----> [1](vscode-notebook-cell:?execution_count=41&line=1) chain.invoke({"question":query,"context":complete_context})
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\runnables\base.py:1514](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1514), in RunnableSequence.invoke(self, input, config)
[1512](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1512) try:
[1513](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1513) for i, step in enumerate(self.steps):
-> [1514](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1514) input = step.invoke(
[1515](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1515) input,
[1516](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1516) # mark each step as a child run
[1517](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1517) patch_config(
[1518](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1518) config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
[1519](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1519) ),
[1520](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1520) )
[1521](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1521) # finish the root run
[1522](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:1522) except BaseException as e:
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\prompts\base.py:94](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:94), in BasePromptTemplate.invoke(self, input, config)
[91](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:91) def invoke(
[92](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:92) self, input: Dict, config: Optional[RunnableConfig] = None
[93](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:93) ) -> PromptValue:
---> [94](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:94) return self._call_with_config(
[95](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:95) self._format_prompt_with_error_handling,
[96](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:96) input,
[97](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:97) config,
[98](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:98) run_type="prompt",
[99](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:99) )
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\runnables\base.py:886](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:886), in Runnable._call_with_config(self, func, input, config, run_type, **kwargs)
[879](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:879) run_manager = callback_manager.on_chain_start(
[880](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:880) dumpd(self),
[881](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:881) input,
[882](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:882) run_type=run_type,
[883](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:883) name=config.get("run_name"),
[884](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:884) )
[885](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:885) try:
--> [886](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:886) output = call_func_with_variable_args(
[887](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:887) func, input, config, run_manager, **kwargs
[888](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:888) )
[889](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:889) except BaseException as e:
[890](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/base.py:890) run_manager.on_chain_error(e)
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\runnables\config.py:308](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/config.py:308), in call_func_with_variable_args(func, input, config, run_manager, **kwargs)
[306](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/config.py:306) if run_manager is not None and accepts_run_manager(func):
[307](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/config.py:307) kwargs["run_manager"] = run_manager
--> [308](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/runnables/config.py:308) return func(input, **kwargs)
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\prompts\base.py:89](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:89), in BasePromptTemplate._format_prompt_with_error_handling(self, inner_input)
[83](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:83) except KeyError as e:
[84](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:84) raise KeyError(
[85](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:85) f"Input to {self.__class__.__name__} is missing variable {e}. "
[86](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:86) f" Expected: {self.input_variables}"
[87](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:87) f" Received: {list(inner_input.keys())}"
[88](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:88) ) from e
---> [89](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/base.py:89) return self.format_prompt(**input_dict)
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\prompts\string.py:161](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/string.py:161), in StringPromptTemplate.format_prompt(self, **kwargs)
[159](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/string.py:159) def format_prompt(self, **kwargs: Any) -> PromptValue:
[160](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/string.py:160) """Create Chat Messages."""
--> [161](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/string.py:161) return StringPromptValue(text=self.format(**kwargs))
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\prompts\prompt.py:132](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:132), in PromptTemplate.format(self, **kwargs)
[117](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:117) """Format the prompt with the inputs.
[118](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:118)
[119](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:119) Args:
(...)
[129](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:129) prompt.format(variable1="foo")
[130](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:130) """
[131](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:131) kwargs = self._merge_partial_and_user_variables(**kwargs)
--> [132](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/prompts/prompt.py:132) return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
File [C:\Program](file:///C:/Program) Files\Python39\lib\string.py:161, in Formatter.format(self, format_string, *args, **kwargs)
[160](file:///C:/Program%20Files/Python39/lib/string.py:160) def format(self, format_string, /, *args, **kwargs):
--> [161](file:///C:/Program%20Files/Python39/lib/string.py:161) return self.vformat(format_string, args, kwargs)
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\utils\formatting.py:29](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:29), in StrictFormatter.vformat(self, format_string, args, kwargs)
[24](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:24) if len(args) > 0:
[25](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:25) raise ValueError(
[26](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:26) "No arguments should be provided, "
[27](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:27) "everything should be passed as keyword arguments."
[28](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:28) )
---> [29](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:29) return super().vformat(format_string, args, kwargs)
File [C:\Program](file:///C:/Program) Files\Python39\lib\string.py:166, in Formatter.vformat(self, format_string, args, kwargs)
[164](file:///C:/Program%20Files/Python39/lib/string.py:164) used_args = set()
[165](file:///C:/Program%20Files/Python39/lib/string.py:165) result, _ = self._vformat(format_string, args, kwargs, used_args, 2)
--> [166](file:///C:/Program%20Files/Python39/lib/string.py:166) self.check_unused_args(used_args, args, kwargs)
[167](file:///C:/Program%20Files/Python39/lib/string.py:167) return result
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\langchain_core\utils\formatting.py:18](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:18), in StrictFormatter.check_unused_args(self, used_args, args, kwargs)
[16](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:16) extra = set(kwargs).difference(used_args)
[17](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:17) if extra:
---> [18](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/langchain_core/utils/formatting.py:18) raise KeyError(extra)
KeyError: {'format_instructions'}
### 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
Sample code
```
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain.prompts import PromptTemplate
response_schemas = [
ResponseSchema(name="result", description="answer to the user's question"),
ResponseSchema(
name="source_documents",
description="source used to answer the user's question",
),
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
#
format_instructions = output_parser.get_format_instructions()
#
llms = LlamaCpp(streaming=True,
model_path=r"C:\Users\PLNAYAK\Documents\Local_LLM_Inference\zephyr-7b-alpha.Q4_K_M.gguf",
max_tokens = 500,
temperature=0.75,
top_p=1,
model_kwargs={"gpu_layers":0,"stream":True},
verbose=True,n_threads = int(os.cpu_count()/2),
n_ctx=4096)
#
prompt = PromptTemplate(
template="Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}\n\nQuestion: {question}\nHelpful Answer:",
input_variables=["context","question"],
partial_variables={"format_instructions": format_instructions},
output_parser=output_parser
)
#
chain = prompt | llms | output_parser
chain.invoke({"question":query,"context":complete_context})
```
### Expected behavior
It should return output in a structured format | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15800 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15800/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15800/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15800/events | https://github.com/langchain-ai/langchain/issues/15800 | 2,073,641,136 | I_kwDOIPDwls57mUCw | 15,800 | printing intermediate output from RAG chains | {
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] | closed | false | null | [] | null | 3 | 2024-01-10T05:52:27 | 2024-01-11T01:16:50 | 2024-01-11T01:16:50 | NONE | null | ### System Info
langchain==0.0.352
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
# Define LLM to generate response
llm = VertexAI(model_name='text-bison@001', max_output_tokens=512, temperature=0.2)
if not message:
message = request.form.get('userInput')
template = """
appropriate custom prompt context...
{context}
Question: {question}
"""
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", template),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{question}"),
]
)
rag_chain = (
RunnablePassthrough.assign(
context=contextualized_question | sub_retriever
)
| qa_prompt
| llm
| remove_prefix
)
response = rag_chain.invoke(({"question": message, "chat_history": memory.get_history()}))
memory.add_interaction(message, response)
```
### Expected behavior
I want to get intermediate output of contextualized_question chain, in
```python
RunnablePassthrough.assign(
context=contextualized_question | sub_retriever
)
```
so that I can easily debug the whole process.
For now, I am just getting final response from the chain which is,
```python
response = rag_chain.invoke(({"question": message, "chat_history": memory.get_history()}))
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15799 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15799/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15799/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15799/events | https://github.com/langchain-ai/langchain/pull/15799 | 2,073,608,341 | PR_kwDOIPDwls5jpNnD | 15,799 | semantic chunker | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15798 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15798/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15798/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15798/events | https://github.com/langchain-ai/langchain/pull/15798 | 2,073,593,087 | PR_kwDOIPDwls5jpKSV | 15,798 | add dspy notebook | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15797 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15797/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15797/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15797/events | https://github.com/langchain-ai/langchain/issues/15797 | 2,073,590,016 | I_kwDOIPDwls57mHkA | 15,797 | Issue: How to use ChatPromptTemplate? | {
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] | open | false | null | [] | null | 4 | 2024-01-10T05:00:37 | 2024-01-10T07:10:53 | null | NONE | null | ### Issue you'd like to raise.
Please look at the example below, I used ChatPromptTemplate to chat wit gpt, the output of the gpt always have a prefix "AI: "，how to remove it.
```python
def chat(self, messages):
history = [("system", SYSTEM)]
for message in messages:
if message["role"] == "user":
history.append(("human", message["content"]))
else:
history.append(("ai", message["content"]))
prompt = ChatPromptTemplate.from_messages(history)
chat_chain = prompt | self.model
res = chat_chain.stream({})
return res
```
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15796 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15796/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15796/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15796/events | https://github.com/langchain-ai/langchain/pull/15796 | 2,073,520,208 | PR_kwDOIPDwls5jo6kd | 15,796 | <Community>: Add Initial Support for TiDB Vector Store | {
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} | [] | open | false | null | [] | null | 2 | 2024-01-10T03:34:04 | 2024-01-15T18:57:54 | null | CONTRIBUTOR | null | This pull request introduces initial support for the TiDB vector store. The current version is basic, laying the foundation for the vector store integration. While this implementation provides the essential features, we plan to expand and improve the TiDB vector store support with additional enhancements in future updates.
Upcoming Enhancements:
* Support for Vector Index Creation: To enhance the efficiency and performance of the vector store.
* Support for max marginal relevance search.
* Customized Table Structure Support: Recognizing the need for flexibility, we plan for more tailored and efficient data store solutions.
Simple use case exmaple
```
## Loading Environment Variables
from typing import List, Tuple
from langchain.docstore.document import Document
from langchain_community.vectorstores.tidb_vector import TiDBVector
from langchain_openai import OpenAIEmbeddings
collection_name = "tidb_vector_langchain"
db = TiDBVector.from_texts(
embedding=embeddings,
texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'],
collection_name=collection_name,
connection_string=tidb_connection_url,
distance_strategy="cosine",
)
query = "Can you tell me about Alexandra?"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15795 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15795/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15795/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15795/events | https://github.com/langchain-ai/langchain/issues/15795 | 2,073,517,905 | I_kwDOIPDwls57l19R | 15,795 | Issue: ImportError in Langchain Community Library When Importing OpenAI Package Due to Typing_Extensions Issue | {
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] | open | false | null | [] | null | 1 | 2024-01-10T03:30:46 | 2024-01-10T03:38:13 | null | NONE | null | ### Issue you'd like to raise.
I am developing in a Colab environment and I have Typing_Extensions Issue
Package Version
-------------------------------- ---------------------
absl-py 1.4.0
aiohttp 3.9.1
aiosignal 1.3.1
alabaster 0.7.13
albumentations 1.3.1
altair 4.2.2
anyio 3.7.1
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------------------------------------------------------------------------------------------------------------------------------------------
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import OpenAIEmbeddings
from langchain.vectorstores.chroma import Chroma
**There has Error!!**
**embeddings = OpenAIEmbeddings()**
emb = embeddings.embed_query("beef dishes")
#print(emb)
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=100,
chunk_overlap=0
)
loader = TextLoader("/content/drive/MyDrive/food.txt", encoding='utf-8')
#loader = TextLoader("facts.txt")
docs = loader.load_and_split(
text_splitter=text_splitter
)
db = Chroma(embedding_function=embeddings)
db.add_documents(docs, persist_directory="emb")
results = db.similarity_search_with_score("looking for beef dishes?")
for result in results:
print("\n")
print(result[1])
print(result[0].page_content)
-----------------------------------------------------------------------------------------
**ImportError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/langchain_community/embeddings/openai.py](https://localhost:8080/#) in validate_environment(cls, values)
326 try:
--> 327 import openai
328 except ImportError:
10 frames
ImportError: cannot import name 'Iterator' from 'typing_extensions' (/usr/local/lib/python3.10/dist-packages/typing_extensions.py)
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/langchain_community/embeddings/openai.py](https://localhost:8080/#) in validate_environment(cls, values)
327 import openai
328 except ImportError:
--> 329 raise ImportError(
330 "Could not import openai python package. "
331 "Please install it with `pip install openai`."**
ImportError: Could not import openai python package. Please install it with `pip install openai`.
### Suggestion:
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users to see individual llm tokens if streaming=True is not set explicitly on
the underlying chat model.
The majority of this PR is testing code:
1. Create a test chat model that makes it easier to test streaming and supports
AIMessages that include function invocation information.
2. Tests for the chat model
3. Tests for RunnableAgent (previously untested)
4. Tests for openai agent (previously untested) | {
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